diff --git a/.gitignore b/.gitignore
index 3990ba831..e599f8860 100644
--- a/.gitignore
+++ b/.gitignore
@@ -78,6 +78,9 @@ target/
 # Jupyter Notebook
 .ipynb_checkpoints
 
+# Local scratch / experiments (not part of the tracked docs)
+/scratch/
+
 # IPython
 profile_default/
 ipython_config.py
diff --git a/conftest.py b/conftest.py
index b1f7403a3..74fab05ba 100644
--- a/conftest.py
+++ b/conftest.py
@@ -536,7 +536,7 @@ def boundary_forcing(use_dask: bool, small_grid: Grid) -> BoundaryForcing:
         start_time=datetime(2012, 1, 1),
         end_time=datetime(2012, 12, 31),
         source={"name": "GLORYS", "path": [fname1, fname2]},
-        apply_2d_horizontal_fill=False,
+        prefill=None,
         use_dask=use_dask,
     )
 
@@ -551,7 +551,7 @@ def boundary_forcing_with_2d_fill(use_dask: bool, small_grid: Grid) -> BoundaryF
         start_time=datetime(2012, 1, 1),
         end_time=datetime(2012, 12, 31),
         source={"name": "GLORYS", "path": [fname1, fname2]},
-        apply_2d_horizontal_fill=True,
+        prefill="2d_lateral_fill",
         use_dask=use_dask,
     )
 
@@ -583,7 +583,10 @@ def bgc_boundary_forcing_from_climatology(use_dask: bool) -> BoundaryForcing:
         end_time=datetime(2021, 6, 30),
         source={"path": fname_bgc, "name": "CESM_REGRIDDED", "climatology": True},  # type: ignore[dict-item]
         type="bgc",
-        apply_2d_horizontal_fill=True,
+        prefill="2d_lateral_fill",
+        # scipy regrid keeps this fixture byte-identical to the legacy AMG output
+        # (the new default 'auto' would use xESMF); see test_validation regression.
+        regrid_method="scipy",
         use_dask=use_dask,
     )
 
@@ -613,7 +616,10 @@ def bgc_boundary_forcing_from_unified_climatology(use_dask: bool) -> BoundaryFor
         end_time=datetime(2021, 6, 30),
         source={"path": fname_bgc, "name": "UNIFIED", "climatology": True},  # type: ignore[dict-item]
         type="bgc",
-        apply_2d_horizontal_fill=True,
+        prefill="2d_lateral_fill",
+        # scipy regrid keeps this fixture byte-identical to the legacy AMG output
+        # (the new default 'auto' would use xESMF); see test_validation regression.
+        regrid_method="scipy",
         use_dask=use_dask,
     )
 
diff --git a/docs/boundary_forcing.ipynb b/docs/boundary_forcing.ipynb
index 5a171685d..dec7cd287 100644
--- a/docs/boundary_forcing.ipynb
+++ b/docs/boundary_forcing.ipynb
@@ -48,7 +48,7 @@
     "    N=100,\n",
     "    topography_source={\n",
     "        \"name\": \"SRTM15\",\n",
-    "        \"path\": \"/global/cfs/projectdirs/m4746/Datasets/SRTM15/SRTM15_V2.6.nc\",\n",
+    "        \"path\": \"/anvil/projects/x-ees250129/Datasets/SRTM15/SRTM15_V2.6.nc\",\n",
     "    },\n",
     ")"
    ]
@@ -61,7 +61,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1300x700 with 2 Axes>"
       ]
@@ -148,14 +148,14 @@
    "outputs": [],
    "source": [
     "glorys_path = [\n",
-    "    \"/global/cfs/projectdirs/m4746/Datasets/GLORYS/NA/2012/mercatorglorys12v1_gl12_mean_20120129.nc\",  # include data from day before start time\n",
-    "    \"/global/cfs/projectdirs/m4746/Datasets/GLORYS/NA/2012/mercatorglorys12v1_gl12_mean_20120130.nc\",\n",
-    "    \"/global/cfs/projectdirs/m4746/Datasets/GLORYS/NA/2012/mercatorglorys12v1_gl12_mean_20120131.nc\",\n",
-    "    \"/global/cfs/projectdirs/m4746/Datasets/GLORYS/NA/2012/mercatorglorys12v1_gl12_mean_20120201.nc\",\n",
-    "    \"/global/cfs/projectdirs/m4746/Datasets/GLORYS/NA/2012/mercatorglorys12v1_gl12_mean_20120202.nc\",\n",
-    "    \"/global/cfs/projectdirs/m4746/Datasets/GLORYS/NA/2012/mercatorglorys12v1_gl12_mean_20120203.nc\",\n",
-    "    \"/global/cfs/projectdirs/m4746/Datasets/GLORYS/NA/2012/mercatorglorys12v1_gl12_mean_20120204.nc\",\n",
-    "    \"/global/cfs/projectdirs/m4746/Datasets/GLORYS/NA/2012/mercatorglorys12v1_gl12_mean_20120205.nc\",  # include data from day after end time\n",
+    "    \"/anvil/projects/x-ees250129/cstar-forge-data/source-data/GLORYS_GLOBAL/cmems_mod_glo_phy_my_0.083deg_P1D-m_GLOBAL_20120129.nc\",  # include data from day before start time\n",
+    "    \"/anvil/projects/x-ees250129/cstar-forge-data/source-data/GLORYS_GLOBAL/cmems_mod_glo_phy_my_0.083deg_P1D-m_GLOBAL_20120130.nc\",\n",
+    "    \"/anvil/projects/x-ees250129/cstar-forge-data/source-data/GLORYS_GLOBAL/cmems_mod_glo_phy_my_0.083deg_P1D-m_GLOBAL_20120131.nc\",\n",
+    "    \"/anvil/projects/x-ees250129/cstar-forge-data/source-data/GLORYS_GLOBAL/cmems_mod_glo_phy_my_0.083deg_P1D-m_GLOBAL_20120201.nc\",\n",
+    "    \"/anvil/projects/x-ees250129/cstar-forge-data/source-data/GLORYS_GLOBAL/cmems_mod_glo_phy_my_0.083deg_P1D-m_GLOBAL_20120202.nc\",\n",
+    "    \"/anvil/projects/x-ees250129/cstar-forge-data/source-data/GLORYS_GLOBAL/cmems_mod_glo_phy_my_0.083deg_P1D-m_GLOBAL_20120203.nc\",\n",
+    "    \"/anvil/projects/x-ees250129/cstar-forge-data/source-data/GLORYS_GLOBAL/cmems_mod_glo_phy_my_0.083deg_P1D-m_GLOBAL_20120204.nc\",\n",
+    "    \"/anvil/projects/x-ees250129/cstar-forge-data/source-data/GLORYS_GLOBAL/cmems_mod_glo_phy_my_0.083deg_P1D-m_GLOBAL_20120205.nc\",  # include data from day after end time\n",
     "]"
    ]
   },
@@ -168,7 +168,7 @@
     "\n",
     "Note\n",
     "\n",
-    "We could have also specified the data location via a wildcard, e.g., `glorys_path=\"/global/cfs/projectdirs/m4746/Datasets/GLORYS/NA/2012/*.nc\"`. But with this latter choice, `ROMS-Tools` will operate quite a bit slower. More specific filenames are better!\n",
+    "We could have also specified the data location via a wildcard, e.g., `glorys_path=\"/anvil/projects/x-ees250129/cstar-forge-data/source-data/GLORYS_GLOBAL/cmems_mod_glo_phy_my_0.083deg_P1D-m_GLOBAL_2012*.nc\"`. But with this latter choice, `ROMS-Tools` will operate quite a bit slower. More specific filenames are better!\n",
     "\n",
     "</div>"
    ]
@@ -178,7 +178,7 @@
    "id": "a111510c-77ce-4674-8daa-166d20a79bd1",
    "metadata": {},
    "source": [
-    "You can also download your own GLORYS data, which must span the desired ROMS domain and temporal range. For more details, please refer to [this page](https://roms-tools.readthedocs.io/en/latest/datasets.html)."
+    "You can also download your own GLORYS data, which must span the desired ROMS domain and temporal range. For more details, please refer to [the datasets page](https://roms-tools.readthedocs.io/en/latest/datasets.html)."
    ]
   },
   {
@@ -199,15 +199,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-12-20 13:48:43 - INFO - Using boundary configuration: {'south': True, 'east': True, 'north': False, 'west': True}\n"
+      "2026-06-22 17:58:27 - INFO - Using boundary configuration: {'south': True, 'east': True, 'north': False, 'west': True}\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 4.93 s, sys: 2.03 s, total: 6.95 s\n",
-      "Wall time: 8.12 s\n"
+      "CPU times: user 5.54 s, sys: 910 ms, total: 6.45 s\n",
+      "Wall time: 7.77 s\n"
      ]
     }
    ],
@@ -279,9 +279,7 @@
        "</symbol>\n",
        "</defs>\n",
        "</svg>\n",
-       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
-       " *\n",
-       " */\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
        "\n",
        ":root {\n",
        "  --xr-font-color0: var(\n",
@@ -360,6 +358,8 @@
        "  display: block !important;\n",
        "  min-width: 300px;\n",
        "  max-width: 700px;\n",
+       "  line-height: 1.6;\n",
+       "  padding-bottom: 4px;\n",
        "}\n",
        "\n",
        ".xr-text-repr-fallback {\n",
@@ -370,8 +370,11 @@
        ".xr-header {\n",
        "  padding-top: 6px;\n",
        "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
        "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "  margin-bottom: 4px;\n",
        "}\n",
        "\n",
        ".xr-header > div,\n",
@@ -382,46 +385,62 @@
        "}\n",
        "\n",
        ".xr-obj-type,\n",
-       ".xr-array-name {\n",
+       ".xr-obj-name {\n",
        "  margin-left: 2px;\n",
        "  margin-right: 10px;\n",
        "}\n",
        "\n",
-       ".xr-obj-type {\n",
+       ".xr-obj-type,\n",
+       ".xr-group-box-contents > label {\n",
        "  color: var(--xr-font-color2);\n",
+       "  display: block;\n",
        "}\n",
        "\n",
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
        "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
        "  display: contents;\n",
        "}\n",
        "\n",
-       ".xr-section-item input {\n",
-       "  display: inline-block;\n",
+       ".xr-section-item > input,\n",
+       ".xr-group-box-contents > input,\n",
+       ".xr-array-wrap > input {\n",
+       "  display: block;\n",
        "  opacity: 0;\n",
        "  height: 0;\n",
+       "  margin: 0;\n",
        "}\n",
        "\n",
-       ".xr-section-item input + label {\n",
+       ".xr-section-item > input + label,\n",
+       ".xr-var-item > input + label {\n",
        "  color: var(--xr-disabled-color);\n",
-       "  border: 2px solid transparent !important;\n",
        "}\n",
        "\n",
-       ".xr-section-item input:enabled + label {\n",
+       ".xr-section-item > input:enabled + label,\n",
+       ".xr-var-item > input:enabled + label,\n",
+       ".xr-array-wrap > input:enabled + label,\n",
+       ".xr-group-box-contents > input:enabled + label {\n",
        "  cursor: pointer;\n",
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
-       ".xr-section-item input:focus + label {\n",
-       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       ".xr-section-item > input:focus-visible + label,\n",
+       ".xr-var-item > input:focus-visible + label,\n",
+       ".xr-array-wrap > input:focus-visible + label,\n",
+       ".xr-group-box-contents > input:focus-visible + label {\n",
+       "  outline: auto;\n",
        "}\n",
        "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
+       ".xr-section-item > input:enabled + label:hover,\n",
+       ".xr-var-item > input:enabled + label:hover,\n",
+       ".xr-array-wrap > input:enabled + label:hover,\n",
+       ".xr-group-box-contents > input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
        "\n",
@@ -429,11 +448,25 @@
        "  grid-column: 1;\n",
        "  color: var(--xr-font-color2);\n",
        "  font-weight: 500;\n",
+       "  white-space: nowrap;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > em {\n",
+       "  font-weight: normal;\n",
+       "}\n",
+       "\n",
+       ".xr-span-grid {\n",
+       "  grid-column-end: -1;\n",
        "}\n",
        "\n",
        ".xr-section-summary > span {\n",
        "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
+       "  padding-left: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.6em;\n",
        "}\n",
        "\n",
        ".xr-section-summary-in:disabled + label {\n",
@@ -461,9 +494,9 @@
        "}\n",
        "\n",
        ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
+       ".xr-section-inline-details,\n",
+       ".xr-group-box-contents > label {\n",
        "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
        "}\n",
        "\n",
        ".xr-section-inline-details {\n",
@@ -471,15 +504,81 @@
        "}\n",
        "\n",
        ".xr-section-details {\n",
-       "  display: none;\n",
        "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
        "  margin-bottom: 5px;\n",
        "}\n",
        "\n",
+       ".xr-section-summary-in ~ .xr-section-details {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
        ".xr-section-summary-in:checked ~ .xr-section-details {\n",
        "  display: contents;\n",
        "}\n",
        "\n",
+       ".xr-children {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 100%;\n",
+       "  grid-column: 1 / -1;\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 30px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 26px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > label::before {\n",
+       "  content: \"📂\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label::before {\n",
+       "  content: \"📁\";\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label {\n",
+       "  padding-bottom: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked ~ .xr-sections {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-ellipsis {\n",
+       "  font-size: 1.4em;\n",
+       "  font-weight: 900;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  letter-spacing: 0.15em;\n",
+       "  cursor: default;\n",
+       "}\n",
+       "\n",
        ".xr-array-wrap {\n",
        "  grid-column: 1 / -1;\n",
        "  display: grid;\n",
@@ -707,42 +806,45 @@
        "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
        "  stroke-width: 0.8px;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 17MB\n",
-       "Dimensions:     (bry_time: 7, s_rho: 100, xi_u: 501, xi_rho: 502, eta_rho: 502,\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 19MB\n",
+       "Dimensions:     (bry_time: 8, s_rho: 100, xi_u: 501, xi_rho: 502, eta_rho: 502,\n",
        "                 eta_v: 501)\n",
        "Coordinates:\n",
-       "    abs_time    (bry_time) datetime64[ns] 56B 2012-01-29T12:00:00 ... 2012-02...\n",
-       "  * bry_time    (bry_time) float64 56B 4.412e+03 4.412e+03 ... 4.418e+03\n",
+       "  * bry_time    (bry_time) float64 64B 4.411e+03 4.412e+03 ... 4.418e+03\n",
+       "    abs_time    (bry_time) datetime64[ns] 64B 2012-01-29 ... 2012-02-05\n",
        "Dimensions without coordinates: s_rho, xi_u, xi_rho, eta_rho, eta_v\n",
        "Data variables: (12/21)\n",
-       "    u_south     (bry_time, s_rho, xi_u) float32 1MB dask.array&lt;chunksize=(1, 100, 501), meta=np.ndarray&gt;\n",
-       "    v_south     (bry_time, s_rho, xi_rho) float32 1MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
-       "    temp_south  (bry_time, s_rho, xi_rho) float32 1MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
-       "    salt_south  (bry_time, s_rho, xi_rho) float32 1MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
-       "    zeta_south  (bry_time, xi_rho) float32 14kB dask.array&lt;chunksize=(1, 502), meta=np.ndarray&gt;\n",
-       "    ubar_south  (bry_time, xi_u) float32 14kB dask.array&lt;chunksize=(1, 501), meta=np.ndarray&gt;\n",
+       "    u_south     (bry_time, s_rho, xi_u) float32 2MB dask.array&lt;chunksize=(1, 100, 501), meta=np.ndarray&gt;\n",
+       "    v_south     (bry_time, s_rho, xi_rho) float32 2MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
+       "    temp_south  (bry_time, s_rho, xi_rho) float32 2MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
+       "    salt_south  (bry_time, s_rho, xi_rho) float32 2MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
+       "    zeta_south  (bry_time, xi_rho) float32 16kB dask.array&lt;chunksize=(1, 502), meta=np.ndarray&gt;\n",
+       "    ubar_south  (bry_time, xi_u) float32 16kB dask.array&lt;chunksize=(1, 501), meta=np.ndarray&gt;\n",
        "    ...          ...\n",
-       "    v_west      (bry_time, s_rho, eta_v) float32 1MB dask.array&lt;chunksize=(1, 100, 501), meta=np.ndarray&gt;\n",
-       "    temp_west   (bry_time, s_rho, eta_rho) float32 1MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
-       "    salt_west   (bry_time, s_rho, eta_rho) float32 1MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
-       "    zeta_west   (bry_time, eta_rho) float32 14kB dask.array&lt;chunksize=(1, 502), meta=np.ndarray&gt;\n",
-       "    ubar_west   (bry_time, eta_rho) float32 14kB dask.array&lt;chunksize=(1, 502), meta=np.ndarray&gt;\n",
-       "    vbar_west   (bry_time, eta_v) float32 14kB dask.array&lt;chunksize=(1, 501), meta=np.ndarray&gt;\n",
-       "Attributes:\n",
+       "    v_west      (bry_time, s_rho, eta_v) float32 2MB dask.array&lt;chunksize=(1, 100, 501), meta=np.ndarray&gt;\n",
+       "    temp_west   (bry_time, s_rho, eta_rho) float32 2MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
+       "    salt_west   (bry_time, s_rho, eta_rho) float32 2MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
+       "    zeta_west   (bry_time, eta_rho) float32 16kB dask.array&lt;chunksize=(1, 502), meta=np.ndarray&gt;\n",
+       "    ubar_west   (bry_time, eta_rho) float32 16kB dask.array&lt;chunksize=(1, 502), meta=np.ndarray&gt;\n",
+       "    vbar_west   (bry_time, eta_v) float32 16kB dask.array&lt;chunksize=(1, 501), meta=np.ndarray&gt;\n",
+       "Attributes: (12/13)\n",
        "    title:                                ROMS boundary forcing file created ...\n",
-       "    roms_tools_version:                   3.1.2.dev23+gc2b82bc5c\n",
+       "    roms_tools_version:                   10000.dev361+g2e6c47bd8\n",
        "    start_time:                           2012-01-30 00:00:00\n",
        "    end_time:                             2012-02-04 00:00:00\n",
        "    source:                               GLORYS\n",
        "    model_reference_date:                 2000-01-01 00:00:00\n",
-       "    apply_2d_horizontal_fill:             False\n",
+       "    ...                                   ...\n",
+       "    regrid_method:                        xesmf\n",
+       "    extrap_method:                        inverse_dist\n",
        "    adjust_depth_for_sea_surface_height:  False\n",
        "    theta_s:                              5.0\n",
        "    theta_b:                              2.0\n",
-       "    hc:                                   300.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-7b92ce5a-d478-4e0d-9888-af4902736b09' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7b92ce5a-d478-4e0d-9888-af4902736b09' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>bry_time</span>: 7</li><li><span>s_rho</span>: 100</li><li><span>xi_u</span>: 501</li><li><span>xi_rho</span>: 502</li><li><span>eta_rho</span>: 502</li><li><span>eta_v</span>: 501</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f82134fc-92be-4df5-871d-f4dd80605273' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f82134fc-92be-4df5-871d-f4dd80605273' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-01-29T12:00:00 ... 2012-02-...</div><input id='attrs-5144cc4e-bf0a-4322-bd11-83862a4e094b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5144cc4e-bf0a-4322-bd11-83862a4e094b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad2513fc-3e1f-4d6c-b1c2-de35323d38c7' class='xr-var-data-in' type='checkbox'><label for='data-ad2513fc-3e1f-4d6c-b1c2-de35323d38c7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>absolute time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-01-29T12:00:00.000000000&#x27;, &#x27;2012-01-30T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2012-01-31T12:00:00.000000000&#x27;, &#x27;2012-02-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2012-02-02T12:00:00.000000000&#x27;, &#x27;2012-02-03T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2012-02-04T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>bry_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.412e+03 4.412e+03 ... 4.418e+03</div><input id='attrs-01f64906-5f8c-4fbb-903a-d2a815da0121' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01f64906-5f8c-4fbb-903a-d2a815da0121' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58a9ee0a-9773-424c-8719-6d16cf909049' class='xr-var-data-in' type='checkbox'><label for='data-58a9ee0a-9773-424c-8719-6d16cf909049' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>relative time: days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([4411.5, 4412.5, 4413.5, 4414.5, 4415.5, 4416.5, 4417.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a53f123b-aa3e-4c1a-ac94-a0b2c406a45d' class='xr-section-summary-in' type='checkbox'  ><label for='section-a53f123b-aa3e-4c1a-ac94-a0b2c406a45d' class='xr-section-summary' >Data variables: <span>(21)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>u_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 501), meta=np.ndarray&gt;</div><input id='attrs-038a7c7e-e2a9-4ca9-b913-ce4055687449' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-038a7c7e-e2a9-4ca9-b913-ce4055687449' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab543263-f674-4301-83f3-d90ae9c86038' class='xr-var-data-in' type='checkbox'><label for='data-ab543263-f674-4301-83f3-d90ae9c86038' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    hc:                                   300.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-a45adc16-33c1-4340-a671-3b937a2b8668' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-a45adc16-33c1-4340-a671-3b937a2b8668' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>bry_time</span>: 8</li><li><span>s_rho</span>: 100</li><li><span>xi_u</span>: 501</li><li><span>xi_rho</span>: 502</li><li><span>eta_rho</span>: 502</li><li><span>eta_v</span>: 501</li></ul></div></li><li class='xr-section-item'><input id='section-6b694f82-92a0-4940-9074-b06dacfefef2' class='xr-section-summary-in' type='checkbox' checked /><label for='section-6b694f82-92a0-4940-9074-b06dacfefef2' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>bry_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.411e+03 4.412e+03 ... 4.418e+03</div><input id='attrs-7aedb65b-adfd-4c15-ac49-6adb2c90c474' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7aedb65b-adfd-4c15-ac49-6adb2c90c474' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49fadfba-895b-49de-914a-1b82b8cbfac2' class='xr-var-data-in' type='checkbox'><label for='data-49fadfba-895b-49de-914a-1b82b8cbfac2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>relative time: days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([4411., 4412., 4413., 4414., 4415., 4416., 4417., 4418.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-01-29 ... 2012-02-05</div><input id='attrs-39d801a0-cd55-4b03-b5da-e39e60d4b20d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-39d801a0-cd55-4b03-b5da-e39e60d4b20d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae229acf-258f-4f41-824f-e4a1c904c9ab' class='xr-var-data-in' type='checkbox'><label for='data-ae229acf-258f-4f41-824f-e4a1c904c9ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>absolute time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-01-29T00:00:00.000000000&#x27;, &#x27;2012-01-30T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-01-31T00:00:00.000000000&#x27;, &#x27;2012-02-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-02-02T00:00:00.000000000&#x27;, &#x27;2012-02-03T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-02-04T00:00:00.000000000&#x27;, &#x27;2012-02-05T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c217b4a2-dc10-4155-bd8a-deb853a3cddd' class='xr-section-summary-in' type='checkbox' /><label for='section-c217b4a2-dc10-4155-bd8a-deb853a3cddd' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(21)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>u_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 501), meta=np.ndarray&gt;</div><input id='attrs-8dbe6dad-81f4-451e-8dcf-2f8fe6fbb061' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8dbe6dad-81f4-451e-8dcf-2f8fe6fbb061' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc3403d3-e14f-4706-9ae2-cc52349d0f08' class='xr-var-data-in' type='checkbox'><label for='data-cc3403d3-e14f-4706-9ae2-cc52349d0f08' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>unit_long :</span></dt><dd>Meters per second</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>long_name :</span></dt><dd>southern boundary u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -757,18 +859,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.34 MiB </td>\n",
+       "                        <td> 1.53 MiB </td>\n",
        "                        <td> 195.70 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 100, 501) </td>\n",
+       "                        <td> (8, 100, 501) </td>\n",
        "                        <td> (1, 100, 501) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 120 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 83 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -778,61 +880,63 @@
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        "        </td>\n",
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        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-f92a93c8-02a3-45d4-bea0-6fbdf08dce70' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f92a93c8-02a3-45d4-bea0-6fbdf08dce70' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-344ba056-f41a-44d4-951c-0a9d2ced44f7' class='xr-var-data-in' type='checkbox'><label for='data-344ba056-f41a-44d4-951c-0a9d2ced44f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-11d56eeb-47c7-4a0b-8b4f-588dfece86b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-11d56eeb-47c7-4a0b-8b4f-588dfece86b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49ab98d7-66cd-45e3-8487-c4010ecd351c' class='xr-var-data-in' type='checkbox'><label for='data-49ab98d7-66cd-45e3-8487-c4010ecd351c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>unit_long :</span></dt><dd>Meters per second</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>long_name :</span></dt><dd>southern boundary v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -847,18 +951,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.34 MiB </td>\n",
+       "                        <td> 1.53 MiB </td>\n",
        "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 100, 502) </td>\n",
+       "                        <td> (8, 100, 502) </td>\n",
        "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 120 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 83 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -868,61 +972,63 @@
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        "        </td>\n",
        "        <td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-bd60925f-856f-4f22-8025-4e3040396f2c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd60925f-856f-4f22-8025-4e3040396f2c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-955211c6-28b5-4e14-8e82-15f15981addd' class='xr-var-data-in' type='checkbox'><label for='data-955211c6-28b5-4e14-8e82-15f15981addd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary potential temperature</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-f16b058e-0b9c-4027-8415-224416d682e7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f16b058e-0b9c-4027-8415-224416d682e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8d68b29-8137-4301-9c22-2a2b69277193' class='xr-var-data-in' type='checkbox'><label for='data-f8d68b29-8137-4301-9c22-2a2b69277193' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>long_name :</span></dt><dd>southern boundary potential temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>unit_long :</span></dt><dd>Degrees Celsius</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd><dt><span>valid_max :</span></dt><dd>21306</dd><dt><span>valid_min :</span></dt><dd>-32766</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -937,18 +1043,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.34 MiB </td>\n",
+       "                        <td> 1.53 MiB </td>\n",
        "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 100, 502) </td>\n",
+       "                        <td> (8, 100, 502) </td>\n",
        "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 42 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -958,61 +1064,63 @@
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        "        <td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salt_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-2931a454-7b63-4f52-b3cf-f8d3badabc85' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2931a454-7b63-4f52-b3cf-f8d3badabc85' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66906195-d9da-4dca-9347-1ed6fd659a68' class='xr-var-data-in' type='checkbox'><label for='data-66906195-d9da-4dca-9347-1ed6fd659a68' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary salinity</dd><dt><span>units :</span></dt><dd>PSU</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salt_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-78c24627-dd6b-445a-9e80-49a1e8c4abae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-78c24627-dd6b-445a-9e80-49a1e8c4abae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e47cd0f-78da-4762-b542-5888804f7565' class='xr-var-data-in' type='checkbox'><label for='data-7e47cd0f-78da-4762-b542-5888804f7565' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>long_name :</span></dt><dd>southern boundary salinity</dd><dt><span>standard_name :</span></dt><dd>sea_water_salinity</dd><dt><span>unit_long :</span></dt><dd>Practical Salinity Unit</dd><dt><span>units :</span></dt><dd>PSU</dd><dt><span>valid_max :</span></dt><dd>28249</dd><dt><span>valid_min :</span></dt><dd>1</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1027,18 +1135,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.34 MiB </td>\n",
+       "                        <td> 1.53 MiB </td>\n",
        "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 100, 502) </td>\n",
+       "                        <td> (8, 100, 502) </td>\n",
        "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 42 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -1048,61 +1156,63 @@
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        "        </td>\n",
        "        <td>\n",
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-       "  <text x=\"7.963157\" y=\"69.956160\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.963157,69.956160)\">7</text>\n",
+       "  <text x=\"86.34181321463146\" y=\"78.33481550079244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"166.34181321463146\" y=\"37.33831435771195\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,166.34181321463146,37.33831435771195)\">100</text>\n",
+       "  <text x=\"8.170906607315729\" y=\"70.1639088934767\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.170906607315729,70.1639088934767)\">8</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zeta_south</span></div><div class='xr-var-dims'>(bry_time, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 502), meta=np.ndarray&gt;</div><input id='attrs-14669924-a91c-4980-86e0-f1dbccf49d11' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-14669924-a91c-4980-86e0-f1dbccf49d11' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcd6d04a-e771-4673-a846-dd3f9d0ea63b' class='xr-var-data-in' type='checkbox'><label for='data-bcd6d04a-e771-4673-a846-dd3f9d0ea63b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary sea surface height</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_geoid</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>unit_long :</span></dt><dd>Meters</dd><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>_ChunkSizes :</span></dt><dd>[   1  681 1440]</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zeta_south</span></div><div class='xr-var-dims'>(bry_time, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 502), meta=np.ndarray&gt;</div><input id='attrs-4098e966-85ae-478a-922a-f858391a515b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4098e966-85ae-478a-922a-f858391a515b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc13b020-0ac1-472b-8b13-4976fba4d99d' class='xr-var-data-in' type='checkbox'><label for='data-dc13b020-0ac1-472b-8b13-4976fba4d99d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>long_name :</span></dt><dd>southern boundary sea surface height</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_geoid</dd><dt><span>unit_long :</span></dt><dd>Meters</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_max :</span></dt><dd>5684</dd><dt><span>valid_min :</span></dt><dd>-6228</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1117,18 +1227,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 13.73 kiB </td>\n",
+       "                        <td> 15.69 kiB </td>\n",
        "                        <td> 1.96 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 502) </td>\n",
+       "                        <td> (8, 502) </td>\n",
        "                        <td> (1, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 46 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 32 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -1143,11 +1253,12 @@
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
        "  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n",
-       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
-       "  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n",
-       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
-       "  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n",
-       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
+       "  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n",
+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
        "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -1155,15 +1266,15 @@
        "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"27\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,27.07473454868028 0.0,27.07473454868028\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,27.781082464873474 0.0,27.781082464873474\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"47.074735\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"140.000000\" y=\"13.537367\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,13.537367)\">7</text>\n",
+       "  <text x=\"60.0\" y=\"47.78108246487348\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"140.0\" y=\"13.890541232436737\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,13.890541232436737)\">8</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ubar_south</span></div><div class='xr-var-dims'>(bry_time, xi_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 501), meta=np.ndarray&gt;</div><input id='attrs-099772ac-b422-4aa4-98fa-774617fc4600' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-099772ac-b422-4aa4-98fa-774617fc4600' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b55c51b-0698-46b8-8503-ccd84f12ba08' class='xr-var-data-in' type='checkbox'><label for='data-0b55c51b-0698-46b8-8503-ccd84f12ba08' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary vertically integrated u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ubar_south</span></div><div class='xr-var-dims'>(bry_time, xi_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 501), meta=np.ndarray&gt;</div><input id='attrs-67d8f8b6-35a0-4994-bec4-c0859209cdfe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-67d8f8b6-35a0-4994-bec4-c0859209cdfe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f356523-e59b-4b33-88c5-5b964d05e419' class='xr-var-data-in' type='checkbox'><label for='data-5f356523-e59b-4b33-88c5-5b964d05e419' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary vertically integrated u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>unit_long :</span></dt><dd>Meters per second</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1178,18 +1289,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 13.70 kiB </td>\n",
+       "                        <td> 15.66 kiB </td>\n",
        "                        <td> 1.96 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 501) </td>\n",
+       "                        <td> (8, 501) </td>\n",
        "                        <td> (1, 501) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 130 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 93 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -1204,11 +1315,12 @@
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
        "  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n",
-       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
-       "  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n",
-       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
-       "  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n",
-       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
+       "  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n",
+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
        "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -1216,15 +1328,15 @@
        "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"27\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,27.08512214385328 0.0,27.08512214385328\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,27.79179338938244 0.0,27.79179338938244\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"47.085122\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >501</text>\n",
-       "  <text x=\"140.000000\" y=\"13.542561\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,13.542561)\">7</text>\n",
+       "  <text x=\"60.0\" y=\"47.79179338938244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >501</text>\n",
+       "  <text x=\"140.0\" y=\"13.89589669469122\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,13.89589669469122)\">8</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vbar_south</span></div><div class='xr-var-dims'>(bry_time, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 502), meta=np.ndarray&gt;</div><input id='attrs-497589d5-2ffd-4341-acda-a1c5c74173e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-497589d5-2ffd-4341-acda-a1c5c74173e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18df6bc5-64e4-4ef0-a775-b9c9c11e91f0' class='xr-var-data-in' type='checkbox'><label for='data-18df6bc5-64e4-4ef0-a775-b9c9c11e91f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary vertically integrated v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vbar_south</span></div><div class='xr-var-dims'>(bry_time, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 502), meta=np.ndarray&gt;</div><input id='attrs-c883a159-1777-4147-af9f-367528d13760' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c883a159-1777-4147-af9f-367528d13760' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41c13df5-602d-46f4-ad12-dcbf6e38235f' class='xr-var-data-in' type='checkbox'><label for='data-41c13df5-602d-46f4-ad12-dcbf6e38235f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary vertically integrated v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>unit_long :</span></dt><dd>Meters per second</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1239,18 +1351,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 13.73 kiB </td>\n",
+       "                        <td> 15.69 kiB </td>\n",
        "                        <td> 1.96 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 502) </td>\n",
+       "                        <td> (8, 502) </td>\n",
        "                        <td> (1, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 130 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 93 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -1265,11 +1377,12 @@
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
        "  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n",
-       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
-       "  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n",
-       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
-       "  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n",
-       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
+       "  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n",
+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
        "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -1277,15 +1390,15 @@
        "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"27\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,27.07473454868028 0.0,27.07473454868028\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,27.781082464873474 0.0,27.781082464873474\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"47.074735\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"140.000000\" y=\"13.537367\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,13.537367)\">7</text>\n",
+       "  <text x=\"60.0\" y=\"47.78108246487348\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"140.0\" y=\"13.890541232436737\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,13.890541232436737)\">8</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-035e0856-888a-45ff-b78c-ca06a3020206' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-035e0856-888a-45ff-b78c-ca06a3020206' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8782ddbd-a3b1-455f-86c3-eb39cbe83ba8' class='xr-var-data-in' type='checkbox'><label for='data-8782ddbd-a3b1-455f-86c3-eb39cbe83ba8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-7ca0e4d5-8bb4-4339-8968-0c74f6364d4a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7ca0e4d5-8bb4-4339-8968-0c74f6364d4a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-236fd051-a6c2-49e8-9300-f16f100e5015' class='xr-var-data-in' type='checkbox'><label for='data-236fd051-a6c2-49e8-9300-f16f100e5015' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>unit_long :</span></dt><dd>Meters per second</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>long_name :</span></dt><dd>eastern boundary u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1300,18 +1413,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.34 MiB </td>\n",
+       "                        <td> 1.53 MiB </td>\n",
        "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 100, 502) </td>\n",
+       "                        <td> (8, 100, 502) </td>\n",
        "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 121 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 83 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -1321,61 +1434,63 @@
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        "        </td>\n",
        "        <td>\n",
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        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_v)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 501), meta=np.ndarray&gt;</div><input id='attrs-a6a78ab0-6f8d-4b24-b6b8-814ac247ddbb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a6a78ab0-6f8d-4b24-b6b8-814ac247ddbb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34e271a5-a213-47a8-a96c-03fe7fded7ea' class='xr-var-data-in' type='checkbox'><label for='data-34e271a5-a213-47a8-a96c-03fe7fded7ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_v)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 501), meta=np.ndarray&gt;</div><input id='attrs-b4de577a-70e1-467c-beb7-ce0b5e18163c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b4de577a-70e1-467c-beb7-ce0b5e18163c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9826b42-c2ec-478c-adaa-02f1646b29b4' class='xr-var-data-in' type='checkbox'><label for='data-b9826b42-c2ec-478c-adaa-02f1646b29b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>unit_long :</span></dt><dd>Meters per second</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>long_name :</span></dt><dd>eastern boundary v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1390,18 +1505,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.34 MiB </td>\n",
+       "                        <td> 1.53 MiB </td>\n",
        "                        <td> 195.70 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 100, 501) </td>\n",
+       "                        <td> (8, 100, 501) </td>\n",
        "                        <td> (1, 100, 501) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 121 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 83 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -1411,61 +1526,63 @@
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        "        </td>\n",
        "        <td>\n",
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        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-d4702263-423c-4f6b-97b3-f9e2ead0515a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d4702263-423c-4f6b-97b3-f9e2ead0515a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-449836e8-2599-47bd-8778-b5878a6b5df0' class='xr-var-data-in' type='checkbox'><label for='data-449836e8-2599-47bd-8778-b5878a6b5df0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary potential temperature</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-32c32c32-ec0c-4b95-aae5-e2708230b585' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32c32c32-ec0c-4b95-aae5-e2708230b585' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f71bf022-9e6e-4033-a42f-b1c4387d1caf' class='xr-var-data-in' type='checkbox'><label for='data-f71bf022-9e6e-4033-a42f-b1c4387d1caf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>long_name :</span></dt><dd>eastern boundary potential temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>unit_long :</span></dt><dd>Degrees Celsius</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd><dt><span>valid_max :</span></dt><dd>21306</dd><dt><span>valid_min :</span></dt><dd>-32766</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1480,18 +1597,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.34 MiB </td>\n",
+       "                        <td> 1.53 MiB </td>\n",
        "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 100, 502) </td>\n",
+       "                        <td> (8, 100, 502) </td>\n",
        "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 76 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 42 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -1501,61 +1618,63 @@
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        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salt_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-1394b9fa-1355-45e2-b87a-481b368a7b40' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1394b9fa-1355-45e2-b87a-481b368a7b40' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92b62599-402c-4290-8255-a9f3db90125a' class='xr-var-data-in' type='checkbox'><label for='data-92b62599-402c-4290-8255-a9f3db90125a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary salinity</dd><dt><span>units :</span></dt><dd>PSU</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salt_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-8acd8d71-90e8-4885-add3-fba18e9a0529' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8acd8d71-90e8-4885-add3-fba18e9a0529' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83e4f75d-99b8-4ddd-bf61-bb7410e50a34' class='xr-var-data-in' type='checkbox'><label for='data-83e4f75d-99b8-4ddd-bf61-bb7410e50a34' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>long_name :</span></dt><dd>eastern boundary salinity</dd><dt><span>standard_name :</span></dt><dd>sea_water_salinity</dd><dt><span>unit_long :</span></dt><dd>Practical Salinity Unit</dd><dt><span>units :</span></dt><dd>PSU</dd><dt><span>valid_max :</span></dt><dd>28249</dd><dt><span>valid_min :</span></dt><dd>1</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1570,18 +1689,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.34 MiB </td>\n",
+       "                        <td> 1.53 MiB </td>\n",
        "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 100, 502) </td>\n",
+       "                        <td> (8, 100, 502) </td>\n",
        "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 76 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 42 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -1591,61 +1710,63 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
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        "\n",
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-       "  <text x=\"85.926314\" y=\"77.919317\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"165.926314\" y=\"36.922816\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,165.926314,36.922816)\">100</text>\n",
-       "  <text x=\"7.963157\" y=\"69.956160\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.963157,69.956160)\">7</text>\n",
+       "  <text x=\"86.34181321463146\" y=\"78.33481550079244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"166.34181321463146\" y=\"37.33831435771195\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,166.34181321463146,37.33831435771195)\">100</text>\n",
+       "  <text x=\"8.170906607315729\" y=\"70.1639088934767\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.170906607315729,70.1639088934767)\">8</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zeta_east</span></div><div class='xr-var-dims'>(bry_time, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 502), meta=np.ndarray&gt;</div><input id='attrs-a60ca54e-af53-4344-96ad-61acc0119ca9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a60ca54e-af53-4344-96ad-61acc0119ca9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da75128a-ac77-470f-a2cf-c425972ea229' class='xr-var-data-in' type='checkbox'><label for='data-da75128a-ac77-470f-a2cf-c425972ea229' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary sea surface height</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_geoid</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>unit_long :</span></dt><dd>Meters</dd><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>_ChunkSizes :</span></dt><dd>[   1  681 1440]</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zeta_east</span></div><div class='xr-var-dims'>(bry_time, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 502), meta=np.ndarray&gt;</div><input id='attrs-a130e5cb-3edd-4cc2-80b2-14d0df5d7df4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a130e5cb-3edd-4cc2-80b2-14d0df5d7df4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8523af49-9f26-4789-8c12-3c6c37ded3aa' class='xr-var-data-in' type='checkbox'><label for='data-8523af49-9f26-4789-8c12-3c6c37ded3aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>long_name :</span></dt><dd>eastern boundary sea surface height</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_geoid</dd><dt><span>unit_long :</span></dt><dd>Meters</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_max :</span></dt><dd>5684</dd><dt><span>valid_min :</span></dt><dd>-6228</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1660,18 +1781,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 13.73 kiB </td>\n",
+       "                        <td> 15.69 kiB </td>\n",
        "                        <td> 1.96 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 502) </td>\n",
+       "                        <td> (8, 502) </td>\n",
        "                        <td> (1, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 46 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 32 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -1686,11 +1807,12 @@
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
        "  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n",
-       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
-       "  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n",
-       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
-       "  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n",
-       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
+       "  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n",
+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
        "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -1698,15 +1820,15 @@
        "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"27\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,27.07473454868028 0.0,27.07473454868028\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,27.781082464873474 0.0,27.781082464873474\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"47.074735\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"140.000000\" y=\"13.537367\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,13.537367)\">7</text>\n",
+       "  <text x=\"60.0\" y=\"47.78108246487348\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"140.0\" y=\"13.890541232436737\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,13.890541232436737)\">8</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ubar_east</span></div><div class='xr-var-dims'>(bry_time, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 502), meta=np.ndarray&gt;</div><input id='attrs-c86a6f4c-a296-42cc-be1e-cb49a5332289' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c86a6f4c-a296-42cc-be1e-cb49a5332289' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bbff6796-1f7e-41fd-86c4-b5a9d83a6d31' class='xr-var-data-in' type='checkbox'><label for='data-bbff6796-1f7e-41fd-86c4-b5a9d83a6d31' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary vertically integrated u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ubar_east</span></div><div class='xr-var-dims'>(bry_time, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 502), meta=np.ndarray&gt;</div><input id='attrs-b7fb23a3-fe19-4107-ace6-2f2c3bf333d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b7fb23a3-fe19-4107-ace6-2f2c3bf333d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c03f7f46-c37b-4e94-b433-16530211a253' class='xr-var-data-in' type='checkbox'><label for='data-c03f7f46-c37b-4e94-b433-16530211a253' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary vertically integrated u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>unit_long :</span></dt><dd>Meters per second</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1721,18 +1843,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 13.73 kiB </td>\n",
+       "                        <td> 15.69 kiB </td>\n",
        "                        <td> 1.96 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 502) </td>\n",
+       "                        <td> (8, 502) </td>\n",
        "                        <td> (1, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 131 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 93 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -1747,11 +1869,12 @@
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
        "  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n",
-       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
-       "  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n",
-       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
-       "  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n",
-       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
+       "  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n",
+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
        "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -1759,15 +1882,15 @@
        "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"27\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,27.07473454868028 0.0,27.07473454868028\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,27.781082464873474 0.0,27.781082464873474\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"47.074735\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"140.000000\" y=\"13.537367\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,13.537367)\">7</text>\n",
+       "  <text x=\"60.0\" y=\"47.78108246487348\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"140.0\" y=\"13.890541232436737\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,13.890541232436737)\">8</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vbar_east</span></div><div class='xr-var-dims'>(bry_time, eta_v)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 501), meta=np.ndarray&gt;</div><input id='attrs-240168e7-951a-415b-810a-308f4f6e6344' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-240168e7-951a-415b-810a-308f4f6e6344' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66799c49-6c84-486b-b061-15b1225f9818' class='xr-var-data-in' type='checkbox'><label for='data-66799c49-6c84-486b-b061-15b1225f9818' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary vertically integrated v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vbar_east</span></div><div class='xr-var-dims'>(bry_time, eta_v)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 501), meta=np.ndarray&gt;</div><input id='attrs-46a26d48-b3b5-4886-966a-0cea512e82e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-46a26d48-b3b5-4886-966a-0cea512e82e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-406515a6-8778-4ec0-8749-8b992bcbae7d' class='xr-var-data-in' type='checkbox'><label for='data-406515a6-8778-4ec0-8749-8b992bcbae7d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary vertically integrated v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>unit_long :</span></dt><dd>Meters per second</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1782,18 +1905,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 13.70 kiB </td>\n",
+       "                        <td> 15.66 kiB </td>\n",
        "                        <td> 1.96 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 501) </td>\n",
+       "                        <td> (8, 501) </td>\n",
        "                        <td> (1, 501) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 131 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 93 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -1808,11 +1931,12 @@
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
        "  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n",
-       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
-       "  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n",
-       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
-       "  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n",
-       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
+       "  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n",
+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
        "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -1820,15 +1944,15 @@
        "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"27\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,27.08512214385328 0.0,27.08512214385328\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,27.79179338938244 0.0,27.79179338938244\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"47.085122\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >501</text>\n",
-       "  <text x=\"140.000000\" y=\"13.542561\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,13.542561)\">7</text>\n",
+       "  <text x=\"60.0\" y=\"47.79179338938244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >501</text>\n",
+       "  <text x=\"140.0\" y=\"13.89589669469122\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,13.89589669469122)\">8</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-33db9e8f-ba76-48a5-be9c-6d2a16a35f36' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-33db9e8f-ba76-48a5-be9c-6d2a16a35f36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a49ce717-8f08-475d-8e34-ba686c5a01c9' class='xr-var-data-in' type='checkbox'><label for='data-a49ce717-8f08-475d-8e34-ba686c5a01c9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-1a2c14be-2a55-4659-a994-fd0a43f9e6d3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a2c14be-2a55-4659-a994-fd0a43f9e6d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-675dbe6c-cb25-4410-9c3e-afb681679db4' class='xr-var-data-in' type='checkbox'><label for='data-675dbe6c-cb25-4410-9c3e-afb681679db4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>unit_long :</span></dt><dd>Meters per second</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>long_name :</span></dt><dd>western boundary u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1843,18 +1967,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.34 MiB </td>\n",
+       "                        <td> 1.53 MiB </td>\n",
        "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 100, 502) </td>\n",
+       "                        <td> (8, 100, 502) </td>\n",
        "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 121 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 83 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -1864,61 +1988,63 @@
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        "        </td>\n",
        "        <td>\n",
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        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_v)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 501), meta=np.ndarray&gt;</div><input id='attrs-9d513528-84f2-4f27-a4d0-5c0a28800177' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d513528-84f2-4f27-a4d0-5c0a28800177' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd8c11fe-9391-4e48-aa5e-567b0f57e027' class='xr-var-data-in' type='checkbox'><label for='data-bd8c11fe-9391-4e48-aa5e-567b0f57e027' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_v)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 501), meta=np.ndarray&gt;</div><input id='attrs-921fc315-843a-49dd-a9a1-4f73137fd82a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-921fc315-843a-49dd-a9a1-4f73137fd82a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97dfcf58-9a19-4bc4-94bd-35fa479f3494' class='xr-var-data-in' type='checkbox'><label for='data-97dfcf58-9a19-4bc4-94bd-35fa479f3494' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>unit_long :</span></dt><dd>Meters per second</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>long_name :</span></dt><dd>western boundary v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1933,18 +2059,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.34 MiB </td>\n",
+       "                        <td> 1.53 MiB </td>\n",
        "                        <td> 195.70 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 100, 501) </td>\n",
+       "                        <td> (8, 100, 501) </td>\n",
        "                        <td> (1, 100, 501) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 121 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 83 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -1954,61 +2080,63 @@
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        "        </td>\n",
        "        <td>\n",
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        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-88d26a7e-abe4-48b4-a238-3baa0d53e618' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-88d26a7e-abe4-48b4-a238-3baa0d53e618' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6bd4aca9-ecee-45f7-86d6-aaf502337316' class='xr-var-data-in' type='checkbox'><label for='data-6bd4aca9-ecee-45f7-86d6-aaf502337316' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary potential temperature</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-c3e31fae-7aad-4c04-ac53-2994dbb01a92' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c3e31fae-7aad-4c04-ac53-2994dbb01a92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-475aa928-cf10-40a6-811a-144d2d037f72' class='xr-var-data-in' type='checkbox'><label for='data-475aa928-cf10-40a6-811a-144d2d037f72' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>long_name :</span></dt><dd>western boundary potential temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>unit_long :</span></dt><dd>Degrees Celsius</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd><dt><span>valid_max :</span></dt><dd>21306</dd><dt><span>valid_min :</span></dt><dd>-32766</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2023,18 +2151,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.34 MiB </td>\n",
+       "                        <td> 1.53 MiB </td>\n",
        "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 100, 502) </td>\n",
+       "                        <td> (8, 100, 502) </td>\n",
        "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 76 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 42 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -2044,61 +2172,63 @@
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        "        <td>\n",
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        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salt_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-85727380-c9fc-4154-83c6-079dfe10f5ac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-85727380-c9fc-4154-83c6-079dfe10f5ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17e7cb51-d498-41a5-82b9-cbb83c85056f' class='xr-var-data-in' type='checkbox'><label for='data-17e7cb51-d498-41a5-82b9-cbb83c85056f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary salinity</dd><dt><span>units :</span></dt><dd>PSU</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salt_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-fd888dba-a9f7-443b-ba9e-f5a6f1187bc6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fd888dba-a9f7-443b-ba9e-f5a6f1187bc6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80f87cf3-eea4-4308-82ce-e0f0cfe0adab' class='xr-var-data-in' type='checkbox'><label for='data-80f87cf3-eea4-4308-82ce-e0f0cfe0adab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>long_name :</span></dt><dd>western boundary salinity</dd><dt><span>standard_name :</span></dt><dd>sea_water_salinity</dd><dt><span>unit_long :</span></dt><dd>Practical Salinity Unit</dd><dt><span>units :</span></dt><dd>PSU</dd><dt><span>valid_max :</span></dt><dd>28249</dd><dt><span>valid_min :</span></dt><dd>1</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2113,18 +2243,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.34 MiB </td>\n",
+       "                        <td> 1.53 MiB </td>\n",
        "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 100, 502) </td>\n",
+       "                        <td> (8, 100, 502) </td>\n",
        "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 76 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 42 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -2134,61 +2264,63 @@
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        "        </td>\n",
        "        <td>\n",
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-       "  <text x=\"85.926314\" y=\"77.919317\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"165.926314\" y=\"36.922816\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,165.926314,36.922816)\">100</text>\n",
-       "  <text x=\"7.963157\" y=\"69.956160\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.963157,69.956160)\">7</text>\n",
+       "  <text x=\"86.34181321463146\" y=\"78.33481550079244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"166.34181321463146\" y=\"37.33831435771195\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,166.34181321463146,37.33831435771195)\">100</text>\n",
+       "  <text x=\"8.170906607315729\" y=\"70.1639088934767\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.170906607315729,70.1639088934767)\">8</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zeta_west</span></div><div class='xr-var-dims'>(bry_time, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 502), meta=np.ndarray&gt;</div><input id='attrs-94aa4495-727d-4f19-a52f-88cb1a739431' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94aa4495-727d-4f19-a52f-88cb1a739431' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28e422b1-5fd7-4e42-a65e-51b0d3f72bfe' class='xr-var-data-in' type='checkbox'><label for='data-28e422b1-5fd7-4e42-a65e-51b0d3f72bfe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary sea surface height</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_geoid</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>unit_long :</span></dt><dd>Meters</dd><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>_ChunkSizes :</span></dt><dd>[   1  681 1440]</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zeta_west</span></div><div class='xr-var-dims'>(bry_time, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 502), meta=np.ndarray&gt;</div><input id='attrs-0645ddba-301b-46a7-89fe-7e6943c44401' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0645ddba-301b-46a7-89fe-7e6943c44401' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d780c025-5b07-4bca-8608-3051896bacfc' class='xr-var-data-in' type='checkbox'><label for='data-d780c025-5b07-4bca-8608-3051896bacfc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>long_name :</span></dt><dd>western boundary sea surface height</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_geoid</dd><dt><span>unit_long :</span></dt><dd>Meters</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_max :</span></dt><dd>5684</dd><dt><span>valid_min :</span></dt><dd>-6228</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2203,18 +2335,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 13.73 kiB </td>\n",
+       "                        <td> 15.69 kiB </td>\n",
        "                        <td> 1.96 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 502) </td>\n",
+       "                        <td> (8, 502) </td>\n",
        "                        <td> (1, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 46 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 32 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -2229,11 +2361,12 @@
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
        "  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n",
-       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
-       "  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n",
-       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
-       "  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n",
-       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
+       "  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n",
+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
        "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -2241,15 +2374,15 @@
        "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"27\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,27.07473454868028 0.0,27.07473454868028\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,27.781082464873474 0.0,27.781082464873474\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"47.074735\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"140.000000\" y=\"13.537367\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,13.537367)\">7</text>\n",
+       "  <text x=\"60.0\" y=\"47.78108246487348\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"140.0\" y=\"13.890541232436737\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,13.890541232436737)\">8</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ubar_west</span></div><div class='xr-var-dims'>(bry_time, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 502), meta=np.ndarray&gt;</div><input id='attrs-e4f558d9-0fb1-49b5-bd8b-3d6a9649eb68' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e4f558d9-0fb1-49b5-bd8b-3d6a9649eb68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9446517-924c-4226-9131-124349a345d1' class='xr-var-data-in' type='checkbox'><label for='data-b9446517-924c-4226-9131-124349a345d1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary vertically integrated u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ubar_west</span></div><div class='xr-var-dims'>(bry_time, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 502), meta=np.ndarray&gt;</div><input id='attrs-5e9c9905-50a7-4c06-a2fd-af64e5416045' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e9c9905-50a7-4c06-a2fd-af64e5416045' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ba63b04-30ca-4f5e-b856-0fea082c1229' class='xr-var-data-in' type='checkbox'><label for='data-8ba63b04-30ca-4f5e-b856-0fea082c1229' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary vertically integrated u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>unit_long :</span></dt><dd>Meters per second</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2264,18 +2397,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 13.73 kiB </td>\n",
+       "                        <td> 15.69 kiB </td>\n",
        "                        <td> 1.96 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 502) </td>\n",
+       "                        <td> (8, 502) </td>\n",
        "                        <td> (1, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 131 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 93 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -2290,11 +2423,12 @@
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
        "  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n",
-       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
-       "  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n",
-       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
-       "  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n",
-       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
+       "  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n",
+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
        "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -2302,15 +2436,15 @@
        "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"27\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,27.07473454868028 0.0,27.07473454868028\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,27.781082464873474 0.0,27.781082464873474\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"47.074735\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"140.000000\" y=\"13.537367\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,13.537367)\">7</text>\n",
+       "  <text x=\"60.0\" y=\"47.78108246487348\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"140.0\" y=\"13.890541232436737\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,13.890541232436737)\">8</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vbar_west</span></div><div class='xr-var-dims'>(bry_time, eta_v)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 501), meta=np.ndarray&gt;</div><input id='attrs-f04b0379-f7e1-4b8f-84a7-6b682332072a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f04b0379-f7e1-4b8f-84a7-6b682332072a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd528c71-25b6-4527-a27e-238eda260522' class='xr-var-data-in' type='checkbox'><label for='data-fd528c71-25b6-4527-a27e-238eda260522' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary vertically integrated v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vbar_west</span></div><div class='xr-var-dims'>(bry_time, eta_v)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 501), meta=np.ndarray&gt;</div><input id='attrs-4c0cf95b-0556-4d1f-8f63-64e270490720' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c0cf95b-0556-4d1f-8f63-64e270490720' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-abbf439a-196b-4112-9b11-ce34ea4f6f7f' class='xr-var-data-in' type='checkbox'><label for='data-abbf439a-196b-4112-9b11-ce34ea4f6f7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary vertically integrated v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>unit_long :</span></dt><dd>Meters per second</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2325,18 +2459,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 13.70 kiB </td>\n",
+       "                        <td> 15.66 kiB </td>\n",
        "                        <td> 1.96 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 501) </td>\n",
+       "                        <td> (8, 501) </td>\n",
        "                        <td> (1, 501) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 131 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 93 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -2351,11 +2485,12 @@
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        "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
        "  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n",
-       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
-       "  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n",
-       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
-       "  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n",
-       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
+       "  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n",
+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
        "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -2363,46 +2498,48 @@
        "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"27\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,27.08512214385328 0.0,27.08512214385328\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,27.79179338938244 0.0,27.79179338938244\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"47.085122\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >501</text>\n",
-       "  <text x=\"140.000000\" y=\"13.542561\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,13.542561)\">7</text>\n",
+       "  <text x=\"60.0\" y=\"47.79179338938244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >501</text>\n",
+       "  <text x=\"140.0\" y=\"13.89589669469122\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,13.89589669469122)\">8</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-ee5a996b-613a-47f6-ba3a-4061b6e8163e' class='xr-section-summary-in' type='checkbox'  ><label for='section-ee5a996b-613a-47f6-ba3a-4061b6e8163e' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>bry_time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-f16f339d-47da-4175-b3e6-94bde25e1573' class='xr-index-data-in' type='checkbox'/><label for='index-f16f339d-47da-4175-b3e6-94bde25e1573' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([4411.5, 4412.5, 4413.5, 4414.5, 4415.5, 4416.5, 4417.5], dtype=&#x27;float64&#x27;, name=&#x27;bry_time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-740407f0-3d57-4c1b-b110-f346a5bfdfb2' class='xr-section-summary-in' type='checkbox'  ><label for='section-740407f0-3d57-4c1b-b110-f346a5bfdfb2' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>ROMS boundary forcing file created by ROMS-Tools</dd><dt><span>roms_tools_version :</span></dt><dd>3.1.2.dev23+gc2b82bc5c</dd><dt><span>start_time :</span></dt><dd>2012-01-30 00:00:00</dd><dt><span>end_time :</span></dt><dd>2012-02-04 00:00:00</dd><dt><span>source :</span></dt><dd>GLORYS</dd><dt><span>model_reference_date :</span></dt><dd>2000-01-01 00:00:00</dd><dt><span>apply_2d_horizontal_fill :</span></dt><dd>False</dd><dt><span>adjust_depth_for_sea_surface_height :</span></dt><dd>False</dd><dt><span>theta_s :</span></dt><dd>5.0</dd><dt><span>theta_b :</span></dt><dd>2.0</dd><dt><span>hc :</span></dt><dd>300.0</dd></dl></div></li></ul></div></div>"
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-58e39478-2600-472b-988e-c6610d65eb09' class='xr-section-summary-in' type='checkbox' /><label for='section-58e39478-2600-472b-988e-c6610d65eb09' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>ROMS boundary forcing file created by ROMS-Tools</dd><dt><span>roms_tools_version :</span></dt><dd>10000.dev361+g2e6c47bd8</dd><dt><span>start_time :</span></dt><dd>2012-01-30 00:00:00</dd><dt><span>end_time :</span></dt><dd>2012-02-04 00:00:00</dd><dt><span>source :</span></dt><dd>GLORYS</dd><dt><span>model_reference_date :</span></dt><dd>2000-01-01 00:00:00</dd><dt><span>prefill :</span></dt><dd>None</dd><dt><span>regrid_method :</span></dt><dd>xesmf</dd><dt><span>extrap_method :</span></dt><dd>inverse_dist</dd><dt><span>adjust_depth_for_sea_surface_height :</span></dt><dd>False</dd><dt><span>theta_s :</span></dt><dd>5.0</dd><dt><span>theta_b :</span></dt><dd>2.0</dd><dt><span>hc :</span></dt><dd>300.0</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.Dataset> Size: 17MB\n",
-       "Dimensions:     (bry_time: 7, s_rho: 100, xi_u: 501, xi_rho: 502, eta_rho: 502,\n",
+       "<xarray.Dataset> Size: 19MB\n",
+       "Dimensions:     (bry_time: 8, s_rho: 100, xi_u: 501, xi_rho: 502, eta_rho: 502,\n",
        "                 eta_v: 501)\n",
        "Coordinates:\n",
-       "    abs_time    (bry_time) datetime64[ns] 56B 2012-01-29T12:00:00 ... 2012-02...\n",
-       "  * bry_time    (bry_time) float64 56B 4.412e+03 4.412e+03 ... 4.418e+03\n",
+       "  * bry_time    (bry_time) float64 64B 4.411e+03 4.412e+03 ... 4.418e+03\n",
+       "    abs_time    (bry_time) datetime64[ns] 64B 2012-01-29 ... 2012-02-05\n",
        "Dimensions without coordinates: s_rho, xi_u, xi_rho, eta_rho, eta_v\n",
        "Data variables: (12/21)\n",
-       "    u_south     (bry_time, s_rho, xi_u) float32 1MB dask.array<chunksize=(1, 100, 501), meta=np.ndarray>\n",
-       "    v_south     (bry_time, s_rho, xi_rho) float32 1MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
-       "    temp_south  (bry_time, s_rho, xi_rho) float32 1MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
-       "    salt_south  (bry_time, s_rho, xi_rho) float32 1MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
-       "    zeta_south  (bry_time, xi_rho) float32 14kB dask.array<chunksize=(1, 502), meta=np.ndarray>\n",
-       "    ubar_south  (bry_time, xi_u) float32 14kB dask.array<chunksize=(1, 501), meta=np.ndarray>\n",
+       "    u_south     (bry_time, s_rho, xi_u) float32 2MB dask.array<chunksize=(1, 100, 501), meta=np.ndarray>\n",
+       "    v_south     (bry_time, s_rho, xi_rho) float32 2MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
+       "    temp_south  (bry_time, s_rho, xi_rho) float32 2MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
+       "    salt_south  (bry_time, s_rho, xi_rho) float32 2MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
+       "    zeta_south  (bry_time, xi_rho) float32 16kB dask.array<chunksize=(1, 502), meta=np.ndarray>\n",
+       "    ubar_south  (bry_time, xi_u) float32 16kB dask.array<chunksize=(1, 501), meta=np.ndarray>\n",
        "    ...          ...\n",
-       "    v_west      (bry_time, s_rho, eta_v) float32 1MB dask.array<chunksize=(1, 100, 501), meta=np.ndarray>\n",
-       "    temp_west   (bry_time, s_rho, eta_rho) float32 1MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
-       "    salt_west   (bry_time, s_rho, eta_rho) float32 1MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
-       "    zeta_west   (bry_time, eta_rho) float32 14kB dask.array<chunksize=(1, 502), meta=np.ndarray>\n",
-       "    ubar_west   (bry_time, eta_rho) float32 14kB dask.array<chunksize=(1, 502), meta=np.ndarray>\n",
-       "    vbar_west   (bry_time, eta_v) float32 14kB dask.array<chunksize=(1, 501), meta=np.ndarray>\n",
-       "Attributes:\n",
+       "    v_west      (bry_time, s_rho, eta_v) float32 2MB dask.array<chunksize=(1, 100, 501), meta=np.ndarray>\n",
+       "    temp_west   (bry_time, s_rho, eta_rho) float32 2MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
+       "    salt_west   (bry_time, s_rho, eta_rho) float32 2MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
+       "    zeta_west   (bry_time, eta_rho) float32 16kB dask.array<chunksize=(1, 502), meta=np.ndarray>\n",
+       "    ubar_west   (bry_time, eta_rho) float32 16kB dask.array<chunksize=(1, 502), meta=np.ndarray>\n",
+       "    vbar_west   (bry_time, eta_v) float32 16kB dask.array<chunksize=(1, 501), meta=np.ndarray>\n",
+       "Attributes: (12/13)\n",
        "    title:                                ROMS boundary forcing file created ...\n",
-       "    roms_tools_version:                   3.1.2.dev23+gc2b82bc5c\n",
+       "    roms_tools_version:                   10000.dev361+g2e6c47bd8\n",
        "    start_time:                           2012-01-30 00:00:00\n",
        "    end_time:                             2012-02-04 00:00:00\n",
        "    source:                               GLORYS\n",
        "    model_reference_date:                 2000-01-01 00:00:00\n",
-       "    apply_2d_horizontal_fill:             False\n",
+       "    ...                                   ...\n",
+       "    regrid_method:                        xesmf\n",
+       "    extrap_method:                        inverse_dist\n",
        "    adjust_depth_for_sea_surface_height:  False\n",
        "    theta_s:                              5.0\n",
        "    theta_b:                              2.0\n",
@@ -2452,9 +2589,7 @@
        "</symbol>\n",
        "</defs>\n",
        "</svg>\n",
-       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
-       " *\n",
-       " */\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
        "\n",
        ":root {\n",
        "  --xr-font-color0: var(\n",
@@ -2533,6 +2668,8 @@
        "  display: block !important;\n",
        "  min-width: 300px;\n",
        "  max-width: 700px;\n",
+       "  line-height: 1.6;\n",
+       "  padding-bottom: 4px;\n",
        "}\n",
        "\n",
        ".xr-text-repr-fallback {\n",
@@ -2543,8 +2680,11 @@
        ".xr-header {\n",
        "  padding-top: 6px;\n",
        "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
        "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "  margin-bottom: 4px;\n",
        "}\n",
        "\n",
        ".xr-header > div,\n",
@@ -2555,46 +2695,62 @@
        "}\n",
        "\n",
        ".xr-obj-type,\n",
-       ".xr-array-name {\n",
+       ".xr-obj-name {\n",
        "  margin-left: 2px;\n",
        "  margin-right: 10px;\n",
        "}\n",
        "\n",
-       ".xr-obj-type {\n",
+       ".xr-obj-type,\n",
+       ".xr-group-box-contents > label {\n",
        "  color: var(--xr-font-color2);\n",
+       "  display: block;\n",
        "}\n",
        "\n",
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
        "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
        "  display: contents;\n",
        "}\n",
        "\n",
-       ".xr-section-item input {\n",
-       "  display: inline-block;\n",
+       ".xr-section-item > input,\n",
+       ".xr-group-box-contents > input,\n",
+       ".xr-array-wrap > input {\n",
+       "  display: block;\n",
        "  opacity: 0;\n",
        "  height: 0;\n",
+       "  margin: 0;\n",
        "}\n",
        "\n",
-       ".xr-section-item input + label {\n",
+       ".xr-section-item > input + label,\n",
+       ".xr-var-item > input + label {\n",
        "  color: var(--xr-disabled-color);\n",
-       "  border: 2px solid transparent !important;\n",
        "}\n",
        "\n",
-       ".xr-section-item input:enabled + label {\n",
+       ".xr-section-item > input:enabled + label,\n",
+       ".xr-var-item > input:enabled + label,\n",
+       ".xr-array-wrap > input:enabled + label,\n",
+       ".xr-group-box-contents > input:enabled + label {\n",
        "  cursor: pointer;\n",
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
-       ".xr-section-item input:focus + label {\n",
-       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       ".xr-section-item > input:focus-visible + label,\n",
+       ".xr-var-item > input:focus-visible + label,\n",
+       ".xr-array-wrap > input:focus-visible + label,\n",
+       ".xr-group-box-contents > input:focus-visible + label {\n",
+       "  outline: auto;\n",
        "}\n",
        "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
+       ".xr-section-item > input:enabled + label:hover,\n",
+       ".xr-var-item > input:enabled + label:hover,\n",
+       ".xr-array-wrap > input:enabled + label:hover,\n",
+       ".xr-group-box-contents > input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
        "\n",
@@ -2602,11 +2758,25 @@
        "  grid-column: 1;\n",
        "  color: var(--xr-font-color2);\n",
        "  font-weight: 500;\n",
+       "  white-space: nowrap;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > em {\n",
+       "  font-weight: normal;\n",
+       "}\n",
+       "\n",
+       ".xr-span-grid {\n",
+       "  grid-column-end: -1;\n",
        "}\n",
        "\n",
        ".xr-section-summary > span {\n",
        "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
+       "  padding-left: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.6em;\n",
        "}\n",
        "\n",
        ".xr-section-summary-in:disabled + label {\n",
@@ -2634,9 +2804,9 @@
        "}\n",
        "\n",
        ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
+       ".xr-section-inline-details,\n",
+       ".xr-group-box-contents > label {\n",
        "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
        "}\n",
        "\n",
        ".xr-section-inline-details {\n",
@@ -2644,15 +2814,81 @@
        "}\n",
        "\n",
        ".xr-section-details {\n",
-       "  display: none;\n",
        "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
        "  margin-bottom: 5px;\n",
        "}\n",
        "\n",
+       ".xr-section-summary-in ~ .xr-section-details {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
        ".xr-section-summary-in:checked ~ .xr-section-details {\n",
        "  display: contents;\n",
        "}\n",
        "\n",
+       ".xr-children {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 100%;\n",
+       "  grid-column: 1 / -1;\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 30px auto;\n",
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+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 26px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > label::before {\n",
+       "  content: \"📂\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label::before {\n",
+       "  content: \"📁\";\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label {\n",
+       "  padding-bottom: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked ~ .xr-sections {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input + label > span {\n",
+       "  display: none;\n",
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+       "\n",
+       ".xr-group-box-ellipsis {\n",
+       "  font-size: 1.4em;\n",
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@@ -2880,24 +3116,25 @@
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;bry_time&#x27; (bry_time: 7)&gt; Size: 56B\n",
-       "array([4411.5, 4412.5, 4413.5, 4414.5, 4415.5, 4416.5, 4417.5])\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;bry_time&#x27; (bry_time: 8)&gt; Size: 64B\n",
+       "array([4411., 4412., 4413., 4414., 4415., 4416., 4417., 4418.])\n",
        "Coordinates:\n",
-       "    abs_time  (bry_time) datetime64[ns] 56B 2012-01-29T12:00:00 ... 2012-02-0...\n",
-       "  * bry_time  (bry_time) float64 56B 4.412e+03 4.412e+03 ... 4.416e+03 4.418e+03\n",
+       "  * bry_time  (bry_time) float64 64B 4.411e+03 4.412e+03 ... 4.417e+03 4.418e+03\n",
+       "    abs_time  (bry_time) datetime64[ns] 64B 2012-01-29 2012-01-30 ... 2012-02-05\n",
        "Attributes:\n",
        "    long_name:  relative time: days since 2000-01-01 00:00:00\n",
-       "    units:      days</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'bry_time'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>bry_time</span>: 7</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-29fb40fb-78f5-4a93-807c-84d2e2ee2029' class='xr-array-in' type='checkbox' checked><label for='section-29fb40fb-78f5-4a93-807c-84d2e2ee2029' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>4.412e+03 4.412e+03 4.414e+03 4.414e+03 4.416e+03 4.416e+03 4.418e+03</span></div><div class='xr-array-data'><pre>array([4411.5, 4412.5, 4413.5, 4414.5, 4415.5, 4416.5, 4417.5])</pre></div></div></li><li class='xr-section-item'><input id='section-1c6279cc-0938-4e48-a2cc-e01ab3b536b5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1c6279cc-0938-4e48-a2cc-e01ab3b536b5' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-01-29T12:00:00 ... 2012-02-...</div><input id='attrs-e0f17b33-e0de-468e-88e8-7d97136220e7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e0f17b33-e0de-468e-88e8-7d97136220e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ba65477-9029-4eeb-8fc4-2a19532e054d' class='xr-var-data-in' type='checkbox'><label for='data-6ba65477-9029-4eeb-8fc4-2a19532e054d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>absolute time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-01-29T12:00:00.000000000&#x27;, &#x27;2012-01-30T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2012-01-31T12:00:00.000000000&#x27;, &#x27;2012-02-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2012-02-02T12:00:00.000000000&#x27;, &#x27;2012-02-03T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2012-02-04T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>bry_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.412e+03 4.412e+03 ... 4.418e+03</div><input id='attrs-78d4b87b-600c-400d-9524-b87860517eb7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-78d4b87b-600c-400d-9524-b87860517eb7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd14d832-3ab8-4efb-bfe3-ed5277abf559' class='xr-var-data-in' type='checkbox'><label for='data-bd14d832-3ab8-4efb-bfe3-ed5277abf559' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>relative time: days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([4411.5, 4412.5, 4413.5, 4414.5, 4415.5, 4416.5, 4417.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-85e8fe72-5a13-4ad5-8492-0be63b42cfa0' class='xr-section-summary-in' type='checkbox'  ><label for='section-85e8fe72-5a13-4ad5-8492-0be63b42cfa0' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>bry_time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-3658f73d-63d2-4cfb-b573-154f2cf01b53' class='xr-index-data-in' type='checkbox'/><label for='index-3658f73d-63d2-4cfb-b573-154f2cf01b53' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([4411.5, 4412.5, 4413.5, 4414.5, 4415.5, 4416.5, 4417.5], dtype=&#x27;float64&#x27;, name=&#x27;bry_time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8e59a785-235d-4ead-9464-fbd877ca5a06' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8e59a785-235d-4ead-9464-fbd877ca5a06' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>relative time: days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div></li></ul></div></div>"
+       "    units:      days</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-obj-name'>&#x27;bry_time&#x27;</div><ul class='xr-dim-list'><li><span class='xr-has-index'>bry_time</span>: 8</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-c4425bbe-7e13-4479-b4f9-52777e14c092' class='xr-array-in' type='checkbox' checked><label for='section-c4425bbe-7e13-4479-b4f9-52777e14c092' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>4.411e+03 4.412e+03 4.413e+03 ... 4.416e+03 4.417e+03 4.418e+03</span></div><div class='xr-array-data'><pre>array([4411., 4412., 4413., 4414., 4415., 4416., 4417., 4418.])</pre></div></div></li><li class='xr-section-item'><input id='section-2879039c-e85c-4ad4-8c4f-a53a5b2d123c' class='xr-section-summary-in' type='checkbox' checked /><label for='section-2879039c-e85c-4ad4-8c4f-a53a5b2d123c' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>bry_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.411e+03 4.412e+03 ... 4.418e+03</div><input id='attrs-22b5f6d4-85b1-4724-9185-ae996177cee1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-22b5f6d4-85b1-4724-9185-ae996177cee1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e053ff3-42f0-4caf-be1e-95171a2e44f1' class='xr-var-data-in' type='checkbox'><label for='data-1e053ff3-42f0-4caf-be1e-95171a2e44f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>relative time: days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([4411., 4412., 4413., 4414., 4415., 4416., 4417., 4418.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-01-29 ... 2012-02-05</div><input id='attrs-a76e9169-27d6-4840-a22c-c4d4c3d4dd63' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a76e9169-27d6-4840-a22c-c4d4c3d4dd63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7554a59-f521-4d47-8a94-78a859991154' class='xr-var-data-in' type='checkbox'><label for='data-d7554a59-f521-4d47-8a94-78a859991154' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>absolute time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-01-29T00:00:00.000000000&#x27;, &#x27;2012-01-30T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-01-31T00:00:00.000000000&#x27;, &#x27;2012-02-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-02-02T00:00:00.000000000&#x27;, &#x27;2012-02-03T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-02-04T00:00:00.000000000&#x27;, &#x27;2012-02-05T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-658273e1-d52a-4abc-a96e-0cf6b0079507' class='xr-section-summary-in' type='checkbox' checked /><label for='section-658273e1-d52a-4abc-a96e-0cf6b0079507' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>relative time: days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.DataArray 'bry_time' (bry_time: 7)> Size: 56B\n",
-       "array([4411.5, 4412.5, 4413.5, 4414.5, 4415.5, 4416.5, 4417.5])\n",
+       "<xarray.DataArray 'bry_time' (bry_time: 8)> Size: 64B\n",
+       "array([4411., 4412., 4413., 4414., 4415., 4416., 4417., 4418.])\n",
        "Coordinates:\n",
-       "    abs_time  (bry_time) datetime64[ns] 56B 2012-01-29T12:00:00 ... 2012-02-0...\n",
-       "  * bry_time  (bry_time) float64 56B 4.412e+03 4.412e+03 ... 4.416e+03 4.418e+03\n",
+       "  * bry_time  (bry_time) float64 64B 4.411e+03 4.412e+03 ... 4.417e+03 4.418e+03\n",
+       "    abs_time  (bry_time) datetime64[ns] 64B 2012-01-29 2012-01-30 ... 2012-02-05\n",
        "Attributes:\n",
        "    long_name:  relative time: days since 2000-01-01 00:00:00\n",
        "    units:      days"
@@ -2923,7 +3160,7 @@
     "\n",
     "The `bry_time` variable shows relative time, i.e., days since the model reference date (here set to January 1, 2000 by default). The `abs_time` coordinate shows the absolute time. The GLORYS data provided to `ROMS-Tools` has daily frequency; this temporal frequency is inherited by `boundary_forcing`. \n",
     "\n",
-    "`boundary_forcing` has 7 time entries because `ROMS-Tools` makes sure to include one time entry at or before the `start_time`, and one time entry at or after the `end_time`. This is essential for proper functioning within ROMS. If the provided data does not meet this requirement, `ROMS-Tools` will issue a warning.\n",
+    "`boundary_forcing` has 8 time entries because `ROMS-Tools` makes sure to include one time entry at or before the `start_time`, and one time entry at or after the `end_time`. This is essential for proper functioning within ROMS. If the provided data does not meet this requirement, `ROMS-Tools` will issue a warning.\n",
     "    \n",
     "</div>"
    ]
@@ -2939,19 +3176,19 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "30e29133-adff-4534-a7b4-63d22620ebfb",
+   "id": "f4c30235-da45-48fb-b8c7-277baa2c75d6",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[########################################] | 100% Completed | 304.13 ms\n"
+      "[########################################] | 100% Completed | 202.55 ms\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 900x500 with 2 Axes>"
       ]
@@ -2975,19 +3212,19 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "aa1c620a-81e0-426a-a110-561991d6b471",
+   "id": "a4b1b6ed-2860-437e-85ab-2b6ea3487c74",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[########################################] | 100% Completed | 305.95 ms\n"
+      "[########################################] | 100% Completed | 202.52 ms\n"
      ]
     },
     {
      "data": {
-      "image/png": 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dismTJzuOqwNiPUaMGIHZs2fju+++ww8//IAGDRogPT094Ovq1q3rdRUl9/c/e/ZslzvITZo00d3HunXrek1R8fZZK4qCUaNGISkpCePHj8f06dNx0003YciQIX6v0aZNGyiKgp07d6JLly5ez1F/9rt370br1q0dxy0WCzIzM8Py+713716XWZ99+/ZBURSX31dFUbB3716XTeSOHz+O3NxcR5/1CMffK1cnIiKjsOaASIeePXvit99+w5o1axzBQf369XHBBRc4VvNxzjdOT09HUlISpk+fjrKyMo/21JVjioqKPJZPbNOmDerUqeOyXGKtWrW8DhhvueUWZGRkYPny5R7P5ebmwmq16n+zQbBarXjzzTcd31ssFrz55pto0KCBYwOuAQMGIDs7G59++qnL615//XXUrl0bvXv3BmAflJlMJo/863nz5gXdvwEDBsBqteKNN95wHLPZbHj99dddzlPv1LrfmXVfRUaLTp06oVOnTnjnnXfw5ZdfYtiwYQHz8wH7zz8vLw/btm1zHDt27JjH6jhdu3ZF3759HQ8tKT7errVr1y6XlYz+/PNP/PLLLx7nvvTSS9iwYQPeeustPPfcc7jiiivwwAMP4OTJk36vMXjwYMiyjGnTpnnMvKifc9++fWE2m/Haa6+5fPbvvvsu8vLyMHDgQN3vLRB1CVCV+rtw7bXXArD/zgCeP/uXXnoJAILqUzj+XtWA3Nu/D0REenDmgEiHnj174v/+7/9w+PBhlyCgV69eePPNN9GyZUuce+65juNJSUl44403MHz4cFxyySUYNmwYGjRogKysLCxduhRXXnkl5syZgz179uCaa67BLbfcgg4dOiAuLg5ff/01jh8/7lJw2bVrV7zxxhv473//i7Zt26Jhw4a4+uqr8fjjj+Pbb7/Fdddd51g2sbCwENu3b8cXX3yBgwcPon79+mH/fJo0aYKZM2fi4MGDOP/88/Hpp59i69ateOuttxxLUN5777148803MWrUKGzatAktW7bEF198gV9++QWvvPKKIxc/OTkZN998M15//XVIkoQ2bdpgyZIlHvnYelx//fW48sor8dRTT+HgwYPo0KEDvvrqK48agqSkJPTq1QuzZs1CWVkZmjZtihUrViAzMzOo644YMQKPPfYYAGhKKQKAYcOG4cknn8SNN96Ihx9+2LEE7vnnn++1IDoUd999N1566SWkp6dj9OjRyMnJwfz583HhhRe6FKL//fffeOaZZzBq1Chcf/31AICFCxeiS5cuePDBB/HZZ5/5vEbbtm3xn//8B8899xx69uyJIUOGICEhAb///juaNGmCGTNmoEGDBpg0aRKmTp2K/v37Y9CgQdi9ezfmzZuHSy+9VPNnp0dmZiYGDRqE/v37IyMjAx999BFuv/12x7KsnTt3xsiRI/HWW28hNzcXvXv3xsaNG/H+++9j8ODBuOqqq3RfMxx/r2rw/fDDDyM9PR0mk8nl3w4iIs0it1ASUezJz88XJpNJ1KlTR1itVsfxjz76SAAQw4cP9/q6NWvWiPT0dJGcnCwSExNFmzZtxKhRo8Qff/whhBDi5MmTYuzYsaJ9+/aiVq1aIjk5WXTv3l189tlnLu1kZ2eLgQMHijp16ggALstPnj17VkyaNEm0bdtWmM1mUb9+fXHFFVeIF1980bGMqLo0orclU9WlHU+cOOFy/L333hMARGZmpt/Ppnfv3uLCCy8Uf/zxh0hLSxOJiYmiRYsWYs6cOR7nHj9+XNx1112ifv36wmw2i44dO3pd2vPEiRNi6NChombNmqJu3brivvvuE3/99ZfXpUxr1arl8z05O3XqlBg+fLhISkoSycnJYvjw4WLLli0ebf7zzz/ixhtvFCkpKSI5OVncfPPN4ujRowKAmDJlSsDPzdmxY8eEyWQS559/vu8P0IsVK1aIiy66SJjNZtGuXTvx0UcfhWUpUyHsv8OtW7cWZrNZdOnSRSxfvtxlKVOr1SouvfRSce6554rc3FyX17766qsCgPj0008D9mnBggXi4osvFgkJCaJu3bqid+/eYuXKlS7nzJkzR7Rv317Ex8eLRo0aiQceeECcOXPG5Rz1903LexZCCABi7Nixju/Vz3Hnzp3ipptuEnXq1BF169YV48aNE8XFxS6vLSsrE1OnThWtWrUS8fHxolmzZmLSpEmipKRE07W9LRUb6t+r+++h1WoVDz30kGjQoIGQJInLmhJR0CQhWNFERBROJ0+eROPGjTF58mQ888wzke4OoWKztRMnTlTKrBoRUaxgzQERUZgtXLgQNpsNw4cPj3RXiIiI/GLNARFRmKxevRo7d+7E//3f/2Hw4MGOFXCIiIiiFYMDIqIwmTZtGjZs2IArr7zSY0UkIiKiaFStag7mzp2LF154AdnZ2ejcuTNef/11XHbZZZHuFhERERFRVKg2NQeffvopJkyYgClTpmDz5s3o3Lkz0tPTQ1oWkYiIiIioKqk2Mwfdu3fHpZdeijlz5gCw7/LZrFkzPPTQQ3jqqaci3DsiIiIiosirFjUHFosFmzZtwqRJkxzHZFlG3759kZGR4XF+aWmpy660iqLg9OnTOOecc7hFPREREUU1IQTOnj2LJk2aQJYjnyRSUlICi8ViWHtmsxmJiYmGtUeuqkVwcPLkSdhsNjRq1MjleKNGjbBr1y6P82fMmIGpU6dWVveIiIiIDHf48GGce+65Ee1DSUkJzqlRG0WwGdZmamoqMjMzGSCESbUIDvSaNGkSJkyY4Pg+Ly8PzZs3x649e1GnTp2g2/WVv6VoTOySvUxaKMJ+h8CZJEmQJcAkSTDZSiGVlUAqKy5/2GdEJMUKCAE4zYQIOQ6QTRBxZkC2/2oIU1z5cftDyCb7OXIcFEiwlV+7zCYcfVFgL2ZR+yFLcMy4mCQgTiq/vmIDhAKpvA0hSYAkq28CkGTY5HiUKQI2RaBMEbDa7O27f6aK2obzZyoApfyITQGsikCpVUGpVaBUUVBcZkNBqRXFZTactVgBAAUlNhSWWpFXXIbC0jKcLbGhuNQKa5kNNqtwfNY2qwIownFd2W1GSXH6mQghIMp/yNYyAZtVgSh/rWKzvxvFpsBqKYO1pBClBWegWIpRVnQWZSWFEIoNkmyyf37mRMSZayAuoSZqNWqB5AYpqFMvEQ3q1USr+rXQOCUBliP7UXJgK4TN6vkLQ0REVV5paSlefvnlkMYsRrFYLCiCDSPQFGYDSl0tUPBB9hFYLBYGB2FSLYKD+vXrw2Qy4fjx4y7Hjx8/jtTUVI/zExISkJCQ4HG8dp06qJOUFHQ/vAUB3ko+FOE9EFCpA231td7alSXAJEuIRy3IlkJIlnhIZfGQrOXTeooNklAgHINxGZAkCFMcYDLbAwIAwhQPmNTAIM4RGFgFYBOArfziibB/bRMV/ZfKAxR7oGK/jEmW7McgXAIE9foVb1J2XMeqCFhsAmXlA3z7e3YafLt9vupTCoTja5sClCkKZKv9IVkVKGU2lMXZUGaxQpbK7J+bsALCCtjKoChlEDYrJMUKSSiQJMVxEUmyD/AlRUAq/2Gp/xWKcPzzJ8kSZEmCHGc/IssSFEXAZJIhhIBsUl8DWMtsKCu1wlJqQ3FBKWxWBZazpyEUG5Tyn5ui2BBnrgFJNsFcuy5MCTVgMteAnFADNlMC2hcfxJ+H/0JCfBwQXy3+vImIyIdoSoU2Q4ZZMiDFqVpUykZWtRg9mM1mdO3aFatWrcLgwYMB2OsIVq1ahXHjxmluRxGuA3F/A3hfswG+ggGtr5cl1zYU4f3vRBGACSgfdDs9nPvi7Y/U+c690zGhBg+SbL/D7/Y+FAEogNPsgQSTVPG12pokAAnlA2pJtg+44RQYlF9f7ZtwalsI4TFD4PxZ+QoKAMAmRPnshrB/XT4ToX6vspXPUtgUxfG1UITj7r86A6B+rZT/Usg+fhnUwCAuXoYsS5BN9v/Gxcn2QKn8dSZZQrHFhtJSK8pKbUisFQ+rxYaypETYbAoUa/kMgyJgipMhyRISasShRu0E1KpjRp3EOKTk7MSfJ/Z67QcREVEkmST7zcGQ24HEACHMqkVwAAATJkzAyJEj0a1bN1x22WV45ZVXUFhYiLvuuivoNtVBqfO40F+KkNbAQD3k7U/IeVYhUDqSIuyNiPLAQPIVsavHZdeBufOxYIjy6wfkFBg4X1sNQtzfpq/AQE0hcgQJXj4fm+J6XA0MbE4HnYMF4daI4uVn6I8kSzCZZMTFm2CKk2EuDwpqmu1pQua4ivebECfjrCyhWJIgmySUmWTEmU2wWlzzNE1xMkwmGfEJcahVx4zGKTXQVJxGPAMDIiKKUs5ZBCG1AzA4CLNqExzceuutOHHiBCZPnozs7Gx06dIFy5Yt8yhS1svf7EGofI2ttdYoeKUO9pXyAWcIU3xaVsFVYJ/B8HmmJNtzarwdR4jvVSObj4uox5XyGQLnQEGdNRBCeJ22dU41MsVJkGUJ5jgZNcwmJDhmDWTHzIGqRnnQUAx7CpK1TLGnIKnpTLLkCDjiE0yoYTbBbAIaZu9AniRp+pkQERER+VJtggMAGDdunK40In98BQX+ZhEkL4M3WfI9APYVd2idOVDPk4QCSR2AK14G4kGSvKQXefRBPdfXCd4CA/W4ZApr8KVyH6CHQnJrSy4fzMeVzxrElacSmeNMLmlF7v1Q04xkkwJrmc3lY5Jk++yBuTzQSMrLQm5urmHvgYiIyGiGphVRWFWr4MAIesaR/gb+gYTyqy9BRz/VYmDAHjjI9mBC2CsWHMe0kiV7oXKw1CDGaz1EFJEC/HDV1YskSYIkSY5AwJ5WVFFvoP5DaZIl2CQBmONgkmVYrDaY42RYrAosVhOsTjUHgD3oMMkSzCYJNU/tD/O7JSIiCo3JoLQiU+hNUAAMDnQI5gaz+11+95WG1HOcx5n+LuPcB00zCM63nP3epfczGPf1Ove+AbChYglToxZJcG5GliQoTnUIzp+dDAkKBPxNaJhkQHZ6O/7uYqh38n0WG8uSy0SMOmsgS5JTWlHF83FOxcdqYKDWHMTJkmMlpopzbIgrDygs1ooLqelO5jgZiaVnYLIU+HwPRERERHowOAiRr6Gl+9g00CxCKLMM3tqSJCm0iTf3vROEAiFk+519IUOW7HscqONmAd8zBu5ja1mSHHsbOK4lSR5BivM56r4Jar/UJj0+5/IAQb2uY0gtXPcicL974ZLS43Se5BQgCD/TIs6BgTv1Lr/9OrIjMDDJkkfAECdLMFkl2BRRfkyBxWpznOccQMTJEpLOHIEsy1AMTBcjIiIyGtOKYgeDgyAF+tWU4D9A8DaDEIi/OgefdQtCsQ+6Nd7913wevKcQSZI6kHca0KOi787vQXKZ1RAuUw3qHgxaPmc1CHD+KN1nD7z9e+TYe8E5aHAaxGsluwUGatGw+pwkS3BfttTkdkydQVCDgtLymQI1FUmdObApSsV5ElCr8DgDAyIiinpMK4odDA500vN77evutss5TkXK/gb57oGBe3DhMuh27EzsthawUCruvDsNzIW6YpBkqjhPbUsoEM4BhrDf4Zdkk33PAnVArK7ao3e1HCFcZwzUazvNIDgHFvagQ/K6pKh6eef0IvcPX3a7c+HtTr+/AmX1fEVd/VW9tttGaL44tx3nFiSoz1msimMWwVq+14LZJLu0YVMEygpyYbKV+r0eERERkR4MDnQINuB1HqPqSS/yNc50XjrT2wpILnTNGjin+qiv0RejywAUt7oI97fhGLS798tt9sD+vFz+GtfAwzFIdwus3AME9TO0CXu9QZni+nr1a28BgTpgtwYY8LsHBJIsOWYTJC+Df+e2XQuVK2YW1ABB3YjN26zD2TOnA//8iYiIogDTimIHg4MQud959rVJlr8AwX2Ap7fw2X2dfXVA7nLYzwBSTeFxfO1exKwogKR4rTtQS4OF5Fp34JxS5NxHybld9/86V++Wfy9Lkn22QBKOdhWnOMG5QNk9QHD5TCDBBgGTJDl+Zv5mDdyDBam8yNh9UzSXc5zSifxtWW+SJKclTWWXAAGoSC1yLkJWqefXiDfBWpjj8xpERETRRIKuxQ/9tkPhxeAgSN4Glu7H9e6mq4W/Qaf9+moefnnRr5q2A+iaRXAsKapj5kAq75/s9L7VmgBf7atfO1Kb1H86JMl+jlRxfee3rn6tXspbjYf7a2SnPCNHEKDWHbjd1ff5Hp2mIpxnDFxmIpyOS5LnjIHz186BgZo6ZJMrViNSAwQ1cDDJEuJlGWbYgLzjnDUgIiIiQzE4CIKvwMDbeYrbQNl59gBwLVDWO9BzbsOzJgH2O/7qnX+PFB7XXbWcZw9czxMV5/uoO1D7j/LVi2yiou7Bva8uKxUJ4QgSfF3fPmvg/L09+FCcn3eqQXCePZBFxcpF3uoP3AME52NAxdKj3gIy55+ttzoD97dinymoGOB7e8SXtxMPyZ4GJTmlE5X3IV6WEG+SYcn5R1fxOBERUSQxrSh2MDjQSWtg4Hy+1hkEfwGCt9Qhb1+rswaA287IcFsdyJma0iMU6K4xUAujISDcAgN11sDrbIfzLIa3gmTILnOH6nSkzU9ffM0euPfX5KUh99kC9/QiSbYHG7YAaUWyLLm8X197JDhfR50NcDlfEVA/AG+zEgXHDnIJUyIiihlcrSh2MDjQQW9g4Pw6x11muA5gA9UfeL9r7XkNyfGcPUffeY8A5yDBZ4Cg8rIjsn3HZOd6AOGoOxBOI3ipfFbAvQjZ+XvHjIajrYoAQYLbzshCcd0PwblNtbvwPnugPicL+zc2dfUipz4B9sG2+wA+zi0wcPksyo8JRdj3eoDv2QOVv2Jn5+dk2emuilxeX+H0UdksJSjMPoqSYwdRdDzL5/WIiIiIgsXgwADBrOCprd0QQ2x/NQZelgz1eM5bn3ylH3nhc9ZAC6d6A+cZCFkSUOC0/CucNjqD79kDNd3J9ZjvgmRf3zuuU16cLMmSSzuOfR00LG3qsgKRVJE6JJcXTSv2ZZdgyT+N/H3bUHzsIAABWcceDERERNHA+f9zIbVjQF/IPwYHBnEvkI24QDMEjjSiyucxe+GU1qT+s2EvUPa+lwGc9ldwXeXJPqD2l15kkgFZCRwUOHMM9L0VLng7z8vY3V9RsnP/1SlXq01B4bGDyDu4E5bTx12uzVQiIiKKNUwrih0MDkKkNQj2V3vgLbVI8/U1nxlegfpRUQdRUYysSfkKRuqsgU14fl7uy5uq/ZElp1kFUbGcKeB6l96de7qPFoE2PwvUrmOXZVlCaf4ZHN24CpaC3KB20iYiIiIKFoODEHgbw2uZQdBSOOuLe/Gx1/a1DiQ1pgeFi7/6B+f0JVmSYHNOI5Kcaw7c94jwvXuyO293MHze1S/fkEwdxKuFyb7qUCQf7fg9JgFlBXnI+uk7CGsZAAYFRERUNXC1otjB4CBIBvx+G8pxZz6UyMOQfvjfAdon903QvAj0kTt2R9axQhQQXA6kc2GycyDg/hb8zUC4L1MKxYrDG5ZBWMsYFBARUZUiG5RWxKq78ONnXJW571FQzdn3THC9cxHoHyo96UXuxchaOPcl56/fYC0uYGBAREREEcOZgyBE26yBNwGXLK1Euj4uf6soeWGvK6jYGM199sBpQ2O317muMuQSMMiuAYSWegIt+xmo4rzUHpSeOYG8g7sCXoeIiCgWMa0odjA4MFhl3fTV/KcRRUGCVxr7J0v+N0Bz5m1pWediZM+2g/+HxlfgILkFG+p/3YMQwF5XkLsjI6hdsomIiGIBVyuKHUwrqk4qcQlMxyZjofxD4K9gWfKcwTHil9lfGpH7ZIYaGFT8V19Kkaos9wTK8k4xMCAiIqKI48xBVRTqbIFBqxiFvIkbytOjpIr7BFKIO86pdQdamPxsNibLEhQfldfeVipybst9SdOSo/shyzL3LyAioiqLMwexg8FBEISIrroDlx16K7kI2XHpMN70NmoFJuddkk2yesz3fgfe+6J/34E4P7MJQrGh9FgmBAMDIiKqwlhzEDuYVmQgf+NF56U1q2rySEgpRHquo/U8SYIE3/UZulYiCnCu+yyJLEseKUbeNkEryz0BYSvT3A8iIiKicOLMQZDUsb6WTc8qg887+EKJfOeigL/ZB/dpTj07JAcKGpyLkb09Z80/xUJkIiKq8kwwKK2I/7sMOwYHVVA0LWNa3Uiy5DGL4C/QKMs7Ge4uERERRZz7PkOhtEPhxbQiA4R60zeoHYXDxU8RbiwJ9t8OPalGAfvgnlbkpVM1SnI5a0BERERRgzMHVZUQrjMI/mYTDFqdKBaFGgwIRXhNLfJ2zCYETKhIWRJWC/Jyc0O6PhERUSwwbLUiThyEHYMDqmBAkCBJEuQwlVw7rzYUdBuQYJKF9h3VygkN0zt69ziwnT2jrxNEREQxyrDViphWFHbV95ZxVeU+Q8AlMqOWrehspLtARERE5IIzB5VA0ZlTLoQwZAMxXdesIqlFMoBgwiGt05RqfYCvDdA82pUl2BRR8ZDs/wUApaSQm58REVG1wLSi2MHgoCpSZw/c/2tEuxEOIrylFmlNN3KOt8K52oHsZ/lSZ6KkMGx9ICIiiiZMK4odVeN2MUWc+2C7smY+1MsEcz29tch6Yiwthc624rOcNSAiIqKowpmDEERT8Cq5py5Vxl4HQgEkU/ivE2Z6C4mdSeU7IasTKnqClNqiFHlBX5mIiCh2yJJkyKw99zkIPwYHlSxQ9oua0x6JuoNKE0Tg4vxJSJIEWc399/cayb46EWBfRtRIsizpqjtw33VZCIGzZ1mQTERE1YNkkrwu8627nao6NooiTCuKMep4VB2WeqTzuO1tEC27JUtS5c20GP1LrWUZU8D73gbOTJJTgFBWwpQiIiKiSjB37ly0bNkSiYmJ6N69OzZu3KjpdZ988gkkScLgwYPD28Eow+AgwiK+O3IYC4wN3Gw4ImwaB+9B3cUoLdL/GiIiohglmyTDHnp8+umnmDBhAqZMmYLNmzejc+fOSE9PR05Ojt/XHTx4EI899hh69uwZytuOSQwOqiL33ZGByqlB8EKC2+xGhPoRjkAlmFoFNb1IsjA4ICKiasQkQzLgAZO+oetLL72EMWPG4K677kKHDh0wf/581KxZEwsWLPD5GpvNhjvuuANTp05F69atQ33nMYfBQZAqI0VGGJwnX1mqQj6gzW1Kx1d9gRogyLJkr4XwEjA41xzElT8vSouqxOdEREQUCfn5+S6P0tJSj3MsFgs2bdqEvn37Oo7Jsoy+ffsiIyPDZ9vTpk1Dw4YNMXr06LD0PdoxOKjKhFKpdQfhHupGYjDtHiQ4UxThNWgIWHsgS0BZCYMDIiKqNiRZshclh/oo/39ss2bNkJyc7HjMmDHD45onT56EzWZDo0aNXI43atQI2dnZXvv5888/491338Xbb79t/IcQI7haURSJ1ZmCaORvp2QjlkHTslKRc2Dhse9BWUnIfSAiIooVwdQLeG2n/Fbk4cOHkZSU5DiekJAQcttnz57F8OHD8fbbb6N+/fohtxerGBxUJ5UQfMhSeIusQ/1nRZIQeD1ZH9TgLVBgIBQBm5fjzulForSYqxUREREFKSkpySU48KZ+/fowmUw4fvy4y/Hjx48jNTXV4/z9+/fj4MGDuP766x3H1P9Xx8XFYffu3WjTpo0BvY9uTCsKQrRlg7j0pzyVKFKFv+HgscGbr/N0/GD0bL/uHAxo3dsA8L8EqrAUa26HiIgo1kmybNhDK7PZjK5du2LVqlWOY4qiYNWqVUhLS/M4v3379ti+fTu2bt3qeAwaNAhXXXUVtm7dimbNmhnyWUQ7zhwYQJJ835RXQrxbr96trpT89CCuoQ7chd57+hEIXmRIsLlNG2hNMdLaXUURkGXJJTAwybJnMMLggIiIqhGj04q0mjBhAkaOHIlu3brhsssuwyuvvILCwkLcddddAIARI0agadOmmDFjBhITE3HRRRe5vD4lJQUAPI5XZQwOqgpvS5cqij1qCcdAXChh3SPBH1kCbJVQnuGvGNkfNUBwF1eeUiRLgGK1hNo9IiIiCuDWW2/FiRMnMHnyZGRnZ6NLly5YtmyZo0g5KysLso7ZiOqAwQG5EJJsyKA/UhugSZLkUtjtuGGvY5zvHBQEGyAAflKQrBYWnxMRUbWirjYUcjtBVB+OGzcO48aN8/rc2rVr/b524cKFuq8X6xgckD0giDFqmpUsvBf/Av5XLArE5jR49zaQF4rwu2Spe2CgFiIDACxcqYiIiKoXe3AQ+nhDCvr/7KRV7I0KIyzaipG9Ufc1qKz9DXz2I6JXd2VEX9wH/P4KjgHfOyiLMtYbEBERUXTizEGEuI8rI55m4jZ7oHc2IZQlQiPJvWDcVxqR+89Hcis6difLEuLi7J+h8x4HJlnizAEREVU7kSpIJv2ieubg2WefhSRJLo/27ds7ni8pKcHYsWNxzjnnoHbt2hg6dKjHWrZZWVkYOHAgatasiYYNG+Lxxx+H1Wqt7LdS+dRZA3+zCDGUTqRn4zK9KzspioBNeC9y9ldzIMkSZFmCvUzD/rWv2QIX3B2ZiIiqGUmy724c8oP//wy7qJ85uPDCC/Hjjz86vo+Lq+jyo48+iqVLl+Lzzz9HcnIyxo0bhyFDhuCXX34BANhsNgwcOBCpqanYsGEDjh07hhEjRiA+Ph7Tp0+v9PcSCiGEtj+ISM9AoPKLkcM1axFsMbIpToZJlmCOk5EQ5xmAifLgIOKzRURERERuoj44iIuL87qLXV5eHt59910sXrwYV199NQDgvffewwUXXIBff/0Vl19+OVasWIGdO3fixx9/RKNGjdClSxc899xzePLJJ/Hss8/CbDZ7vWZpaSlKS0sd3+fn54fnzVUxsiS5FPJWdXL5HQz1v1J50bF7UGFyj5bKSkFERFSdyCYZsgEFybKInayHWBX1n/DevXvRpEkTtG7dGnfccQeysrIAAJs2bUJZWRn69u3rOLd9+/Zo3rw5MjIyAAAZGRno2LGjYy1bAEhPT0d+fj527Njh85ozZsxAcnKy4xELO+JJQqlIHXL6rxSO3ZJ1tKcnHUhbe4Y251Uoy5cCgDlOhjlORk2zyTMwACAsJY7t2ImIiKoDdSlTIx4UXlEdHHTv3h0LFy7EsmXL8MYbbyAzMxM9e/bE2bNnkZ2dDbPZ7Ni5TtWoUSNkZ2cDALKzs10CA/V59TlfJk2ahLy8PMfj8OHDAGJjpaJIC+dHVJmBgXOA4K/wWK03MJUHBOrDb/tcrYiIiIiiVFSnFV177bWOrzt16oTu3bujRYsW+Oyzz1CjRo2wXTchIQEJCQmGt1vVEm4koWha1SgWgirnVYtCnTkAgJpmE2qYXf+8bIqwzyQwrYiIiKoZwzZBEzEwqIhxUT1z4C4lJQXnn38+9u3bh9TUVFgsFuTm5rqcc/z4cUeNQmpqqsfqRer33uoYqgQ15UdRIlqcXBX+dK0Blip1rjcA3DY6c8KaAyIiqu7UmgMjHhReMfUJFxQUYP/+/WjcuDG6du2K+Ph4rFq1yvH87t27kZWVhbS0NABAWloatm/fjpycHMc5K1euRFJSEjp06FDp/ffFfdUaX2PSiKxuEwu3/b3wtbKT1tQkb+lF/jgHAOqsgbl81SIXis3+ICIiIopCUZ1W9Nhjj+H6669HixYtcPToUUyZMgUmkwm33XYbkpOTMXr0aEyYMAH16tVDUlISHnroIaSlpeHyyy8HAPTr1w8dOnTA8OHDMWvWLGRnZ+Ppp5/G2LFjw5I2FPXCsWOy2qZk8nuaFMWrGHnb38Cd++7IvnhbutSFlbMGRERUDRlVTMy0orCL6uDgn3/+wW233YZTp06hQYMG6NGjB3799Vc0aNAAAPDyyy9DlmUMHToUpaWlSE9Px7x58xyvN5lMWLJkCR544AGkpaWhVq1aGDlyJKZNmxapt1Q53FcuEoo9zaiSxPKfrZ56A+fNz5xTisxxJsesgUmWYHKaxVAsDA6IiKj6kSWNG4VqaIfCK6qDg08++cTv84mJiZg7dy7mzp3r85wWLVrg+++/N7prYacI3ykw/p4LiSwHvWuyJIQ9BcnICYIQdnD2lYLla+zvvD+Dv1qDQLzVHDi3G896AyIiIopiUR0cUPgJqTwg0BuJCxGeKYIgAoJQMpbcZwq0zhx42/xMDQx8FSYDYFoRERFVS5JJhmRAMbGkxFS5bExicFCVhavGQOMAXtfUX5CzBM7vUAjhsxDZnU0In7MI/gSaEvU3cwAAkrUUkiRFpriciIgoQmSTBNmAmgNZYVpRuDH8MohRKXDBDFgNL/YNIZ3HqFxALfsn+Hytzs9D8XG+1lkEtb7AvRjZ2+vV4ICIiIgoGnHmgIKnYRYh4DhYkg2d4TDqjryWwEAtRtaFNQdERFQNGbYJGmcOwo7BQVVUvkKR5PxfA2nZGTkcqwkYsHGxT0bsimyvNbDPIji3ZxMCpvICDamsBEolrhxFREQUDVhzEDv4CVcVYQoEjBZs/3yl/qiCnTHw1q57oOBtjwN7DXdFMXKg/Q3UNiVLUVD9JCIiIqoMnDmIAK03qcO2ZKkRhAJIJsdgXwA+N0LTXBPhYzZCSJJjSaJwzh4AGtOJ/MyKBCxILivW3SciIqJYJ5tgUEGyAZ0hvxgcVHWOzdDcBr0hFPxWJl/pS+Fc7cdfgKAGBs61Bo7NzuQAn6kQXMqUiIiqJUm2z7Yb0Q6FV2yMEEm/UAfPUnAboklSEPUGEQ5UbCF8Vu4zBe7fOwcakrU0pnePJiIioqqPMwdRJNwpM37JcsWGaEGShLCnAIUgmCVMg/3c/G2AZguiaNh5EzSvy5gypYiIiKopWZYhG1CQLNt4XzvcGByQPs7LlwbYJVlXnGDA7IECbVNhWoqQA1GXMTW57Y7sjU0RMJkkyGUluq5BRERUVRi2lKkBbZB/DL+qGqEA6l1vtd4gxKUzHXfzjVwJyTkY8BYY2JcDMu56GnkLEtT6AvW//uIY5yChRrxrgbZkZXBARERE0Y0zB1FOHatWZv1NMKk9Afc+8BVYuL/GSxthrD3WLFBgoO5v4Hm8IsVILiuGLMvc54CIiKodw/Y5MKAN8o/BQTXmqDFQ79DrCAokodiXLxX+X+tvGVMhyS77HngNLsJUrBxo3wQt1D0O4pxXLpLKU4xMQEKcDIut4v1JTCsiIqJqSpJlSIFW9dPYDoUXgwPSz7nuwAe9GUGBAgM9Q3lvtQe+Xm8zcFbC28yB2STDJgmYZAlyWTFnDYiIiCiqMTioKsKVexPkcqahX9d3I/5qhxUfX4eTt6AgIU5GDbMJpVbFcY5cWlBJPSIiIooussmg1YqYVhR2DA4MUll58UIIvzv0Bt2uGgToDQbUNy7BsWuy3us6Uou8FCHbd0cOfHl34Q4MJMn7KkUmWUJCXMVnmBAn2wMEISBbisLcKyIioihlUM0BGByEHT/hMNKS1x7OnX41CyZ/T8PKRf7qDewn+F+xyD3VyN9npSUYUCC8BhNKgGVM1d0Yve2O7MzkFNjUMJtQw2wPlBLiZMQrFpf6CiIiIqJoxJmDqkoogFBiYkAazkJkf/GJnqLkgIFB+fGEOBlmp5mDGmYTii02SEwpIiKiakySDVqtiAXJYcfggLwLMnVJ9y7Jkuw6C+ElKDBiZSFnNi/xkt5N0LQwx8kotthgjpMhyphSRERE1RdXK4od/ITJTl3WVP1ay0ucB/X+Zii0zl7oCCoUVF7BsRbutQdqSpE6iyCXFEDmP2hEREQU5ThzUEmioLLAe4qR7jVHnYqHQ+qM5Jrz460fTtcIw419TUyyBGuA550DA5MsOQKCGvH2lCKTLEFYCqOjvoSIiCgC7Jug6Vu0xHs7NgN6Q/4wOKiCvAYBlVh7ELAQ2eMFgYuR/Qlm0O0ttcjxnIb2ZB8rFanUAMEcJ8NiVSCVnGVwQERE1RZ3SI4d/IRjnKQuIRoFjCh+FpLsNzDwN7wO21YP5bMaFVlXnrMcamDgPHOgMsfJLEgmIiKimMCZA3LZ40DzHXt1l2RHmpGPqUJ/AYNHapGXGQSDBvy+0pL0Fju7BwZxsgSTl1qCGvEmxJefK2xWSFaLrusQERFVJbIsG1J7x/q98GNwUJ0ZtFyoT3pmErymFvnfAM3rJUVF+YKRMwnu3ZP8pBU5zxyY42SUFRSBGZJERFSdMa0odvATJmNpCQg8RtqS63POKychuGLuUAIDdTMz98G/7LYZmsocJzte4xwYqOeLEqYUERERUWzgzEFVY0T9gSQ5Bufa04wEEMzWCE4DbW/Xck77CXXFIiHsuyRrYU8V8rzf7x4YuAcQKjWlyGySYbIU+V3xiIiIqKrjzEHsYHAQJSK1VKeDWm+gJ9VIrTsI4ZpqMOMtMAj27r/z6/zVFNg0tK8O/t13RnZfwhQAappNMDvtkKzOJijF9j0OFCU6CseJiIgqmyQZtAlauFOiiWlF1V4wf6haZicCnRPgj1vXLstB0FOI7Egn8hMouK9Q5HyOUlLAwICIiIhiAmcOqhEhyYYsNxr4QvquIeSKX0O9QUEwMy7ugYGWGQSVt8BApQYI8eUBl1z+XkTRWf2dJCIiqkKYVhQ7GBzEMDnMd9d185Nm5DMocS48lj2XQ9U6bvc1ESACPB+IyW3WwPm4c2CQ4LTpGVCRUiRLEhQWJBMRUTXH4CB28BMmO731BurLhBJwNsLX8y51BgGKkYPZXVjvHgbu4twCAvdiZJXJT5CmWC2AjeXIREREFBs4cxAmvgamES88LhdoYB64gdCKkQOtguRvXB9MoBAKSZZcFi5ynkVw3gCthtk+8xFvkiBLEkwSgJLCyuomERFR1JJNMmQD7vob0Qb5x+CgunDezTjaOKcWeRn3G1FXoLbjfljx0bivJUqdA4M4L/UGNeJdU6NsxUwpIiIikmTJmNWKfPz/mYzD8KuqEEr4Bv86/pj1Fjy7FCMbNCEQajqRXu4rFakFyXEmGdZizhwQERFR7ODMAbly2gAtIOfUIqEAkmdBsVZCkjyqj4MZ4msNDGx+znOfNTDJkssmZiaXtCLX2QN18zNT+WFbSYH9M63kgIWIiCiasCA5djA4qM4kybHxmeadkPU0H8JMRrju/itC+A0MAlGDAffVitxnDwB7gGArLoSE4AIdIiKiqoLBQexgcECGCxQUuAciznsbeAsKtBYg+wsoFIigbt47ljL1syKR+6pGakEyAFiLzlZ6ATURERFRsBgcUKXSGxg41ws7P6t1vO2vmDnQ7IR7epH7Bmju55hNMhLiKlKrZEliQTIREREASZKNKUgOQ6YDuWJwEKOch6nh2PVYc5qRem1Jto/Y9SwiEOAazgN7v4N8X13T0ZVg+EorUusOFMUGxVIS5l4QERFFP8lkgmwKvjbRuR0KL4ZfBqiMrJFK2R9BTzGyEmJA4j6DYPD789dcMPUM7rMGvpY6dVZWxJWKiIiIKLZw5qASVKmM81CDAi+8jdV9fWbBBkla4wEtg35fHBuflX9tKcwPui0iIqKqhAXJsYOfMGmnJTDQu8+Bl+VLXWsL9EcD7rUJoc5KOIqSnVYqcmexVrxvtRi5tIDBAREREVARHBjxqCpKSnynHh87dqwSe+Kq6nzCFHOE2wpARsywOLfhvxg5uPadAwNbeSPW8v/al0mtONdSlK89TYuIiIiqlUsuuQRbt271OP7ll1+iU6dOld+hchy5xCDnG9dSJJfJNLAQ2uh9DbwN/hUI2DR22eQ2S6Bnu3b1vVgKz4Zv12oiIqIYIsmyYY+qok+fPrj88ssxc+ZMAEBhYSFGjRqF4cOH49///nfE+sWaA4OFa/OusCnfBC2Sd7gDLWHq7y6/9j0QAp8TqN7APWAAKmYPbG4XUIRA6dk8TX0jIiKq6lhz4GnevHkYOHAg7rnnHixZsgTHjh1D7dq1sXHjRlx00UUR61dEP+H169fj+uuvR5MmTSBJEr755huX54UQmDx5Mho3bowaNWqgb9++2Lt3r8s5p0+fxh133IGkpCSkpKRg9OjRKChwXVt+27Zt6NmzJxITE9GsWTPMmjUr3G8tagl1N2RJBiIVffsJRBQRXMqPr6BMgYCiIWHJecDvHgT4ew6oqDew2BSX1KKyorM63gERERFVN9deey2GDBmCX375BVlZWZg5c2ZEAwMgwsFBYWEhOnfujLlz53p9ftasWXjttdcwf/58/Pbbb6hVqxbS09NdCjjuuOMO7NixAytXrsSSJUuwfv163HvvvY7n8/Pz0a9fP7Ro0QKbNm3CCy+8gGeffRZvvfVW2N9flRKGVYoA10G90cu1GjmJo3cVI5ulFIq1zLgOEBERxTBJlowpSA5hVcFos3//fqSlpWHJkiVYvnw5nnjiCQwaNAhPPPEEysoiN4aIaHBw7bXX4r///S9uvPFGj+eEEHjllVfw9NNP44YbbkCnTp3wwQcf4OjRo44Zhr///hvLli3DO++8g+7du6NHjx54/fXX8cknn+Do0aMAgEWLFsFisWDBggW48MILMWzYMDz88MN46aWXKvOtVnnBbMTmPHj3No53ThlyKTTW2bbzMUVorzsAgLgA/wip6UTOqxUBgIWzBkRERA6RrDmYO3cuWrZsicTERHTv3h0bN270ee7bb7+Nnj17om7duqhbty769u3r9/xQdOnSBa1atcKff/6Jf/3rX/jvf/+LNWvW4KuvvsJll10WlmtqEbWJW5mZmcjOzkbfvn0dx5KTk9G9e3dkZGQAADIyMpCSkoJu3bo5zunbty9kWcZvv/3mOKdXr14wm82Oc9LT07F7926cOXPG67VLS0uRn5/v8ogWkhTGiLk83Ufz7sghcF+pyOP5EJcw9WxPd3MefM0e2ERFgOD4mnscEBERRdynn36KCRMmYMqUKdi8eTM6d+6M9PR05OTkeD1/7dq1uO2227BmzRpkZGSgWbNm6NevH44cOWJ43+bNm4dPPvkEKSkpjmNXXHEFtmzZgksuucTw62kVtcFBdnY2AKBRo0Yuxxs1auR4Ljs7Gw0bNnR5Pi4uDvXq1XM5x1sbztdwN2PGDCQnJzsezZo1C/0NVTdC0bxSj/u+BsEEBs6c05P87XPgXkRsJEvh2fAGckRERDFEkk2GPQB43MQtLS31et2XXnoJY8aMwV133YUOHTpg/vz5qFmzJhYsWOD1/EWLFuHBBx9Ely5d0L59e7zzzjtQFAWrVq0y/DMZPny41+N16tTBu+++a/j1tOJqRV5MmjQJEyZMcHyfn59fNQIESa6cpTV1XCPQ8FzrqkVez/fTulGBgU1RAJgcaUWKIqAIAUtBPiRJCjnQISIiqhJkk/1hRDuAx7hsypQpePbZZ12OWSwWbNq0CZMmTap4uSyjb9++jiyUQIqKilBWVoZ69eqF1m8vPvjgA5/PSZLkM3gIt6gNDlJTUwEAx48fR+PGjR3Hjx8/ji5dujjOcZ8WslqtOH36tOP1qampOH78uMs56vfqOe4SEhKQkJBgyPuoErQWIwsFgAx4uWOuJVUp2IG0npfZNJ7srwDZZ3qRU8BhKcyHEqYibiIiouru8OHDSEpKcnzvbdx28uRJ2Gw2rxkku3bt0nSdJ598Ek2aNHFJczfKI4884vJ9WVkZioqKYDabUbNmzYgFB1GbVtSqVSukpqa6TOPk5+fjt99+Q1paGgAgLS0Nubm52LRpk+Oc1atXQ1EUdO/e3XHO+vXrXaq+V65ciXbt2qFu3bqV9G7CwzEGFwqk8kel05E+5C7QSkVGZv0oELr3oDCVFz15W8rUmXsxsqIImEoLPM4jIiKqtmTZuAeApKQkl0c4buo+//zz+OSTT/D1118jMTHR8PbPnDnj8igoKMDu3bvRo0cPfPzxx4ZfT6uIBgcFBQXYunWrY+vozMxMbN26FVlZWZAkCePHj8d///tffPvtt9i+fTtGjBiBJk2aYPDgwQCACy64AP3798eYMWOwceNG/PLLLxg3bhyGDRuGJk2aAABuv/12mM1mjB49Gjt27MCnn36KV1991SVtiABIUsUeCEbyETgEGqe71A3ovaRbKlIYSwscswVWRZQXJANCUXD2LFcrIiIiUkkmk2EPrerXrw+TyeQ1g8RX9ojqxRdfxPPPP48VK1agU6dOQb3nYJx33nl4/vnnPWYVKlNEg4M//vgDF198MS6++GIAwIQJE3DxxRdj8uTJAIAnnngCDz30EO69915ceumlKCgowLJly1yit0WLFqF9+/a45pprMGDAAPTo0cNlD4Pk5GSsWLECmZmZ6Nq1KyZOnIjJkye77IUQilhOKVc3Q6uM1Ylcr+t69z24Tc8CBxBGrlCkZ58Da3EBaw2IiIgizGw2o2vXri5ZKGpxsZqF4s2sWbPw3HPPYdmyZS4rYlaWuLg4x5L8kRDRmoM+ffr4HURJkoRp06Zh2rRpPs+pV68eFi9e7Pc6nTp1wk8//RR0P0Phsj4/x4seuyNH4jPxl16kp9bApgiYZMml1kARApZCzhoQERG5MLggWasJEyZg5MiR6NatGy677DK88sorKCwsxF133QUAGDFiBJo2bYoZM2YAAGbOnInJkydj8eLFaNmypWNly9q1a6N27dqh99/Jt99+6/K9EALHjh3DnDlzcOWVVxp6LT2itiCZSC9/KxUJAa+bn9k0BCdaZg0sVqViQzQGB0RERK5k2aDgQF+2w6233ooTJ05g8uTJyM7ORpcuXbBs2TJHkXJWVhZkpzbfeOMNWCwW3HTTTS7teFsNKVRqmrxKkiQ0aNAAV199NWbPnm3otfRgcBDr3HP6QygQjiStaTju53l7p86BQTDZPd52RdayQhEAWGwKRFE+ZFnmakVERERRYNy4cRg3bpzX59auXevy/cGDB8PfoXLROk5gcEBBkYQCYVDJSrjy88OVsuQeKFisNiCh4k+prPAsaw6IiIicSLIMSeddf1/tUHgxOIhCAkB13VvXfUAfykpF3tsXmvc6cGeSJUdg4D67YBPCMYtQVpjH4ICIiMiZZFDNgWRAGxGkZ7XMl156KYw98Y3BQYREbPCoFgR72ajMMEIE1b6/O/0uKxMJ9+c8X+jt49W/z0FFIGDR8TqptFDXdYiIiKh62LJli6bzpHCO0wJgcFBdSFLUrbsaqDdGzBqo9QfeAgM9wYJzKlFCnOxRa+Bos6wUFoueUIKIiKgaiNBqRdHm1VdfxYUXXgiTjv0aKhsTt8gYIRZB+0sn0ls7oGXMHygwMEme+xu41xp4K1JWirkzMhERkTu15sCIRyy7+OKLcfr0aQBA69atcerUqQj3yFNsf8LVUFTlsodQZe88ONdaZ+DvvatthLpSUSAJcfY/GV+rFzE4ICIiIl9SUlJw4MABAPaVkaJxxSKmFUWpaC5KlrTMEvipO/A3aDdqPO9rtsHbXgehcg4UlOKzkCQpuoI4IiKiSGNaEQBg6NCh6N27Nxo3bgxJktCtWzefKUZqEFHZGBwYSG/Ba6zRFBQEICTJJQJwHkRH86fnnl7kbXdkAFCK8hkcEBERuYvQJmjR5q233sKQIUOwb98+PPzwwxgzZgzq1KkT6W65YHBQnQRZlKwrKBBKxYpIAQSzD4He8CTUMbpJlh1XjXNaytT9vypbYX5UThESERFRdOjfvz8AYNOmTXjkkUcYHFDlUAf0QpLtY/UgB6x6AgNJKBAaAwN3wYzhA92dVwyaizDJEqwaIxmlKN+QaxIREVUlkskEyYAVeoxoI1q89957jq8//vhjDBo0CLVq1Qr4unr16um6jiRJ2Lx5M1q0aKHpfAYHMUoO0/q3RqQOaeFvqK0IwEe9b8XrfQQGRmbzeEsbsh+XYVOU8v/aKp6wWSHKSo3rABERUVUhy8akBMV4WpEv9913H7p3747WrVsHPDc3NxevvPIKkpOTA54rhMCDDz4Im80W8FwVg4MYomXQHOvcx+Jabth7O8U9SLAp0LwzsreViNx3RPaqlCsVERERkX56axWHDRuGhg0bajr3oYce0tU2g4MoFvYVi4JMATKS8+DfXyCg5Y/GWxpRsEXiJlkC3IJsc5xcXoMAx3+dSSUMDoiIiLziakWG0VvbePbsWV3nMziobtR0pMqelgsiENE0a+BtV+QQUou8zRr4Si9yJ5VwGVMiIiJvJNkEyYCBvRFtRKMffvgBTZo0iXQ3AHATNKoEvoqUtWxqFui8inO0HXNvWyt/OyWrpGIWIxMREZF+PXr0QGJiou7Xvf/++1i6dKnj+yeeeAIpKSm44oorcOjQoaD6wuCAQiMU+8MXDTMG4bjP7ivFyF+aka9Zg4qvZb/nSkVnOGtARETkjSRXFCWH8oiClGijtGrVCq1bt/b50GL69OmoUaMGACAjIwNz587FrFmzUL9+fTz66KNB9YtpRVWBQQPSylqpSA9vg+2g9kcI8Bn5mg3QfJ5QgBJ9OX1ERETVBdOKPI0fP97l+7KyMmzZsgXLli3D448/rqmNw4cPo23btgCAb775BkOHDsW9996LK6+8En369AmqXwwOqjup8qNwJUy7IqvNOscBNh/xji3EugR1h2THZmiWQkicNSAiIiKNHnnkEa/H586diz/++ENTG7Vr18apU6fQvHlzrFixAhMmTAAAJCYmori4OKh+VZ25mSqiqg8v9YyfQ03RCXalIpWWOgNVXAnrDYiIiHyS5YoVi0J6VP2h67XXXosvv/xS07n/+te/cM899+Cee+7Bnj17MGDAAADAjh070LJly6CuX/U/YbIL5Y+pEtKN3FOFtC1dGrgdZ75mEbRymSlwCxbiS/MhV4N/sIiIiIJiRL2BURupRbkvvvhC8y7Ic+fORVpaGk6cOIEvv/wS55xzDgBg06ZNuO2224K6PtOKyD+tgYFQDEtP8hUYKD6+9t6G6/daN0DTwzlAiCvJ073uMBEREVVfF198MSSpYiwhhEB2djZOnDiBefPm+X3tggULMGjQINSvXx9z5szxeH7q1KlB94vBQVURhcXEAHwGDb42PwsmlcjfbIERm6CZZBm2AAP/+OLcoK5DRERUHUgmEySTAQXJBrQRLQYPHuzyvSzLaNCgAfr06YP27dv7fe1HH32EBx98EJdccgluuOEG3HDDDQFfoxWDA4oJekIfb8uYGs15YzTZZoHJWhL2axIREcUs7pDsYcqUKUG/dvXq1Thz5gyWLl2Kb7/9Fv/3f/+HRo0aYdCgQbjhhhvQo0ePoNOdq37iFhlLywxFkLMYwWx2ps4MeAsIvNUY2IQIuNux1mVNVQkWFiMTERFR5apbty7uvPNOfPbZZzh58iRef/11FBcX44477kDDhg0xYsQIfPHFFygsLNTVLoOD6qa8mMfXrsV+aRj069krQWs6kZ5QQ20m1JWK9EgozXfJGSQiIiI3hqxUZNDsQxVkNpvRv39/zJs3D4cPH8ayZcvQsmVLPPfcc3jppZd0tcW0okokS8Ft4GVcB8K8YpGBRckKPCPXUMb7RgcLzrMP5lLOHBAREfkjyTIkA1YaMqKN6qBbt27o1q0bpk2bhrKyMl2vZXBA3lVigbPzrIH7Vb31wudqRt42QdMQFJicbvq77G3gNhugbnzmLrE0L+Q9GYiIiIj0EkLgiy++wJo1a5CTk+OycqIkSfjyyy8RHx+vq02GXzrExPjPiOU0jQoMKuED83cFI2YLAtYfCAXxZfpy+YiIiKodyaCUIqnqpBW99957KCoqCqmN8ePHY/jw4cjMzETt2rWRnJzseCQlJQXVJmcOYpQihMedbUMYFhj4TjFyHrK7LGnqdl4wtQaBGJHW5TyDYLYUQqry+1oTERGFSJKMST2uQjV+Tz31FB555BHcfPPNGD16NK644grdbXz44Yf46quvHDsjG4EzByGojJkEAf93x425SJTukRCAt89fXbXIfaUimxLaTIKvFY7MXKmIiIiIgnDkyBG8//77OHnypGNvg5kzZyI7O1tzG8nJyWjdurWh/WJwYCA51qJZPRF8GCIhlxkEva8V3r92pwhhaDGye5BgtpwNeh1hIiKiakOSjXtUEXFxcbjxxhvxv//9D4cPH8aYMWOwaNEiNG/eHIMGDcL//vc/lxoCb5599llMnToVxcXFxvXLsJbIKwmVcOc/3EIcXEtC0bV0qhDBzRpGZBlTS37AP1wiIqLqTkhBLqPupZ2qqFGjRujRowf27NmDPXv2YPv27Rg5ciTq1q2L9957D3369PH6ultuuQUff/wxGjZsiJYtW3oUH2/evFl3XzQFB/n5+lMngi2CoCgR5gG2rxWK3C8bbKAAaFupyButm6DZFIEELmNKREREQTp+/Dg+/PBDvPfeezhw4AAGDx6MJUuWoG/fvigsLMS0adMwcuRIHDp0yOvrR44ciU2bNuHOO+9Eo0aNDNl3SVNwkJKSoutikiRhz549hudAkUahDuwjtCyTv8sa0SU9wUKcLMHi/npFgclp8xVJsSLOVhp6x4iIiKo6o1KCqtDMwfXXX4/ly5fj/PPPx5gxYzBixAjUq1fP8XytWrUwceJEvPDCCz7bWLp0KZYvX44ePXoY1i/NaUVffPGFS4d9EUIYWjEdbUK5k20URbiuzW8IIaA589/Azc787Q/gteA4wPjeV2qR0XUHJllCnKXAsDaJiIiqNEkyZgAV6UGYgRo2bIh169YhLS3N5zkNGjRAZmamz+ebNWtmeLaOpuCgRYsW6NWrF8455xxNjbZu3Vr3hgtUdeitMQDCN2vgvmqRkeJLz4avcSIiIqqyysrKcPDgQdSvX9/veZIkoUWLFj6fnz17Np544gnMnz8fLVu2NKRvmoIDfxGLN3/99VdQnaHwcwza9d75FwoAudIi9mBmDXwtY+rZjveGtNQaOK9WFG8pgIDEfQ6IiIgCkWX7w4h2qoD4+Hhs27Yt5HbuvPNOFBUVoU2bNqhZs6bHzfnTp0/rbpOrFUWIWsPhL63GSCFV90fZPghae+MtCPC1X4HWImRn8ZazDAyIiIgoKHfeeSfeffddPP/880G38corrxjXoXJBBQe///471qxZg5ycHI9lHF966SVDOkYGMGJQHyWBga9ZA0W4/9e4wbpJlgCb9+dsikA8VyoiIiLShEuZerJarViwYAF+/PFHdO3aFbVq1XJ5XsuYeuTIkYb3S3dwMH36dDz99NNo166dx5JJRiyfRFGikoIC56soAnC+ge/+PeC2BKqfQCDYZUw1EwriyorCew0iIqKqgqsVefjrr79wySWXAAD27Nnj8py/MXV+fr6uIuSzZ8+iTp06ms/XHRy8+uqrWLBgAUaNGqX3pVVGNKxYFDYRWsbUH2+zBorLPgm+++wrgPAXWHhLMVLTkUzl/ybFWwpQVX8FiIiIKPzWrFkT1Ovq1q2LY8eOoWHDhprOb9q0KbZu3ap5iwHdwYEsy7jyyiv1voxCJICwDEZ1T88ZGBk5D/rdA4BAxcf+29VWmOxPoBoErlRERESkA2cODCOEwDvvvIPatWtrOr+srExX+7qDg0cffRRz584NSwEEVSKj/kjdGbAHQiiBQWW1G285y5WKiIiItGJw4NUff/yBzz77DFlZWbBYXLdf/eqrr7y+pnnz5nj77bc1XyM1NVXXFgO6g4PHHnsMAwcORJs2bdChQwePi/l6IxQlgvijCmbfgkCcB+rCgBmEQNlQ3moQbF5eo3XVoviSfMhSVGZhERERUQz45JNPMGLECKSnp2PFihXo168f9uzZg+PHj+PGG2/0+bqDBw+GtV+6g4OHH34Ya9aswVVXXYVzzjmHRchRLtSBvRQlqxVFE5uiwFyaV2nL0BIREcU6IUkGrVZUdcad06dPx8svv4yxY8eiTp06ePXVV9GqVSvcd999aNy4ccT6pTs4eP/99/Hll19i4MCB4egPGSWUQb3W1wbaiEQogGQKvh+VzGLV9r4lxQaTtTjMvSEiIqpCmFbkYf/+/Y7xtNlsRmFhISRJwqOPPoqrr74aU6dOjUi/dH/C9erVQ5s2bQy5+Pr163H99dejSZMmkCQJ33zzjcvzo0aNgiRJLo/+/fu7nHP69GnccccdSEpKQkpKCkaPHo2CggKXc7Zt24aePXsiMTERzZo1w6xZswzpf5UjhObAIFD0b+SMg3qH3tedevVwKDfytQYGNkWB2XKWKxURERFRSOrWrYuzZ+0LnDRt2hR//fUXACA3NxdFRZFbLl13cPDss89iypQphnS6sLAQnTt3xty5c32e079/fxw7dszx+Pjjj12ev+OOO7Bjxw6sXLkSS5Yswfr163Hvvfc6ns/Pz0e/fv3QokULbNq0CS+88AKeffZZvPXWWyH1vdpmlEhSpUTt/lJ2wvnZF1s8dz2zuW30l1CayzJkIiIiPSTJuEcV0atXL6xcuRIAcPPNN+ORRx7BmDFjcNttt+Gaa66JWL90pxW99tpr2L9/Pxo1aoSWLVt6FCRv3rxZc1vXXnstrr32Wr/nJCQkIDU11etzf//9N5YtW4bff/8d3bp1AwC8/vrrGDBgAF588UU0adIEixYtgsViwYIFC2A2m3HhhRdi69ateOmll1yCiGgX838K6kyChjQj58AgUF6/+9Pq8qXu+xj4K2wuc3vSW4DgLLE0D/afCEMEIiIiTZhW5GHOnDkoKSkBAPznP/9BfHw8NmzYgKFDh+Lpp5/W1EZWVhaaNWvmUQMshMDhw4fRvHlz3f3SHRwMHjxY90VCsXbtWjRs2BB169bF1Vdfjf/+978455xzAAAZGRlISUlxBAYA0LdvX8iyjN9++w033ngjMjIy0KtXL5jNZsc56enpmDlzJs6cOYO6det6XLO0tBSlpaWO7/Pz88P4DmNDSIXNXpY3lSXvqwX5Th2yH3e+h69laK5np2Tn1KJSq+LY+MxdQkkulzAlIiKikNSrV8/xtSzLeOqpp3S30apVK68bop0+fRqtWrWCzeb/hqc3uoODKVOm6L5IsPr3748hQ4agVatW2L9/P/7973/j2muvRUZGBkwmE7Kzsz0+jLi4ONSrVw/Z2dkAgOzsbLRq1crlnEaNGjme8xYczJgxw9AikJi+x+y44x9sYBCe7aSN+jzVGQY9xcjmskKDrk5ERFQ9CEk2aLWi2J450HPDOSkpKeA5QgivK4cWFBQgMTFRV99UuoODyjRs2DDH1x07dkSnTp3Qpk0brF27Nqy5WJMmTcKECRMc3+fn56NZs2Zhu1400lxQHMY/0mCXClVnC9xTi3wdcxYopchsyeesARERkV6SHHiVQ63txLCUlBTN2wD4u+uvjlMlScIzzzyDmjVrurzut99+Q5cuXYLqo6bgoF69etizZw/q16+vqdHmzZvjp59+QosWLYLqlC+tW7dG/fr1sW/fPlxzzTVITU1FTk6OyzlWqxWnT5921Cmkpqbi+PHjLueo3/uqZUhISEBCQoKhfY8lmgIDI/7AdQp1/SNfaUIWm2vLpX5mEWoUn4ZAFagBISIiokq3Zs0ax9cHDx7EU089hVGjRiEtLQ2APWX+/fffx4wZM/y2s2XLFgD2G6nbt293SZ83m83o3LkzHnvssaD6qCk4yM3NxQ8//IDk5GRNjZ46dSqoHKdA/vnnH5w6dcqxMURaWhpyc3OxadMmdO3aFQCwevVqKIqC7t27O875z3/+g7KyMkfx9MqVK9GuXTuvKUXVViVtdiY55Vj5qjsIJNDd/2A5zxr4CiQSS05DliRugEZERKQHC5IBAL1793Z8PW3aNLz00ku47bbbHMcGDRqEjh074q233sLIkSN9tqMGGXfddRdeffVVTSlIWmlOK/LXwWAVFBRg3759ju8zMzOxdetW1KtXD/Xq1cPUqVMxdOhQpKamYv/+/XjiiSfQtm1bpKenAwAuuOAC9O/fH2PGjMH8+fNRVlaGcePGYdiwYWjSpAkA4Pbbb8fUqVMxevRoPPnkk/jrr7/w6quv4uWXXzb8/ZBv6myEgL5N0RTY19sNV+iipiAFSicCAAhhnzlgYEBERKQPgwMPGRkZmD9/vsfxbt264Z577tHUxnvvvWd0t7QFB4oSnqHZH3/8gauuusrxvZo/NXLkSLzxxhvYtm0b3n//feTm5qJJkybo168fnnvuOZeUn0WLFmHcuHG45pprIMsyhg4ditdee83xfHJyMlasWIGxY8eia9euqF+/PiZPnhxTy5hSBedxub/lSf3xN1tRZLHBJHtPGjKXFcAkrMFdlIiIiMhJs2bN8Pbbb3tszvvOO+9ornUtLCzE888/j1WrViEnJ8djzH7gwAHd/YpoQXKfPn383oVdvnx5wDbq1auHxYsX+z2nU6dO+Omnn3T3r8oJZ7Ttq22haNrbAPAsQPYXknoLDPSmG/k6v7jMBpOXYqGaJad0tU9ERETlOHPg4eWXX8bQoUPxww8/ONLhN27ciL179+LLL7/U1MY999yDdevWYfjw4WjcuLHmYmd/onq1Iqqa9P7aqkFCuLJ51GVM3Zczdd8joVbRCUisNyAiItJNSJJBS5lWnSVBBgwYgL1792LevHnYtWsXAOD666/H/fffr3nm4IcffsDSpUtx5ZVXGtYvBgfknZYC5TBG71oT2RQI1/+WD9wrdkrW1k7AfQ6EgpolpxgYEBERkWHOPfdcTJ8+PejX161b12UzNSMwOCAXWpYxDSXylzQMritn3aTya2mMHmqW5kKupBWdiIiIqhymFXmVm5uLjRs3eq0XGDFiRMDXP/fcc5g8eTLef/99l70OQsHggIwnlKD/eN2H3+oGy3pu2DtmD4K4y19cZoNNETDH2fuvLmlau/gEZFkOW3E+ERFRlSZJ5euZG9BOFfHdd9/hjjvuQEFBAZKSklzqBSRJ0hQczJ49G/v370ejRo3QsmVLx7L9qs2bN+vuV1DBgaIo2Ldvn9cop1evXsE0SbFAR3Gx6+uER6GBpHfE70ewqxap1ACgyM9ypnWKshkYEBERkWEmTpyIu+++G9OnTw/6rv/gwYON7RSCCA5+/fVX3H777Th06JBH/rUkSWHZ/Kw6Mzw+Vu/ox8C0nJHp/b42NVP5CwziLYVIsBYZ1xkiIqLqJoJpRXPnzsULL7yA7OxsdO7cGa+//jouu+wyn+d//vnneOaZZ3Dw4EGcd955mDlzJgYMGBBKr706cuQIHn744ZDSgaZMmWJgj+x0f8L3338/unXrhr/++gunT5/GmTNnHI/Tp08b3kGKAgbn2stO02ZafgHdgwQjS4Ld9zwotnjuY1Cr6LghS4MRERFVV0KSDXvo8emnn2LChAmYMmUKNm/ejM6dOyM9PR05OTlez9+wYQNuu+02jB49Glu2bMHgwYMxePBg/PXXX0Z8DC7S09Pxxx9/hNxObm4u3nnnHUyaNMkxFt+8eTOOHDkSVHu6Zw727t2LL774Am3btg3qghQjDFitSBJK0MXLkVwUyGK1zyKom6HVPnuEqxQRERHFoJdeegljxozBXXfdBQCYP38+li5digULFuCpp57yOP/VV19F//798fjjjwOwF/yuXLkSc+bM8bqbcSgGDhyIxx9/HDt37kTHjh096gUGDRoUsI1t27ahb9++SE5OxsGDBzFmzBjUq1cPX331FbKysvDBBx/o7pfu4KB79+7Yt28fg4NoVj6w17LykL/X+1XJaUmKEC4zDoD+AMLXpmeOfQ5snu/bVFaEREu+vgsRERGRK4PTivLzXf/fnJCQgISEBJdjFosFmzZtwqRJkxzHZFlG3759kZGR4bX5jIwMTJgwweVYeno6vvnmm9D77mbMmDEAgGnTpnk8pzVVf8KECRg1ahRmzZqFOnXqOI4PGDAAt99+e1D90hQcbNu2zfH1Qw89hIkTJyI7O9trlNOpU6egOkLGCDogUF8bzuU6DWhbHeAbfSPfW2AAADXzj3DjMyIiohDZN0ELPUVXbcN9k7ApU6bg2WefdTl28uRJ2Gw2NGrUyOV4o0aNHJuOucvOzvZ6fnZ2dog992TEQie///473nzzTY/jTZs2DbrPmoKDLl26eAyQ7r77bsfX6nMsSCafKnmPAOdZAn/FyO6zCUUWG2qaTbApAiZZQq38fxgYEBERRZnDhw8jKSnJ8b37rEF1kZCQ4DGLAgB79uxBgwYNgmpTU3CQmZkZVONEAIJeAlVNJVIH8L7SgvwJtEqRNxarghpmGXGlZ2G2nNX9eiIiInIlhDGz/mobSUlJLsGBN/Xr14fJZMLx48ddjh8/fhypqaleX5Oamqrr/FB4SydyNnny5IBtDBo0CNOmTcNnn30GwH7DPisrC08++SSGDh0aVL80BQctWrRwfL1+/XpcccUViItzfanVasWGDRtczqUqJMiNzUIpStbC3z80WuMCR82B1XV2o0b+EQiEYTlZIiKiakYRIqibfN7a0cpsNqNr165YtWqVYz8ARVGwatUqjBs3zutr0tLSsGrVKowfP95xbOXKlUhLSwul2159/fXXLt+XlZUhMzMTcXFxaNOmjabgYPbs2bjpppvQsGFDFBcXo3fv3sjOzkZaWhr+7//+L6h+6S5Ivuqqq3Ds2DE0bNjQ5XheXh6uuuoqphVVN+rAv5LThozmHhgAQI28fxgYEBERxbAJEyZg5MiR6NatGy677DK88sorKCwsdKxeNGLECDRt2hQzZswAADzyyCPo3bs3Zs+ejYEDB+KTTz7BH3/8gbfeesvwvm3ZssXjWH5+PkaNGoUbb7xRUxvJyclYuXIlfv75Z2zbtg0FBQW45JJL0Ldv36D7pTs4UGsL3J06dQq1atUKuiMUgyphxSL3OwT+7hf4qCn22o7jNW7Hiy02mONk1BRnEV9WqLWbRERE5IeAMfsU6W3j1ltvxYkTJzB58mRkZ2ejS5cuWLZsmaPoOCsrC7JcMZ654oorsHjxYjz99NP497//jfPOOw/ffPMNLrroIgN6H1hSUhKmTp2K66+/HsOHD9f8uh49eqBHjx6G9EFzcDBkyBAA9lymUaNGuRR+2Gw2bNu2DVdccYUhnaIQlQ/ahSSHtHqRlmvo5lR/EM678rYQ6hQAoFbhcaYUERERGUQR2tN9A7Wj17hx43ymEa1du9bj2M0334ybb75Z/4UMkpeXh7y8PM3nr1q1CqtWrUJOTo7HCkgLFizQfX3NwUFycjIA+8xBnTp1UKNGDcdzZrMZl19+uWO9VoocIcmQEObULjlAYFDJeyDo4bwjsuL2L4xz8XLtwmwGBkRERBQ2r732msv3QggcO3YMH374Ia699lpNbUydOhXTpk1Dt27d0LhxY6/ZPXppDg7ee+89AEDLli3x2GOPMYWoCtE1uxBo4O/teS/HZAm6Qxh16K71roH7SkU2P69zPldWrEgsOaOzd0REROSLEMKQpcGr0vLiL7/8ssv3siyjQYMGGDlypMvGbf7Mnz8fCxcu1JWCFIjumoMpU6YAAHJycrB7924AQLt27TwKlCk2aA4MtOxsGCUzBt5qD/wFBu5qlpzirAEREZGBIplWFK2M2CrAYrEYntavezR39uxZDB8+HE2bNkXv3r3Ru3dvNG3aFHfeeaeu/CiKEHUXZINrEYTsPc70toyp84yXEdNfKpOX3+YyRaBM578kSaWnIBgeEBERUSX5559/8M8//+h+3T333IPFixcb2hfdMwf33HMPtmzZgiVLljjWfM3IyMAjjzyC++67D5988omhHayqqsq0mL89DIzc36AyP62U0pOQKvWKREREVR//z+pKURT897//xezZs1FQUAAAqFOnDiZOnIj//Oc/Lqso+VJSUoK33noLP/74Izp16oT4+HiX51966SXd/dIdHCxZsgTLly93WS4pPT0db7/9Nvr376+7AxSlNOxfUFmBQbD0zhgAQIKtGAm24jD0hoiIqPpiWpGn//znP3j33Xfx/PPP48orrwQA/Pzzz3j22WdRUlKiaROzbdu2oUuXLgCAv/76y+W5YLMzdAcH55xzjmPlImfJycmoW7duUJ2gGGRgYCBJCPvthDKbgnhveUdukktPcglTIiIiCrv3338f77zzDgYNGuQ41qlTJzRt2hQPPvigpuBgzZo1hvdL9+3dp59+GhMmTEB2drbjWHZ2Nh5//HE888wzhnaOolQlzwqEGjeU+dsdzU1y6akQr0ZERETu1NWKjHhUFadPn0b79u09jrdv3x6nT5/W3V6wdQvudI/y3njjDfz6669o3rw52rZti7Zt26J58+bYsGED3nzzTVxyySWOB4WHHMHb2oFmBaIhnQiomHbUExhAKEi2cKUiIiIioykGPqqKzp07Y86cOR7H58yZg86dO2tqQ1EUTJs2DcnJyWjRogVatGiBlJQUPPfccx4bommlO61o8ODBQV2IopBQNM8CCEl2XWbI2zlynGeNQpQEC4B9t2TZz3tILDoFU5X6Z4eIiIii1axZszBw4ED8+OOPLov8HD58GN9//72mNoyoW3AX9D4H5CrQwDOmaRnghxgEyJL3vQhCmTxUnKYeFSFgCjAnUKvgGBRIkLmeAhERkaGEsD+MaKeq6N27N/bs2YO5c+di165dAIAhQ4bgwQcfRJMmTTS1YUTdgjvdwQEA5Obm4osvvsD+/fvx+OOPo169eti8eTMaNWqEpk2bBtNklaAE+RtbaZX3OmYKHELZ+CzAa30FBEYrUwTiA+ViCYFaBccYGBAREYUBVyvyrkmTJkEN4FVG1y0AQQQH27ZtQ9++fZGcnIyDBw9izJgxqFevHr766itkZWXhgw8+CKojFINCCAzCQXZa9shW/q+H1uVME0pOw6RYwtU1IiIiIg9nzpzBu+++i7///hsA0KFDB9x1112oV6+epterdQuvvfaay3E9dQvudI/gJkyYgFGjRmHv3r1ITEx0HB8wYADWr18fVCcofMJVIOy3XZ3XjIaqhNr5/3BXZCIiojDhakWe1q9fj5YtW+K1117DmTNncObMGbz22mto1aqV5jH1rFmzsGDBAnTo0AGjR4/G6NGj0aFDByxcuBAvvPBCUP3SPS77/fffcd9993kcb9q0qcvyplSFGRBweBuGB7tZhy/uKxXZfM0iKDbUPnuEuyITERGFCVcr8jR27FjceuutyMzMxFdffYWvvvoKBw4cwLBhwzB27FhNbah1CzfeeCNyc3ORm5uLIUOGYPfu3ejZs2dQ/dKdVpSQkID8/HyP43v27EGDBg2C6gTFCEkGAm3lLZs8q4X8DPq11h0own6ur/G9DAk2P4N7m/C9BKx05ihkYQvcCSIiIiKD7Nu3D1988QVMJpPjmMlkwoQJE3Sl6Ydat+BOd3AwaNAgTJs2DZ999hkA+93erKwsPPnkkxg6dKhhHaNKorVIWdM5XkbfEV7BSSmPJmSTnwAlZx8kSapSU5VERETRRMCg1YpCbyJqXHLJJfj777/Rrl07l+N///23rnqBUOsW3OkODmbPno2bbroJDRs2RHFxMXr37o3s7GykpaUZGrXEimBXKIo4PSsXRUlgoM4aaPnIff1c3JectRXmQT57okr9Y0NERBRtFCEMGTPF7Lir3LZt2xxfP/zww3jkkUewb98+XH755QCAX3/9FXPnzsXzzz+vqb3169fj+uuvR3JyMrp16wYAeO211zBt2jR899136NWrl+4+SiLI26U///wztm3bhoKCAlxyySXo27dvMM3EhPz8fCQnJ+NY9nEkJSW5PKf1l1Q9q2KA67wGv+s5ztyH17IEmGQJEsr/q9gg2cog2cpX2rFaKr5XrL47JMkVg37nwb+PTcyEHAfIJghTHCDH2b/3tTGa+t68PCfkOAjZBJsiYBOAVRGOrxUB2MpfK5y+dk8lcv7IbUJAERXnl1oVlFgVFFisOGuxwepUd1Aj3gSTLEGWJChCoKDUiqwtP8Pyzx7OGhARUZVRUlKC559/Hnl5eR7jlsqmjqH+PPAP6tQJvS9nz+ajc+tzo+K9BUOWZU3ZCpIkwWYLnPLcsWNHpKWl4Y033nCkJ9lsNjz44IPYsGEDtm/frruPQe1zAAA9evRAjx49gn05RbtgZwu0PGcQxUs45W0jOl/7HNgspSg9srdq7ahCREQUhQSMSQmK9f9jZ2ZmGtqeUXULznQFB4qiYOHChfjqq69w8OBBSJKEVq1a4aabbsLw4cMNX22GQiTJnjMBRrbti2wK33XLeQsMvPG3z0Hewb8BpSqte0BERBSduAmaXYsWLQxtz6i6BWeagwMhBAYNGoTvv/8enTt3RseOHSGEwN9//41Ro0bhq6++wjfffBNUJygMggkMtJ4fZZufaaUIAVN5opatrBRn9m0L8AoiIiKi6BWobsG5xqFTp06a2tQcHCxcuBDr16/HqlWrcNVVV7k8t3r1agwePBgffPABRowYobVJiiZGBAZBUOeanDY3NoxzvYH7cqk5e7ZBsZYZe0EiIiLyThiUxRvjMwdGu+222wAATzzxhNfn1PoGrTUMgI7g4OOPP8a///1vj8AAAK6++mo89dRTWLRoEYODMFNTt7zl1gdNS2AgSYF3W47SWYMym4KEuIpcvJKzuTj+99bIdYiIiKiaUSA0pwQHaocqGF3DAOgIDrZt24ZZs2b5fP7aa6/Fa6+9ZkinYkVlL6flaxOvoGlczjRgUAB4byecNQ8aue+SrNisyPxtNQT/cSEiIqIYZ3QNA6AjODh9+jQaNWrk8/lGjRrhzJkzhnSqulN3A44JvgIHHbMIkiQZvmKQt12XLRYLsv9YjaLTJ8B5SSIiosojDEorqooLDFosFuTk5EBxWySlefPmml5/9OhR/Pzzz17bePjhh3X3R3NwYLPZEBfn+3STyQSr1c+6+uRVZVXdS+V38DXNAsQo90wr5wChJO8kTvy+CmUlhWBgQEREVLm4WpGnvXv34u6778aGDRtcjuupEVi4cCHuu+8+mM1mnHPOOS4rh0qSFN7gQAiBUaNGISEhwevzpaWlui9OUSrEAEJIsiMY8UWWJMcmZ/4Y8Y9A8ZkTOL3xh/LGqtC/KkRERBSzRo0ahbi4OCxZsgSNGzcOakuAZ555BpMnT8akSZMgy8bcANYcHIwcOTLgOSxGjhKhFCsbEBh4bVNH7YEMwFusLIS2t+ZcC2IpKcaZTT8yMCAiIoogphV52rp1KzZt2oT27dsH3UZRURGGDRtmWGAA6AgO3nvvPcMuStWIl2BBEgIC3kf5Rv1qqwFCwb4tEGUWMDAgIiKKHK5W5KlDhw44efJkSG2MHj0an3/+OZ566imDeqVzh2SKLpIQAdN3KlO01TOU5J9B8eE9ke4GERERkYeZM2fiiSeewPTp09GxY0fEx8e7PJ+UlBSwjRkzZuC6667DsmXLvLbx0ksv6e4XgwMyhLfAIFDtgSx5Tx/yex0dNwzyM/+CLMselftERERUuZhW5Klv374AgGuuucbluJ6C5BkzZmD58uVo164dAHgUJAcjosHBjBkz8NVXX2HXrl2oUaMGrrjiCsycOdPxBgGgpKQEEydOxCeffILS0lKkp6dj3rx5LsuqZmVl4YEHHsCaNWtQu3ZtjBw5EjNmzHBZXWnt2rWYMGECduzYgWbNmuHpp5/GqFGjKvPtRheNexwACKoOQchxnnUGAdqRJe9LkAL6/zGwWkpR8M9+gIEBERFRxClCGLI/VGXvMRVOa9asCbmN2bNnY8GCBYaOaSMaHKxbtw5jx47FpZdeCqvVin//+9/o168fdu7ciVq1agEAHn30USxduhSff/45kpOTMW7cOAwZMgS//PILAPsSqwMHDkRqaio2bNiAY8eOYcSIEYiPj8f06dMB2HePGzhwIO6//34sWrQIq1atwj333IPGjRsjPT09Yu/fn2jZ68BvqpCeoKGSU47OHNoLKHrnJYiIiIjCr6ysDNOmTcP8+fNx3nnnBd1OQkICrrzySgN7BkhCRE8IduLECTRs2BDr1q1Dr169kJeXhwYNGmDx4sW46aabAAC7du3CBRdcgIyMDFx++eX44YcfcN111+Ho0aOO2YT58+fjySefxIkTJ2A2m/Hkk09i6dKl+OuvvxzXGjZsGHJzc7Fs2TKPfpSWlroszZqfn49mzZrhWPZxl/wvPdGreqa6NKf6sTsv1el+jizBpWxXluxTRLIEmCQJMgQkxQooNkg2i/0kqwWSYoVkKwMUq8vde0koroN9SfYctJd/73meZJ8NMMUBchyEKd7ltR7nA4BQPNKKRPk1FdiXMrUpAmUKYBMCQgC28jdvE/ZjFZ+XazfVgiRRvm6yEPZjFqtAgcWK35Z+iuIzoRX5EBERxaKSkhI8//zzyMvL05S3Hk75+flITk7G2h2HULtO6H0pOJuPPhe2iIr3FqoGDRpgw4YNIQUHM2bMwLFjx/Daa68Z1q+oqiDNy8sDANSrVw8AsGnTJpSVlTlysgCgffv2aN68OTIyMgAAGRkZ6Nixo0uaUXp6OvLz87Fjxw7HOc5tqOeobbibMWMGkpOTHY9mzZoZ9yYrk7fBvwaBCov1ziZoKVT2d4be8LX4bB4DAyIioiiiphUZ8agq7rzzTrz77rshtbFx40a8//77aN26Na6//noMGTLE5RGMqClIVhQF48ePx5VXXomLLroIAJCdnQ2z2YyUlBSXcxs1aoTs7GzHOc6Bgfq8+py/c/Lz81FcXIwaNWq4PDdp0iRMmDDB8b06cxATZBlQtG1E5kFLIKEz2PBeqCyFdWXRnIN7YZ9zqTr/gBAREVHVYrVasWDBAvz444/o2rWrI6VepWWloZSUlKCDAF+iJjgYO3Ys/vrrL/z888+R7goSEhJ87gRdZWkZ9AfaYCNKljLNObQXDAyIiIiihyIEbCxIdvHXX3/hkksuAQDs2eO69LrWlYbCsQ9ZVAQH48aNw5IlS7B+/Xqce+65juOpqamwWCzIzc11mT04fvw4UlNTHeds3LjRpb3jx487nlP/qx5zPicpKclj1oB8CGbg721nZC/t+FulSK+is3kozD1tTGNERERkCEUYM7BXqk5sYMhqRaoTJ05g9+7dAIB27dqhQYMGQbcV0Vu9QgiMGzcOX3/9NVavXo1WrVq5PN+1a1fEx8dj1apVjmO7d+9GVlYW0tLSAABpaWnYvn07cnJyHOesXLkSSUlJ6NChg+Mc5zbUc9Q2KAA/0auQZN+pQ17ODbfsQ/vDfg0iIiIio+zbtw/Lly9HcXExgIpFa7QoLCzE3XffjcaNG6NXr17o1asXmjRpgtGjR6OoqCio/kQ0OBg7diw++ugjLF68GHXq1EF2djays7MdH05ycjJGjx6NCRMmYM2aNdi0aRPuuusupKWl4fLLLwcA9OvXDx06dMDw4cPx559/Yvny5Xj66acxduxYR2rQ/fffjwMHDuCJJ57Arl27MG/ePHz22Wd49NFHI/betRKIcIKMQQP6yto9+djBfZVyHSIiItLOphj3qCpOnTqFa665Bueffz4GDBiAY8eOAQBGjx6NiRMnampjwoQJWLduHb777jvk5uYiNzcX//vf/7Bu3TrNbbiLaHDwxhtvIC8vD3369EHjxo0dj08//dRxzssvv4zrrrsOQ4cORa9evZCamoqvvvrK8bzJZMKSJUtgMpmQlpaGO++8EyNGjMC0adMc57Rq1QpLly7FypUr0blzZ8yePRvvvPOO4XscyEHuRAd4LmMacUGudAQACiKzQUNpcRHO5ByLyLWJiIjIN65W5OnRRx9FfHw8srKyULNmTcfxW2+91etS+958+eWXePfdd3HttdciKSkJSUlJGDBgAN5++2188cUXQfUrojUHWqZNEhMTMXfuXMydO9fnOS1atMD333/vt50+ffpgy5YtuvtI+oRrJSIFAnKAoONYVqbxFyYiIiIKgxUrVmD58uUu9bYAcN555+HQoUOa2igqKvJYkRMAGjZsGJtpRdVJFGx2rInh6T+VuILR0cx9mqv7iYiIqPLYylcrMuJRVRQWFrrMGKhOnz6tedXMtLQ0TJkyBSUlJY5jxcXFmDp1atC1tVGxWhEZwGOHYlt4r+GFtyLkUDn/G+Bv9sBaVoacfw7pKuIhIiKiyqHAmNTpKlRygJ49e+KDDz7Ac889B8C+fKmiKJg1axauuuoqTW28+uqrSE9Px7nnnovOnTsDAP78808kJiZi+fLlQfWLwUF1JxRNd/cDzij4WLFIqqTB+vF/DkFRqtI/GURERFSVzZo1C9dccw3++OMPWCwWPPHEE9ixYwdOnz6NX375RVMbF110Efbu3YtFixZh165dAIDbbrsNd9xxR9DL9TM4oMCCSA0SwssKqEEXOAcOMI4ctKcUceaAiIgo+tgUAZsBUwdGtBEtLrroIuzZswdz5sxBnTp1UFBQgCFDhmDs2LFo3Lix5nZq1qyJMWPGGNYvBgfVjFS+KZnm2gLZZNCF9QcGWv/+FUXB0YP7GRgQERFFKWHQSkNV6f/1WVlZaNasGf7zn/94fa558+YB25gxYwYaNWqEu+++2+X4ggULcOLECTz55JO6+8WC5GpEV7GxJPnd/Mwfjz/+MBcln8w+AmuZJazXICIiIjJSq1atcOLECY/jp06d8tgY2Jc333wT7du39zh+4YUXYv78+UH1izMHBlP3OoiqdXgl2V5boOf8IEViuu/owf1MKSIiIopiNmF/GNFOVSGE8LrKYkFBARITEzW1kZ2d7TUFqUGDBo5N1fRicFCVqYN8IwMDnYFDuIuShRD458A+BgZERERRzKgNzKLq5muQJkyYAMC+OtEzzzzjspypzWbDb7/9hi5dumhqq1mzZvjll188Zhp++eUXNGnSJKj+MTioqvTOFiC4FYlcXi88vzd6dVP35UzzTp9CceFZYy9CREREFCbqprxCCGzfvh1ms9nxnNlsRufOnfHYY49pamvMmDEYP348ysrKcPXVVwMAVq1ahSeeeAITJ04Mqn8MDkgbWcMeBxEI5rlKERERUfTjakUV1qxZAwC466678OqrryIpKSnoth5//HGcOnUKDz74ICwWe/1lYmIinnzySUyaNCmoNhkcUGh8zCYoQjjqL3w9D9in1KDm3PkZ4PuahTh8YC8DAyIioijHtCJP7733nsv3+fn5WL16Ndq3b++1yNgbSZIwc+ZMPPPMM/j7779Ro0YNnHfeeZp3WPaGqxVRQLpWOQoj938PCgvOIu/Uych0hoiIiCgEt9xyC+bMmQMAKC4uRrdu3XDLLbegY8eO+PLLL3W1Vbt2bVx66aW46KKLQgoMAAYHMSUSM2nBBAbu3RQhFB742wDtSOb+oNslIiKiyqOuVmTEo6pYv349evbsCQD4+uuvIYRAbm4uXnvtNfz3v/+NWL8YHBjEXwqNXmqaTETT6iTZdwGyn4BBS5c9tkHQ3isAFQHDPwf2GV/xTERERIZT04qMeFQVeXl5qFevHgBg2bJlGDp0KGrWrImBAwdi7969EesXg4MwMTJYqHSGBjrhacNSWoITx/4x5gJERERElaxZs2bIyMhAYWEhli1bhn79+gEAzpw5o3mfg3BgQXIYyZIU0QhXSDIkncuZhsL9vXorSjZqVaOjhw6yEJmIiChGKIqAYkBKhBFtRIvx48fjjjvuQO3atdG8eXP06dMHgD3dqGPHjhHrF4ODSiZLYU4XkmR7UABbGC8SnFBqD9z9k8klTImIiGKFYlC9QBWKDfDggw/isssuw+HDh/Gvf/0Lcvmy8a1bt45ozQGDg+pOa8Gx3p2R4VpLYGRgYLNacexQJgMDIiIiimndunVDp06dkJmZiTZt2iAuLg4DBw6MaJ9Yc0CB+dgALZxLnPpLx8r+Jws2mzVs1yYiIiJjsSDZU1FREUaPHo2aNWviwgsvRFZWFgDgoYcewvPPPx+xfjE4IMO5T/lp/TvWOrlw+MBex9QbERERRT+bEIY9qopJkybhzz//xNq1a10KkPv27YtPP/00Yv3iCKuaEeU1CUadF4hREb4acNhsNvxzYC8UpfIKrYmIiKjqO336NO644w4kJSUhJSUFo0ePRkFBgd/zH3roIbRr1w41atRA8+bN8fDDDyMvL0/T9b755hvMmTMHPXr0gOR0h/TCCy/E/v2R28uJNQdRxPmOuxDC5RclZJIMaF25KEp2RPYm58hhWMvKIt0NIiIi0iEWViu64447cOzYMaxcuRJlZWW46667cO+992Lx4sVezz969CiOHj2KF198ER06dMChQ4dw//334+jRo/jiiy8CXu/EiRNo2LChx/HCwkJjx4A6MTiIsMpYvQiAjsDA/y+jntkE96JkI6gpRZw5ICIiih02GLNaUbjWYvz777+xbNky/P777+jWrRsA4PXXX8eAAQPw4osvokmTJh6vueiii/Dll186vm/Tpg3+7//+D3feeSesVivi4vwPs7t164alS5fioYceAgBHQPDOO+8gLS3NqLemG4ODMIvYXgfB7CcQaOAf4RkFRVFwmClFRERE1V5+fr7L9wkJCUhISAi6vYyMDKSkpDgCA8Ce+y/LMn777TfceOONmtrJy8tDUlJSwMAAAKZPn45rr70WO3fuhNVqxauvvoqdO3diw4YNWLduXdDvJVTRmz9ShagbgUXtnsla6guiINXo5LEjKCstjXQ3iIiISCejVytq1qwZkpOTHY8ZM2aE1L/s7GyPFJ+4uDjUq1cP2dnZmto4efIknnvuOdx7772azu/Rowe2bt0Kq9WKjh07YsWKFWjYsCEyMjLQtWtX3e/BKJw5oMCiIDAA7BufMaWIiIgo9hi10pDaxuHDh5GUlOQ47mvW4KmnnsLMmTP9tvn333+H3K/8/HwMHDgQHTp0wLPPPqv5dW3atMHbb78d8vWNxOCgutI64I+SJUOFEPgncx8DAyIiIkJSUpJLcODLxIkTMWrUKL/ntG7dGqmpqcjJyXE5brVacfr0aaSmpvp9/dmzZ9G/f3/UqVMHX3/9NeLj4wP2CwBGjBiBq666Cr1790br1q01vaYyMDgIUqQ24RBCaN8QoAo5m3saxYW+lxMjIiKi6KUoArYIrFbUoEEDNGjQIOB5aWlpyM3NxaZNmxwpPatXr4aiKOjevbvP1+Xn5yM9PR0JCQn49ttvXfYrCMRsNmPGjBkYPXo0mjZtit69e6NPnz7o3bs3zjvvPM3tGC06bgvHOLkSBuuGhCJRkh4UjKOHDlTLoIiIiKgqsJUHB0Y8wuGCCy5A//79MWbMGGzcuBG//PILxo0bh2HDhjlWKjpy5Ajat2+PjRs3ArAHBv369UNhYSHeffdd5OfnIzs7G9nZ2bDZAq+r9M4772DPnj04fPgwZs2ahdq1a2P27Nlo3749zj333LC8Ty04cxBlRBXa+c9Ix7IytW+1TERERKTTokWLMG7cOFxzzTWQZRlDhw7Fa6+95ni+rKwMu3fvRlFREQBg8+bN+O233wAAbdu2dWkrMzMTLVu21HTdunXr4pxzzkHdunWRkpKCuLg4TbMd4cLgwGDqzW2OY41jLSvDqexjke4GERERBcmou/7hmjkAgHr16vnc8AwAWrZs6XITt0+fPiHd1P33v/+NtWvXYsuWLbjgggvQu3dvPPXUU+jVqxfq1q0bdLuhYnAQIepGF5wpCOxk9lEIrZu4ERERUdSxKcYM7G1VaDjw/PPPo0GDBpgyZQqGDBmC888/P9JdAsDgoNJEbDM0DfTsehwJx/85BEmSGSAQERFRlbFlyxasW7cOa9euxezZs2E2mx1FyX369IlYsMDgIMYJSYYU7GbikhwTRcrHDh9kYEBERBTDYiGtqLJ17twZnTt3xsMPPwwA+PPPP/Hyyy9j7NixUBRFU1FzODA4CEK0zgBUKoOCikBpVaUlxcg7fcqQaxEREVFkMDjwJITAli1bsHbtWqxduxY///wz8vPz0alTJ/Tu3Tti/WJwUN3E2OZnOUf/iXQXiIiIiAxXr149FBQUoHPnzujduzfGjBmDnj17IiUlJaL9YnAQ5dQAWa7EJf6jqQYh58hh1hsQERHFuEhtghbNPvroI/Ts2VPTTs+VicGBgYzao0uBwbvTqYN9LQPsKAoMAODY4UMMDIiIiGKcTRiUVlSFUrsHDhwY6S54FV0jwSokKjbzlWR9swBR0ekKhWfzUZCXG+luEBEREVUbnDkII18boslSRbpQ1IiyGQPAvkoRERERxT4WJMcOBgeVQJIqAgQJgOG/1iHc8dc0sxChwOHIoUxIksSN4oiIiGIcg4PYEX23i6OYIkRULWMqQk0DikBgoPXjs9lsyD6cxcCAiIiIqBJx5qC6MWKwH2RQomecn/1PFqzWsqCuQ0RERNHFqgiYDLjrb+XMQdgxOKhODAkMgmtD7wTAof17mVJERERURTCtKHYwrSiKGTowjsKCY19/34qiIGv/XgYGRERERJWMMwcGMXoVUMP3OjBAZW2OduyfLFgspZVyLSIiIgo/boIWOxgcVFWSDAibse1Vkv27dzGliIiIqAqxCWHIBmZVaRO0aBXRm9MzZszApZdeijp16qBhw4YYPHgwdu/e7XJOnz59IEmSy+P+++93OScrKwsDBw5EzZo10bBhQzz++OOwWq0u56xduxaXXHIJEhIS0LZtWyxcuDDcby8yJBmQdf5YoyjlyGotQ+a+3QwMiIiIiCIgoqPCdevWYezYsfj111+xcuVKlJWVoV+/figsLHQ5b8yYMTh27JjjMWvWLMdzNpsNAwcOhMViwYYNG/D+++9j4cKFmDx5suOczMxMDBw4EFdddRW2bt2K8ePH45577sHy5csr7b1KEiCHcQdioXc3ZFRempAehw9megR2REREFNvUgmQjHhReEU0rWrZsmcv3CxcuRMOGDbFp0yb06tXLcbxmzZpITU312saKFSuwc+dO/Pjjj2jUqBG6dOmC5557Dk8++SSeffZZmM1mzJ8/H61atcLs2bMBABdccAF+/vlnvPzyy0hPTw/fGwwjRQiYQgk2onTzs327djKliIiIqIrhakWxI6puHefl5QEA6tWr53J80aJFqF+/Pi666CJMmjQJRUVFjucyMjLQsWNHNGrUyHEsPT0d+fn52LFjh+Ocvn37urSZnp6OjIwMr/0oLS1Ffn6+y6PaCTEw8Pe362sjudKSEhw+lMnAgIiIiChCoqYgWVEUjB8/HldeeSUuuugix/Hbb78dLVq0QJMmTbBt2zY8+eST2L17N7766isAQHZ2tktgAMDxfXZ2tt9z8vPzUVxcjBo1arg8N2PGDEydOtXw9xhxkgwIJfB5emsWDJK5fy+EoqF/REREFFM4cxA7oiY4GDt2LP766y/8/PPPLsfvvfdex9cdO3ZE48aNcc0112D//v1o06ZNWPoyadIkTJgwwfF9fn4+mjVr5vXccNYRGEVIMiQtQQEiW4ewfw9XKSIiIqqKbEKBzYAbgDaN4xkKXlSkFY0bNw5LlizBmjVrcO655/o9t3v37gCAffv2AQBSU1Nx/Phxl3PU79U6BV/nJCUlecwaAEBCQgKSkpJcHlpIIQ5qhRBQoH83YcNUYmDg/hZLSkpwJCuLgQERERFRBEU0OBBCYNy4cfj666+xevVqtGrVKuBrtm7dCgBo3LgxACAtLQ3bt29HTk6O45yVK1ciKSkJHTp0cJyzatUql3ZWrlyJtLQ0g95JBUkIxyOm+JsBqYSg4eCBfQwMiIiIqijFoJWKuAla+EU0rWjs2LFYvHgx/ve//6FOnTqOGoHk5GTUqFED+/fvx+LFizFgwACcc8452LZtGx599FH06tULnTp1AgD069cPHTp0wPDhwzFr1ixkZ2fj6aefxtixY5GQkAAAuP/++zFnzhw88cQTuPvuu7F69Wp89tlnWLp0aVjfnxogiGhOPYqS5UwP7t/PlCIiIqIqyqYIyKw5iAkRHRm+8cYbyMvLQ58+fdC4cWPH49NPPwUAmM1m/Pjjj+jXrx/at2+PiRMnYujQofjuu+8cbZhMJixZsgQmkwlpaWm48847MWLECEybNs1xTqtWrbB06VKsXLkSnTt3xuzZs/HOO+/E7DKmlaoSggebzYasg1yliIiIiCjSIjpzEGgw2KxZM6xbty5gOy1atMD333/v95w+ffpgy5YtuvoXrRQBmKJ4MsKdIoTfwu2jR/6B1VpWiT0iIiKiymRVAMmAu/5W1iOHXdSsVkSVJEwzAaHc9D+UeQCSJENwBQIiIqIqiWlFsSM6Es4pIEV4rvCjRySXKA0k88B+BgZEREREUYAzBwYIR72xEIAiAXL5LXkpwkXN4QouzubnI/fMmbC0TURERNGBMwexg8FBlAjL77okAdC4K3KEHDp0MNJdICIiojBjcBA7ojfXhCpPBGclDmVmRnxWhIiIiIjsOHNQnUW4DkFRFBziEqZERERVnroJmhHtUHgxOKgEkhBRtxFaNBQoZx87irIyLmFKRERU1dkUYchSpkwrCj8GB9VMNAQFqoPlKUWcOSAiIiKKDgwOqpGQAwODA4sD+/cxMCAiIqoGhBAQBtz157gh/BgchMDfrr/kX0FBAU6eOBHpbhAREVElUBRhSL0Aaw7CL3pyTGKcZEAkq6B8fwOjf++13PGv5HSjg5kHKvV6RERERBQYZw6M5LyfgI7BdjhiYCHJkMK1v4EBgcT+fftYb0BERFRNCCEM+X8+xw3hx+AgXNSBeSQKgCUZ9nkILedWfmqU1WrFoYMH+QdORERUTQjFoJoDphWFHdOKqqtggxYDgolDhw7CarWG3A4RERERGYszB5VEEgKQJMiQYIv2O+Zhnu3Yt3cvJFmGUMKU9kRERERRhQXJsYPBAbmSwxsYKIqCvXv2MjAgIiKqRoTiWpoZSjsUXkwrIofK2CAtK+sQSktLwn4dIiIiItKPMwchCmc9rxAAqthWCn/v/JspRURERNUMVyuKHQwOooTzL7sQAgokyKEGBuqqRYH+kPzNGBg4m2C1WrF79y4GBkRERNUMaw5iB9OKyDeD04z27N6NsrIyQ9skIiIiIuNw5qCqUAfykqy9WsdfTlQY6g+2bNnMjc+IiIiqIe5zEDsYHISbUMK/EZokA8IW3muEKCcnB0ePHo10N4iIiCgSDAoOwOAg7JhWVN1IckR2bf711wxIEdiNmYiIiIi048xBkOQIDXQVAZiCubTBAYGeZU9Pnz6F3bt2GXp9IiIiih2KEPYNYQ1oh8KLwQEB8D/YD3X/g5/X/8RaAyIiompMCINqDjiWCDsGBwYwIhL2RS0tNoXtCuF17OhR7N27J9LdICIiIiINGBwYRSiQhBLSXXYhDKyzkWXApmHVojDWHwghsHbtGs4aEBERVXNcrSh2MDgwmOS0jGio6ThRL8D7y8w8gKNHjlRSZ4iIiChaKQogGbIJmgGdIb+q+Oi1Ggg2AAlzQbUQAj+vW88VioiIiIhiCGcOKoPbXgcxOVzWGYTs27MHJ0+eCFNniIiIKJYIIQxJMWaacvgxOAhCpJYxjRVCCPzy83r77AT/iImIiKo9odgfRrRD4cW0ohDETIxQyR3du3s3cs+cYWBAREREFGM4cxAjhBBhG+SHtMKS2qfyOEBRFGz45SfOGhAREZGDogiDCpI5tgg3BgdRRggBBYAsBBRIwe2GrOd6Bq+otEedNSAiIiIqx6VMYwfTiiIsor/jegODADMXiqJgw88/hdAhIiIiIookzhyEUaibooVFGOsPdu7cgdzc3LC1T0RERLGJMwexI8pGrhSrrFYrfv6JswZERETkSRHCsEe4nD59GnfccQeSkpKQkpKC0aNHo6CgQNNrhRC49tprIUkSvvnmm7D1sTIwOCBD/PH77yjU+AdEREREFG3uuOMO7NixAytXrsSSJUuwfv163HvvvZpe+8orr1SZjV+ZVkQhy83NRUbGhkh3g4iIiKKU0WlF+fn5LscTEhKQkJAQdLt///03li1bht9//x3dunUDALz++usYMGAAXnzxRTRp0sTna7du3YrZs2fjjz/+QOPGjYPuQ7TgzEGIpGq+XKfNZsO3334Lm80W6a4QERFRlBJCOAKEkB7l465mzZohOTnZ8ZgxY0ZI/cvIyEBKSoojMACAvn37QpZl/Pbbbz5fV1RUhNtvvx1z585FampqSH2IFpw5MIJQIBm8ZZ8CwGRoi8YTQmDFihXIPnYs0l0hIiKiauTw4cNISkpyfB/KrAEAZGdno2HDhi7H4uLiUK9ePWRnZ/t83aOPPoorrrgCN9xwQ0jXjyYMDozkHCDoWKVIEfZgINZs3LgR27dti3Q3iIiIKMoJRRiygZmaVpSUlOQSHPjy1FNPYebMmX7P+fvvv4Pqy7fffovVq1djy5YtQb0+WjE4oKAcyMzE2nXrIt0NIiIiigFCVKQEhdqOHhMnTsSoUaP8ntO6dWukpqYiJyfH5bjVasXp06d9pgutXr0a+/fvR0pKisvxoUOHomfPnli7dq2uvkYLBgc6yVWkEj0URUVFWLJkaaS7QURERORXgwYN0KBBg4DnpaWlITc3F5s2bULXrl0B2Af/iqKge/fuXl/z1FNP4Z577nE51rFjR7z88su4/vrrQ+98hDA4IN2Wr1yFktLSSHeDiIiIYkS0b4J2wQUXoH///hgzZgzmz5+PsrIyjBs3DsOGDXOsVHTkyBFcc801+OCDD3DZZZchNTXV66xC8+bN0apVq7D0szIwOCBddu/dh91790a6G0RERBRDFEXYiyyNaCdMFi1ahHHjxuGaa66BLMsYOnQoXnvtNcfzZWVl2L17N4qKisLWh2jA4IA0Ky4uxrIVP0a6G0RERESGq1evHhYvXuzz+ZYtWwaseTCiriLSGBxEOcfvWIRLHYQQWMZ0IiIiIgqCUGwQSuh7IhnRBvkX0U3Q3njjDXTq1MmxHFVaWhp++OEHx/MlJSUYO3YszjnnHNSuXRtDhw7F8ePHXdrIysrCwIEDUbNmTTRs2BCPP/44rFaryzlr167FJZdcgoSEBLRt2xYLFy6sjLenm7qkaTQGndv+2oHde/dViYiYiIiIKpcaHBjxoPCKaHBw7rnn4vnnn8emTZvwxx9/4Oqrr8YNN9yAHTt2ALBvLPHdd9/h888/x7p163D06FEMGTLE8XqbzYaBAwfCYrFgw4YNeP/997Fw4UJMnjzZcU5mZiYGDhyIq666Clu3bsX48eNxzz33YPny5ZX+fmPV8ZwTWLF6baS7QURERERhJokouxVcr149vPDCC7jpppvQoEEDLF68GDfddBMAYNeuXbjggguQkZGByy+/HD/88AOuu+46HD16FI0aNQIAzJ8/H08++SROnDgBs9mMJ598EkuXLsVff/3luMawYcOQm5uLZcuWaepTfn4+kpOTceRYNlKSkx3HJQmQhKjYIdnLJmhC3QxNkiEkCUIAihCwlX/qNsX+tU0I2BT7f4Wwt22SJMjl6UQmCTDJEiTY/+v4XghIihUoj6TtX1vtfbJZK/qk/pidl2KV5Ir+qWTZ3vfy4yWWMrz70Sc4W1DIWQMiIqIYUFJSgueffx55eXmaNgoLJ3UM1fS2NyGba4TcnmIpxpGP74uK91ZVRXTmwJnNZsMnn3yCwsJCpKWlYdOmTSgrK0Pfvn0d57Rv3x7NmzdHRkYGACAjIwMdO3Z0BAYAkJ6ejvz8fMfsQ0ZGhksb6jlqG96UlpYiPz/f5VFdrVi9noEBERERhUTYbIY9KLwiHhxs374dtWvXRkJCAu6//358/fXX6NChA7Kzs2E2mz12nWvUqBGys7MBANnZ2S6Bgfq8+py/c/Lz81FcXOy1TzNmzEBycrLj0axZM49zqsNeaLv27seO3XsYGBARERFVExEPDtq1a4etW7fit99+wwMPPICRI0di586dEe3TpEmTkJeX53gcPnw4ov2JhKKiYvywal2ku0FERERVgBAGFSQLzhyEW8SXMjWbzWjbti0AoGvXrvj999/x6quv4tZbb4XFYkFubq7L7MHx48cdu9GlpqZi48aNLu2pqxk5n+O+wtHx48eRlJSEGjW8574lJCQgISHBkPcXDEUIyJAgICIyRSGEwHcrVqHUYqn0axMRERFR5ER85sCdoigoLS1F165dER8fj1WrVjme2717N7KyspCWlgYASEtLw/bt25GTk+M4Z+XKlUhKSkKHDh0c5zi3oZ6jtkGeMv7YigOHDjOdiIiIiAzBpUxjR0RnDiZNmoRrr70WzZs3x9mzZ7F48WKsXbsWy5cvR3JyMkaPHo0JEyagXr16SEpKwkMPPYS0tDRcfvnlAIB+/fqhQ4cOGD58OGbNmoXs7Gw8/fTTGDt2rOPO//333485c+bgiSeewN13343Vq1fjs88+w9KlS8P75oTiWPEnlmz/ew/WZWwMfCIRERGRRtwELXZENDjIycnBiBEjcOzYMSQnJ6NTp05Yvnw5/vWvfwEAXn75ZciyjKFDh6K0tBTp6emYN2+e4/UmkwlLlizBAw88gLS0NNSqVQsjR47EtGnTHOe0atUKS5cuxaOPPopXX30V5557Lt555x2kp6dX+vuNdn/u3IPv1/wU6W4QERERUYRE3T4H0cjbPgdqKYDPfQ4A130EdOxzYFMEZKliLwMgvPscCCGQselPrPttk9EfHREREVWyaNznoOENsyDHG7DPQVkxcv73RFS8t6oq4gXJsUyqAnGVzWbDsrW/YNuuvZHuChEREVVRQlEMSitSAp9EIWFwECr32YIYUlRcgs9/WIUj2TmBTyYiIiKiKo/BgQG8phTpJIR9CdHKmow4dSYPHy9ZibOFRZVzQSIiIqq2FMXmSIEOuR0KKwYHRhKR2ZdAr6M5J/HxdytgKbNyuVIiIiIKO65WFDsYHFQzR4+fwKJvV8BqszEwICIiIiIXDA7CTBJKxYpFEZZ98hQWf8fAgIiIiCoXZw5iB4ODaiL7xCks+m4FyqwMDIiIiKiS2WwQsgEDexuDg3BjcFAN7Np/CN+u+gk2RWFgQEREREQ+MTiIUooQkCQJphDaKC4pxY8//4bte/Yb1i8iIiIivYQwZrUiIThzEG4MDqKA+918ASCUNY+EENi5ex9Wrt+AklJLSH0jIiIiCpVQFGOCA26CFnYMDoymDvQjtKRp1j9HsHr9LziWcyIi1yciIiKi2MXgIAjRtpWBEAKZmQeRsfF3HD5yFFK0dZCIiIiqNWHQJmhcrSj8GBzosOz771GzVk306tULdevWjXR3cPz4cfy9cyd27tyJgoICR1DAomMiIiKKJva0otBTgphWFH4MDnTYu3cPEhMTcezoUdw5fDjq1KpVaddWAEgCMEnA8exsrFuzCkePHIEkSY5ggEEBEREREYWCwUEQzp49i08/+QS333YbatZIDOu1lPLxvkkCrNYyZGz4GVs2/cFZAiIiIooZTCuKHQwOgiCEwOnTp/HBhx/ipqFD0KCeMSlGCuxLmHqTffQI1iz/Afn5eY4+EBEREcUCBgexg8FBkIQQyM/Px3sL38cVl3fHZd0ugTnO+I/zbF4uNv26Aft277TPFjAoICIiIqIwYXAQAiEEhBD4eUMGNm3egrTul+LiTp0QH6//Y1XbKisrQ0F+Hk4eP4asA/twJOugY88DzhYQERFRLFIUGyTOHMQEBgcGKS4pwep1P+HXjX+g5xWXo3OnjpBNst/XWK1W/LphA/7a9idKSoo9nldnChgSEBERUSwTNgWQDAgObFytKNwYHBisqLgYy1etwR9btqJ/v35odm5Tr+dZrVb876sv8c/hLJ8zApwpICIiIqLK5P/WNgXt9JlcLPrkU6z4cTUsFovH82t+XOk3MCAiIiKqKoSwQSgGPATTisKNMwdhog76t/z5J/bt34/+/fujZevWAIBNf/yOHX9tj2T3iIiIiCqNUGzGpBWx5iDsGByEmRACZwsK8Nnnn6NRaipq1ayJAwcORLpbREREREQeGBxUAnUW4Xh2tmPzMiIiIqLqgjMHsYPBQSVjjQERERFVNwwOYgeDAw3UAX1paWmEe0JERETknzpeiaobkrYyY5Zmt5UZ0Qr5IYmo+s2JTv/88w+aNWsW6W4QERERaXb48GGce+65Ee1DSUkJWrVqhezsbMPaTE1NRWZmJhITEw1rkyowONBAURTs3r0bHTp0wOHDh5GUlBTpLpEO+fn5aNasGX92MYg/u9jFn11s488vdqk/u507d6Jdu3aQ5civWl9SUuJ1Wfdgmc1mBgZhxLQiDWRZRtOm9s3MkpKS+A9ljOLPLnbxZxe7+LOLbfz5xa6mTZtGRWAAAImJiRzMx5Do+K0hIiIiIqKIY3BAREREREQAGBxolpCQgClTpiAhISHSXSGd+LOLXfzZxS7+7GIbf36xiz87ChULkomIiIiICABnDoiIiIiIqByDAyIiIiIiAsDggIiIiIiIyjE4ICIiIiIiAAwONJk7dy5atmyJxMREdO/eHRs3box0l6q99evX4/rrr0eTJk0g/X979x5TZf3HAfx9kIsg4OF+MxgtRJFgBslO6ZhxEhEbWivnaEk3VsGGaG2nP8RcW4dla2lrarYJbS1MG13wsohbg/AMjjAR6QwYeFyBDBDkItfz+f0RPuuEor/fDzkg79f2bJzv98t5Pg/vPWwfnuc5qFT44YcfrOZFBDk5OQgICICzszO0Wi2am5ut1vT29iI1NRXu7u5Qq9V4/fXXMTg4OIdHsTjp9Xo8+eSTcHNzg6+vL7Zt2waTyWS1ZmRkBBkZGfDy8oKrqyteeOEFXL9+3WqN2WxGcnIyXFxc4Ovri/feew8TExNzeSiLzpEjRxAVFaX8YyyNRoNz584p88xt4cjNzYVKpcLu3buVMeY3f33wwQdQqVRW26pVq5R5Zkezic3BPZw8eRJ79uzB/v37cfHiRURHRyMxMRFdXV22Lm1RGxoaQnR0NL744os7zn/88cc4fPgwjh49CoPBgGXLliExMREjIyPKmtTUVDQ2NqK4uBhFRUX47bffkJ6ePleHsGhVVFQgIyMDFy5cQHFxMcbHx7Fp0yYMDQ0pa7Kzs/Hzzz/j1KlTqKiowF9//YXnn39emZ+cnERycjLGxsbw+++/Iz8/H3l5ecjJybHFIS0aK1asQG5uLoxGI2pra/HMM88gJSUFjY2NAJjbQlFTU4Njx44hKirKapz5zW9r1qxBR0eHslVWVipzzI5mldCM1q1bJxkZGcrryclJCQwMFL1eb8Oq6J8ASGFhofLaYrGIv7+/HDx4UBnr6+sTJycn+fbbb0VE5MqVKwJAampqlDXnzp0TlUolf/7555zVTiJdXV0CQCoqKkTk76wcHBzk1KlTypqmpiYBINXV1SIicvbsWbGzs5POzk5lzZEjR8Td3V1GR0fn9gAWOQ8PD/nqq6+Y2wIxMDAgYWFhUlxcLPHx8ZKVlSUiPO/mu/3790t0dPQd55gdzTZeOZjB2NgYjEYjtFqtMmZnZwetVovq6mobVkYzaWtrQ2dnp1Vuy5cvR1xcnJJbdXU11Go1YmNjlTVarRZ2dnYwGAxzXvNi1t/fDwDw9PQEABiNRoyPj1vlt2rVKgQHB1vl9/jjj8PPz09Zk5iYiJs3byp/xaYHa3JyEgUFBRgaGoJGo2FuC0RGRgaSk5OtcgJ43i0Ezc3NCAwMxKOPPorU1FSYzWYAzI5mn72tC5jPuru7MTk5aXUyAYCfnx/++OMPG1VF99LZ2QkAd8zt9lxnZyd8fX2t5u3t7eHp6amsoQfPYrFg9+7dePrppxEZGQng72wcHR2hVqut1v47vzvle3uOHpyGhgZoNBqMjIzA1dUVhYWFiIiIQH19PXOb5woKCnDx4kXU1NRMm+N5N7/FxcUhLy8P4eHh6OjowIEDB7BhwwZcvnyZ2dGsY3NARDaTkZGBy5cvW907S/NbeHg46uvr0d/fj9OnT2PXrl2oqKiwdVl0D9euXUNWVhaKi4uxdOlSW5dD/6WkpCTl66ioKMTFxSEkJATfffcdnJ2dbVgZPYx4W9EMvL29sWTJkmlP/F+/fh3+/v42qoru5XY2M+Xm7+8/7aHyiYkJ9Pb2Mts5kpmZiaKiIpSVlWHFihXKuL+/P8bGxtDX12e1/t/53Snf23P04Dg6OuKxxx5DTEwM9Ho9oqOjcejQIeY2zxmNRnR1deGJJ56Avb097O3tUVFRgcOHD8Pe3h5+fn7MbwFRq9VYuXIlWlpaeO7RrGNzMANHR0fExMSgpKREGbNYLCgpKYFGo7FhZTST0NBQ+Pv7W+V28+ZNGAwGJTeNRoO+vj4YjUZlTWlpKSwWC+Li4ua85sVERJCZmYnCwkKUlpYiNDTUaj4mJgYODg5W+ZlMJpjNZqv8GhoarBq84uJiuLu7IyIiYm4OhAD8/TtxdHSUuc1zCQkJaGhoQH19vbLFxsYiNTVV+Zr5LRyDg4NobW1FQEAAzz2afbZ+Inq+KygoECcnJ8nLy5MrV65Ienq6qNVqqyf+ae4NDAxIXV2d1NXVCQD59NNPpa6uTq5evSoiIrm5uaJWq+XHH3+US5cuSUpKioSGhsqtW7eU99i8ebOsXbtWDAaDVFZWSlhYmOzcudNWh7RovP3227J8+XIpLy+Xjo4OZRseHlbWvPXWWxIcHCylpaVSW1srGo1GNBqNMj8xMSGRkZGyadMmqa+vl/Pnz4uPj4+8//77tjikRUOn00lFRYW0tbXJpUuXRKfTiUqlkl9++UVEmNtC889PKxJhfvPZ3r17pby8XNra2qSqqkq0Wq14e3tLV1eXiDA7ml1sDu7D559/LsHBweLo6Cjr1q2TCxcu2LqkRa+srEwATNt27dolIn9/nOm+ffvEz89PnJycJCEhQUwmk9V79PT0yM6dO8XV1VXc3d3l1VdflYGBARsczeJyp9wAyIkTJ5Q1t27dknfeeUc8PDzExcVFtm/fLh0dHVbv097eLklJSeLs7Cze3t6yd+9eGR8fn+OjWVxee+01CQkJEUdHR/Hx8ZGEhASlMRBhbgvNv5sD5jd/7dixQwICAsTR0VGCgoJkx44d0tLSoswzO5pNKhER21yzICIiIiKi+YTPHBAREREREQA2B0RERERENIXNARERERERAWBzQEREREREU9gcEBERERERADYHREREREQ0hc0BEREREREBYHNARERERERT2BwQES0AaWlp2LZtm63LICKihxybAyKi/1F5eTlUKhX6+vpsXQoREdGsYHNARDTPjY+P27oEIiJaJNgcEBHNwGKxQK/XIzQ0FM7OzoiOjsbp06fR3t6OjRs3AgA8PDygUqmQlpYGADh//jzWr18PtVoNLy8vbN26Fa2trfe1v/b2dqhUKpw8eRLx8fFYunQpvvnmG2X+k08+QUBAALy8vJCRkWHVONy4cQOvvPIKPDw84OLigqSkJDQ3N8/eD4OIiB56bA6IiGag1+vx9ddf4+jRo2hsbER2djZefvllXL16Fd9//z0AwGQyoaOjA4cOHQIADA0NYc+ePaitrUVJSQns7Oywfft2WCyW+96vTqdDVlYWmpqakJiYCAAoKytDa2srysrKkJ+fj7y8POTl5Snfk5aWhtraWvz000+orq6GiGDLli288kBERPdNJSJi6yKIiOaj0dFReHp64tdff4VGo1HG33jjDQwPDyM9PR0bN27EjRs3oFar7/o+3d3d8PHxQUNDAyIjI2fcZ3t7O0JDQ/HZZ58hKytLGU9LS0N5eTlaW1uxZMkSAMBLL70EOzs7FBQUoLm5GStXrkRVVRWeeuopAEBPTw8eeeQR5Ofn48UXX/w/fhJERLRY2Nu6ACKi+aqlpQXDw8N49tlnrcbHxsawdu3au35fc3MzcnJyYDAY0N3drVwxMJvN92wObouNjZ02tmbNGqUxAICAgAA0NDQAAJqammBvb4+4uDhl3svLC+Hh4WhqarqvfRIREbE5ICK6i8HBQQDAmTNnEBQUZDXn5OR01+cInnvuOYSEhOD48eMIDAyExWJBZGQkxsbG7nvfy5Ytmzbm4OBg9VqlUv1XtyoRERHdC5sDIqK7iIiIgJOTE8xmM+Lj46fNX7t2DQAwOTmpjPX09MBkMuH48ePYsGEDAKCysvKB17p69WpMTEzAYDBY3VZkMpkQERHxwPdPREQPBzYHRER34ebmhnfffRfZ2dmwWCxYv349+vv7UVVVBXd3d2i1WqhUKhQVFWHLli1wdnaGh4cHvLy88OWXXyIgIABmsxk6ne6B1xoWFoaUlBS8+eabOHbsGNzc3KDT6RAUFISUlJQHvn8iIno48NOKiIhm8OGHH2Lfvn3Q6/VYvXo1Nm/ejDNnziA0NBRBQUE4cOAAdDod/Pz8kJmZqTwgbDQaERkZiezsbBw8eHBOaj1x4gRiYmKwdetWaDQaiAjOnj077XYkIiKiu+GnFREREREREQBeOSAiIiIioilsDoiI5tBHH30EV1fXO25JSUm2Lo+IiBY53lZERDSHent70dvbe8c5Z2fnaR+ZSkRENJfYHBAREREREQDeVkRERERERFPYHBAREREREQA2B0RERERENIXNARERERERAWBzQEREREREU9gcEBERERERADYHREREREQ05T9hHCQBCfGTBwAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "<Figure size 900x500 with 2 Axes>"
       ]
@@ -3011,7 +3248,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "1b679c8e-8142-4752-af3f-40213f78219a",
+   "id": "e3d94944-1893-4c0f-a6c5-006ead9a93a5",
    "metadata": {
     "tags": []
    },
@@ -3020,12 +3257,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[########################################] | 100% Completed | 101.47 ms\n"
+      "[########################################] | 100% Completed | 101.16 ms\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 700x400 with 1 Axes>"
       ]
@@ -3049,7 +3286,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "09d365cf-7fb3-4c27-a765-1384fcd12fd6",
+   "id": "0b9dbb5d-5bfd-41ee-92fa-8449233dec2f",
    "metadata": {
     "tags": []
    },
@@ -3058,12 +3295,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[########################################] | 100% Completed | 304.25 ms\n"
+      "[########################################] | 100% Completed | 101.99 ms\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 700x400 with 1 Axes>"
       ]
@@ -3103,8 +3340,8 @@
    },
    "outputs": [],
    "source": [
-    "cesm_bgc_path = \"/global/cfs/projectdirs/m4746/Datasets/CESM_REGRIDDED/CESM-climatology_lowres_regridded.nc\"\n",
-    "unified_bgc_path = \"/global/cfs/projectdirs/m4746/Datasets/UNIFIED/BGCdataset.nc\""
+    "cesm_bgc_path = \"/anvil/projects/x-ees250129/Datasets/CESM_REGRIDDED/CESM-climatology_lowres_regridded.nc\"\n",
+    "unified_bgc_path = \"/anvil/projects/x-ees250129/Datasets/UNIFIED/BGCdataset.nc\""
    ]
   },
   {
@@ -3119,15 +3356,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-12-20 13:49:49 - INFO - No `boundaries` provided. Using mask-based defaults: {'south': True, 'east': True, 'north': False, 'west': True}\n"
+      "2026-06-22 17:58:37 - INFO - No `boundaries` provided. Using mask-based defaults: {'south': True, 'east': True, 'north': False, 'west': True}\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 5.03 s, sys: 648 ms, total: 5.68 s\n",
-      "Wall time: 8.58 s\n"
+      "CPU times: user 2.02 s, sys: 103 ms, total: 2.12 s\n",
+      "Wall time: 2.21 s\n"
      ]
     }
    ],
@@ -3170,9 +3407,7 @@
        "</symbol>\n",
        "</defs>\n",
        "</svg>\n",
-       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
-       " *\n",
-       " */\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
        "\n",
        ":root {\n",
        "  --xr-font-color0: var(\n",
@@ -3251,6 +3486,8 @@
        "  display: block !important;\n",
        "  min-width: 300px;\n",
        "  max-width: 700px;\n",
+       "  line-height: 1.6;\n",
+       "  padding-bottom: 4px;\n",
        "}\n",
        "\n",
        ".xr-text-repr-fallback {\n",
@@ -3261,8 +3498,11 @@
        ".xr-header {\n",
        "  padding-top: 6px;\n",
        "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
        "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "  margin-bottom: 4px;\n",
        "}\n",
        "\n",
        ".xr-header > div,\n",
@@ -3273,46 +3513,62 @@
        "}\n",
        "\n",
        ".xr-obj-type,\n",
-       ".xr-array-name {\n",
+       ".xr-obj-name {\n",
        "  margin-left: 2px;\n",
        "  margin-right: 10px;\n",
        "}\n",
        "\n",
-       ".xr-obj-type {\n",
+       ".xr-obj-type,\n",
+       ".xr-group-box-contents > label {\n",
        "  color: var(--xr-font-color2);\n",
+       "  display: block;\n",
        "}\n",
        "\n",
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
        "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
        "  display: contents;\n",
        "}\n",
        "\n",
-       ".xr-section-item input {\n",
-       "  display: inline-block;\n",
+       ".xr-section-item > input,\n",
+       ".xr-group-box-contents > input,\n",
+       ".xr-array-wrap > input {\n",
+       "  display: block;\n",
        "  opacity: 0;\n",
        "  height: 0;\n",
+       "  margin: 0;\n",
        "}\n",
        "\n",
-       ".xr-section-item input + label {\n",
+       ".xr-section-item > input + label,\n",
+       ".xr-var-item > input + label {\n",
        "  color: var(--xr-disabled-color);\n",
-       "  border: 2px solid transparent !important;\n",
        "}\n",
        "\n",
-       ".xr-section-item input:enabled + label {\n",
+       ".xr-section-item > input:enabled + label,\n",
+       ".xr-var-item > input:enabled + label,\n",
+       ".xr-array-wrap > input:enabled + label,\n",
+       ".xr-group-box-contents > input:enabled + label {\n",
        "  cursor: pointer;\n",
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
-       ".xr-section-item input:focus + label {\n",
-       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       ".xr-section-item > input:focus-visible + label,\n",
+       ".xr-var-item > input:focus-visible + label,\n",
+       ".xr-array-wrap > input:focus-visible + label,\n",
+       ".xr-group-box-contents > input:focus-visible + label {\n",
+       "  outline: auto;\n",
        "}\n",
        "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
+       ".xr-section-item > input:enabled + label:hover,\n",
+       ".xr-var-item > input:enabled + label:hover,\n",
+       ".xr-array-wrap > input:enabled + label:hover,\n",
+       ".xr-group-box-contents > input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
        "\n",
@@ -3320,11 +3576,25 @@
        "  grid-column: 1;\n",
        "  color: var(--xr-font-color2);\n",
        "  font-weight: 500;\n",
+       "  white-space: nowrap;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > em {\n",
+       "  font-weight: normal;\n",
+       "}\n",
+       "\n",
+       ".xr-span-grid {\n",
+       "  grid-column-end: -1;\n",
        "}\n",
        "\n",
        ".xr-section-summary > span {\n",
        "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
+       "  padding-left: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.6em;\n",
        "}\n",
        "\n",
        ".xr-section-summary-in:disabled + label {\n",
@@ -3352,9 +3622,9 @@
        "}\n",
        "\n",
        ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
+       ".xr-section-inline-details,\n",
+       ".xr-group-box-contents > label {\n",
        "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
        "}\n",
        "\n",
        ".xr-section-inline-details {\n",
@@ -3362,50 +3632,116 @@
        "}\n",
        "\n",
        ".xr-section-details {\n",
-       "  display: none;\n",
        "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
        "  margin-bottom: 5px;\n",
        "}\n",
        "\n",
+       ".xr-section-summary-in ~ .xr-section-details {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
        ".xr-section-summary-in:checked ~ .xr-section-details {\n",
        "  display: contents;\n",
        "}\n",
        "\n",
-       ".xr-array-wrap {\n",
+       ".xr-children {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 100%;\n",
        "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
+       "  padding-top: 4px;\n",
        "}\n",
        "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 30px auto;\n",
        "}\n",
        "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
        "}\n",
        "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 26px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
        "}\n",
        "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "  padding-bottom: 4px;\n",
        "}\n",
        "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
+       ".xr-group-box-contents > label::before {\n",
+       "  content: \"📂\";\n",
+       "  padding-right: 0.3em;\n",
        "}\n",
        "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
+       ".xr-group-box-contents > input:checked + label::before {\n",
+       "  content: \"📁\";\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label {\n",
+       "  padding-bottom: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked ~ .xr-sections {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-ellipsis {\n",
+       "  font-size: 1.4em;\n",
+       "  font-weight: 900;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  letter-spacing: 0.15em;\n",
+       "  cursor: default;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
        "  margin: 0;\n",
        "}\n",
        "\n",
@@ -3601,43 +3937,44 @@
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 231MB\n",
        "Dimensions:            (bry_time: 12, s_rho: 100, xi_rho: 502, eta_rho: 502)\n",
        "Coordinates:\n",
+       "  * bry_time           (bry_time) float64 96B 15.0 45.0 74.0 ... 319.0 349.0\n",
        "    month              (bry_time) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12\n",
        "    abs_time           (bry_time) datetime64[ns] 96B 2000-01-16 ... 2000-12-15\n",
-       "  * bry_time           (bry_time) float64 96B 15.0 45.0 74.0 ... 319.0 349.0\n",
        "Dimensions without coordinates: s_rho, xi_rho, eta_rho\n",
        "Data variables: (12/96)\n",
-       "    PO4_south          (bry_time, s_rho, xi_rho) float32 2MB dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;\n",
-       "    NO3_south          (bry_time, s_rho, xi_rho) float32 2MB dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;\n",
-       "    SiO3_south         (bry_time, s_rho, xi_rho) float32 2MB dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;\n",
-       "    NH4_south          (bry_time, s_rho, xi_rho) float32 2MB dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;\n",
-       "    Fe_south           (bry_time, s_rho, xi_rho) float32 2MB dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;\n",
-       "    Lig_south          (bry_time, s_rho, xi_rho) float32 2MB dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;\n",
+       "    PO4_south          (bry_time, s_rho, xi_rho) float32 2MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
+       "    NO3_south          (bry_time, s_rho, xi_rho) float32 2MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
+       "    SiO3_south         (bry_time, s_rho, xi_rho) float32 2MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
+       "    NH4_south          (bry_time, s_rho, xi_rho) float32 2MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
+       "    Fe_south           (bry_time, s_rho, xi_rho) float32 2MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
+       "    Lig_south          (bry_time, s_rho, xi_rho) float32 2MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
        "    ...                 ...\n",
-       "    diazChl_west       (bry_time, s_rho, eta_rho) float32 2MB dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;\n",
-       "    diazC_west         (bry_time, s_rho, eta_rho) float32 2MB dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;\n",
-       "    diazP_west         (bry_time, s_rho, eta_rho) float32 2MB dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;\n",
-       "    diazFe_west        (bry_time, s_rho, eta_rho) float32 2MB dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;\n",
-       "    spCaCO3_west       (bry_time, s_rho, eta_rho) float32 2MB dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;\n",
-       "    zooC_west          (bry_time, s_rho, eta_rho) float32 2MB dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;\n",
-       "Attributes:\n",
+       "    diazChl_west       (bry_time, s_rho, eta_rho) float32 2MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
+       "    diazC_west         (bry_time, s_rho, eta_rho) float32 2MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
+       "    diazP_west         (bry_time, s_rho, eta_rho) float32 2MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
+       "    diazFe_west        (bry_time, s_rho, eta_rho) float32 2MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
+       "    spCaCO3_west       (bry_time, s_rho, eta_rho) float32 2MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
+       "    zooC_west          (bry_time, s_rho, eta_rho) float32 2MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
+       "Attributes: (12/14)\n",
        "    title:                                ROMS boundary forcing file created ...\n",
-       "    roms_tools_version:                   3.1.2.dev23+gc2b82bc5c\n",
+       "    roms_tools_version:                   10000.dev361+g2e6c47bd8\n",
        "    start_time:                           2012-01-30 00:00:00\n",
        "    end_time:                             2012-02-04 00:00:00\n",
        "    source:                               CESM_REGRIDDED\n",
        "    model_reference_date:                 2000-01-01 00:00:00\n",
-       "    apply_2d_horizontal_fill:             False\n",
+       "    ...                                   ...\n",
+       "    extrap_method:                        inverse_dist\n",
        "    adjust_depth_for_sea_surface_height:  False\n",
        "    theta_s:                              5.0\n",
        "    theta_b:                              2.0\n",
        "    hc:                                   300.0\n",
-       "    climatology:                          True</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-240c8e01-f327-4d73-ba46-e0f9d6b3e375' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-240c8e01-f327-4d73-ba46-e0f9d6b3e375' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>bry_time</span>: 12</li><li><span>s_rho</span>: 100</li><li><span>xi_rho</span>: 502</li><li><span>eta_rho</span>: 502</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-00e5b770-f03c-4f4a-9dfa-a1a34075a26f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-00e5b770-f03c-4f4a-9dfa-a1a34075a26f' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>month</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-c98f1b1d-3732-41a8-95e6-77441fbb75cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c98f1b1d-3732-41a8-95e6-77441fbb75cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30d425d1-1fa2-4a58-adc0-9541e124e548' class='xr-var-data-in' type='checkbox'><label for='data-30d425d1-1fa2-4a58-adc0-9541e124e548' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Month index (1-12)</dd></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-16 ... 2000-12-15</div><input id='attrs-d79f0b46-5140-4fd0-b86d-fe7ac1370d1e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d79f0b46-5140-4fd0-b86d-fe7ac1370d1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e654eec-4de6-404f-bcc2-f72cfd28f82f' class='xr-var-data-in' type='checkbox'><label for='data-0e654eec-4de6-404f-bcc2-f72cfd28f82f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>absolute time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-16T00:00:00.000000000&#x27;, &#x27;2000-02-15T00:00:00.000000000&#x27;,\n",
+       "    climatology:                          True</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-297d0858-f3f0-4f6f-be0f-8b431bc729e5' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-297d0858-f3f0-4f6f-be0f-8b431bc729e5' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>bry_time</span>: 12</li><li><span>s_rho</span>: 100</li><li><span>xi_rho</span>: 502</li><li><span>eta_rho</span>: 502</li></ul></div></li><li class='xr-section-item'><input id='section-e97b8419-091e-4a0f-8e04-06d929a9e2b9' class='xr-section-summary-in' type='checkbox' checked /><label for='section-e97b8419-091e-4a0f-8e04-06d929a9e2b9' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>bry_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.0 45.0 74.0 ... 319.0 349.0</div><input id='attrs-203728b4-c034-4e9e-afc6-771ba1960e93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-203728b4-c034-4e9e-afc6-771ba1960e93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25b7c26f-56b4-4f4e-b1ad-397123f57a51' class='xr-var-data-in' type='checkbox'><label for='data-25b7c26f-56b4-4f4e-b1ad-397123f57a51' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cycle_length :</span></dt><dd>365.25</dd><dt><span>long_name :</span></dt><dd>relative time: days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([ 15.,  45.,  74., 105., 135., 166., 196., 227., 258., 288., 319., 349.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>month</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-9291bdbe-dedf-456f-821e-338cc941841f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9291bdbe-dedf-456f-821e-338cc941841f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b3df781-5dca-495e-bbe6-ad6720c6e884' class='xr-var-data-in' type='checkbox'><label for='data-1b3df781-5dca-495e-bbe6-ad6720c6e884' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Month index (1-12)</dd></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-16 ... 2000-12-15</div><input id='attrs-97034d14-106f-4444-afb0-b762f847fc84' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-97034d14-106f-4444-afb0-b762f847fc84' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ef25709-c008-40d5-aa0b-8fdebe53dda8' class='xr-var-data-in' type='checkbox'><label for='data-1ef25709-c008-40d5-aa0b-8fdebe53dda8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>absolute time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-16T00:00:00.000000000&#x27;, &#x27;2000-02-15T00:00:00.000000000&#x27;,\n",
        "       &#x27;2000-03-15T00:00:00.000000000&#x27;, &#x27;2000-04-15T00:00:00.000000000&#x27;,\n",
        "       &#x27;2000-05-15T00:00:00.000000000&#x27;, &#x27;2000-06-15T00:00:00.000000000&#x27;,\n",
        "       &#x27;2000-07-15T00:00:00.000000000&#x27;, &#x27;2000-08-15T00:00:00.000000000&#x27;,\n",
        "       &#x27;2000-09-15T00:00:00.000000000&#x27;, &#x27;2000-10-15T00:00:00.000000000&#x27;,\n",
        "       &#x27;2000-11-15T00:00:00.000000000&#x27;, &#x27;2000-12-15T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>bry_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.0 45.0 74.0 ... 319.0 349.0</div><input id='attrs-c6b94f49-abca-499d-9b12-7f1036b2237c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c6b94f49-abca-499d-9b12-7f1036b2237c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b3ad345-05ee-485a-b342-0d4027754bec' class='xr-var-data-in' type='checkbox'><label for='data-2b3ad345-05ee-485a-b342-0d4027754bec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cycle_length :</span></dt><dd>365.25</dd><dt><span>long_name :</span></dt><dd>relative time: days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([ 15.,  45.,  74., 105., 135., 166., 196., 227., 258., 288., 319., 349.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4722dbb5-c2da-4425-ab13-5847a54c03f8' class='xr-section-summary-in' type='checkbox'  ><label for='section-4722dbb5-c2da-4425-ab13-5847a54c03f8' class='xr-section-summary' >Data variables: <span>(96)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>PO4_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-2f600ed9-2d7f-4b89-b96e-cb2e40610a98' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2f600ed9-2d7f-4b89-b96e-cb2e40610a98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4199afb7-02d4-4d34-a482-3bba67f7b543' class='xr-var-data-in' type='checkbox'><label for='data-4199afb7-02d4-4d34-a482-3bba67f7b543' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved inorganic phosphate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3b7e63a0-273c-4d13-b5be-d6d335e24fd8' class='xr-section-summary-in' type='checkbox' /><label for='section-3b7e63a0-273c-4d13-b5be-d6d335e24fd8' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(96)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>PO4_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-d5fbcdd7-ddce-4fbf-af5a-0d800e2696cf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d5fbcdd7-ddce-4fbf-af5a-0d800e2696cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c1b1a49-da0e-41e7-a146-847265cddbce' class='xr-var-data-in' type='checkbox'><label for='data-8c1b1a49-da0e-41e7-a146-847265cddbce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>southern boundary dissolved inorganic phosphate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3653,17 +3990,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -3681,6 +4018,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -3688,6 +4036,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -3709,13 +4068,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NO3_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-9136e4be-f9d6-46f3-9f6c-9c9b8e0f9b5b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9136e4be-f9d6-46f3-9f6c-9c9b8e0f9b5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65631027-4266-4437-aec6-f87e17fa8f3d' class='xr-var-data-in' type='checkbox'><label for='data-65631027-4266-4437-aec6-f87e17fa8f3d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved inorganic nitrate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NO3_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-0a48fec8-d69d-4b89-ad3f-4a120980adcd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a48fec8-d69d-4b89-ad3f-4a120980adcd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6fdf3c12-1881-4358-a2f0-5be6cde5b64d' class='xr-var-data-in' type='checkbox'><label for='data-6fdf3c12-1881-4358-a2f0-5be6cde5b64d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>southern boundary dissolved inorganic nitrate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3731,17 +4090,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -3759,6 +4118,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -3766,6 +4136,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -3787,13 +4168,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SiO3_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-b7ccc402-4c68-40aa-815d-680d2e976fdd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b7ccc402-4c68-40aa-815d-680d2e976fdd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa1bc6cc-afeb-4d43-8138-4f092b4eaf5d' class='xr-var-data-in' type='checkbox'><label for='data-fa1bc6cc-afeb-4d43-8138-4f092b4eaf5d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved inorganic silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SiO3_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-28640df0-31af-4ca0-9ef8-749cf684497e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28640df0-31af-4ca0-9ef8-749cf684497e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c88317e-faf5-4072-8b1f-e798a08ff83a' class='xr-var-data-in' type='checkbox'><label for='data-8c88317e-faf5-4072-8b1f-e798a08ff83a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>southern boundary dissolved inorganic silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3809,17 +4190,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -3837,6 +4218,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -3844,6 +4236,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -3865,13 +4268,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NH4_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-5cf5fda2-da30-4963-baa8-8de0ebfa75ca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5cf5fda2-da30-4963-baa8-8de0ebfa75ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30fb4e3d-8d86-4c36-93b4-7b39fb413e3a' class='xr-var-data-in' type='checkbox'><label for='data-30fb4e3d-8d86-4c36-93b4-7b39fb413e3a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved ammonia</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NH4_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-04a129cb-9fec-4382-98f6-510edd7ba0e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-04a129cb-9fec-4382-98f6-510edd7ba0e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-415bd0ee-d65b-43a2-8b65-c8ed24d33e5f' class='xr-var-data-in' type='checkbox'><label for='data-415bd0ee-d65b-43a2-8b65-c8ed24d33e5f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>southern boundary dissolved ammonia</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3887,17 +4290,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -3915,6 +4318,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -3922,6 +4336,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -3943,13 +4368,13 @@
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Fe_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-7bdc564d-89dc-49ad-8a8b-eefc34133299' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7bdc564d-89dc-49ad-8a8b-eefc34133299' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6eeb3b8a-f107-4735-a229-12ba34cad5aa' class='xr-var-data-in' type='checkbox'><label for='data-6eeb3b8a-f107-4735-a229-12ba34cad5aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved inorganic iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Fe_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-2aef2b08-63e3-41e4-b65f-92c254034282' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2aef2b08-63e3-41e4-b65f-92c254034282' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f191e9f-5eba-4917-8732-2798569d44ac' class='xr-var-data-in' type='checkbox'><label for='data-7f191e9f-5eba-4917-8732-2798569d44ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>southern boundary dissolved inorganic iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3965,17 +4390,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -3993,6 +4418,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -4000,6 +4436,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -4021,13 +4468,13 @@
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Lig_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-23c80df9-ec50-4f5a-a2e1-cc3c94061e1b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-23c80df9-ec50-4f5a-a2e1-cc3c94061e1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0379a14b-33a2-4111-bb81-da597f557624' class='xr-var-data-in' type='checkbox'><label for='data-0379a14b-33a2-4111-bb81-da597f557624' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary iron binding ligand</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Lig_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-db205c27-b19a-4ade-aa48-ab1513fbdeb9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db205c27-b19a-4ade-aa48-ab1513fbdeb9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4418434-9cc8-45d8-9495-63a48e08c8b4' class='xr-var-data-in' type='checkbox'><label for='data-e4418434-9cc8-45d8-9495-63a48e08c8b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>southern boundary iron binding ligand</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4043,17 +4490,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -4071,6 +4518,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -4078,6 +4536,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -4099,13 +4568,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>O2_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-670bbc62-642d-4b31-9077-40e689043903' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-670bbc62-642d-4b31-9077-40e689043903' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f72bf417-6409-42e4-b1a1-614ded0ee304' class='xr-var-data-in' type='checkbox'><label for='data-f72bf417-6409-42e4-b1a1-614ded0ee304' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved oxygen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>O2_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-72a4c1b3-19a4-426e-8ee0-ad0924f70a15' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-72a4c1b3-19a4-426e-8ee0-ad0924f70a15' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8dce032-7d82-4035-81e2-fc036ec9f695' class='xr-var-data-in' type='checkbox'><label for='data-a8dce032-7d82-4035-81e2-fc036ec9f695' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>southern boundary dissolved oxygen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4121,17 +4590,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -4149,6 +4618,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -4156,6 +4636,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -4177,13 +4668,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIC_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-a8281130-c4c3-4270-84c5-8ea72abc411d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a8281130-c4c3-4270-84c5-8ea72abc411d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0be57e9d-3775-4ccb-a755-1dd4019f86f7' class='xr-var-data-in' type='checkbox'><label for='data-0be57e9d-3775-4ccb-a755-1dd4019f86f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved inorganic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIC_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-8f2a573b-bddf-432d-8bb4-611eec38ee0a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8f2a573b-bddf-432d-8bb4-611eec38ee0a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-331b24fa-5e4c-4f35-a23b-a2ba2c0baa58' class='xr-var-data-in' type='checkbox'><label for='data-331b24fa-5e4c-4f35-a23b-a2ba2c0baa58' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>southern boundary dissolved inorganic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4199,17 +4690,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -4227,6 +4718,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -4234,6 +4736,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -4255,13 +4768,13 @@
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
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-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIC_ALT_CO2_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-8de5ebe1-15e3-4215-8e7a-31609c280e30' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8de5ebe1-15e3-4215-8e7a-31609c280e30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18d4f917-e228-44de-9dd9-d3911b944925' class='xr-var-data-in' type='checkbox'><label for='data-18d4f917-e228-44de-9dd9-d3911b944925' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved inorganic carbon, alternative CO2</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIC_ALT_CO2_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-9ca0bbdf-23d0-4b5e-bc87-85f380ec0614' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9ca0bbdf-23d0-4b5e-bc87-85f380ec0614' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19cf7d67-c52c-4f15-a1a5-d53a475a0456' class='xr-var-data-in' type='checkbox'><label for='data-19cf7d67-c52c-4f15-a1a5-d53a475a0456' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>southern boundary dissolved inorganic carbon, alternative CO2</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4277,17 +4790,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -4305,6 +4818,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -4312,6 +4836,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -4333,13 +4868,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALK_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-e100c4ce-319a-4ef1-988e-adfd262e297d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e100c4ce-319a-4ef1-988e-adfd262e297d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-164812e3-0067-4130-8652-aa1787c566da' class='xr-var-data-in' type='checkbox'><label for='data-164812e3-0067-4130-8652-aa1787c566da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary alkalinity</dd><dt><span>units :</span></dt><dd>meq/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALK_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-5d687aad-98ab-4c9f-b4a4-6b4fb68f37f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5d687aad-98ab-4c9f-b4a4-6b4fb68f37f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30ee1040-0d95-49cf-8fe3-098ec36b93be' class='xr-var-data-in' type='checkbox'><label for='data-30ee1040-0d95-49cf-8fe3-098ec36b93be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>southern boundary alkalinity</dd><dt><span>units :</span></dt><dd>meq/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4355,17 +4890,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -4383,6 +4918,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -4390,6 +4936,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -4411,13 +4968,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALK_ALT_CO2_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-ce736526-7017-4e24-84e6-4c237e65d520' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce736526-7017-4e24-84e6-4c237e65d520' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b858039a-1953-4305-8cc2-b5fb2916f330' class='xr-var-data-in' type='checkbox'><label for='data-b858039a-1953-4305-8cc2-b5fb2916f330' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary alkalinity, alternative CO2</dd><dt><span>units :</span></dt><dd>meq/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALK_ALT_CO2_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-3332e8fc-bcff-43f2-b9db-a629e5233165' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3332e8fc-bcff-43f2-b9db-a629e5233165' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-464cc0d3-2a2e-4ec0-be17-645d4e7606e2' class='xr-var-data-in' type='checkbox'><label for='data-464cc0d3-2a2e-4ec0-be17-645d4e7606e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>southern boundary alkalinity, alternative CO2</dd><dt><span>units :</span></dt><dd>meq/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4433,17 +4990,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -4461,6 +5018,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
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@@ -4468,6 +5036,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
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        "\n",
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@@ -4489,13 +5068,13 @@
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOC_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-49405179-3248-4751-9799-b84b5a008aa6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49405179-3248-4751-9799-b84b5a008aa6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc632c6f-565c-44ec-97ac-ce4f2c960dd0' class='xr-var-data-in' type='checkbox'><label for='data-dc632c6f-565c-44ec-97ac-ce4f2c960dd0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved organic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOC_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-40ee1e49-4033-4343-a61c-305ce76b5c6f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40ee1e49-4033-4343-a61c-305ce76b5c6f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7252eed4-1abd-47f0-b887-56daad134688' class='xr-var-data-in' type='checkbox'><label for='data-7252eed4-1abd-47f0-b887-56daad134688' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>southern boundary dissolved organic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4511,17 +5090,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -4539,13 +5118,35 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
        "  <polygon points=\"10.0,0.0 27.676686162348723,17.676686162348723 27.676686162348723,59.6696884485097 10.0,41.99300228616098\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -4567,13 +5168,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DON_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-fcdb85af-75f5-47b9-9eec-e72c2c4669fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fcdb85af-75f5-47b9-9eec-e72c2c4669fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08d31548-9d72-42cc-8cfe-7abbd5cbc4ba' class='xr-var-data-in' type='checkbox'><label for='data-08d31548-9d72-42cc-8cfe-7abbd5cbc4ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DON_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-978fae75-f7bf-419c-a14a-3de22445701e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-978fae75-f7bf-419c-a14a-3de22445701e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6ceb8f4-bc36-4dcc-b4c4-57790b558bb8' class='xr-var-data-in' type='checkbox'><label for='data-a6ceb8f4-bc36-4dcc-b4c4-57790b558bb8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>southern boundary dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4589,17 +5190,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -4617,6 +5218,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -4624,6 +5236,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -4645,13 +5268,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOP_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-309550f4-22a7-492b-bf7e-ccf46e6bc415' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-309550f4-22a7-492b-bf7e-ccf46e6bc415' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4955f86b-a739-4341-a34d-a35b52a9938a' class='xr-var-data-in' type='checkbox'><label for='data-4955f86b-a739-4341-a34d-a35b52a9938a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOP_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-6796947f-6125-4683-bfa5-cd96b65ad7f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6796947f-6125-4683-bfa5-cd96b65ad7f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c1d4050-79ec-458a-903c-7cdddd69c11c' class='xr-var-data-in' type='checkbox'><label for='data-4c1d4050-79ec-458a-903c-7cdddd69c11c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>southern boundary dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4667,17 +5290,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -4695,6 +5318,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -4702,6 +5336,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -4723,13 +5368,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOPr_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-dce5b76c-1ed3-45b4-9371-280ce3fe7fd5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dce5b76c-1ed3-45b4-9371-280ce3fe7fd5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ddcc612-20db-4580-b608-b1a4edb78fab' class='xr-var-data-in' type='checkbox'><label for='data-5ddcc612-20db-4580-b608-b1a4edb78fab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary refractory dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOPr_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-b7eb00e0-0fcf-4d56-9c00-94d8f4f5fc82' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b7eb00e0-0fcf-4d56-9c00-94d8f4f5fc82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f418a683-9e7c-4be6-8c13-75d41f3e0ab5' class='xr-var-data-in' type='checkbox'><label for='data-f418a683-9e7c-4be6-8c13-75d41f3e0ab5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>southern boundary refractory dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4745,17 +5390,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -4773,6 +5418,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -4780,6 +5436,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
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@@ -4801,13 +5468,13 @@
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DONr_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-10333f7b-5ed2-4dd5-a0b1-b92fa4313b0a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-10333f7b-5ed2-4dd5-a0b1-b92fa4313b0a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aabcfbf4-b15d-490d-bbbc-258d6a516dba' class='xr-var-data-in' type='checkbox'><label for='data-aabcfbf4-b15d-490d-bbbc-258d6a516dba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary refractory dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DONr_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-9159aa30-3777-43e9-97d0-2f14456dc21f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9159aa30-3777-43e9-97d0-2f14456dc21f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0eebbb3b-36a3-44e1-9803-3aa855923dd2' class='xr-var-data-in' type='checkbox'><label for='data-0eebbb3b-36a3-44e1-9803-3aa855923dd2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>southern boundary refractory dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4823,17 +5490,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -4851,6 +5518,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -4858,6 +5536,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -4879,13 +5568,13 @@
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOCr_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-fc6f3410-60e7-4a1e-a218-b1c28523212e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc6f3410-60e7-4a1e-a218-b1c28523212e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d2ee83e-1191-4d8c-a248-41680a38a4d5' class='xr-var-data-in' type='checkbox'><label for='data-0d2ee83e-1191-4d8c-a248-41680a38a4d5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary refractory dissolved organic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOCr_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-3c00eb13-7825-40af-bec7-68dbc2801391' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3c00eb13-7825-40af-bec7-68dbc2801391' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68edc559-d715-42f4-aad8-ba840e6ba01d' class='xr-var-data-in' type='checkbox'><label for='data-68edc559-d715-42f4-aad8-ba840e6ba01d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>southern boundary refractory dissolved organic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4901,17 +5590,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -4929,6 +5618,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -4936,6 +5636,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -4957,13 +5668,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spChl_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-c89c35f4-8f11-4f9c-b148-4fe3b45779c4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c89c35f4-8f11-4f9c-b148-4fe3b45779c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db61cf2a-92d0-4b58-beb0-dee9a0d3262b' class='xr-var-data-in' type='checkbox'><label for='data-db61cf2a-92d0-4b58-beb0-dee9a0d3262b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary small phytoplankton chlorophyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spChl_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-0897f2c7-c0f7-4386-b924-85ce8c9da988' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0897f2c7-c0f7-4386-b924-85ce8c9da988' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17bc8d54-ec96-4b5f-a5ba-32256b06b8a7' class='xr-var-data-in' type='checkbox'><label for='data-17bc8d54-ec96-4b5f-a5ba-32256b06b8a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>southern boundary small phytoplankton chlorophyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4979,17 +5690,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -5007,6 +5718,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -5014,6 +5736,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -5035,13 +5768,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spC_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-f6d4645b-7588-4a0d-9577-3afc045c7e22' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f6d4645b-7588-4a0d-9577-3afc045c7e22' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3da40821-94e4-4f91-9b5a-d194a503013c' class='xr-var-data-in' type='checkbox'><label for='data-3da40821-94e4-4f91-9b5a-d194a503013c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary small phytoplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spC_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-a8e0c880-249e-4276-aaeb-4f172bf4b099' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a8e0c880-249e-4276-aaeb-4f172bf4b099' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a1bc491-0701-4f80-82b0-376a869aafc6' class='xr-var-data-in' type='checkbox'><label for='data-6a1bc491-0701-4f80-82b0-376a869aafc6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>southern boundary small phytoplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5057,17 +5790,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -5085,6 +5818,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -5092,6 +5836,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -5113,13 +5868,13 @@
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spP_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-b94e8a4c-5b87-4517-ae8b-4612f888af82' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b94e8a4c-5b87-4517-ae8b-4612f888af82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00e12cd9-deb1-4bef-880c-dfbe954e44ec' class='xr-var-data-in' type='checkbox'><label for='data-00e12cd9-deb1-4bef-880c-dfbe954e44ec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary small phytoplankton phosphorous</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spP_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-4d4cb6ab-f1c0-47cb-9371-b2fe27bb8a10' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4d4cb6ab-f1c0-47cb-9371-b2fe27bb8a10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f75c900-aac9-4683-996c-58b6706326f3' class='xr-var-data-in' type='checkbox'><label for='data-8f75c900-aac9-4683-996c-58b6706326f3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>southern boundary small phytoplankton phosphorous</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5135,17 +5890,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -5163,6 +5918,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -5170,6 +5936,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -5191,13 +5968,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spFe_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-9767749c-8002-4785-af31-8445289809ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9767749c-8002-4785-af31-8445289809ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9aca1721-4ce1-49d1-a9fd-c2ebbb22560a' class='xr-var-data-in' type='checkbox'><label for='data-9aca1721-4ce1-49d1-a9fd-c2ebbb22560a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary small phytoplankton iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spFe_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-6a33f888-87c9-432a-8a29-442f19db1ec3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6a33f888-87c9-432a-8a29-442f19db1ec3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-761ac0c2-66bc-4984-8b88-10f04227935d' class='xr-var-data-in' type='checkbox'><label for='data-761ac0c2-66bc-4984-8b88-10f04227935d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>southern boundary small phytoplankton iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5213,17 +5990,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -5241,6 +6018,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -5248,6 +6036,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -5269,13 +6068,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatChl_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-f040a7f2-5ec2-4f86-a22e-9cab99b6ca7f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f040a7f2-5ec2-4f86-a22e-9cab99b6ca7f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c195d11f-6711-4fce-9de6-f795c44a950a' class='xr-var-data-in' type='checkbox'><label for='data-c195d11f-6711-4fce-9de6-f795c44a950a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary diatom chloropyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatChl_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-5e97dffd-972a-461f-8b3f-2cefa11ff821' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e97dffd-972a-461f-8b3f-2cefa11ff821' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49db7afb-cce2-456d-9042-84cf8f78a673' class='xr-var-data-in' type='checkbox'><label for='data-49db7afb-cce2-456d-9042-84cf8f78a673' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>southern boundary diatom chloropyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5291,17 +6090,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -5319,6 +6118,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -5326,6 +6136,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
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+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -5347,13 +6168,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
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-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatC_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-12c0ab11-4938-457e-9dcf-58d60d17d041' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12c0ab11-4938-457e-9dcf-58d60d17d041' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc3ac591-c6b3-43c7-9c59-dabf4be1d2c6' class='xr-var-data-in' type='checkbox'><label for='data-fc3ac591-c6b3-43c7-9c59-dabf4be1d2c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary diatom carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatC_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-06ec36b0-2156-4231-8986-28f4a40a237a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06ec36b0-2156-4231-8986-28f4a40a237a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c6051e2-0d1d-4ac8-a778-7ccc9e1d9c85' class='xr-var-data-in' type='checkbox'><label for='data-3c6051e2-0d1d-4ac8-a778-7ccc9e1d9c85' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>southern boundary diatom carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5369,17 +6190,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -5397,6 +6218,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -5404,6 +6236,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -5425,13 +6268,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatP_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-ac274f4c-5833-45e0-8f4d-1b2007ba0edb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac274f4c-5833-45e0-8f4d-1b2007ba0edb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50973c6c-b08c-4dce-9f99-2aca9184e6d9' class='xr-var-data-in' type='checkbox'><label for='data-50973c6c-b08c-4dce-9f99-2aca9184e6d9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary diatom phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatP_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-3ccc2432-c87e-4974-8078-751beb4be28f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ccc2432-c87e-4974-8078-751beb4be28f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a50b22b7-917b-4e24-84fc-c871628d5721' class='xr-var-data-in' type='checkbox'><label for='data-a50b22b7-917b-4e24-84fc-c871628d5721' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>southern boundary diatom phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5447,17 +6290,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -5475,6 +6318,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -5482,6 +6336,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -5503,13 +6368,13 @@
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatFe_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-2ca8b147-509e-481e-bd3d-14fbc9856479' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2ca8b147-509e-481e-bd3d-14fbc9856479' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33025987-ee10-466e-a5b5-3591aaeb3a40' class='xr-var-data-in' type='checkbox'><label for='data-33025987-ee10-466e-a5b5-3591aaeb3a40' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary diatom iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatFe_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-435ac084-bcb5-4f5b-aff2-8ba0623e235b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-435ac084-bcb5-4f5b-aff2-8ba0623e235b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31cb898b-9343-4d06-8b4d-c22890700ec1' class='xr-var-data-in' type='checkbox'><label for='data-31cb898b-9343-4d06-8b4d-c22890700ec1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>southern boundary diatom iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5525,17 +6390,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -5553,6 +6418,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
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+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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        "\n",
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@@ -5560,6 +6436,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
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@@ -5581,13 +6468,13 @@
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatSi_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-55a91a0b-f27a-49ca-9efb-1640a2176e73' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-55a91a0b-f27a-49ca-9efb-1640a2176e73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6807ef80-2735-4b48-9fca-f2c754ffd005' class='xr-var-data-in' type='checkbox'><label for='data-6807ef80-2735-4b48-9fca-f2c754ffd005' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary diatom silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatSi_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-d9297243-c79a-424a-a5c0-83e3e6b9e4d9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9297243-c79a-424a-a5c0-83e3e6b9e4d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9432515-860c-4b97-a7e5-c7070f624040' class='xr-var-data-in' type='checkbox'><label for='data-c9432515-860c-4b97-a7e5-c7070f624040' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>southern boundary diatom silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5603,17 +6490,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -5631,13 +6518,35 @@
        "\n",
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+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
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+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
        "  <polygon points=\"10.0,0.0 27.676686162348723,17.676686162348723 27.676686162348723,59.6696884485097 10.0,41.99300228616098\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
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@@ -5659,13 +6568,13 @@
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        "\n",
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-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazChl_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-59262349-3d83-4622-b6d1-80ab057d175c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-59262349-3d83-4622-b6d1-80ab057d175c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3222de97-9be9-4d3f-97bf-867ce79eebf5' class='xr-var-data-in' type='checkbox'><label for='data-3222de97-9be9-4d3f-97bf-867ce79eebf5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary diazotroph chloropyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazChl_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-57ec20b3-c45a-490a-bcef-eacd722d7b8f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-57ec20b3-c45a-490a-bcef-eacd722d7b8f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9e6aa7d-ce9e-409b-a5ad-91ec6daf56fa' class='xr-var-data-in' type='checkbox'><label for='data-d9e6aa7d-ce9e-409b-a5ad-91ec6daf56fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>southern boundary diazotroph chloropyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5681,17 +6590,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -5709,6 +6618,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
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@@ -5716,6 +6636,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
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+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
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+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
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@@ -5737,13 +6668,13 @@
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        "\n",
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-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
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-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
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+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazC_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-58025507-68b4-4795-a24f-fc228d55e22b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-58025507-68b4-4795-a24f-fc228d55e22b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-036fd348-cf89-46f3-915f-4179e82da6b4' class='xr-var-data-in' type='checkbox'><label for='data-036fd348-cf89-46f3-915f-4179e82da6b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary diazotroph carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazC_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-ea187b1e-b2bf-488d-a9d3-4998eb47b892' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea187b1e-b2bf-488d-a9d3-4998eb47b892' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d101b9d4-c422-4721-97a3-c7c7b5d7c3e9' class='xr-var-data-in' type='checkbox'><label for='data-d101b9d4-c422-4721-97a3-c7c7b5d7c3e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>southern boundary diazotroph carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5759,17 +6690,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -5787,6 +6718,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
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@@ -5794,6 +6736,17 @@
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        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -5815,13 +6768,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazP_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-4495e990-7f89-4389-a745-108dee54d175' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4495e990-7f89-4389-a745-108dee54d175' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3a74e7f-3649-4d07-a4c4-386cadbf0e56' class='xr-var-data-in' type='checkbox'><label for='data-a3a74e7f-3649-4d07-a4c4-386cadbf0e56' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary diazotroph phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazP_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-8421705d-ea49-4450-82ff-1097d5f951ac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8421705d-ea49-4450-82ff-1097d5f951ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50c9b618-3828-48ac-b8af-f9ee942d3266' class='xr-var-data-in' type='checkbox'><label for='data-50c9b618-3828-48ac-b8af-f9ee942d3266' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>southern boundary diazotroph phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5837,17 +6790,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -5865,6 +6818,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -5872,6 +6836,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -5893,13 +6868,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazFe_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-64f7a7a9-2e1c-4f92-a5af-14b6eac2516b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-64f7a7a9-2e1c-4f92-a5af-14b6eac2516b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18c46f6c-6ba1-4eaf-8a6a-06767d9b48d9' class='xr-var-data-in' type='checkbox'><label for='data-18c46f6c-6ba1-4eaf-8a6a-06767d9b48d9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary diazotroph iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazFe_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-0d85c50c-998b-4801-a687-dab8f62f1579' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0d85c50c-998b-4801-a687-dab8f62f1579' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e64f533-f922-4459-8d57-193be6f68045' class='xr-var-data-in' type='checkbox'><label for='data-3e64f533-f922-4459-8d57-193be6f68045' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>southern boundary diazotroph iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5915,17 +6890,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -5943,6 +6918,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -5950,6 +6936,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -5971,13 +6968,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spCaCO3_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-7270b031-6042-4da3-9856-f869b157a647' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7270b031-6042-4da3-9856-f869b157a647' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7fda2e0-d260-4e5b-aaf2-5d7130c04a06' class='xr-var-data-in' type='checkbox'><label for='data-e7fda2e0-d260-4e5b-aaf2-5d7130c04a06' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary small phytoplankton CaCO3</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spCaCO3_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-a66ae730-204c-4246-a145-787ca985d04d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a66ae730-204c-4246-a145-787ca985d04d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc3a811b-28d7-4076-90a9-902484042cac' class='xr-var-data-in' type='checkbox'><label for='data-dc3a811b-28d7-4076-90a9-902484042cac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>southern boundary small phytoplankton CaCO3</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5993,17 +6990,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -6021,6 +7018,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -6028,6 +7036,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -6049,13 +7068,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zooC_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-566e3d67-c0c0-4896-85a2-35c273aedebe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-566e3d67-c0c0-4896-85a2-35c273aedebe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-282e39f5-3d76-411a-a599-da47ebda3ede' class='xr-var-data-in' type='checkbox'><label for='data-282e39f5-3d76-411a-a599-da47ebda3ede' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary zooplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zooC_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-ce568c4d-3101-442b-a5d5-5f754032b7b7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce568c4d-3101-442b-a5d5-5f754032b7b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42b74273-bfc1-475f-8fc4-399074eed6bd' class='xr-var-data-in' type='checkbox'><label for='data-42b74273-bfc1-475f-8fc4-399074eed6bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>long_name :</span></dt><dd>southern boundary zooplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6071,17 +7090,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -6099,6 +7118,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -6106,6 +7136,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -6127,13 +7168,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PO4_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-c383c058-4544-4290-8ce9-fce489202fd7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c383c058-4544-4290-8ce9-fce489202fd7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97a79ea9-345b-410e-b706-cc0ed8248bb0' class='xr-var-data-in' type='checkbox'><label for='data-97a79ea9-345b-410e-b706-cc0ed8248bb0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved inorganic phosphate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PO4_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-cea75439-0315-4eb7-b3ed-fbb48240bc05' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cea75439-0315-4eb7-b3ed-fbb48240bc05' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc373785-47bb-42f0-b045-09a4b944fcb6' class='xr-var-data-in' type='checkbox'><label for='data-fc373785-47bb-42f0-b045-09a4b944fcb6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>eastern boundary dissolved inorganic phosphate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6149,17 +7190,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -6177,6 +7218,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -6184,6 +7236,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -6205,13 +7268,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NO3_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-c72ae891-54af-404e-b2e9-3dac19b751e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c72ae891-54af-404e-b2e9-3dac19b751e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-475123c8-42d2-48ca-957a-a3180315c3ea' class='xr-var-data-in' type='checkbox'><label for='data-475123c8-42d2-48ca-957a-a3180315c3ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved inorganic nitrate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NO3_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-751f9d07-c438-4abf-8838-c3fd8f8810ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-751f9d07-c438-4abf-8838-c3fd8f8810ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6ef73eb-03b6-4c75-ac7e-c447fb2b0c9c' class='xr-var-data-in' type='checkbox'><label for='data-a6ef73eb-03b6-4c75-ac7e-c447fb2b0c9c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>eastern boundary dissolved inorganic nitrate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6227,17 +7290,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -6255,6 +7318,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -6262,6 +7336,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -6283,13 +7368,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SiO3_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-8e7c285f-5656-4796-9360-0f728bc66fa0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8e7c285f-5656-4796-9360-0f728bc66fa0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8375745f-98fe-45c0-9a01-ba5599503180' class='xr-var-data-in' type='checkbox'><label for='data-8375745f-98fe-45c0-9a01-ba5599503180' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved inorganic silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SiO3_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-28a09322-c13a-4c6e-8c20-2ff7657a3f78' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28a09322-c13a-4c6e-8c20-2ff7657a3f78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58b1149b-d019-4d99-b040-645b8cec33e0' class='xr-var-data-in' type='checkbox'><label for='data-58b1149b-d019-4d99-b040-645b8cec33e0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>eastern boundary dissolved inorganic silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6305,17 +7390,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -6333,6 +7418,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -6340,6 +7436,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -6361,13 +7468,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NH4_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-eb2ca88d-a66c-4034-a62b-6b375ad74acc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eb2ca88d-a66c-4034-a62b-6b375ad74acc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-515aeae7-d688-4a1d-8241-83b0187517fa' class='xr-var-data-in' type='checkbox'><label for='data-515aeae7-d688-4a1d-8241-83b0187517fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved ammonia</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NH4_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-7abab7ce-4678-4b3d-9641-5c223f44c688' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7abab7ce-4678-4b3d-9641-5c223f44c688' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0beb78e2-c6ba-4ba6-b951-361dc2563d00' class='xr-var-data-in' type='checkbox'><label for='data-0beb78e2-c6ba-4ba6-b951-361dc2563d00' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>eastern boundary dissolved ammonia</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6383,17 +7490,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -6411,6 +7518,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -6418,6 +7536,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -6439,13 +7568,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Fe_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-9b8ff40b-d0af-4a82-8cc5-1d32744c8ddd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b8ff40b-d0af-4a82-8cc5-1d32744c8ddd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f947cfef-5810-43c3-a8e5-d9cc48c8ccbc' class='xr-var-data-in' type='checkbox'><label for='data-f947cfef-5810-43c3-a8e5-d9cc48c8ccbc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved inorganic iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Fe_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-70cba416-6150-4bab-ae4c-b7d6807f54db' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70cba416-6150-4bab-ae4c-b7d6807f54db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11fcd4b3-916e-4fef-9f4c-89da736c5438' class='xr-var-data-in' type='checkbox'><label for='data-11fcd4b3-916e-4fef-9f4c-89da736c5438' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>eastern boundary dissolved inorganic iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6461,17 +7590,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -6489,6 +7618,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -6496,6 +7636,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -6517,13 +7668,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Lig_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-d578b3ac-eb55-4e24-8501-71df6a384027' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d578b3ac-eb55-4e24-8501-71df6a384027' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d09408d7-1bbc-494f-81df-40656cf3f1d5' class='xr-var-data-in' type='checkbox'><label for='data-d09408d7-1bbc-494f-81df-40656cf3f1d5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary iron binding ligand</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Lig_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-96ea5965-d582-406b-9eae-339a1b791330' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-96ea5965-d582-406b-9eae-339a1b791330' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5e1bb5c-2bbf-4f3d-b2db-377641ff103e' class='xr-var-data-in' type='checkbox'><label for='data-e5e1bb5c-2bbf-4f3d-b2db-377641ff103e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>eastern boundary iron binding ligand</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6539,17 +7690,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -6567,6 +7718,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -6574,6 +7736,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -6595,13 +7768,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>O2_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-914309c9-7a83-451c-8be9-ba85d2701640' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-914309c9-7a83-451c-8be9-ba85d2701640' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-054c837e-41f3-4437-965c-fbe3bc3fb06d' class='xr-var-data-in' type='checkbox'><label for='data-054c837e-41f3-4437-965c-fbe3bc3fb06d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved oxygen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>O2_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-578d8cb9-c8bb-4ff1-af27-875d3a8c0fa9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-578d8cb9-c8bb-4ff1-af27-875d3a8c0fa9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e055a4f-b62f-43a2-b34b-e3f04149da3b' class='xr-var-data-in' type='checkbox'><label for='data-2e055a4f-b62f-43a2-b34b-e3f04149da3b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>eastern boundary dissolved oxygen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6617,17 +7790,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -6645,6 +7818,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -6652,6 +7836,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
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+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -6673,13 +7868,13 @@
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIC_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-24bd3744-ea01-4836-9dc5-38623682e8bc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-24bd3744-ea01-4836-9dc5-38623682e8bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67d6bfe8-79b2-4f88-870b-17f03a2ae91c' class='xr-var-data-in' type='checkbox'><label for='data-67d6bfe8-79b2-4f88-870b-17f03a2ae91c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved inorganic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIC_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-547e7260-fbb9-4211-a08c-99530ba062a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-547e7260-fbb9-4211-a08c-99530ba062a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7cd7231-3e11-4316-a306-eb5bf439d3f7' class='xr-var-data-in' type='checkbox'><label for='data-f7cd7231-3e11-4316-a306-eb5bf439d3f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>eastern boundary dissolved inorganic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6695,17 +7890,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -6723,13 +7918,35 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
        "  <polygon points=\"10.0,0.0 27.676686162348723,17.676686162348723 27.676686162348723,59.6696884485097 10.0,41.99300228616098\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -6751,13 +7968,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIC_ALT_CO2_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-81463886-67ca-4dd1-a418-c1d6a31292a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-81463886-67ca-4dd1-a418-c1d6a31292a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e3d4dbc-c698-4a49-a594-0e966990c84a' class='xr-var-data-in' type='checkbox'><label for='data-2e3d4dbc-c698-4a49-a594-0e966990c84a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved inorganic carbon, alternative CO2</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIC_ALT_CO2_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-36a21a73-c281-4db3-ad60-9b62799b13a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36a21a73-c281-4db3-ad60-9b62799b13a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b24cf30b-872f-4440-88d6-00c01b85cabf' class='xr-var-data-in' type='checkbox'><label for='data-b24cf30b-872f-4440-88d6-00c01b85cabf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>eastern boundary dissolved inorganic carbon, alternative CO2</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6773,17 +7990,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -6801,6 +8018,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -6808,6 +8036,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -6829,13 +8068,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALK_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-1a819c8e-98f5-40cc-8adf-b030aefcb22e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a819c8e-98f5-40cc-8adf-b030aefcb22e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53838636-3573-4aa2-a476-de4f117b06dc' class='xr-var-data-in' type='checkbox'><label for='data-53838636-3573-4aa2-a476-de4f117b06dc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary alkalinity</dd><dt><span>units :</span></dt><dd>meq/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALK_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-9892f532-20bd-4fce-b09b-4a87aeb43aa2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9892f532-20bd-4fce-b09b-4a87aeb43aa2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ebdee7c5-5911-429f-a46a-fe1b91551fd3' class='xr-var-data-in' type='checkbox'><label for='data-ebdee7c5-5911-429f-a46a-fe1b91551fd3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>eastern boundary alkalinity</dd><dt><span>units :</span></dt><dd>meq/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6851,17 +8090,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -6879,6 +8118,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
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@@ -6886,6 +8136,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -6907,13 +8168,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALK_ALT_CO2_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-83485134-f571-4dbe-bc88-360320875a1a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-83485134-f571-4dbe-bc88-360320875a1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d5e5cd2-1d0b-437a-a354-057ab7a77e3e' class='xr-var-data-in' type='checkbox'><label for='data-1d5e5cd2-1d0b-437a-a354-057ab7a77e3e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary alkalinity, alternative CO2</dd><dt><span>units :</span></dt><dd>meq/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALK_ALT_CO2_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-0ee21e0f-cec2-4e2c-8a4c-b2a368450c95' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ee21e0f-cec2-4e2c-8a4c-b2a368450c95' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-003e242b-e261-4b53-a680-7d31aa0ffd13' class='xr-var-data-in' type='checkbox'><label for='data-003e242b-e261-4b53-a680-7d31aa0ffd13' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>eastern boundary alkalinity, alternative CO2</dd><dt><span>units :</span></dt><dd>meq/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6929,17 +8190,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -6957,6 +8218,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -6964,6 +8236,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -6985,13 +8268,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOC_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-0efcec30-578b-4fbe-af11-9b820f907b0e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0efcec30-578b-4fbe-af11-9b820f907b0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07230907-3da2-46af-8699-812d868fbbf5' class='xr-var-data-in' type='checkbox'><label for='data-07230907-3da2-46af-8699-812d868fbbf5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved organic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOC_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-d2f00dc5-0873-4cdb-8f2e-f17a4a2f093f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2f00dc5-0873-4cdb-8f2e-f17a4a2f093f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e10fa099-5720-4ad3-a0f9-f69347106047' class='xr-var-data-in' type='checkbox'><label for='data-e10fa099-5720-4ad3-a0f9-f69347106047' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>eastern boundary dissolved organic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7007,17 +8290,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -7035,6 +8318,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -7042,6 +8336,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -7063,13 +8368,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DON_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-17752a78-e222-4441-b8d3-fa58339b7efc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-17752a78-e222-4441-b8d3-fa58339b7efc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c0fa0dc-0266-4e5a-b6bf-c4869f86d7ed' class='xr-var-data-in' type='checkbox'><label for='data-8c0fa0dc-0266-4e5a-b6bf-c4869f86d7ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DON_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-57593c7d-b936-46d8-b15f-ef55ce89f119' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-57593c7d-b936-46d8-b15f-ef55ce89f119' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-adde6edb-7c05-406a-935e-4779fe245775' class='xr-var-data-in' type='checkbox'><label for='data-adde6edb-7c05-406a-935e-4779fe245775' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>eastern boundary dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7085,17 +8390,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -7113,6 +8418,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -7120,6 +8436,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -7141,13 +8468,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOP_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-d4e82b4c-f940-4cc6-89cf-1e7330f8ac6f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d4e82b4c-f940-4cc6-89cf-1e7330f8ac6f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0281846d-a305-48f5-934d-601c46448c43' class='xr-var-data-in' type='checkbox'><label for='data-0281846d-a305-48f5-934d-601c46448c43' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOP_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-df13400d-fb8d-4729-bf01-a7e53cb80056' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-df13400d-fb8d-4729-bf01-a7e53cb80056' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ef64225-338b-4d28-b738-231d7386cac0' class='xr-var-data-in' type='checkbox'><label for='data-8ef64225-338b-4d28-b738-231d7386cac0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>eastern boundary dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7163,17 +8490,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -7191,6 +8518,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -7198,6 +8536,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -7219,13 +8568,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOPr_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-b778f1f5-9723-4dac-8631-353c194fa841' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b778f1f5-9723-4dac-8631-353c194fa841' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa65717e-6f87-4178-999a-66254c499d02' class='xr-var-data-in' type='checkbox'><label for='data-aa65717e-6f87-4178-999a-66254c499d02' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary refractory dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOPr_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-28969d85-b423-41a9-80ab-8da7b3166557' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28969d85-b423-41a9-80ab-8da7b3166557' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c97b0db5-7496-453c-9971-aa9892970d9a' class='xr-var-data-in' type='checkbox'><label for='data-c97b0db5-7496-453c-9971-aa9892970d9a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>eastern boundary refractory dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7241,17 +8590,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -7269,6 +8618,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -7276,6 +8636,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -7297,13 +8668,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DONr_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-d982e2f8-4cb6-4f38-b438-ead862391eaa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d982e2f8-4cb6-4f38-b438-ead862391eaa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3fea980-617d-43f7-baef-7f861fa60b83' class='xr-var-data-in' type='checkbox'><label for='data-f3fea980-617d-43f7-baef-7f861fa60b83' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary refractory dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DONr_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-d55cee43-fa64-4545-a141-d621a5508f75' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d55cee43-fa64-4545-a141-d621a5508f75' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad5d2bb7-f646-4a83-ae22-6148385d39fe' class='xr-var-data-in' type='checkbox'><label for='data-ad5d2bb7-f646-4a83-ae22-6148385d39fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>eastern boundary refractory dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7319,17 +8690,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -7347,6 +8718,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -7354,6 +8736,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -7375,13 +8768,13 @@
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOCr_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-28814bbe-2e2f-4b09-8094-835987a61f3b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28814bbe-2e2f-4b09-8094-835987a61f3b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d5f7e9e-847f-433c-a2f3-920a5c953c86' class='xr-var-data-in' type='checkbox'><label for='data-0d5f7e9e-847f-433c-a2f3-920a5c953c86' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary refractory dissolved organic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOCr_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-743c55d4-bdbe-417e-8bf2-5e86b36061f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-743c55d4-bdbe-417e-8bf2-5e86b36061f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-606dcc9b-a375-4592-a47c-d36ada9b813d' class='xr-var-data-in' type='checkbox'><label for='data-606dcc9b-a375-4592-a47c-d36ada9b813d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>eastern boundary refractory dissolved organic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7397,17 +8790,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -7425,6 +8818,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -7432,6 +8836,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
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+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
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+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
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@@ -7453,13 +8868,13 @@
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        "\n",
        "  <!-- Text -->\n",
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-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
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+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spChl_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-ad79516b-9c77-4efe-ab72-5d351bb6d443' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad79516b-9c77-4efe-ab72-5d351bb6d443' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d33b943-3607-4a37-96d4-0c9cdc128850' class='xr-var-data-in' type='checkbox'><label for='data-5d33b943-3607-4a37-96d4-0c9cdc128850' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary small phytoplankton chlorophyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spChl_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-c2bd23d6-e224-4401-8d10-b5b441db376d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c2bd23d6-e224-4401-8d10-b5b441db376d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74445cc1-2322-4d10-821e-896572f91654' class='xr-var-data-in' type='checkbox'><label for='data-74445cc1-2322-4d10-821e-896572f91654' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>eastern boundary small phytoplankton chlorophyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7475,17 +8890,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -7503,6 +8918,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
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@@ -7510,6 +8936,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
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        "\n",
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@@ -7531,13 +8968,13 @@
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+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spC_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-3bfa4b18-55a8-4b62-bed2-3b6eb618322c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3bfa4b18-55a8-4b62-bed2-3b6eb618322c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75f08c49-9462-494b-b0a5-fa019068f775' class='xr-var-data-in' type='checkbox'><label for='data-75f08c49-9462-494b-b0a5-fa019068f775' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary small phytoplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spC_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-351c767d-60ae-482e-924c-0ac09001c8ce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-351c767d-60ae-482e-924c-0ac09001c8ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-877698e4-97d2-456e-8736-60c28ee51d0b' class='xr-var-data-in' type='checkbox'><label for='data-877698e4-97d2-456e-8736-60c28ee51d0b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>eastern boundary small phytoplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7553,17 +8990,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -7581,6 +9018,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -7588,6 +9036,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -7609,13 +9068,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spP_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-25a9f76b-8214-4222-b1d8-3fcc8ffd7d60' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-25a9f76b-8214-4222-b1d8-3fcc8ffd7d60' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc878b15-b2dd-4c85-b224-826e55a90471' class='xr-var-data-in' type='checkbox'><label for='data-cc878b15-b2dd-4c85-b224-826e55a90471' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary small phytoplankton phosphorous</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spP_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-f00a94f9-328e-4837-90ec-6831b7369a06' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f00a94f9-328e-4837-90ec-6831b7369a06' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c40afa6-5c16-46ed-a4e2-0aaa8bf2673e' class='xr-var-data-in' type='checkbox'><label for='data-8c40afa6-5c16-46ed-a4e2-0aaa8bf2673e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>eastern boundary small phytoplankton phosphorous</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7631,17 +9090,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -7659,6 +9118,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -7666,6 +9136,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -7687,13 +9168,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spFe_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-24032694-6f6f-4147-bd51-ffb1a9c5e4c0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-24032694-6f6f-4147-bd51-ffb1a9c5e4c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4af3083c-8b8d-4e86-b618-e0d173c00fdd' class='xr-var-data-in' type='checkbox'><label for='data-4af3083c-8b8d-4e86-b618-e0d173c00fdd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary small phytoplankton iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spFe_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-5694df29-72bb-4316-bc2d-38c5830212e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5694df29-72bb-4316-bc2d-38c5830212e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75eceb94-f6cb-45f8-800b-0de507f48c06' class='xr-var-data-in' type='checkbox'><label for='data-75eceb94-f6cb-45f8-800b-0de507f48c06' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>eastern boundary small phytoplankton iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7709,17 +9190,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -7737,6 +9218,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -7744,6 +9236,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
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+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -7765,13 +9268,13 @@
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatChl_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-999ca558-94b3-4a21-9794-0ea90af1d265' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-999ca558-94b3-4a21-9794-0ea90af1d265' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c4bb920-56d5-43c1-a144-072525f5eae0' class='xr-var-data-in' type='checkbox'><label for='data-1c4bb920-56d5-43c1-a144-072525f5eae0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary diatom chloropyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatChl_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-24bf4c17-caaf-4b70-bf2a-9f217f4bc782' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-24bf4c17-caaf-4b70-bf2a-9f217f4bc782' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f0a0053-d467-4048-ab0a-b4a270a04adb' class='xr-var-data-in' type='checkbox'><label for='data-7f0a0053-d467-4048-ab0a-b4a270a04adb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>eastern boundary diatom chloropyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7787,17 +9290,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -7815,13 +9318,35 @@
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+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
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+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
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+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
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        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
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@@ -7843,13 +9368,13 @@
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        "\n",
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-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatC_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-8a315b10-f812-45e1-8f6e-7b413809706e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a315b10-f812-45e1-8f6e-7b413809706e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-133e1f75-d1a9-46ce-931a-126225099950' class='xr-var-data-in' type='checkbox'><label for='data-133e1f75-d1a9-46ce-931a-126225099950' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary diatom carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatC_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-6a4d40ad-4edc-4116-9d5a-6646dcda361f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6a4d40ad-4edc-4116-9d5a-6646dcda361f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d313c3d-f172-4b60-889c-cca07ede964e' class='xr-var-data-in' type='checkbox'><label for='data-9d313c3d-f172-4b60-889c-cca07ede964e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>eastern boundary diatom carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7865,17 +9390,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -7893,6 +9418,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
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+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
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@@ -7900,6 +9436,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
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@@ -7921,13 +9468,13 @@
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        "\n",
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-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
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+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatP_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-5975e5e7-7009-4c4a-b57b-9a1533b95885' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5975e5e7-7009-4c4a-b57b-9a1533b95885' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24d556f7-262f-44dd-8cba-24d04ec340e3' class='xr-var-data-in' type='checkbox'><label for='data-24d556f7-262f-44dd-8cba-24d04ec340e3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary diatom phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatP_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-df45f5da-8cbd-4c0f-b843-9a8a913c3bdb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-df45f5da-8cbd-4c0f-b843-9a8a913c3bdb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7730c410-fea5-4abd-baf3-e6e04181f0ee' class='xr-var-data-in' type='checkbox'><label for='data-7730c410-fea5-4abd-baf3-e6e04181f0ee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>eastern boundary diatom phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7943,17 +9490,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -7971,6 +9518,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
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@@ -7978,6 +9536,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -7999,13 +9568,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatFe_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-0186ab56-a6da-4734-9f68-248fec399a5b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0186ab56-a6da-4734-9f68-248fec399a5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00ec0a8c-2a86-422b-93d2-e02a7808af5d' class='xr-var-data-in' type='checkbox'><label for='data-00ec0a8c-2a86-422b-93d2-e02a7808af5d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary diatom iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatFe_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-019752c0-1d4c-4fb3-a4aa-e07879be3111' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-019752c0-1d4c-4fb3-a4aa-e07879be3111' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05800a78-9bc4-4d78-a42a-1273047572c9' class='xr-var-data-in' type='checkbox'><label for='data-05800a78-9bc4-4d78-a42a-1273047572c9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>eastern boundary diatom iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -8021,17 +9590,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -8049,6 +9618,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -8056,6 +9636,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -8077,13 +9668,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatSi_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-6d4196de-6b68-45f8-86ee-3fd954eb7954' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6d4196de-6b68-45f8-86ee-3fd954eb7954' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa3feffe-3d08-4e06-882d-df7a6b645c97' class='xr-var-data-in' type='checkbox'><label for='data-fa3feffe-3d08-4e06-882d-df7a6b645c97' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary diatom silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatSi_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-9da702ce-e378-449c-877c-6af5c5397bcf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9da702ce-e378-449c-877c-6af5c5397bcf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-031cc6a6-2ee4-4988-858c-ff8cf1e82df7' class='xr-var-data-in' type='checkbox'><label for='data-031cc6a6-2ee4-4988-858c-ff8cf1e82df7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>eastern boundary diatom silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -8099,17 +9690,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -8127,6 +9718,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -8134,6 +9736,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -8155,13 +9768,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazChl_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-8b9f203c-f719-4ae7-a167-e4491275a47c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b9f203c-f719-4ae7-a167-e4491275a47c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3aa4fd28-7992-4018-8362-0291a9556a8b' class='xr-var-data-in' type='checkbox'><label for='data-3aa4fd28-7992-4018-8362-0291a9556a8b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary diazotroph chloropyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazChl_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-cebfa394-ee86-498c-98bd-83a31a95b3d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cebfa394-ee86-498c-98bd-83a31a95b3d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-194ea590-5648-417a-ad5e-058f4dccc3e2' class='xr-var-data-in' type='checkbox'><label for='data-194ea590-5648-417a-ad5e-058f4dccc3e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>eastern boundary diazotroph chloropyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -8177,17 +9790,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -8205,6 +9818,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -8212,6 +9836,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -8233,13 +9868,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazC_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-6d7c9f1f-31e5-4f1f-8850-e233149d6533' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6d7c9f1f-31e5-4f1f-8850-e233149d6533' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0166fef8-f5e5-4ff0-82d7-458c33c89a38' class='xr-var-data-in' type='checkbox'><label for='data-0166fef8-f5e5-4ff0-82d7-458c33c89a38' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary diazotroph carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazC_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-70afc68f-a451-458f-bcd3-ac3274a4cb31' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70afc68f-a451-458f-bcd3-ac3274a4cb31' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59b89080-2fee-4ba2-b25f-5529a9e30ead' class='xr-var-data-in' type='checkbox'><label for='data-59b89080-2fee-4ba2-b25f-5529a9e30ead' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>eastern boundary diazotroph carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -8255,17 +9890,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -8283,6 +9918,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -8290,6 +9936,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -8311,13 +9968,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazP_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-157e054b-9f46-44f5-9471-80a72fe304b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-157e054b-9f46-44f5-9471-80a72fe304b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d18b1617-84ce-4f4c-b659-9bbf264eae89' class='xr-var-data-in' type='checkbox'><label for='data-d18b1617-84ce-4f4c-b659-9bbf264eae89' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary diazotroph phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazP_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-1441c570-ef79-4d04-ac6e-24136d531eba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1441c570-ef79-4d04-ac6e-24136d531eba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8bf1f48a-6180-4c25-903d-8b464336823e' class='xr-var-data-in' type='checkbox'><label for='data-8bf1f48a-6180-4c25-903d-8b464336823e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>eastern boundary diazotroph phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -8333,17 +9990,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -8361,6 +10018,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -8368,6 +10036,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -8389,13 +10068,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazFe_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-709311cf-3cc2-4170-acf7-2177063c5c8f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-709311cf-3cc2-4170-acf7-2177063c5c8f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-abdeeae8-b3cc-456b-b51f-1be2f5ac79fe' class='xr-var-data-in' type='checkbox'><label for='data-abdeeae8-b3cc-456b-b51f-1be2f5ac79fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary diazotroph iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazFe_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-6a5df47c-eba5-45a3-b198-63aa9557b8fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6a5df47c-eba5-45a3-b198-63aa9557b8fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b7a9223-5c08-47a6-b393-c3c2179b4afa' class='xr-var-data-in' type='checkbox'><label for='data-7b7a9223-5c08-47a6-b393-c3c2179b4afa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>eastern boundary diazotroph iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -8411,17 +10090,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -8439,6 +10118,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -8446,6 +10136,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -8467,13 +10168,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spCaCO3_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-191bbb85-8ea3-4c64-9511-a54fe7036d12' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-191bbb85-8ea3-4c64-9511-a54fe7036d12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d195781e-9e10-4072-a75c-3da788b59bf2' class='xr-var-data-in' type='checkbox'><label for='data-d195781e-9e10-4072-a75c-3da788b59bf2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary small phytoplankton CaCO3</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spCaCO3_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-ded424fc-0b1a-4aee-a2df-c2ec62891024' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ded424fc-0b1a-4aee-a2df-c2ec62891024' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b48ffab-169b-454f-b2d6-ebbd8cf5793c' class='xr-var-data-in' type='checkbox'><label for='data-1b48ffab-169b-454f-b2d6-ebbd8cf5793c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>eastern boundary small phytoplankton CaCO3</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -8489,17 +10190,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -8517,6 +10218,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -8524,6 +10236,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -8545,13 +10268,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zooC_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-a0177927-8e7b-4e90-a57a-94670d84680b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a0177927-8e7b-4e90-a57a-94670d84680b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e074ccdc-3c37-460b-b228-c2103d86774a' class='xr-var-data-in' type='checkbox'><label for='data-e074ccdc-3c37-460b-b228-c2103d86774a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary zooplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zooC_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-4c136d20-9e6f-46ca-863a-8cabb3d9e62b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c136d20-9e6f-46ca-863a-8cabb3d9e62b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d020d65-7733-471f-bd05-9628da719137' class='xr-var-data-in' type='checkbox'><label for='data-0d020d65-7733-471f-bd05-9628da719137' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>long_name :</span></dt><dd>eastern boundary zooplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -8567,17 +10290,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -8595,6 +10318,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -8602,6 +10336,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -8623,13 +10368,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PO4_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-f7979199-9bc1-41dd-baf1-bf6ddc908235' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f7979199-9bc1-41dd-baf1-bf6ddc908235' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f92e11b-7ff9-4d8b-9ce7-bc99c9e2e349' class='xr-var-data-in' type='checkbox'><label for='data-5f92e11b-7ff9-4d8b-9ce7-bc99c9e2e349' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary dissolved inorganic phosphate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PO4_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-d6a89f8f-c9d3-4275-aede-cbb4648e2b32' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6a89f8f-c9d3-4275-aede-cbb4648e2b32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b95af420-3ceb-4b7e-bd20-5c5e46c1e57e' class='xr-var-data-in' type='checkbox'><label for='data-b95af420-3ceb-4b7e-bd20-5c5e46c1e57e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>western boundary dissolved inorganic phosphate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -8645,17 +10390,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -8673,6 +10418,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -8680,6 +10436,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -8701,13 +10468,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NO3_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-b188feab-3894-4155-a76d-4e4159d5192c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b188feab-3894-4155-a76d-4e4159d5192c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b04b899-dab9-4370-8307-d995b1633431' class='xr-var-data-in' type='checkbox'><label for='data-2b04b899-dab9-4370-8307-d995b1633431' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary dissolved inorganic nitrate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NO3_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-94379f2f-7f01-44f6-a980-d2f8222d533b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94379f2f-7f01-44f6-a980-d2f8222d533b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a33cdc03-eccf-4589-acc1-bda7b146756a' class='xr-var-data-in' type='checkbox'><label for='data-a33cdc03-eccf-4589-acc1-bda7b146756a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>western boundary dissolved inorganic nitrate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -8723,17 +10490,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -8751,6 +10518,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -8758,6 +10536,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -8779,13 +10568,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SiO3_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-8d7c60ac-59f1-427b-99f5-45e7396c3fdb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d7c60ac-59f1-427b-99f5-45e7396c3fdb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2468fb5-c593-4129-86fc-b7657ffeb85b' class='xr-var-data-in' type='checkbox'><label for='data-d2468fb5-c593-4129-86fc-b7657ffeb85b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary dissolved inorganic silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SiO3_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-b231e4a5-4235-406b-a195-3584e81b8d5b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b231e4a5-4235-406b-a195-3584e81b8d5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0606d952-f552-416e-93f4-26327bd66b27' class='xr-var-data-in' type='checkbox'><label for='data-0606d952-f552-416e-93f4-26327bd66b27' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>western boundary dissolved inorganic silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -8801,17 +10590,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -8829,6 +10618,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
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@@ -8836,6 +10636,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
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        "\n",
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@@ -8857,13 +10668,13 @@
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        "\n",
        "  <!-- Text -->\n",
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-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NH4_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-6a6226ad-d208-4c3c-943c-19ea247c1245' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6a6226ad-d208-4c3c-943c-19ea247c1245' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f20f808-4550-437f-9d18-14521056a2fd' class='xr-var-data-in' type='checkbox'><label for='data-9f20f808-4550-437f-9d18-14521056a2fd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary dissolved ammonia</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NH4_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-37bfe0df-525b-4cda-bef0-8e53cd4ad2cb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-37bfe0df-525b-4cda-bef0-8e53cd4ad2cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38b1fa2c-220e-4b25-a321-5cef1a01166b' class='xr-var-data-in' type='checkbox'><label for='data-38b1fa2c-220e-4b25-a321-5cef1a01166b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>western boundary dissolved ammonia</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -8879,17 +10690,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -8907,13 +10718,35 @@
        "\n",
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-       "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
        "  <polygon points=\"10.0,0.0 27.676686162348723,17.676686162348723 27.676686162348723,59.6696884485097 10.0,41.99300228616098\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
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+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
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@@ -8935,13 +10768,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Fe_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-2eed4256-3ca1-4b32-b8a0-b7419acf502f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2eed4256-3ca1-4b32-b8a0-b7419acf502f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7387a912-a1c0-4e7c-af78-c6db4be5bfee' class='xr-var-data-in' type='checkbox'><label for='data-7387a912-a1c0-4e7c-af78-c6db4be5bfee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary dissolved inorganic iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Fe_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-eb57b1a3-4a8c-4749-99fe-5cc74ebd7bc8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eb57b1a3-4a8c-4749-99fe-5cc74ebd7bc8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d2ac3aa-d32a-44d5-a9bf-e9bb9f0adbe1' class='xr-var-data-in' type='checkbox'><label for='data-3d2ac3aa-d32a-44d5-a9bf-e9bb9f0adbe1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>western boundary dissolved inorganic iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -8957,17 +10790,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -8985,6 +10818,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
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@@ -8992,6 +10836,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
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+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
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+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
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@@ -9013,13 +10868,13 @@
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        "\n",
        "  <!-- Text -->\n",
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-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Lig_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-7174aa6f-5dab-4875-956e-b7c916d37b43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7174aa6f-5dab-4875-956e-b7c916d37b43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e0383ec-fdd1-4253-b490-b0ad44f516ae' class='xr-var-data-in' type='checkbox'><label for='data-3e0383ec-fdd1-4253-b490-b0ad44f516ae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary iron binding ligand</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Lig_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-d9918a1d-164d-4eaf-a0b1-f05b15e81c39' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9918a1d-164d-4eaf-a0b1-f05b15e81c39' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7cf5e6c-88c6-40c2-97bb-9d87697bcb97' class='xr-var-data-in' type='checkbox'><label for='data-c7cf5e6c-88c6-40c2-97bb-9d87697bcb97' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>western boundary iron binding ligand</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -9035,17 +10890,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -9063,6 +10918,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
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@@ -9070,6 +10936,17 @@
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        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -9091,13 +10968,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>O2_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-9959b53e-d2f0-4328-84a0-84ce842d44a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9959b53e-d2f0-4328-84a0-84ce842d44a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d369f94-6fa8-431e-968c-258e8fd532bd' class='xr-var-data-in' type='checkbox'><label for='data-6d369f94-6fa8-431e-968c-258e8fd532bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary dissolved oxygen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>O2_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-392e7a96-1b03-4a05-b50c-2bfff9c965f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-392e7a96-1b03-4a05-b50c-2bfff9c965f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea03f241-8682-4694-86d7-c7087b40d98e' class='xr-var-data-in' type='checkbox'><label for='data-ea03f241-8682-4694-86d7-c7087b40d98e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>western boundary dissolved oxygen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -9113,17 +10990,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -9141,6 +11018,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -9148,6 +11036,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -9169,13 +11068,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIC_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-58c8ff82-0fd3-4418-b17d-7e23394d4adb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-58c8ff82-0fd3-4418-b17d-7e23394d4adb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ee54ded-aa7e-4850-8595-5d13388fdd7f' class='xr-var-data-in' type='checkbox'><label for='data-6ee54ded-aa7e-4850-8595-5d13388fdd7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary dissolved inorganic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIC_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-d83571e0-e321-4199-b133-581b0328bdf9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d83571e0-e321-4199-b133-581b0328bdf9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-336cbf74-9ae7-499d-9bb4-2243c887c67f' class='xr-var-data-in' type='checkbox'><label for='data-336cbf74-9ae7-499d-9bb4-2243c887c67f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>western boundary dissolved inorganic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -9191,17 +11090,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -9219,6 +11118,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -9226,6 +11136,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -9247,13 +11168,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIC_ALT_CO2_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-f3e47e2f-94a9-458c-9c70-a1e8ea8f0f97' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f3e47e2f-94a9-458c-9c70-a1e8ea8f0f97' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b57a17b-93bc-4755-89ea-eeee16bd5f8c' class='xr-var-data-in' type='checkbox'><label for='data-8b57a17b-93bc-4755-89ea-eeee16bd5f8c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary dissolved inorganic carbon, alternative CO2</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIC_ALT_CO2_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-de1c0283-174e-479c-a2d1-ccc1c4da0db7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de1c0283-174e-479c-a2d1-ccc1c4da0db7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ddb4610-0888-4066-abf1-0f17d892ba63' class='xr-var-data-in' type='checkbox'><label for='data-2ddb4610-0888-4066-abf1-0f17d892ba63' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>western boundary dissolved inorganic carbon, alternative CO2</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -9269,17 +11190,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -9297,6 +11218,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -9304,6 +11236,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -9325,13 +11268,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALK_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-b03fb2b2-d910-48a2-bd89-a398c8170e43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b03fb2b2-d910-48a2-bd89-a398c8170e43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fbf31496-68be-4466-a642-d445edfb5b72' class='xr-var-data-in' type='checkbox'><label for='data-fbf31496-68be-4466-a642-d445edfb5b72' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary alkalinity</dd><dt><span>units :</span></dt><dd>meq/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALK_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-f1f952e6-ade5-4b33-9b2e-29b8853026c1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f1f952e6-ade5-4b33-9b2e-29b8853026c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53a80c38-cfd2-4702-941c-1dfc6d0869de' class='xr-var-data-in' type='checkbox'><label for='data-53a80c38-cfd2-4702-941c-1dfc6d0869de' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>western boundary alkalinity</dd><dt><span>units :</span></dt><dd>meq/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -9347,17 +11290,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -9375,6 +11318,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -9382,6 +11336,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -9403,13 +11368,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALK_ALT_CO2_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-eda54c39-769e-4d35-87eb-7f333836e4b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eda54c39-769e-4d35-87eb-7f333836e4b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1229704f-1c79-47ec-ac0f-55fb6ef6eec2' class='xr-var-data-in' type='checkbox'><label for='data-1229704f-1c79-47ec-ac0f-55fb6ef6eec2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary alkalinity, alternative CO2</dd><dt><span>units :</span></dt><dd>meq/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALK_ALT_CO2_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-06b24727-eb03-4aca-a7f7-6ef59b943a39' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06b24727-eb03-4aca-a7f7-6ef59b943a39' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-177b3f7b-11e0-4049-bf6e-a2c32e3b391b' class='xr-var-data-in' type='checkbox'><label for='data-177b3f7b-11e0-4049-bf6e-a2c32e3b391b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>western boundary alkalinity, alternative CO2</dd><dt><span>units :</span></dt><dd>meq/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -9425,17 +11390,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -9453,6 +11418,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -9460,6 +11436,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -9481,13 +11468,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOC_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-a1ea1e06-be4e-4d13-81bd-680a16195188' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a1ea1e06-be4e-4d13-81bd-680a16195188' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81572e4c-9a77-4ff7-acb9-1212978d59be' class='xr-var-data-in' type='checkbox'><label for='data-81572e4c-9a77-4ff7-acb9-1212978d59be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary dissolved organic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOC_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-c76ef7d9-c268-45a3-ac5e-0329cb560ce4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c76ef7d9-c268-45a3-ac5e-0329cb560ce4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df4782d6-599f-4a96-92b2-0133757e0d54' class='xr-var-data-in' type='checkbox'><label for='data-df4782d6-599f-4a96-92b2-0133757e0d54' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>western boundary dissolved organic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -9503,17 +11490,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -9531,6 +11518,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -9538,6 +11536,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -9559,13 +11568,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DON_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-33fd6377-b9d4-45fd-9f65-e3b85c91e231' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-33fd6377-b9d4-45fd-9f65-e3b85c91e231' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8aba772-64df-480b-ae01-a1bc1ba88204' class='xr-var-data-in' type='checkbox'><label for='data-a8aba772-64df-480b-ae01-a1bc1ba88204' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DON_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-e8e48ac8-e568-4081-87a9-85b60061e22b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e8e48ac8-e568-4081-87a9-85b60061e22b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79d1d1fa-1724-4cb4-9b52-92ace2dad580' class='xr-var-data-in' type='checkbox'><label for='data-79d1d1fa-1724-4cb4-9b52-92ace2dad580' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>western boundary dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -9581,17 +11590,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -9609,6 +11618,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -9616,6 +11636,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
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+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
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+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -9637,13 +11668,13 @@
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        "\n",
        "  <!-- Text -->\n",
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-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
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+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOP_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-083cc12f-b00d-4540-8a64-9df20e466675' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-083cc12f-b00d-4540-8a64-9df20e466675' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a2f921d-4eab-4a69-8a46-b2d00c99a746' class='xr-var-data-in' type='checkbox'><label for='data-9a2f921d-4eab-4a69-8a46-b2d00c99a746' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOP_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-7d380843-6248-4c93-bbb9-f67b05c3d585' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7d380843-6248-4c93-bbb9-f67b05c3d585' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6706df30-ef80-4f30-9e37-5765d10f42ed' class='xr-var-data-in' type='checkbox'><label for='data-6706df30-ef80-4f30-9e37-5765d10f42ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>western boundary dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -9659,17 +11690,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -9687,6 +11718,17 @@
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        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
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@@ -9694,6 +11736,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
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        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOPr_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-32f8d62b-a74a-468b-82c9-43e40996fde5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32f8d62b-a74a-468b-82c9-43e40996fde5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-288f48e8-3370-48a2-9bf8-39af0a98cc03' class='xr-var-data-in' type='checkbox'><label for='data-288f48e8-3370-48a2-9bf8-39af0a98cc03' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary refractory dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOPr_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-4e3eb594-3f23-4f68-989a-a4af7323692a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e3eb594-3f23-4f68-989a-a4af7323692a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f49fa6c-f3fb-430f-9257-aa8bee505510' class='xr-var-data-in' type='checkbox'><label for='data-6f49fa6c-f3fb-430f-9257-aa8bee505510' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>western boundary refractory dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -9737,17 +11790,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -9765,6 +11818,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -9772,6 +11836,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
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+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
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+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
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@@ -9793,13 +11868,13 @@
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        "\n",
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-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DONr_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-4b725a0f-99bf-4e6e-9155-9f5e5184a14b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b725a0f-99bf-4e6e-9155-9f5e5184a14b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66576abd-df14-475c-8d90-92d1ca47d438' class='xr-var-data-in' type='checkbox'><label for='data-66576abd-df14-475c-8d90-92d1ca47d438' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary refractory dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DONr_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-c603ffa0-bf0e-4ff8-b149-3c875fc2bda9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c603ffa0-bf0e-4ff8-b149-3c875fc2bda9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55b6c876-5c25-4302-b942-0994dab9d1cf' class='xr-var-data-in' type='checkbox'><label for='data-55b6c876-5c25-4302-b942-0994dab9d1cf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>western boundary refractory dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -9815,17 +11890,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -9843,6 +11918,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
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@@ -9850,6 +11936,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
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@@ -9871,13 +11968,13 @@
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-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
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+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOCr_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-07cc6e08-b643-4dd6-b694-e805712a281d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-07cc6e08-b643-4dd6-b694-e805712a281d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-176ca834-c26b-4618-8453-0e41e001332a' class='xr-var-data-in' type='checkbox'><label for='data-176ca834-c26b-4618-8453-0e41e001332a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary refractory dissolved organic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOCr_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-9e46c970-3a06-443c-8c26-c429d8328cea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9e46c970-3a06-443c-8c26-c429d8328cea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e14bfe97-020c-444c-ac26-549c8b3765b4' class='xr-var-data-in' type='checkbox'><label for='data-e14bfe97-020c-444c-ac26-549c8b3765b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>western boundary refractory dissolved organic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -9893,17 +11990,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -9921,6 +12018,17 @@
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        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
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-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spChl_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-58947713-55a7-4cb2-b7e9-7f5b9d9a16bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-58947713-55a7-4cb2-b7e9-7f5b9d9a16bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40f1dc5b-23d1-4382-b933-40c8aac554b1' class='xr-var-data-in' type='checkbox'><label for='data-40f1dc5b-23d1-4382-b933-40c8aac554b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary small phytoplankton chlorophyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spChl_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-79ba5a93-df88-4553-97f7-9ff9093d11a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-79ba5a93-df88-4553-97f7-9ff9093d11a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e66ba7d-1922-4357-b9c8-f48c4f6773ff' class='xr-var-data-in' type='checkbox'><label for='data-7e66ba7d-1922-4357-b9c8-f48c4f6773ff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>western boundary small phytoplankton chlorophyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -9971,17 +12090,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -9999,13 +12118,35 @@
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+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
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+       "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
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        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
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@@ -10027,13 +12168,13 @@
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-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spC_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-391ea0a5-b32f-4275-8a7a-9e9b4dd9766e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-391ea0a5-b32f-4275-8a7a-9e9b4dd9766e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2dc424ec-1a06-4f16-95a9-dd7435c103bf' class='xr-var-data-in' type='checkbox'><label for='data-2dc424ec-1a06-4f16-95a9-dd7435c103bf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary small phytoplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spC_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-60bbbad9-30fd-4a9d-a960-eff30c269a44' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-60bbbad9-30fd-4a9d-a960-eff30c269a44' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9ecd561-2bdd-45f7-a8ed-a1ddf5c36d2e' class='xr-var-data-in' type='checkbox'><label for='data-e9ecd561-2bdd-45f7-a8ed-a1ddf5c36d2e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>western boundary small phytoplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -10049,17 +12190,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -10077,6 +12218,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
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+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
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+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
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+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
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@@ -10084,6 +12236,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
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@@ -10105,13 +12268,13 @@
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        "\n",
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+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
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        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spP_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-ff178f65-c6ed-4988-ad46-c57fb2d73541' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ff178f65-c6ed-4988-ad46-c57fb2d73541' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6101fc2c-f734-424b-bb46-e1e059c4a7c8' class='xr-var-data-in' type='checkbox'><label for='data-6101fc2c-f734-424b-bb46-e1e059c4a7c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary small phytoplankton phosphorous</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spP_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-07679e10-ca20-4e76-93c9-162997c5c70b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-07679e10-ca20-4e76-93c9-162997c5c70b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1810d363-01f6-4162-934b-359f74881885' class='xr-var-data-in' type='checkbox'><label for='data-1810d363-01f6-4162-934b-359f74881885' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>western boundary small phytoplankton phosphorous</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -10127,17 +12290,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -10155,6 +12318,17 @@
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        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
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@@ -10162,6 +12336,17 @@
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        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -10183,13 +12368,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spFe_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-030004bb-7778-47ee-9049-a17d5371393a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-030004bb-7778-47ee-9049-a17d5371393a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a36d18c3-9d26-4dba-a70c-6d3966d54f22' class='xr-var-data-in' type='checkbox'><label for='data-a36d18c3-9d26-4dba-a70c-6d3966d54f22' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary small phytoplankton iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spFe_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-de9b42ff-6dff-47bd-8b04-99332ce783f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de9b42ff-6dff-47bd-8b04-99332ce783f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a34dc140-8e05-434a-8669-14e766da1cd8' class='xr-var-data-in' type='checkbox'><label for='data-a34dc140-8e05-434a-8669-14e766da1cd8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>western boundary small phytoplankton iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -10205,17 +12390,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -10233,6 +12418,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -10240,6 +12436,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -10261,13 +12468,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatChl_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-0528a9cb-021e-416d-976c-4bf23c2c5d8b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0528a9cb-021e-416d-976c-4bf23c2c5d8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11606cce-a080-48e0-b5a7-10f86d457a71' class='xr-var-data-in' type='checkbox'><label for='data-11606cce-a080-48e0-b5a7-10f86d457a71' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary diatom chloropyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatChl_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-f4dbf228-81be-4bb6-81ce-4ef0853a63a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4dbf228-81be-4bb6-81ce-4ef0853a63a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4058664c-20a2-4c47-8ccc-9f72b65bc5a2' class='xr-var-data-in' type='checkbox'><label for='data-4058664c-20a2-4c47-8ccc-9f72b65bc5a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>western boundary diatom chloropyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -10283,17 +12490,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -10311,6 +12518,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -10318,6 +12536,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -10339,13 +12568,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatC_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-841087b7-88d8-4708-87bf-de95035ac0fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-841087b7-88d8-4708-87bf-de95035ac0fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42e30d80-cfb9-4c06-be10-61dea7f3b89f' class='xr-var-data-in' type='checkbox'><label for='data-42e30d80-cfb9-4c06-be10-61dea7f3b89f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary diatom carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatC_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-12699d44-aedb-4bde-beaa-634682ae104e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12699d44-aedb-4bde-beaa-634682ae104e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8b6814e-d8e4-401f-b1b6-07c6af3210f1' class='xr-var-data-in' type='checkbox'><label for='data-c8b6814e-d8e4-401f-b1b6-07c6af3210f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>western boundary diatom carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -10361,17 +12590,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -10389,6 +12618,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -10396,6 +12636,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -10417,13 +12668,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatP_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-89a9a8ee-37de-47da-9261-51cf70ab7ec6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-89a9a8ee-37de-47da-9261-51cf70ab7ec6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5a31749-c143-499f-89ca-2a8aa2363334' class='xr-var-data-in' type='checkbox'><label for='data-e5a31749-c143-499f-89ca-2a8aa2363334' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary diatom phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatP_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-aa0d60fa-f6ea-4bf8-9d56-d4b9092c59b7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa0d60fa-f6ea-4bf8-9d56-d4b9092c59b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8e1bd60-b3c0-459b-8090-20176074cc4a' class='xr-var-data-in' type='checkbox'><label for='data-a8e1bd60-b3c0-459b-8090-20176074cc4a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>western boundary diatom phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -10439,17 +12690,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -10467,6 +12718,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -10474,6 +12736,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -10495,13 +12768,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatFe_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-d8875bd1-7a80-4a39-b5ed-c75d357817bf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d8875bd1-7a80-4a39-b5ed-c75d357817bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-748d42f5-f48b-48d1-b280-cd681f09691e' class='xr-var-data-in' type='checkbox'><label for='data-748d42f5-f48b-48d1-b280-cd681f09691e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary diatom iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatFe_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-fa032a4d-175d-4db1-96d6-3f8143b1a1f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa032a4d-175d-4db1-96d6-3f8143b1a1f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8aa94d8-4b28-4575-b097-f10a68d05ce7' class='xr-var-data-in' type='checkbox'><label for='data-e8aa94d8-4b28-4575-b097-f10a68d05ce7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>western boundary diatom iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -10517,17 +12790,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -10545,6 +12818,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -10552,6 +12836,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -10573,13 +12868,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatSi_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-dcc2852f-6979-40d6-92d8-555e9cdd7fab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dcc2852f-6979-40d6-92d8-555e9cdd7fab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1363f37c-a395-48d8-9e01-d401a93976e6' class='xr-var-data-in' type='checkbox'><label for='data-1363f37c-a395-48d8-9e01-d401a93976e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary diatom silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatSi_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-980260d1-264b-402e-858c-75890b0a02f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-980260d1-264b-402e-858c-75890b0a02f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a110242-b0c1-4294-b4ce-9e3178bac655' class='xr-var-data-in' type='checkbox'><label for='data-1a110242-b0c1-4294-b4ce-9e3178bac655' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>western boundary diatom silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -10595,17 +12890,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -10623,6 +12918,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -10630,6 +12936,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -10651,13 +12968,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazChl_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-03019f01-c37a-4271-b9a7-34037a69b283' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03019f01-c37a-4271-b9a7-34037a69b283' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a42a0aa9-9ad3-46fa-98c4-b2c0714146e4' class='xr-var-data-in' type='checkbox'><label for='data-a42a0aa9-9ad3-46fa-98c4-b2c0714146e4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary diazotroph chloropyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazChl_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-58abf834-a533-4865-b0e6-7bf6ecf49bce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-58abf834-a533-4865-b0e6-7bf6ecf49bce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bea82976-f118-4a19-beb4-e1c921121096' class='xr-var-data-in' type='checkbox'><label for='data-bea82976-f118-4a19-beb4-e1c921121096' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>western boundary diazotroph chloropyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -10673,17 +12990,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -10701,6 +13018,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -10708,6 +13036,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -10729,13 +13068,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazC_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-20580383-86d9-4cd4-9555-50070dd2eceb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20580383-86d9-4cd4-9555-50070dd2eceb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53bb00f9-280f-4d5d-a237-4cd9014cb146' class='xr-var-data-in' type='checkbox'><label for='data-53bb00f9-280f-4d5d-a237-4cd9014cb146' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary diazotroph carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazC_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-ce39fa4f-8b95-4334-a2c0-6e8a0c24b21a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce39fa4f-8b95-4334-a2c0-6e8a0c24b21a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0fc3f58-2579-4c85-88f3-8447dc7d32e5' class='xr-var-data-in' type='checkbox'><label for='data-d0fc3f58-2579-4c85-88f3-8447dc7d32e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>western boundary diazotroph carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -10751,17 +13090,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -10779,6 +13118,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
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+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -10786,6 +13136,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -10807,13 +13168,13 @@
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        "\n",
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-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazP_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-e2d5eeaa-0cec-46fd-8dba-a2abfd7299b9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2d5eeaa-0cec-46fd-8dba-a2abfd7299b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4edb9d4f-8b8b-4703-8b67-634debe03ea4' class='xr-var-data-in' type='checkbox'><label for='data-4edb9d4f-8b8b-4703-8b67-634debe03ea4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary diazotroph phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazP_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-827254a5-a8bf-4338-8734-cf7bab685ff4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-827254a5-a8bf-4338-8734-cf7bab685ff4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2820d3eb-ba52-4db6-a059-fb4250c17bd8' class='xr-var-data-in' type='checkbox'><label for='data-2820d3eb-ba52-4db6-a059-fb4250c17bd8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>western boundary diazotroph phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -10829,17 +13190,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -10857,6 +13218,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -10864,6 +13236,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -10885,13 +13268,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazFe_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-bbed8558-aa28-4280-9a7d-bcf2c86dfbe2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bbed8558-aa28-4280-9a7d-bcf2c86dfbe2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7e8662f-fd18-4e4a-963c-032993b2ed69' class='xr-var-data-in' type='checkbox'><label for='data-e7e8662f-fd18-4e4a-963c-032993b2ed69' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary diazotroph iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazFe_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-b5cfd448-c6e6-47ee-bfa5-ca33df7f0d74' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b5cfd448-c6e6-47ee-bfa5-ca33df7f0d74' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da89eab9-a8c4-4c54-9a10-eea21e96b60c' class='xr-var-data-in' type='checkbox'><label for='data-da89eab9-a8c4-4c54-9a10-eea21e96b60c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>western boundary diazotroph iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -10907,17 +13290,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -10935,6 +13318,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -10942,6 +13336,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -10963,13 +13368,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spCaCO3_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-4e856c7b-0062-4bc6-8b15-fdd8a926478a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e856c7b-0062-4bc6-8b15-fdd8a926478a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-623d4b8a-c5c5-4360-adce-6db242180300' class='xr-var-data-in' type='checkbox'><label for='data-623d4b8a-c5c5-4360-adce-6db242180300' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary small phytoplankton CaCO3</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spCaCO3_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-21f0986f-c1dc-468a-bdad-38c8340bc875' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-21f0986f-c1dc-468a-bdad-38c8340bc875' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-189871fa-8f8e-413c-a9b9-026b4d94a430' class='xr-var-data-in' type='checkbox'><label for='data-189871fa-8f8e-413c-a9b9-026b4d94a430' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>western boundary small phytoplankton CaCO3</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -10985,17 +13390,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -11013,6 +13418,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -11020,6 +13436,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -11041,13 +13468,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
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        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zooC_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-89351e14-96a2-4d75-9d06-b9386d8ce0c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-89351e14-96a2-4d75-9d06-b9386d8ce0c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b4c54f3-2c5e-4b07-9117-2acb89a20b30' class='xr-var-data-in' type='checkbox'><label for='data-6b4c54f3-2c5e-4b07-9117-2acb89a20b30' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary zooplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zooC_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-50de29be-b72e-4094-8445-3cc810d92439' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-50de29be-b72e-4094-8445-3cc810d92439' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-143c7eb4-ba5a-4b34-97ba-eaf3d7b22426' class='xr-var-data-in' type='checkbox'><label for='data-143c7eb4-ba5a-4b34-97ba-eaf3d7b22426' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>long_name :</span></dt><dd>western boundary zooplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -11063,17 +13490,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 2.30 MiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (12, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -11091,13 +13518,35 @@
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-       "\n",
-       "  <!-- Colored Rectangle -->\n",
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        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
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        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-afaeb741-8701-42a4-93c6-995b33b2e7c9' class='xr-section-summary-in' type='checkbox'  ><label for='section-afaeb741-8701-42a4-93c6-995b33b2e7c9' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>bry_time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-064d15f3-4a44-411a-b3a9-e4949be7a1d1' class='xr-index-data-in' type='checkbox'/><label for='index-064d15f3-4a44-411a-b3a9-e4949be7a1d1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([15.000000000000002,               45.0,               74.0,\n",
-       "                    105.0,              135.0,              166.0,\n",
-       "                    196.0,              227.0,              258.0,\n",
-       "                    288.0,              319.0,              349.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;bry_time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-37677ac2-3a98-4492-97b9-0357adf19f07' class='xr-section-summary-in' type='checkbox'  ><label for='section-37677ac2-3a98-4492-97b9-0357adf19f07' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>ROMS boundary forcing file created by ROMS-Tools</dd><dt><span>roms_tools_version :</span></dt><dd>3.1.2.dev23+gc2b82bc5c</dd><dt><span>start_time :</span></dt><dd>2012-01-30 00:00:00</dd><dt><span>end_time :</span></dt><dd>2012-02-04 00:00:00</dd><dt><span>source :</span></dt><dd>CESM_REGRIDDED</dd><dt><span>model_reference_date :</span></dt><dd>2000-01-01 00:00:00</dd><dt><span>apply_2d_horizontal_fill :</span></dt><dd>False</dd><dt><span>adjust_depth_for_sea_surface_height :</span></dt><dd>False</dd><dt><span>theta_s :</span></dt><dd>5.0</dd><dt><span>theta_b :</span></dt><dd>2.0</dd><dt><span>hc :</span></dt><dd>300.0</dd><dt><span>climatology :</span></dt><dd>True</dd></dl></div></li></ul></div></div>"
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-1ec123c4-9e7f-4982-af25-815a089d1bb0' class='xr-section-summary-in' type='checkbox' /><label for='section-1ec123c4-9e7f-4982-af25-815a089d1bb0' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(14)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>ROMS boundary forcing file created by ROMS-Tools</dd><dt><span>roms_tools_version :</span></dt><dd>10000.dev361+g2e6c47bd8</dd><dt><span>start_time :</span></dt><dd>2012-01-30 00:00:00</dd><dt><span>end_time :</span></dt><dd>2012-02-04 00:00:00</dd><dt><span>source :</span></dt><dd>CESM_REGRIDDED</dd><dt><span>model_reference_date :</span></dt><dd>2000-01-01 00:00:00</dd><dt><span>prefill :</span></dt><dd>None</dd><dt><span>regrid_method :</span></dt><dd>xesmf</dd><dt><span>extrap_method :</span></dt><dd>inverse_dist</dd><dt><span>adjust_depth_for_sea_surface_height :</span></dt><dd>False</dd><dt><span>theta_s :</span></dt><dd>5.0</dd><dt><span>theta_b :</span></dt><dd>2.0</dd><dt><span>hc :</span></dt><dd>300.0</dd><dt><span>climatology :</span></dt><dd>True</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset> Size: 231MB\n",
        "Dimensions:            (bry_time: 12, s_rho: 100, xi_rho: 502, eta_rho: 502)\n",
        "Coordinates:\n",
+       "  * bry_time           (bry_time) float64 96B 15.0 45.0 74.0 ... 319.0 349.0\n",
        "    month              (bry_time) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12\n",
        "    abs_time           (bry_time) datetime64[ns] 96B 2000-01-16 ... 2000-12-15\n",
-       "  * bry_time           (bry_time) float64 96B 15.0 45.0 74.0 ... 319.0 349.0\n",
        "Dimensions without coordinates: s_rho, xi_rho, eta_rho\n",
        "Data variables: (12/96)\n",
-       "    PO4_south          (bry_time, s_rho, xi_rho) float32 2MB dask.array<chunksize=(12, 100, 502), meta=np.ndarray>\n",
-       "    NO3_south          (bry_time, s_rho, xi_rho) float32 2MB dask.array<chunksize=(12, 100, 502), meta=np.ndarray>\n",
-       "    SiO3_south         (bry_time, s_rho, xi_rho) float32 2MB dask.array<chunksize=(12, 100, 502), meta=np.ndarray>\n",
-       "    NH4_south          (bry_time, s_rho, xi_rho) float32 2MB dask.array<chunksize=(12, 100, 502), meta=np.ndarray>\n",
-       "    Fe_south           (bry_time, s_rho, xi_rho) float32 2MB dask.array<chunksize=(12, 100, 502), meta=np.ndarray>\n",
-       "    Lig_south          (bry_time, s_rho, xi_rho) float32 2MB dask.array<chunksize=(12, 100, 502), meta=np.ndarray>\n",
+       "    PO4_south          (bry_time, s_rho, xi_rho) float32 2MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
+       "    NO3_south          (bry_time, s_rho, xi_rho) float32 2MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
+       "    SiO3_south         (bry_time, s_rho, xi_rho) float32 2MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
+       "    NH4_south          (bry_time, s_rho, xi_rho) float32 2MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
+       "    Fe_south           (bry_time, s_rho, xi_rho) float32 2MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
+       "    Lig_south          (bry_time, s_rho, xi_rho) float32 2MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
        "    ...                 ...\n",
-       "    diazChl_west       (bry_time, s_rho, eta_rho) float32 2MB dask.array<chunksize=(12, 100, 502), meta=np.ndarray>\n",
-       "    diazC_west         (bry_time, s_rho, eta_rho) float32 2MB dask.array<chunksize=(12, 100, 502), meta=np.ndarray>\n",
-       "    diazP_west         (bry_time, s_rho, eta_rho) float32 2MB dask.array<chunksize=(12, 100, 502), meta=np.ndarray>\n",
-       "    diazFe_west        (bry_time, s_rho, eta_rho) float32 2MB dask.array<chunksize=(12, 100, 502), meta=np.ndarray>\n",
-       "    spCaCO3_west       (bry_time, s_rho, eta_rho) float32 2MB dask.array<chunksize=(12, 100, 502), meta=np.ndarray>\n",
-       "    zooC_west          (bry_time, s_rho, eta_rho) float32 2MB dask.array<chunksize=(12, 100, 502), meta=np.ndarray>\n",
-       "Attributes:\n",
+       "    diazChl_west       (bry_time, s_rho, eta_rho) float32 2MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
+       "    diazC_west         (bry_time, s_rho, eta_rho) float32 2MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
+       "    diazP_west         (bry_time, s_rho, eta_rho) float32 2MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
+       "    diazFe_west        (bry_time, s_rho, eta_rho) float32 2MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
+       "    spCaCO3_west       (bry_time, s_rho, eta_rho) float32 2MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
+       "    zooC_west          (bry_time, s_rho, eta_rho) float32 2MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
+       "Attributes: (12/14)\n",
        "    title:                                ROMS boundary forcing file created ...\n",
-       "    roms_tools_version:                   3.1.2.dev23+gc2b82bc5c\n",
+       "    roms_tools_version:                   10000.dev361+g2e6c47bd8\n",
        "    start_time:                           2012-01-30 00:00:00\n",
        "    end_time:                             2012-02-04 00:00:00\n",
        "    source:                               CESM_REGRIDDED\n",
        "    model_reference_date:                 2000-01-01 00:00:00\n",
-       "    apply_2d_horizontal_fill:             False\n",
+       "    ...                                   ...\n",
+       "    extrap_method:                        inverse_dist\n",
        "    adjust_depth_for_sea_surface_height:  False\n",
        "    theta_s:                              5.0\n",
        "    theta_b:                              2.0\n",
@@ -11187,16 +13633,16 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-12-20 13:49:58 - INFO - No `boundaries` provided. Using mask-based defaults: {'south': True, 'east': True, 'north': False, 'west': True}\n",
-      "2025-12-20 13:49:58 - WARNING - Optional variables missing (but not critical): ['Lig', 'DIC_ALT_CO2', 'Alk_ALT_CO2']\n"
+      "2026-06-22 17:58:39 - INFO - No `boundaries` provided. Using mask-based defaults: {'south': True, 'east': True, 'north': False, 'west': True}\n",
+      "2026-06-22 17:58:39 - WARNING - Optional variables missing (but not critical): ['Lig', 'DIC_ALT_CO2', 'Alk_ALT_CO2']\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 4.45 s, sys: 329 ms, total: 4.78 s\n",
-      "Wall time: 4.87 s\n"
+      "CPU times: user 1.41 s, sys: 24 ms, total: 1.43 s\n",
+      "Wall time: 1.69 s\n"
      ]
     }
    ],
@@ -11237,9 +13683,7 @@
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        "</defs>\n",
        "</svg>\n",
-       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
-       " *\n",
-       " */\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
        "\n",
        ":root {\n",
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@@ -11318,6 +13762,8 @@
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        "  min-width: 300px;\n",
        "  max-width: 700px;\n",
+       "  line-height: 1.6;\n",
+       "  padding-bottom: 4px;\n",
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        "\n",
        ".xr-text-repr-fallback {\n",
@@ -11328,8 +13774,11 @@
        ".xr-header {\n",
        "  padding-top: 6px;\n",
        "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
        "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "  margin-bottom: 4px;\n",
        "}\n",
        "\n",
        ".xr-header > div,\n",
@@ -11340,46 +13789,62 @@
        "}\n",
        "\n",
        ".xr-obj-type,\n",
-       ".xr-array-name {\n",
+       ".xr-obj-name {\n",
        "  margin-left: 2px;\n",
        "  margin-right: 10px;\n",
        "}\n",
        "\n",
-       ".xr-obj-type {\n",
+       ".xr-obj-type,\n",
+       ".xr-group-box-contents > label {\n",
        "  color: var(--xr-font-color2);\n",
+       "  display: block;\n",
        "}\n",
        "\n",
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
        "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
        "  display: contents;\n",
        "}\n",
        "\n",
-       ".xr-section-item input {\n",
-       "  display: inline-block;\n",
+       ".xr-section-item > input,\n",
+       ".xr-group-box-contents > input,\n",
+       ".xr-array-wrap > input {\n",
+       "  display: block;\n",
        "  opacity: 0;\n",
        "  height: 0;\n",
+       "  margin: 0;\n",
        "}\n",
        "\n",
-       ".xr-section-item input + label {\n",
+       ".xr-section-item > input + label,\n",
+       ".xr-var-item > input + label {\n",
        "  color: var(--xr-disabled-color);\n",
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        "}\n",
        "\n",
-       ".xr-section-item input:enabled + label {\n",
+       ".xr-section-item > input:enabled + label,\n",
+       ".xr-var-item > input:enabled + label,\n",
+       ".xr-array-wrap > input:enabled + label,\n",
+       ".xr-group-box-contents > input:enabled + label {\n",
        "  cursor: pointer;\n",
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
-       ".xr-section-item input:focus + label {\n",
-       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       ".xr-section-item > input:focus-visible + label,\n",
+       ".xr-var-item > input:focus-visible + label,\n",
+       ".xr-array-wrap > input:focus-visible + label,\n",
+       ".xr-group-box-contents > input:focus-visible + label {\n",
+       "  outline: auto;\n",
        "}\n",
        "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
+       ".xr-section-item > input:enabled + label:hover,\n",
+       ".xr-var-item > input:enabled + label:hover,\n",
+       ".xr-array-wrap > input:enabled + label:hover,\n",
+       ".xr-group-box-contents > input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
        "\n",
@@ -11387,11 +13852,25 @@
        "  grid-column: 1;\n",
        "  color: var(--xr-font-color2);\n",
        "  font-weight: 500;\n",
+       "  white-space: nowrap;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > em {\n",
+       "  font-weight: normal;\n",
+       "}\n",
+       "\n",
+       ".xr-span-grid {\n",
+       "  grid-column-end: -1;\n",
        "}\n",
        "\n",
        ".xr-section-summary > span {\n",
        "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
+       "  padding-left: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.6em;\n",
        "}\n",
        "\n",
        ".xr-section-summary-in:disabled + label {\n",
@@ -11419,9 +13898,9 @@
        "}\n",
        "\n",
        ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
+       ".xr-section-inline-details,\n",
+       ".xr-group-box-contents > label {\n",
        "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
        "}\n",
        "\n",
        ".xr-section-inline-details {\n",
@@ -11429,15 +13908,81 @@
        "}\n",
        "\n",
        ".xr-section-details {\n",
-       "  display: none;\n",
        "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
        "  margin-bottom: 5px;\n",
        "}\n",
        "\n",
+       ".xr-section-summary-in ~ .xr-section-details {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
        ".xr-section-summary-in:checked ~ .xr-section-details {\n",
        "  display: contents;\n",
        "}\n",
        "\n",
+       ".xr-children {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 100%;\n",
+       "  grid-column: 1 / -1;\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 30px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 26px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > label::before {\n",
+       "  content: \"📂\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label::before {\n",
+       "  content: \"📁\";\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label {\n",
+       "  padding-bottom: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked ~ .xr-sections {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-ellipsis {\n",
+       "  font-size: 1.4em;\n",
+       "  font-weight: 900;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  letter-spacing: 0.15em;\n",
+       "  cursor: default;\n",
+       "}\n",
+       "\n",
        ".xr-array-wrap {\n",
        "  grid-column: 1 / -1;\n",
        "  display: grid;\n",
@@ -11668,44 +14213,45 @@
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 231MB\n",
        "Dimensions:            (bry_time: 12, s_rho: 100, xi_rho: 502, eta_rho: 502)\n",
        "Coordinates:\n",
+       "  * bry_time           (bry_time) float64 96B 15.5 45.0 74.5 ... 319.0 349.5\n",
        "    month              (bry_time) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12\n",
        "    abs_time           (bry_time) datetime64[ns] 96B 2000-01-16T12:00:00 ... ...\n",
-       "  * bry_time           (bry_time) float64 96B 15.5 45.0 74.5 ... 319.0 349.5\n",
        "Dimensions without coordinates: s_rho, xi_rho, eta_rho\n",
        "Data variables: (12/96)\n",
-       "    PO4_south          (bry_time, s_rho, xi_rho) float32 2MB dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;\n",
-       "    NO3_south          (bry_time, s_rho, xi_rho) float32 2MB dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;\n",
-       "    SiO3_south         (bry_time, s_rho, xi_rho) float32 2MB dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;\n",
-       "    Fe_south           (bry_time, s_rho, xi_rho) float32 2MB dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;\n",
-       "    O2_south           (bry_time, s_rho, xi_rho) float32 2MB dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;\n",
-       "    DIC_south          (bry_time, s_rho, xi_rho) float32 2MB dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;\n",
+       "    PO4_south          (bry_time, s_rho, xi_rho) float32 2MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
+       "    NO3_south          (bry_time, s_rho, xi_rho) float32 2MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
+       "    SiO3_south         (bry_time, s_rho, xi_rho) float32 2MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
+       "    Fe_south           (bry_time, s_rho, xi_rho) float32 2MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
+       "    O2_south           (bry_time, s_rho, xi_rho) float32 2MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
+       "    DIC_south          (bry_time, s_rho, xi_rho) float32 2MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
        "    ...                 ...\n",
-       "    diazFe_west        (bry_time, s_rho, eta_rho) float32 2MB dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;\n",
-       "    spCaCO3_west       (bry_time, s_rho, eta_rho) float32 2MB dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;\n",
-       "    zooC_west          (bry_time, s_rho, eta_rho) float32 2MB dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;\n",
-       "    Lig_west           (bry_time, s_rho, eta_rho) float32 2MB dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;\n",
-       "    DIC_ALT_CO2_west   (bry_time, s_rho, eta_rho) float32 2MB dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;\n",
-       "    ALK_ALT_CO2_west   (bry_time, s_rho, eta_rho) float32 2MB dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;\n",
-       "Attributes:\n",
+       "    diazFe_west        (bry_time, s_rho, eta_rho) float32 2MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
+       "    spCaCO3_west       (bry_time, s_rho, eta_rho) float32 2MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
+       "    zooC_west          (bry_time, s_rho, eta_rho) float32 2MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
+       "    Lig_west           (bry_time, s_rho, eta_rho) float32 2MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
+       "    DIC_ALT_CO2_west   (bry_time, s_rho, eta_rho) float32 2MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
+       "    ALK_ALT_CO2_west   (bry_time, s_rho, eta_rho) float32 2MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
+       "Attributes: (12/14)\n",
        "    title:                                ROMS boundary forcing file created ...\n",
-       "    roms_tools_version:                   3.1.2.dev23+gc2b82bc5c\n",
+       "    roms_tools_version:                   10000.dev361+g2e6c47bd8\n",
        "    start_time:                           2012-01-30 00:00:00\n",
        "    end_time:                             2012-02-04 00:00:00\n",
        "    source:                               UNIFIED\n",
        "    model_reference_date:                 2000-01-01 00:00:00\n",
-       "    apply_2d_horizontal_fill:             False\n",
+       "    ...                                   ...\n",
+       "    extrap_method:                        inverse_dist\n",
        "    adjust_depth_for_sea_surface_height:  False\n",
        "    theta_s:                              5.0\n",
        "    theta_b:                              2.0\n",
        "    hc:                                   300.0\n",
-       "    climatology:                          True</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-bdd11521-487e-475b-94c6-8336473e20d6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bdd11521-487e-475b-94c6-8336473e20d6' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>bry_time</span>: 12</li><li><span>s_rho</span>: 100</li><li><span>xi_rho</span>: 502</li><li><span>eta_rho</span>: 502</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-fde9f1d5-9d87-49bb-b284-bbe89fc8aa64' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fde9f1d5-9d87-49bb-b284-bbe89fc8aa64' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>month</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-33d77ecd-611c-4735-bb07-0308faef8a82' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-33d77ecd-611c-4735-bb07-0308faef8a82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af10c743-cd0e-40e6-8492-29d3afb2de0d' class='xr-var-data-in' type='checkbox'><label for='data-af10c743-cd0e-40e6-8492-29d3afb2de0d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Month index (1-12)</dd></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-16T12:00:00 ... 2000-12-...</div><input id='attrs-7ee824ca-97ca-4e4c-bb4c-02855c45e976' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7ee824ca-97ca-4e4c-bb4c-02855c45e976' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7109ce80-b8af-42a5-8818-bf2e34a022f4' class='xr-var-data-in' type='checkbox'><label for='data-7109ce80-b8af-42a5-8818-bf2e34a022f4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>absolute time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-16T12:00:00.000000000&#x27;, &#x27;2000-02-15T00:00:00.000000000&#x27;,\n",
+       "    climatology:                          True</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b59e469d-b79c-42f2-8619-c04ccf20be5b' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-b59e469d-b79c-42f2-8619-c04ccf20be5b' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>bry_time</span>: 12</li><li><span>s_rho</span>: 100</li><li><span>xi_rho</span>: 502</li><li><span>eta_rho</span>: 502</li></ul></div></li><li class='xr-section-item'><input id='section-a8a55d08-211d-426a-9d4c-72a7db490ad6' class='xr-section-summary-in' type='checkbox' checked /><label for='section-a8a55d08-211d-426a-9d4c-72a7db490ad6' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>bry_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.5 45.0 74.5 ... 319.0 349.5</div><input id='attrs-4488185d-3ea6-4b89-aaca-7996055f5f8b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4488185d-3ea6-4b89-aaca-7996055f5f8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-961dfa1c-28f1-4910-b57e-a69790999b8a' class='xr-var-data-in' type='checkbox'><label for='data-961dfa1c-28f1-4910-b57e-a69790999b8a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cycle_length :</span></dt><dd>365.25</dd><dt><span>long_name :</span></dt><dd>relative time: days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([ 15.5,  45. ,  74.5, 105. , 135.5, 166. , 196. , 227.5, 258. , 288.5,\n",
+       "       319. , 349.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>month</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-d358abd4-080e-402b-9dbe-f0c54d6a9fda' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d358abd4-080e-402b-9dbe-f0c54d6a9fda' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77950fed-b042-453e-835b-972005e68cb1' class='xr-var-data-in' type='checkbox'><label for='data-77950fed-b042-453e-835b-972005e68cb1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Month index (1-12)</dd></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-16T12:00:00 ... 2000-12-...</div><input id='attrs-34fa9aff-5a8e-4d39-a17f-98990a4e56c9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34fa9aff-5a8e-4d39-a17f-98990a4e56c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2b3ee9d-a8e8-4730-87a5-f3b1dff41d0c' class='xr-var-data-in' type='checkbox'><label for='data-a2b3ee9d-a8e8-4730-87a5-f3b1dff41d0c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>absolute time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-16T12:00:00.000000000&#x27;, &#x27;2000-02-15T00:00:00.000000000&#x27;,\n",
        "       &#x27;2000-03-15T12:00:00.000000000&#x27;, &#x27;2000-04-15T00:00:00.000000000&#x27;,\n",
        "       &#x27;2000-05-15T12:00:00.000000000&#x27;, &#x27;2000-06-15T00:00:00.000000000&#x27;,\n",
        "       &#x27;2000-07-15T00:00:00.000000000&#x27;, &#x27;2000-08-15T12:00:00.000000000&#x27;,\n",
        "       &#x27;2000-09-15T00:00:00.000000000&#x27;, &#x27;2000-10-15T12:00:00.000000000&#x27;,\n",
        "       &#x27;2000-11-15T00:00:00.000000000&#x27;, &#x27;2000-12-15T12:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>bry_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.5 45.0 74.5 ... 319.0 349.5</div><input id='attrs-85d9b705-c394-4c9e-a24b-f7b2ab835024' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-85d9b705-c394-4c9e-a24b-f7b2ab835024' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d3ee8ec-3b21-4000-8f42-bd0d7a2d42dc' class='xr-var-data-in' type='checkbox'><label for='data-3d3ee8ec-3b21-4000-8f42-bd0d7a2d42dc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cycle_length :</span></dt><dd>365.25</dd><dt><span>long_name :</span></dt><dd>relative time: days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([ 15.5,  45. ,  74.5, 105. , 135.5, 166. , 196. , 227.5, 258. , 288.5,\n",
-       "       319. , 349.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-77c14ce0-a4be-4004-8c0f-792b7bf59fee' class='xr-section-summary-in' type='checkbox'  ><label for='section-77c14ce0-a4be-4004-8c0f-792b7bf59fee' class='xr-section-summary' >Data variables: <span>(96)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>PO4_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-18eb489a-cb12-4e5d-a498-9ed0af4eff3e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-18eb489a-cb12-4e5d-a498-9ed0af4eff3e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-545a937d-be47-4f69-b45f-4221333fd502' class='xr-var-data-in' type='checkbox'><label for='data-545a937d-be47-4f69-b45f-4221333fd502' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved inorganic phosphate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ce1628b3-2b10-4cec-af2c-42b7b55adde6' class='xr-section-summary-in' type='checkbox' /><label for='section-ce1628b3-2b10-4cec-af2c-42b7b55adde6' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(96)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>PO4_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-3f499c6c-ab19-4d0c-b794-4bf1d08b7fdf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3f499c6c-ab19-4d0c-b794-4bf1d08b7fdf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8dcf80de-a1dd-4ba5-8f00-eec070220a22' class='xr-var-data-in' type='checkbox'><label for='data-8dcf80de-a1dd-4ba5-8f00-eec070220a22' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved inorganic phosphate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>WOA18</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/WOA18/phos/woa18_all_p*_01.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -11721,17 +14267,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -11749,11 +14295,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -11761,11 +14313,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -11787,13 +14345,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NO3_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-64426604-e02e-42d5-84e7-908e4d4ab80d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-64426604-e02e-42d5-84e7-908e4d4ab80d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1163ed60-5dad-47bb-9484-38ed0dd306e9' class='xr-var-data-in' type='checkbox'><label for='data-1163ed60-5dad-47bb-9484-38ed0dd306e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved inorganic nitrate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NO3_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-c81052b6-46d0-4452-9319-b5979c32ae88' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c81052b6-46d0-4452-9319-b5979c32ae88' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9560e8d6-bff7-414c-9d8e-7cea8f988c69' class='xr-var-data-in' type='checkbox'><label for='data-9560e8d6-bff7-414c-9d8e-7cea8f988c69' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved inorganic nitrate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>WOA18</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/WOA18/nitr/woa18_all_n*_01.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -11809,17 +14367,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -11837,11 +14395,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -11849,11 +14413,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -11875,13 +14445,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SiO3_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-6187012c-bd0d-4eae-b892-628cf741a50e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6187012c-bd0d-4eae-b892-628cf741a50e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94e3c6c0-9f2a-4db7-9adf-6af8e49aca7c' class='xr-var-data-in' type='checkbox'><label for='data-94e3c6c0-9f2a-4db7-9adf-6af8e49aca7c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved inorganic silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SiO3_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-b3ebb354-e028-4050-b324-f98727cfa43d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b3ebb354-e028-4050-b324-f98727cfa43d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9996de8f-fbdf-4726-a232-f061c56c0be4' class='xr-var-data-in' type='checkbox'><label for='data-9996de8f-fbdf-4726-a232-f061c56c0be4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved inorganic silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>WOA18</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/WOA18/silc/woa18_all_i*_01.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -11897,17 +14467,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -11925,11 +14495,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -11937,11 +14513,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -11963,13 +14545,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Fe_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-43c05a54-cc37-4fa7-be9d-b742ea0ccced' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-43c05a54-cc37-4fa7-be9d-b742ea0ccced' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d0c5df3-aac9-443d-b834-7ebec16b9130' class='xr-var-data-in' type='checkbox'><label for='data-2d0c5df3-aac9-443d-b834-7ebec16b9130' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved inorganic iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Fe_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-43da2288-e97b-47e5-b026-51bb1f96d8d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-43da2288-e97b-47e5-b026-51bb1f96d8d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2576e379-b502-4988-9a02-bec16303665f' class='xr-var-data-in' type='checkbox'><label for='data-2576e379-b502-4988-9a02-bec16303665f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved inorganic iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Monthly reconstructions based on in situ data</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/MappedIron/Monthly_dFe.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -11985,17 +14567,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -12013,11 +14595,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -12025,11 +14613,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -12051,13 +14645,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>O2_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-5449ec26-917e-4d4c-816f-7c54861ade24' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5449ec26-917e-4d4c-816f-7c54861ade24' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a444146-6648-46b2-8c26-d11c2170483f' class='xr-var-data-in' type='checkbox'><label for='data-3a444146-6648-46b2-8c26-d11c2170483f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved oxygen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>O2_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-73d51efd-0e15-4615-8e80-39c38a5a1a5e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-73d51efd-0e15-4615-8e80-39c38a5a1a5e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50e8b9bf-b244-417e-83e2-3bf95dcafd55' class='xr-var-data-in' type='checkbox'><label for='data-50e8b9bf-b244-417e-83e2-3bf95dcafd55' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved oxygen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>WOA18</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/WOA18/oxyg/woa18_all_o*_01.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -12073,17 +14667,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -12101,11 +14695,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -12113,11 +14713,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -12139,13 +14745,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIC_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-e8d642ff-4ef9-458d-9f22-d274fbd5af54' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e8d642ff-4ef9-458d-9f22-d274fbd5af54' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25c7f1ba-6ec7-40be-8e94-81ab5fed04aa' class='xr-var-data-in' type='checkbox'><label for='data-25c7f1ba-6ec7-40be-8e94-81ab5fed04aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved inorganic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIC_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-f16c7a7a-bd1e-4ba1-8def-aeac690aeab5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f16c7a7a-bd1e-4ba1-8def-aeac690aeab5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88df0aff-7649-424a-aba6-6e1b59ad8a2b' class='xr-var-data-in' type='checkbox'><label for='data-88df0aff-7649-424a-aba6-6e1b59ad8a2b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved inorganic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>GLODAPv2</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/GLODAP/GLODAPv2.2016b_MappedClimatologies/GLODAPv2.2016b.TCO2.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -12161,17 +14767,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -12189,11 +14795,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -12201,11 +14813,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
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@@ -12227,13 +14845,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
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-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALK_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-edfa4294-8069-471c-8c36-876cafa782ac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-edfa4294-8069-471c-8c36-876cafa782ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6dec9716-1d6b-428a-9f8c-95c0ab24f5fb' class='xr-var-data-in' type='checkbox'><label for='data-6dec9716-1d6b-428a-9f8c-95c0ab24f5fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary alkalinity</dd><dt><span>units :</span></dt><dd>meq/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALK_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-3fa6b788-7f2e-4141-89f3-cb2b6d708560' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3fa6b788-7f2e-4141-89f3-cb2b6d708560' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ba26d55-5efd-42b6-a9f3-ccb18d94ddfb' class='xr-var-data-in' type='checkbox'><label for='data-9ba26d55-5efd-42b6-a9f3-ccb18d94ddfb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary alkalinity</dd><dt><span>units :</span></dt><dd>meq/m^3</dd><dt><span>source :</span></dt><dd>GLODAPv2</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/GLODAP/GLODAPv2.2016b_MappedClimatologies/GLODAPv2.2016b.TAlk.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -12249,17 +14867,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -12277,11 +14895,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -12289,11 +14913,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
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@@ -12315,13 +14945,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NH4_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-c9efd2cb-7609-4533-bb89-d045869bb927' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c9efd2cb-7609-4533-bb89-d045869bb927' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40c2c75c-2bcb-4c7b-b87f-9c299b03324a' class='xr-var-data-in' type='checkbox'><label for='data-40c2c75c-2bcb-4c7b-b87f-9c299b03324a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved ammonia</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NH4_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-626f361c-e553-4bba-9c1e-26173873b108' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-626f361c-e553-4bba-9c1e-26173873b108' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7131aa1c-57fd-4bdc-b71a-34f156a934ac' class='xr-var-data-in' type='checkbox'><label for='data-7131aa1c-57fd-4bdc-b71a-34f156a934ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved ammonia</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>based on 3D CESM climatology regridded at low-resolution by Abigale Wyatt</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM-climatology_lowres_regridded.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -12337,17 +14967,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -12365,11 +14995,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
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        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
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        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
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        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
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@@ -12403,13 +15045,13 @@
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-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
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+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOC_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-cfbb63af-1bb6-48c1-b035-ebebca08818f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cfbb63af-1bb6-48c1-b035-ebebca08818f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce37fffe-e670-414e-9ba8-c3192a0b9915' class='xr-var-data-in' type='checkbox'><label for='data-ce37fffe-e670-414e-9ba8-c3192a0b9915' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved organic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOC_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-0caf6efa-938d-4479-88f8-8ed97f9124d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0caf6efa-938d-4479-88f8-8ed97f9124d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75ba3df8-1d46-4c01-a8d8-9666f6577496' class='xr-var-data-in' type='checkbox'><label for='data-75ba3df8-1d46-4c01-a8d8-9666f6577496' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved organic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>based on 3D CESM climatology regridded at low-resolution by Abigale Wyatt</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM-climatology_lowres_regridded.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -12425,17 +15067,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -12453,11 +15095,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -12465,11 +15113,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -12491,13 +15145,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DON_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-a46a972b-e02a-4070-a95c-1c9e1b0dc73a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a46a972b-e02a-4070-a95c-1c9e1b0dc73a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55e159da-35ca-40bb-bf65-7cb177cb02a8' class='xr-var-data-in' type='checkbox'><label for='data-55e159da-35ca-40bb-bf65-7cb177cb02a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DON_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-4deaa145-84fa-4289-be1b-c6cb5fd99d24' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4deaa145-84fa-4289-be1b-c6cb5fd99d24' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a326125-2db8-46a1-85e7-bb731174d36b' class='xr-var-data-in' type='checkbox'><label for='data-1a326125-2db8-46a1-85e7-bb731174d36b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>based on 3D CESM climatology regridded at low-resolution by Abigale Wyatt</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM-climatology_lowres_regridded.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -12513,17 +15167,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -12541,11 +15195,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -12553,11 +15213,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -12579,13 +15245,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOP_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-f4770cb4-25bf-4f06-9652-2797737d5bfc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4770cb4-25bf-4f06-9652-2797737d5bfc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66628d33-9d27-4886-b084-2aa57b423546' class='xr-var-data-in' type='checkbox'><label for='data-66628d33-9d27-4886-b084-2aa57b423546' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOP_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-c6707a13-6b94-4596-a556-61fd2a2f2ea2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c6707a13-6b94-4596-a556-61fd2a2f2ea2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8efe2220-ad5a-4d4e-b2da-5e5b36fdfc56' class='xr-var-data-in' type='checkbox'><label for='data-8efe2220-ad5a-4d4e-b2da-5e5b36fdfc56' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>based on 3D CESM climatology regridded at low-resolution by Abigale Wyatt</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM-climatology_lowres_regridded.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -12601,17 +15267,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -12629,11 +15295,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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        "\n",
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@@ -12641,11 +15313,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
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@@ -12667,13 +15345,13 @@
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOPr_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-af132760-f8d8-490c-84e8-746a39c66cf3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-af132760-f8d8-490c-84e8-746a39c66cf3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1afdab7-3748-4ab5-9e34-ac5a8553d57f' class='xr-var-data-in' type='checkbox'><label for='data-b1afdab7-3748-4ab5-9e34-ac5a8553d57f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary refractory dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOPr_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-7cd1b1dd-e4d1-45f0-b5c1-6d11299ecbf3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7cd1b1dd-e4d1-45f0-b5c1-6d11299ecbf3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc2e2737-b562-4eaa-a0d0-8202c715c9cc' class='xr-var-data-in' type='checkbox'><label for='data-fc2e2737-b562-4eaa-a0d0-8202c715c9cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary refractory dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>based on 3D CESM climatology regridded at low-resolution by Abigale Wyatt</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM-climatology_lowres_regridded.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -12689,17 +15367,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -12717,11 +15395,17 @@
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        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
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@@ -12729,11 +15413,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
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        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
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-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DONr_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-5a49672c-7f04-4bfc-a76d-38b21b8e8216' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5a49672c-7f04-4bfc-a76d-38b21b8e8216' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b64d1282-6614-489b-9d40-e2edde3113c4' class='xr-var-data-in' type='checkbox'><label for='data-b64d1282-6614-489b-9d40-e2edde3113c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary refractory dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DONr_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-ba890ae1-4b68-4330-8768-5f4817e9b439' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba890ae1-4b68-4330-8768-5f4817e9b439' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-98529ae3-4bfc-44de-bb1f-e021c312f780' class='xr-var-data-in' type='checkbox'><label for='data-98529ae3-4bfc-44de-bb1f-e021c312f780' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary refractory dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>based on 3D CESM climatology regridded at low-resolution by Abigale Wyatt</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM-climatology_lowres_regridded.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -12777,17 +15467,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -12805,11 +15495,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
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        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
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        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
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@@ -12817,11 +15513,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
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        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
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        "  <!-- Text -->\n",
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+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
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        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOCr_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-310897a8-ec5c-4bfe-8bf4-ea329b556448' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-310897a8-ec5c-4bfe-8bf4-ea329b556448' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-373e7f5b-0ddc-4c99-ab14-c55380c7b391' class='xr-var-data-in' type='checkbox'><label for='data-373e7f5b-0ddc-4c99-ab14-c55380c7b391' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary refractory dissolved organic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOCr_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-d1ba3252-8454-4cd7-ad29-041489253dbc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d1ba3252-8454-4cd7-ad29-041489253dbc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ebe61f38-4840-48c4-8bd7-3a43bc228041' class='xr-var-data-in' type='checkbox'><label for='data-ebe61f38-4840-48c4-8bd7-3a43bc228041' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary refractory dissolved organic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>based on 3D CESM climatology regridded at low-resolution by Abigale Wyatt</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM-climatology_lowres_regridded.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -12865,17 +15567,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -12893,11 +15595,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -12905,11 +15613,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -12931,13 +15645,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spChl_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-ca7b71c5-af10-498a-86ca-59bd4fb3cd58' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca7b71c5-af10-498a-86ca-59bd4fb3cd58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a61bacb-1e2d-490a-ba07-274ae7934352' class='xr-var-data-in' type='checkbox'><label for='data-4a61bacb-1e2d-490a-ba07-274ae7934352' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary small phytoplankton chlorophyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spChl_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-9ca50473-04e3-4152-a2e0-fc42b4281cf7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9ca50473-04e3-4152-a2e0-fc42b4281cf7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcad6196-cc4f-4cf7-9883-af31d1c80081' class='xr-var-data-in' type='checkbox'><label for='data-bcad6196-cc4f-4cf7-9883-af31d1c80081' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary small phytoplankton chlorophyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -12953,17 +15667,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -12981,11 +15695,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -12993,11 +15713,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -13019,13 +15745,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spC_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-8d4c7938-b479-4fa2-a6a4-261a8aa8c03d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d4c7938-b479-4fa2-a6a4-261a8aa8c03d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e1174db-de15-424c-bd2f-0af11bb5f00f' class='xr-var-data-in' type='checkbox'><label for='data-5e1174db-de15-424c-bd2f-0af11bb5f00f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary small phytoplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spC_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-90430ed5-fb79-4cad-be11-814ae4d6f74e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-90430ed5-fb79-4cad-be11-814ae4d6f74e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20dd5557-5f90-4caa-a370-80fc1ec59191' class='xr-var-data-in' type='checkbox'><label for='data-20dd5557-5f90-4caa-a370-80fc1ec59191' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary small phytoplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -13041,17 +15767,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -13069,11 +15795,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -13081,11 +15813,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -13107,13 +15845,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spP_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-3f7b376a-bd48-4d08-a2f1-c7ac53cf5776' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3f7b376a-bd48-4d08-a2f1-c7ac53cf5776' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0bfc8782-dc27-415c-8214-12586cb5086b' class='xr-var-data-in' type='checkbox'><label for='data-0bfc8782-dc27-415c-8214-12586cb5086b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary small phytoplankton phosphorous</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spP_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-fb834164-5588-4c0b-94fe-9963cf919c61' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fb834164-5588-4c0b-94fe-9963cf919c61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a74f6f2-f380-494d-80fe-b09bb23f00a8' class='xr-var-data-in' type='checkbox'><label for='data-0a74f6f2-f380-494d-80fe-b09bb23f00a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary small phytoplankton phosphorous</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -13129,17 +15867,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -13157,11 +15895,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -13169,11 +15913,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -13195,13 +15945,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spFe_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-23619401-1095-43bc-b96d-7c08ed591f64' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-23619401-1095-43bc-b96d-7c08ed591f64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bfc06386-78b7-40ed-ac91-1a41e786ecc6' class='xr-var-data-in' type='checkbox'><label for='data-bfc06386-78b7-40ed-ac91-1a41e786ecc6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary small phytoplankton iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spFe_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-3de5e076-bc8a-46af-9f52-5930959ad9d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3de5e076-bc8a-46af-9f52-5930959ad9d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83ddcde6-74c2-48ee-8bb1-ab74f8350af7' class='xr-var-data-in' type='checkbox'><label for='data-83ddcde6-74c2-48ee-8bb1-ab74f8350af7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary small phytoplankton iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -13217,17 +15967,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -13245,11 +15995,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -13257,11 +16013,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -13283,13 +16045,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatChl_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-6c4cdc61-1377-4065-ae99-a3dbc2eabc42' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c4cdc61-1377-4065-ae99-a3dbc2eabc42' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ffc55444-fd9b-4ec8-83c0-79994083e1ab' class='xr-var-data-in' type='checkbox'><label for='data-ffc55444-fd9b-4ec8-83c0-79994083e1ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary diatom chloropyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatChl_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-6e4c4f07-d23a-47b2-912d-f53eb622f9ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e4c4f07-d23a-47b2-912d-f53eb622f9ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1cf550fe-a41c-4852-bcb9-916648a110e6' class='xr-var-data-in' type='checkbox'><label for='data-1cf550fe-a41c-4852-bcb9-916648a110e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary diatom chloropyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -13305,17 +16067,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -13333,11 +16095,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -13345,11 +16113,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
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        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
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        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -13371,13 +16145,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
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-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
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-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatC_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-857b706a-68e9-42a6-9927-b390386b2203' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-857b706a-68e9-42a6-9927-b390386b2203' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a802ec7-dd48-44ba-b218-036c7d70bed9' class='xr-var-data-in' type='checkbox'><label for='data-0a802ec7-dd48-44ba-b218-036c7d70bed9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary diatom carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatC_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-a5e67dbe-d1bc-4a5c-8556-45fdb0f7b2e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a5e67dbe-d1bc-4a5c-8556-45fdb0f7b2e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db3a708c-bd84-475c-9b9b-1ce65032c7ec' class='xr-var-data-in' type='checkbox'><label for='data-db3a708c-bd84-475c-9b9b-1ce65032c7ec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary diatom carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -13393,17 +16167,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -13421,11 +16195,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -13433,11 +16213,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -13459,13 +16245,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
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-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatP_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-3d9730bd-3dc6-4c7a-8dbb-037a0cf6a052' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3d9730bd-3dc6-4c7a-8dbb-037a0cf6a052' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71ab45a3-3977-4712-bd9a-4349a683f1da' class='xr-var-data-in' type='checkbox'><label for='data-71ab45a3-3977-4712-bd9a-4349a683f1da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary diatom phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatP_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-56afdd44-ecfa-4b74-ab3b-9097c6abd18a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-56afdd44-ecfa-4b74-ab3b-9097c6abd18a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b1f05ea-2291-485d-a5a1-c2d95cc95681' class='xr-var-data-in' type='checkbox'><label for='data-9b1f05ea-2291-485d-a5a1-c2d95cc95681' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary diatom phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -13481,17 +16267,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -13509,11 +16295,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
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        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
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@@ -13521,11 +16313,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
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        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
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        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
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        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
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@@ -13547,13 +16345,13 @@
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        "\n",
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-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
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+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatFe_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-78ff2d0a-f31c-447b-8058-025d7b189d15' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-78ff2d0a-f31c-447b-8058-025d7b189d15' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f1b6f66-d22a-45e7-b92a-97bec07acc1a' class='xr-var-data-in' type='checkbox'><label for='data-6f1b6f66-d22a-45e7-b92a-97bec07acc1a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary diatom iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatFe_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-47c11679-a285-4051-bd5f-92145dcd2512' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-47c11679-a285-4051-bd5f-92145dcd2512' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4341d470-7bde-48b6-b492-b086021c01f0' class='xr-var-data-in' type='checkbox'><label for='data-4341d470-7bde-48b6-b492-b086021c01f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary diatom iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -13569,17 +16367,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -13597,11 +16395,17 @@
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        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
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        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
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        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatSi_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-6c3cc08d-0d6c-4c77-8f08-526e60cfbe7d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c3cc08d-0d6c-4c77-8f08-526e60cfbe7d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd663864-3629-49ca-bc2c-86dfd6fd3ff9' class='xr-var-data-in' type='checkbox'><label for='data-dd663864-3629-49ca-bc2c-86dfd6fd3ff9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary diatom silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatSi_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-cd5215ac-bee6-4c8a-8037-b50c7da49ee9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd5215ac-bee6-4c8a-8037-b50c7da49ee9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59c8608e-b090-4fc3-a3c0-465a1813fe1f' class='xr-var-data-in' type='checkbox'><label for='data-59c8608e-b090-4fc3-a3c0-465a1813fe1f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary diatom silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -13657,17 +16467,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -13685,11 +16495,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -13697,11 +16513,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -13723,13 +16545,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazChl_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-efdb7d8a-f678-4b0d-bc6c-dcdba2896e56' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-efdb7d8a-f678-4b0d-bc6c-dcdba2896e56' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4d41564-43ab-4cd1-9783-83140d54b4ec' class='xr-var-data-in' type='checkbox'><label for='data-a4d41564-43ab-4cd1-9783-83140d54b4ec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary diazotroph chloropyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazChl_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-dbfa95ca-2f38-4953-b705-dc6ee322a15c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dbfa95ca-2f38-4953-b705-dc6ee322a15c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1534f3d1-e500-4436-9660-7c35950910cb' class='xr-var-data-in' type='checkbox'><label for='data-1534f3d1-e500-4436-9660-7c35950910cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary diazotroph chloropyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -13745,17 +16567,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -13773,11 +16595,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -13785,11 +16613,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -13811,13 +16645,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
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-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazC_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-7e4eda0b-974a-47be-b661-edb484586ce0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7e4eda0b-974a-47be-b661-edb484586ce0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b22cc726-7171-480e-ba98-5a7832ca486b' class='xr-var-data-in' type='checkbox'><label for='data-b22cc726-7171-480e-ba98-5a7832ca486b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary diazotroph carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazC_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-855bb832-56fa-4d07-9ef7-dbd37ecf03a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-855bb832-56fa-4d07-9ef7-dbd37ecf03a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c34779bc-fb8c-49d4-8f3d-03e08b183dbf' class='xr-var-data-in' type='checkbox'><label for='data-c34779bc-fb8c-49d4-8f3d-03e08b183dbf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary diazotroph carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -13833,17 +16667,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -13861,11 +16695,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
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        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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@@ -13873,11 +16713,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -13899,13 +16745,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazP_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-04e13bdc-2d11-4452-92d7-9b0e447bb3f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-04e13bdc-2d11-4452-92d7-9b0e447bb3f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ed0bbf3-7d0c-471c-808d-57f931ed3f5d' class='xr-var-data-in' type='checkbox'><label for='data-0ed0bbf3-7d0c-471c-808d-57f931ed3f5d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary diazotroph phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazP_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-edc32c07-3b32-4823-bfc5-1fb6674e0905' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-edc32c07-3b32-4823-bfc5-1fb6674e0905' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52956416-b87e-496b-89e5-58827e1f9b7d' class='xr-var-data-in' type='checkbox'><label for='data-52956416-b87e-496b-89e5-58827e1f9b7d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary diazotroph phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -13921,17 +16767,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -13949,11 +16795,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -13961,11 +16813,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
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        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -13987,13 +16845,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazFe_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-06dc3b42-ab40-479e-9e41-92c6d73de049' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06dc3b42-ab40-479e-9e41-92c6d73de049' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc33f7ec-7136-4c96-a111-5497e2e9838d' class='xr-var-data-in' type='checkbox'><label for='data-fc33f7ec-7136-4c96-a111-5497e2e9838d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary diazotroph iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazFe_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-26fa8395-6de2-41e0-9c8c-a52d20f3c40c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-26fa8395-6de2-41e0-9c8c-a52d20f3c40c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a094ba19-d158-45d9-9d03-ef6ef69f4ba4' class='xr-var-data-in' type='checkbox'><label for='data-a094ba19-d158-45d9-9d03-ef6ef69f4ba4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary diazotroph iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -14009,17 +16867,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -14037,11 +16895,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -14049,11 +16913,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -14075,13 +16945,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spCaCO3_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-f5f6c990-7be9-47f6-b78b-025e670c88f5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f5f6c990-7be9-47f6-b78b-025e670c88f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f13e4710-7d15-40ee-a291-7474ea88bd58' class='xr-var-data-in' type='checkbox'><label for='data-f13e4710-7d15-40ee-a291-7474ea88bd58' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary small phytoplankton CaCO3</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spCaCO3_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-9a838c5c-6498-4c0e-8c91-a0e6bdcc096f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9a838c5c-6498-4c0e-8c91-a0e6bdcc096f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f76351f-0b6b-44be-997f-80dcfc9bf113' class='xr-var-data-in' type='checkbox'><label for='data-9f76351f-0b6b-44be-997f-80dcfc9bf113' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary small phytoplankton CaCO3</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -14097,17 +16967,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -14125,11 +16995,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -14137,11 +17013,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -14163,13 +17045,13 @@
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zooC_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-d62fbe0d-d5a7-44ac-a9e0-c0ac9c280120' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d62fbe0d-d5a7-44ac-a9e0-c0ac9c280120' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9b0bac7-da28-4c50-9d63-e413a3b3d72e' class='xr-var-data-in' type='checkbox'><label for='data-e9b0bac7-da28-4c50-9d63-e413a3b3d72e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary zooplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zooC_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-c30625e1-d7f2-4187-9bbf-1887985d289e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c30625e1-d7f2-4187-9bbf-1887985d289e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ad8a850-a753-4608-a3ea-2d016bc6a82e' class='xr-var-data-in' type='checkbox'><label for='data-6ad8a850-a753-4608-a3ea-2d016bc6a82e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary zooplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -14185,17 +17067,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -14213,11 +17095,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -14225,11 +17113,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
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        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
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        "\n",
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@@ -14251,13 +17145,13 @@
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-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Lig_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-561dd0a4-f071-4765-ba99-46fedd1495cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-561dd0a4-f071-4765-ba99-46fedd1495cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac31d7cd-9e11-4207-957d-d440a4c89a9f' class='xr-var-data-in' type='checkbox'><label for='data-ac31d7cd-9e11-4207-957d-d440a4c89a9f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary iron binding ligand</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Lig_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-7559c94a-55be-4976-aed3-45296899799d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7559c94a-55be-4976-aed3-45296899799d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b18d990-6171-47ab-a8fa-fa2f2c6bf289' class='xr-var-data-in' type='checkbox'><label for='data-2b18d990-6171-47ab-a8fa-fa2f2c6bf289' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary iron binding ligand</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Monthly reconstructions based on in situ data</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/MappedIron/Monthly_dFe.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -14273,17 +17167,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 51 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -14301,11 +17195,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
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        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
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        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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@@ -14313,11 +17213,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
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+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIC_ALT_CO2_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-d2ecd846-353f-44b4-9779-964e3e99649f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2ecd846-353f-44b4-9779-964e3e99649f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a263c3c7-b8f4-4330-ac04-4d3363a8068a' class='xr-var-data-in' type='checkbox'><label for='data-a263c3c7-b8f4-4330-ac04-4d3363a8068a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved inorganic carbon, alternative CO2</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIC_ALT_CO2_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-1b53b850-76c4-426f-b543-490577586afc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b53b850-76c4-426f-b543-490577586afc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b909c2d-ef6e-4805-bdca-92d7eff11c16' class='xr-var-data-in' type='checkbox'><label for='data-8b909c2d-ef6e-4805-bdca-92d7eff11c16' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved inorganic carbon, alternative CO2</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>GLODAPv2</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/GLODAP/GLODAPv2.2016b_MappedClimatologies/GLODAPv2.2016b.TCO2.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -14361,17 +17267,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 51 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -14389,11 +17295,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -14401,11 +17313,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -14427,13 +17345,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALK_ALT_CO2_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-46f01372-24c7-4e3c-b882-cdafcf9d113d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-46f01372-24c7-4e3c-b882-cdafcf9d113d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5d543b7-b17b-4794-a5d4-a412b24bc99c' class='xr-var-data-in' type='checkbox'><label for='data-f5d543b7-b17b-4794-a5d4-a412b24bc99c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary alkalinity, alternative CO2</dd><dt><span>units :</span></dt><dd>meq/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALK_ALT_CO2_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-3666a2d8-9e12-4e96-8f86-b76d082a28ca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3666a2d8-9e12-4e96-8f86-b76d082a28ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be89b592-9de6-47b0-8120-01ec1262acfb' class='xr-var-data-in' type='checkbox'><label for='data-be89b592-9de6-47b0-8120-01ec1262acfb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary alkalinity, alternative CO2</dd><dt><span>units :</span></dt><dd>meq/m^3</dd><dt><span>source :</span></dt><dd>GLODAPv2</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/GLODAP/GLODAPv2.2016b_MappedClimatologies/GLODAPv2.2016b.TAlk.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -14449,17 +17367,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 51 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -14477,11 +17395,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -14489,11 +17413,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -14515,13 +17445,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PO4_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-30338416-41b6-4901-b9c1-b71992ab8e22' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-30338416-41b6-4901-b9c1-b71992ab8e22' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3812b7c1-38ff-4be7-a8d1-765ebc81608a' class='xr-var-data-in' type='checkbox'><label for='data-3812b7c1-38ff-4be7-a8d1-765ebc81608a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved inorganic phosphate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PO4_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-7ef4d6bf-e5b4-426a-9228-336102bcc37c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7ef4d6bf-e5b4-426a-9228-336102bcc37c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb2b2def-c4fe-4d60-a160-6bb96c3df8f6' class='xr-var-data-in' type='checkbox'><label for='data-fb2b2def-c4fe-4d60-a160-6bb96c3df8f6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved inorganic phosphate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>WOA18</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/WOA18/phos/woa18_all_p*_01.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -14537,17 +17467,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -14565,11 +17495,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -14577,11 +17513,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
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+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
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        "\n",
        "  <!-- Vertical lines -->\n",
@@ -14603,13 +17545,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NO3_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-f802d63a-3a10-40e4-b013-c41b9181c268' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f802d63a-3a10-40e4-b013-c41b9181c268' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d4800541-15c6-4361-80fc-92c163166b68' class='xr-var-data-in' type='checkbox'><label for='data-d4800541-15c6-4361-80fc-92c163166b68' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved inorganic nitrate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NO3_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-f8e7f09e-3b30-45ba-9e0c-891cae64f6a5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f8e7f09e-3b30-45ba-9e0c-891cae64f6a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d38db4a2-dea2-4a51-a2e9-88f1a237a03e' class='xr-var-data-in' type='checkbox'><label for='data-d38db4a2-dea2-4a51-a2e9-88f1a237a03e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved inorganic nitrate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>WOA18</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/WOA18/nitr/woa18_all_n*_01.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -14625,17 +17567,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -14653,11 +17595,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -14665,11 +17613,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -14691,13 +17645,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SiO3_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-f167656d-30a9-4f5d-a67e-72b6f7b4874f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f167656d-30a9-4f5d-a67e-72b6f7b4874f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c808541d-d1b2-48f6-bc8e-704dc0deceda' class='xr-var-data-in' type='checkbox'><label for='data-c808541d-d1b2-48f6-bc8e-704dc0deceda' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved inorganic silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SiO3_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-732278ee-433c-46b4-a9c6-57bccc14cfc5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-732278ee-433c-46b4-a9c6-57bccc14cfc5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53874b36-d3ff-43ff-bdba-8e1709b61fe2' class='xr-var-data-in' type='checkbox'><label for='data-53874b36-d3ff-43ff-bdba-8e1709b61fe2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved inorganic silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>WOA18</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/WOA18/silc/woa18_all_i*_01.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -14713,17 +17667,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -14741,11 +17695,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -14753,11 +17713,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -14779,13 +17745,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Fe_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-10e4dab9-78d9-4d03-89af-a7410ba2a075' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-10e4dab9-78d9-4d03-89af-a7410ba2a075' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74d3ffb7-8af5-4b75-b4c1-4d6810fd1eff' class='xr-var-data-in' type='checkbox'><label for='data-74d3ffb7-8af5-4b75-b4c1-4d6810fd1eff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved inorganic iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Fe_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-fa06423b-c89f-4103-80c6-4cc17fed14b9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa06423b-c89f-4103-80c6-4cc17fed14b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1aa975bd-f552-473f-baa4-9ccccab434e1' class='xr-var-data-in' type='checkbox'><label for='data-1aa975bd-f552-473f-baa4-9ccccab434e1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved inorganic iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Monthly reconstructions based on in situ data</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/MappedIron/Monthly_dFe.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -14801,17 +17767,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -14829,11 +17795,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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        "\n",
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@@ -14841,11 +17813,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -14867,13 +17845,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>O2_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-81032387-0612-4b3e-bf93-8fa2b4e538c6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-81032387-0612-4b3e-bf93-8fa2b4e538c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3bb09779-24ea-4df3-bd2d-05b432be0923' class='xr-var-data-in' type='checkbox'><label for='data-3bb09779-24ea-4df3-bd2d-05b432be0923' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved oxygen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>O2_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-d2af8127-87a3-4f26-8db5-726e562bf224' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2af8127-87a3-4f26-8db5-726e562bf224' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3963a09c-4f1a-48d9-b451-4a746a700175' class='xr-var-data-in' type='checkbox'><label for='data-3963a09c-4f1a-48d9-b451-4a746a700175' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved oxygen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>WOA18</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/WOA18/oxyg/woa18_all_o*_01.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -14889,17 +17867,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -14917,11 +17895,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -14929,11 +17913,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -14955,13 +17945,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIC_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-31634754-aa37-4db0-9fec-361dae4d1ed7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-31634754-aa37-4db0-9fec-361dae4d1ed7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-834af28c-3ce1-4269-85ef-270bd14d994e' class='xr-var-data-in' type='checkbox'><label for='data-834af28c-3ce1-4269-85ef-270bd14d994e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved inorganic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIC_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-fcdb32de-edaf-47ef-a7df-331485bce082' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fcdb32de-edaf-47ef-a7df-331485bce082' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f671299-d402-46c2-85a2-4943699f9dca' class='xr-var-data-in' type='checkbox'><label for='data-7f671299-d402-46c2-85a2-4943699f9dca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved inorganic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>GLODAPv2</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/GLODAP/GLODAPv2.2016b_MappedClimatologies/GLODAPv2.2016b.TCO2.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -14977,17 +17967,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -15005,11 +17995,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -15017,11 +18013,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -15043,13 +18045,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALK_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-7c8c4804-49f6-4ef9-a976-41e365dc5210' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c8c4804-49f6-4ef9-a976-41e365dc5210' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c24f5837-f3c8-48fd-b8e2-210032e74688' class='xr-var-data-in' type='checkbox'><label for='data-c24f5837-f3c8-48fd-b8e2-210032e74688' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary alkalinity</dd><dt><span>units :</span></dt><dd>meq/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALK_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-5d141ff9-2439-48d4-a4bb-7230aa2d03fc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5d141ff9-2439-48d4-a4bb-7230aa2d03fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed7d954d-3911-4bda-b81a-7eeea59e279f' class='xr-var-data-in' type='checkbox'><label for='data-ed7d954d-3911-4bda-b81a-7eeea59e279f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary alkalinity</dd><dt><span>units :</span></dt><dd>meq/m^3</dd><dt><span>source :</span></dt><dd>GLODAPv2</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/GLODAP/GLODAPv2.2016b_MappedClimatologies/GLODAPv2.2016b.TAlk.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -15065,17 +18067,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -15093,11 +18095,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -15105,11 +18113,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -15131,13 +18145,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NH4_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-e6d65bdd-348b-444d-9f8f-43aaf110a95d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e6d65bdd-348b-444d-9f8f-43aaf110a95d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2eac8a25-695b-4086-a56a-5b4b2e5de6b7' class='xr-var-data-in' type='checkbox'><label for='data-2eac8a25-695b-4086-a56a-5b4b2e5de6b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved ammonia</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NH4_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-94b6f1d4-6718-411d-a92d-699032c61533' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94b6f1d4-6718-411d-a92d-699032c61533' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6bbcfb0-a6f8-4a24-8a63-530f8bea9a6d' class='xr-var-data-in' type='checkbox'><label for='data-e6bbcfb0-a6f8-4a24-8a63-530f8bea9a6d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved ammonia</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>based on 3D CESM climatology regridded at low-resolution by Abigale Wyatt</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM-climatology_lowres_regridded.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -15153,17 +18167,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -15181,11 +18195,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -15193,11 +18213,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
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@@ -15219,13 +18245,13 @@
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
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-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOC_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-a6af868b-1035-4a74-b75d-10c7407d8a75' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a6af868b-1035-4a74-b75d-10c7407d8a75' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81c5a012-8312-4478-b2ce-a6e8ab4e1572' class='xr-var-data-in' type='checkbox'><label for='data-81c5a012-8312-4478-b2ce-a6e8ab4e1572' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved organic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOC_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-556ccd93-f2e2-49fe-af16-d88ad1c48d85' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-556ccd93-f2e2-49fe-af16-d88ad1c48d85' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b97fea99-eb2b-45ef-b0af-d0f0c2fff563' class='xr-var-data-in' type='checkbox'><label for='data-b97fea99-eb2b-45ef-b0af-d0f0c2fff563' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved organic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>based on 3D CESM climatology regridded at low-resolution by Abigale Wyatt</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM-climatology_lowres_regridded.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -15241,17 +18267,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -15269,11 +18295,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
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        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
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        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -15281,11 +18313,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
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        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
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        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
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@@ -15307,13 +18345,13 @@
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        "\n",
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+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DON_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-0046a700-ffa6-4a05-b9c5-d4de4aaa5ce3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0046a700-ffa6-4a05-b9c5-d4de4aaa5ce3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac76ea95-8410-43a6-9ab6-52a641aff3fb' class='xr-var-data-in' type='checkbox'><label for='data-ac76ea95-8410-43a6-9ab6-52a641aff3fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DON_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-3499d8d8-1772-419a-83ac-4e3ed4c4c996' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3499d8d8-1772-419a-83ac-4e3ed4c4c996' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d683f394-d53e-4891-a8c1-ff6171b5d5dd' class='xr-var-data-in' type='checkbox'><label for='data-d683f394-d53e-4891-a8c1-ff6171b5d5dd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>based on 3D CESM climatology regridded at low-resolution by Abigale Wyatt</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM-climatology_lowres_regridded.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -15329,17 +18367,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -15357,11 +18395,17 @@
        "\n",
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        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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        "\n",
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@@ -15369,11 +18413,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
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        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
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        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
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        "\n",
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-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOP_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-8e44bc38-fc29-45bc-951e-70ceb6b1c92e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8e44bc38-fc29-45bc-951e-70ceb6b1c92e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24dd2960-9aa0-4750-a51b-ba3e3ae2a6df' class='xr-var-data-in' type='checkbox'><label for='data-24dd2960-9aa0-4750-a51b-ba3e3ae2a6df' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOP_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-064d6f87-09ec-4ce1-9200-73cd6b403d1c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-064d6f87-09ec-4ce1-9200-73cd6b403d1c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f796d1d8-b911-4c92-8028-4246a1ba12c1' class='xr-var-data-in' type='checkbox'><label for='data-f796d1d8-b911-4c92-8028-4246a1ba12c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>based on 3D CESM climatology regridded at low-resolution by Abigale Wyatt</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM-climatology_lowres_regridded.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -15417,17 +18467,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -15445,11 +18495,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
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        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
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        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOPr_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-1f5c1043-e1eb-402b-8610-5c1cf75ca159' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1f5c1043-e1eb-402b-8610-5c1cf75ca159' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61ac2b84-75a9-4380-a1e8-6cda0e6908d5' class='xr-var-data-in' type='checkbox'><label for='data-61ac2b84-75a9-4380-a1e8-6cda0e6908d5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary refractory dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOPr_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-ccff52b8-ce66-4a56-b0d7-fc7e3858818c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ccff52b8-ce66-4a56-b0d7-fc7e3858818c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e14b682-6b41-4ade-95c2-bc0c90daf935' class='xr-var-data-in' type='checkbox'><label for='data-2e14b682-6b41-4ade-95c2-bc0c90daf935' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary refractory dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>based on 3D CESM climatology regridded at low-resolution by Abigale Wyatt</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM-climatology_lowres_regridded.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -15505,17 +18567,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -15533,11 +18595,17 @@
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        "  <!-- Vertical lines -->\n",
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        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
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@@ -15571,13 +18645,13 @@
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        "\n",
        "  <!-- Text -->\n",
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-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DONr_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-afc82787-62ff-463c-9ab6-773e52189180' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-afc82787-62ff-463c-9ab6-773e52189180' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9355ab54-198b-4db6-bf91-fb03c72404c9' class='xr-var-data-in' type='checkbox'><label for='data-9355ab54-198b-4db6-bf91-fb03c72404c9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary refractory dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DONr_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-105fad90-ab62-4de5-afb8-aaabe9e8506c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-105fad90-ab62-4de5-afb8-aaabe9e8506c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e86b71ee-38c9-4f6d-9c50-fe33aa183c6d' class='xr-var-data-in' type='checkbox'><label for='data-e86b71ee-38c9-4f6d-9c50-fe33aa183c6d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary refractory dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>based on 3D CESM climatology regridded at low-resolution by Abigale Wyatt</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM-climatology_lowres_regridded.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -15593,17 +18667,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -15621,11 +18695,17 @@
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        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
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        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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@@ -15633,11 +18713,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
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        "\n",
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-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOCr_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-ac85ac15-2f4d-4315-800c-b87d0530944e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac85ac15-2f4d-4315-800c-b87d0530944e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-237aa1ea-8508-4650-a366-2d419f89d8d4' class='xr-var-data-in' type='checkbox'><label for='data-237aa1ea-8508-4650-a366-2d419f89d8d4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary refractory dissolved organic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOCr_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-a67e815a-9c32-4fa4-90c5-f691a44bdd0b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a67e815a-9c32-4fa4-90c5-f691a44bdd0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17f5f124-3479-415b-aa8f-69fdd46b1ca2' class='xr-var-data-in' type='checkbox'><label for='data-17f5f124-3479-415b-aa8f-69fdd46b1ca2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary refractory dissolved organic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>based on 3D CESM climatology regridded at low-resolution by Abigale Wyatt</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM-climatology_lowres_regridded.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -15681,17 +18767,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -15709,11 +18795,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
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        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
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@@ -15721,11 +18813,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
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        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
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        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
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        "  <!-- Text -->\n",
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        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spChl_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-7c04b208-d623-488d-90de-34413710b488' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c04b208-d623-488d-90de-34413710b488' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-116c0da5-8965-4c35-80f3-a6b663e773ce' class='xr-var-data-in' type='checkbox'><label for='data-116c0da5-8965-4c35-80f3-a6b663e773ce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary small phytoplankton chlorophyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spChl_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-69959b66-aa4f-4995-a00f-fff075bdceea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-69959b66-aa4f-4995-a00f-fff075bdceea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a00fd66-a930-4c6e-996d-a802e2c0ec99' class='xr-var-data-in' type='checkbox'><label for='data-5a00fd66-a930-4c6e-996d-a802e2c0ec99' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary small phytoplankton chlorophyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -15769,17 +18867,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -15797,11 +18895,17 @@
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        "\n",
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@@ -15809,11 +18913,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -15835,13 +18945,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spC_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-28564223-08fe-4817-bd99-a1b0a7a36f3a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28564223-08fe-4817-bd99-a1b0a7a36f3a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-975b727a-72db-4f03-b32e-ac27081ddef2' class='xr-var-data-in' type='checkbox'><label for='data-975b727a-72db-4f03-b32e-ac27081ddef2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary small phytoplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spC_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-99c7b836-fd08-45c7-8d1e-9a6ea7d00bed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99c7b836-fd08-45c7-8d1e-9a6ea7d00bed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f09cc28-b89f-4a18-a74c-43edfd0d9ddd' class='xr-var-data-in' type='checkbox'><label for='data-3f09cc28-b89f-4a18-a74c-43edfd0d9ddd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary small phytoplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -15857,17 +18967,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -15885,11 +18995,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -15897,11 +19013,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -15923,13 +19045,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spP_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-d80d9022-eceb-4ce8-b0d6-cc9a037e157d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d80d9022-eceb-4ce8-b0d6-cc9a037e157d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92d27042-4eac-40d1-b683-dcd1d27ecae4' class='xr-var-data-in' type='checkbox'><label for='data-92d27042-4eac-40d1-b683-dcd1d27ecae4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary small phytoplankton phosphorous</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spP_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-eca15d9b-cc66-4ff7-a2ac-dd1adc65a608' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eca15d9b-cc66-4ff7-a2ac-dd1adc65a608' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4449b6af-1a24-4162-adc2-1c7cde22e506' class='xr-var-data-in' type='checkbox'><label for='data-4449b6af-1a24-4162-adc2-1c7cde22e506' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary small phytoplankton phosphorous</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -15945,17 +19067,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -15973,11 +19095,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -15985,11 +19113,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -16011,13 +19145,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spFe_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-7677a4df-c761-40c3-9242-1f1bb1747484' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7677a4df-c761-40c3-9242-1f1bb1747484' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a66f1917-dd72-4e82-a955-5fa0cd99d5af' class='xr-var-data-in' type='checkbox'><label for='data-a66f1917-dd72-4e82-a955-5fa0cd99d5af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary small phytoplankton iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spFe_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-3fdd8353-ee92-48c2-ab08-005aa06a97e9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3fdd8353-ee92-48c2-ab08-005aa06a97e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63849d7f-a461-4592-b68f-ea8e069e8ba1' class='xr-var-data-in' type='checkbox'><label for='data-63849d7f-a461-4592-b68f-ea8e069e8ba1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary small phytoplankton iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -16033,17 +19167,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -16061,11 +19195,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -16073,11 +19213,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -16099,13 +19245,13 @@
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatChl_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-b7c06dae-e7eb-4f79-a668-81bcafb6b9f0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b7c06dae-e7eb-4f79-a668-81bcafb6b9f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7d58623-c99f-48dc-a1e6-1687a4369363' class='xr-var-data-in' type='checkbox'><label for='data-b7d58623-c99f-48dc-a1e6-1687a4369363' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary diatom chloropyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatChl_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-b4528048-1c25-48eb-8ac3-f742306c03b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b4528048-1c25-48eb-8ac3-f742306c03b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-afff6027-d976-4650-ac50-0bb6565a6a81' class='xr-var-data-in' type='checkbox'><label for='data-afff6027-d976-4650-ac50-0bb6565a6a81' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary diatom chloropyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -16121,17 +19267,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -16149,11 +19295,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -16161,11 +19313,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
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        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
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@@ -16187,13 +19345,13 @@
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        "\n",
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-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatC_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-527ca359-0743-49fa-bd04-2e391e097f8f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-527ca359-0743-49fa-bd04-2e391e097f8f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97726cd1-4861-4a97-ac03-d5c9a2a9e741' class='xr-var-data-in' type='checkbox'><label for='data-97726cd1-4861-4a97-ac03-d5c9a2a9e741' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary diatom carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatC_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-043ed62b-b403-463b-b36f-43b0ba50779e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-043ed62b-b403-463b-b36f-43b0ba50779e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27826cda-8bda-4136-970f-b0e966a5e9be' class='xr-var-data-in' type='checkbox'><label for='data-27826cda-8bda-4136-970f-b0e966a5e9be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary diatom carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -16209,17 +19367,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -16237,11 +19395,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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        "\n",
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@@ -16249,11 +19413,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
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        "\n",
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@@ -16275,13 +19445,13 @@
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-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
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+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatP_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-14cffa87-8989-40ba-93f9-1beb8b4bf03a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-14cffa87-8989-40ba-93f9-1beb8b4bf03a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21dcb832-ca89-49ce-831f-5fb8530ec75c' class='xr-var-data-in' type='checkbox'><label for='data-21dcb832-ca89-49ce-831f-5fb8530ec75c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary diatom phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatP_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-ed3535e4-c46a-4d17-8489-980fbd83ef94' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed3535e4-c46a-4d17-8489-980fbd83ef94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d40fb50-a03d-40f4-a0c5-2942dfc0b62f' class='xr-var-data-in' type='checkbox'><label for='data-6d40fb50-a03d-40f4-a0c5-2942dfc0b62f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary diatom phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -16297,17 +19467,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -16325,11 +19495,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -16337,11 +19513,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -16363,13 +19545,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatFe_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-6938f7b2-dd1f-469e-8152-acf43ac1e464' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6938f7b2-dd1f-469e-8152-acf43ac1e464' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd671949-acb6-4e74-b7d2-a2a18952a35a' class='xr-var-data-in' type='checkbox'><label for='data-dd671949-acb6-4e74-b7d2-a2a18952a35a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary diatom iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatFe_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-1a228ffa-b417-464e-bab1-20f3b82d3c3f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a228ffa-b417-464e-bab1-20f3b82d3c3f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7af07f09-f580-43a8-a537-58b571c7a42c' class='xr-var-data-in' type='checkbox'><label for='data-7af07f09-f580-43a8-a537-58b571c7a42c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary diatom iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -16385,17 +19567,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -16413,11 +19595,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -16425,11 +19613,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -16451,13 +19645,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatSi_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-a723ffcb-db76-4273-ac8c-e1a1189f9813' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a723ffcb-db76-4273-ac8c-e1a1189f9813' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a85504cf-00f8-448e-a7b1-68b2986dfe27' class='xr-var-data-in' type='checkbox'><label for='data-a85504cf-00f8-448e-a7b1-68b2986dfe27' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary diatom silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatSi_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-53a79f5c-4bd9-492a-bb1f-17d42d1a224c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-53a79f5c-4bd9-492a-bb1f-17d42d1a224c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-187d6443-5bd4-4a0c-8ddb-7912d25e66ee' class='xr-var-data-in' type='checkbox'><label for='data-187d6443-5bd4-4a0c-8ddb-7912d25e66ee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary diatom silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -16473,17 +19667,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -16501,11 +19695,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
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        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
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        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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@@ -16513,11 +19713,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
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        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
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        "\n",
        "  <!-- Vertical lines -->\n",
@@ -16539,13 +19745,13 @@
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazChl_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-ccba588f-30d2-4df8-be5c-786a1fea0ca1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ccba588f-30d2-4df8-be5c-786a1fea0ca1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b81ca29f-3a0c-404f-8f7e-0e4ece52dcee' class='xr-var-data-in' type='checkbox'><label for='data-b81ca29f-3a0c-404f-8f7e-0e4ece52dcee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary diazotroph chloropyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazChl_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-31a045c8-0c98-4222-b5f0-eb3b4e11256c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-31a045c8-0c98-4222-b5f0-eb3b4e11256c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89401baa-6414-4e20-8f70-8322bc06ea8c' class='xr-var-data-in' type='checkbox'><label for='data-89401baa-6414-4e20-8f70-8322bc06ea8c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary diazotroph chloropyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -16561,17 +19767,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -16589,11 +19795,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
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        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
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@@ -16601,11 +19813,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
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        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
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        "\n",
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@@ -16627,13 +19845,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
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-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazC_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-c3193729-64d5-4ed3-9ce2-dcb6dcbd9721' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c3193729-64d5-4ed3-9ce2-dcb6dcbd9721' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f363f5e0-f525-48d3-b382-7ff913464161' class='xr-var-data-in' type='checkbox'><label for='data-f363f5e0-f525-48d3-b382-7ff913464161' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary diazotroph carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazC_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-24ed2fc7-6d61-4f47-b9d2-3feca2356c9f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-24ed2fc7-6d61-4f47-b9d2-3feca2356c9f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f3d18ac-1295-4b8f-886a-907576b98b82' class='xr-var-data-in' type='checkbox'><label for='data-1f3d18ac-1295-4b8f-886a-907576b98b82' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary diazotroph carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -16649,17 +19867,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -16677,11 +19895,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
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        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
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        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
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        "  <!-- Colored Rectangle -->\n",
@@ -16689,11 +19913,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
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        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
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        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
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        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
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@@ -16715,13 +19945,13 @@
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        "\n",
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+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
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+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazP_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-45968f2a-2e43-4dd4-a271-f1131827c054' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45968f2a-2e43-4dd4-a271-f1131827c054' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e61bc4b0-be61-42a7-a625-5e7835666ffb' class='xr-var-data-in' type='checkbox'><label for='data-e61bc4b0-be61-42a7-a625-5e7835666ffb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary diazotroph phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazP_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-f4a3e3b5-d3e6-4a85-a0a5-604426ce6944' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4a3e3b5-d3e6-4a85-a0a5-604426ce6944' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b64a2352-9c6f-4c2a-808c-9770ff7422bc' class='xr-var-data-in' type='checkbox'><label for='data-b64a2352-9c6f-4c2a-808c-9770ff7422bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary diazotroph phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -16737,17 +19967,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -16765,11 +19995,17 @@
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        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -16777,11 +20013,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -16803,13 +20045,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazFe_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-ddbc1c7b-cd02-48e1-a4e1-6ad0f21106de' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ddbc1c7b-cd02-48e1-a4e1-6ad0f21106de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a3ebdb3-5ae9-46ee-ae8d-0aaaeee97929' class='xr-var-data-in' type='checkbox'><label for='data-3a3ebdb3-5ae9-46ee-ae8d-0aaaeee97929' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary diazotroph iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazFe_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-7efcc9db-da3e-4232-af4a-fcae148356ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7efcc9db-da3e-4232-af4a-fcae148356ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b5fa7aa-9b3a-4b26-93ba-e5ab0be3f227' class='xr-var-data-in' type='checkbox'><label for='data-4b5fa7aa-9b3a-4b26-93ba-e5ab0be3f227' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary diazotroph iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -16825,17 +20067,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -16853,11 +20095,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -16865,11 +20113,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -16891,13 +20145,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spCaCO3_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-d9729718-3caf-4e45-89cf-28e746282968' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9729718-3caf-4e45-89cf-28e746282968' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a744f4f2-4fbf-4b77-a9fe-48d72303f3e4' class='xr-var-data-in' type='checkbox'><label for='data-a744f4f2-4fbf-4b77-a9fe-48d72303f3e4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary small phytoplankton CaCO3</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spCaCO3_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-0c915c92-1547-44eb-8a15-df3e778306cf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0c915c92-1547-44eb-8a15-df3e778306cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d4483d2-773b-4e6e-9681-48a1e0e0dc07' class='xr-var-data-in' type='checkbox'><label for='data-6d4483d2-773b-4e6e-9681-48a1e0e0dc07' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary small phytoplankton CaCO3</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -16913,17 +20167,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -16941,11 +20195,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -16953,11 +20213,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -16979,13 +20245,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zooC_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-3c81894b-504a-4afc-bf9a-eaa644bcaee9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3c81894b-504a-4afc-bf9a-eaa644bcaee9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30d72913-f9bf-43db-a042-f04ee1696367' class='xr-var-data-in' type='checkbox'><label for='data-30d72913-f9bf-43db-a042-f04ee1696367' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary zooplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zooC_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-a3a4c0b8-a403-403b-a427-5ac5becfd312' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3a4c0b8-a403-403b-a427-5ac5becfd312' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a0f85af-9799-4982-8c6c-c1a1b897c434' class='xr-var-data-in' type='checkbox'><label for='data-7a0f85af-9799-4982-8c6c-c1a1b897c434' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary zooplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -17001,17 +20267,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -17029,11 +20295,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -17041,11 +20313,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -17067,13 +20345,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Lig_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-097cc8ec-6104-466a-abfd-1f4d80108634' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-097cc8ec-6104-466a-abfd-1f4d80108634' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44210d9f-640b-49ba-9778-3592fecbca0c' class='xr-var-data-in' type='checkbox'><label for='data-44210d9f-640b-49ba-9778-3592fecbca0c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary iron binding ligand</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Lig_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-2bcc0655-d24c-410b-bb31-168022ecdbe1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2bcc0655-d24c-410b-bb31-168022ecdbe1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-475cd5a3-eeb7-48ef-a8dd-d57b506241e8' class='xr-var-data-in' type='checkbox'><label for='data-475cd5a3-eeb7-48ef-a8dd-d57b506241e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary iron binding ligand</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Monthly reconstructions based on in situ data</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/MappedIron/Monthly_dFe.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -17089,17 +20367,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 51 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -17117,11 +20395,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -17129,11 +20413,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -17155,13 +20445,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIC_ALT_CO2_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-afef5f8e-fd87-4674-927c-d51ca63474e2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-afef5f8e-fd87-4674-927c-d51ca63474e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-517f1b7a-573b-45ff-9902-d9bacd10e841' class='xr-var-data-in' type='checkbox'><label for='data-517f1b7a-573b-45ff-9902-d9bacd10e841' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved inorganic carbon, alternative CO2</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIC_ALT_CO2_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-ca8b93b6-7794-4aa3-957c-599f49660928' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca8b93b6-7794-4aa3-957c-599f49660928' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2916653d-fb4d-43df-83da-89f609f4e105' class='xr-var-data-in' type='checkbox'><label for='data-2916653d-fb4d-43df-83da-89f609f4e105' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved inorganic carbon, alternative CO2</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>GLODAPv2</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/GLODAP/GLODAPv2.2016b_MappedClimatologies/GLODAPv2.2016b.TCO2.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -17177,17 +20467,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 51 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -17205,11 +20495,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -17217,11 +20513,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -17243,13 +20545,13 @@
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
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-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALK_ALT_CO2_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-8c4254c1-cc2e-420c-a9e1-1a1263e405ea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8c4254c1-cc2e-420c-a9e1-1a1263e405ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-891941bf-c723-444e-9da6-4dd170825dea' class='xr-var-data-in' type='checkbox'><label for='data-891941bf-c723-444e-9da6-4dd170825dea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary alkalinity, alternative CO2</dd><dt><span>units :</span></dt><dd>meq/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALK_ALT_CO2_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-ee0216ca-4280-4ae2-b385-22b9b2d1b8a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ee0216ca-4280-4ae2-b385-22b9b2d1b8a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf1c125d-e44a-4f0b-91d6-f4a4663470cb' class='xr-var-data-in' type='checkbox'><label for='data-cf1c125d-e44a-4f0b-91d6-f4a4663470cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary alkalinity, alternative CO2</dd><dt><span>units :</span></dt><dd>meq/m^3</dd><dt><span>source :</span></dt><dd>GLODAPv2</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/GLODAP/GLODAPv2.2016b_MappedClimatologies/GLODAPv2.2016b.TAlk.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -17265,17 +20567,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 51 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -17293,11 +20595,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -17305,11 +20613,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -17331,13 +20645,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PO4_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-4f44b20e-61c0-4a1b-a1eb-2477300232af' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f44b20e-61c0-4a1b-a1eb-2477300232af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cdb03874-b5c5-4d35-bfe0-ea8e667e8dd5' class='xr-var-data-in' type='checkbox'><label for='data-cdb03874-b5c5-4d35-bfe0-ea8e667e8dd5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary dissolved inorganic phosphate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PO4_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-d0ec86d4-9e3e-4e3f-8edd-742d2dee3d07' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d0ec86d4-9e3e-4e3f-8edd-742d2dee3d07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4dbc1f6-1c28-4bd6-aaed-410e8082f614' class='xr-var-data-in' type='checkbox'><label for='data-f4dbc1f6-1c28-4bd6-aaed-410e8082f614' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary dissolved inorganic phosphate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>WOA18</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/WOA18/phos/woa18_all_p*_01.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -17353,17 +20667,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -17381,11 +20695,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -17393,11 +20713,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -17419,13 +20745,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NO3_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-47852a3f-55db-4211-a0e4-e15b39e324b1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-47852a3f-55db-4211-a0e4-e15b39e324b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0aa9570a-55c3-4bf3-ac8b-dde1a624d4a8' class='xr-var-data-in' type='checkbox'><label for='data-0aa9570a-55c3-4bf3-ac8b-dde1a624d4a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary dissolved inorganic nitrate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NO3_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-9596baeb-9af3-4961-b304-c647dc36c47c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9596baeb-9af3-4961-b304-c647dc36c47c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c0d021d-2613-460f-bd60-811463e31f96' class='xr-var-data-in' type='checkbox'><label for='data-6c0d021d-2613-460f-bd60-811463e31f96' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary dissolved inorganic nitrate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>WOA18</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/WOA18/nitr/woa18_all_n*_01.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -17441,17 +20767,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -17469,11 +20795,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
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        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -17481,11 +20813,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
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        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -17507,13 +20845,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SiO3_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-a8f63411-8290-418e-bb05-2e78d0695354' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a8f63411-8290-418e-bb05-2e78d0695354' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57d2383e-07ea-4ad1-916d-ca99e7853920' class='xr-var-data-in' type='checkbox'><label for='data-57d2383e-07ea-4ad1-916d-ca99e7853920' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary dissolved inorganic silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SiO3_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-749d37bc-ba76-483c-ac3c-483a6f4d9732' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-749d37bc-ba76-483c-ac3c-483a6f4d9732' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-117619c4-f4a0-4c3f-81a9-ecaaee428c9f' class='xr-var-data-in' type='checkbox'><label for='data-117619c4-f4a0-4c3f-81a9-ecaaee428c9f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary dissolved inorganic silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>WOA18</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/WOA18/silc/woa18_all_i*_01.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -17529,17 +20867,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -17557,11 +20895,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -17569,11 +20913,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -17595,13 +20945,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Fe_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-59013949-1786-4851-9d02-76df32121e1d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-59013949-1786-4851-9d02-76df32121e1d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84380ce4-2d6c-4ac3-97ba-a7527789909d' class='xr-var-data-in' type='checkbox'><label for='data-84380ce4-2d6c-4ac3-97ba-a7527789909d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary dissolved inorganic iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Fe_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-aa50b435-6a4c-456c-a0ab-ff90d3442243' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa50b435-6a4c-456c-a0ab-ff90d3442243' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-123d1e3d-a5d7-4b40-bc52-321810676fd8' class='xr-var-data-in' type='checkbox'><label for='data-123d1e3d-a5d7-4b40-bc52-321810676fd8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary dissolved inorganic iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Monthly reconstructions based on in situ data</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/MappedIron/Monthly_dFe.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -17617,17 +20967,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -17645,11 +20995,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -17657,11 +21013,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -17683,13 +21045,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>O2_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-27e122a8-5e1a-4956-9e03-6eb341370acf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-27e122a8-5e1a-4956-9e03-6eb341370acf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b91500ef-0910-4eec-8eee-53d8c46fd55a' class='xr-var-data-in' type='checkbox'><label for='data-b91500ef-0910-4eec-8eee-53d8c46fd55a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary dissolved oxygen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>O2_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-cd28f9fc-c634-4ada-b64b-8f613d486ac7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd28f9fc-c634-4ada-b64b-8f613d486ac7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aca15c54-79e5-40c9-adf2-49f90190c591' class='xr-var-data-in' type='checkbox'><label for='data-aca15c54-79e5-40c9-adf2-49f90190c591' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary dissolved oxygen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>WOA18</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/WOA18/oxyg/woa18_all_o*_01.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -17705,17 +21067,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -17733,11 +21095,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -17745,11 +21113,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -17771,13 +21145,13 @@
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIC_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-51c91ecc-1ae0-473c-b6e0-ae9313791b53' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-51c91ecc-1ae0-473c-b6e0-ae9313791b53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45cbc633-1d59-4562-9e17-24f96b934175' class='xr-var-data-in' type='checkbox'><label for='data-45cbc633-1d59-4562-9e17-24f96b934175' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary dissolved inorganic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIC_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-68fbbdc2-70f7-43a2-844b-22c9c55cc03c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-68fbbdc2-70f7-43a2-844b-22c9c55cc03c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e13e951-1780-49a2-afd4-353a4881011b' class='xr-var-data-in' type='checkbox'><label for='data-0e13e951-1780-49a2-afd4-353a4881011b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary dissolved inorganic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>GLODAPv2</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/GLODAP/GLODAPv2.2016b_MappedClimatologies/GLODAPv2.2016b.TCO2.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -17793,17 +21167,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -17821,11 +21195,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -17833,11 +21213,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
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        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
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@@ -17859,13 +21245,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALK_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-691b7a53-f27e-4e17-841c-d23d7fc6a183' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-691b7a53-f27e-4e17-841c-d23d7fc6a183' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8bcec19-bc55-4413-9463-76c74a5e59db' class='xr-var-data-in' type='checkbox'><label for='data-c8bcec19-bc55-4413-9463-76c74a5e59db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary alkalinity</dd><dt><span>units :</span></dt><dd>meq/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALK_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-166d5b39-d92e-43e4-9059-4df71f5607b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-166d5b39-d92e-43e4-9059-4df71f5607b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2b06f1a-6003-441b-bdc0-54c66c0ded27' class='xr-var-data-in' type='checkbox'><label for='data-c2b06f1a-6003-441b-bdc0-54c66c0ded27' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary alkalinity</dd><dt><span>units :</span></dt><dd>meq/m^3</dd><dt><span>source :</span></dt><dd>GLODAPv2</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/GLODAP/GLODAPv2.2016b_MappedClimatologies/GLODAPv2.2016b.TAlk.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -17881,17 +21267,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -17909,11 +21295,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -17921,11 +21313,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -17947,13 +21345,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NH4_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-94580c53-c1e8-4bea-811c-daa004efeb98' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94580c53-c1e8-4bea-811c-daa004efeb98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb1680c7-57e4-4eac-b201-856c44714f5f' class='xr-var-data-in' type='checkbox'><label for='data-eb1680c7-57e4-4eac-b201-856c44714f5f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary dissolved ammonia</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NH4_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-26255f0e-3833-4aa9-8e71-32ea7e63c850' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-26255f0e-3833-4aa9-8e71-32ea7e63c850' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-234f0958-1cd1-424d-838c-9cc36c1c4915' class='xr-var-data-in' type='checkbox'><label for='data-234f0958-1cd1-424d-838c-9cc36c1c4915' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary dissolved ammonia</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>based on 3D CESM climatology regridded at low-resolution by Abigale Wyatt</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM-climatology_lowres_regridded.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -17969,17 +21367,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -17997,11 +21395,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -18009,11 +21413,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
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@@ -18035,13 +21445,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOC_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-5e812cf2-32b4-4c0b-a64d-96170c27c54c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e812cf2-32b4-4c0b-a64d-96170c27c54c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4142a3d3-8855-4699-91e3-595db4dd1a72' class='xr-var-data-in' type='checkbox'><label for='data-4142a3d3-8855-4699-91e3-595db4dd1a72' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary dissolved organic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOC_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-12b3af99-b6cc-4198-81d0-05eddd82e143' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12b3af99-b6cc-4198-81d0-05eddd82e143' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9a9620c-6577-4d2b-9452-b8db93cb928c' class='xr-var-data-in' type='checkbox'><label for='data-d9a9620c-6577-4d2b-9452-b8db93cb928c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary dissolved organic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>based on 3D CESM climatology regridded at low-resolution by Abigale Wyatt</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM-climatology_lowres_regridded.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -18057,17 +21467,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -18085,11 +21495,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -18097,11 +21513,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
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@@ -18123,13 +21545,13 @@
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        "\n",
        "  <!-- Text -->\n",
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-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DON_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-57eb4b01-631a-41d4-b732-574c5bb53c29' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-57eb4b01-631a-41d4-b732-574c5bb53c29' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42d155d3-e273-4954-b349-74616d82f198' class='xr-var-data-in' type='checkbox'><label for='data-42d155d3-e273-4954-b349-74616d82f198' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DON_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-4b6deae7-ab48-4e59-8e1f-b19460fdd39e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b6deae7-ab48-4e59-8e1f-b19460fdd39e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1180f027-af3f-49d2-9f24-3e1de85eb421' class='xr-var-data-in' type='checkbox'><label for='data-1180f027-af3f-49d2-9f24-3e1de85eb421' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>based on 3D CESM climatology regridded at low-resolution by Abigale Wyatt</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM-climatology_lowres_regridded.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -18145,17 +21567,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -18173,11 +21595,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
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        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
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        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
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        "  <!-- Colored Rectangle -->\n",
@@ -18185,11 +21613,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
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        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
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        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
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        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
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        "\n",
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@@ -18211,13 +21645,13 @@
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
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-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOP_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-11e6f7fb-0d22-49b8-afcb-421219883de9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-11e6f7fb-0d22-49b8-afcb-421219883de9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a69a006-c90d-47e9-bae4-75881a83e669' class='xr-var-data-in' type='checkbox'><label for='data-1a69a006-c90d-47e9-bae4-75881a83e669' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOP_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-efafc9f2-3019-4acc-89e6-6b1d39486cbf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-efafc9f2-3019-4acc-89e6-6b1d39486cbf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04ae2759-87db-4244-871b-1c1269c73e7c' class='xr-var-data-in' type='checkbox'><label for='data-04ae2759-87db-4244-871b-1c1269c73e7c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>based on 3D CESM climatology regridded at low-resolution by Abigale Wyatt</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM-climatology_lowres_regridded.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -18233,17 +21667,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -18261,11 +21695,17 @@
        "\n",
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        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
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@@ -18273,11 +21713,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
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        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
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        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
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        "\n",
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-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOPr_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-47d81e46-d996-48e2-8127-bca9ef70da15' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-47d81e46-d996-48e2-8127-bca9ef70da15' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-379630d0-e799-4f0f-b629-0394b654931c' class='xr-var-data-in' type='checkbox'><label for='data-379630d0-e799-4f0f-b629-0394b654931c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary refractory dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOPr_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-abc1f6a9-2324-4480-9a7e-bba537812517' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-abc1f6a9-2324-4480-9a7e-bba537812517' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-305c81dd-d7fd-4eea-908e-4d47815a1d5e' class='xr-var-data-in' type='checkbox'><label for='data-305c81dd-d7fd-4eea-908e-4d47815a1d5e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary refractory dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>based on 3D CESM climatology regridded at low-resolution by Abigale Wyatt</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM-climatology_lowres_regridded.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -18321,17 +21767,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -18349,11 +21795,17 @@
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        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
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        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
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        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DONr_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-b4f4cc20-9d0e-471c-8e7d-28b86fdfd391' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b4f4cc20-9d0e-471c-8e7d-28b86fdfd391' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8dbf680-7d82-4c87-bd40-071e6d0b2d08' class='xr-var-data-in' type='checkbox'><label for='data-b8dbf680-7d82-4c87-bd40-071e6d0b2d08' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary refractory dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DONr_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-80c4a038-49dc-4940-a949-9cb01f9d6a52' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-80c4a038-49dc-4940-a949-9cb01f9d6a52' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3dc8a2cf-3783-433c-89f7-cca015571f68' class='xr-var-data-in' type='checkbox'><label for='data-3dc8a2cf-3783-433c-89f7-cca015571f68' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary refractory dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>based on 3D CESM climatology regridded at low-resolution by Abigale Wyatt</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM-climatology_lowres_regridded.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -18409,17 +21867,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -18437,11 +21895,17 @@
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        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
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@@ -18475,13 +21945,13 @@
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOCr_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-2c8a8f4c-46ae-4229-9b9b-e9624f0ca19f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c8a8f4c-46ae-4229-9b9b-e9624f0ca19f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10dc5d14-4515-4667-8881-19fd3dff1fdf' class='xr-var-data-in' type='checkbox'><label for='data-10dc5d14-4515-4667-8881-19fd3dff1fdf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary refractory dissolved organic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOCr_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-5bfcbbdc-c22f-43d1-a401-b5c2bc47397a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5bfcbbdc-c22f-43d1-a401-b5c2bc47397a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3de8d709-8b07-4c00-a995-fc04a7fe4a1e' class='xr-var-data-in' type='checkbox'><label for='data-3de8d709-8b07-4c00-a995-fc04a7fe4a1e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary refractory dissolved organic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>based on 3D CESM climatology regridded at low-resolution by Abigale Wyatt</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM-climatology_lowres_regridded.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -18497,17 +21967,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -18525,11 +21995,17 @@
        "\n",
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        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
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        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
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@@ -18537,11 +22013,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
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        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
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        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spChl_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-9137ceef-6439-49a1-a326-11f6f41bfb2d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9137ceef-6439-49a1-a326-11f6f41bfb2d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ee17ddf-b167-42d4-add6-21c9e53ac54b' class='xr-var-data-in' type='checkbox'><label for='data-5ee17ddf-b167-42d4-add6-21c9e53ac54b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary small phytoplankton chlorophyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spChl_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-183f52ab-b835-43fd-b2b6-1da9c1c94746' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-183f52ab-b835-43fd-b2b6-1da9c1c94746' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33d090b3-7bc3-4a0b-9a3b-e4281295c59c' class='xr-var-data-in' type='checkbox'><label for='data-33d090b3-7bc3-4a0b-9a3b-e4281295c59c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary small phytoplankton chlorophyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -18585,17 +22067,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -18613,11 +22095,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
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        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
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@@ -18625,11 +22113,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
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        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
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        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
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@@ -18651,13 +22145,13 @@
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        "\n",
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-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
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        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spC_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-384e1d0b-51de-4106-afe4-132d5885ff41' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-384e1d0b-51de-4106-afe4-132d5885ff41' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-923effc2-6e04-4aba-9e57-04691590b72d' class='xr-var-data-in' type='checkbox'><label for='data-923effc2-6e04-4aba-9e57-04691590b72d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary small phytoplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spC_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-1fc56516-e8a0-4077-9de6-ef9db67bbd78' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1fc56516-e8a0-4077-9de6-ef9db67bbd78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31c53c52-3c82-4303-a719-5a49cd6bb7c4' class='xr-var-data-in' type='checkbox'><label for='data-31c53c52-3c82-4303-a719-5a49cd6bb7c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary small phytoplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -18673,17 +22167,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -18701,11 +22195,17 @@
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        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
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        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -18713,11 +22213,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -18739,13 +22245,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spP_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-7fca1902-f4f3-4bf0-8c82-4ae0859c2710' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7fca1902-f4f3-4bf0-8c82-4ae0859c2710' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1997caa-f9d9-4e08-8161-132b2abd4ac4' class='xr-var-data-in' type='checkbox'><label for='data-e1997caa-f9d9-4e08-8161-132b2abd4ac4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary small phytoplankton phosphorous</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spP_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-98e0f5ad-5e51-4a45-ae92-2f7ad3f9d38a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-98e0f5ad-5e51-4a45-ae92-2f7ad3f9d38a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aaabb5ea-c7e6-4bdc-aea3-e4116a44421b' class='xr-var-data-in' type='checkbox'><label for='data-aaabb5ea-c7e6-4bdc-aea3-e4116a44421b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary small phytoplankton phosphorous</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -18761,17 +22267,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -18789,11 +22295,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -18801,11 +22313,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -18827,13 +22345,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spFe_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-c918fef2-e31c-4f0d-a5d2-ab5d0bf4c127' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c918fef2-e31c-4f0d-a5d2-ab5d0bf4c127' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91307735-ecc2-498d-ae3c-ebe29749b25d' class='xr-var-data-in' type='checkbox'><label for='data-91307735-ecc2-498d-ae3c-ebe29749b25d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary small phytoplankton iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spFe_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-279d9fbf-b590-49b5-a7e8-cd453c39c650' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-279d9fbf-b590-49b5-a7e8-cd453c39c650' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3eee315f-ec24-4e73-9ed5-fe5b7c4ebc3d' class='xr-var-data-in' type='checkbox'><label for='data-3eee315f-ec24-4e73-9ed5-fe5b7c4ebc3d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary small phytoplankton iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -18849,17 +22367,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -18877,11 +22395,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -18889,11 +22413,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -18915,13 +22445,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatChl_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-06d72d1e-628b-47e7-b995-035b28458ee0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06d72d1e-628b-47e7-b995-035b28458ee0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef70c1ec-bce6-4f92-b84d-9aec753b5026' class='xr-var-data-in' type='checkbox'><label for='data-ef70c1ec-bce6-4f92-b84d-9aec753b5026' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary diatom chloropyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatChl_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-1430d244-6c35-4d81-8ea6-424d8fb20362' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1430d244-6c35-4d81-8ea6-424d8fb20362' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-517766a9-f6a9-4787-a091-6116863f229a' class='xr-var-data-in' type='checkbox'><label for='data-517766a9-f6a9-4787-a091-6116863f229a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary diatom chloropyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -18937,17 +22467,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -18965,11 +22495,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -18977,11 +22513,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -19003,13 +22545,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
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-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatC_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-e8175bc5-a448-4bf9-a7f7-4f5853ef1076' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e8175bc5-a448-4bf9-a7f7-4f5853ef1076' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d19dfc5-6558-4da1-bec4-e713df7cfe6e' class='xr-var-data-in' type='checkbox'><label for='data-4d19dfc5-6558-4da1-bec4-e713df7cfe6e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary diatom carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatC_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-28b42db1-4c90-4e7d-8519-fb25d61c5148' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28b42db1-4c90-4e7d-8519-fb25d61c5148' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86156f30-d7e9-4655-be01-53424dd11ddf' class='xr-var-data-in' type='checkbox'><label for='data-86156f30-d7e9-4655-be01-53424dd11ddf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary diatom carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -19025,17 +22567,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -19053,11 +22595,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -19065,11 +22613,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
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        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
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        "\n",
        "  <!-- Text -->\n",
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-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatP_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-fa804ba6-4cc8-4fac-bfdc-78e86c01d44b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa804ba6-4cc8-4fac-bfdc-78e86c01d44b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ea62f7c-52ab-416f-add9-6cc472e65820' class='xr-var-data-in' type='checkbox'><label for='data-1ea62f7c-52ab-416f-add9-6cc472e65820' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary diatom phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatP_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-94350208-0199-46dc-b6e9-9b6ad2d00fa2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94350208-0199-46dc-b6e9-9b6ad2d00fa2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9b9cacc-0802-4048-8516-e96cfacddbf0' class='xr-var-data-in' type='checkbox'><label for='data-e9b9cacc-0802-4048-8516-e96cfacddbf0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary diatom phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -19113,17 +22667,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -19141,11 +22695,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
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        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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@@ -19153,11 +22713,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
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        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
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        "\n",
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@@ -19179,13 +22745,13 @@
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-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatFe_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-ac7ba98c-ef02-4362-804d-a881cb38abc6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac7ba98c-ef02-4362-804d-a881cb38abc6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ed60bc7-485b-40b3-ba93-64e57fbc2641' class='xr-var-data-in' type='checkbox'><label for='data-0ed60bc7-485b-40b3-ba93-64e57fbc2641' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary diatom iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatFe_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-61c612b0-ef45-40fa-bd25-ffe4e6746e01' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-61c612b0-ef45-40fa-bd25-ffe4e6746e01' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4717c0eb-e5b6-4b4a-b211-ff62dd6ed1f1' class='xr-var-data-in' type='checkbox'><label for='data-4717c0eb-e5b6-4b4a-b211-ff62dd6ed1f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary diatom iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -19201,17 +22767,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -19229,11 +22795,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -19241,11 +22813,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -19267,13 +22845,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatSi_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-d6b4a4ba-760c-4f41-9e2c-a9df7f6d4892' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6b4a4ba-760c-4f41-9e2c-a9df7f6d4892' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15d929c3-cb74-4d39-ba63-55c88392e672' class='xr-var-data-in' type='checkbox'><label for='data-15d929c3-cb74-4d39-ba63-55c88392e672' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary diatom silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatSi_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-78a68872-9b65-4a22-8877-067448a2da60' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-78a68872-9b65-4a22-8877-067448a2da60' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04675759-cc44-4b70-8871-bc0b3162ccff' class='xr-var-data-in' type='checkbox'><label for='data-04675759-cc44-4b70-8871-bc0b3162ccff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary diatom silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -19289,17 +22867,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -19317,11 +22895,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
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        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
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        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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        "\n",
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@@ -19329,11 +22913,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
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        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
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        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
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        "\n",
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-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
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+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazChl_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-4cd5bdf0-95b9-47aa-a179-974c6f0c490f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4cd5bdf0-95b9-47aa-a179-974c6f0c490f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a014094-c20b-4fda-910e-1bf00a630b44' class='xr-var-data-in' type='checkbox'><label for='data-8a014094-c20b-4fda-910e-1bf00a630b44' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary diazotroph chloropyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazChl_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-ed60397c-7149-440c-8ad0-ef8bda100269' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed60397c-7149-440c-8ad0-ef8bda100269' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-225558a5-8db2-4564-a70e-8a3fe76d7e03' class='xr-var-data-in' type='checkbox'><label for='data-225558a5-8db2-4564-a70e-8a3fe76d7e03' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary diazotroph chloropyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -19377,17 +22967,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -19405,11 +22995,17 @@
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        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
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        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
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        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
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-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazC_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-d7bdf670-a43e-4944-b6ef-c51c1c863d65' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d7bdf670-a43e-4944-b6ef-c51c1c863d65' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22a544a8-d533-468f-8715-a012d97a3f78' class='xr-var-data-in' type='checkbox'><label for='data-22a544a8-d533-468f-8715-a012d97a3f78' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary diazotroph carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazC_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-028c9ac8-21b6-4214-ad03-8be4c5b146d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-028c9ac8-21b6-4214-ad03-8be4c5b146d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b2c3433-bd3a-4350-99d6-e9bbf21506d9' class='xr-var-data-in' type='checkbox'><label for='data-1b2c3433-bd3a-4350-99d6-e9bbf21506d9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary diazotroph carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -19465,17 +23067,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -19493,11 +23095,17 @@
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        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
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        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
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        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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@@ -19505,11 +23113,17 @@
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        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
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-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazP_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-d4c87ba1-73f0-4516-87b1-4ec1617fc792' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d4c87ba1-73f0-4516-87b1-4ec1617fc792' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76cf3a9f-5064-4424-bed3-a119e504ae70' class='xr-var-data-in' type='checkbox'><label for='data-76cf3a9f-5064-4424-bed3-a119e504ae70' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary diazotroph phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazP_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-1906efbe-887d-4a5e-8ffe-147e0c65d94f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1906efbe-887d-4a5e-8ffe-147e0c65d94f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fbe4ea66-5447-4808-8563-d61050b9244b' class='xr-var-data-in' type='checkbox'><label for='data-fbe4ea66-5447-4808-8563-d61050b9244b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary diazotroph phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -19553,17 +23167,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -19581,11 +23195,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
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        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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        "\n",
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@@ -19593,11 +23213,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
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@@ -19619,13 +23245,13 @@
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        "\n",
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+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazFe_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-78d5c547-0bfb-4160-91c7-10126aa56151' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-78d5c547-0bfb-4160-91c7-10126aa56151' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e07087b-233d-477d-9a02-b214fea228e4' class='xr-var-data-in' type='checkbox'><label for='data-0e07087b-233d-477d-9a02-b214fea228e4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary diazotroph iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazFe_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-0b829479-a21d-495d-9c75-128e1169894c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0b829479-a21d-495d-9c75-128e1169894c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa4d2449-7544-4297-aaf0-cb6a1cb8fe54' class='xr-var-data-in' type='checkbox'><label for='data-fa4d2449-7544-4297-aaf0-cb6a1cb8fe54' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary diazotroph iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -19641,17 +23267,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -19669,11 +23295,17 @@
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        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
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@@ -19681,11 +23313,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -19707,13 +23345,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spCaCO3_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-4226f56d-f5b8-40b8-b74e-8ff5795c9eec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4226f56d-f5b8-40b8-b74e-8ff5795c9eec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40d49b72-b3f8-4fd4-b1c1-57acbf5d5f42' class='xr-var-data-in' type='checkbox'><label for='data-40d49b72-b3f8-4fd4-b1c1-57acbf5d5f42' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary small phytoplankton CaCO3</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spCaCO3_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-ac636f84-a0ae-460e-8d0a-68271ae01db5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac636f84-a0ae-460e-8d0a-68271ae01db5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-101276c7-8a08-4d82-9fda-b7d2ec7faf0c' class='xr-var-data-in' type='checkbox'><label for='data-101276c7-8a08-4d82-9fda-b7d2ec7faf0c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary small phytoplankton CaCO3</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -19729,17 +23367,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -19757,11 +23395,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
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        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
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        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
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@@ -19769,11 +23413,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
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        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
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        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
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        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
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-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zooC_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-97b5491b-e6ea-4954-bc51-3754c99990f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-97b5491b-e6ea-4954-bc51-3754c99990f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3d8ffe7-c75a-444c-85e4-3f466aa9e866' class='xr-var-data-in' type='checkbox'><label for='data-b3d8ffe7-c75a-444c-85e4-3f466aa9e866' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary zooplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zooC_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-30fd1016-0328-4e6c-a969-86c4ec769fae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-30fd1016-0328-4e6c-a969-86c4ec769fae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78d901d9-763d-4429-b4b0-228be7d31a10' class='xr-var-data-in' type='checkbox'><label for='data-78d901d9-763d-4429-b4b0-228be7d31a10' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary zooplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Scaled on CHL with mean ratio from CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/CESM_BGC_annual_profiles.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -19817,17 +23467,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 50 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -19845,11 +23495,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
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        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -19857,11 +23513,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
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        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
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        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
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        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
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        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
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        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
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        "\n",
        "  <!-- Text -->\n",
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-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
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+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Lig_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-d5986973-5225-459d-9e6b-20c46f59753f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d5986973-5225-459d-9e6b-20c46f59753f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-034eb613-1ec4-44d6-a5e2-75de776596c7' class='xr-var-data-in' type='checkbox'><label for='data-034eb613-1ec4-44d6-a5e2-75de776596c7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary iron binding ligand</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Lig_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-609577ac-422b-4536-8f83-aea7c0a91de1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-609577ac-422b-4536-8f83-aea7c0a91de1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0146d645-4f7a-446a-9124-da6815175398' class='xr-var-data-in' type='checkbox'><label for='data-0146d645-4f7a-446a-9124-da6815175398' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary iron binding ligand</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>Monthly reconstructions based on in situ data</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/MappedIron/Monthly_dFe.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -19905,17 +23567,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 51 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -19933,11 +23595,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -19945,11 +23613,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -19971,13 +23645,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIC_ALT_CO2_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-271c435f-5cfb-4c16-b549-cc78922fce97' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-271c435f-5cfb-4c16-b549-cc78922fce97' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9624abf6-221e-4c6a-8f06-cb70fdd07dd0' class='xr-var-data-in' type='checkbox'><label for='data-9624abf6-221e-4c6a-8f06-cb70fdd07dd0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary dissolved inorganic carbon, alternative CO2</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIC_ALT_CO2_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-3b42069c-cf06-4bcb-9e93-69b8f4693beb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b42069c-cf06-4bcb-9e93-69b8f4693beb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3715c06-8d43-49bb-88b2-ada1e317c724' class='xr-var-data-in' type='checkbox'><label for='data-b3715c06-8d43-49bb-88b2-ada1e317c724' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary dissolved inorganic carbon, alternative CO2</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>source :</span></dt><dd>GLODAPv2</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/GLODAP/GLODAPv2.2016b_MappedClimatologies/GLODAPv2.2016b.TCO2.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -19993,17 +23667,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 51 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -20021,11 +23695,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -20033,11 +23713,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -20059,13 +23745,13 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALK_ALT_CO2_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-a83a81f1-a700-4358-b98a-6c4148448dfa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a83a81f1-a700-4358-b98a-6c4148448dfa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80afdf25-e820-48f0-92b8-5a3b77980884' class='xr-var-data-in' type='checkbox'><label for='data-80afdf25-e820-48f0-92b8-5a3b77980884' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary alkalinity, alternative CO2</dd><dt><span>units :</span></dt><dd>meq/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALK_ALT_CO2_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-68824b1f-779b-411f-99e7-03619c880a39' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-68824b1f-779b-411f-99e7-03619c880a39' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73bdc55d-40bf-456c-b842-ff8c8f1258d0' class='xr-var-data-in' type='checkbox'><label for='data-73bdc55d-40bf-456c-b842-ff8c8f1258d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary alkalinity, alternative CO2</dd><dt><span>units :</span></dt><dd>meq/m^3</dd><dt><span>source :</span></dt><dd>GLODAPv2</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/GLODAP/GLODAPv2.2016b_MappedClimatologies/GLODAPv2.2016b.TAlk.nc</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -20081,17 +23767,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 2.30 MiB </td>\n",
-       "                        <td> 392.19 kiB </td>\n",
+       "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 100, 502) </td>\n",
-       "                        <td> (2, 100, 502) </td>\n",
+       "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 51 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 26 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -20109,11 +23795,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"43\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"47\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -20121,11 +23813,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
        "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
        "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -20147,48 +23845,45 @@
        "  <polygon points=\"27.676686162348723,17.676686162348723 147.67668616234872,17.676686162348723 147.67668616234872,59.6696884485097 27.676686162348723,59.6696884485097\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"87.676686\" y=\"79.669688\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"167.676686\" y=\"38.673187\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.676686,38.673187)\">100</text>\n",
-       "  <text x=\"8.838343\" y=\"70.831345\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343,70.831345)\">12</text>\n",
+       "  <text x=\"87.67668616234872\" y=\"79.6696884485097\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"167.67668616234872\" y=\"38.67318730542921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,167.67668616234872,38.67318730542921)\">100</text>\n",
+       "  <text x=\"8.838343081174362\" y=\"70.83134536733535\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.838343081174362,70.83134536733535)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-9aa4afab-b9a1-4be2-a112-d39bf4645e95' class='xr-section-summary-in' type='checkbox'  ><label for='section-9aa4afab-b9a1-4be2-a112-d39bf4645e95' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>bry_time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-c2fec7db-2d9a-4eca-ab09-1d8208198af5' class='xr-index-data-in' type='checkbox'/><label for='index-c2fec7db-2d9a-4eca-ab09-1d8208198af5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([15.500000000000002,               45.0,               74.5,\n",
-       "                    105.0,              135.5,              166.0,\n",
-       "                    196.0,              227.5,              258.0,\n",
-       "                    288.5,              319.0,              349.5],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;bry_time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-20ce28ad-4e06-43e7-bd74-0c4c883d7651' class='xr-section-summary-in' type='checkbox'  ><label for='section-20ce28ad-4e06-43e7-bd74-0c4c883d7651' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>ROMS boundary forcing file created by ROMS-Tools</dd><dt><span>roms_tools_version :</span></dt><dd>3.1.2.dev23+gc2b82bc5c</dd><dt><span>start_time :</span></dt><dd>2012-01-30 00:00:00</dd><dt><span>end_time :</span></dt><dd>2012-02-04 00:00:00</dd><dt><span>source :</span></dt><dd>UNIFIED</dd><dt><span>model_reference_date :</span></dt><dd>2000-01-01 00:00:00</dd><dt><span>apply_2d_horizontal_fill :</span></dt><dd>False</dd><dt><span>adjust_depth_for_sea_surface_height :</span></dt><dd>False</dd><dt><span>theta_s :</span></dt><dd>5.0</dd><dt><span>theta_b :</span></dt><dd>2.0</dd><dt><span>hc :</span></dt><dd>300.0</dd><dt><span>climatology :</span></dt><dd>True</dd></dl></div></li></ul></div></div>"
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-1a78a285-dedf-45bf-9579-57187e1475cb' class='xr-section-summary-in' type='checkbox' /><label for='section-1a78a285-dedf-45bf-9579-57187e1475cb' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(14)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>ROMS boundary forcing file created by ROMS-Tools</dd><dt><span>roms_tools_version :</span></dt><dd>10000.dev361+g2e6c47bd8</dd><dt><span>start_time :</span></dt><dd>2012-01-30 00:00:00</dd><dt><span>end_time :</span></dt><dd>2012-02-04 00:00:00</dd><dt><span>source :</span></dt><dd>UNIFIED</dd><dt><span>model_reference_date :</span></dt><dd>2000-01-01 00:00:00</dd><dt><span>prefill :</span></dt><dd>None</dd><dt><span>regrid_method :</span></dt><dd>xesmf</dd><dt><span>extrap_method :</span></dt><dd>inverse_dist</dd><dt><span>adjust_depth_for_sea_surface_height :</span></dt><dd>False</dd><dt><span>theta_s :</span></dt><dd>5.0</dd><dt><span>theta_b :</span></dt><dd>2.0</dd><dt><span>hc :</span></dt><dd>300.0</dd><dt><span>climatology :</span></dt><dd>True</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset> Size: 231MB\n",
        "Dimensions:            (bry_time: 12, s_rho: 100, xi_rho: 502, eta_rho: 502)\n",
        "Coordinates:\n",
+       "  * bry_time           (bry_time) float64 96B 15.5 45.0 74.5 ... 319.0 349.5\n",
        "    month              (bry_time) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12\n",
        "    abs_time           (bry_time) datetime64[ns] 96B 2000-01-16T12:00:00 ... ...\n",
-       "  * bry_time           (bry_time) float64 96B 15.5 45.0 74.5 ... 319.0 349.5\n",
        "Dimensions without coordinates: s_rho, xi_rho, eta_rho\n",
        "Data variables: (12/96)\n",
-       "    PO4_south          (bry_time, s_rho, xi_rho) float32 2MB dask.array<chunksize=(2, 100, 502), meta=np.ndarray>\n",
-       "    NO3_south          (bry_time, s_rho, xi_rho) float32 2MB dask.array<chunksize=(2, 100, 502), meta=np.ndarray>\n",
-       "    SiO3_south         (bry_time, s_rho, xi_rho) float32 2MB dask.array<chunksize=(2, 100, 502), meta=np.ndarray>\n",
-       "    Fe_south           (bry_time, s_rho, xi_rho) float32 2MB dask.array<chunksize=(2, 100, 502), meta=np.ndarray>\n",
-       "    O2_south           (bry_time, s_rho, xi_rho) float32 2MB dask.array<chunksize=(2, 100, 502), meta=np.ndarray>\n",
-       "    DIC_south          (bry_time, s_rho, xi_rho) float32 2MB dask.array<chunksize=(2, 100, 502), meta=np.ndarray>\n",
+       "    PO4_south          (bry_time, s_rho, xi_rho) float32 2MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
+       "    NO3_south          (bry_time, s_rho, xi_rho) float32 2MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
+       "    SiO3_south         (bry_time, s_rho, xi_rho) float32 2MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
+       "    Fe_south           (bry_time, s_rho, xi_rho) float32 2MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
+       "    O2_south           (bry_time, s_rho, xi_rho) float32 2MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
+       "    DIC_south          (bry_time, s_rho, xi_rho) float32 2MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
        "    ...                 ...\n",
-       "    diazFe_west        (bry_time, s_rho, eta_rho) float32 2MB dask.array<chunksize=(2, 100, 502), meta=np.ndarray>\n",
-       "    spCaCO3_west       (bry_time, s_rho, eta_rho) float32 2MB dask.array<chunksize=(2, 100, 502), meta=np.ndarray>\n",
-       "    zooC_west          (bry_time, s_rho, eta_rho) float32 2MB dask.array<chunksize=(2, 100, 502), meta=np.ndarray>\n",
-       "    Lig_west           (bry_time, s_rho, eta_rho) float32 2MB dask.array<chunksize=(2, 100, 502), meta=np.ndarray>\n",
-       "    DIC_ALT_CO2_west   (bry_time, s_rho, eta_rho) float32 2MB dask.array<chunksize=(2, 100, 502), meta=np.ndarray>\n",
-       "    ALK_ALT_CO2_west   (bry_time, s_rho, eta_rho) float32 2MB dask.array<chunksize=(2, 100, 502), meta=np.ndarray>\n",
-       "Attributes:\n",
+       "    diazFe_west        (bry_time, s_rho, eta_rho) float32 2MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
+       "    spCaCO3_west       (bry_time, s_rho, eta_rho) float32 2MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
+       "    zooC_west          (bry_time, s_rho, eta_rho) float32 2MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
+       "    Lig_west           (bry_time, s_rho, eta_rho) float32 2MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
+       "    DIC_ALT_CO2_west   (bry_time, s_rho, eta_rho) float32 2MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
+       "    ALK_ALT_CO2_west   (bry_time, s_rho, eta_rho) float32 2MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
+       "Attributes: (12/14)\n",
        "    title:                                ROMS boundary forcing file created ...\n",
-       "    roms_tools_version:                   3.1.2.dev23+gc2b82bc5c\n",
+       "    roms_tools_version:                   10000.dev361+g2e6c47bd8\n",
        "    start_time:                           2012-01-30 00:00:00\n",
        "    end_time:                             2012-02-04 00:00:00\n",
        "    source:                               UNIFIED\n",
        "    model_reference_date:                 2000-01-01 00:00:00\n",
-       "    apply_2d_horizontal_fill:             False\n",
+       "    ...                                   ...\n",
+       "    extrap_method:                        inverse_dist\n",
        "    adjust_depth_for_sea_surface_height:  False\n",
        "    theta_s:                              5.0\n",
        "    theta_b:                              2.0\n",
@@ -20237,9 +23932,7 @@
        "</symbol>\n",
        "</defs>\n",
        "</svg>\n",
-       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
-       " *\n",
-       " */\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
        "\n",
        ":root {\n",
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@@ -20318,6 +24011,8 @@
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        "  min-width: 300px;\n",
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+       "  line-height: 1.6;\n",
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        "\n",
        ".xr-text-repr-fallback {\n",
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        ".xr-header {\n",
        "  padding-top: 6px;\n",
        "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
        "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "  margin-bottom: 4px;\n",
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        "\n",
        ".xr-header > div,\n",
@@ -20340,46 +24038,62 @@
        "}\n",
        "\n",
        ".xr-obj-type,\n",
-       ".xr-array-name {\n",
+       ".xr-obj-name {\n",
        "  margin-left: 2px;\n",
        "  margin-right: 10px;\n",
        "}\n",
        "\n",
-       ".xr-obj-type {\n",
+       ".xr-obj-type,\n",
+       ".xr-group-box-contents > label {\n",
        "  color: var(--xr-font-color2);\n",
+       "  display: block;\n",
        "}\n",
        "\n",
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
        "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
        "  display: contents;\n",
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        "\n",
-       ".xr-section-item input {\n",
-       "  display: inline-block;\n",
+       ".xr-section-item > input,\n",
+       ".xr-group-box-contents > input,\n",
+       ".xr-array-wrap > input {\n",
+       "  display: block;\n",
        "  opacity: 0;\n",
        "  height: 0;\n",
+       "  margin: 0;\n",
        "}\n",
        "\n",
-       ".xr-section-item input + label {\n",
+       ".xr-section-item > input + label,\n",
+       ".xr-var-item > input + label {\n",
        "  color: var(--xr-disabled-color);\n",
-       "  border: 2px solid transparent !important;\n",
        "}\n",
        "\n",
-       ".xr-section-item input:enabled + label {\n",
+       ".xr-section-item > input:enabled + label,\n",
+       ".xr-var-item > input:enabled + label,\n",
+       ".xr-array-wrap > input:enabled + label,\n",
+       ".xr-group-box-contents > input:enabled + label {\n",
        "  cursor: pointer;\n",
        "  color: var(--xr-font-color2);\n",
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        "\n",
-       ".xr-section-item input:focus + label {\n",
-       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       ".xr-section-item > input:focus-visible + label,\n",
+       ".xr-var-item > input:focus-visible + label,\n",
+       ".xr-array-wrap > input:focus-visible + label,\n",
+       ".xr-group-box-contents > input:focus-visible + label {\n",
+       "  outline: auto;\n",
        "}\n",
        "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
+       ".xr-section-item > input:enabled + label:hover,\n",
+       ".xr-var-item > input:enabled + label:hover,\n",
+       ".xr-array-wrap > input:enabled + label:hover,\n",
+       ".xr-group-box-contents > input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
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@@ -20387,11 +24101,25 @@
        "  grid-column: 1;\n",
        "  color: var(--xr-font-color2);\n",
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+       ".xr-span-grid {\n",
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+       ".xr-group-box-contents > input:checked + label > span {\n",
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@@ -20419,9 +24147,9 @@
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        "\n",
        ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
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        ".xr-section-details {\n",
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@@ -20668,31 +24462,27 @@
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;bry_time&#x27; (bry_time: 12)&gt; Size: 96B\n",
        "array([ 15.,  45.,  74., 105., 135., 166., 196., 227., 258., 288., 319., 349.])\n",
        "Coordinates:\n",
+       "  * bry_time  (bry_time) float64 96B 15.0 45.0 74.0 105.0 ... 288.0 319.0 349.0\n",
        "    month     (bry_time) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12\n",
        "    abs_time  (bry_time) datetime64[ns] 96B 2000-01-16 2000-02-15 ... 2000-12-15\n",
-       "  * bry_time  (bry_time) float64 96B 15.0 45.0 74.0 105.0 ... 288.0 319.0 349.0\n",
        "Attributes:\n",
        "    cycle_length:  365.25\n",
        "    long_name:     relative time: days since 2000-01-01 00:00:00\n",
-       "    units:         days</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'bry_time'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>bry_time</span>: 12</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-0d4909a8-a4b4-4134-a726-b8b6559860a4' class='xr-array-in' type='checkbox' checked><label for='section-0d4909a8-a4b4-4134-a726-b8b6559860a4' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>15.0 45.0 74.0 105.0 135.0 166.0 196.0 227.0 258.0 288.0 319.0 349.0</span></div><div class='xr-array-data'><pre>array([ 15.,  45.,  74., 105., 135., 166., 196., 227., 258., 288., 319., 349.])</pre></div></div></li><li class='xr-section-item'><input id='section-7ff650d9-ef81-4475-876d-090e1227853e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7ff650d9-ef81-4475-876d-090e1227853e' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>month</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-2a5df858-ea39-428e-a6e7-3df32428e1c4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2a5df858-ea39-428e-a6e7-3df32428e1c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab36159e-80e8-4d2b-8301-4436df6e0259' class='xr-var-data-in' type='checkbox'><label for='data-ab36159e-80e8-4d2b-8301-4436df6e0259' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Month index (1-12)</dd></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-16 ... 2000-12-15</div><input id='attrs-e7bde78b-5e88-4d16-833b-0fb0bc77a3ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7bde78b-5e88-4d16-833b-0fb0bc77a3ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6ea7f48-9e68-4eb5-b633-e08720a3a8a5' class='xr-var-data-in' type='checkbox'><label for='data-b6ea7f48-9e68-4eb5-b633-e08720a3a8a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>absolute time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-16T00:00:00.000000000&#x27;, &#x27;2000-02-15T00:00:00.000000000&#x27;,\n",
+       "    units:         days</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-obj-name'>&#x27;bry_time&#x27;</div><ul class='xr-dim-list'><li><span class='xr-has-index'>bry_time</span>: 12</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-219642e6-b3fd-4bf2-b4c6-04e454326f64' class='xr-array-in' type='checkbox' checked><label for='section-219642e6-b3fd-4bf2-b4c6-04e454326f64' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>15.0 45.0 74.0 105.0 135.0 166.0 196.0 227.0 258.0 288.0 319.0 349.0</span></div><div class='xr-array-data'><pre>array([ 15.,  45.,  74., 105., 135., 166., 196., 227., 258., 288., 319., 349.])</pre></div></div></li><li class='xr-section-item'><input id='section-493bdb9e-ddd7-409b-a7cf-6cd5f809ccf4' class='xr-section-summary-in' type='checkbox' checked /><label for='section-493bdb9e-ddd7-409b-a7cf-6cd5f809ccf4' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>bry_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.0 45.0 74.0 ... 319.0 349.0</div><input id='attrs-c3150aab-0b8f-43d0-867d-f267bdc270b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c3150aab-0b8f-43d0-867d-f267bdc270b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ceda1d5-94b2-4133-8feb-134271071c19' class='xr-var-data-in' type='checkbox'><label for='data-7ceda1d5-94b2-4133-8feb-134271071c19' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cycle_length :</span></dt><dd>365.25</dd><dt><span>long_name :</span></dt><dd>relative time: days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([ 15.,  45.,  74., 105., 135., 166., 196., 227., 258., 288., 319., 349.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>month</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-6dfacf5b-dcda-41f7-9a4d-dd93b1d9a7ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6dfacf5b-dcda-41f7-9a4d-dd93b1d9a7ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce8d16e1-f696-4771-9b90-26c4ba0eded9' class='xr-var-data-in' type='checkbox'><label for='data-ce8d16e1-f696-4771-9b90-26c4ba0eded9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Month index (1-12)</dd></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-16 ... 2000-12-15</div><input id='attrs-e8f596a7-fe6e-41da-891e-e75af07e2041' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e8f596a7-fe6e-41da-891e-e75af07e2041' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c31185be-9122-4cbe-bdfd-9a0c64cbcdf7' class='xr-var-data-in' type='checkbox'><label for='data-c31185be-9122-4cbe-bdfd-9a0c64cbcdf7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>absolute time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-16T00:00:00.000000000&#x27;, &#x27;2000-02-15T00:00:00.000000000&#x27;,\n",
        "       &#x27;2000-03-15T00:00:00.000000000&#x27;, &#x27;2000-04-15T00:00:00.000000000&#x27;,\n",
        "       &#x27;2000-05-15T00:00:00.000000000&#x27;, &#x27;2000-06-15T00:00:00.000000000&#x27;,\n",
        "       &#x27;2000-07-15T00:00:00.000000000&#x27;, &#x27;2000-08-15T00:00:00.000000000&#x27;,\n",
        "       &#x27;2000-09-15T00:00:00.000000000&#x27;, &#x27;2000-10-15T00:00:00.000000000&#x27;,\n",
        "       &#x27;2000-11-15T00:00:00.000000000&#x27;, &#x27;2000-12-15T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>bry_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.0 45.0 74.0 ... 319.0 349.0</div><input id='attrs-c6981bab-4802-4604-a247-7b6c933c0679' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c6981bab-4802-4604-a247-7b6c933c0679' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5815a38-c8b4-456c-b749-e3caa5c7acbe' class='xr-var-data-in' type='checkbox'><label for='data-b5815a38-c8b4-456c-b749-e3caa5c7acbe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cycle_length :</span></dt><dd>365.25</dd><dt><span>long_name :</span></dt><dd>relative time: days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([ 15.,  45.,  74., 105., 135., 166., 196., 227., 258., 288., 319., 349.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8d34eaa9-b9c1-443d-89aa-03ce64549c30' class='xr-section-summary-in' type='checkbox'  ><label for='section-8d34eaa9-b9c1-443d-89aa-03ce64549c30' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>bry_time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-8c60c189-5cb7-4991-8c8d-60d5c75c298d' class='xr-index-data-in' type='checkbox'/><label for='index-8c60c189-5cb7-4991-8c8d-60d5c75c298d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([15.000000000000002,               45.0,               74.0,\n",
-       "                    105.0,              135.0,              166.0,\n",
-       "                    196.0,              227.0,              258.0,\n",
-       "                    288.0,              319.0,              349.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;bry_time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2e4f9156-54be-421d-8bc8-17a69f8bcebc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2e4f9156-54be-421d-8bc8-17a69f8bcebc' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>cycle_length :</span></dt><dd>365.25</dd><dt><span>long_name :</span></dt><dd>relative time: days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8de1bdf6-1b79-49ea-88be-d2a7082b370e' class='xr-section-summary-in' type='checkbox' checked /><label for='section-8de1bdf6-1b79-49ea-88be-d2a7082b370e' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>cycle_length :</span></dt><dd>365.25</dd><dt><span>long_name :</span></dt><dd>relative time: days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.DataArray 'bry_time' (bry_time: 12)> Size: 96B\n",
        "array([ 15.,  45.,  74., 105., 135., 166., 196., 227., 258., 288., 319., 349.])\n",
        "Coordinates:\n",
+       "  * bry_time  (bry_time) float64 96B 15.0 45.0 74.0 105.0 ... 288.0 319.0 349.0\n",
        "    month     (bry_time) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12\n",
        "    abs_time  (bry_time) datetime64[ns] 96B 2000-01-16 2000-02-15 ... 2000-12-15\n",
-       "  * bry_time  (bry_time) float64 96B 15.0 45.0 74.0 105.0 ... 288.0 319.0 349.0\n",
        "Attributes:\n",
        "    cycle_length:  365.25\n",
        "    long_name:     relative time: days since 2000-01-01 00:00:00\n",
@@ -20734,9 +24524,7 @@
        "</symbol>\n",
        "</defs>\n",
        "</svg>\n",
-       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
-       " *\n",
-       " */\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
        "\n",
        ":root {\n",
        "  --xr-font-color0: var(\n",
@@ -20815,6 +24603,8 @@
        "  display: block !important;\n",
        "  min-width: 300px;\n",
        "  max-width: 700px;\n",
+       "  line-height: 1.6;\n",
+       "  padding-bottom: 4px;\n",
        "}\n",
        "\n",
        ".xr-text-repr-fallback {\n",
@@ -20825,8 +24615,11 @@
        ".xr-header {\n",
        "  padding-top: 6px;\n",
        "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
        "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "  margin-bottom: 4px;\n",
        "}\n",
        "\n",
        ".xr-header > div,\n",
@@ -20837,46 +24630,62 @@
        "}\n",
        "\n",
        ".xr-obj-type,\n",
-       ".xr-array-name {\n",
+       ".xr-obj-name {\n",
        "  margin-left: 2px;\n",
        "  margin-right: 10px;\n",
        "}\n",
        "\n",
-       ".xr-obj-type {\n",
+       ".xr-obj-type,\n",
+       ".xr-group-box-contents > label {\n",
        "  color: var(--xr-font-color2);\n",
+       "  display: block;\n",
        "}\n",
        "\n",
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
        "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
        "  display: contents;\n",
        "}\n",
        "\n",
-       ".xr-section-item input {\n",
-       "  display: inline-block;\n",
+       ".xr-section-item > input,\n",
+       ".xr-group-box-contents > input,\n",
+       ".xr-array-wrap > input {\n",
+       "  display: block;\n",
        "  opacity: 0;\n",
        "  height: 0;\n",
+       "  margin: 0;\n",
        "}\n",
        "\n",
-       ".xr-section-item input + label {\n",
+       ".xr-section-item > input + label,\n",
+       ".xr-var-item > input + label {\n",
        "  color: var(--xr-disabled-color);\n",
-       "  border: 2px solid transparent !important;\n",
        "}\n",
        "\n",
-       ".xr-section-item input:enabled + label {\n",
+       ".xr-section-item > input:enabled + label,\n",
+       ".xr-var-item > input:enabled + label,\n",
+       ".xr-array-wrap > input:enabled + label,\n",
+       ".xr-group-box-contents > input:enabled + label {\n",
        "  cursor: pointer;\n",
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
-       ".xr-section-item input:focus + label {\n",
-       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       ".xr-section-item > input:focus-visible + label,\n",
+       ".xr-var-item > input:focus-visible + label,\n",
+       ".xr-array-wrap > input:focus-visible + label,\n",
+       ".xr-group-box-contents > input:focus-visible + label {\n",
+       "  outline: auto;\n",
        "}\n",
        "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
+       ".xr-section-item > input:enabled + label:hover,\n",
+       ".xr-var-item > input:enabled + label:hover,\n",
+       ".xr-array-wrap > input:enabled + label:hover,\n",
+       ".xr-group-box-contents > input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
        "\n",
@@ -20884,11 +24693,25 @@
        "  grid-column: 1;\n",
        "  color: var(--xr-font-color2);\n",
        "  font-weight: 500;\n",
+       "  white-space: nowrap;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > em {\n",
+       "  font-weight: normal;\n",
+       "}\n",
+       "\n",
+       ".xr-span-grid {\n",
+       "  grid-column-end: -1;\n",
        "}\n",
        "\n",
        ".xr-section-summary > span {\n",
        "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
+       "  padding-left: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.6em;\n",
        "}\n",
        "\n",
        ".xr-section-summary-in:disabled + label {\n",
@@ -20916,9 +24739,9 @@
        "}\n",
        "\n",
        ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
+       ".xr-section-inline-details,\n",
+       ".xr-group-box-contents > label {\n",
        "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
        "}\n",
        "\n",
        ".xr-section-inline-details {\n",
@@ -20926,15 +24749,81 @@
        "}\n",
        "\n",
        ".xr-section-details {\n",
-       "  display: none;\n",
        "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
        "  margin-bottom: 5px;\n",
        "}\n",
        "\n",
+       ".xr-section-summary-in ~ .xr-section-details {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
        ".xr-section-summary-in:checked ~ .xr-section-details {\n",
        "  display: contents;\n",
        "}\n",
        "\n",
+       ".xr-children {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 100%;\n",
+       "  grid-column: 1 / -1;\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 30px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 26px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > label::before {\n",
+       "  content: \"📂\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label::before {\n",
+       "  content: \"📁\";\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label {\n",
+       "  padding-bottom: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked ~ .xr-sections {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-ellipsis {\n",
+       "  font-size: 1.4em;\n",
+       "  font-weight: 900;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  letter-spacing: 0.15em;\n",
+       "  cursor: default;\n",
+       "}\n",
+       "\n",
        ".xr-array-wrap {\n",
        "  grid-column: 1 / -1;\n",
        "  display: grid;\n",
@@ -21166,34 +25055,30 @@
        "array([ 15.5,  45. ,  74.5, 105. , 135.5, 166. , 196. , 227.5, 258. , 288.5,\n",
        "       319. , 349.5])\n",
        "Coordinates:\n",
+       "  * bry_time  (bry_time) float64 96B 15.5 45.0 74.5 105.0 ... 288.5 319.0 349.5\n",
        "    month     (bry_time) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12\n",
        "    abs_time  (bry_time) datetime64[ns] 96B 2000-01-16T12:00:00 ... 2000-12-1...\n",
-       "  * bry_time  (bry_time) float64 96B 15.5 45.0 74.5 105.0 ... 288.5 319.0 349.5\n",
        "Attributes:\n",
        "    cycle_length:  365.25\n",
        "    long_name:     relative time: days since 2000-01-01 00:00:00\n",
-       "    units:         days</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'bry_time'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>bry_time</span>: 12</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-44b07382-7337-4085-8666-c6a1bbba9de6' class='xr-array-in' type='checkbox' checked><label for='section-44b07382-7337-4085-8666-c6a1bbba9de6' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>15.5 45.0 74.5 105.0 135.5 166.0 196.0 227.5 258.0 288.5 319.0 349.5</span></div><div class='xr-array-data'><pre>array([ 15.5,  45. ,  74.5, 105. , 135.5, 166. , 196. , 227.5, 258. , 288.5,\n",
-       "       319. , 349.5])</pre></div></div></li><li class='xr-section-item'><input id='section-920cbb9e-c387-4f66-b0e7-bd4be4265d18' class='xr-section-summary-in' type='checkbox'  checked><label for='section-920cbb9e-c387-4f66-b0e7-bd4be4265d18' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>month</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-8e3f06aa-db66-4b9b-ac79-8d8f22789300' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8e3f06aa-db66-4b9b-ac79-8d8f22789300' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3cc854e8-1f04-4d9c-83a0-05f2acf126a9' class='xr-var-data-in' type='checkbox'><label for='data-3cc854e8-1f04-4d9c-83a0-05f2acf126a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Month index (1-12)</dd></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-16T12:00:00 ... 2000-12-...</div><input id='attrs-2cccf47b-1109-483d-a4ec-45ec49fc9439' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2cccf47b-1109-483d-a4ec-45ec49fc9439' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21cd386a-f4e2-4bc0-ba66-6b9967e55817' class='xr-var-data-in' type='checkbox'><label for='data-21cd386a-f4e2-4bc0-ba66-6b9967e55817' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>absolute time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-16T12:00:00.000000000&#x27;, &#x27;2000-02-15T00:00:00.000000000&#x27;,\n",
+       "    units:         days</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-obj-name'>&#x27;bry_time&#x27;</div><ul class='xr-dim-list'><li><span class='xr-has-index'>bry_time</span>: 12</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-1711131f-4456-480c-9d65-f5b07eb9e3b2' class='xr-array-in' type='checkbox' checked><label for='section-1711131f-4456-480c-9d65-f5b07eb9e3b2' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>15.5 45.0 74.5 105.0 135.5 166.0 196.0 227.5 258.0 288.5 319.0 349.5</span></div><div class='xr-array-data'><pre>array([ 15.5,  45. ,  74.5, 105. , 135.5, 166. , 196. , 227.5, 258. , 288.5,\n",
+       "       319. , 349.5])</pre></div></div></li><li class='xr-section-item'><input id='section-0053911c-352b-46d3-a298-aa85770860f1' class='xr-section-summary-in' type='checkbox' checked /><label for='section-0053911c-352b-46d3-a298-aa85770860f1' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>bry_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.5 45.0 74.5 ... 319.0 349.5</div><input id='attrs-8c727ab3-27b9-49d2-8631-e6ea08365df6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8c727ab3-27b9-49d2-8631-e6ea08365df6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a028c08d-ab26-41d4-886b-f96e3466c4c0' class='xr-var-data-in' type='checkbox'><label for='data-a028c08d-ab26-41d4-886b-f96e3466c4c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cycle_length :</span></dt><dd>365.25</dd><dt><span>long_name :</span></dt><dd>relative time: days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([ 15.5,  45. ,  74.5, 105. , 135.5, 166. , 196. , 227.5, 258. , 288.5,\n",
+       "       319. , 349.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>month</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-e5f85601-0af4-40c8-a940-27ce66048c6a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e5f85601-0af4-40c8-a940-27ce66048c6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6926c1e-8454-434a-b2d8-14bdad3e38c2' class='xr-var-data-in' type='checkbox'><label for='data-d6926c1e-8454-434a-b2d8-14bdad3e38c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Month index (1-12)</dd></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-16T12:00:00 ... 2000-12-...</div><input id='attrs-748470b0-5324-40e4-8e4a-02cb848a911e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-748470b0-5324-40e4-8e4a-02cb848a911e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5760f4f6-82ae-4a1d-93f8-f857deb1db05' class='xr-var-data-in' type='checkbox'><label for='data-5760f4f6-82ae-4a1d-93f8-f857deb1db05' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>absolute time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-16T12:00:00.000000000&#x27;, &#x27;2000-02-15T00:00:00.000000000&#x27;,\n",
        "       &#x27;2000-03-15T12:00:00.000000000&#x27;, &#x27;2000-04-15T00:00:00.000000000&#x27;,\n",
        "       &#x27;2000-05-15T12:00:00.000000000&#x27;, &#x27;2000-06-15T00:00:00.000000000&#x27;,\n",
        "       &#x27;2000-07-15T00:00:00.000000000&#x27;, &#x27;2000-08-15T12:00:00.000000000&#x27;,\n",
        "       &#x27;2000-09-15T00:00:00.000000000&#x27;, &#x27;2000-10-15T12:00:00.000000000&#x27;,\n",
        "       &#x27;2000-11-15T00:00:00.000000000&#x27;, &#x27;2000-12-15T12:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>bry_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.5 45.0 74.5 ... 319.0 349.5</div><input id='attrs-7f90e868-4c88-4972-8ed4-a8292a85ead9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f90e868-4c88-4972-8ed4-a8292a85ead9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba4b45ae-c49a-41c0-910e-86d7de9cbbee' class='xr-var-data-in' type='checkbox'><label for='data-ba4b45ae-c49a-41c0-910e-86d7de9cbbee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cycle_length :</span></dt><dd>365.25</dd><dt><span>long_name :</span></dt><dd>relative time: days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([ 15.5,  45. ,  74.5, 105. , 135.5, 166. , 196. , 227.5, 258. , 288.5,\n",
-       "       319. , 349.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d8a50ea0-f47c-4043-a280-1075ebb8eba7' class='xr-section-summary-in' type='checkbox'  ><label for='section-d8a50ea0-f47c-4043-a280-1075ebb8eba7' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>bry_time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-ccd4dd0f-e5e7-4d22-a11c-f60b44bf8b85' class='xr-index-data-in' type='checkbox'/><label for='index-ccd4dd0f-e5e7-4d22-a11c-f60b44bf8b85' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([15.500000000000002,               45.0,               74.5,\n",
-       "                    105.0,              135.5,              166.0,\n",
-       "                    196.0,              227.5,              258.0,\n",
-       "                    288.5,              319.0,              349.5],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;bry_time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-875fb857-ddf2-4297-a562-444a0494a4fb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-875fb857-ddf2-4297-a562-444a0494a4fb' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>cycle_length :</span></dt><dd>365.25</dd><dt><span>long_name :</span></dt><dd>relative time: days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-90d96466-99e0-4000-8c42-7046f0344e66' class='xr-section-summary-in' type='checkbox' checked /><label for='section-90d96466-99e0-4000-8c42-7046f0344e66' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>cycle_length :</span></dt><dd>365.25</dd><dt><span>long_name :</span></dt><dd>relative time: days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.DataArray 'bry_time' (bry_time: 12)> Size: 96B\n",
        "array([ 15.5,  45. ,  74.5, 105. , 135.5, 166. , 196. , 227.5, 258. , 288.5,\n",
        "       319. , 349.5])\n",
        "Coordinates:\n",
+       "  * bry_time  (bry_time) float64 96B 15.5 45.0 74.5 105.0 ... 288.5 319.0 349.5\n",
        "    month     (bry_time) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12\n",
        "    abs_time  (bry_time) datetime64[ns] 96B 2000-01-16T12:00:00 ... 2000-12-1...\n",
-       "  * bry_time  (bry_time) float64 96B 15.5 45.0 74.5 105.0 ... 288.5 319.0 349.5\n",
        "Attributes:\n",
        "    cycle_length:  365.25\n",
        "    long_name:     relative time: days since 2000-01-01 00:00:00\n",
@@ -21236,7 +25121,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "aabbf660-ef8d-423c-8e99-c618e02169a1",
+   "id": "802c7642-f403-4074-a0b5-c2cc4a683dac",
    "metadata": {
     "tags": []
    },
@@ -21245,12 +25130,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[########################################] | 100% Completed | 504.51 ms\n"
+      "[########################################] | 100% Completed | 201.95 ms\n"
      ]
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 900x500 with 2 Axes>"
       ]
@@ -21273,12 +25158,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[########################################] | 100% Completed | 102.05 ms\n"
+      "[########################################] | 100% Completed | 101.61 ms\n"
      ]
     },
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       "text/plain": [
        "<Figure size 900x500 with 2 Axes>"
       ]
@@ -21311,7 +25196,7 @@
    },
    "outputs": [],
    "source": [
-    "filepath = \"/pscratch/sd/n/nloose/forcing/my_bgc_boundary_forcing.nc\""
+    "filepath = \"/anvil/scratch/x-kthyng/forcing/my_bgc_boundary_forcing.nc\""
    ]
   },
   {
@@ -21326,23 +25211,25 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-12-20 13:50:17 - INFO - Writing the following NetCDF files:\n",
-      "/pscratch/sd/n/nloose/forcing/my_bgc_boundary_forcing_clim.nc\n"
+      "2026-06-22 17:58:41 - INFO - Writing the following NetCDF files:\n",
+      "/anvil/scratch/x-kthyng/forcing/my_bgc_boundary_forcing_clim.nc\n",
+      "/home/x-kthyng/packages/roms-tools/roms_tools/utils.py:916: UserWarning: Unlimited dimension(s) {'time'} declared in 'dataset.encoding', but not part of current dataset dimensions. Consider removing {'time'} from 'dataset.encoding'.\n",
+      "  xr.save_mfdataset(dataset_list, output_filenames)\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[########################################] | 100% Completed | 7.95 ss\n",
-      "CPU times: user 1min 3s, sys: 44.5 s, total: 1min 47s\n",
-      "Wall time: 8.58 s\n"
+      "[########################################] | 100% Completed | 7.61 ss\n",
+      "CPU times: user 7.59 s, sys: 2.04 s, total: 9.62 s\n",
+      "Wall time: 8.32 s\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "[PosixPath('/pscratch/sd/n/nloose/forcing/my_bgc_boundary_forcing_clim.nc')]"
+       "[PosixPath('/anvil/scratch/x-kthyng/forcing/my_bgc_boundary_forcing_clim.nc')]"
       ]
      },
      "execution_count": 24,
@@ -21371,7 +25258,7 @@
    },
    "outputs": [],
    "source": [
-    "filepath = \"/pscratch/sd/n/nloose/forcing/my_boundary_forcing.nc\""
+    "filepath = \"/anvil/scratch/x-kthyng/forcing/my_boundary_forcing.nc\""
    ]
   },
   {
@@ -21386,26 +25273,30 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-12-20 13:50:25 - INFO - Writing the following NetCDF files:\n",
-      "/pscratch/sd/n/nloose/forcing/my_boundary_forcing_201201.nc\n",
-      "/pscratch/sd/n/nloose/forcing/my_boundary_forcing_201202.nc\n"
+      "2026-06-22 17:58:50 - INFO - Writing the following NetCDF files:\n",
+      "/anvil/scratch/x-kthyng/forcing/my_boundary_forcing_201201.nc\n",
+      "/anvil/scratch/x-kthyng/forcing/my_boundary_forcing_201202.nc\n",
+      "/home/x-kthyng/packages/roms-tools/roms_tools/utils.py:916: UserWarning: Unlimited dimension(s) {'time'} declared in 'dataset.encoding', but not part of current dataset dimensions. Consider removing {'time'} from 'dataset.encoding'.\n",
+      "  xr.save_mfdataset(dataset_list, output_filenames)\n",
+      "/home/x-kthyng/packages/roms-tools/roms_tools/utils.py:916: UserWarning: Unlimited dimension(s) {'time'} declared in 'dataset.encoding', but not part of current dataset dimensions. Consider removing {'time'} from 'dataset.encoding'.\n",
+      "  xr.save_mfdataset(dataset_list, output_filenames)\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[########################################] | 100% Completed | 1.25 sms\n",
-      "[########################################] | 100% Completed | 1.67 sms\n",
-      "CPU times: user 7.85 s, sys: 3.83 s, total: 11.7 s\n",
-      "Wall time: 3.25 s\n"
+      "[########################################] | 100% Completed | 7.25 ss\n",
+      "[########################################] | 100% Completed | 23.13 s\n",
+      "CPU times: user 3.24 s, sys: 3.53 s, total: 6.76 s\n",
+      "Wall time: 31 s\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "[PosixPath('/pscratch/sd/n/nloose/forcing/my_boundary_forcing_201201.nc'),\n",
-       " PosixPath('/pscratch/sd/n/nloose/forcing/my_boundary_forcing_201202.nc')]"
+       "[PosixPath('/anvil/scratch/x-kthyng/forcing/my_boundary_forcing_201201.nc'),\n",
+       " PosixPath('/anvil/scratch/x-kthyng/forcing/my_boundary_forcing_201202.nc')]"
       ]
      },
      "execution_count": 26,
@@ -21450,7 +25341,7 @@
    },
    "outputs": [],
    "source": [
-    "yaml_filepath = \"/pscratch/sd/n/nloose/forcing/my_boundary_forcing.yaml\""
+    "yaml_filepath = \"/anvil/scratch/x-kthyng/forcing/my_boundary_forcing.yaml\""
    ]
   },
   {
@@ -21486,7 +25377,7 @@
      "output_type": "stream",
      "text": [
       "---\n",
-      "roms_tools_version: 3.1.2.dev23+gc2b82bc5c\n",
+      "roms_tools_version: 10000.dev361+g2e6c47bd8\n",
       "---\n",
       "Grid:\n",
       "  nx: 500\n",
@@ -21502,9 +25393,11 @@
       "  hc: 300.0\n",
       "  topography_source:\n",
       "    name: SRTM15\n",
-      "    path: /global/cfs/projectdirs/m4746/Datasets/SRTM15/SRTM15_V2.6.nc\n",
+      "    path: /anvil/projects/x-ees250129/Datasets/SRTM15/SRTM15_V2.6.nc\n",
       "  mask_shapefile: null\n",
+      "  close_narrow_channels: false\n",
       "  hmin: 5.0\n",
+      "  filename: null\n",
       "BoundaryForcing:\n",
       "  start_time: '2012-01-30T00:00:00'\n",
       "  end_time: '2012-02-04T00:00:00'\n",
@@ -21516,18 +25409,24 @@
       "  source:\n",
       "    name: GLORYS\n",
       "    path:\n",
-      "    - /global/cfs/projectdirs/m4746/Datasets/GLORYS/NA/2012/mercatorglorys12v1_gl12_mean_20120129.nc\n",
-      "    - /global/cfs/projectdirs/m4746/Datasets/GLORYS/NA/2012/mercatorglorys12v1_gl12_mean_20120130.nc\n",
-      "    - /global/cfs/projectdirs/m4746/Datasets/GLORYS/NA/2012/mercatorglorys12v1_gl12_mean_20120131.nc\n",
-      "    - /global/cfs/projectdirs/m4746/Datasets/GLORYS/NA/2012/mercatorglorys12v1_gl12_mean_20120201.nc\n",
-      "    - /global/cfs/projectdirs/m4746/Datasets/GLORYS/NA/2012/mercatorglorys12v1_gl12_mean_20120202.nc\n",
-      "    - /global/cfs/projectdirs/m4746/Datasets/GLORYS/NA/2012/mercatorglorys12v1_gl12_mean_20120203.nc\n",
-      "    - /global/cfs/projectdirs/m4746/Datasets/GLORYS/NA/2012/mercatorglorys12v1_gl12_mean_20120204.nc\n",
-      "    - /global/cfs/projectdirs/m4746/Datasets/GLORYS/NA/2012/mercatorglorys12v1_gl12_mean_20120205.nc\n",
+      "    - /anvil/projects/x-ees250129/cstar-forge-data/source-data/GLORYS_GLOBAL/cmems_mod_glo_phy_my_0.083deg_P1D-m_GLOBAL_20120129.nc\n",
+      "    - /anvil/projects/x-ees250129/cstar-forge-data/source-data/GLORYS_GLOBAL/cmems_mod_glo_phy_my_0.083deg_P1D-m_GLOBAL_20120130.nc\n",
+      "    - /anvil/projects/x-ees250129/cstar-forge-data/source-data/GLORYS_GLOBAL/cmems_mod_glo_phy_my_0.083deg_P1D-m_GLOBAL_20120131.nc\n",
+      "    - /anvil/projects/x-ees250129/cstar-forge-data/source-data/GLORYS_GLOBAL/cmems_mod_glo_phy_my_0.083deg_P1D-m_GLOBAL_20120201.nc\n",
+      "    - /anvil/projects/x-ees250129/cstar-forge-data/source-data/GLORYS_GLOBAL/cmems_mod_glo_phy_my_0.083deg_P1D-m_GLOBAL_20120202.nc\n",
+      "    - /anvil/projects/x-ees250129/cstar-forge-data/source-data/GLORYS_GLOBAL/cmems_mod_glo_phy_my_0.083deg_P1D-m_GLOBAL_20120203.nc\n",
+      "    - /anvil/projects/x-ees250129/cstar-forge-data/source-data/GLORYS_GLOBAL/cmems_mod_glo_phy_my_0.083deg_P1D-m_GLOBAL_20120204.nc\n",
+      "    - /anvil/projects/x-ees250129/cstar-forge-data/source-data/GLORYS_GLOBAL/cmems_mod_glo_phy_my_0.083deg_P1D-m_GLOBAL_20120205.nc\n",
       "    climatology: false\n",
       "  type: physics\n",
-      "  apply_2d_horizontal_fill: false\n",
+      "  prefill: null\n",
+      "  prefill_kwargs: null\n",
+      "  regrid_method: null\n",
+      "  extrap_method: null\n",
+      "  extrap_kwargs: null\n",
       "  model_reference_date: '2000-01-01T00:00:00'\n",
+      "  chunks: null\n",
+      "  initial_slice_bounds: null\n",
       "  bypass_validation: false\n",
       "\n"
      ]
@@ -21562,15 +25461,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-12-20 13:50:36 - INFO - Using boundary configuration: {'south': True, 'east': True, 'north': False, 'west': True}\n"
+      "2026-06-22 17:59:25 - INFO - Using boundary configuration: {'south': True, 'east': True, 'north': False, 'west': True}\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 8.39 s, sys: 2.36 s, total: 10.8 s\n",
-      "Wall time: 11.3 s\n"
+      "CPU times: user 8.21 s, sys: 977 ms, total: 9.18 s\n",
+      "Wall time: 11.4 s\n"
      ]
     }
    ],
@@ -21604,9 +25503,7 @@
        "</symbol>\n",
        "</defs>\n",
        "</svg>\n",
-       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
-       " *\n",
-       " */\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
        "\n",
        ":root {\n",
        "  --xr-font-color0: var(\n",
@@ -21685,6 +25582,8 @@
        "  display: block !important;\n",
        "  min-width: 300px;\n",
        "  max-width: 700px;\n",
+       "  line-height: 1.6;\n",
+       "  padding-bottom: 4px;\n",
        "}\n",
        "\n",
        ".xr-text-repr-fallback {\n",
@@ -21695,8 +25594,11 @@
        ".xr-header {\n",
        "  padding-top: 6px;\n",
        "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
        "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "  margin-bottom: 4px;\n",
        "}\n",
        "\n",
        ".xr-header > div,\n",
@@ -21707,46 +25609,62 @@
        "}\n",
        "\n",
        ".xr-obj-type,\n",
-       ".xr-array-name {\n",
+       ".xr-obj-name {\n",
        "  margin-left: 2px;\n",
        "  margin-right: 10px;\n",
        "}\n",
        "\n",
-       ".xr-obj-type {\n",
+       ".xr-obj-type,\n",
+       ".xr-group-box-contents > label {\n",
        "  color: var(--xr-font-color2);\n",
+       "  display: block;\n",
        "}\n",
        "\n",
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
        "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
        "  display: contents;\n",
        "}\n",
        "\n",
-       ".xr-section-item input {\n",
-       "  display: inline-block;\n",
+       ".xr-section-item > input,\n",
+       ".xr-group-box-contents > input,\n",
+       ".xr-array-wrap > input {\n",
+       "  display: block;\n",
        "  opacity: 0;\n",
        "  height: 0;\n",
+       "  margin: 0;\n",
        "}\n",
        "\n",
-       ".xr-section-item input + label {\n",
+       ".xr-section-item > input + label,\n",
+       ".xr-var-item > input + label {\n",
        "  color: var(--xr-disabled-color);\n",
-       "  border: 2px solid transparent !important;\n",
        "}\n",
        "\n",
-       ".xr-section-item input:enabled + label {\n",
+       ".xr-section-item > input:enabled + label,\n",
+       ".xr-var-item > input:enabled + label,\n",
+       ".xr-array-wrap > input:enabled + label,\n",
+       ".xr-group-box-contents > input:enabled + label {\n",
        "  cursor: pointer;\n",
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
-       ".xr-section-item input:focus + label {\n",
-       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       ".xr-section-item > input:focus-visible + label,\n",
+       ".xr-var-item > input:focus-visible + label,\n",
+       ".xr-array-wrap > input:focus-visible + label,\n",
+       ".xr-group-box-contents > input:focus-visible + label {\n",
+       "  outline: auto;\n",
        "}\n",
        "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
+       ".xr-section-item > input:enabled + label:hover,\n",
+       ".xr-var-item > input:enabled + label:hover,\n",
+       ".xr-array-wrap > input:enabled + label:hover,\n",
+       ".xr-group-box-contents > input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
        "\n",
@@ -21754,11 +25672,25 @@
        "  grid-column: 1;\n",
        "  color: var(--xr-font-color2);\n",
        "  font-weight: 500;\n",
+       "  white-space: nowrap;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > em {\n",
+       "  font-weight: normal;\n",
+       "}\n",
+       "\n",
+       ".xr-span-grid {\n",
+       "  grid-column-end: -1;\n",
        "}\n",
        "\n",
        ".xr-section-summary > span {\n",
        "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
+       "  padding-left: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.6em;\n",
        "}\n",
        "\n",
        ".xr-section-summary-in:disabled + label {\n",
@@ -21786,9 +25718,9 @@
        "}\n",
        "\n",
        ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
+       ".xr-section-inline-details,\n",
+       ".xr-group-box-contents > label {\n",
        "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
        "}\n",
        "\n",
        ".xr-section-inline-details {\n",
@@ -21796,15 +25728,81 @@
        "}\n",
        "\n",
        ".xr-section-details {\n",
-       "  display: none;\n",
        "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
        "  margin-bottom: 5px;\n",
        "}\n",
        "\n",
+       ".xr-section-summary-in ~ .xr-section-details {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
        ".xr-section-summary-in:checked ~ .xr-section-details {\n",
        "  display: contents;\n",
        "}\n",
        "\n",
+       ".xr-children {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 100%;\n",
+       "  grid-column: 1 / -1;\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 30px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 26px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > label::before {\n",
+       "  content: \"📂\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label::before {\n",
+       "  content: \"📁\";\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label {\n",
+       "  padding-bottom: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked ~ .xr-sections {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-ellipsis {\n",
+       "  font-size: 1.4em;\n",
+       "  font-weight: 900;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  letter-spacing: 0.15em;\n",
+       "  cursor: default;\n",
+       "}\n",
+       "\n",
        ".xr-array-wrap {\n",
        "  grid-column: 1 / -1;\n",
        "  display: grid;\n",
@@ -22032,42 +26030,45 @@
        "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
        "  stroke-width: 0.8px;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 17MB\n",
-       "Dimensions:     (bry_time: 7, s_rho: 100, xi_u: 501, xi_rho: 502, eta_rho: 502,\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 19MB\n",
+       "Dimensions:     (bry_time: 8, s_rho: 100, xi_u: 501, xi_rho: 502, eta_rho: 502,\n",
        "                 eta_v: 501)\n",
        "Coordinates:\n",
-       "    abs_time    (bry_time) datetime64[ns] 56B 2012-01-29T12:00:00 ... 2012-02...\n",
-       "  * bry_time    (bry_time) float64 56B 4.412e+03 4.412e+03 ... 4.418e+03\n",
+       "  * bry_time    (bry_time) float64 64B 4.411e+03 4.412e+03 ... 4.418e+03\n",
+       "    abs_time    (bry_time) datetime64[ns] 64B 2012-01-29 ... 2012-02-05\n",
        "Dimensions without coordinates: s_rho, xi_u, xi_rho, eta_rho, eta_v\n",
        "Data variables: (12/21)\n",
-       "    u_south     (bry_time, s_rho, xi_u) float32 1MB dask.array&lt;chunksize=(1, 100, 501), meta=np.ndarray&gt;\n",
-       "    v_south     (bry_time, s_rho, xi_rho) float32 1MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
-       "    temp_south  (bry_time, s_rho, xi_rho) float32 1MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
-       "    salt_south  (bry_time, s_rho, xi_rho) float32 1MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
-       "    zeta_south  (bry_time, xi_rho) float32 14kB dask.array&lt;chunksize=(1, 502), meta=np.ndarray&gt;\n",
-       "    ubar_south  (bry_time, xi_u) float32 14kB dask.array&lt;chunksize=(1, 501), meta=np.ndarray&gt;\n",
+       "    u_south     (bry_time, s_rho, xi_u) float32 2MB dask.array&lt;chunksize=(1, 100, 501), meta=np.ndarray&gt;\n",
+       "    v_south     (bry_time, s_rho, xi_rho) float32 2MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
+       "    temp_south  (bry_time, s_rho, xi_rho) float32 2MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
+       "    salt_south  (bry_time, s_rho, xi_rho) float32 2MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
+       "    zeta_south  (bry_time, xi_rho) float32 16kB dask.array&lt;chunksize=(1, 502), meta=np.ndarray&gt;\n",
+       "    ubar_south  (bry_time, xi_u) float32 16kB dask.array&lt;chunksize=(1, 501), meta=np.ndarray&gt;\n",
        "    ...          ...\n",
-       "    v_west      (bry_time, s_rho, eta_v) float32 1MB dask.array&lt;chunksize=(1, 100, 501), meta=np.ndarray&gt;\n",
-       "    temp_west   (bry_time, s_rho, eta_rho) float32 1MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
-       "    salt_west   (bry_time, s_rho, eta_rho) float32 1MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
-       "    zeta_west   (bry_time, eta_rho) float32 14kB dask.array&lt;chunksize=(1, 502), meta=np.ndarray&gt;\n",
-       "    ubar_west   (bry_time, eta_rho) float32 14kB dask.array&lt;chunksize=(1, 502), meta=np.ndarray&gt;\n",
-       "    vbar_west   (bry_time, eta_v) float32 14kB dask.array&lt;chunksize=(1, 501), meta=np.ndarray&gt;\n",
-       "Attributes:\n",
+       "    v_west      (bry_time, s_rho, eta_v) float32 2MB dask.array&lt;chunksize=(1, 100, 501), meta=np.ndarray&gt;\n",
+       "    temp_west   (bry_time, s_rho, eta_rho) float32 2MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
+       "    salt_west   (bry_time, s_rho, eta_rho) float32 2MB dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;\n",
+       "    zeta_west   (bry_time, eta_rho) float32 16kB dask.array&lt;chunksize=(1, 502), meta=np.ndarray&gt;\n",
+       "    ubar_west   (bry_time, eta_rho) float32 16kB dask.array&lt;chunksize=(1, 502), meta=np.ndarray&gt;\n",
+       "    vbar_west   (bry_time, eta_v) float32 16kB dask.array&lt;chunksize=(1, 501), meta=np.ndarray&gt;\n",
+       "Attributes: (12/13)\n",
        "    title:                                ROMS boundary forcing file created ...\n",
-       "    roms_tools_version:                   3.1.2.dev23+gc2b82bc5c\n",
+       "    roms_tools_version:                   10000.dev361+g2e6c47bd8\n",
        "    start_time:                           2012-01-30 00:00:00\n",
        "    end_time:                             2012-02-04 00:00:00\n",
        "    source:                               GLORYS\n",
        "    model_reference_date:                 2000-01-01 00:00:00\n",
-       "    apply_2d_horizontal_fill:             False\n",
+       "    ...                                   ...\n",
+       "    regrid_method:                        xesmf\n",
+       "    extrap_method:                        inverse_dist\n",
        "    adjust_depth_for_sea_surface_height:  False\n",
        "    theta_s:                              5.0\n",
        "    theta_b:                              2.0\n",
-       "    hc:                                   300.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d41737f0-2d3a-4e5e-9d85-4ea2c735de1e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d41737f0-2d3a-4e5e-9d85-4ea2c735de1e' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>bry_time</span>: 7</li><li><span>s_rho</span>: 100</li><li><span>xi_u</span>: 501</li><li><span>xi_rho</span>: 502</li><li><span>eta_rho</span>: 502</li><li><span>eta_v</span>: 501</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-da26a0a5-c8c0-4a99-941b-a68a961d490c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-da26a0a5-c8c0-4a99-941b-a68a961d490c' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-01-29T12:00:00 ... 2012-02-...</div><input id='attrs-e7a29a19-3d28-487f-8e21-47b688dd85f0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7a29a19-3d28-487f-8e21-47b688dd85f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c63d1a16-d84a-4b46-b4b1-3aa3bf290674' class='xr-var-data-in' type='checkbox'><label for='data-c63d1a16-d84a-4b46-b4b1-3aa3bf290674' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>absolute time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-01-29T12:00:00.000000000&#x27;, &#x27;2012-01-30T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2012-01-31T12:00:00.000000000&#x27;, &#x27;2012-02-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2012-02-02T12:00:00.000000000&#x27;, &#x27;2012-02-03T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2012-02-04T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>bry_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.412e+03 4.412e+03 ... 4.418e+03</div><input id='attrs-60ee353a-7274-491d-a2cc-f6c230bfb8e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-60ee353a-7274-491d-a2cc-f6c230bfb8e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-98da2c98-8f44-44e3-b79f-61141f084d47' class='xr-var-data-in' type='checkbox'><label for='data-98da2c98-8f44-44e3-b79f-61141f084d47' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>relative time: days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([4411.5, 4412.5, 4413.5, 4414.5, 4415.5, 4416.5, 4417.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1792e777-d880-4e5b-a4dd-f12906e0fc98' class='xr-section-summary-in' type='checkbox'  ><label for='section-1792e777-d880-4e5b-a4dd-f12906e0fc98' class='xr-section-summary' >Data variables: <span>(21)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>u_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 501), meta=np.ndarray&gt;</div><input id='attrs-61793a45-3c8d-4d7b-b0a9-82b55ea825ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-61793a45-3c8d-4d7b-b0a9-82b55ea825ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3a92a32-d8ab-4899-ae03-15a215a9572a' class='xr-var-data-in' type='checkbox'><label for='data-a3a92a32-d8ab-4899-ae03-15a215a9572a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    hc:                                   300.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-1eab8d89-5ad0-43bd-bf3e-895483d47719' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-1eab8d89-5ad0-43bd-bf3e-895483d47719' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>bry_time</span>: 8</li><li><span>s_rho</span>: 100</li><li><span>xi_u</span>: 501</li><li><span>xi_rho</span>: 502</li><li><span>eta_rho</span>: 502</li><li><span>eta_v</span>: 501</li></ul></div></li><li class='xr-section-item'><input id='section-fe457074-30b6-49cd-9cb6-8baa7fc501a6' class='xr-section-summary-in' type='checkbox' checked /><label for='section-fe457074-30b6-49cd-9cb6-8baa7fc501a6' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>bry_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.411e+03 4.412e+03 ... 4.418e+03</div><input id='attrs-99f763ec-d536-4eb1-9af2-1b5c94621159' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99f763ec-d536-4eb1-9af2-1b5c94621159' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bdc44f47-8b52-455a-b784-d68fc5384530' class='xr-var-data-in' type='checkbox'><label for='data-bdc44f47-8b52-455a-b784-d68fc5384530' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>relative time: days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([4411., 4412., 4413., 4414., 4415., 4416., 4417., 4418.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-01-29 ... 2012-02-05</div><input id='attrs-b1aa1afc-5952-4d22-a257-7aa9eb5ad2e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b1aa1afc-5952-4d22-a257-7aa9eb5ad2e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5bb8f2bd-935b-4615-bd92-4da0ad9d6bd1' class='xr-var-data-in' type='checkbox'><label for='data-5bb8f2bd-935b-4615-bd92-4da0ad9d6bd1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>absolute time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-01-29T00:00:00.000000000&#x27;, &#x27;2012-01-30T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-01-31T00:00:00.000000000&#x27;, &#x27;2012-02-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-02-02T00:00:00.000000000&#x27;, &#x27;2012-02-03T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-02-04T00:00:00.000000000&#x27;, &#x27;2012-02-05T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5739482d-1e85-44bd-8beb-6a7891340e11' class='xr-section-summary-in' type='checkbox' /><label for='section-5739482d-1e85-44bd-8beb-6a7891340e11' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(21)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>u_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 501), meta=np.ndarray&gt;</div><input id='attrs-d2ee57bd-afe3-4d99-9eb1-02c2d404e667' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2ee57bd-afe3-4d99-9eb1-02c2d404e667' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0dccdad5-8c60-4c03-816d-9c89a1a60eae' class='xr-var-data-in' type='checkbox'><label for='data-0dccdad5-8c60-4c03-816d-9c89a1a60eae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>unit_long :</span></dt><dd>Meters per second</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>long_name :</span></dt><dd>southern boundary u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -22082,18 +26083,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.34 MiB </td>\n",
+       "                        <td> 1.53 MiB </td>\n",
        "                        <td> 195.70 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 100, 501) </td>\n",
+       "                        <td> (8, 100, 501) </td>\n",
        "                        <td> (1, 100, 501) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 120 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 83 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -22103,61 +26104,63 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"195\" height=\"107\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "        <svg width=\"196\" height=\"108\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
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        "\n",
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        "\n",
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+       "  <line x1=\"10\" y1=\"0\" x2=\"26\" y2=\"16\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"130\" y1=\"0\" x2=\"146\" y2=\"16\" style=\"stroke-width:2\" />\n",
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+       "  <polygon points=\"26.34811375846026,16.34811375846026 146.34811375846027,16.34811375846026 146.34811375846027,58.3495559274114 26.34811375846026,58.3495559274114\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
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+       "  <text x=\"86.34811375846027\" y=\"78.3495559274114\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >501</text>\n",
+       "  <text x=\"166.34811375846027\" y=\"37.34883484293583\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,166.34811375846027,37.34883484293583)\">100</text>\n",
+       "  <text x=\"8.174056879230129\" y=\"70.17549904818128\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.174056879230129,70.17549904818128)\">8</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-d3568047-7aa0-472e-9de7-9090b29a5cd5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d3568047-7aa0-472e-9de7-9090b29a5cd5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d24b47ff-4063-4517-86c1-d8318a4d6a21' class='xr-var-data-in' type='checkbox'><label for='data-d24b47ff-4063-4517-86c1-d8318a4d6a21' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-711f38a9-c07d-4980-81fb-73aa726ae21b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-711f38a9-c07d-4980-81fb-73aa726ae21b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d42a1b60-decb-4577-a1b9-23dbb758f8ee' class='xr-var-data-in' type='checkbox'><label for='data-d42a1b60-decb-4577-a1b9-23dbb758f8ee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>unit_long :</span></dt><dd>Meters per second</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>long_name :</span></dt><dd>southern boundary v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -22172,18 +26175,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.34 MiB </td>\n",
+       "                        <td> 1.53 MiB </td>\n",
        "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 100, 502) </td>\n",
+       "                        <td> (8, 100, 502) </td>\n",
        "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 120 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 83 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -22193,61 +26196,63 @@
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-f6b82804-1230-4020-879c-26d0e707eba0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f6b82804-1230-4020-879c-26d0e707eba0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8295ad96-d6af-4a80-b02f-82f15f062587' class='xr-var-data-in' type='checkbox'><label for='data-8295ad96-d6af-4a80-b02f-82f15f062587' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary potential temperature</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-e8093ef0-d019-47b5-8101-2723ac6c7857' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e8093ef0-d019-47b5-8101-2723ac6c7857' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-685e75e3-8220-4892-a0c0-08be6676eecb' class='xr-var-data-in' type='checkbox'><label for='data-685e75e3-8220-4892-a0c0-08be6676eecb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>long_name :</span></dt><dd>southern boundary potential temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>unit_long :</span></dt><dd>Degrees Celsius</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd><dt><span>valid_max :</span></dt><dd>21306</dd><dt><span>valid_min :</span></dt><dd>-32766</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -22262,18 +26267,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.34 MiB </td>\n",
+       "                        <td> 1.53 MiB </td>\n",
        "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 100, 502) </td>\n",
+       "                        <td> (8, 100, 502) </td>\n",
        "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 42 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -22283,61 +26288,63 @@
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        "        </td>\n",
        "        <td>\n",
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        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salt_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-50b57944-7527-44a9-a01e-002be42f69cf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-50b57944-7527-44a9-a01e-002be42f69cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32a0b074-5eb9-431d-b329-6b56e91eb7f8' class='xr-var-data-in' type='checkbox'><label for='data-32a0b074-5eb9-431d-b329-6b56e91eb7f8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary salinity</dd><dt><span>units :</span></dt><dd>PSU</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salt_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-58ac0852-d280-4d43-8157-188fe24213d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-58ac0852-d280-4d43-8157-188fe24213d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc0ced6f-4976-4f4d-bfd1-d61f02bf37b5' class='xr-var-data-in' type='checkbox'><label for='data-bc0ced6f-4976-4f4d-bfd1-d61f02bf37b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>long_name :</span></dt><dd>southern boundary salinity</dd><dt><span>standard_name :</span></dt><dd>sea_water_salinity</dd><dt><span>unit_long :</span></dt><dd>Practical Salinity Unit</dd><dt><span>units :</span></dt><dd>PSU</dd><dt><span>valid_max :</span></dt><dd>28249</dd><dt><span>valid_min :</span></dt><dd>1</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -22352,18 +26359,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.34 MiB </td>\n",
+       "                        <td> 1.53 MiB </td>\n",
        "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 100, 502) </td>\n",
+       "                        <td> (8, 100, 502) </td>\n",
        "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 42 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -22373,61 +26380,63 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"195\" height=\"107\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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        "\n",
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-       "  <line x1=\"10\" y1=\"0\" x2=\"25\" y2=\"15\" style=\"stroke-width:2\" />\n",
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        "\n",
        "  <!-- Horizontal lines -->\n",
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-       "  <line x1=\"25\" y1=\"57\" x2=\"145\" y2=\"57\" style=\"stroke-width:2\" />\n",
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-       "  <text x=\"7.963157\" y=\"69.956160\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.963157,69.956160)\">7</text>\n",
+       "  <text x=\"86.34181321463146\" y=\"78.33481550079244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"166.34181321463146\" y=\"37.33831435771195\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,166.34181321463146,37.33831435771195)\">100</text>\n",
+       "  <text x=\"8.170906607315729\" y=\"70.1639088934767\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.170906607315729,70.1639088934767)\">8</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zeta_south</span></div><div class='xr-var-dims'>(bry_time, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 502), meta=np.ndarray&gt;</div><input id='attrs-b5189d3a-9458-46b8-93e2-1ac8af50cf13' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b5189d3a-9458-46b8-93e2-1ac8af50cf13' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6af1de23-2084-40fb-907c-6226b780b61e' class='xr-var-data-in' type='checkbox'><label for='data-6af1de23-2084-40fb-907c-6226b780b61e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary sea surface height</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_geoid</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>unit_long :</span></dt><dd>Meters</dd><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>_ChunkSizes :</span></dt><dd>[   1  681 1440]</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zeta_south</span></div><div class='xr-var-dims'>(bry_time, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 502), meta=np.ndarray&gt;</div><input id='attrs-1a1b806c-6e6f-4df2-8d9b-f11f726e621d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a1b806c-6e6f-4df2-8d9b-f11f726e621d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67082b1d-61fa-4c7d-9f84-62ae3b422736' class='xr-var-data-in' type='checkbox'><label for='data-67082b1d-61fa-4c7d-9f84-62ae3b422736' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>long_name :</span></dt><dd>southern boundary sea surface height</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_geoid</dd><dt><span>unit_long :</span></dt><dd>Meters</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_max :</span></dt><dd>5684</dd><dt><span>valid_min :</span></dt><dd>-6228</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -22442,18 +26451,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 13.73 kiB </td>\n",
+       "                        <td> 15.69 kiB </td>\n",
        "                        <td> 1.96 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 502) </td>\n",
+       "                        <td> (8, 502) </td>\n",
        "                        <td> (1, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 46 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 32 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -22468,11 +26477,12 @@
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        "  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n",
-       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
-       "  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n",
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-       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
+       "  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n",
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        "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" style=\"stroke-width:2\" />\n",
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@@ -22480,15 +26490,15 @@
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        "\n",
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+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,27.781082464873474 0.0,27.781082464873474\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
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-       "  <text x=\"60.000000\" y=\"47.074735\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"140.000000\" y=\"13.537367\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,13.537367)\">7</text>\n",
+       "  <text x=\"60.0\" y=\"47.78108246487348\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
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        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ubar_south</span></div><div class='xr-var-dims'>(bry_time, xi_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 501), meta=np.ndarray&gt;</div><input id='attrs-a3ed951d-0267-471f-9340-c3fd4c978f33' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3ed951d-0267-471f-9340-c3fd4c978f33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0cc26d0a-8908-4ace-9975-ae79bb05f3f5' class='xr-var-data-in' type='checkbox'><label for='data-0cc26d0a-8908-4ace-9975-ae79bb05f3f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary vertically integrated u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ubar_south</span></div><div class='xr-var-dims'>(bry_time, xi_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 501), meta=np.ndarray&gt;</div><input id='attrs-9493050b-c471-4f72-b1ef-3f025d1e41aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9493050b-c471-4f72-b1ef-3f025d1e41aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f4ae16d-97b3-45a7-bb99-1c9e1269bf2e' class='xr-var-data-in' type='checkbox'><label for='data-5f4ae16d-97b3-45a7-bb99-1c9e1269bf2e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary vertically integrated u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>unit_long :</span></dt><dd>Meters per second</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -22503,18 +26513,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 13.70 kiB </td>\n",
+       "                        <td> 15.66 kiB </td>\n",
        "                        <td> 1.96 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 501) </td>\n",
+       "                        <td> (8, 501) </td>\n",
        "                        <td> (1, 501) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 130 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 93 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -22529,11 +26539,12 @@
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        "  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n",
-       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
-       "  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n",
-       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
-       "  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n",
-       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
+       "  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n",
+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
        "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -22541,15 +26552,15 @@
        "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"27\" style=\"stroke-width:2\" />\n",
        "\n",
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-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,27.08512214385328 0.0,27.08512214385328\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,27.79179338938244 0.0,27.79179338938244\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
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+       "  <text x=\"140.0\" y=\"13.89589669469122\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,13.89589669469122)\">8</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vbar_south</span></div><div class='xr-var-dims'>(bry_time, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 502), meta=np.ndarray&gt;</div><input id='attrs-5e871089-b090-497e-8c51-8dbfc62f2ba3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e871089-b090-497e-8c51-8dbfc62f2ba3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97679739-edbd-42ef-bc91-587cfa28734a' class='xr-var-data-in' type='checkbox'><label for='data-97679739-edbd-42ef-bc91-587cfa28734a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary vertically integrated v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vbar_south</span></div><div class='xr-var-dims'>(bry_time, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 502), meta=np.ndarray&gt;</div><input id='attrs-5546af4c-99e7-4ab5-8d8b-0423ea57dc3c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5546af4c-99e7-4ab5-8d8b-0423ea57dc3c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b58e621-f3e1-4115-92b5-3f24bbc19c6f' class='xr-var-data-in' type='checkbox'><label for='data-6b58e621-f3e1-4115-92b5-3f24bbc19c6f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary vertically integrated v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>unit_long :</span></dt><dd>Meters per second</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -22564,18 +26575,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 13.73 kiB </td>\n",
+       "                        <td> 15.69 kiB </td>\n",
        "                        <td> 1.96 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 502) </td>\n",
+       "                        <td> (8, 502) </td>\n",
        "                        <td> (1, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 130 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 93 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -22590,11 +26601,12 @@
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        "  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n",
-       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
-       "  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n",
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-       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
+       "  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
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+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
        "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -22602,15 +26614,15 @@
        "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"27\" style=\"stroke-width:2\" />\n",
        "\n",
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        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-3ab87b1c-66b7-4b9d-8390-f58ce7ca8126' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ab87b1c-66b7-4b9d-8390-f58ce7ca8126' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2671367a-eac3-4886-b6b6-11ea105d9fd1' class='xr-var-data-in' type='checkbox'><label for='data-2671367a-eac3-4886-b6b6-11ea105d9fd1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-0e39c8c2-e050-4495-9bb4-c50e41291ffc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e39c8c2-e050-4495-9bb4-c50e41291ffc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3023476d-522e-44f1-86dd-515ab16c47e2' class='xr-var-data-in' type='checkbox'><label for='data-3023476d-522e-44f1-86dd-515ab16c47e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>unit_long :</span></dt><dd>Meters per second</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>long_name :</span></dt><dd>eastern boundary u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -22625,18 +26637,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.34 MiB </td>\n",
+       "                        <td> 1.53 MiB </td>\n",
        "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 100, 502) </td>\n",
+       "                        <td> (8, 100, 502) </td>\n",
        "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 121 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 83 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -22646,61 +26658,63 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"195\" height=\"107\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "        <svg width=\"196\" height=\"108\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"25\" y2=\"15\" style=\"stroke-width:2\" />\n",
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        "\n",
        "  <!-- Vertical lines -->\n",
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+       "  <text x=\"8.170906607315729\" y=\"70.1639088934767\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.170906607315729,70.1639088934767)\">8</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_v)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 501), meta=np.ndarray&gt;</div><input id='attrs-ef33ec34-4640-4fa7-879a-027d79866b43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef33ec34-4640-4fa7-879a-027d79866b43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e5cfe92-29dc-4ee9-a08a-37cee174b82d' class='xr-var-data-in' type='checkbox'><label for='data-4e5cfe92-29dc-4ee9-a08a-37cee174b82d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_v)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 501), meta=np.ndarray&gt;</div><input id='attrs-c0c42a86-4f08-4606-b4ac-f276dc4c3258' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c0c42a86-4f08-4606-b4ac-f276dc4c3258' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92b69b88-b8d4-4030-b958-b21156b1b89a' class='xr-var-data-in' type='checkbox'><label for='data-92b69b88-b8d4-4030-b958-b21156b1b89a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>unit_long :</span></dt><dd>Meters per second</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>long_name :</span></dt><dd>eastern boundary v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -22715,18 +26729,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.34 MiB </td>\n",
+       "                        <td> 1.53 MiB </td>\n",
        "                        <td> 195.70 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 100, 501) </td>\n",
+       "                        <td> (8, 100, 501) </td>\n",
        "                        <td> (1, 100, 501) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 121 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 83 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -22736,61 +26750,63 @@
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-6e77e660-8d12-4055-b354-8b4d037e8d30' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e77e660-8d12-4055-b354-8b4d037e8d30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a2d3fc7-2068-4431-ba93-851e77533545' class='xr-var-data-in' type='checkbox'><label for='data-6a2d3fc7-2068-4431-ba93-851e77533545' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary potential temperature</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-82d11c7a-7a02-4394-9704-5629596b10c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-82d11c7a-7a02-4394-9704-5629596b10c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bfcfb834-137d-452a-81f9-021dd29e9dfa' class='xr-var-data-in' type='checkbox'><label for='data-bfcfb834-137d-452a-81f9-021dd29e9dfa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>long_name :</span></dt><dd>eastern boundary potential temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>unit_long :</span></dt><dd>Degrees Celsius</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd><dt><span>valid_max :</span></dt><dd>21306</dd><dt><span>valid_min :</span></dt><dd>-32766</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -22805,18 +26821,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.34 MiB </td>\n",
+       "                        <td> 1.53 MiB </td>\n",
        "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 100, 502) </td>\n",
+       "                        <td> (8, 100, 502) </td>\n",
        "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 76 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 42 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -22826,61 +26842,63 @@
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        "        </td>\n",
        "        <td>\n",
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        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salt_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-a6eb488b-7da8-4396-96f5-337528434dbe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a6eb488b-7da8-4396-96f5-337528434dbe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f67fade7-e2f4-4250-8784-ab8999cb08e5' class='xr-var-data-in' type='checkbox'><label for='data-f67fade7-e2f4-4250-8784-ab8999cb08e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary salinity</dd><dt><span>units :</span></dt><dd>PSU</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salt_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-cce930ac-7b90-4ad1-9c51-35f2b53f85d5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cce930ac-7b90-4ad1-9c51-35f2b53f85d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27f9150c-a405-41b0-b965-79ba2a3e1403' class='xr-var-data-in' type='checkbox'><label for='data-27f9150c-a405-41b0-b965-79ba2a3e1403' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>long_name :</span></dt><dd>eastern boundary salinity</dd><dt><span>standard_name :</span></dt><dd>sea_water_salinity</dd><dt><span>unit_long :</span></dt><dd>Practical Salinity Unit</dd><dt><span>units :</span></dt><dd>PSU</dd><dt><span>valid_max :</span></dt><dd>28249</dd><dt><span>valid_min :</span></dt><dd>1</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -22895,18 +26913,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.34 MiB </td>\n",
+       "                        <td> 1.53 MiB </td>\n",
        "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 100, 502) </td>\n",
+       "                        <td> (8, 100, 502) </td>\n",
        "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 76 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 42 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -22916,61 +26934,63 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"195\" height=\"107\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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        "\n",
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+       "  <text x=\"86.34181321463146\" y=\"78.33481550079244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
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+       "  <text x=\"8.170906607315729\" y=\"70.1639088934767\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.170906607315729,70.1639088934767)\">8</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zeta_east</span></div><div class='xr-var-dims'>(bry_time, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 502), meta=np.ndarray&gt;</div><input id='attrs-057dcc4e-f463-4517-845a-d96121615a79' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-057dcc4e-f463-4517-845a-d96121615a79' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f4dc713-aece-46c5-8643-b77a852ca14b' class='xr-var-data-in' type='checkbox'><label for='data-2f4dc713-aece-46c5-8643-b77a852ca14b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary sea surface height</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_geoid</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>unit_long :</span></dt><dd>Meters</dd><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>_ChunkSizes :</span></dt><dd>[   1  681 1440]</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zeta_east</span></div><div class='xr-var-dims'>(bry_time, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 502), meta=np.ndarray&gt;</div><input id='attrs-19d515d3-f490-4162-a782-8aa5c83099d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-19d515d3-f490-4162-a782-8aa5c83099d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8acea65-1858-4ffd-af5e-0841de8713bb' class='xr-var-data-in' type='checkbox'><label for='data-f8acea65-1858-4ffd-af5e-0841de8713bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>long_name :</span></dt><dd>eastern boundary sea surface height</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_geoid</dd><dt><span>unit_long :</span></dt><dd>Meters</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_max :</span></dt><dd>5684</dd><dt><span>valid_min :</span></dt><dd>-6228</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -22985,18 +27005,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 13.73 kiB </td>\n",
+       "                        <td> 15.69 kiB </td>\n",
        "                        <td> 1.96 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 502) </td>\n",
+       "                        <td> (8, 502) </td>\n",
        "                        <td> (1, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 46 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 32 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -23011,11 +27031,12 @@
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        "  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n",
-       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
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-       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
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        "\n",
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@@ -23023,15 +27044,15 @@
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        "\n",
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"47.074735\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
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        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ubar_east</span></div><div class='xr-var-dims'>(bry_time, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 502), meta=np.ndarray&gt;</div><input id='attrs-f550b526-0e51-44c3-994f-568aeeb571ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f550b526-0e51-44c3-994f-568aeeb571ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76e68ce6-1b41-4dd0-a5a9-9ff663c316e5' class='xr-var-data-in' type='checkbox'><label for='data-76e68ce6-1b41-4dd0-a5a9-9ff663c316e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary vertically integrated u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ubar_east</span></div><div class='xr-var-dims'>(bry_time, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 502), meta=np.ndarray&gt;</div><input id='attrs-dfb31fd8-2df8-49b8-a427-5e004a2d49d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dfb31fd8-2df8-49b8-a427-5e004a2d49d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0148c587-1e1a-452a-ba09-e9d8144fd8ca' class='xr-var-data-in' type='checkbox'><label for='data-0148c587-1e1a-452a-ba09-e9d8144fd8ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary vertically integrated u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>unit_long :</span></dt><dd>Meters per second</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -23046,18 +27067,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 13.73 kiB </td>\n",
+       "                        <td> 15.69 kiB </td>\n",
        "                        <td> 1.96 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 502) </td>\n",
+       "                        <td> (8, 502) </td>\n",
        "                        <td> (1, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 131 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 93 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -23072,11 +27093,12 @@
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        "  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n",
-       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
-       "  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n",
-       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
-       "  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n",
-       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
+       "  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n",
+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
        "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" style=\"stroke-width:2\" />\n",
        "\n",
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@@ -23084,15 +27106,15 @@
        "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"27\" style=\"stroke-width:2\" />\n",
        "\n",
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-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,27.07473454868028 0.0,27.07473454868028\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,27.781082464873474 0.0,27.781082464873474\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
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-       "  <text x=\"60.000000\" y=\"47.074735\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
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+       "  <text x=\"140.0\" y=\"13.890541232436737\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,13.890541232436737)\">8</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vbar_east</span></div><div class='xr-var-dims'>(bry_time, eta_v)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 501), meta=np.ndarray&gt;</div><input id='attrs-a06a63e7-f953-4f68-b694-d236cf459311' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a06a63e7-f953-4f68-b694-d236cf459311' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2938e82-df97-4a57-81c8-1c94cfcc0315' class='xr-var-data-in' type='checkbox'><label for='data-b2938e82-df97-4a57-81c8-1c94cfcc0315' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary vertically integrated v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vbar_east</span></div><div class='xr-var-dims'>(bry_time, eta_v)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 501), meta=np.ndarray&gt;</div><input id='attrs-76d10698-5730-4274-97b9-d916cbdcb96c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-76d10698-5730-4274-97b9-d916cbdcb96c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf065025-ae4c-4e99-9c04-e79c6ee70481' class='xr-var-data-in' type='checkbox'><label for='data-bf065025-ae4c-4e99-9c04-e79c6ee70481' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary vertically integrated v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>unit_long :</span></dt><dd>Meters per second</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -23107,18 +27129,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 13.70 kiB </td>\n",
+       "                        <td> 15.66 kiB </td>\n",
        "                        <td> 1.96 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 501) </td>\n",
+       "                        <td> (8, 501) </td>\n",
        "                        <td> (1, 501) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 131 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 93 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -23133,11 +27155,12 @@
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        "  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n",
-       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
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-       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
+       "  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
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+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
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        "\n",
        "  <!-- Vertical lines -->\n",
@@ -23145,15 +27168,15 @@
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        "\n",
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-       "  <text x=\"60.000000\" y=\"47.085122\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >501</text>\n",
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        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-f7115c94-8a93-4ee2-82ab-e254a3b035b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f7115c94-8a93-4ee2-82ab-e254a3b035b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59f43be0-d37e-45cf-9025-31da24adb968' class='xr-var-data-in' type='checkbox'><label for='data-59f43be0-d37e-45cf-9025-31da24adb968' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-c3076e02-bcb5-4619-8994-a423f0201000' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c3076e02-bcb5-4619-8994-a423f0201000' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d674c34-9902-4b9e-abea-c958a522f791' class='xr-var-data-in' type='checkbox'><label for='data-3d674c34-9902-4b9e-abea-c958a522f791' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>unit_long :</span></dt><dd>Meters per second</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>long_name :</span></dt><dd>western boundary u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -23168,18 +27191,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.34 MiB </td>\n",
+       "                        <td> 1.53 MiB </td>\n",
        "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 100, 502) </td>\n",
+       "                        <td> (8, 100, 502) </td>\n",
        "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 121 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 83 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -23189,61 +27212,63 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"195\" height=\"107\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "        <svg width=\"196\" height=\"108\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"25\" y2=\"15\" style=\"stroke-width:2\" />\n",
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        "\n",
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        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_v)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 501), meta=np.ndarray&gt;</div><input id='attrs-4256dd6c-1f7d-40a0-85aa-01d5e55b499f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4256dd6c-1f7d-40a0-85aa-01d5e55b499f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2bdbf72-1264-4a35-bb22-48a795515cce' class='xr-var-data-in' type='checkbox'><label for='data-e2bdbf72-1264-4a35-bb22-48a795515cce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_v)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 501), meta=np.ndarray&gt;</div><input id='attrs-c6134678-7658-43f3-8b9f-2c5919a7865d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c6134678-7658-43f3-8b9f-2c5919a7865d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88d698d4-b114-4a20-96e0-7957c0d5d15d' class='xr-var-data-in' type='checkbox'><label for='data-88d698d4-b114-4a20-96e0-7957c0d5d15d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>unit_long :</span></dt><dd>Meters per second</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>long_name :</span></dt><dd>western boundary v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -23258,18 +27283,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.34 MiB </td>\n",
+       "                        <td> 1.53 MiB </td>\n",
        "                        <td> 195.70 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 100, 501) </td>\n",
+       "                        <td> (8, 100, 501) </td>\n",
        "                        <td> (1, 100, 501) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 121 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 83 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -23279,61 +27304,63 @@
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        "        <td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-84c9e96e-b13a-4e14-8bc7-f13d283be7de' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84c9e96e-b13a-4e14-8bc7-f13d283be7de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb8d85a1-0fcc-4b6f-afe4-288522004e67' class='xr-var-data-in' type='checkbox'><label for='data-eb8d85a1-0fcc-4b6f-afe4-288522004e67' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary potential temperature</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-137f556e-1657-42ec-8578-6c2576bc462e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-137f556e-1657-42ec-8578-6c2576bc462e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cbe9750d-c9bc-4654-8027-6c92fe291707' class='xr-var-data-in' type='checkbox'><label for='data-cbe9750d-c9bc-4654-8027-6c92fe291707' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>long_name :</span></dt><dd>western boundary potential temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>unit_long :</span></dt><dd>Degrees Celsius</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd><dt><span>valid_max :</span></dt><dd>21306</dd><dt><span>valid_min :</span></dt><dd>-32766</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -23348,18 +27375,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.34 MiB </td>\n",
+       "                        <td> 1.53 MiB </td>\n",
        "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 100, 502) </td>\n",
+       "                        <td> (8, 100, 502) </td>\n",
        "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 76 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 42 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -23369,61 +27396,63 @@
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        "        </td>\n",
        "        <td>\n",
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        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salt_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-3385c4f5-24e7-46fb-b92e-eed2144855a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3385c4f5-24e7-46fb-b92e-eed2144855a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77fb07b2-f831-4780-a01a-4a2d4203991c' class='xr-var-data-in' type='checkbox'><label for='data-77fb07b2-f831-4780-a01a-4a2d4203991c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary salinity</dd><dt><span>units :</span></dt><dd>PSU</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salt_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 502), meta=np.ndarray&gt;</div><input id='attrs-d5aff762-04d5-4401-a615-bbeeaf826962' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d5aff762-04d5-4401-a615-bbeeaf826962' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f50d8bd-0fd7-4ca1-bb64-1ca32cbe4b56' class='xr-var-data-in' type='checkbox'><label for='data-1f50d8bd-0fd7-4ca1-bb64-1ca32cbe4b56' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>long_name :</span></dt><dd>western boundary salinity</dd><dt><span>standard_name :</span></dt><dd>sea_water_salinity</dd><dt><span>unit_long :</span></dt><dd>Practical Salinity Unit</dd><dt><span>units :</span></dt><dd>PSU</dd><dt><span>valid_max :</span></dt><dd>28249</dd><dt><span>valid_min :</span></dt><dd>1</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -23438,18 +27467,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 1.34 MiB </td>\n",
+       "                        <td> 1.53 MiB </td>\n",
        "                        <td> 196.09 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 100, 502) </td>\n",
+       "                        <td> (8, 100, 502) </td>\n",
        "                        <td> (1, 100, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 76 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 42 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -23459,61 +27488,63 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"195\" height=\"107\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "  <text x=\"8.170906607315729\" y=\"70.1639088934767\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,8.170906607315729,70.1639088934767)\">8</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zeta_west</span></div><div class='xr-var-dims'>(bry_time, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 502), meta=np.ndarray&gt;</div><input id='attrs-39ba7c90-df47-48fa-bab2-8f40b8ffe516' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-39ba7c90-df47-48fa-bab2-8f40b8ffe516' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d1376fc-c6b3-49ab-bf40-2e6e4b62fdb6' class='xr-var-data-in' type='checkbox'><label for='data-5d1376fc-c6b3-49ab-bf40-2e6e4b62fdb6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary sea surface height</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_geoid</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>unit_long :</span></dt><dd>Meters</dd><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>_ChunkSizes :</span></dt><dd>[   1  681 1440]</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zeta_west</span></div><div class='xr-var-dims'>(bry_time, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 502), meta=np.ndarray&gt;</div><input id='attrs-eb576e20-5dc0-407f-a1c8-dcac294d256c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eb576e20-5dc0-407f-a1c8-dcac294d256c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c2fea33-afb7-47f4-a5e6-b4ba1f39fd38' class='xr-var-data-in' type='checkbox'><label for='data-2c2fea33-afb7-47f4-a5e6-b4ba1f39fd38' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>long_name :</span></dt><dd>western boundary sea surface height</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_geoid</dd><dt><span>unit_long :</span></dt><dd>Meters</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_max :</span></dt><dd>5684</dd><dt><span>valid_min :</span></dt><dd>-6228</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -23528,18 +27559,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 13.73 kiB </td>\n",
+       "                        <td> 15.69 kiB </td>\n",
        "                        <td> 1.96 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 502) </td>\n",
+       "                        <td> (8, 502) </td>\n",
        "                        <td> (1, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 46 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 32 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -23554,11 +27585,12 @@
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-       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
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@@ -23566,15 +27598,15 @@
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        "\n",
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        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ubar_west</span></div><div class='xr-var-dims'>(bry_time, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 502), meta=np.ndarray&gt;</div><input id='attrs-21f9df9c-25c0-4b25-9da2-58cc01255d25' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-21f9df9c-25c0-4b25-9da2-58cc01255d25' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7dba5e0-dd44-4c54-8ae8-546b1c1a28d1' class='xr-var-data-in' type='checkbox'><label for='data-e7dba5e0-dd44-4c54-8ae8-546b1c1a28d1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary vertically integrated u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ubar_west</span></div><div class='xr-var-dims'>(bry_time, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 502), meta=np.ndarray&gt;</div><input id='attrs-e4c94c30-c8f8-4691-94b7-18accb962749' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e4c94c30-c8f8-4691-94b7-18accb962749' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-124f27d4-8db2-424b-9470-0207cd8b4fc3' class='xr-var-data-in' type='checkbox'><label for='data-124f27d4-8db2-424b-9470-0207cd8b4fc3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary vertically integrated u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>unit_long :</span></dt><dd>Meters per second</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -23589,18 +27621,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 13.73 kiB </td>\n",
+       "                        <td> 15.69 kiB </td>\n",
        "                        <td> 1.96 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 502) </td>\n",
+       "                        <td> (8, 502) </td>\n",
        "                        <td> (1, 502) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 131 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 93 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -23615,11 +27647,12 @@
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
        "  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n",
-       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
-       "  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n",
-       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
-       "  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n",
-       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
+       "  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n",
+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
        "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -23627,15 +27660,15 @@
        "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"27\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
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+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,27.781082464873474 0.0,27.781082464873474\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"47.074735\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
-       "  <text x=\"140.000000\" y=\"13.537367\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,13.537367)\">7</text>\n",
+       "  <text x=\"60.0\" y=\"47.78108246487348\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >502</text>\n",
+       "  <text x=\"140.0\" y=\"13.890541232436737\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,13.890541232436737)\">8</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vbar_west</span></div><div class='xr-var-dims'>(bry_time, eta_v)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 501), meta=np.ndarray&gt;</div><input id='attrs-e07eb3c7-a7df-4788-8239-23471b918201' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e07eb3c7-a7df-4788-8239-23471b918201' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8af7b1fd-4d81-45f8-999c-d8ced714b1db' class='xr-var-data-in' type='checkbox'><label for='data-8af7b1fd-4d81-45f8-999c-d8ced714b1db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary vertically integrated v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vbar_west</span></div><div class='xr-var-dims'>(bry_time, eta_v)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 501), meta=np.ndarray&gt;</div><input id='attrs-b55fde0c-bc5a-4d6d-a92e-24b18303a1ce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b55fde0c-bc5a-4d6d-a92e-24b18303a1ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6e120ac-6144-49c9-b783-56506dc34a0a' class='xr-var-data-in' type='checkbox'><label for='data-a6e120ac-6144-49c9-b783-56506dc34a0a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary vertically integrated v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>unit_long :</span></dt><dd>Meters per second</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -23650,18 +27683,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 13.70 kiB </td>\n",
+       "                        <td> 15.66 kiB </td>\n",
        "                        <td> 1.96 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (7, 501) </td>\n",
+       "                        <td> (8, 501) </td>\n",
        "                        <td> (1, 501) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 7 chunks in 131 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 93 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -23676,11 +27709,12 @@
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        "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
        "  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n",
-       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
-       "  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n",
-       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
-       "  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n",
-       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
+       "  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
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+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
        "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -23688,46 +27722,48 @@
        "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"27\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,27.08512214385328 0.0,27.08512214385328\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"47.085122\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >501</text>\n",
-       "  <text x=\"140.000000\" y=\"13.542561\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,13.542561)\">7</text>\n",
+       "  <text x=\"60.0\" y=\"47.79179338938244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >501</text>\n",
+       "  <text x=\"140.0\" y=\"13.89589669469122\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,13.89589669469122)\">8</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-26b5343c-ac0f-4876-8f2f-893169fa0076' class='xr-section-summary-in' type='checkbox'  ><label for='section-26b5343c-ac0f-4876-8f2f-893169fa0076' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>bry_time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-af22a6c9-6f3e-463b-a329-73ce8da60eae' class='xr-index-data-in' type='checkbox'/><label for='index-af22a6c9-6f3e-463b-a329-73ce8da60eae' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([4411.5, 4412.5, 4413.5, 4414.5, 4415.5, 4416.5, 4417.5], dtype=&#x27;float64&#x27;, name=&#x27;bry_time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2fdff3f9-5a37-4514-a35c-5ac6c09b3a98' class='xr-section-summary-in' type='checkbox'  ><label for='section-2fdff3f9-5a37-4514-a35c-5ac6c09b3a98' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>ROMS boundary forcing file created by ROMS-Tools</dd><dt><span>roms_tools_version :</span></dt><dd>3.1.2.dev23+gc2b82bc5c</dd><dt><span>start_time :</span></dt><dd>2012-01-30 00:00:00</dd><dt><span>end_time :</span></dt><dd>2012-02-04 00:00:00</dd><dt><span>source :</span></dt><dd>GLORYS</dd><dt><span>model_reference_date :</span></dt><dd>2000-01-01 00:00:00</dd><dt><span>apply_2d_horizontal_fill :</span></dt><dd>False</dd><dt><span>adjust_depth_for_sea_surface_height :</span></dt><dd>False</dd><dt><span>theta_s :</span></dt><dd>5.0</dd><dt><span>theta_b :</span></dt><dd>2.0</dd><dt><span>hc :</span></dt><dd>300.0</dd></dl></div></li></ul></div></div>"
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-fef88a71-cd0b-4fe2-a728-e7773a1743d1' class='xr-section-summary-in' type='checkbox' /><label for='section-fef88a71-cd0b-4fe2-a728-e7773a1743d1' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>ROMS boundary forcing file created by ROMS-Tools</dd><dt><span>roms_tools_version :</span></dt><dd>10000.dev361+g2e6c47bd8</dd><dt><span>start_time :</span></dt><dd>2012-01-30 00:00:00</dd><dt><span>end_time :</span></dt><dd>2012-02-04 00:00:00</dd><dt><span>source :</span></dt><dd>GLORYS</dd><dt><span>model_reference_date :</span></dt><dd>2000-01-01 00:00:00</dd><dt><span>prefill :</span></dt><dd>None</dd><dt><span>regrid_method :</span></dt><dd>xesmf</dd><dt><span>extrap_method :</span></dt><dd>inverse_dist</dd><dt><span>adjust_depth_for_sea_surface_height :</span></dt><dd>False</dd><dt><span>theta_s :</span></dt><dd>5.0</dd><dt><span>theta_b :</span></dt><dd>2.0</dd><dt><span>hc :</span></dt><dd>300.0</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.Dataset> Size: 17MB\n",
-       "Dimensions:     (bry_time: 7, s_rho: 100, xi_u: 501, xi_rho: 502, eta_rho: 502,\n",
+       "<xarray.Dataset> Size: 19MB\n",
+       "Dimensions:     (bry_time: 8, s_rho: 100, xi_u: 501, xi_rho: 502, eta_rho: 502,\n",
        "                 eta_v: 501)\n",
        "Coordinates:\n",
-       "    abs_time    (bry_time) datetime64[ns] 56B 2012-01-29T12:00:00 ... 2012-02...\n",
-       "  * bry_time    (bry_time) float64 56B 4.412e+03 4.412e+03 ... 4.418e+03\n",
+       "  * bry_time    (bry_time) float64 64B 4.411e+03 4.412e+03 ... 4.418e+03\n",
+       "    abs_time    (bry_time) datetime64[ns] 64B 2012-01-29 ... 2012-02-05\n",
        "Dimensions without coordinates: s_rho, xi_u, xi_rho, eta_rho, eta_v\n",
        "Data variables: (12/21)\n",
-       "    u_south     (bry_time, s_rho, xi_u) float32 1MB dask.array<chunksize=(1, 100, 501), meta=np.ndarray>\n",
-       "    v_south     (bry_time, s_rho, xi_rho) float32 1MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
-       "    temp_south  (bry_time, s_rho, xi_rho) float32 1MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
-       "    salt_south  (bry_time, s_rho, xi_rho) float32 1MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
-       "    zeta_south  (bry_time, xi_rho) float32 14kB dask.array<chunksize=(1, 502), meta=np.ndarray>\n",
-       "    ubar_south  (bry_time, xi_u) float32 14kB dask.array<chunksize=(1, 501), meta=np.ndarray>\n",
+       "    u_south     (bry_time, s_rho, xi_u) float32 2MB dask.array<chunksize=(1, 100, 501), meta=np.ndarray>\n",
+       "    v_south     (bry_time, s_rho, xi_rho) float32 2MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
+       "    temp_south  (bry_time, s_rho, xi_rho) float32 2MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
+       "    salt_south  (bry_time, s_rho, xi_rho) float32 2MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
+       "    zeta_south  (bry_time, xi_rho) float32 16kB dask.array<chunksize=(1, 502), meta=np.ndarray>\n",
+       "    ubar_south  (bry_time, xi_u) float32 16kB dask.array<chunksize=(1, 501), meta=np.ndarray>\n",
        "    ...          ...\n",
-       "    v_west      (bry_time, s_rho, eta_v) float32 1MB dask.array<chunksize=(1, 100, 501), meta=np.ndarray>\n",
-       "    temp_west   (bry_time, s_rho, eta_rho) float32 1MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
-       "    salt_west   (bry_time, s_rho, eta_rho) float32 1MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
-       "    zeta_west   (bry_time, eta_rho) float32 14kB dask.array<chunksize=(1, 502), meta=np.ndarray>\n",
-       "    ubar_west   (bry_time, eta_rho) float32 14kB dask.array<chunksize=(1, 502), meta=np.ndarray>\n",
-       "    vbar_west   (bry_time, eta_v) float32 14kB dask.array<chunksize=(1, 501), meta=np.ndarray>\n",
-       "Attributes:\n",
+       "    v_west      (bry_time, s_rho, eta_v) float32 2MB dask.array<chunksize=(1, 100, 501), meta=np.ndarray>\n",
+       "    temp_west   (bry_time, s_rho, eta_rho) float32 2MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
+       "    salt_west   (bry_time, s_rho, eta_rho) float32 2MB dask.array<chunksize=(1, 100, 502), meta=np.ndarray>\n",
+       "    zeta_west   (bry_time, eta_rho) float32 16kB dask.array<chunksize=(1, 502), meta=np.ndarray>\n",
+       "    ubar_west   (bry_time, eta_rho) float32 16kB dask.array<chunksize=(1, 502), meta=np.ndarray>\n",
+       "    vbar_west   (bry_time, eta_v) float32 16kB dask.array<chunksize=(1, 501), meta=np.ndarray>\n",
+       "Attributes: (12/13)\n",
        "    title:                                ROMS boundary forcing file created ...\n",
-       "    roms_tools_version:                   3.1.2.dev23+gc2b82bc5c\n",
+       "    roms_tools_version:                   10000.dev361+g2e6c47bd8\n",
        "    start_time:                           2012-01-30 00:00:00\n",
        "    end_time:                             2012-02-04 00:00:00\n",
        "    source:                               GLORYS\n",
        "    model_reference_date:                 2000-01-01 00:00:00\n",
-       "    apply_2d_horizontal_fill:             False\n",
+       "    ...                                   ...\n",
+       "    regrid_method:                        xesmf\n",
+       "    extrap_method:                        inverse_dist\n",
        "    adjust_depth_for_sea_surface_height:  False\n",
        "    theta_s:                              5.0\n",
        "    theta_b:                              2.0\n",
@@ -23745,96 +27781,87 @@
   },
   {
    "cell_type": "markdown",
-   "id": "12e1c9d6-6197-4d0d-a5c6-717f2e95cf43",
-   "metadata": {},
-   "source": [
-    "## 1D versus 2D horizontal fill\n",
-    "\n",
-    "The horizontal fill process controls how ocean values are extended into land regions. This matters when the source data (such as GLORYS) and the ROMS grid have differing land masks, often due to different resolutions.\n",
-    "Without applying horizontal fill, land mask discrepancies could lead to NaN values at certain ocean points in the ROMS grid, where the source data (GLORYS) considers them land.\n",
-    "\n",
-    "When creating the boundary forcing, users can select between two horizontal fill methods:\n",
-    "\n",
-    "* **1D Horizontal Fill** (`apply_2d_horizontal_fill = False`): This method applies extrapolation along each boundary edge individually after regridding, using a [forward fill](https://docs.xarray.dev/en/stable/generated/xarray.DataArray.ffill.html) followed by a [backward fill](https://docs.xarray.dev/en/stable/generated/xarray.DataArray.bfill.html) to fill missing values.\n",
-    "* **2D Horizontal Fill** (`apply_2d_horizontal_fill = True`): This method fills missing values across the grid using a 2D diffusion AMG (Algebraic Multigrid) algorithm before the regridding step, allowing for more spatially smooth and consistent coverage across the entire area.\n",
-    "\n",
-    "Note that the two methods coincide for boundaries that are entirely over open ocean (without any land interference)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dad24e20-afda-491f-887e-f08d4bce0a2e",
-   "metadata": {},
-   "source": [
-    "<img src=\"images/horizontal_fill.png\" alt=\"Horizontal fill\" width=\"1000\"/>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "68eec771-f574-4085-a177-6cd8c841d420",
+   "id": "c9f5d973",
    "metadata": {},
    "source": [
-    "The **1D horizontal fill** method is **faster** and the **default** choice. The 2D horizontal fill, while more thorough, is slower as it fills an entire 2D domain at each depth level, often covering interior areas that won’t ultimately be used for the boundary forcing. For example, in the scenario above, the 2D fill would extend values across both Iceland and Greenland.\n",
+    "## Horizontal regridding near land and coarse source data\n",
     "\n",
-    "<div class=\"alert alert-info\">\n",
-    "\n",
-    "Note\n",
+    "The source data (such as GLORYS or CESM) and the ROMS grid generally have\n",
+    "different land masks, often because they have different resolutions. A naive\n",
+    "linear interpolation would propagate NaNs into the ROMS boundary wherever any of\n",
+    "the surrounding source cells is land, leaving holes at ocean points that ROMS\n",
+    "needs.\n",
     "\n",
-    "To minimize unnecessary calculations, `ROMS-Tools` limits the 2D fill to a small area surrounding the target ROMS domain, so it wouldn't fill the entire North Atlantic in the scenario above. However, a buffer zone around the target ROMS domain is included to avoid edge artifacts from the 2D fill near the boundaries.\n",
-    "    \n",
-    "</div>\n",
+    "`ROMS-Tools` avoids this with a NaN-aware horizontal regridding that needs no\n",
+    "separate pre-fill step:\n",
     "\n",
-    "However, the 1D fill method may **not** be **safe in all situations**. Its **limitations** include:\n",
+    "* When [xESMF](https://xesmf.readthedocs.io/) is available (the default for conda\n",
+    "  installs), boundary data is regridded with **masked bilinear interpolation**:\n",
+    "  the interpolation weights are renormalized over only the valid (ocean) source\n",
+    "  cells, so a target point surrounded by mostly land still receives a sensible\n",
+    "  ocean value. Target points whose source neighbors are *all* land are then filled\n",
+    "  by **inverse distance extrapolation**. Together these guarantee NaN-free\n",
+    "  boundaries.\n",
+    "* When xESMF is not available (e.g. Windows/pip installs), `ROMS-Tools` falls back\n",
+    "  to a **nearest-neighbor pre-fill** of the source data followed by scipy's linear\n",
+    "  interpolation, accessed through xarray. Because every masked source cell is first filled with its nearest\n",
+    "  valid ocean value, the subsequent interpolation cannot propagate NaNs. This is\n",
+    "  numerically close to, though not identical to, the masked-bilinear result.\n",
     "\n",
-    "* **Land Segments Dividing a Boundary Segment**: When land segments divide a boundary segment, the 1D fill method may extend values across the land, from one end to the other, potentially leading to unphysical effects. In such cases, `ROMS-Tools` will issue a warning suggesting that the 2D fill method is a safer alternative. We will explore such an example in Case study 1 below.\n",
-    "* **Coarse Resolution of Source Data**: If the source data has a coarse resolution, a boundary segment may appear entirely on land in the source data, even though it crosses into the ocean in the target ROMS domain. For such segments, the 1D fill method is unable to properly fill NaNs and will raise an error. In contrast, the 2D fill method applies a spatial diffusion algorithm before regridding, thus providing a reliable solution. This scenario will be explored in Case study 2 below."
+    "This replaces as the default the older, slower 2D Poisson (AMG) fill that was previously required\n",
+    "to plug NaN holes before regridding, giving a substantial speedup and memory\n",
+    "reduction on large, multi-year domains. The AMG fill is still available as a prefill option.\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f020cb73-6cd9-46b4-8b7a-e1339fa73f21",
+   "id": "3fece001",
    "metadata": {},
    "source": [
-    "### Case study 1: Land segments dividing a boundary section\n",
+    "### Coarse resolution source data\n",
     "\n",
-    "Let's examine an example with an island in the boundary to better understand the first limitation of the 1D fill method."
+    "Let's look at special case that can arise: source data that is so coarse that a\n",
+    "grid appears to lie almost entirely on land in the source grid. We\n",
+    "create a grid at the entrance to the Puget Sound, WA, USA, to demonstrate.\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 32,
-   "id": "59b420dd-8b8c-4b1e-b294-5c61129a6b7d",
-   "metadata": {
-    "tags": []
-   },
+   "id": "92de0415-a34b-4b35-8bdf-5d6df8f4eacb",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "grid = Grid(\n",
-    "    nx=100,\n",
-    "    ny=100,\n",
-    "    size_x=1000,\n",
-    "    size_y=1000,\n",
-    "    center_lon=-31,\n",
-    "    center_lat=64,\n",
-    "    rot=25,\n",
+    "grid_kwargs = dict(\n",
+    "    nx=50,  # number of grid points in x-direction\n",
+    "    ny=100,  # number of grid points in y-direction\n",
+    "    size_x=40,  # domain size in x-direction (in km)\n",
+    "    size_y=60,  # domain size in y-direction (in km)\n",
+    "    rot=0,  # rotation of the grid (in degrees)\n",
     "    topography_source={\n",
     "        \"name\": \"SRTM15\",\n",
-    "        \"path\": \"/global/cfs/projectdirs/m4746/Datasets/SRTM15/SRTM15_V2.6.nc\",\n",
+    "        \"path\": \"/anvil/projects/x-ees250129/Datasets/SRTM15/SRTM15_V2.6.nc\",\n",
     "    },\n",
+    ")\n",
+    "\n",
+    "grid = Grid(\n",
+    "    **grid_kwargs,\n",
+    "    center_lon=-122.75,  # longitude of the center of the domain\n",
+    "    center_lat=48.1,  # latitude of the center of the domain\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 33,
-   "id": "09b47006-6b4b-4007-b9fb-2b0a321bde7c",
+   "id": "18b3ef37-7ada-4df3-99cc-d0fe36cdaf8c",
    "metadata": {
     "tags": []
    },
    "outputs": [
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 1300x700 with 2 Axes>"
       ]
@@ -23849,214 +27876,686 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e40793d7-ebba-4c14-a7f9-7cc3174cdf68",
-   "metadata": {},
-   "source": [
-    "The eastern boundary is divided by Iceland!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8bcf0541-42f1-46d4-aadb-9591c7083819",
+   "id": "b5f69ff1-6071-4f44-8afc-a1aa9e9760e6",
    "metadata": {},
    "source": [
-    "We now create the boundary forcing twice, once with a 1D horizontal fill, and once with a 2D horizontal fill. By choosing `use_dask = False`, we trigger computation upon creating these two objects, and we can get a comparison of the timings."
+    "Before generating the boundary forcing, let's first examine the **source data** that we will be regridding: physics data from the GLORYS simulation output."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "12c5583f-4ec1-4405-9158-cc723c66adc2",
+   "id": "bb5448ac-1f8a-4d82-bd12-0abb50be12ea",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "kwargs = {\n",
-    "    \"grid\": grid,\n",
-    "    \"start_time\": start_time,\n",
-    "    \"end_time\": end_time,\n",
-    "    \"boundaries\": {\n",
-    "        \"south\": True,\n",
-    "        \"east\": True,\n",
-    "        \"north\": True,\n",
-    "        \"west\": True,\n",
-    "    },\n",
-    "    \"source\": {\"name\": \"GLORYS\", \"path\": glorys_path},\n",
-    "    \"use_dask\": False,\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9a1d7e60-0e5d-4545-bcfc-145ed18efd6c",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-info\">\n",
-    "\n",
-    "Note\n",
-    "    \n",
-    "In this example, the northern boundary is entirely over land, which means we could exclude it from the boundary generation by setting `boundaries = {\"south\": True, \"east\": True, \"north\": False, \"west\": True}` or by leaving `boundaries` unspecified, in which case `ROMS-Tools` will automatically infer the valid boundaries. However, we keep the northern boundary in this demonstration to illustrate the `ROMS-Tools` behavior when an entire boundary edge lies over land.\n",
-    "\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "8255bc26-39dc-4f95-8a30-89a7c54a74d7",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-12-20 13:50:46 - INFO - Using boundary configuration: {'south': True, 'east': True, 'north': True, 'west': True}\n",
-      "2025-12-20 13:50:54 - WARNING - The eastern boundary is divided by land. It would be safer (but slower and more memory-intensive) to use `apply_2d_horizontal_fill = True`.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 4.7 s, sys: 2.49 s, total: 7.19 s\n",
-      "Wall time: 9.46 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "\n",
-    "boundary_forcing_with_1d_fill = BoundaryForcing(\n",
-    "    **kwargs,\n",
-    "    apply_2d_horizontal_fill=False,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a38462aa-9fda-48f2-9cdf-c7381a76ce55",
-   "metadata": {},
-   "source": [
-    "Fortunately, `ROMS-Tools` provides a warning for the eastern boundary, alerting us to the issue."
+    "import xarray as xr"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 36,
-   "id": "36d69260-65d6-4ccf-955c-5d65a4428ef2",
-   "metadata": {
-    "tags": []
-   },
+   "id": "37a40144-7343-45f9-bd46-689f5ff3259b",
+   "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-12-20 13:50:56 - INFO - Using boundary configuration: {'south': True, 'east': True, 'north': True, 'west': True}\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 38min 51s, sys: 2.88 s, total: 38min 54s\n",
-      "Wall time: 28.3 s\n"
-     ]
+     "data": {
+      "text/html": [
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+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
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+       "    --jp-border-color2,\n",
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+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
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+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "  line-height: 1.6;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "  margin-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-obj-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-group-box-contents > label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input,\n",
+       ".xr-group-box-contents > input,\n",
+       ".xr-array-wrap > input {\n",
+       "  display: block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input + label,\n",
+       ".xr-var-item > input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:enabled + label,\n",
+       ".xr-var-item > input:enabled + label,\n",
+       ".xr-array-wrap > input:enabled + label,\n",
+       ".xr-group-box-contents > input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:focus-visible + label,\n",
+       ".xr-var-item > input:focus-visible + label,\n",
+       ".xr-array-wrap > input:focus-visible + label,\n",
+       ".xr-group-box-contents > input:focus-visible + label {\n",
+       "  outline: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:enabled + label:hover,\n",
+       ".xr-var-item > input:enabled + label:hover,\n",
+       ".xr-array-wrap > input:enabled + label:hover,\n",
+       ".xr-group-box-contents > input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "  white-space: nowrap;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > em {\n",
+       "  font-weight: normal;\n",
+       "}\n",
+       "\n",
+       ".xr-span-grid {\n",
+       "  grid-column-end: -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.6em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details,\n",
+       ".xr-group-box-contents > label {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in ~ .xr-section-details {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-children {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 100%;\n",
+       "  grid-column: 1 / -1;\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 30px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 26px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > label::before {\n",
+       "  content: \"📂\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label::before {\n",
+       "  content: \"📁\";\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label {\n",
+       "  padding-bottom: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked ~ .xr-sections {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-ellipsis {\n",
+       "  font-size: 1.4em;\n",
+       "  font-weight: 900;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  letter-spacing: 0.15em;\n",
+       "  cursor: default;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 14GB\n",
+       "Dimensions:    (time: 1, depth: 50, latitude: 2041, longitude: 4320)\n",
+       "Coordinates:\n",
+       "  * time       (time) datetime64[ns] 8B 2012-01-29\n",
+       "  * depth      (depth) float32 200B 0.494 1.541 2.646 ... 5.275e+03 5.728e+03\n",
+       "  * latitude   (latitude) float32 8kB -80.0 -79.92 -79.83 ... 89.83 89.92 90.0\n",
+       "  * longitude  (longitude) float32 17kB -180.0 -179.9 -179.8 ... 179.8 179.9\n",
+       "Data variables:\n",
+       "    thetao     (time, depth, latitude, longitude) float64 4GB ...\n",
+       "    so         (time, depth, latitude, longitude) float64 4GB ...\n",
+       "    uo         (time, depth, latitude, longitude) float64 4GB ...\n",
+       "    vo         (time, depth, latitude, longitude) float64 4GB ...\n",
+       "    zos        (time, latitude, longitude) float64 71MB ...\n",
+       "Attributes: (12/25)\n",
+       "    Conventions:               CF-1.4\n",
+       "    bulletin_date:             2021-07-07 00:00:00\n",
+       "    bulletin_type:             operational\n",
+       "    comment:                   CMEMS product\n",
+       "    domain_name:               GL12\n",
+       "    easting:                   longitude\n",
+       "    ...                        ...\n",
+       "    references:                http://www.mercator-ocean.fr\n",
+       "    source:                    MERCATOR GLORYS12V1\n",
+       "    title:                     daily mean fields from Global Ocean Physics An...\n",
+       "    z_max:                     5727.9169921875\n",
+       "    z_min:                     0.49402499198913574\n",
+       "    copernicusmarine_version:  2.3.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-51954dd7-c154-47ef-a9dd-b0a01e964789' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-51954dd7-c154-47ef-a9dd-b0a01e964789' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>depth</span>: 50</li><li><span class='xr-has-index'>latitude</span>: 2041</li><li><span class='xr-has-index'>longitude</span>: 4320</li></ul></div></li><li class='xr-section-item'><input id='section-92c99ee6-b3f6-483c-a25e-fa03c5820b9e' class='xr-section-summary-in' type='checkbox' checked /><label for='section-92c99ee6-b3f6-483c-a25e-fa03c5820b9e' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-01-29</div><input id='attrs-11499e75-9fd4-42c9-8502-50087df65962' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-11499e75-9fd4-42c9-8502-50087df65962' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-510c9263-21e2-411d-a68b-2942753cff5c' class='xr-var-data-in' type='checkbox'><label for='data-510c9263-21e2-411d-a68b-2942753cff5c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>unit_long :</span></dt><dd>Hours Since 1950-01-01</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>Time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-01-29T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>depth</span></div><div class='xr-var-dims'>(depth)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.494 1.541 ... 5.275e+03 5.728e+03</div><input id='attrs-024052e8-5c77-400d-807f-cd8191b529e8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-024052e8-5c77-400d-807f-cd8191b529e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d21c28e3-a5d6-4bfd-9cb9-cf53b4225295' class='xr-var-data-in' type='checkbox'><label for='data-d21c28e3-a5d6-4bfd-9cb9-cf53b4225295' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>unit_long :</span></dt><dd>Meters</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>Depth</dd></dl></div><div class='xr-var-data'><pre>array([4.940250e-01, 1.541375e+00, 2.645669e+00, 3.819495e+00, 5.078224e+00,\n",
+       "       6.440614e+00, 7.929560e+00, 9.572997e+00, 1.140500e+01, 1.346714e+01,\n",
+       "       1.581007e+01, 1.849556e+01, 2.159882e+01, 2.521141e+01, 2.944473e+01,\n",
+       "       3.443415e+01, 4.034405e+01, 4.737369e+01, 5.576429e+01, 6.580727e+01,\n",
+       "       7.785385e+01, 9.232607e+01, 1.097293e+02, 1.306660e+02, 1.558507e+02,\n",
+       "       1.861256e+02, 2.224752e+02, 2.660403e+02, 3.181274e+02, 3.802130e+02,\n",
+       "       4.539377e+02, 5.410889e+02, 6.435668e+02, 7.633331e+02, 9.023393e+02,\n",
+       "       1.062440e+03, 1.245291e+03, 1.452251e+03, 1.684284e+03, 1.941893e+03,\n",
+       "       2.225078e+03, 2.533336e+03, 2.865703e+03, 3.220820e+03, 3.597032e+03,\n",
+       "       3.992484e+03, 4.405224e+03, 4.833291e+03, 5.274784e+03, 5.727917e+03],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-80.0 -79.92 -79.83 ... 89.92 90.0</div><input id='attrs-08b68a7c-9139-48e1-804a-957b0ba287d5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08b68a7c-9139-48e1-804a-957b0ba287d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c88d6da-b478-48b8-a5c6-5e343fbb3dd8' class='xr-var-data-in' type='checkbox'><label for='data-2c88d6da-b478-48b8-a5c6-5e343fbb3dd8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>unit_long :</span></dt><dd>Degrees North</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd></dl></div><div class='xr-var-data'><pre>array([-80.      , -79.916664, -79.833336, ...,  89.833336,  89.916664,\n",
+       "        90.      ], shape=(2041,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-180.0 -179.9 ... 179.8 179.9</div><input id='attrs-2a9e0474-33fe-4a62-9f10-3ee621ac0d57' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2a9e0474-33fe-4a62-9f10-3ee621ac0d57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c2775bd-e013-42d9-a586-b616feeac775' class='xr-var-data-in' type='checkbox'><label for='data-0c2775bd-e013-42d9-a586-b616feeac775' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>unit_long :</span></dt><dd>Degrees East</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>Longitude</dd></dl></div><div class='xr-var-data'><pre>array([-180.     , -179.91667, -179.83333, ...,  179.75   ,  179.83333,\n",
+       "        179.91667], shape=(4320,), dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-42db401c-cedc-4cb0-abd4-3464069d2120' class='xr-section-summary-in' type='checkbox' checked /><label for='section-42db401c-cedc-4cb0-abd4-3464069d2120' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>thetao</span></div><div class='xr-var-dims'>(time, depth, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cd4cd736-a3ae-4cd8-aaf4-3a80cba70197' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd4cd736-a3ae-4cd8-aaf4-3a80cba70197' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28f953c7-6e2f-47cb-a0d8-42fdc0dbbd23' class='xr-var-data-in' type='checkbox'><label for='data-28f953c7-6e2f-47cb-a0d8-42fdc0dbbd23' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>unit_long :</span></dt><dd>Degrees Celsius</dd><dt><span>units :</span></dt><dd>degrees_C</dd><dt><span>valid_max :</span></dt><dd>21306</dd><dt><span>valid_min :</span></dt><dd>-32766</dd></dl></div><div class='xr-var-data'><pre>[440856000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>so</span></div><div class='xr-var-dims'>(time, depth, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-904f07d7-cf90-4e4c-b333-4e389e0120ca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-904f07d7-cf90-4e4c-b333-4e389e0120ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-113eb35a-d4e2-46c3-8c3c-45e87cb053a9' class='xr-var-data-in' type='checkbox'><label for='data-113eb35a-d4e2-46c3-8c3c-45e87cb053a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>long_name :</span></dt><dd>Salinity</dd><dt><span>standard_name :</span></dt><dd>sea_water_salinity</dd><dt><span>unit_long :</span></dt><dd>Practical Salinity Unit</dd><dt><span>units :</span></dt><dd>1e-3</dd><dt><span>valid_max :</span></dt><dd>28249</dd><dt><span>valid_min :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[440856000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>uo</span></div><div class='xr-var-dims'>(time, depth, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9cae7891-eb0d-4ab8-b78f-9f575fbe658f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9cae7891-eb0d-4ab8-b78f-9f575fbe658f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ef934bd-531f-4994-9e05-763e451508ae' class='xr-var-data-in' type='checkbox'><label for='data-6ef934bd-531f-4994-9e05-763e451508ae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>long_name :</span></dt><dd>Eastward velocity</dd><dt><span>standard_name :</span></dt><dd>eastward_sea_water_velocity</dd><dt><span>unit_long :</span></dt><dd>Meters per second</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>valid_max :</span></dt><dd>4314</dd><dt><span>valid_min :</span></dt><dd>-3123</dd></dl></div><div class='xr-var-data'><pre>[440856000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vo</span></div><div class='xr-var-dims'>(time, depth, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-95b6c540-1bb5-4f12-97a2-3e977a1aa9e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95b6c540-1bb5-4f12-97a2-3e977a1aa9e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fdc0c6b-9409-4ec9-8973-d031b7208629' class='xr-var-data-in' type='checkbox'><label for='data-1fdc0c6b-9409-4ec9-8973-d031b7208629' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>long_name :</span></dt><dd>Northward velocity</dd><dt><span>standard_name :</span></dt><dd>northward_sea_water_velocity</dd><dt><span>unit_long :</span></dt><dd>Meters per second</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>valid_max :</span></dt><dd>3639</dd><dt><span>valid_min :</span></dt><dd>-3680</dd></dl></div><div class='xr-var-data'><pre>[440856000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zos</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d71b0f6d-741c-4db3-a2a8-62465dc1fa23' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d71b0f6d-741c-4db3-a2a8-62465dc1fa23' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9cac8765-8f80-408e-b11a-f88775e877ba' class='xr-var-data-in' type='checkbox'><label for='data-9cac8765-8f80-408e-b11a-f88775e877ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>long_name :</span></dt><dd>Sea surface height</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_geoid</dd><dt><span>unit_long :</span></dt><dd>Meters</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_max :</span></dt><dd>5684</dd><dt><span>valid_min :</span></dt><dd>-6228</dd></dl></div><div class='xr-var-data'><pre>[8817120 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0204c114-c9d3-40a4-8e7d-a14fa0111526' class='xr-section-summary-in' type='checkbox' /><label for='section-0204c114-c9d3-40a4-8e7d-a14fa0111526' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(25)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.4</dd><dt><span>bulletin_date :</span></dt><dd>2021-07-07 00:00:00</dd><dt><span>bulletin_type :</span></dt><dd>operational</dd><dt><span>comment :</span></dt><dd>CMEMS product</dd><dt><span>domain_name :</span></dt><dd>GL12</dd><dt><span>easting :</span></dt><dd>longitude</dd><dt><span>field_date :</span></dt><dd>2021-06-30 00:00:00</dd><dt><span>field_julian_date :</span></dt><dd>26113.0</dd><dt><span>field_type :</span></dt><dd>mean</dd><dt><span>forecast_range :</span></dt><dd></dd><dt><span>forecast_type :</span></dt><dd></dd><dt><span>history :</span></dt><dd>2023/06/01 16:20:05 MERCATOR OCEAN Netcdf creation</dd><dt><span>institution :</span></dt><dd>MERCATOR OCEAN</dd><dt><span>julian_day_unit :</span></dt><dd>days since 1950-01-01 00:00:00</dd><dt><span>latitude_max :</span></dt><dd>90.0</dd><dt><span>latitude_min :</span></dt><dd>-80.0</dd><dt><span>longitude_max :</span></dt><dd>179.9166717529297</dd><dt><span>longitude_min :</span></dt><dd>-180.0</dd><dt><span>northing :</span></dt><dd>latitude</dd><dt><span>references :</span></dt><dd>http://www.mercator-ocean.fr</dd><dt><span>source :</span></dt><dd>MERCATOR GLORYS12V1</dd><dt><span>title :</span></dt><dd>daily mean fields from Global Ocean Physics Analysis and Forecast updated Daily</dd><dt><span>z_max :</span></dt><dd>5727.9169921875</dd><dt><span>z_min :</span></dt><dd>0.49402499198913574</dd><dt><span>copernicusmarine_version :</span></dt><dd>2.3.0</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 14GB\n",
+       "Dimensions:    (time: 1, depth: 50, latitude: 2041, longitude: 4320)\n",
+       "Coordinates:\n",
+       "  * time       (time) datetime64[ns] 8B 2012-01-29\n",
+       "  * depth      (depth) float32 200B 0.494 1.541 2.646 ... 5.275e+03 5.728e+03\n",
+       "  * latitude   (latitude) float32 8kB -80.0 -79.92 -79.83 ... 89.83 89.92 90.0\n",
+       "  * longitude  (longitude) float32 17kB -180.0 -179.9 -179.8 ... 179.8 179.9\n",
+       "Data variables:\n",
+       "    thetao     (time, depth, latitude, longitude) float64 4GB ...\n",
+       "    so         (time, depth, latitude, longitude) float64 4GB ...\n",
+       "    uo         (time, depth, latitude, longitude) float64 4GB ...\n",
+       "    vo         (time, depth, latitude, longitude) float64 4GB ...\n",
+       "    zos        (time, latitude, longitude) float64 71MB ...\n",
+       "Attributes: (12/25)\n",
+       "    Conventions:               CF-1.4\n",
+       "    bulletin_date:             2021-07-07 00:00:00\n",
+       "    bulletin_type:             operational\n",
+       "    comment:                   CMEMS product\n",
+       "    domain_name:               GL12\n",
+       "    easting:                   longitude\n",
+       "    ...                        ...\n",
+       "    references:                http://www.mercator-ocean.fr\n",
+       "    source:                    MERCATOR GLORYS12V1\n",
+       "    title:                     daily mean fields from Global Ocean Physics An...\n",
+       "    z_max:                     5727.9169921875\n",
+       "    z_min:                     0.49402499198913574\n",
+       "    copernicusmarine_version:  2.3.0"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "%%time\n",
-    "\n",
-    "boundary_forcing_with_2d_fill = BoundaryForcing(\n",
-    "    **kwargs,\n",
-    "    apply_2d_horizontal_fill=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d6c9e483-1036-4b6e-82eb-a0078df06703",
-   "metadata": {},
-   "source": [
-    "Creating the boundary forcing with the safer 2D horizontal fill took about 3 times as long as with the 1D horizontal fill. "
+    "ds_source = xr.open_dataset(glorys_path[0])\n",
+    "ds_source"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e9b05b74-5c50-4961-b9f2-59b5bf2f30fd",
+   "id": "4bdd0be3-662c-42ed-ba80-ee6bb56e2233",
    "metadata": {},
    "source": [
-    "Let's compare the results! As an example, we will look at temperature along the four boundaries at the first time step."
+    "Here, we zoom in on the coarser GLORYS source data as compared with the relatively small domain of interest in the Puget Sound:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 37,
-   "id": "99be25e0-cecb-49a2-b093-5f2ce56ef15f",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "bdry_coords = {\n",
-    "    \"south\": {\"eta_rho\": 0},\n",
-    "    \"east\": {\"xi_rho\": -1},\n",
-    "    \"north\": {\"eta_rho\": -1},\n",
-    "    \"west\": {\"xi_rho\": 0},\n",
-    "}\n",
-    "\n",
-    "\n",
-    "def compare_fill_methods(\n",
-    "    direction,\n",
-    "    bry_time=0,\n",
-    "    bf_2d_fill=boundary_forcing_with_2d_fill,\n",
-    "    bf_1d_fill=boundary_forcing_with_1d_fill,\n",
-    "):\n",
-    "\n",
-    "    fig, axs = plt.subplots(1, 3, figsize=(17, 3))\n",
-    "\n",
-    "    var_name = \"temp\"\n",
-    "    kwargs = {\"cbar_kwargs\": {\"label\": \"degrees Celsius\"}}\n",
-    "    title = \"Temperature\"\n",
-    "\n",
-    "    mask = grid.ds.mask_rho.isel(**bdry_coords[direction])\n",
-    "\n",
-    "    bf_2d_fill.ds[f\"{var_name}_{direction}\"].isel(bry_time=bry_time).plot(\n",
-    "        ax=axs[0], **kwargs\n",
-    "    )\n",
-    "    axs[0].set_title(\"2D horizontal fill\")\n",
-    "    bf_1d_fill.ds[f\"{var_name}_{direction}\"].isel(bry_time=bry_time).plot(\n",
-    "        ax=axs[1], **kwargs\n",
-    "    )\n",
-    "    axs[1].set_title(\"1D horizontal fill\")\n",
-    "    (bf_2d_fill.ds - bf_1d_fill.ds)[f\"{var_name}_{direction}\"].where(mask).isel(\n",
-    "        bry_time=bry_time\n",
-    "    ).plot(ax=axs[2], **kwargs)\n",
-    "    axs[2].set_title(\"Difference at wet points\")\n",
-    "\n",
-    "    fig.suptitle(f\"{title} at {direction}ern boundary\", y=1.05)\n",
-    "\n",
-    "    return fig"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "b4822008-2c6c-4c88-82a1-f9a650f36656",
+   "id": "e4b57b54-b003-42e3-8279-7b9836096906",
    "metadata": {
     "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<Figure size 1700x300 with 6 Axes>"
+       "<matplotlib.legend.Legend at 0x1465d7c446e0>"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -24064,92 +28563,151 @@
     }
    ],
    "source": [
-    "fig = compare_fill_methods(\"south\")"
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "lon_min = grid.ds.lon_rho.min() - 360\n",
+    "lon_max = grid.ds.lon_rho.max() - 360\n",
+    "lat_min = grid.ds.lat_rho.min()\n",
+    "lat_max = grid.ds.lat_rho.max()\n",
+    "\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ds_source.thetao[0,0].sel(latitude=slice(lat_min-1,lat_max+1), longitude=slice(lon_min-1,lon_max+1)).plot()\n",
+    "\n",
+    "\n",
+    "ax.plot(\n",
+    "    [lon_min, lon_min, lon_max, lon_max, lon_min],\n",
+    "    [lat_min, lat_max, lat_max, lat_min, lat_min],\n",
+    "    color=\"r\",\n",
+    "    linewidth=2,\n",
+    "    label=\"ROMS domain\",\n",
+    ")\n",
+    "\n",
+    "ax.legend(loc=\"upper left\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "2228f93b-6440-407a-a953-2cdfc5c6a44c",
+   "id": "6000a636-f263-4e9b-818e-89aea4a7ae54",
+   "metadata": {},
+   "source": [
+    "As shown above, all the boundaries of this ROMS domain lie almost entirely on\n",
+    "land in the 1/12 degree GLORYS source. In this data-starved case, pre-filling the source is necessary so that \n",
+    "the boundary calculations have some values to work with. If you do not run pre-fill,\n",
+    "the southern boundary is entirely NaN and `ROMS-Tools` raises an error.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "bb300b09-76d5-4d66-a407-40c1c0d3d137",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "The southern boundary does not contain any land points, so southern boundary temperature is the same for both fill methods, as expected."
+    "kwargs = {\n",
+    "    \"start_time\": start_time,\n",
+    "    \"end_time\": end_time,\n",
+    "    \"source\": {\"name\": \"GLORYS\", \"path\": glorys_path},\n",
+    "    \"type\": \"physics\",  # \"physics\" or \"bgc\"; default is \"physics\"\n",
+    "    \"use_dask\": True,\n",
+    "}"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 39,
-   "id": "94664b9c-997b-4893-ba01-b94440bb7873",
-   "metadata": {
-    "tags": []
-   },
+   "id": "f7e85fdb-77ce-4fc6-ac08-fbe884b0f9a1",
+   "metadata": {},
    "outputs": [
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1700x300 with 6 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-06-22 17:59:34 - INFO - No `boundaries` provided. Using mask-based defaults: {'south': True, 'east': True, 'north': True, 'west': True}\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "zeta_south consists entirely of NaNs after regridding. This may be due to the southern boundary being entirely on land in the GLORYS data, which could have a coarser resolution than the ROMS domain. Try setting a `prefill` method (e.g. 'inverse_dist', 'nearest_neighbor', or '2d_lateral_fill') to fill the source before regridding; see https://roms-tools.readthedocs.io/en/latest/boundary_forcing.html for details.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[39]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m bf = BoundaryForcing(\n\u001b[32m      2\u001b[39m         grid=grid,\n\u001b[32m      3\u001b[39m     **kwargs,\n\u001b[32m      4\u001b[39m     \u001b[38;5;66;03m# apply_2d_horizontal_fill = False,\u001b[39;00m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m<string>:20\u001b[39m, in \u001b[36m__create_fn__.<locals>.__init__\u001b[39m\u001b[34m(self, grid, start_time, end_time, boundaries, source, type, prefill, prefill_kwargs, regrid_method, extrap_method, extrap_kwargs, apply_2d_horizontal_fill, model_reference_date, use_dask, chunks, initial_slice_bounds, bypass_validation)\u001b[39m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/packages/roms-tools/roms_tools/setup/boundary_forcing.py:565\u001b[39m, in \u001b[36mBoundaryForcing.__post_init__\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m    562\u001b[39m ds = \u001b[38;5;28mself\u001b[39m._add_global_metadata(data, ds)\n\u001b[32m    564\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m.bypass_validation:\n\u001b[32m--> \u001b[39m\u001b[32m565\u001b[39m     \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_validate\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mds\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    567\u001b[39m \u001b[38;5;66;03m# substitute NaNs over land by a fill value to avoid blow-up of ROMS\u001b[39;00m\n\u001b[32m    568\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m var_name \u001b[38;5;129;01min\u001b[39;00m ds.data_vars:\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/packages/roms-tools/roms_tools/setup/boundary_forcing.py:1033\u001b[39m, in \u001b[36mBoundaryForcing._validate\u001b[39m\u001b[34m(self, ds)\u001b[39m\n\u001b[32m   1023\u001b[39m \u001b[38;5;66;03m# Check for NaN values at the first time step using the nan_check function\u001b[39;00m\n\u001b[32m   1024\u001b[39m error_message = (\n\u001b[32m   1025\u001b[39m     \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbdry_var_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m consists entirely of NaNs after regridding. \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m   1026\u001b[39m     \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mThis may be due to the \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdirection\u001b[38;5;132;01m}\u001b[39;00m\u001b[33mern boundary being entirely on land in the \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m   (...)\u001b[39m\u001b[32m   1030\u001b[39m     \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mhttps://roms-tools.readthedocs.io/en/latest/boundary_forcing.html for details.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m   1031\u001b[39m )\n\u001b[32m-> \u001b[39m\u001b[32m1033\u001b[39m \u001b[30;43mnan_check\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m   1034\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mds\u001b[39;49m\u001b[30;43m[\u001b[39;49m\u001b[30;43mbdry_var_name\u001b[39;49m\u001b[30;43m]\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43misel\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mbry_time\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43m0\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1035\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mmask\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43misel\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mbdry_coords\u001b[39;49m\u001b[30;43m[\u001b[39;49m\u001b[30;43mlocation\u001b[39;49m\u001b[30;43m]\u001b[39;49m\u001b[30;43m[\u001b[39;49m\u001b[30;43mdirection\u001b[39;49m\u001b[30;43m]\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1036\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43merror_message\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43merror_message\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1037\u001b[39m \u001b[30;43m\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/packages/roms-tools/roms_tools/setup/utils.py:291\u001b[39m, in \u001b[36mnan_check\u001b[39m\u001b[34m(field, mask, error_message)\u001b[39m\n\u001b[32m    289\u001b[39m \u001b[38;5;66;03m# Check if any NaN values exist in the modified field\u001b[39;00m\n\u001b[32m    290\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m da.isnull().any().values:\n\u001b[32m--> \u001b[39m\u001b[32m291\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(error_message)\n",
+      "\u001b[31mValueError\u001b[39m: zeta_south consists entirely of NaNs after regridding. This may be due to the southern boundary being entirely on land in the GLORYS data, which could have a coarser resolution than the ROMS domain. Try setting a `prefill` method (e.g. 'inverse_dist', 'nearest_neighbor', or '2d_lateral_fill') to fill the source before regridding; see https://roms-tools.readthedocs.io/en/latest/boundary_forcing.html for details."
+     ]
     }
    ],
    "source": [
-    "fig = compare_fill_methods(\"west\")"
+    "bf = BoundaryForcing(\n",
+    "    grid=grid,\n",
+    "    **kwargs,\n",
+    "    prefill=None,\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b1de81ee-0122-4151-9314-8ab3cc2c362b",
+   "id": "493d8550-888a-4a19-a8a9-1677d4c2a076",
    "metadata": {},
    "source": [
-    "The western boundary intersects Greenland for `eta_rho > 67`, where the fill values vary between the two fill methods. However, in this example, the values are nearly identical at most wet points, with only minor differences of up to 0.22$^\\circ$C. As a result, the choice of fill method has a minimal impact on the ROMS online simulation outcome in this case."
+    "Let’s try a horizontal pre-fill instead, as suggested in the error message."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 40,
-   "id": "9f522d18-9670-403e-b7f0-f5c2ac27161f",
-   "metadata": {
-    "tags": []
-   },
+   "id": "8256f1e3-0f9b-44b9-ba15-bec0c3c26c1d",
+   "metadata": {},
    "outputs": [
     {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1700x300 with 6 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-06-22 18:00:51 - INFO - No `boundaries` provided. Using mask-based defaults: {'south': True, 'east': True, 'north': True, 'west': True}\n"
+     ]
     }
    ],
    "source": [
-    "fig = compare_fill_methods(\"north\")"
+    "bf_prefill = BoundaryForcing(\n",
+    "    grid=grid,\n",
+    "    **kwargs,\n",
+    "    prefill=\"inverse_dist\",\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "2b95236a-4bce-412f-a9d7-df77810b49f9",
+   "id": "1da7f4ab-68e0-4f85-8a9c-beb32b83e624",
    "metadata": {},
    "source": [
-    "The northern boundary lies entirely over land (Greenland), and might as well have been skipped in the boundary forcing generation. The fill values generated by the 2D fill are more sophisticated than those by the 1D fill (simply zero). But this won't have any influence on the online model."
+    "This worked, and the boundary forcing has reasonable values."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 41,
-   "id": "595bde97-bfb3-4310-9561-bb086ea12d7e",
-   "metadata": {
-    "tags": []
-   },
+   "id": "2b04c396-1989-4789-a599-7ff585242bee",
+   "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[########################################] | 100% Completed | 302.24 ms\n",
+      "[########################################] | 100% Completed | 101.84 ms\n",
+      "[########################################] | 100% Completed | 101.62 ms\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 1700x300 with 6 Axes>"
+       "<Figure size 2000x400 with 6 Axes>"
       ]
      },
      "metadata": {},
@@ -24157,64 +28715,69 @@
     }
    ],
    "source": [
-    "fig = compare_fill_methods(\"east\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "74a6b9e0-a04a-497a-b949-2418a1852a12",
-   "metadata": {},
-   "source": [
-    "The eastern boundary is divided by Iceland, highlighting one of the limitations of the 1D fill method. As noted earlier, the 1D fill applied a straightforward forward fill, which inadvertently spread values from the region south of Iceland to the region north of Iceland, creating an artifact in the eastern boundary data north of Iceland (at about `eta_rho` = 50). This is exactly what `ROMS-Tools` warned us about when we generated the boundary forcing with the 1D fill!"
+    "fig, axs = plt.subplots(1, 3, figsize=(20, 4))\n",
+    "\n",
+    "bf_prefill.plot(f\"salt_west\", layer_contours=True, ax=axs[0])\n",
+    "bf_prefill.plot(f\"salt_east\", layer_contours=True, ax=axs[1])\n",
+    "bf_prefill.plot(f\"salt_south\", layer_contours=True, ax=axs[2])\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "dd90d37d-908b-4af8-b917-58392c631c42",
+   "id": "71a04ebc-aa04-45cb-87a4-6e13cee5b472",
    "metadata": {},
    "source": [
-    "### Case study 2: Coarse resolution of source data\n",
-    "\n",
-    "Let's examine an example where the resolution of the source data is very coarse to better understand the second limitation of the 1D fill method. First, we create a grid that encompasses the waters surrounding Wales."
+    "Alternatively, since the boundary processing reads in a buffer of 3 grid cells on either side of each boundary in an effort to expand the data available, a shift in the grid itself can make enough data available for the original command to work:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
-   "id": "bece594e-7dff-4795-b567-d41efda3d7c5",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
+   "execution_count": 48,
+   "id": "5cdfba89-61bc-4fde-a438-98e15f87dd90",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-06-22 18:12:46 - INFO - No `boundaries` provided. Using mask-based defaults: {'south': True, 'east': True, 'north': True, 'west': True}\n"
+     ]
+    }
+   ],
    "source": [
     "grid = Grid(\n",
-    "    nx=30,\n",
-    "    ny=30,\n",
-    "    size_x=240,\n",
-    "    size_y=240,\n",
-    "    center_lon=-4.1,\n",
-    "    center_lat=52.36,\n",
-    "    rot=0,\n",
-    "    topography_source={\n",
-    "        \"name\": \"SRTM15\",\n",
-    "        \"path\": \"/global/cfs/projectdirs/m4746/Datasets/SRTM15/SRTM15_V2.6.nc\",\n",
-    "    },\n",
+    "    **grid_kwargs,\n",
+    "    center_lon=-122.8,  # longitude of the center of the domain\n",
+    "    center_lat=48.2,  # latitude of the center of the domain\n",
+    ")\n",
+    "\n",
+    "bf_shift = BoundaryForcing(\n",
+    "    grid=grid,\n",
+    "    **kwargs,\n",
+    "    prefill=None,\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
-   "id": "c04dd014-c3fd-48cc-8d35-662d75ed4989",
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": 49,
+   "id": "7cb206c1-f768-4e39-9f1e-23aa3bbe334b",
+   "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[########################################] | 100% Completed | 101.39 ms\n",
+      "[########################################] | 100% Completed | 101.08 ms\n",
+      "[########################################] | 100% Completed | 101.18 ms\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 1300x700 with 2 Axes>"
+       "<Figure size 2000x400 with 6 Axes>"
       ]
      },
      "metadata": {},
@@ -24222,55 +28785,81 @@
     }
    ],
    "source": [
-    "grid.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "eedd4ef9-f04a-4d81-a8dd-add9a484faea",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "kwargs = {\n",
-    "    \"grid\": grid,\n",
-    "    \"start_time\": start_time,\n",
-    "    \"end_time\": end_time,\n",
-    "    \"source\": {\"name\": \"CESM_REGRIDDED\", \"path\": cesm_bgc_path, \"climatology\": True},\n",
-    "    \"type\": \"bgc\",\n",
-    "    \"use_dask\": False,\n",
-    "}"
+    "fig, axs = plt.subplots(1, 3, figsize=(20, 4))\n",
+    "\n",
+    "bf_shift.plot(f\"salt_west\", layer_contours=True, ax=axs[0])\n",
+    "bf_shift.plot(f\"salt_east\", layer_contours=True, ax=axs[1])\n",
+    "bf_shift.plot(f\"salt_south\", layer_contours=True, ax=axs[2])\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e3db9620-6004-412f-8870-7c1873e161fd",
+   "id": "dad5898c",
    "metadata": {},
    "source": [
-    "Before generating the boundary forcing, let's first examine the **source data** that we will be regridding: BGC data from a **coarse 1x1 degree** CESM simulation."
+    "### Pre-filling the source data\n",
+    "\n",
+    "Here we show a deeper dive into the options available in case you want to **fill the source's\n",
+    "land cells first** and then regrid the now NaN-free source. This is controlled\n",
+    "by the `prefill` argument (with method-specific options in `prefill_kwargs`):\n",
+    "\n",
+    "| Strategy | `prefill` value | Needs xESMF | Cost | Character |\n",
+    "|---|---|---|---|---|\n",
+    "| Masked bilinear + extrapolation (default) | `None` | yes\\* | low | Interpolates over real ocean only; `extrap_method` fills boundary points with no valid source neighbor. Extrapolation will fail if there are no source cells in the boundary strip or within 3 buffer cells. \\*Falls back to a nearest-neighbor pre-fill + scipy when xESMF is unavailable. |\n",
+    "| AMG Poisson fill (legacy) | `\"2d_lateral_fill\"` | no | high | Smoothest; a global harmonic fill of the whole source. Slower and more memory-hungry on large domains. |\n",
+    "| Inverse-distance fill | `\"inverse_dist\"` | yes | medium | Smooth inverse-distance-weighted fill of the source; tune with `num_src_pnts` / `dist_exponent`. |\n",
+    "| Nearest-source fill | `\"nearest_s2d\"` | yes | low | Blocky (single nearest source cell). |\n",
+    "| Nearest-neighbor fill | `\"nearest_neighbor\"` | no | lowest | Blocky and connectivity-blind; also the automatic fallback when xESMF is unavailable. |\n",
+    "\n",
+    "There are two independent stages here: **`prefill`** fills the *source* before\n",
+    "regridding (after which the regrid is a plain bilinear interpolation, so\n",
+    "`extrap_method` no longer applies), while **`extrap_method`** fills *destination*\n",
+    "boundary points during the default masked-bilinear regrid. The regrid engine itself\n",
+    "is selected with `regrid_method` (`\"auto\"`, `\"xesmf\"`, `\"scipy\"`).\n",
+    "\n",
+    "Let's visualize how each method fills the coarse CESM source over an example domain around Wales.\n",
+    "We crop the source to the domain (plus a buffer) and apply each fill in turn:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
-   "id": "10fd45eb-cb68-47a4-8564-0f01c5255032",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
+   "execution_count": 44,
+   "id": "51a5056b-7652-4394-9125-f06c1100b801",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1300x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "import xarray as xr"
+    "grid = Grid(\n",
+    "    nx=30,\n",
+    "    ny=30,\n",
+    "    size_x=240,\n",
+    "    size_y=240,\n",
+    "    center_lon=-4.1,\n",
+    "    center_lat=52.36,\n",
+    "    rot=0,\n",
+    "    topography_source={\n",
+    "        \"name\": \"SRTM15\",\n",
+    "        \"path\": \"/anvil/projects/x-ees250129/Datasets/SRTM15/SRTM15_V2.6.nc\",\n",
+    "    },\n",
+    ")\n",
+    "grid.plot()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
-   "id": "59050072-276b-41e3-aa5a-39e671552b23",
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": 45,
+   "id": "f6e84876-f98a-460b-b9fd-af390a9dd814",
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -24290,9 +28879,7 @@
        "</symbol>\n",
        "</defs>\n",
        "</svg>\n",
-       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
-       " *\n",
-       " */\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
        "\n",
        ":root {\n",
        "  --xr-font-color0: var(\n",
@@ -24371,6 +28958,8 @@
        "  display: block !important;\n",
        "  min-width: 300px;\n",
        "  max-width: 700px;\n",
+       "  line-height: 1.6;\n",
+       "  padding-bottom: 4px;\n",
        "}\n",
        "\n",
        ".xr-text-repr-fallback {\n",
@@ -24381,8 +28970,11 @@
        ".xr-header {\n",
        "  padding-top: 6px;\n",
        "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
        "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "  margin-bottom: 4px;\n",
        "}\n",
        "\n",
        ".xr-header > div,\n",
@@ -24393,46 +28985,62 @@
        "}\n",
        "\n",
        ".xr-obj-type,\n",
-       ".xr-array-name {\n",
+       ".xr-obj-name {\n",
        "  margin-left: 2px;\n",
        "  margin-right: 10px;\n",
        "}\n",
        "\n",
-       ".xr-obj-type {\n",
+       ".xr-obj-type,\n",
+       ".xr-group-box-contents > label {\n",
        "  color: var(--xr-font-color2);\n",
+       "  display: block;\n",
        "}\n",
        "\n",
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
        "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
        "  display: contents;\n",
        "}\n",
        "\n",
-       ".xr-section-item input {\n",
-       "  display: inline-block;\n",
+       ".xr-section-item > input,\n",
+       ".xr-group-box-contents > input,\n",
+       ".xr-array-wrap > input {\n",
+       "  display: block;\n",
        "  opacity: 0;\n",
        "  height: 0;\n",
+       "  margin: 0;\n",
        "}\n",
        "\n",
-       ".xr-section-item input + label {\n",
+       ".xr-section-item > input + label,\n",
+       ".xr-var-item > input + label {\n",
        "  color: var(--xr-disabled-color);\n",
-       "  border: 2px solid transparent !important;\n",
        "}\n",
        "\n",
-       ".xr-section-item input:enabled + label {\n",
+       ".xr-section-item > input:enabled + label,\n",
+       ".xr-var-item > input:enabled + label,\n",
+       ".xr-array-wrap > input:enabled + label,\n",
+       ".xr-group-box-contents > input:enabled + label {\n",
        "  cursor: pointer;\n",
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
-       ".xr-section-item input:focus + label {\n",
-       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       ".xr-section-item > input:focus-visible + label,\n",
+       ".xr-var-item > input:focus-visible + label,\n",
+       ".xr-array-wrap > input:focus-visible + label,\n",
+       ".xr-group-box-contents > input:focus-visible + label {\n",
+       "  outline: auto;\n",
        "}\n",
        "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
+       ".xr-section-item > input:enabled + label:hover,\n",
+       ".xr-var-item > input:enabled + label:hover,\n",
+       ".xr-array-wrap > input:enabled + label:hover,\n",
+       ".xr-group-box-contents > input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
        "\n",
@@ -24440,11 +29048,25 @@
        "  grid-column: 1;\n",
        "  color: var(--xr-font-color2);\n",
        "  font-weight: 500;\n",
+       "  white-space: nowrap;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > em {\n",
+       "  font-weight: normal;\n",
+       "}\n",
+       "\n",
+       ".xr-span-grid {\n",
+       "  grid-column-end: -1;\n",
        "}\n",
        "\n",
        ".xr-section-summary > span {\n",
        "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
+       "  padding-left: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.6em;\n",
        "}\n",
        "\n",
        ".xr-section-summary-in:disabled + label {\n",
@@ -24472,9 +29094,9 @@
        "}\n",
        "\n",
        ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
+       ".xr-section-inline-details,\n",
+       ".xr-group-box-contents > label {\n",
        "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
        "}\n",
        "\n",
        ".xr-section-inline-details {\n",
@@ -24482,15 +29104,81 @@
        "}\n",
        "\n",
        ".xr-section-details {\n",
-       "  display: none;\n",
        "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
        "  margin-bottom: 5px;\n",
        "}\n",
        "\n",
+       ".xr-section-summary-in ~ .xr-section-details {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
        ".xr-section-summary-in:checked ~ .xr-section-details {\n",
        "  display: contents;\n",
        "}\n",
        "\n",
+       ".xr-children {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 100%;\n",
+       "  grid-column: 1 / -1;\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 30px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 26px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > label::before {\n",
+       "  content: \"📂\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label::before {\n",
+       "  content: \"📁\";\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label {\n",
+       "  padding-bottom: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked ~ .xr-sections {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-ellipsis {\n",
+       "  font-size: 1.4em;\n",
+       "  font-weight: 900;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  letter-spacing: 0.15em;\n",
+       "  cursor: default;\n",
+       "}\n",
+       "\n",
        ".xr-array-wrap {\n",
        "  grid-column: 1 / -1;\n",
        "  display: grid;\n",
@@ -24723,9 +29411,9 @@
        "Coordinates:\n",
        "  * month        (month) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12\n",
        "  * z_t          (z_t) float32 240B 500.0 1.5e+03 ... 5.125e+05 5.375e+05\n",
-       "  * z_t_150m     (z_t_150m) float32 60B 500.0 1.5e+03 ... 1.35e+04 1.45e+04\n",
-       "  * lon          (lon) float64 3kB -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n",
        "  * lat          (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n",
+       "  * lon          (lon) float64 3kB -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n",
+       "  * z_t_150m     (z_t_150m) float32 60B 500.0 1.5e+03 ... 1.35e+04 1.45e+04\n",
        "Data variables: (12/32)\n",
        "    ALK          (month, z_t, lat, lon) float32 187MB ...\n",
        "    ALK_ALT_CO2  (month, z_t, lat, lon) float32 187MB ...\n",
@@ -24743,7 +29431,7 @@
        "Attributes:\n",
        "    Title:          Climatology computed from the 1x1 degree CESM-MARBL simul...\n",
        "    original_path:  /glade/campaign/collections/cmip/CMIP6/CESM-HR/FOSI_BGC/L...\n",
-       "    regrid_method:  conservative</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-652ddce3-9607-4b86-8e0a-c42ebb143d7e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-652ddce3-9607-4b86-8e0a-c42ebb143d7e' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>month</span>: 12</li><li><span class='xr-has-index'>z_t</span>: 60</li><li><span class='xr-has-index'>lat</span>: 180</li><li><span class='xr-has-index'>lon</span>: 360</li><li><span class='xr-has-index'>z_t_150m</span>: 15</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f30517dc-3279-46ce-a4d2-8a4aaff43b06' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f30517dc-3279-46ce-a4d2-8a4aaff43b06' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>month</span></div><div class='xr-var-dims'>(month)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-588aa664-b6f2-4eb2-9b1b-02ac9b93ac2a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-588aa664-b6f2-4eb2-9b1b-02ac9b93ac2a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e989367f-4738-46d8-80ee-e99e679233c3' class='xr-var-data-in' type='checkbox'><label for='data-e989367f-4738-46d8-80ee-e99e679233c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z_t</span></div><div class='xr-var-dims'>(z_t)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>500.0 1.5e+03 ... 5.375e+05</div><input id='attrs-4775d2c2-695f-4c7a-b839-2da0597b191c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4775d2c2-695f-4c7a-b839-2da0597b191c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a8f0167-352d-4b4e-b211-1047fa800c69' class='xr-var-data-in' type='checkbox'><label for='data-3a8f0167-352d-4b4e-b211-1047fa800c69' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>depth from surface to midpoint of layer</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>units :</span></dt><dd>centimeters</dd><dt><span>valid_max :</span></dt><dd>537500.0</dd><dt><span>valid_min :</span></dt><dd>500.0</dd></dl></div><div class='xr-var-data'><pre>array([5.000000e+02, 1.500000e+03, 2.500000e+03, 3.500000e+03, 4.500000e+03,\n",
+       "    regrid_method:  conservative</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-52e2a37f-1cca-4322-a432-85456325f90e' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-52e2a37f-1cca-4322-a432-85456325f90e' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>month</span>: 12</li><li><span class='xr-has-index'>z_t</span>: 60</li><li><span class='xr-has-index'>lat</span>: 180</li><li><span class='xr-has-index'>lon</span>: 360</li><li><span class='xr-has-index'>z_t_150m</span>: 15</li></ul></div></li><li class='xr-section-item'><input id='section-aafe0ee0-a626-4c19-a67d-6b94ccefdebb' class='xr-section-summary-in' type='checkbox' checked /><label for='section-aafe0ee0-a626-4c19-a67d-6b94ccefdebb' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>month</span></div><div class='xr-var-dims'>(month)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-77120b27-cb3d-49b7-8031-8ad18a3f2926' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-77120b27-cb3d-49b7-8031-8ad18a3f2926' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0aeaae26-2ea7-458b-b509-296a41ebae47' class='xr-var-data-in' type='checkbox'><label for='data-0aeaae26-2ea7-458b-b509-296a41ebae47' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z_t</span></div><div class='xr-var-dims'>(z_t)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>500.0 1.5e+03 ... 5.375e+05</div><input id='attrs-b7937668-b60d-43dd-9e19-781186336417' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b7937668-b60d-43dd-9e19-781186336417' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fdee0eff-c605-49d4-b18d-5cb54e65ca97' class='xr-var-data-in' type='checkbox'><label for='data-fdee0eff-c605-49d4-b18d-5cb54e65ca97' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>depth from surface to midpoint of layer</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>units :</span></dt><dd>centimeters</dd><dt><span>valid_max :</span></dt><dd>537500.0</dd><dt><span>valid_min :</span></dt><dd>500.0</dd></dl></div><div class='xr-var-data'><pre>array([5.000000e+02, 1.500000e+03, 2.500000e+03, 3.500000e+03, 4.500000e+03,\n",
        "       5.500000e+03, 6.500000e+03, 7.500000e+03, 8.500000e+03, 9.500000e+03,\n",
        "       1.050000e+04, 1.150000e+04, 1.250000e+04, 1.350000e+04, 1.450000e+04,\n",
        "       1.550000e+04, 1.650984e+04, 1.754790e+04, 1.862913e+04, 1.976603e+04,\n",
@@ -24755,8 +29443,7 @@
        "       1.968944e+05, 2.186457e+05, 2.413972e+05, 2.649001e+05, 2.889385e+05,\n",
        "       3.133405e+05, 3.379793e+05, 3.627670e+05, 3.876452e+05, 4.125768e+05,\n",
        "       4.375392e+05, 4.625190e+05, 4.875083e+05, 5.125028e+05, 5.375000e+05],\n",
-       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z_t_150m</span></div><div class='xr-var-dims'>(z_t_150m)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>500.0 1.5e+03 ... 1.35e+04 1.45e+04</div><input id='attrs-7c7e8c39-4580-4beb-a602-697b09729ee3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c7e8c39-4580-4beb-a602-697b09729ee3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2eeb2ac-6c31-4bea-80e9-9e58861b7e12' class='xr-var-data-in' type='checkbox'><label for='data-f2eeb2ac-6c31-4bea-80e9-9e58861b7e12' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>depth from surface to midpoint of layer</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>units :</span></dt><dd>centimeters</dd><dt><span>valid_max :</span></dt><dd>14500.0</dd><dt><span>valid_min :</span></dt><dd>500.0</dd></dl></div><div class='xr-var-data'><pre>array([  500.,  1500.,  2500.,  3500.,  4500.,  5500.,  6500.,  7500.,  8500.,\n",
-       "        9500., 10500., 11500., 12500., 13500., 14500.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-179.5 -178.5 ... 178.5 179.5</div><input id='attrs-8eb513fe-b309-4df6-a426-eb5061c1d2d3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8eb513fe-b309-4df6-a426-eb5061c1d2d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00c58f70-efee-4cd7-bd31-e181f9a86718' class='xr-var-data-in' type='checkbox'><label for='data-00c58f70-efee-4cd7-bd31-e181f9a86718' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([-179.5, -178.5, -177.5, ...,  177.5,  178.5,  179.5], shape=(360,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.5 -88.5 -87.5 ... 88.5 89.5</div><input id='attrs-10a8561a-0b60-4ab6-bae1-a92c097ddbf9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-10a8561a-0b60-4ab6-bae1-a92c097ddbf9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d8bf0f3-b063-40fe-86cf-adc2e51beb8e' class='xr-var-data-in' type='checkbox'><label for='data-6d8bf0f3-b063-40fe-86cf-adc2e51beb8e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.5 -88.5 -87.5 ... 88.5 89.5</div><input id='attrs-74f4acf1-469e-4f4a-ae27-2231d563b273' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-74f4acf1-469e-4f4a-ae27-2231d563b273' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16ff7d22-1a4a-44ca-bee6-84753427a3b3' class='xr-var-data-in' type='checkbox'><label for='data-16ff7d22-1a4a-44ca-bee6-84753427a3b3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
        "       -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,\n",
        "       -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,\n",
        "       -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,\n",
@@ -24773,32 +29460,8 @@
        "        50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,\n",
        "        60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,\n",
        "        70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,\n",
-       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1f05afb2-6ddb-4de4-8a52-95eacf0be9fd' class='xr-section-summary-in' type='checkbox'  ><label for='section-1f05afb2-6ddb-4de4-8a52-95eacf0be9fd' class='xr-section-summary' >Data variables: <span>(32)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>ALK</span></div><div class='xr-var-dims'>(month, z_t, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9f10ce53-cde2-42a7-a5a0-e02789d1a8ac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f10ce53-cde2-42a7-a5a0-e02789d1a8ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c14f0a35-5b44-423a-a667-0935fbd7cdfb' class='xr-var-data-in' type='checkbox'><label for='data-c14f0a35-5b44-423a-a667-0935fbd7cdfb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>Alkalinity</dd><dt><span>units :</span></dt><dd>meq/m^3</dd></dl></div><div class='xr-var-data'><pre>[46656000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALK_ALT_CO2</span></div><div class='xr-var-dims'>(month, z_t, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-03a68a91-3eb0-4978-9702-9a6330925906' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03a68a91-3eb0-4978-9702-9a6330925906' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b2222a2-719a-49e2-b435-03421e58a006' class='xr-var-data-in' type='checkbox'><label for='data-1b2222a2-719a-49e2-b435-03421e58a006' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>Alkalinity, Alternative CO2</dd><dt><span>units :</span></dt><dd>meq/m^3</dd></dl></div><div class='xr-var-data'><pre>[46656000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIC</span></div><div class='xr-var-dims'>(month, z_t, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7e5a66d2-2c4b-463e-abcf-d5f27b074d30' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7e5a66d2-2c4b-463e-abcf-d5f27b074d30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-031c0079-8d74-4582-9aad-857393e8b42f' class='xr-var-data-in' type='checkbox'><label for='data-031c0079-8d74-4582-9aad-857393e8b42f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>Dissolved Inorganic Carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[46656000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIC_ALT_CO2</span></div><div class='xr-var-dims'>(month, z_t, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-253e1a2a-b95a-4599-b654-3e65fde28abd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-253e1a2a-b95a-4599-b654-3e65fde28abd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e85d2b6a-5adf-4deb-bac3-3b07ffbfc45b' class='xr-var-data-in' type='checkbox'><label for='data-e85d2b6a-5adf-4deb-bac3-3b07ffbfc45b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>Dissolved Inorganic Carbon, Alternative CO2</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[46656000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOC</span></div><div class='xr-var-dims'>(month, z_t, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-16f60d7a-4bff-4186-84f7-9066dec144f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-16f60d7a-4bff-4186-84f7-9066dec144f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a6a610b-decd-4873-bf46-b098cc103b08' class='xr-var-data-in' type='checkbox'><label for='data-6a6a610b-decd-4873-bf46-b098cc103b08' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>Dissolved Organic Carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[46656000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOCr</span></div><div class='xr-var-dims'>(month, z_t, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6e600f37-537d-4d27-8531-bcfbd6226ddb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e600f37-537d-4d27-8531-bcfbd6226ddb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-477aa0d2-4aa6-4c98-a7bb-57c163cc2456' class='xr-var-data-in' type='checkbox'><label for='data-477aa0d2-4aa6-4c98-a7bb-57c163cc2456' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>Refractory DOC</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[46656000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DON</span></div><div class='xr-var-dims'>(month, z_t, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1cef471e-1a85-401c-8b76-709781666409' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1cef471e-1a85-401c-8b76-709781666409' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab3c2a87-89a1-45b3-93df-0e06b761828b' class='xr-var-data-in' type='checkbox'><label for='data-ab3c2a87-89a1-45b3-93df-0e06b761828b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>Dissolved Organic Nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[46656000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DONr</span></div><div class='xr-var-dims'>(month, z_t, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f5f429c8-1d24-499d-8e31-2fa28708af9b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f5f429c8-1d24-499d-8e31-2fa28708af9b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-602eaf97-f752-4e67-8045-10fbbb5979c8' class='xr-var-data-in' type='checkbox'><label for='data-602eaf97-f752-4e67-8045-10fbbb5979c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>Refractory DON</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[46656000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOP</span></div><div class='xr-var-dims'>(month, z_t, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a6e0525c-6784-487b-8ef0-ac9704d56436' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a6e0525c-6784-487b-8ef0-ac9704d56436' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55ce2dfa-aaf8-45e0-a484-eec554910ee3' class='xr-var-data-in' type='checkbox'><label for='data-55ce2dfa-aaf8-45e0-a484-eec554910ee3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>Dissolved Organic Phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[46656000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOPr</span></div><div class='xr-var-dims'>(month, z_t, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-60e9c5ae-bc46-4058-baae-f9eef10e93c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-60e9c5ae-bc46-4058-baae-f9eef10e93c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e203d495-5e1e-4de6-91ff-cd4f4d518d67' class='xr-var-data-in' type='checkbox'><label for='data-e203d495-5e1e-4de6-91ff-cd4f4d518d67' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>Refractory DOP</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[46656000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Fe</span></div><div class='xr-var-dims'>(month, z_t, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-450f47c4-c781-4584-a72b-99f5f71a1b7c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-450f47c4-c781-4584-a72b-99f5f71a1b7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26cbdf1f-4617-4cb5-93fe-58267312a717' class='xr-var-data-in' type='checkbox'><label for='data-26cbdf1f-4617-4cb5-93fe-58267312a717' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>Dissolved Inorganic Iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[46656000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Lig</span></div><div class='xr-var-dims'>(month, z_t, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-606553a2-3b59-40fc-a320-4c4bfb7c5315' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-606553a2-3b59-40fc-a320-4c4bfb7c5315' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5795307e-7078-4063-ade3-171294466907' class='xr-var-data-in' type='checkbox'><label for='data-5795307e-7078-4063-ade3-171294466907' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>Iron Binding Ligand</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[46656000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NH4</span></div><div class='xr-var-dims'>(month, z_t, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5740876a-398b-4421-b8df-3f928ab98ac6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5740876a-398b-4421-b8df-3f928ab98ac6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-947af980-007d-411a-b1e1-e34b97aeb4e2' class='xr-var-data-in' type='checkbox'><label for='data-947af980-007d-411a-b1e1-e34b97aeb4e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>Dissolved Ammonia</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[46656000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NO3</span></div><div class='xr-var-dims'>(month, z_t, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2767a262-316f-48d8-b366-4cfb16425648' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2767a262-316f-48d8-b366-4cfb16425648' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d3cab54-b3fe-4016-b38c-41aca339f5c0' class='xr-var-data-in' type='checkbox'><label for='data-0d3cab54-b3fe-4016-b38c-41aca339f5c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>Dissolved Inorganic Nitrate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[46656000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>O2</span></div><div class='xr-var-dims'>(month, z_t, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-eca857f4-3b79-4a74-abfd-bc770968e48c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eca857f4-3b79-4a74-abfd-bc770968e48c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e4d749d-115e-4e1a-8101-e852e60e7a44' class='xr-var-data-in' type='checkbox'><label for='data-1e4d749d-115e-4e1a-8101-e852e60e7a44' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>Dissolved Oxygen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[46656000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PO4</span></div><div class='xr-var-dims'>(month, z_t, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-09551cf8-4ec6-4329-ac28-a83f7d79c696' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-09551cf8-4ec6-4329-ac28-a83f7d79c696' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb1cfd4c-5c2b-47a2-bf80-eeb1041600a7' class='xr-var-data-in' type='checkbox'><label for='data-fb1cfd4c-5c2b-47a2-bf80-eeb1041600a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>Dissolved Inorganic Phosphate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[46656000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SiO3</span></div><div class='xr-var-dims'>(month, z_t, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3f19f82c-3105-4019-ae36-658c20196f7b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3f19f82c-3105-4019-ae36-658c20196f7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e1313ff-c9a0-40ed-8ecc-3d357a8be829' class='xr-var-data-in' type='checkbox'><label for='data-2e1313ff-c9a0-40ed-8ecc-3d357a8be829' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>Dissolved Inorganic Silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[46656000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatC</span></div><div class='xr-var-dims'>(month, z_t_150m, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-41c1bc52-3fec-4328-b213-73420f570081' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41c1bc52-3fec-4328-b213-73420f570081' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0b9b2e5-b7a0-4505-a0b9-83af6f1b1aef' class='xr-var-data-in' type='checkbox'><label for='data-b0b9b2e5-b7a0-4505-a0b9-83af6f1b1aef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>Diatom Carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[11664000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatChl</span></div><div class='xr-var-dims'>(month, z_t_150m, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5cc9e613-ced3-4bba-bfe3-29ad0108f692' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5cc9e613-ced3-4bba-bfe3-29ad0108f692' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d0fb7bc-a90a-4360-8120-efd7cdf0f76f' class='xr-var-data-in' type='checkbox'><label for='data-2d0fb7bc-a90a-4360-8120-efd7cdf0f76f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>Diatom Chlorophyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><pre>[11664000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatFe</span></div><div class='xr-var-dims'>(month, z_t_150m, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7ca13590-7b48-4d95-ad6c-19d0f003b84c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7ca13590-7b48-4d95-ad6c-19d0f003b84c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3dd39b3d-58d5-450f-bbe7-bd4cc23aff2b' class='xr-var-data-in' type='checkbox'><label for='data-3dd39b3d-58d5-450f-bbe7-bd4cc23aff2b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>Diatom Iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[11664000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatP</span></div><div class='xr-var-dims'>(month, z_t_150m, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4448c483-3adc-45c3-af3e-afaf5fb14c37' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4448c483-3adc-45c3-af3e-afaf5fb14c37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5e9c282-e6ca-4763-afda-27f82c610f74' class='xr-var-data-in' type='checkbox'><label for='data-f5e9c282-e6ca-4763-afda-27f82c610f74' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>Diatom Phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[11664000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatSi</span></div><div class='xr-var-dims'>(month, z_t_150m, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-96aeb666-2b31-4ba9-911f-4a8185660010' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-96aeb666-2b31-4ba9-911f-4a8185660010' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96db811b-7661-48d9-8738-f7f72e28797f' class='xr-var-data-in' type='checkbox'><label for='data-96db811b-7661-48d9-8738-f7f72e28797f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>Diatom Silicon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[11664000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazC</span></div><div class='xr-var-dims'>(month, z_t_150m, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9b38005f-284a-452b-9b34-1cd2521a4e4f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b38005f-284a-452b-9b34-1cd2521a4e4f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0762655d-29a4-45a9-bd78-f766d5e19fbb' class='xr-var-data-in' type='checkbox'><label for='data-0762655d-29a4-45a9-bd78-f766d5e19fbb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>Diazotroph Carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[11664000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazChl</span></div><div class='xr-var-dims'>(month, z_t_150m, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5bd5a262-bb1b-46f2-907f-ed70194fb466' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5bd5a262-bb1b-46f2-907f-ed70194fb466' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d6ec121-6295-4bf1-beab-2f3b1344a137' class='xr-var-data-in' type='checkbox'><label for='data-9d6ec121-6295-4bf1-beab-2f3b1344a137' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>Diazotroph Chlorophyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><pre>[11664000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazFe</span></div><div class='xr-var-dims'>(month, z_t_150m, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fe8d3034-63cc-48c9-95fd-712e9c91e1aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe8d3034-63cc-48c9-95fd-712e9c91e1aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d660ad2c-060a-4f31-b6f1-1ac0c8953635' class='xr-var-data-in' type='checkbox'><label for='data-d660ad2c-060a-4f31-b6f1-1ac0c8953635' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>Diazotroph Iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[11664000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazP</span></div><div class='xr-var-dims'>(month, z_t_150m, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9609d53f-67dc-443d-8f12-79debfa14624' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9609d53f-67dc-443d-8f12-79debfa14624' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af099193-36b9-49b8-8238-e184954db893' class='xr-var-data-in' type='checkbox'><label for='data-af099193-36b9-49b8-8238-e184954db893' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>Diazotroph Phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[11664000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spC</span></div><div class='xr-var-dims'>(month, z_t_150m, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9fcf1938-831e-40b9-8c3f-eaf1e2baa213' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9fcf1938-831e-40b9-8c3f-eaf1e2baa213' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44156a35-cd11-4617-9a02-7ee74720ea11' class='xr-var-data-in' type='checkbox'><label for='data-44156a35-cd11-4617-9a02-7ee74720ea11' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>Small Phyto Carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[11664000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spCaCO3</span></div><div class='xr-var-dims'>(month, z_t_150m, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-75a34ba7-ae8a-4a43-8b97-bcc162e535e2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75a34ba7-ae8a-4a43-8b97-bcc162e535e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ffdbf40-6b20-4ac5-bd07-89ed114a8387' class='xr-var-data-in' type='checkbox'><label for='data-5ffdbf40-6b20-4ac5-bd07-89ed114a8387' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>Coccolithophores CaCO3</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[11664000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spChl</span></div><div class='xr-var-dims'>(month, z_t_150m, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e08b78d9-a155-44d3-937c-461bc7fdc65c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e08b78d9-a155-44d3-937c-461bc7fdc65c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7b8eb41-8a70-4eb9-8ac3-9c8784a21991' class='xr-var-data-in' type='checkbox'><label for='data-d7b8eb41-8a70-4eb9-8ac3-9c8784a21991' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>Small Phyto Chlorophyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><pre>[11664000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spFe</span></div><div class='xr-var-dims'>(month, z_t_150m, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-04320337-6773-4409-bde7-98db7cd10ad9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-04320337-6773-4409-bde7-98db7cd10ad9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd99e4d2-393b-4cb3-9df0-cd5b4e60e3f7' class='xr-var-data-in' type='checkbox'><label for='data-cd99e4d2-393b-4cb3-9df0-cd5b4e60e3f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>Small Phyto Iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[11664000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spP</span></div><div class='xr-var-dims'>(month, z_t_150m, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-74f3e0a4-e60f-49ce-9817-dfe8bd8a3478' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-74f3e0a4-e60f-49ce-9817-dfe8bd8a3478' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-426ad2a1-7540-4866-ab67-3bb266a2b3c0' class='xr-var-data-in' type='checkbox'><label for='data-426ad2a1-7540-4866-ab67-3bb266a2b3c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>Small Phyto Phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[11664000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zooC</span></div><div class='xr-var-dims'>(month, z_t_150m, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d6bf09c5-4b15-45d2-abb5-0bb14037f878' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d6bf09c5-4b15-45d2-abb5-0bb14037f878' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e728dca-dbc6-48a0-a0d5-1c9f498e4229' class='xr-var-data-in' type='checkbox'><label for='data-6e728dca-dbc6-48a0-a0d5-1c9f498e4229' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[11664000 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-aacd864b-f0f5-4ce1-931c-70c5f5a9d538' class='xr-section-summary-in' type='checkbox'  ><label for='section-aacd864b-f0f5-4ce1-931c-70c5f5a9d538' class='xr-section-summary' >Indexes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>month</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-f596ded7-07e1-4fdb-949d-772536b0f01b' class='xr-index-data-in' type='checkbox'/><label for='index-f596ded7-07e1-4fdb-949d-772536b0f01b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=&#x27;int64&#x27;, name=&#x27;month&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>z_t</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-bf28798f-d4dd-429d-a1cc-4a8eb6ea8c37' class='xr-index-data-in' type='checkbox'/><label for='index-bf28798f-d4dd-429d-a1cc-4a8eb6ea8c37' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([          500.0,          1500.0,          2500.0,          3500.0,\n",
-       "                4500.0,          5500.0,          6500.0,          7500.0,\n",
-       "                8500.0,          9500.0,         10500.0,         11500.0,\n",
-       "               12500.0,         13500.0,         14500.0,         15500.0,\n",
-       "        16509.83984375, 17547.904296875, 18629.126953125,  19766.02734375,\n",
-       "       20971.138671875,    22257.828125,   23640.8828125,    25137.015625,\n",
-       "       26765.419921875, 28548.365234375,    30511.921875, 32686.798828125,\n",
-       "        35109.34765625,  37822.76171875,  40878.46484375,  44337.76953125,\n",
-       "          48273.671875,  52772.80078125,   57937.2890625,  63886.26171875,\n",
-       "          70756.328125,        78700.25,   87882.5234375,   98470.5859375,\n",
-       "         110620.421875,     124456.6875,    140049.71875,   157394.640625,\n",
-       "         176400.328125,   196894.421875,    218645.65625,    241397.15625,\n",
-       "            264900.125,    288938.46875,    313340.46875,    337979.34375,\n",
-       "          362767.03125,     387645.1875,     412576.8125,       437539.25,\n",
-       "          462519.03125,    487508.34375,     512502.8125,        537500.0],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;z_t&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>z_t_150m</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-8b7403f3-73f4-4914-88a6-0915ddd76a52' class='xr-index-data-in' type='checkbox'/><label for='index-8b7403f3-73f4-4914-88a6-0915ddd76a52' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  500.0,  1500.0,  2500.0,  3500.0,  4500.0,  5500.0,  6500.0,  7500.0,\n",
-       "        8500.0,  9500.0, 10500.0, 11500.0, 12500.0, 13500.0, 14500.0],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;z_t_150m&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-d81032fa-4189-4c66-8fb0-de1cf9b58b1c' class='xr-index-data-in' type='checkbox'/><label for='index-d81032fa-4189-4c66-8fb0-de1cf9b58b1c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-179.5, -178.5, -177.5, -176.5, -175.5, -174.5, -173.5, -172.5, -171.5,\n",
-       "       -170.5,\n",
-       "       ...\n",
-       "        170.5,  171.5,  172.5,  173.5,  174.5,  175.5,  176.5,  177.5,  178.5,\n",
-       "        179.5],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=360))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-a7f928e6-2b1b-4180-b057-0a9d8c6f5a26' class='xr-index-data-in' type='checkbox'/><label for='index-a7f928e6-2b1b-4180-b057-0a9d8c6f5a26' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
-       "       ...\n",
-       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=180))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-626a6800-441d-4860-b6f2-b93f3fa9820f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-626a6800-441d-4860-b6f2-b93f3fa9820f' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Title :</span></dt><dd>Climatology computed from the 1x1 degree CESM-MARBL simulation.</dd><dt><span>original_path :</span></dt><dd>/glade/campaign/collections/cmip/CMIP6/CESM-HR/FOSI_BGC/LR/g.e22.GOMIPECOIAF_JRA-1p4-2018.TL319_g17.4p2z.002branch/ocn/proc/tseries/month_1/</dd><dt><span>regrid_method :</span></dt><dd>conservative</dd></dl></div></li></ul></div></div>"
+       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-179.5 -178.5 ... 178.5 179.5</div><input id='attrs-f7a4f420-6b22-45e9-b1d1-8e946e910dc2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f7a4f420-6b22-45e9-b1d1-8e946e910dc2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de0954d5-74a5-445a-9ef0-07cf833671ca' class='xr-var-data-in' type='checkbox'><label for='data-de0954d5-74a5-445a-9ef0-07cf833671ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([-179.5, -178.5, -177.5, ...,  177.5,  178.5,  179.5], shape=(360,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z_t_150m</span></div><div class='xr-var-dims'>(z_t_150m)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>500.0 1.5e+03 ... 1.35e+04 1.45e+04</div><input id='attrs-7a5f0897-31ec-42ee-afd1-860dc0475dff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7a5f0897-31ec-42ee-afd1-860dc0475dff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e55dc56-b2cc-48d7-a57d-d07fa6793018' class='xr-var-data-in' type='checkbox'><label for='data-5e55dc56-b2cc-48d7-a57d-d07fa6793018' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>depth from surface to midpoint of layer</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>units :</span></dt><dd>centimeters</dd><dt><span>valid_max :</span></dt><dd>14500.0</dd><dt><span>valid_min :</span></dt><dd>500.0</dd></dl></div><div class='xr-var-data'><pre>array([  500.,  1500.,  2500.,  3500.,  4500.,  5500.,  6500.,  7500.,  8500.,\n",
+       "        9500., 10500., 11500., 12500., 13500., 14500.], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f74fbc64-452e-4399-a318-1245e81d523c' class='xr-section-summary-in' type='checkbox' /><label for='section-f74fbc64-452e-4399-a318-1245e81d523c' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(32)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>ALK</span></div><div class='xr-var-dims'>(month, z_t, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dab11c8e-c733-4349-b155-566e3bf24134' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dab11c8e-c733-4349-b155-566e3bf24134' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56a680ae-6b40-4cdb-87f7-d114bc8bb080' class='xr-var-data-in' type='checkbox'><label for='data-56a680ae-6b40-4cdb-87f7-d114bc8bb080' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>Alkalinity</dd><dt><span>units :</span></dt><dd>meq/m^3</dd></dl></div><div class='xr-var-data'><pre>[46656000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALK_ALT_CO2</span></div><div class='xr-var-dims'>(month, z_t, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-33a401bd-277b-4f82-90dc-7dd39c8983d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-33a401bd-277b-4f82-90dc-7dd39c8983d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-559208d2-ee09-42ca-bf23-e06a47b293d9' class='xr-var-data-in' type='checkbox'><label for='data-559208d2-ee09-42ca-bf23-e06a47b293d9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>Alkalinity, Alternative CO2</dd><dt><span>units :</span></dt><dd>meq/m^3</dd></dl></div><div class='xr-var-data'><pre>[46656000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIC</span></div><div class='xr-var-dims'>(month, z_t, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-365a4278-4038-40b3-8e92-6cc25f78b076' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-365a4278-4038-40b3-8e92-6cc25f78b076' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f0956f8-0528-4645-bd05-9c901fde67d5' class='xr-var-data-in' type='checkbox'><label for='data-7f0956f8-0528-4645-bd05-9c901fde67d5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>Dissolved Inorganic Carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[46656000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIC_ALT_CO2</span></div><div class='xr-var-dims'>(month, z_t, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9496eeb0-4325-4c1e-b2f4-d8542671a5ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9496eeb0-4325-4c1e-b2f4-d8542671a5ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-968f381f-bd78-4c6a-93aa-be002ab3302c' class='xr-var-data-in' type='checkbox'><label for='data-968f381f-bd78-4c6a-93aa-be002ab3302c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>Dissolved Inorganic Carbon, Alternative CO2</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[46656000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOC</span></div><div class='xr-var-dims'>(month, z_t, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-777d556c-134f-4e1c-a5a6-245531edc811' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-777d556c-134f-4e1c-a5a6-245531edc811' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-786877fa-e7c2-420a-bb9b-b5c083fb7523' class='xr-var-data-in' type='checkbox'><label for='data-786877fa-e7c2-420a-bb9b-b5c083fb7523' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>Dissolved Organic Carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[46656000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOCr</span></div><div class='xr-var-dims'>(month, z_t, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c71880f7-405d-4c2c-ad5d-5288f66173aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c71880f7-405d-4c2c-ad5d-5288f66173aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60e0e3b6-c22d-4936-986e-de6e345a6183' class='xr-var-data-in' type='checkbox'><label for='data-60e0e3b6-c22d-4936-986e-de6e345a6183' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>Refractory DOC</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[46656000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DON</span></div><div class='xr-var-dims'>(month, z_t, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f6667e09-b6eb-431a-8e87-ad3a44d4b8d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f6667e09-b6eb-431a-8e87-ad3a44d4b8d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e8e5fa2-8b02-4536-9edb-6c1ab5a22f11' class='xr-var-data-in' type='checkbox'><label for='data-1e8e5fa2-8b02-4536-9edb-6c1ab5a22f11' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>Dissolved Organic Nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[46656000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DONr</span></div><div class='xr-var-dims'>(month, z_t, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-809fd7b8-075d-4783-b6ba-82e838672074' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-809fd7b8-075d-4783-b6ba-82e838672074' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-503a125a-b0aa-41cc-8cf0-b267ff13ae6a' class='xr-var-data-in' type='checkbox'><label for='data-503a125a-b0aa-41cc-8cf0-b267ff13ae6a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>Refractory DON</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[46656000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOP</span></div><div class='xr-var-dims'>(month, z_t, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-efaae727-a9af-4cf4-8e27-b683622bb340' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-efaae727-a9af-4cf4-8e27-b683622bb340' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1215946-ed37-4d36-8446-e43bd975b945' class='xr-var-data-in' type='checkbox'><label for='data-d1215946-ed37-4d36-8446-e43bd975b945' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>Dissolved Organic Phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[46656000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOPr</span></div><div class='xr-var-dims'>(month, z_t, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-07814a82-15e2-4eb1-8099-74256c2a3fb3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-07814a82-15e2-4eb1-8099-74256c2a3fb3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36cf3943-5fd1-4839-80c9-a7ed8f285b84' class='xr-var-data-in' type='checkbox'><label for='data-36cf3943-5fd1-4839-80c9-a7ed8f285b84' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>Refractory DOP</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[46656000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Fe</span></div><div class='xr-var-dims'>(month, z_t, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f4946b0b-6ac4-4975-84f3-4b4f90bdba7c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4946b0b-6ac4-4975-84f3-4b4f90bdba7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3826283-1773-429d-9dca-40372f13f61c' class='xr-var-data-in' type='checkbox'><label for='data-e3826283-1773-429d-9dca-40372f13f61c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>Dissolved Inorganic Iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[46656000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Lig</span></div><div class='xr-var-dims'>(month, z_t, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7b922e50-b3b4-49e1-99f8-1d464afda9f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7b922e50-b3b4-49e1-99f8-1d464afda9f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d22d3fe-858c-4678-9602-9d58df520118' class='xr-var-data-in' type='checkbox'><label for='data-5d22d3fe-858c-4678-9602-9d58df520118' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>Iron Binding Ligand</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[46656000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NH4</span></div><div class='xr-var-dims'>(month, z_t, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-75f90b34-6249-4aeb-8dda-878216b75256' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75f90b34-6249-4aeb-8dda-878216b75256' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c3b90b1-e404-4367-beac-8d7b4ece3028' class='xr-var-data-in' type='checkbox'><label for='data-5c3b90b1-e404-4367-beac-8d7b4ece3028' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>Dissolved Ammonia</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[46656000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NO3</span></div><div class='xr-var-dims'>(month, z_t, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c5e4aec1-98c4-4758-bd21-7579360464a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c5e4aec1-98c4-4758-bd21-7579360464a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59dfbe14-6207-4a85-be44-416da3e1ed28' class='xr-var-data-in' type='checkbox'><label for='data-59dfbe14-6207-4a85-be44-416da3e1ed28' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>Dissolved Inorganic Nitrate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[46656000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>O2</span></div><div class='xr-var-dims'>(month, z_t, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4127a310-84b2-4a53-8d18-9398476f88ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4127a310-84b2-4a53-8d18-9398476f88ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18131830-7b8b-4a06-81bc-810a67eb5a95' class='xr-var-data-in' type='checkbox'><label for='data-18131830-7b8b-4a06-81bc-810a67eb5a95' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>Dissolved Oxygen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[46656000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PO4</span></div><div class='xr-var-dims'>(month, z_t, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e230e83a-feef-48d9-b811-22a75fd029c5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e230e83a-feef-48d9-b811-22a75fd029c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a87e4b0-a63c-4423-ae36-94d73aac8aa4' class='xr-var-data-in' type='checkbox'><label for='data-1a87e4b0-a63c-4423-ae36-94d73aac8aa4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>Dissolved Inorganic Phosphate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[46656000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SiO3</span></div><div class='xr-var-dims'>(month, z_t, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6d9817d5-2230-4986-b113-86700ce1c275' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6d9817d5-2230-4986-b113-86700ce1c275' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce3ce696-c096-4dd1-9265-c0ebaf9dfcb1' class='xr-var-data-in' type='checkbox'><label for='data-ce3ce696-c096-4dd1-9265-c0ebaf9dfcb1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>Dissolved Inorganic Silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[46656000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatC</span></div><div class='xr-var-dims'>(month, z_t_150m, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-30765ba0-53fb-447b-ba8d-86b1565ee937' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-30765ba0-53fb-447b-ba8d-86b1565ee937' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59b5d567-2972-4f30-9eaf-e5d219fb3893' class='xr-var-data-in' type='checkbox'><label for='data-59b5d567-2972-4f30-9eaf-e5d219fb3893' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>Diatom Carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[11664000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatChl</span></div><div class='xr-var-dims'>(month, z_t_150m, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d60ce77e-9bbf-41a7-a160-491ea2ebce94' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d60ce77e-9bbf-41a7-a160-491ea2ebce94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3face8e2-8d80-435f-a2e4-e3cb1d8fa46c' class='xr-var-data-in' type='checkbox'><label for='data-3face8e2-8d80-435f-a2e4-e3cb1d8fa46c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>Diatom Chlorophyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><pre>[11664000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatFe</span></div><div class='xr-var-dims'>(month, z_t_150m, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b1e43ff1-eb0b-4134-bb6e-ea43fd138693' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b1e43ff1-eb0b-4134-bb6e-ea43fd138693' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96a00bc4-f227-4572-9ae9-8e624333f70b' class='xr-var-data-in' type='checkbox'><label for='data-96a00bc4-f227-4572-9ae9-8e624333f70b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>Diatom Iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[11664000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatP</span></div><div class='xr-var-dims'>(month, z_t_150m, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-69255c9c-04a9-4b61-9656-cf9b4b8fdb4c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-69255c9c-04a9-4b61-9656-cf9b4b8fdb4c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47086b59-2db1-4ecc-9c56-474e8aa06c90' class='xr-var-data-in' type='checkbox'><label for='data-47086b59-2db1-4ecc-9c56-474e8aa06c90' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>Diatom Phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[11664000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatSi</span></div><div class='xr-var-dims'>(month, z_t_150m, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-42b4c48f-b2c4-4b61-a772-7dcd4c62bca8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42b4c48f-b2c4-4b61-a772-7dcd4c62bca8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa994463-7943-4b15-849e-3be8e6344a6b' class='xr-var-data-in' type='checkbox'><label for='data-aa994463-7943-4b15-849e-3be8e6344a6b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>Diatom Silicon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[11664000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazC</span></div><div class='xr-var-dims'>(month, z_t_150m, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3b4a73cf-08c0-4a49-b37a-137a80a22027' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b4a73cf-08c0-4a49-b37a-137a80a22027' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8029f90-da5c-407a-a097-44213ac4aef8' class='xr-var-data-in' type='checkbox'><label for='data-c8029f90-da5c-407a-a097-44213ac4aef8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>Diazotroph Carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[11664000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazChl</span></div><div class='xr-var-dims'>(month, z_t_150m, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3c201998-3f31-498e-b3e7-e03ef481c658' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3c201998-3f31-498e-b3e7-e03ef481c658' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9eda82c3-14ae-47db-b5e3-8b59ffa8bee2' class='xr-var-data-in' type='checkbox'><label for='data-9eda82c3-14ae-47db-b5e3-8b59ffa8bee2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>Diazotroph Chlorophyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><pre>[11664000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazFe</span></div><div class='xr-var-dims'>(month, z_t_150m, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-79fe1e3b-1739-4dcd-993b-b89dbcba3dd9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-79fe1e3b-1739-4dcd-993b-b89dbcba3dd9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d61e3550-55ca-4f9b-9f0f-a40ce31b6a01' class='xr-var-data-in' type='checkbox'><label for='data-d61e3550-55ca-4f9b-9f0f-a40ce31b6a01' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>Diazotroph Iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[11664000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazP</span></div><div class='xr-var-dims'>(month, z_t_150m, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7cbd376d-3905-478a-8a92-ec83f4ebb11c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7cbd376d-3905-478a-8a92-ec83f4ebb11c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65aca749-612c-4004-bb68-f5cb6d260b64' class='xr-var-data-in' type='checkbox'><label for='data-65aca749-612c-4004-bb68-f5cb6d260b64' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>Diazotroph Phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[11664000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spC</span></div><div class='xr-var-dims'>(month, z_t_150m, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0b3a5097-e495-4880-977d-584b38cd45b7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0b3a5097-e495-4880-977d-584b38cd45b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca325a6e-a6ff-4966-b485-5e761d725534' class='xr-var-data-in' type='checkbox'><label for='data-ca325a6e-a6ff-4966-b485-5e761d725534' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>Small Phyto Carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[11664000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spCaCO3</span></div><div class='xr-var-dims'>(month, z_t_150m, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-286e5e9c-b801-4f7c-b61d-93290b781e48' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-286e5e9c-b801-4f7c-b61d-93290b781e48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbbfa678-a066-4c2a-8509-6f6ee74e83e0' class='xr-var-data-in' type='checkbox'><label for='data-dbbfa678-a066-4c2a-8509-6f6ee74e83e0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>Coccolithophores CaCO3</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[11664000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spChl</span></div><div class='xr-var-dims'>(month, z_t_150m, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a04a005e-d3f6-4ab8-8c28-74395edc15d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a04a005e-d3f6-4ab8-8c28-74395edc15d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d92bbf7-af6e-4b3d-89f7-9f1e1ebe8f9f' class='xr-var-data-in' type='checkbox'><label for='data-6d92bbf7-af6e-4b3d-89f7-9f1e1ebe8f9f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>Small Phyto Chlorophyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><pre>[11664000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spFe</span></div><div class='xr-var-dims'>(month, z_t_150m, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8429a1a2-256b-4b5e-b303-74a92bd06b71' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8429a1a2-256b-4b5e-b303-74a92bd06b71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9645d718-1518-49eb-bec2-d5ca9fb0ac62' class='xr-var-data-in' type='checkbox'><label for='data-9645d718-1518-49eb-bec2-d5ca9fb0ac62' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>Small Phyto Iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[11664000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spP</span></div><div class='xr-var-dims'>(month, z_t_150m, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e60cb964-5f56-437f-b176-f551a713b127' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e60cb964-5f56-437f-b176-f551a713b127' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d21b91c0-a9a8-4ca4-afee-9ea151f8f710' class='xr-var-data-in' type='checkbox'><label for='data-d21b91c0-a9a8-4ca4-afee-9ea151f8f710' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>long_name :</span></dt><dd>Small Phyto Phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><pre>[11664000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zooC</span></div><div class='xr-var-dims'>(month, z_t_150m, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cf1af3a8-254a-4947-984a-3b81478b25f9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cf1af3a8-254a-4947-984a-3b81478b25f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21013fbc-e7b5-4e31-bcc9-71c0dc3e3038' class='xr-var-data-in' type='checkbox'><label for='data-21013fbc-e7b5-4e31-bcc9-71c0dc3e3038' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[11664000 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c67cb9dd-8352-4652-8174-da75d68cf851' class='xr-section-summary-in' type='checkbox' checked /><label for='section-c67cb9dd-8352-4652-8174-da75d68cf851' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Title :</span></dt><dd>Climatology computed from the 1x1 degree CESM-MARBL simulation.</dd><dt><span>original_path :</span></dt><dd>/glade/campaign/collections/cmip/CMIP6/CESM-HR/FOSI_BGC/LR/g.e22.GOMIPECOIAF_JRA-1p4-2018.TL319_g17.4p2z.002branch/ocn/proc/tseries/month_1/</dd><dt><span>regrid_method :</span></dt><dd>conservative</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset> Size: 4GB\n",
@@ -24806,9 +29469,9 @@
        "Coordinates:\n",
        "  * month        (month) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12\n",
        "  * z_t          (z_t) float32 240B 500.0 1.5e+03 ... 5.125e+05 5.375e+05\n",
-       "  * z_t_150m     (z_t_150m) float32 60B 500.0 1.5e+03 ... 1.35e+04 1.45e+04\n",
-       "  * lon          (lon) float64 3kB -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n",
        "  * lat          (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n",
+       "  * lon          (lon) float64 3kB -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n",
+       "  * z_t_150m     (z_t_150m) float32 60B 500.0 1.5e+03 ... 1.35e+04 1.45e+04\n",
        "Data variables: (12/32)\n",
        "    ALK          (month, z_t, lat, lon) float32 187MB ...\n",
        "    ALK_ALT_CO2  (month, z_t, lat, lon) float32 187MB ...\n",
@@ -24829,7 +29492,7 @@
        "    regrid_method:  conservative"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -24841,35 +29504,33 @@
   },
   {
    "cell_type": "markdown",
-   "id": "46a699ea-3054-4047-bf19-da7819a39b0f",
+   "id": "e3463c0f-9c29-4492-9824-33ddab1ea100",
    "metadata": {},
    "source": [
-    "Here, we zoom in on the coarse CESM source data around the Wales domain of interest."
+    "Here we show first the input CESM source data, which has land masked out on a 1x1 degree grid. Following is the same data but prefilled with the listed method."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
-   "id": "8ca33308-fea1-4620-8d87-f23e5d60e605",
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": 46,
+   "id": "4964f476",
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f526e06acf0>"
+       "(np.float64(0.0), np.float64(1.0), np.float64(0.0), np.float64(1.0))"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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DSfHH0OetjSd6/9ixrM5Tcdogc/TRR5tzpY1dRx11lGls0bCj5//UU081DR/agLNz5874/fQ169Gjh7Ro0ULuuece0yjyxhtvyLhx40xw0udh15gxY0yw0DHKZ5xxhrmqp41ve/bssbzvb3/7W/P6aJ21LvrYy5Yti9dVz6UGMQ1EGtBU7MqRNrDYfY0AAP5FNhE599xzZdiwYTJ69Gj57LPPTA5ROnQpRhtHtGFAP+u1N4Y2GvzlL38xeeCTTz4xDTLq22+/lZNOOkn+3//7f+bzV/PHvffeay6c6LFjeS9GP/P1IolmQ20EsftYWlZzg2ZFvbCj+zQH6UWiWIbQDHT66afLaaedFr9ApXnHDr2vnhPdNBdpI9H69evlrbfesryv1kHzmzb4xOjzueSSS0yjk2YnbbjS4+bn5ydkO0U+AVIkCnjcLbfcon/BRidMmJCwf+7cuWb/nDlzzPctW7aMhkKh6Jo1axLKXX755dGaNWtG169fn7D/7rvvNvdftWpVfJ8eY+DAgQnl1q5da8rNnj07vk+/1n16W8zIkSPN/Yvr2bOnKWe16X3LU9rjxY793nvvJZRt3759tF+/fvHvFyxYYMotXLgwvq+oqCjarFmz6Lnnnlvh8xQ7H23atIkWFBQklD3zzDOjnTt3Lve5aN0OP/zw6K5duxL2X3XVVdFq1apFt2/fHrVj9erV5b4vip/TxYsXm336r/rpp5/M99OnTy/3MTp06GDOMwAAxZFN/nsO7rrrroRzc+WVV5rP80gkYr5fvny5KXfPPfcklNu4cWM0Ly8vev3115f65tL7FxYWmlyi93/xxRcPOf9//vOfE+5j97E+/PBDU27+/PnlvrFr1KhhmdFKE8tOO3furND9Hn30UXO/++67L74vHA6bzHb88cfHz6lat25dNDs7+5DsCSA1GAqEtBHrWRGjvVeysrJMy36MDjvRXhTFvfzyy+Zqg/bi0CEtsU3HsSod6lFVHn74YTNUx2rTqw7J0N4e2sukOD0HelUkRp+nlive40R7iPz4448JXVIrep4GDRp0yJUjrYteDbryyivNY+zevTvh9oMHD8o///lPc4VPu8UWfxztQaK3r1ixwtZzj73uZb0vyqMz72uPnP/5n/8xV8K0O3Bs+BQAAHaRTX7JAyVziH6eb926NZ4vdMiLDuEu/rmv2eTYY49NGKar99HeJ82bNzef5ZozWrZsaW7THsml9ZYpzu5j6RAjHSKk85g89NBDpjetk3Qy2lgm+fvf/y4//PCD5X10KLrOuaK9Za+++ur4/jVr1pjMpkOoiw8d0vOivXAAeANDgZA29EOxOP3ArV+/vvz888/xfaWtmrNlyxZ56aWXDmkEiCk5btdJ+sFtdyhQMvT5l6TDVXRivOLnSWecnzFjhhnmovOeaJdSPVf9+vVL+jyVdq61+68Ov5ozZ44JKqFQyAwJ0q64J5xwgnmtNOBoXXSz8zhlib3uZb0vyqPBRBt4tGuwDgXSoVTa2KIB+bbbbpNatWrZqgMAILORTQ7NIrFhs7EsovlCs1BsuE9JOveI0gscffv2NY0IOvSmU6dOJlPofp2ot3i2KSuL2H0sHWakF4z0M//GG2+UHTt2mGPpEGMdzlxWFrJLs48u06zDpnVotg7Z6dChg9x0001mPpqS9GKUDmvS4Ulz585NaEApK+/E9hWffw9A6tCwgrShY19/9atfxb/XP9D1w6b4B3ppk4Dp5GB69UQ/PEujPTSqik6GaqdHzMiRI6t0clwdl6u9M5555hkz3nfBggUyfvx40/CR7Hkq7Vxro8a1115rNm3EefPNN01g0QacjRs3mqtD+pja0KNXZUqj453tiL3uZb0vrOiVnlmzZpmvv/rqK3NFSXsOFRQUmEYhAACskE2sxSZpfeedd0pd5Sa2T+dJ016vmoc0F8XowgJlKZlF7D6W0oYbzUXaEPPpp5+ax9ULLjrh7Q033CCVpfOh6KaNKtobd+rUqabXic7TpvPIFG9U0RWfevbsKc8995yZ/66svFNSafsApAYNK0gb2oLfpUuX+Pf6h7D+EW01Q7tO3vrqq6+aoR/6h72bdCiQnYlUNQhUJZ09Xmem1+FAOmGrfshrY0tVniftGaPdWbX7qzbi6BWV9u3bm+FGOvRGG3FKhoeKiL3uZb0vKkKHj+kVKg00OgFyWb1/AAAojmxiTfOFrlioeUCHxlg1kpRsENEs5fRjlXxcHSY0bdo007jidA7QY2ijieYibUTRDBRrWFm4cKFpVNHJebWHS2mNQboSkPamefrpp82Fq9h50mHfOul+VV4gBGAfDStIG7qMsvaI0G6SsVWB9IPQ6oNTrz7o8n06DlVniNcPKB37q3/oa0OC9k7QZf2qgj6WV+h8KjpTvnax1XNRsm5OnKezzjrLLAGtw350Rnv90J8+fbrpHaKrBildelkDhC4R/fvf/95cudHGJ70ipUOR7MyYH2ss0jHUenztstunTx9ztSu2SlF59MrUVVddJUOHDjX10gYefVzdX/wqVexq1rPPPmu6D+us/roPAABFNrF28skny2WXXWYu6Hz44YdmmIwO8dFVF999913zuap54Ne//rW5uKOfw9qLRIfoai7QbOL0Y+lcLLrinzZq6Oe7Pp6+ltrbVnNm8Ryg87JoPbRxQ4cK28l2urqiLtusPZc1O+lxNf9oXtFGFqX10cfX4Tzau/fjjz9OOIZejNI8o8PF//rXv5qVknSOOh2upMfTXralDQ8CkBo0rCBt6Aeefog8+OCDprVe/4jXP6qtej3oB6F+uOqHkg6H0Q86/WDUISf9+/d3vRdLquhyhNpzRJ9/aUsaO3GetDeK9vp47LHHzMS1+oGvAUUbwWLjlTUo6NUgfRztJaIT1elVHG3g0AlsK0KH8ug4ar3CpOOYO3fubB5fn2t5tF4a3jRU6RAlfT9psNIlDItPGKdLVGsY0xCjjT/aQMRYZgBADNnEHu11ovOk6L/62avzpmhPC20IiU3CrzlBGzCuueYacyFIL6bpRRMdVtyiRQtHH0szh2YPnWdNLzhpltQGk5LDkLQxRIcua67Yv3+/aRQpPtluWbSXsGYqnRx327Zt5rH0opNexNG5VpQ+L+0No7lCl3UubZL+WO9cXcpa6Zx1OheLXpTSxhgdbm6nPgCqXkCXBnLhcYCkaWOK/oGrH0xVPWQGAADACtkEAFAcyy0DAAAAAAAkiaFAADxHJ9gtrzOdDt0pvqIRAACAG6wmyNc5UXQDkFkYCgTAc3TssE58Wxa7Y5wBAACcVHKJ55J0jhadqwVAZqHHCgDP0cnrdEnosuikugAAAG774IMPyr2d+QCBzESPFQAAAAAAgCQxABAAAAAAACBJvh8KpGvX6/r0OnTAakwkACBz6ATJe/bskWbNmjky0eDBgweloKCgUsfIycmRatWqVbou8DayCQCgNGST9OX7hhVtVGnevHmqqwEA8KiNGzfK4YcfXulGldYta8rmreFKHadJkyaydu1aGld8jmwCACgP2ST9+L5hJTbJZY9n/59kVc+RdBGJlt+7Jhgoeylap8vYYec4dvoLuVVnN5+3reOIm69VxJXHCth4Tk6dG6eet1NCDr1WqFoF+wrl2TP/4chkyNpTRRtV1q5sKbVrJdf7ZfeeiLTust4ci14r/hZ7z3V/ZkxaZRM7vJRP0i2beO04fswmXssnZBOURDZJX75vWIkN/9HgklUjV9KFHxtWnPqApmElPcKLcw0iNKygajk5TLRGzV+2ZIQr8FafOnWqPP/88/Lll19KXl6e9OjRQ+68805p27ZtQrnVq1fLH//4R1m6dKkZftKhQwf5+9//Li1atDC36+pbEydOlKeffloOHDggvXv3lgceeCChB8+OHTtk3LhxsmDBAvP9oEGDZMaMGVKnTp3knijSNpvY4aV8km7ZxGvH8WvDipfyCQ0r8FM2yfR8wuS1AACkGQ0iY8eOlRUrVsiiRYukqKhI+vbtK/v27YuX+fbbb+WUU06RX//617JkyRL55JNP5Oabb07oDTN+/Hh54YUX5JlnnpF3331X9u7dK2eeeaaEw/8d0jR8+HD5+OOP5fXXXzebfn3RRRe5/pwBAIC3Lc3gfOL75ZZ3794thx12mJz60pVpdVWIHitlo8dK5dBjpeoxFCg9FOwtkKdOe1p27doltWvXduSzZvOaFpUaCtSk7QYzrrp4fXJzc81Wnm3btkmjRo1MoDn11FPNvvPPP1+ys7PlqaeeKvU++rwbNmxobh82bFjC3B+vvvqq9OvXz1xRat++vQlI3bp1M2X065NOOslcjSp5BQoVe7+csmBsWmUTO+ixUvXnz63j0GPFhXPMMGX4OJtkWj6hxwoAAA6JVPI/pcFBg1Bs0261VjSEqHr16v1Sj0hEXnnlFTn66KNNANFQo8Fj/vz58fusXLlSCgsLzZWkGF0hqWPHjrJs2TLz/fLly00dYqFFde/e3eyLlQEAAN6VqmySafnE93OsAADglnA0arZk76tKuypUHu14eu2115putRo61NatW0232TvuuENuvfVWM75Zu8kOGTJEFi9eLD179pTNmzeb5Z3r1q2bcLzGjRub25T+q6GnJN0XKwMAALwrFdkkE/NJxjSsaHc9p7pXVvUwH2VVVy91tU3Hyd+89rzd7C7q1GR+TjyOm8+b4TmZJ2zxu9bq9mREJGq2ZO+rNLhUpPvvVVddJZ9++qkZgxw/VuSXn5vBgwfLhAkTzNedO3c2V3EeeughE1zKC0LFJ80rbQK9kmWQ/tnEKV7KJ+mWTdIxn6RbNrH7WG49d7JJ5smUbJKJ+YShQAAApKmrr77azIavV3mKz5TfoEEDycrKMuOPi2vXrp1s2LDBfN2kSROztLPOql+cXk3Sq0KxMlu2bCl1zHSsDAAAQKbnExpWAABwiF7ZCSe5VeRqkl6R0StBuqThW2+9Ja1bt064XbvQdu3aVdasWZOw/6uvvpKWLVuar7t06WImj9NZ+2M2bdokn3/+uVkeUekkcDo++v3334+Xee+998y+WBkAAOBdbmWTTM8nGTMUCACAquZEd1s7dCnDefPmyYsvvii1atWKjyfWSdvy8vLM13/4wx/MbPo6C/9pp51mxjC/9NJLZmnDWNnRo0fLddddJ/Xr1zcTy02cOFE6deokffr0iV9B6t+/v4wZM0Yefvhhs++yyy4zSx6yIhAAAN7nVjbJ9HxCwwoAAB6aIM6OBx980Pzbq1evhP2zZ8+WUaNGma/POeccM15ZZ+4fN26cCRrPPfecmUQuZtq0aaZL7nnnnScHDhyQ3r17y+OPPy6hUCheZu7cueb+sdn5Bw0aJDNnzkzqOQIAAH9mk0zPJ4Go9tfxsdj63b1e/r1k1bCevdgrk9em0+Rw6ThBnNeetx8niPPa82aCuMxjNQFcwd4CmXf6PNNttKITspX1WfPl6sZSq1Zyo2z37InIr9ttcaQ+8LbY++XUl670TDZxipfySbplE0cfi8lrq/4cM3ktkkA28S/mWAEAAAAAAEgSQ4EAAHBIbLK3ZO8LAADgJLKJO2hYSQFHurc61I3Ra11p0617sFNdRd3s+uvee9SfQ3icOsdeEpHKD090s5usl4Wjv2zJ3heZRX/Xuvl57ga3no8fs4nX8okfs4lf84kfs4nX8gnZBFZoWAEAwCEa15ON7M5EfQAAgMR8QTapesyxAgAAAAAAkCR6rAAA4GC35XCSXZe91OUZAAD4A9kkA3qstGrVSgKBwCHb2LFjze26EvTkyZOlWbNmkpeXZ9bDXrVqVSqrDABAmSLRym1IPbIJAMBPyCYZ0LDywQcfyKZNm+LbokWLzP6hQ4eaf++66y659957ZebMmaZskyZN5IwzzpA9e/akstoAAJRKe6tUZkPqkU0AAH5CNsmAhpWGDRuaxpLY9vLLL0ubNm2kZ8+eprfK9OnT5aabbpIhQ4ZIx44d5YknnpD9+/fLvHnzUlltAABKRXhJf2QTAICfkE0ybPLagoICmTNnjlx66aVmONDatWtl8+bN0rdv33iZ3Nxc0+iybNmyMo+Tn58vu3fvTtgAAADIJgAAwNeT186fP1927twpo0aNMt9ro4pq3LhxQjn9fv369WUeZ+rUqTJlypRD9gcDUbP5hZ3nYmdNe1vHcbOMjTrbYfVYwYAzC5s69Z5y63nbP07lz0/IxZ83p86fH7k5IWo4ylCWSDRgtmQkez9UHbKJd/OJH7OJ1/KJH7OJIp9kVj4hm/ySL8gmGdRjZdasWTJgwAAzUW1x2nulOB0iVHJfcZMmTZJdu3bFt40bN1ZZnQEAKI7utv5CNgEApDuySQb1WNEeKG+++aY8//zz8X0650qs50rTpk3j+7du3XpIL5bidLiQbgAAuC0sQbMld194CdkEAOAHZJMM6rEye/ZsadSokQwcODC+r3Xr1qZxJbZSUGwelqVLl0qPHj1SVFMAAJAJyCYAACBteqxEIhETXkaOHClZWf+tjg73GT9+vNx+++1y1FFHmU2/rl69ugwfPjyldQYAoDTRSoxj1vvCG8gmAAC/IJtkSMOKDgHasGGDWQ2opOuvv14OHDggV155pezYsUO6desmCxculFq1aqWkrgAA2BnHnIxk7wfnkU0AAH5BNsmQhhVdTlknpC2N9lqZPHmy2QAA8LpwNGi25O7reHWQJLIJAMAvyCYZ0rACAICflo+MJDl9WYRlwwEAgMPIJu7ImIaVYCBqtrIkOya+tMdx5DgWAdvO47haxsYfBI6dm0DEtceyfByH/hBy89zYEXKgPk6dG5T/QemEsEO//yI2emo49R4F4L1sYvexnChDNqn6z2A/ZhNFPsmsfGInmziFjJPZMqZhBQCAqsY4ZgAA4CVkE3fQsAIAgCfGMdPLCwAAOIts4g4aVgAAcHQccyClXacBAACK5wuySdVzb9AZAAAAAACAz9BjBQAAh+iKQGFWBQIAAB5BNnEHDSsAADiEccwAAMBLyCbuoGEFAAAHrwrpltx9mbwWAAA4i2zijoxpWMkNhSU7VFTm7cGAe4E26EB4dqq+Tq23bqc+Tjxvu9x8Pb20pn3IqfeFS6+Vm+fGTZEkV4WpislMw9GAa/W1w83HSsX7S8+3nXNe1n2RWfKyiiQ7K+ibbOK1fEI2qfpz7Mds4td84qVs4sV84gQn6lsVz5ls4o70ercCAAAAAAB4SMb0WAEAoKqFKzF5bZihQAAAwGFkE3fQsAIAgEO0C2+y3XgjUe8MYQQAAP5ANnEHDSsAADiEq0IAAMBLyCbuYI4VAAAAAACAJNFjBQAAh0QqsbqP/9agAAAAqUY2cQcNKwAAOCQiQbMle18AAAAnkU3ckTENK7WyDkpOdtnXAyM2rjAW2ZiQ0M6khcGA9XXJYKDykxjaeU522KlL0MZqFlnBsCP1CTlUH1TuPYrUS7ZnRKaw+n2c7CSz5QlHg2ZL9r7ILFbZxCl28ku6sZNxcoJF4haySdUjm6QP8om3kE3ckTENKwAAVLWIBMyW7H0BAACcRDZxh/8uYQAAAAAAALiEHisAADiE7rYAAMBLyCbuoGEFAACHhCVotmTvCwAA4CSyiTtoWAEAwMEJNZOdONypCccBAACK5wuySdWjYQUAAAeXNEy25wnLLQMAAKeRTdyRMQ0rNbIKJCer7CV47bTi2Vma086qDoWRUKWXRnRzKWU7yxVm21pC2pklJZ1Yihr+4uaVfqeW6LXzu8JquULnlne3LlMVSxMDmc4qm7hpV2GepBM7WSAvVOhKXYBU5xMvZRMn6wOkk4xpWAEAoKppmEw2UBJEAQCA08gm7qBhBQAAh4QlYLZk7wsAAOAksok7aFgBAMAhXBUCAABeQjZxR8oHwP3www9y4YUXSv369aV69erSuXNnWblyZfz2UaNGSSAQSNi6d++e0joDAAD/IpsAAIC06bGyY8cOOfnkk+W0006T1157TRo1aiTffvut1KlTJ6Fc//79Zfbs2fHvc3JyUlBbAADKF67EkB69L1KPbAIA8BOySQb0WLnzzjulefPmptHkxBNPlFatWknv3r2lTZs2CeVyc3OlSZMm8a1evXopqzMAAFbdbZPd7Jo6dap07dpVatWqZS5KnH322bJmzZoyy19++eWmx+f06dMT9ufn58vVV18tDRo0kBo1asigQYPk+++/P6Sh4aKLLpLDDjvMbPr1zp07ffsmIJsAAPzErWyS6fkkpQ0rCxYskBNOOEGGDh1qTvxxxx0njz766CHllixZYm4/+uijZcyYMbJ169Yyj6kvwu7duxM2AADcEI4GK7XZtXTpUhk7dqysWLFCFi1aJEVFRdK3b1/Zt2/fIWXnz58v7733njRr1uyQ28aPHy8vvPCCPPPMM/Luu+/K3r175cwzz5Rw+L/9Z4YPHy4ff/yxvP7662bTrzW8+BXZBADgJ25lk0zPJykdCvTdd9/Jgw8+KNdee63ceOON8v7778u4ceNMD5WLL77YlBkwYIBpeGnZsqWsXbtWbr75Zjn99NPNPCxarrRWsilTphyyPyRRs5UlFCj7trhARJyQFyz0zOoQ2QE6n1c1vy6h6qUVTII2fn4jUffqG7FxbsI26mP13rH3nPz5/vOqqARsvf5l3dcuDRDFac9PvQChn42nnnpqwlwhV111lbzxxhsycODAhPvs2rVLZs2aJU899ZT06dPH7JszZ47pSfrmm29Kv379ZPXq1eaxNCB169bNlNELICeddJK5AtW2bVvxGy9lEzc1yNmb6irAZX7MJ17KJnbyiR+zyS9lvPU6eCmP+jmbZHo+SWnDSiQSMT1Wbr/9dvO99lhZtWqVCTSx8DJs2LB4+Y4dO5ryGmReeeUVGTJkyCHHnDRpkglDMdpjRV8EAADSQcmelvqHeml/rJcMIar4UFn9jNUrN3/4wx+kQ4cOh9xHQ05hYaG5khSjV430s3bZsmUmuCxfvtx0r42FFqUTyOs+LePHhhWyCQAAlc8mmZZPUtpU3bRpU2nfvn3Cvnbt2smGDRvKvY82rHz99del3q4vcO3atRM2AADSpbutXgyIjRfWTXs7lCcajZoLCqeccooJHcXnCsnKyjK9LUqzefNmMxl83bp1E/Y3btzY3BYro1eaStJ9sTJ+QzYBAPhJKrJJJuaTlPZY0RWBSk5m89VXX5mGk7L8/PPPsnHjRhN8AADwEu3+nGwX6Nj99DOu+EUBqytC2pX2008/NWOQi1/tue++++Sjjz4yk8JVhAah4vcp7f4ly/gJ2QQA4CepyCaZmE9S2mNlwoQJZlyUDgX65ptvZN68efLII4+YCW+UTlIzceJE09Vn3bp1ZhLbs846y8wOfM4556Sy6gAAHCIswUptqmSvy/LCi86Yr5OtLl68WA4//PD4/nfeecdM9N6iRQtzVUi39evXy3XXXWdW4FO6yl5BQYGZVb84vZ9eFYqV2bJlyyGPu23btngZvyGbAAD8xO1skqn5JKUNK7oUk872+/TTT5vuQX/961/NUksjRowwt4dCIfnss89k8ODBZkWgkSNHmn+1oUWXcAIAIBPpFRm9EvT888/LW2+9Ja1bt064Xccu61UinSE/tun4ZB3PrBPFqS5dukh2draZtT9m06ZN8vnnn0uPHj3M9zoJnI6P1glcY3QGf90XK+M3ZBMAAJITzeB8ktKhQEqXTdKtNHl5efETDABAJnS3tUN7dmovzxdffNFcaIiNJ9Zxz/rZWb9+fbMVpyFFr/DEJnTTsqNHjzZXibSsTiynvUQ7deoUn4Vf5z3r37+/jBkzRh5++GGz77LLLjOf236cuDaGbAIA8Au3skmm55OUN6wAAOAXEQmaLdn72qWr56levXodsqzhqFGjbB9n2rRpphvueeedJwcOHJDevXvL448/bnqMxsydO9dMMBebnX/QoEEyc+ZM248BAAD8n00yPZ/QsPIfwUBEvCSYZuut22nNdPOxvMRr763If2b3rqyQOPN6WglLwFPnJuJQfUK2fh7Kf+8E0+tHwdH3n1eFowGzJXvfinS1rSidq6ykatWqyYwZM8xWFr1SNGfOnAo/Hvz3+eFHmZpNvPb+Srds4mY+8Vo2CQXClmXyw5n5J6YTvwecei1TkU0yPZ9k5rseAIA0724LAABgJ1+QTaqevy8dAgAAAAAAVCF6rAAA4JBoNJh0l3a9LwAAgJPIJu6gYQUAAAfH3Cc77t7N+YQAAEBmIJu4g4YVAAAcEokmP1eK3hcAAMBJZBN30O8YAAAAAAAgSfRYAQDAIZFKzLHi96WoAQCA+8gm7siYhpVAICLBQCTV1fCtoI1179PxsZzgtSVUvfRzYOcPyZBYv96FNo5j57EiHpvjImTxXg977L3ltfff6u1Nyr29aF++44+p76Fk30dee/+h6pFN/PNZn2719Vo+8VI2cTOfpGM2sSM3VGRZJj+cMX+GphzZxB28owEAcIg2diXb4JWODWUAAMDbyCbuoGEFAACH0N0WAAB4CdnEHQzoBgAAAAAASBI9VgAAcHIcc7LLLafhOHoAAOBtZBN30LACAIBDopWYvFbvCwAA4CSyiTtoWAEAwCHaWyXpHitMXgsAABxGNnEHc6wAAAAAAAAkiR4raSoYiKa6CnD5tfLj1exgIGJZpjAasjXbufVxrMuEPPZzZbX87sFwtuUxiiLW58/N+T+++qmhZZmCIus652+pblkmWFh+nSMHD4rTmHkfmY58klmvlR+ziZv5xE42cYrXMk5uqMiyzD+/bCvp5MiLP6r0MYqiheI0sok7aFgBAMAhdLcFAABeQjZxBw0rAAA4OfN+kr17WBUIAAA4jWziDhpWAABwCFeFAACAl5BN3MHktQAAAAAAAEmixwoAAA7hqhAAAPASsok7aFgBAMAhhBeRIUOGVPi8PfTQQ9KoUSPehwAAOIxs4k4+oWEFSBNuLmHp1vKJdpZJdvOxIi6uRLi3KNeyTFGk/Drvyq9mfQwbz7sobL1k5NbttSzLRH+yfk7BfOv3Vt0vLYtInXzrMlkHy39BiwpF1ouzCC8i8+fPl/POO0/y8vJsnbN58+bJ3r17aVgB0pAfs4mb+cTNHLS3MMe1x/rwuxbiN04spZwqZBN38gkNKwAAOCRaidV9XGzXq3L333+/7SDyj3/8o8rrAwBApiKbuJNPmLwWAAA4ZvHixVKvXj3b5V977TX51a9+xSsAAADSNp/QYwUAAIfQ3VakZ8+eFTpnp5xyCu8/AACqCNnEnXyS8h4rP/zwg1x44YVSv359qV69unTu3FlWrlwZvz0ajcrkyZOlWbNmZjxUr169ZNWqVSmtMwAA5YWXZDe/27Jli2zYsEG8jmwCAPALsok7+SSlDSs7duyQk08+WbKzs01Xmy+++ELuueceqVOnTrzMXXfdJffee6/MnDlTPvjgA2nSpImcccYZsmfPnlRWHQCAQxBefqGf0XrRpGXLljJy5EgpKCiQsWPHStOmTaV169bmqtHu3bs9+Q4imwAA/IRs4k4+SWnDyp133inNmzeX2bNny4knniitWrWS3r17S5s2beK9VaZPny433XSTWR6pY8eO8sQTT8j+/fvNLL0AAMB7brzxRtP7dOLEieYKkM7C//bbb8s777wjS5Yske3bt5sM4EVkEwAA/OnGKswnKW1YWbBggZxwwgkydOhQMzvvcccdJ48++mj89rVr18rmzZulb9++8X25ubmmJWnZsmWlHjM/P9+0MhXfAABwA1eFfvHiiy/KAw88IFdffbXMnTvXfN7ffvvtppfqb37zGxNannvuOU++KckmAAA/IZu4k09SOnntd999Jw8++KBce+21pvXo/fffl3HjxpnGk4svvtg0qqjGjRsn3E+/X79+fanHnDp1qkyZMsWV+gNu8tr8C5Fo5dtlwzaWpbXzvCMOHedgONuyTDBgvShukY1z8/OB6pZl9hfmlHt7flHI8hgFhda/5vMPWj/vyO7y66Jyd1g/75obLYtItR1hyzI5u4ssy4T2lV+mqOigOC0aDZgt2fv6xdatW+XII480X8fmSGvbtm389g4dOsjGjTbeDClANgEyO5u4mU+cyiZu+vC7FqmuAiqIbOJOPklpj5VIJCLHH3+8aSXS3iqXX365jBkzxjS2FBcIJP7S0SFCJffFTJo0SXbt2hXfvBrcAAD+oyG6Mptf6IT027Zti38/ePDghPnT9u7day6ieBHZBADgJ2QTd/JJShtWdJKY9u3bJ+xr165dfEZenahWxXquFG9pKtmLJUZPRO3atRM2AADcQHfbXxxzzDFmwvkYnRdNh/zG6G36ee9FZBMAgJ+QTdzJJyltWNGxTGvWrEnY99VXX5lZepXOzKuNK4sWLYrfrjP3Ll26VHr06OF6fQEAgDUdtzxs2LAyb9eLI7fddpsnTyXZBAAAf5pbhfkkpXOsTJgwwTSQ6FAgnZFX51h55JFHzKZ0uM/48ePN7UcddZTZ9Ovq1avL8OHDU1l1AAAOwTjmX9SrV6/cd8eAAQM8++4hmwAA/IRs4k4+SWnDSteuXeWFF14w86L85S9/MT1UdHnlESNGxMtcf/31cuDAAbnyyitlx44d0q1bN1m4cKHUqlUrlVUHAKDM7rZ+mAQyU5FNAAB+QjZxR0obVtSZZ55ptrJor5XJkyebDQAAL+OqUPFzETUr/c2YMUPSDdkEAOAXZBN38knKG1YAAIC/hMNhueiii2Tt2rWprgoAAECV5xMaVjwqGIimugpwiNe690ei1nNWh11a9tXOubFVXxvHKbJxnIJISJywtzDHskx+2PrXb0G4/PocyLd+nMID1o8TCFr/vgkUWJ/jnJ2WRSR3V8SyTN7mg5ZlsrbtsX6w/QfKvz1SIFVxVSjZn3m9rx8cPHhQhgwZIps2bZLFixenujpwENnEX7yUTwqjznz+pls+cTOb/HywumWZ/ELrzFDrMIvPVhHZsytP0smRF38kfkY2cSef0LACAIBDtIkqmmS7uF+a0/v06SPbt283K/jVqVMn1dUBACCjkU3cySc0rAAA4JCIBMx/yd7XD5YtW2bGLTds2DDVVQEAIOORTdzJJ9b9zwAAQIUmiEt28wNd3W/ixInyyiuvpLoqAABkPLKJO/mEhhUAAOAYnWn/sccek+HDh8tbb73FmQUAAL7PJwwFAgDAwQkPA0n2PPHSRJKVNWLECKlbt65ccMEFsmXLllRXBwCAjEU2cSef0LACAIBDdOLapCev9cvstf/x29/+Vl544YVUVwMAgIxGNnEnn9CwAgCAQyozV4pf5lgprkePHqmuAgAAGY1s4k4+yZiGlWg0aGu9+fIEAxHH6gN/sNN1v7LvO6eFbaw8UhQJuVIXO6ughKPu1TcYsO4ysL8o27JMftj6V2tR2Pp9EYmU/9zzcgssj5GbXWT9ODbOccGGPMsy1XZan7+8bdZ1ztq627JMdMs2yzLhvXvLvb0oWmh5DKAqkU2QCfnETu5wilN5wEv5xKls4qZahx2wLLNnl3WuANJJxjSsAABQ1bgqVPJ8ROUf//iHLF68WLZu3SqRSOIFiueff543JQAAZJO0zyfeupQOAECaXyWuzGbX1KlTpWvXrlKrVi1p1KiRnH322bJmzZr47YWFhfLHP/5ROnXqJDVq1JBmzZrJxRdfLD/++GPCcfLz8+Xqq6+WBg0amHKDBg2S77//PqHMjh075KKLLpLDDjvMbPr1zp07bdXzmmuuMeXXrl0rNWvWjB8jtgEAAH9kk0zPJ/RYAQAgzSaIW7p0qYwdO9aEl6KiIrnpppukb9++8sUXX5gAsn//fvnoo4/k5ptvlmOPPdaEj/Hjx5tg8uGHH8aPo/teeukleeaZZ6R+/fpy3XXXyZlnnikrV66UUOiXLuy6LKGGmddff918f9lll5kwovezMmfOHHPVRyeKAwAA/p68dmkG5xMaVgAAcDS8JDt5rf2ysRARM3v2bHNlSAPHqaeeaq62LFq0KKHMjBkz5MQTT5QNGzZIixYtZNeuXTJr1ix56qmnpE+fPvGg0bx5c3nzzTelX79+snr1avNYK1askG7dupkyjz76qJx00knmClTbtm3LrafW44gjjrD/xAAAQFpmk0zPJwwFAgDAQ3bv3p2waXdYKxpCVL169cotEwgEpE6dOuZ7DTnaJVevJMVol9yOHTvKsmXLzPfLly834SMWWlT37t3NvliZ8kyePFmmTJkiBw5YT2QIAAD8k00yLZ/QYwUAAA9NXqtXZIq75ZZbTAAo+35Rufbaa+WUU04xoaM0Bw8elBtuuMF0m61du7bZt3nzZsnJyZG6desmlG3cuLG5LVZGrzSVpPtiZcozdOhQefrpp035Vq1aSXZ24soV2h0YAAD4K5tkYj6hYQUAAIdoj9kkhzHH77dx48Z4uFC5ubnl3u+qq66STz/9VN59991Sb9erPueff76Z8f6BBx6wrkc0aq4cxRT/uqwyZRk1apS58nThhReaQGTnPgAAIL2zSSbmExpWKiAStR45FQwkLtUE2FFo672V7K/ERAUR6x/7wsgvk0KVp6KzhFelIhvnr8jF55Rl4/dAOGRdJhQsLP8YEev6Rmw8zs7d1S3L5O61LCJ5P4Uty2TtPGh9oAM2ytgQ+M/kZmXeHo2IWFfZ9atCGlyKh5fy6Iz5CxYskLffflsOP/zwUkPLeeedZ2a9f+uttxKO26RJEykoKDATxxW/KqTLDvbo0SNeZsuWLYccd9u2bSaIWHnllVfkjTfeMFerUDXIJkhlNnHKviLrP9LSkVU+ScdskhUqcKQ+4sDCcHt25VmWOXrMF9YHyq1mWSRaVH4mc4ofskmm5hPmWAEAIM3oFRm9EqQz2msgad26dZmh5euvvzaTvems+sV16dLFdH0tPoncpk2b5PPPP48HF50ETsc+v//++/Ey7733ntkXK1Me7TpckSAGAADSVzSD8wk9VgAA8FJ/Wxt0KcN58+bJiy++KLVq1YqPJ9ZJ2/Ly8swSh7/73e/MGOGXX35ZwuFwvIxOIKdjl7Xs6NGjzRKGGmp0/8SJE6VTp07xWfjbtWsn/fv3lzFjxsjDDz8cX85Qlzy0mnFf3XPPPXL99dfLQw89ZMYwAwAAf2aTTM8nNKwAAOCUSnS31fva9eCDD5p/e/Xqdciyhjpu+PvvvzddcFXnzp0TyixevDh+v2nTpklWVpa5cqQz4/fu3Vsef/xxCRUbRjV37lwZN25cfHb+QYMGycyZM23VU8cu79+/X9q0aSPVq1c/ZHK47du3237OAADAu9kk0/MJDSsAADgkGv1lS/a+9suWX1ivvliVUdWqVZMZM2aYrSx6pWjOnDmSDA1GTFgLAID/s0mm5xMaVgAA8NAEcX6wcOFCOe2008zVKQAAkDpkE3fyCZPXAgAAR11xxRXSsGFDGTZsmBlrvXPnTs4wAADwbT7JmB4rYQmYrco5tCSz1dJqTi2966aQjTqHfXTFtiKvd+KovtLZef/aWbbPzlLKdpYvdmu55YhDdbG3JLNDZWw8VtjGcULB8t87IRtN47Z+Vdg4f6H91ofJOmC9PmBwf751dQ5aL7ccLSyyLBPIKv8nK6BdUR1e0tCcS5fGMXvZd999J59++qkZRz19+nS59NJL5eSTT5bBgwebMdBMZOvPbJKO+cSv2cTO6xDx0Etl53PTa5zIJ+mYTZxSLdv6c9xKUztLKTvEKlM49jhkk7TNJ+n3WwwAAI+PY05285NjjjlG/vSnP5mlEDXIDB06VF5//XUzk/+xxx4rf/7zn+XDDz9MdTUBAPA1sok7+YSGFQAAnF7SMNnNp5o1a2a637766qvy008/yc033yzr1q0zSyXefvvtqa4eAAD+RTZxJZ9kzFAgAACQejVq1JDf/e53ZotEIvLzzz+nukoAACDD1ahkPklpw8rkyZNlypQpCfsaN24smzdvNl/rbL1PPPFEwu3dunWTFStWuFpPAADsYOb9RPfff7/tN864ceM88SYjmwAA/IRs4k4+SXmPlQ4dOsibb74Z/z4USpxYU7vhzJ49O/59Tk6Oq/UDAKBCfDykp6KmTZsm27Ztk/3790udOnXMPp2Bv3r16mZW/phAIOCZhhVFNgEA+ArZpMrzScobVrKysqRJkyZl3p6bm1vu7QAAeAVXhRLddttt8sADD8isWbOkbdu2Zt+aNWtkzJgxcvnll8uIESPEi8gmAAC/IJu4k09SPnnt119/bSaNad26tZx//vlmZt7ilixZIo0aNZKjjz7aPNGtW7eWe7z8/HzZvXt3wgYAgCuYIC6BTgI3Y8aMeGhR+rVeKdIZ+b2KbAIA8A2yiSv5JKU9VnS+lCeffNI0mmzZskVuvfVW6dGjh6xatUrq168vAwYMMMsftWzZUtauXWtOwOmnny4rV640PVlKM3Xq1EPmbVGRqPVa8laCAes+VGGx8Rg21pEPBiLl3m7nudipr9eEHKpzuJKvtV12znFRJHF4W7LsvOaFNt5bB8PZlmWKbBzHSmV/3ipyHDtl7Dynooh1mbBDZew8lhPvv1Cw/N8ldkWs3zYSrmb9Xo/ULP13d3HB/BqWZQJZNj6+wuU/96A+qXzrwyB5mzZtksLCwkP2h8Nh87nvRWST5LOJX/NJumUTu7KC4fJvl/JvV1vza0m6sZOD3MonfswmQKbmk5T+9GjDybnnniudOnWSPn36yCuvvGL2xyasHTZsmAwcOFA6duwoZ511lrz22mvy1VdfxcuVZtKkSbJr1674tnHjRteeDwAg0wUquflL7969TW/TDz/8UKLRX/441a+1m61+7nsR2QQA4C9kEzfyScrnWCm5xJE2smgX3NI0bdrU9F4p63alPVnK6s0CAIAr3W2Tva/P/O1vf5ORI0fKiSeeKNnZv1wlLioqkn79+sljjz0m6YBsAgBIa2QTV/KJpxpWdH6U1atXy29+85tSb9e1pLUHijawAADgOYSXBDqz/quvvmp6m3755ZfmqlC7du3MEOB0QTYBAKQ1sokr+SSlDSsTJ040Q3xatGhhJqXVOVZ0slltPdq7d69MnjzZDBXShpR169bJjTfeKA0aNJBzzjknldUGAKB0Op4+2XkUPDb/gpNatWplQkubNm3MijteRjYBAPgK2cSVfJLSOVa+//57ueCCC8wMvEOGDJGcnBxZsWKFGe4TCoXks88+k8GDB5uWI21s0X+XL18utWql30RZAABkmv3798vo0aOlevXq0qFDB9mwYYPZP27cOLnjjjvEi8gmAAD42/4qyCcpvWz0zDPPlHlbXl6evPHGG67WBwCAytD5z/4zB1pS9/UbnVD+k08+kSVLlkj//v3j+3ViuFtuuUVuuOEG8RqyCQDAT8gm7uQTb/fHBQAgnTCOOcH8+fPl2Wefle7du0sg8N+hTu3bt5dvv/3W/dcHAIBMQzZxJZ9kTMNKJBo0WyWPYlkiGLC+5Bi2s6Rmpetqr7522HlOdoRtzB8Qcuix3FIUCVmWyY9Y/5hFbLwn7Jw/O/VxSkG4/MeK2Kivnedt5zhFEeufl7CNMrZeBxvHsfMzk5tVJJ5ho77RXyZML9eeZtbv9WB+dcsyOUHrcxzYX2BdptDiHIfzRbaLsxjHnGDbtm3SqFGjQ07Tvn37EoJMpsrMbOJMPsnkbGLnc9GOnwpqilfYeT232/j8cJMT+cRr2QQ+RTZxJZ+kdI4VAADgX127dpVXXnkl/n0srDz66KNy0kknpbBmAAAgU3WtgnySMT1WAACoanpBMNmLgn68mDh16lQzdvmLL76QoqIiue+++2TVqlVmIvqlS5emunoAAPge2cSdfEKPFQAAnB7HnOzmMz169JB//etfZvZ9Xcpw4cKF0rhxYxNcunTpkurqAQDgf2QTV/IJPVYAAHAK45gP0alTJ3niiSd4jwEAkApkE1fyCQ0rAAA4hZn3S7V161azRSKJk5Yec8wxvPcAAKhKZBNX8gkNKwAAoEqsXLlSRo4cKatXr5ZoNHGsk04UFw6HOfMAACDt8wkNKwAAOIWrQgkuueQSOfroo2XWrFlm7DJLLAMA4DKyiSv5JGMaVnQNeDvrwJcramOteRuTDwYDiV2NSlX519ZWfe2xrm/QxnIWIYeWvAjrOMFKijhwDFMXGy+UnfednedUELH+cc2y8d7KCRZZlhEbj1UUKP/9VWTr58X6eRdFrI+TH7au74HCbMsy1bKsz03Axvs4auN52fmZcep9almXoHVdDja0LpO138b7uHbIskw0WM36sfKsX/NgQflXG8JF1nWpMMJLgrVr18rzzz8vRx55pPPn2gcyMps4lk/8l02c/L1/IJxjWaZGqKDc2/fZOIab6uXutyyzPb+6eMnOg3muPI6d/GJHXnahI8exk7mcsO25lpZlGp673pW6eBrZxJV8wqpAAAA4PUFcspvP9O7dWz755JNUVwMAgMxFNnElnyTVY+XSSy/9/+3dCZgTVbrw8TdJr+z7poCMIoKACzooOoKCLKKoeF0GF1Au6igiKOMMOl7BUVGuigujuA2ooDjfqIjjBo6IOoAojjOCCKggqGyyNXt3J/U97/Em04HuriJdXalK/j+fkqTqpHJSqXRO3jrnPWau59q1ayet37Vrl9xwww3y5z//2a36AQCAgHr66afNGOYlS5ZIx44dJTc3+SrmgAEDXH0+2icAACAd7ZOUAis6LdG99957QGBlz5498txzzxFYAQBkJR1VkOrIApdGJPjK/Pnz5aOPPpK33nrrgG3VkbyW9gkAAPt939I28aR9clBDgYqKimT79u0mc+6OHTvM/fiydetWefPNN6VJkyYHXQkAADJqHHOqS4YZMWKEXH755bJu3TozlWHZxc2gCu0TAAAqQNvEk/bJQfVYqVevnong6KJZdPen68eNG5dSRQAAQGbZvHmzjBo1ymTcr060TwAAQDrbJwcVWJk7d67prXLGGWfIyy+/LA0aNEhsy8vLk9atW0uLFi1cqxwAAEGi6WdTHgokmWfgwIGm7XD44YdX6/PQPgEAoHy0TbxpnxxUYKV79+6J6Ylatmwp4TCTCgEAgPJp79YxY8aYccydOnU6IDmcdsV1A+0TAACQzvZJSslrtWeK2r17t6xZs0aKi4uTtnfu3Fn8JmqFzFIlbl1OtOwDUjGbK57hUMyV+kYcDOoPO7j8GvEw66KT56rye+2isJPECQ6qm+PgPXd0XjhIrZQTth9bWCBVF3PpfYrG7F93YW6JK89leXhu2X323Dp+TlhhB5+7fPv67GwRsS1T60cH9cm1P4/D+yo/L0pLq+HvVlWmTfbR3y03s+7XqlVL5s2bZ5b9hw+7FVgJavsk09omXrZPaJtUv8KI/fdmScz+b7qX6uTuEz/5aXdNT55nb6n9T7qCnFLxipM2156S5B+yqEa0TTxpn6QUWNm0aZNceeWV5WbRVW5n+QcAIBCqkoQ2A5PXag9XL9E+AQBgP7RNPGmfpDSWZ+TIkWYWoIULF0phYaG8/fbbZorDtm3byqxZs1yvJAAAgUDm/bSifQIAwH5om3gipcDKe++9JxMnTpQTTzzR5FnRrreXXXaZTJgwQcaPH+9+LQEAQCDcdNNNsmvXLsfldYzzli1bXHlu2icAACAd7ZOUAitaoSZNmpjbOjOQdr1Vmvjls88+S2WXAAAEnqbFqcqSCR5++GGT48SpP/3pT7Jt2zZXnpv2CQAAyWibeNM+SSnHSrt27WT58uVy2GGHybHHHitPPPGEuT158mRp3rx5KrsEACD4GMcslmWZbPua/M2Jg7l6ZIf2CQAA+6Ft4kn7JCfVMczr1q0zt++44w7p06ePTJs2TfLy8kyuFQAAshKNF5kyZcpBH7amTZu6cvhpnwAAsB/aJp60T1IKrFx66aWJ28cdd5ysXr1avvrqK2nVqpU0atQolV0CABB4VRnSkylDgQYPHpy256Z9AgBAMtom3rRPcg4m2YtTDz74YKr1QUBFdX50GxEf/WoIO6hLzMFrcvZcMftCln26o/xwqW2ZEgf7cVIfJ38YwmFv3s88y3769hwHr2lvNMeV99xyUKbUwfvgZD9enceN6u60LbNuY6Ftmb2N7etTWGpfn6LDIvb72WT/XOHSyt+H0pKUri3AZ2ifIFPaJm7+Xc8JR6u+Hw/bJjFHZexfd56D+hTHvPvbf0S9zVXex9fbGrpSl72l7rzughz7Y+xEYW6JbZk9JbmuPBfgBcefsH/+85+OyjkdswQAQMbRhn+qQTOXgrnZhvYJAACVoG3ir8DK3Llzq7cmAAAEHeOYPUf7BACAStA28URK0y27ZezYsaaHS9mlWbNmSZl7tUyLFi2ksLBQevToIUuXLk1nlQEAqBBTGgYfbRMAQCahbZIFgRV19NFHmxmG4ssXX3yR2DZhwgSTr2XSpEnyySefmKDLmWeeKTt27EhrnQEAgL2pU6fK7t27A3eoaJsAAJC5plZD+yTtgZWcnBwTMIkvjRs3TvRWeeihh+S2226TgQMHSseOHc1UznoAXnjhhXRXGwCAirvbprpkmDFjxpjv9qFDh8r8+fMlKGibAAAyBm0TT9onaQ+srFy50gz1adOmjVxyySXy7bffmvWrVq2S9evXS+/evRNl8/PzpXv37pW++H379klRUVHSAgCAJ/5vuuVUloMJrIwfP15OPPFEqV27tjRp0kTOO+88Wb58eXJVHAyn1e/MG264QRo1aiQ1a9aUAQMGyPfff59UZuvWrXL55ZdL3bp1zaK3t23b5qieuq9p06aZfZx++uly1FFHyX333We+3/2MtgkAIGN41DbJ9vZJWuea7Nq1qzz33HNy5JFHyoYNG+Suu+6Sbt26mQMbf1FNmzZNeoze/+677yp9M8eNG3fQdfHbdHt+4mR6OyfTA3rJ7v10MgWjo+dx8tfG0VPFXJmK0NEzuTSloZ+mPMwJO5hC2nJyjB1MpRxzMKW1g/PCwSTcrqiVW2xbpkH+Htsy0cPtX/emn2rbltlziIP3YVOBbZldLR1Mu7ml8jpH94UDmyBu3rx5cv3115vGS2lpqendqRcivvzyS9MAKTucVru76vesfsfqcFpt4GiDR40cOVJef/11mTFjhjRs2FBuvvlmOfvss2Xx4sUSifw87fWgQYNMA+Ttt98296+++mrTeNHH2dF9aGNIl40bN5pGjNbn9ttvl759+5orReecc46Ew2m/zpNA2yQYMrFt4r/2iXdtE1Rtyma3pmR2a9pmr6ZkdjId86xv/+FKXQb84hQJLA+T187L4vZJWgMr/fr1S9zu1KmTnHzyyXL44YebIT8nnXRSudM3a4SrsimdtVvPTTfdlLivPVZatmxZLfUHACAdjZd4IyJuypQp5sqQNjhOO+20A4bTKv1u1YsTOpz2mmuuke3bt8szzzwjzz//vPTq1cuU0YaFfme+++670qdPH1m2bJl5roULF5qAg3rqqafM97U2gNq1a+e4zlq/U045xTxuxYoVJqfakCFDpF69eqb+esXKD2ibAAAyioeBlbezuH3iqzCzRrE0wKJdcOOzA+3fHUcjSvv3YilLhwvVqVMnaQEAICj2H86q3WHtaCNENWjQwPFwWm3klJSUJJXRbrma0yxeZsGCBaZ7bbzRovTCh65zOiZZe6Tef//9JiGsNk70Nf3tb38zdfzxxx9Nw2rw4MHiV7RNAADZLpW2Sba1T3wVWNE3SKNPzZs3NzlXNLgyZ86cxPbi4mLTvUiHCwEAkIlTGuoVmfh4YV10iGtl9OqP9tQ89dRTTaNDVTacNr5N/83Ly5P69etXWkav5OxP1zkZh6zdaPX1aPfaYcOGyQ8//CAvvvhi4gqUjq3W7r1r164Vv6JtAgAIsnS0TbKxfZLWoUCjR482L6pVq1amJ4qOr9JIkUaGdLiPjq265557pG3btmbR2zVq1DDjqQAAyET6JV62t6VeyanM8OHD5d///rd89NFHB2w72OG05ZUpr7yT/cQbOHpBRLvmVkQvpujVIb+gbQIAQNXaJtnYPklrYEWTzfz617+Wn376yUyzrN13dJxU69atzfZbbrlF9uzZI9ddd53J2KtdfWbPnp1IagMAQKaNYz6YYayaMX/WrFnywQcfyKGHHppYX3Y4rTYMyhtOq2W0J6h+v5a9KqRl4j1DtYx2ld3fpk2bKh2WG6dde48//vgD1uvzakK6K664wjSA4t/7fkDbBACQUTxum2Rr+yStQ4G00jp+SV+Adr95+eWXpUOHDont+mJ0KqZ169bJ3r17TVQp3o0IAIBM7G7rhF6R0StBr7zyirz33ntm+GxZTobTdunSRXJzc5PK6PftkiVLEmX0So6Oj160aFGizMcff2zWORmWe+WVVybGV5e1Y8cOs82PaJsAADKJV22TbG+fpLXHCgAAOHg6laFmz3/ttddML874eGId96zjgp0Mp9WyOp2gjiHWqQw1sZwOg9Ek8vExxu3btzfTDur44yeeeCIxnaFOeegk435FXXK1V4g+PwAAyBzXZ3H7JGsCK5GQZZaKhFPuH1U9wqFYuqvgW2GxPzYxm85YlZ0LB8fB+2Q56BgWcue5cp3sxcFrj1r2FYo5eV02CiIltmVKHTxPLOzoANoqjkVc2U+dHPtM6TEHx7hO3t5Kt7etudF2H7mhqCtlzm38T9syKw79uXtnZRZvbWVbJq+lfX3WbK9nW6Y4Wvn7Gd3tLKP9QfPg6+Txxx83/+4/BaBOC6hTBDodTjtx4kTJycmRiy66yJTt2bOnSeQWifzn2E2fPl1GjBiRyM4/YMAAmTRpUqX1O+6440yDRRfdpz5HXDQaNWOWtUGU7WibZA432ia+a5+41DbJd9Cm3RPNE6/khUttyxTHgvUT6Yh6m13Zz9fbGtqW+fPRz4lfHJXrpOXrjlnf/sOT5ynaEZMmzmcKds6jn7qPZ3H7JFh/NQAAyPBxzI6KWvaF48NpdalIQUGBPProo2apiF4pmjZtmvPKich5551n/v3888+lT58+UqtWrcQ2zfR/2GGHyQUXXHBQ+wQAAP5tm2R7+4TACgAALkllPHLZx2aKO+64w/yrDZSLL77YNJAAAID3aJt40z4hsAIAAKrF4MGDObIAACDj2ycEVgAACGB3W7/SrrkrVqyQRo0amWkSy0sOF7dlyxZP6wYAQNahbeJJ+4TACgAALqG77c8J5+IJ6B566CHOLQAA0oi2iTftEwIrAAC4hatCSd1rGQoEAECa0TbxpH1CYAUAALfQeDlALBaTr7/+WjZu3Ghul3Xaaadx7gEAUJ1om3jSPsmawEpOKGqWoAi7MD1EJIAD9sMS82Q/MQm78jyO6hJy8JosB/UJOXku+3M8ZtnvKOzk+Ni8rpiDCkcd1CXPyfFzoGZOsW2ZpnlFtmWOKvzRtkyeg/chN1RqW6ZhZFel2xtEdrvydyDXwd8bJ/vpVrDGtszQ+ovEDREH59fifY0q3b57R1QucaU2qMjChQtl0KBB8t133x0wBaOObY5Gg/O9XB2ysW0SxPaJV20T37VPXGqbOFEYsf+OdkupFbEtkx+x/452YkeJf2ZEu/WQN+wLHeJFTYDMbJ9kTWAFAIDqxjjmZNdee62ccMIJ8sYbb0jz5s0rTRQHAADcR9vEm/YJgRUAANxCd9skK1eulL/+9a9yxBFHcI4BAJAOtE08aZ94198QAIBsabykumSYrl27mvHLAAAgTWibeNI+occKAACoFjfccIPcfPPNsn79eunUqZPk5uYmbe/cuTNHHgAABL59QmAFAACXMI452QUXXGD+veqqq/5zjEIhkyiO5LUAAFQ/2ibetE8IrAAA4BbGMSdZtWoV5xYAAOlE28ST9gmBFQAAXMJVoWStW7fm3AIAII1om3jTPsmawEo4pEv1ZwaMeJR9MByKubQfd+obcbCfsLhTZzc4qkvIndzOMSvk2bkVlZBn51fMqvz4hB3U163zr17ubtsyh+RvtS3zi7yNtmXqhPfalqntoIyf/p444eTTEHFw+kXcOkcdlDkx/6dKt+8oroa/SVwVklmzZkm/fv3MeGW9XZkBAwZINsu0tonf2idBa5sEsX3iZdvEiVIrIn5SO7fq7YFrmrzvSl2QxWibeNI+yZrACgAAqH7nnXeeSQbXpEkTc7si5FgBAACZ0j4hsAIAgFu4KiSxWKzc2wAAIA1om3jSPiGwAgCAS7TDe6qd3r3rLA8AALIFbRNvEFgBAMAtXBWSRx55xPHhGjFiBOceAADVibaJJ+0TAisAAMA1EydOdFROxzATWAEAAJnQPiGwAgCAS5jSUGTVqlWcTwAA+ARtE2/aJwRWAABwC91tAQCAn9A28UTWBFZyQlHJDYXFD8IaNvRAxKXnCUvMV8cmZgUrxaOz99v+GMcs785fJ88Vs0m1GTZ/xauuMFJsW+aQ/K22ZQ7L+8m2TLOcInFDVOyPX8TBex514Rjb7UPluvReRRw8l5OzOBLy5jNebc/jzZ/4wPj+++9l1qxZsmbNGikuTv48P/jgg5LNsrFt4lb7hLaJF+958NomQTOs8bx0VwHZgrZJtbdPsiawAgBAdaO7bbK///3vMmDAAGnTpo0sX75cOnbsKKtXrxbLsuT444/nhAQAgLZJRrRPMi/0CwAAfGHMmDFy8803y5IlS6SgoEBefvllWbt2rXTv3l0uvPDCdFcPAABkoTHV0D4hsAIAgNvjmFNdMsyyZctk8ODB5nZOTo7s2bNHatWqJXfeeafcd9996a4eAACZj7aJJ+0T3wRWxo8fb6Y2GjlyZGLdkCFDzLqyy0knnZTWegIAYDcUKNUl09SsWVP27dtnbrdo0UK++eabxLaffrLPe+QHtE8AAEFG28Sb9okvcqx88skn8uSTT0rnzp0P2Na3b1+ZMmVK4n5eXp7HtQMAwCEy7yfRiyH/+Mc/pEOHDtK/f3/T7faLL76QV155JRAXSmifAAACj7aJJ+2TtAdWdu7cKZdeeqk89dRTctdddx2wPT8/X5o1a+Z4fxp5ikefVFGRO7N8AACAg6NZ9fV7Xo0dO9bcfumll+SII46QiRMn+vpwutk+oW0CAEBmt0/SHli5/vrrTZSoV69e5TZc3n//fWnSpInUq1fPJJO5++67zf3KuuyOGzfugPW54ZhZgsKNaQS9nDrRS5k4JbPfhEMOzj8Xpj3MD5falmlZsMWVqZRb5GwTrziZStkNJQ7eg1wH72WJg89LQdj+c5frYPriXAcjUMMOpm2OuZCQpDq+AJkVKNmhhx6a6Glao0YNeeyxxwIzFMjN9gltk8xvn9A2yZy2iZuGNv5A/CLq4Ls1konJvnwmN1R56yM3FHX9OWmbeNM+SetfnxkzZshnn31mGhzl6devn0yfPl3ee+89eeCBB0yX3DPOOCOpR0p5GX63b9+eWDS7LwAAniBBXJKLLrpIYrEDfwxt2LBBevTo4duT0u32CW0TAEDa0DbxpH2Sth4rGvC48cYbZfbs2WaKo/JcfPHFids6t/QJJ5wgrVu3ljfeeEMGDhxY7mO0a64uAAB4jnHMSdatWydDhw5NypWm6zQIcfTRR4sfVUf7hLYJACBtaJt40j5JW4+VxYsXy8aNG6VLly5miiNd5s2bJ4888oi5HY0e2A2qefPmpuGycuXKtNQZAIDKkHk/2ZtvvimLFi2SUaNGmfs//PCDuRLUqVMn+ctf/uLLk4n2CQAgk9A28aZ9krYeKz179jSZd8u68sor5aijjpLf/e53EolEDnjM5s2bzZUkDbAAAAB/a9iwobzzzjty6qmnmvvao+P44483w2jCYX/lQoijfQIAQGZrWA3tk7QFVmrXrm26z+4/n7S+SF2vmXk1Q+8FF1xgAimrV6+WW2+9VRo1aiTnn39+uqoNAEDF6G5bboK4OXPmmMbLmWeeKc8//7yEHCQ6ThfaJwCAjELbxJP2SdpnBaqI9ljRHi3PPfecbNu2zQRXTj/9dDMNkjZ6AADwm5BlmSXVx2aC+vXrl9sw2b17t7z++uvmAkrcli32s375De0TAECQ0Dbxpn3iq8CKTl0YV1hYaLrnAAAQGFwVkoceekgyDe0TAEBg0TbxpH3iq8BKdQpLzCxV2odm/vGJSBVfi19FXcqnbPdexSz7bl6OzpeQfX2jDp4rE+WHS23LNM7bYVumRe422zINwjttyzh6z330GXciYr4pq/66Iw5ed9TBocl18HkoDOXalimVqP1zyYF5uPYXs/kM54aqL0Fcqo/NBIMHD053FQIj09ommdo+8apt4mb7JOrgbySqZmjjDzLuEEYl5ErbI1vlhvz505q2iTftE3+++wAAIJCKioocl61Tp0611gUAAMCL9gmBFQAA3EJ3W6lXr55t8jdLc9GEQhKN2vdOAgAAVUDbxJP2CYEVAABcQndbkblz53I+AQDgE7RNvGmfEFgBAMAtXBWS7t27OzpUn3/+OecdAADVjbaJJ+0Td7JxAQAA2Ni+fbs89thjcvzxx0uXLl04XgAAICPaJwRWAABwubttqkumeu+99+Syyy6T5s2by6OPPipnnXWWfPrpp+muFgAAGY+2iTftE4YCAQDgFrrbJnz//fcydepU+fOf/yy7du2Siy66SEpKSuTll1+WDh06cM4BAOAF2iaetE+yJrCSG4pKbig4HXTCAbt0GbPs5713IiIxV/YTtemM5drxtRzU18F559bxc8LJa3dSn/xwaaXbWxZssd1Hm/yNtmWaRHbYlskLMbNIVUTF/v3e5+Cc2GGVOngu+/OvIBSxLZMfyrUtU2JTnxwHrzsVAfvzXS30is9HH30kZ599trkC1LdvX4lEIjJ58uR0V81XaJtUr6C1Tdxsn+SH7P8e74tlzc+Agza08QfprkKg2wwRB9/1bviqpMS2zFG59u0FJ3JDwf680Dap/vZJsM8QAAD8xLJ+XlJ9bIaYPXu2jBgxQn7zm99I27Zt010dAACyF20TT9onwenCAQAAEj744AM555xzpEWLFhIKhWTmzJlJR2fnzp0yfPhwOfTQQ6WwsFDat28vjz/+eFKZffv2yQ033CCNGjWSmjVryoABA0wX2bK2bt0ql19+udStW9csenvbtm2VvhMffvih7NixQ0444QTp2rWrTJo0STZt2sS7BwBABvvAx22T6m6fEFgBACCACeJ0XPAxxxxjGgXlGTVqlLz99tsybdo0WbZsmbmvDZXXXnstUWbkyJHy6quvyowZM0zXWG3waPfYaPQ/w+sGDRpkph7Ufemit7UBU5mTTz5ZnnrqKVm3bp1cc801Zv+HHHKIxGIxmTNnjmnUAACA6kfbxJv2CYEVAADcThCX6nIQ+vXrJ3fddZcMHDiw3O0LFiyQwYMHS48ePeSwww6Tq6++2gRi4tnudWrBZ555Rh544AHp1auXHHfccSYI88UXX8i7775rymhARoMpTz/9tGmMxBskf/vb32T58uW2daxRo4ZcddVVJmij+7355pvl3nvvlSZNmpgrUAAAoJrRNvGkfUJgBQAAl4RiVVtUUVFR0qJdYlNx6qmnyqxZs+SHH34Qy7Jk7ty5smLFCunTp4/ZvnjxYpMFv3fv3onHaNfdjh07yvz58xPBGe1iq91l40466SSzLl7GqXbt2smECRNMd94XX3wxpdcEAAAODm0Tb9onBFYAAPCRli1bJsYM6zJ+/PiU9vPII4+YaQN1HHNeXp7JfP/YY4+ZgItav369WV+/fv2kxzVt2tRsi5fRqzf703XxMgdLs++fd955JugDAAD8L9PbJm60T5gVCAAAt6QwpCfpsSKydu1aqVOnTmJ1fn5+yo2XhQsXmgZC69atTUK56667Tpo3b26G/lRYDcsyCefiyt6uqAwAAPAp2iaeyJrASjhkmcUPIvH+3j4QtdzptOTlsY1Z9o35iHhzjCMOfldEnfzKcvA2lDh4SVErYlsmP1xqW6Z+zm7bMi3ytla+Pbfy7apmqNi2TNjBexkVl37gWe6c61EHb6hX56hbn6m9Yn9uRWP2r6kk9J+kqBXZ5+BvZIkLf0f3WO6/B6kkoS37WKVBlbKBlVTs2bNHbr31VpOYtn///mZd586dTeLZ+++/3wRWmjVrJsXFxSazftkrQxs3bpRu3bqZ21pmw4YNB+xfM+jr1SNUDW2T6m2fZGvbxKkaYfvvYDtFpYXiJxc2XOTKfvZaua7sJzdk3+aylaVtEyecvO4VpVU/z9XRucH92UzbxBsMBQIAwC2WVbXFJZo7RZdwOHxAN1fNfK+6dOkiubm5Jgt+nGbJX7JkSSKwoslqNcntokX/+bHy8ccfm3XxMgAAwMdom3giuKE3AAAy8KqQUzo18tdff524v2rVKtMjpUGDBtKqVSvp3r27/Pa3v5XCwkIzFGjevHny3HPPyYMPPmjK6xjpoUOHmkz4DRs2NI8bPXq0dOrUKTFUqH379mb887Bhw+SJJ54w63R2IZ2SWZO9AQAAf6Nt4g0CKwAABJBOm3z66acn7t90003mX51ieerUqTJjxgwZM2aMXHrppbJlyxYTXLn77rvl2muvTTxm4sSJkpOTIxdddJEZPtSzZ0/zWO3ZEjd9+nQZMWJEYvYgnYZw0qRJnr5WAADgf59mcduEwAoAAD5KEOdUjx49TBLZimh+lClTplS6j4KCAnn00UfNUhHtyTJt2rSDqxwAAPAH2iaeILACAEAAu9sCAAA4aV/QNql+BFYAAHBLVZLQupi8FgAAING+oG1S7ZgVCAAAAAAAIEVZ02MlEoqZpbqFUx5cnywmIfGCF8fkYEStsCtz1vtJWKK2ZXJD9mVE8mxLNMnZYVumbmS3bZlf5G+0LRORqp87xVbE/nlC9p+FmJPzxkl9nXwe3Dr9QlWPa0ccnFtucfK5izr4u7XLwXu+18FVlb2hUtsyBTbnzh7L/b9/dLfFwaBtUvmx8YtMbJu4pV6ufZvitFpfSdA4aZ/kOWi7lVg5Wdc28bp94pWlJfbnuhNH59YQr9E28UbWBFYAAMikBHEAAACO2he0TaodgRUAAFzCVSEAAOAntE28QWAFAAC3xKyfl1QfCwAA4CbaJtmVvHb8+PESCoVk5MiRiXWWZcnYsWOlRYsWUlhYKD169JClS5emtZ4AACB70D4BAACBCKx88skn8uSTT0rnzp2T1k+YMEEefPBBmTRpkinTrFkzOfPMM2XHDvsEnQAApG0cc6oLfIX2CQAg8GibZEdgZefOnXLppZfKU089JfXr10/qrfLQQw/JbbfdJgMHDpSOHTvKs88+K7t375YXXnihwv3t27dPioqKkhYAALwQKjOW+aAX3iJfcbN9QtsEAJAutE2yJMfK9ddfL/3795devXrJXXfdlVi/atUqWb9+vfTu3TuxLj8/X7p37y7z58+Xa665psIuu+PGjTtgfUgs16ZC9kKQ6urmFNJ+ml7Rb+93bmSPbZlWeT/ZlikIl1R5ekAVtZkiMM/BdLhuTXPpaCpCJ3FkBx87JxMIOjqPXfiIRx1MRR0J4N8SJ9M2+5ZOFe1guugKHwvfcLN9QtvEv2ibVE2ugymHt0RriRsaRHZK0KZktm8PZF7bxAjw13h122Pts9leDb+FaJtkfo+VGTNmyGeffWYaHPvTRotq2rRp0nq9H99WnjFjxsj27dsTy9q1a6uh5gAAIFO53T6hbQIAQGZLW48VDXjceOONMnv2bCkoKKiwnCa0LUu74O6/riy9aqQLAABeY0rD4KuO9gltEwBAutA2yfAeK4sXL5aNGzdKly5dJCcnxyzz5s2TRx55xNyOXwna/+qPPmb/q0QAAPgCCeICj/YJACCj0DbJ7MBKz5495YsvvpDPP/88sZxwwgkmUZze/sUvfmFmAZozZ07iMcXFxSb40q1bt3RVGwCACoW010IVFqQf7RMAQCahbZLhQ4Fq165tMumXVbNmTWnYsGFi/ciRI+Wee+6Rtm3bmkVv16hRQwYNGpSmWgMAUAnNOZdq3rnszN3tO7RPAAAZhbZJdswKVJlbbrlF9uzZI9ddd51s3bpVunbtasY8a6MHAAAgHWifAAAA3wZW3n///aT7mgRu7NixZgEAwO+qMqSHoUD+RfsEABBUtE2yMLBSnXTudkfztyMlUcs+XU/YZE7KPm6dd2EH+9ln5dqWKZAS2zJRq+KZtxJClb/ne608213khkrFDTEH9Y06OP8ilcw4lijj5Dx2cqo7eD/tnivqKE1W1Z/Ha3kO6lzg4L3KtzlHix2c5ikniEv1scgqtE2qF20T+/PPC1/vs590onnuNvFK7cgez57Lrn2SiW0TVSwRB/sJXvvEzi9y7F93WtA28UTWBFYAAKh22lsl1SS0JK8FAABuo23iCQIrAAC4JGT9vKT6WAAAADfRNsnw6ZYBAAAAAACCjh4rAAC4he62AADAT2ibeILACgAALtG8f6nmgyS/OgAAcBttE28QWAEAwC1cFQIAAH5C28QT5FgBAAAAAABIUdb0WAmHYmYB3D6vvBIxk9BXbm8s17ZMfsi+TF4oalumxIUZTKKhkG2ZiJOpUkKltkXCYv+aMlHUQfw818Hxc3L+OSnjRG0HIf8aoYhtmdxQ5TsqdnD+HTQ9BKkeBmYFyjq0TVCd55admBWs66u7Yvm2ZWqG99mW2REtdOd70YX2iVttk6jYf5+FM7B94la7w8nxCzTaJp7ImsAKAADVLWRZZkn1sQAAAG6ibeINAisAALiFccwAAMBPaJt4gsAKAABu0U4nqY4QpMMKAABwG20TT/htuB0AAAAAAEBg0GMFAACXMI4ZAAD4CW0TbxBYAQDA1cz7KY7pYSgQAABwG20TTxBYAQDALSSIAwAAfkLbxBNZE1iJuDjXOco5vqGo7WHJ+DniKxCz7FMZhUOpZrtMVmLZf6T3WbmePFfN8D5XnieccibQgxd18F6Jo/fKfj8RF16Xk33kOqhvgYMyuQ7+fkYcfMRzHfwdKAjpX2wg89E2qebjS9vEF5rnbvPsuXbF8l3ZT71IqWSaEvMXp+rtinDIu99TtR18ht1omzhRO2Tfxi6xnLTLSHGaqbImsAIAQLXTNlWqjTjvYogAACBb0DbxBIEVAABcQoI4AADgJ7RNvEFgBQAAtzCOGQAA+AltE08QWAEAwC00XgAAgJ/QNvEE2XMAAAAAAABSRI8VAADcwlUhAADgJ7RNPEFgBQAAt5B5HwAA+AltE09kTWAlHIqZBWl8DxyUiVkZODrNZ+ddsWX/sQ+LZVumIFwiXog5OHMiDuapjTmYA9fJ63ZL1MG5XhAurnx7KGq/DwfnX0HIyfzATo6fvYiD54o6eB8iKc9pXL3IvI+DQdsk/bK2beJh+2RDaR3bMk1zisRPtkVr2pZpmLPTk7pkqkMilbdxflb173raJrRNvJKh3xQAAAAAAADVL2t6rAAAUO0YxwwAAPyEtoknCKwAAOCWmKV9blN/LAAAgJtom2T+UKDHH39cOnfuLHXq1DHLySefLG+99VZi+5AhQyQUCiUtJ510UjqrDACA/VWhVBekHW0TAEBGoW2S+T1WDj30ULn33nvliCOOMPefffZZOffcc+Wf//ynHH300WZd3759ZcqUKYnH5OXlpa2+AABUrioBEgIrfkDbBACQWWibZHyPlXPOOUfOOussOfLII81y9913S61atWThwoWJMvn5+dKsWbPE0qBBg0r3uW/fPikqKkpaAADINB988IH5Hm3RooXp0Tlz5swDyixbtkwGDBggdevWldq1a5ten2vWrEn6zrzhhhukUaNGUrNmTVP2+++/T9rH1q1b5fLLLzf70EVvb9u2TTIVbRMAAFLzQRa3TXyTYyUajcr/+3//T3bt2mWGBMW9//770qRJE6lXr550797dBF/0fkXGjx8v48aNO2B9RCyzwN8iDqaPjfpomlUnUzB6ed45OTYxy8Hxc+EQezm9uZOpiyM+m/a6ps1Uyk6mU64djrkyLXGuo+mW7YUdPJeT+jjaj0t1DnKCOP2+POaYY+TKK6+UCy644IDt33zzjZx66qkydOhQ872oDQ9tzBQUFCTKjBw5Ul5//XWZMWOGNGzYUG6++WY5++yzZfHixRKJREyZQYMGmQbN22+/be5fffXVpgGjj8t0tE0QxLaJH9snCIa9Vq5tmba5bk0z7c1nhrYJbROvhCwrvYO6v/jiCxNI2bt3r+mt8sILL5heLOqll14y61q3bi2rVq2S22+/XUpLS02DT3uylEcjXLrEaY+Vli1byj2LTpeCWr6JI6EK/NR4cdJw8ZKTYEZBqMS2TA0nP/rDle8nN1Rqu4+IOAgMOEgEGnawnzwH9Qk7aGQ6CdA4aazWDu+1LUNgpbL3oep/B4p2xKRFu+9l+/btJs9XlfZVVGQCF71aD5eccPnfT3ZKY/vk3e8mpVQfvSr06quvynnnnZdYd8kll0hubq48//zz5T5Gn6dx48Zm+8UXX2zW/fjjj+Y7880335Q+ffqYQEyHDh1MT9KuXbuaMnpbv7e/+uoradeunWQi2iYIctvEj+0TO41ydkjQNMyxDzDYtYVyJRq4C0Od8twKrHgjaIEV2ibbA9s2SftfXX3hn3/+uTkYv/nNb2Tw4MHy5Zdfmm16MPv37y8dO3Y0XYo0se2KFSvkjTfeqHB/GnCJJ8ONLwAAeMKKVW35vyBN2aXsxQKnYrGY+a7UYbbaCNGentr4KNslVy9SlJSUSO/evRPrtOuufufOnz/f3F+wYIEJGMUbLkq77Oq6eJlMRNsEAJAxaJtkR2BFk9Fq8toTTjjBDOPRbs0PP/xwuWWbN29ueq+sXLnS83oCAOAFvSoTHzOsi343HqyNGzfKzp07TYJ4TQI/e/ZsOf/882XgwIEyb948U2b9+vXmO7h+/fpJj23atKnZFi9T3vBbXRcvk4lomwAA8B+0Tez5bmyMjkyq6Orc5s2bZe3atSbAAgCA77iQY0W/58r2tqxo6KtdjxWlM+2NGjXK3D722GNNL5PJkyebnGUVV8MyQ4viyt6uqEymo20CAAgs2iaZ32Pl1ltvlQ8//FBWr15txjPfdtttJlntpZdeaq60jR492nRD1u26XocDaXZgveoGAIDvxKyqLSIHDGdNJbCi35U5OTlmDHJZ7du3T2Te15n2iouLTWb9/Xu7aK+VeJkNGzYcsP9NmzYlymQa2iYAgIxC2yTzAyvaWNOZBXQsc8+ePeXjjz82sw6ceeaZZjYCDbbo1TYdI665V/RfDbTotEwAAPj2qlCqi4tDWU488URZvnx50nrNU6ZDalWXLl1Mcts5c+Yktq9bt06WLFki3bp1M/c1EZwmuV20aFGijH5X67p4mUxD2wQAkFFom2T+UKBnnnmmwm2FhYXyzjvveFofAACqRGMjKQ8FOrji2rPz66+/TtzX2fM0GXyDBg2kVatW8tvf/tYkgT/ttNPk9NNPNxcudIpk7QGqNH+LTsWsUyzrVMv6OO0p2qlTJ+nVq1eih4vmaBk2bJg88cQTiemWdUrmTJ0RiLYJACCj0DbJzhwr1TkNrZOpaBEAHk0h6GTqRCfnlN+mPCyx7D/2JZb99H+5VuVTCBZ4+HmLudT5LuZoSj57TqaaznVwfGrYlMn1aOo/N6crzA25816FHbzndnXO8dn0qAfr008/NQGTuJtuusn8qz08p06daobNaj4VTX47YsQIEwh5+eWX5dRTT008ZuLEiWbI0EUXXSR79uwxvUf1sdprNG769Onm8fHZgwYMGCCTJk3y9LVmKtomGcTD7/pMbZ8EzebSWrZlmuVukyAJ2lTKThSEnLTc3EHbRLK6bRKyNCNbBtOpKvWq3L2fdJeCWlkTR8poXjUEnDRcnPCy4eKkIRVxcFm8RnhflcvUdLAPJyIhd/5EFYSKXdlPbsg+6FQQKrEtUztsX5/aNs9V4CBoQmClYkU7YtKk3XdmWEvZZLFV+a7p1exqyQnnpbSP0lixvLv+SVfqA3+jbZJ5vPyuD2L7xE6jnB2SiewCK7li36aIeHihKhMDKzVCOYEKrNA2CS4iDQAAuMXMxpNiI/j/ZvIBAABwDW0TTxBYAQDAR1MaAgAAuIa2iSf80wcQAAAAAAAgYOixAgCAW7gqBAAA/IS2iScIrAAA4JaYmdOwCo8FAABwEW0TTxBYAQDAJZYVM0uqjwUAAHATbRNvZE1gJSwxiaQ6U4MPhV2agjZmuTNln1uiTqYpc2HaOSdTDDqZltjJlIdu1NdNTupcYkUclKn8z0eJZT+FYG6oVLxSbFNfleegPk7Oi1wH77ndVMpOp1Ou6rR+TkWcnOsuPZcb0xWqSKjy/URC1dTdNtWeJySvzTqZ1jbJ1PaJV20TN9snTkRsvoectAXc8lNp7aydkhnBmEo5R9z5PNA2yVwkrwUAAAAAAEhR1vRYAQCg2pleJ/RYAQAAPkHbxBMEVgAAcEssJpLqkAByrAAAALfRNvEEgRUAANzCVSEAAOAntE08QWAFAACXWLGYWCn2WGFWIAAA4DbaJt4geS0AAAAAAECK6LECAIBb6G4LAAD8hLaJJ7ImsJIbikpuKJTuavhPyLtOSzHL/vhHxL4LfdRHHa0iqc7+sZ+o+Ovc9OoYR0LuHD+/yZOobZmIC295roPPb8SlcyvsYD8Rn/2Njdokg7XbnpKYJZLqeW0aPsgmtE3S3z7JxLaJm+ennRIr4kldgmp9Sb1Kt7fM3exZXTrl7fTsuYImRzL8PKZt4omsCawAAFDtTHAk1VmBCKwAAACX0TbxROaF1wEAAAAAADxCjxUAAFxixSyxUhwKZNFjBQAAuIy2iTcIrAAA4BaTtyXVoUDVkPMFAABkN9omniCwAgCAS7gqBAAA/IS2iTcIrAAA4BauCgEAAD+hbeKJjA+sxMes791Zmu6q+JKX0wM6mdLQ0X5cqHPM8lfeZt9Nt+xgisWccOWfqZCDfcTC9mXcEnYwPKPEQZ2dHRv758qNOChjc17khtyZJtlv0y07+XS68bp27Iy5ntukVEok1VnYzWORFWib+Kd94qe2iR/bJ3ZKPMwNtScn89ryu3Ls2xSRkDvDRHfkZd5w01K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       "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
+       "<Figure size 1100x1200 with 11 Axes>"
       ]
      },
      "metadata": {},
@@ -24877,146 +29538,74 @@
     }
    ],
    "source": [
-    "fig, ax = plt.subplots()\n",
-    "ds_source[\"ALK\"].isel(z_t=0, month=0).sel(lat=slice(40, 60), lon=slice(-20, 0)).plot(\n",
-    "    ax=ax\n",
-    ")\n",
-    "ax.plot(\n",
-    "    [grid.ds.lon_rho.min() - 360, grid.ds.lon_rho.min() - 360],\n",
-    "    [grid.ds.lat_rho.min(), grid.ds.lat_rho.max()],\n",
-    "    color=\"r\",\n",
-    "    linewidth=2,\n",
-    ")\n",
-    "ax.plot(\n",
-    "    [grid.ds.lon_rho.max() - 360, grid.ds.lon_rho.max() - 360],\n",
-    "    [grid.ds.lat_rho.min(), grid.ds.lat_rho.max()],\n",
-    "    color=\"r\",\n",
-    "    linewidth=2,\n",
-    ")\n",
-    "ax.plot(\n",
-    "    [grid.ds.lon_rho.min() - 360, grid.ds.lon_rho.max() - 360],\n",
-    "    [grid.ds.lat_rho.min(), grid.ds.lat_rho.min()],\n",
-    "    color=\"r\",\n",
-    "    linewidth=2,\n",
-    ")\n",
-    "ax.plot(\n",
-    "    [grid.ds.lon_rho.min() - 360, grid.ds.lon_rho.max() - 360],\n",
-    "    [grid.ds.lat_rho.max(), grid.ds.lat_rho.max()],\n",
-    "    color=\"r\",\n",
-    "    linewidth=2,\n",
-    "    label=\"ROMS domain\",\n",
+    "from roms_tools.datasets.lat_lon_datasets import CESMBGCDataset\n",
+    "from roms_tools.setup.utils import get_target_coords\n",
+    "\n",
+    "target_coords = get_target_coords(grid)\n",
+    "source = CESMBGCDataset(\n",
+    "    filename=cesm_bgc_path,\n",
+    "    start_time=start_time,\n",
+    "    end_time=end_time,\n",
+    "    climatology=True,\n",
+    "    use_dask=True,\n",
     ")\n",
-    "ax.legend(loc=\"upper left\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7ada3994-d1da-4dce-9930-bbbc63f13b3e",
-   "metadata": {},
-   "source": [
-    "As shown in the figure above, all the boundaries of the ROMS domain lie almost entirely on land, with at most one ocean grid point per boundary. This is insufficient to generate valid regridded boundary values if the 2D horizontal fill prior to regridding is skipped, resulting in each boundary being populated entirely with NaN values. For this reason, `ROMS-Tools` raises an error if we try to use the 1D fill in this example, as seen in the next cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "1b44205e-d96d-4f29-8d81-b5e45e33bbd0",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-12-20 13:51:27 - INFO - No `boundaries` provided. Using mask-based defaults: {'south': True, 'east': False, 'north': True, 'west': True}\n"
-     ]
-    },
-    {
-     "ename": "ValueError",
-     "evalue": "ALK_south consists entirely of NaNs after regridding. This may be due to the southern boundary being on land in the CESM_REGRIDDED data, which could have a coarser resolution than the ROMS domain. Try setting `apply_2d_horizontal_fill = True` to resolve this issue.",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)",
-      "\u001b[36mFile \u001b[39m\u001b[32m<timed exec>:1\u001b[39m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m<string>:13\u001b[39m, in \u001b[36m__init__\u001b[39m\u001b[34m(self, grid, start_time, end_time, boundaries, source, type, apply_2d_horizontal_fill, model_reference_date, use_dask, bypass_validation)\u001b[39m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m/global/cfs/cdirs/m4746/Users/nora/roms-tools/roms_tools/setup/boundary_forcing.py:377\u001b[39m, in \u001b[36mBoundaryForcing.__post_init__\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m    374\u001b[39m ds = \u001b[38;5;28mself\u001b[39m._add_global_metadata(data, ds)\n\u001b[32m    376\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m.bypass_validation:\n\u001b[32m--> \u001b[39m\u001b[32m377\u001b[39m     \u001b[38;5;28mself\u001b[39m._validate(ds)\n\u001b[32m    379\u001b[39m \u001b[38;5;66;03m# substitute NaNs over land by a fill value to avoid blow-up of ROMS\u001b[39;00m\n\u001b[32m    380\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m var_name \u001b[38;5;129;01min\u001b[39;00m ds.data_vars:\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m/global/cfs/cdirs/m4746/Users/nora/roms-tools/roms_tools/setup/boundary_forcing.py:866\u001b[39m, in \u001b[36mBoundaryForcing._validate\u001b[39m\u001b[34m(self, ds)\u001b[39m\n\u001b[32m    858\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    859\u001b[39m     error_message = (\n\u001b[32m    860\u001b[39m         \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbdry_var_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m consists entirely of NaNs after regridding. \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    861\u001b[39m         \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mThis may be due to the \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdirection\u001b[38;5;132;01m}\u001b[39;00m\u001b[33mern boundary being on land in the \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    862\u001b[39m         \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m.source[\u001b[33m'\u001b[39m\u001b[33mname\u001b[39m\u001b[33m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m data, which could have a coarser resolution than the ROMS domain. \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    863\u001b[39m         \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mTry setting `apply_2d_horizontal_fill = True` to resolve this issue.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    864\u001b[39m     )\n\u001b[32m--> \u001b[39m\u001b[32m866\u001b[39m nan_check(\n\u001b[32m    867\u001b[39m     ds[bdry_var_name].isel(bry_time=\u001b[32m0\u001b[39m),\n\u001b[32m    868\u001b[39m     mask.isel(**\u001b[38;5;28mself\u001b[39m.bdry_coords[location][direction]),\n\u001b[32m    869\u001b[39m     error_message=error_message,\n\u001b[32m    870\u001b[39m )\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m/global/cfs/cdirs/m4746/Users/nora/roms-tools/roms_tools/setup/utils.py:107\u001b[39m, in \u001b[36mnan_check\u001b[39m\u001b[34m(field, mask, error_message)\u001b[39m\n\u001b[32m    105\u001b[39m \u001b[38;5;66;03m# Check if any NaN values exist in the modified field\u001b[39;00m\n\u001b[32m    106\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m da.isnull().any().values:\n\u001b[32m--> \u001b[39m\u001b[32m107\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(error_message)\n",
-      "\u001b[31mValueError\u001b[39m: ALK_south consists entirely of NaNs after regridding. This may be due to the southern boundary being on land in the CESM_REGRIDDED data, which could have a coarser resolution than the ROMS domain. Try setting `apply_2d_horizontal_fill = True` to resolve this issue."
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
     "\n",
-    "bgc_boundary_forcing_with_1d_fill = BoundaryForcing(\n",
-    "    **kwargs, apply_2d_horizontal_fill=False\n",
-    ")"
+    "lon_name = source.dim_names[\"longitude\"]\n",
+    "lat_name = source.dim_names[\"latitude\"]\n",
+    "\n",
+    "methods = [None, \"2d_lateral_fill\", \"inverse_dist\", \"nearest_s2d\", \"nearest_neighbor\"]\n",
+    "fig, axs = plt.subplots(3, 2, figsize=(11, 12), constrained_layout=True)\n",
+    "\n",
+    "for method, ax in zip(methods, axs.ravel()):\n",
+    "    # A fresh copy per method so each fill starts from the raw (NaN) source.\n",
+    "    sub = source.choose_subdomain(\n",
+    "        target_coords, return_copy=True, unchunk_lateral_dims=True\n",
+    "    )\n",
+    "    sub.convert_to_float64()\n",
+    "    if method is not None:\n",
+    "        sub.extrapolate_deepest_to_bottom()   # fill deep NaNs down each column first, necessary for 2D lateral fill to run\n",
+    "        sub.apply_prefill(method)\n",
+    "\n",
+    "    field = sub.ds[\"ALK\"]\n",
+    "    # reduce to a single (lat, lon) surface slice for plotting\n",
+    "    extra_dims = [d for d in field.dims if d not in (lon_name, lat_name)]\n",
+    "    field.isel({d: 0 for d in extra_dims}).plot(ax=ax, x=lon_name, y=lat_name)\n",
+    "    ax.set_title(f\"prefill='{method}'\")\n",
+    "axs[-1,-1].axis('off')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "8759d843-5ddc-4ca5-901b-f2403d23e2e0",
+   "id": "af73c2b2",
    "metadata": {},
    "source": [
-    "Let's try the 2D horizontal fill instead, as suggested in the error message."
+    "The AMG (`2d_lateral_fill`) and inverse-distance fills produce smooth fields,\n",
+    "while the two nearest-style fills are blockier. For `inverse_dist` you can trade\n",
+    "smoothness against locality via `prefill_kwargs` (more source points and a smaller\n",
+    "distance exponent give a smoother fill):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
-   "id": "82e46670-2c58-470f-a229-3707be8c8dbc",
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": 47,
+   "id": "76449250",
+   "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-12-20 13:52:09 - INFO - No `boundaries` provided. Using mask-based defaults: {'south': True, 'east': False, 'north': True, 'west': True}\n"
-     ]
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x14648d816d50>"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 25.3 s, sys: 1.28 s, total: 26.6 s\n",
-      "Wall time: 39.8 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "\n",
-    "bgc_boundary_forcing_with_2d_fill = BoundaryForcing(\n",
-    "    **kwargs, apply_2d_horizontal_fill=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5a0e7404-a4ea-4aa4-8d39-0fa77b140025",
-   "metadata": {},
-   "source": [
-    "This worked, and the boundary forcing has reasonable values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "672fa99b-f3a8-4142-80ac-2ded47b1bf2b",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 2000x400 with 6 Axes>"
+       "<Figure size 640x480 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -25024,20 +29613,42 @@
     }
    ],
    "source": [
-    "fig, axs = plt.subplots(1, 3, figsize=(20, 4))\n",
+    "sub = source.choose_subdomain(\n",
+    "    target_coords, return_copy=True, unchunk_lateral_dims=True\n",
+    ")\n",
+    "sub.convert_to_float64()\n",
+    "sub.apply_prefill(\n",
+    "    \"inverse_dist\", prefill_kwargs={\"num_src_pnts\": 16, \"dist_exponent\": 1.0}\n",
+    ")\n",
     "\n",
-    "bgc_boundary_forcing_with_2d_fill.plot(f\"ALK_north\", layer_contours=True, ax=axs[0])\n",
-    "bgc_boundary_forcing_with_2d_fill.plot(f\"ALK_west\", layer_contours=True, ax=axs[1])\n",
-    "bgc_boundary_forcing_with_2d_fill.plot(f\"ALK_south\", layer_contours=True, ax=axs[2])"
+    "field = sub.ds[\"ALK\"]\n",
+    "extra_dims = [d for d in field.dims if d not in (lon_name, lat_name)]\n",
+    "field.isel({d: 0 for d in extra_dims}).plot(x=lon_name, y=lat_name)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "675a92b6-28b2-4094-8ac1-a267ac7bd7ba",
+   "cell_type": "markdown",
+   "id": "de1b6b21",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "To use a pre-fill when building the boundary forcing itself, pass `prefill`\n",
+    "(and optionally `prefill_kwargs` and `regrid_method`) to `BoundaryForcing`:\n",
+    "\n",
+    "```python\n",
+    "bf = BoundaryForcing(\n",
+    "    grid=grid,\n",
+    "    start_time=start_time,\n",
+    "    end_time=end_time,\n",
+    "    source={\"name\": \"CESM_REGRIDDED\", \"path\": cesm_bgc_path, \"climatology\": True},\n",
+    "    type=\"bgc\",\n",
+    "    prefill=\"inverse_dist\",\n",
+    "    prefill_kwargs={\"num_src_pnts\": 16},\n",
+    "    use_dask=True,\n",
+    ")\n",
+    "```\n",
+    "\n",
+    "The legacy `apply_2d_horizontal_fill=True` is now equivalent to `prefill=\"2d_lateral_fill\", regrid_method=\"scipy\"`)."
+   ]
   }
  ],
  "metadata": {
@@ -25056,7 +29667,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.5"
+   "version": "3.13.13"
   }
  },
  "nbformat": 4,
diff --git a/docs/end_to_end.ipynb b/docs/end_to_end.ipynb
index 857c95224..7d7e67161 100644
--- a/docs/end_to_end.ipynb
+++ b/docs/end_to_end.ipynb
@@ -141,12 +141,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "f66a0566-f8a5-49ea-876c-6126f1e5c3f9",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    }
+   ],
    "source": [
     "from roms_tools import Grid"
    ]
@@ -161,7 +169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "93596aca-7ee2-4757-8383-bc09267eec6e",
    "metadata": {
     "tags": []
@@ -171,8 +179,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 1.12 s, sys: 36.3 ms, total: 1.16 s\n",
-      "Wall time: 1.18 s\n"
+      "CPU times: user 959 ms, sys: 62.2 ms, total: 1.02 s\n",
+      "Wall time: 1.12 s\n"
      ]
     }
    ],
@@ -215,7 +223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "d37ffa51-a38f-403e-9410-f024b5f835cb",
    "metadata": {},
    "outputs": [
@@ -688,25 +696,25 @@
        "    mask_rho      (eta_rho, xi_rho) int32 33kB 0 0 0 0 0 0 0 0 ... 1 1 1 1 1 1 1\n",
        "    ...            ...\n",
        "    mask_coarse   (eta_coarse, xi_coarse) int32 9kB 0 0 0 0 0 0 ... 1 1 1 1 1 1\n",
-       "    h             (eta_rho, xi_rho) float64 67kB 5.0 5.0 5.0 ... 803.4 803.4\n",
+       "    h             (eta_rho, xi_rho) float64 67kB 5.0 5.0 5.0 ... 807.4 807.4\n",
        "    sigma_r       (s_rho) float32 80B -0.975 -0.925 -0.875 ... -0.075 -0.025\n",
        "    Cs_r          (s_rho) float32 80B -0.9579 -0.8615 ... -0.002245 -0.0002471\n",
        "    sigma_w       (s_w) float32 84B -1.0 -0.95 -0.9 -0.85 ... -0.1 -0.05 0.0\n",
        "    Cs_w          (s_w) float32 84B -1.0 -0.9114 -0.809 ... -0.0009921 0.0\n",
-       "Attributes: (12/13)\n",
+       "Attributes: (12/14)\n",
        "    title:                   ROMS grid created by ROMS-Tools\n",
-       "    roms_tools_version:      3.1.3.dev32+g91d580e67.d20250929\n",
+       "    roms_tools_version:      10000.dev364+g785fa77b7.d20260612\n",
        "    size_x:                  2000\n",
        "    size_y:                  1600\n",
        "    center_lon:              -89\n",
        "    center_lat:              24\n",
        "    ...                      ...\n",
-       "    straddle:                False\n",
+       "    close_narrow_channels:   False\n",
        "    topography_source_name:  ETOPO5\n",
        "    hmin:                    5.0\n",
        "    theta_s:                 5.0\n",
        "    theta_b:                 2.0\n",
-       "    hc:                      300.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-638bc058-f9cd-402a-93f8-6cc369bffd87' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-638bc058-f9cd-402a-93f8-6cc369bffd87' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>eta_rho</span>: 82</li><li><span>xi_rho</span>: 102</li><li><span>xi_u</span>: 101</li><li><span>eta_v</span>: 81</li><li><span>eta_coarse</span>: 42</li><li><span>xi_coarse</span>: 52</li><li><span>s_rho</span>: 20</li><li><span>s_w</span>: 21</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-17d73f38-28dd-4782-82ff-80f2d47a1032' class='xr-section-summary-in' type='checkbox'  checked><label for='section-17d73f38-28dd-4782-82ff-80f2d47a1032' class='xr-section-summary' >Coordinates: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat_rho</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>16.43 16.44 16.46 ... 30.94 30.93</div><input id='attrs-c6d63ff7-2409-49fe-b075-2938a8a69379' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c6d63ff7-2409-49fe-b075-2938a8a69379' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3de55346-94f5-4fe7-aeb0-cc69b478e1b6' class='xr-var-data-in' type='checkbox'><label for='data-3de55346-94f5-4fe7-aeb0-cc69b478e1b6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of rho-points</dd><dt><span>units :</span></dt><dd>degrees North</dd></dl></div><div class='xr-var-data'><pre>array([[16.43294119, 16.44476749, 16.45635985, ..., 16.45635985,\n",
+       "    hc:                      300.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-0267a2c3-f999-4c18-82ff-4ea66ad79446' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0267a2c3-f999-4c18-82ff-4ea66ad79446' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>eta_rho</span>: 82</li><li><span>xi_rho</span>: 102</li><li><span>xi_u</span>: 101</li><li><span>eta_v</span>: 81</li><li><span>eta_coarse</span>: 42</li><li><span>xi_coarse</span>: 52</li><li><span>s_rho</span>: 20</li><li><span>s_w</span>: 21</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9b507b6b-8ee9-4a71-8eea-12b23485d89c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9b507b6b-8ee9-4a71-8eea-12b23485d89c' class='xr-section-summary' >Coordinates: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat_rho</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>16.43 16.44 16.46 ... 30.94 30.93</div><input id='attrs-fcf82b5c-34f7-4547-a3a1-cd0d37e2df27' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fcf82b5c-34f7-4547-a3a1-cd0d37e2df27' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59bcbcd5-a4ed-4e15-a0f4-3e6d7d78173d' class='xr-var-data-in' type='checkbox'><label for='data-59bcbcd5-a4ed-4e15-a0f4-3e6d7d78173d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of rho-points</dd><dt><span>units :</span></dt><dd>degrees North</dd></dl></div><div class='xr-var-data'><pre>array([[16.43294119, 16.44476749, 16.45635985, ..., 16.45635985,\n",
        "        16.44476749, 16.43294119],\n",
        "       [16.61098786, 16.62282971, 16.63443732, ..., 16.63443732,\n",
        "        16.62282971, 16.61098786],\n",
@@ -718,7 +726,7 @@
        "       [30.74818115, 30.76138547, 30.77432957, ..., 30.77432957,\n",
        "        30.76138547, 30.74818115],\n",
        "       [30.92612883, 30.93935248, 30.95231554, ..., 30.95231554,\n",
-       "        30.93935248, 30.92612883]], shape=(82, 102))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_rho</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>261.6 261.8 262.0 ... 281.3 281.5</div><input id='attrs-3274fda1-c73f-4a34-b74a-3898f625d3ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3274fda1-c73f-4a34-b74a-3898f625d3ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7623e593-b2b1-4b77-88d4-7d9b52c6bb64' class='xr-var-data-in' type='checkbox'><label for='data-7623e593-b2b1-4b77-88d4-7d9b52c6bb64' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of rho-points</dd><dt><span>units :</span></dt><dd>degrees East</dd></dl></div><div class='xr-var-data'><pre>array([[261.60373122, 261.78933767, 261.97497137, ..., 280.02502863,\n",
+       "        30.93935248, 30.92612883]], shape=(82, 102))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_rho</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>261.6 261.8 262.0 ... 281.3 281.5</div><input id='attrs-1d5d3fea-cf9f-428b-ae94-2ac7e79ab7bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1d5d3fea-cf9f-428b-ae94-2ac7e79ab7bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59fd7234-b3d4-44d5-8b0a-04ba2e5dc67e' class='xr-var-data-in' type='checkbox'><label for='data-59fd7234-b3d4-44d5-8b0a-04ba2e5dc67e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of rho-points</dd><dt><span>units :</span></dt><dd>degrees East</dd></dl></div><div class='xr-var-data'><pre>array([[261.60373122, 261.78933767, 261.97497137, ..., 280.02502863,\n",
        "        280.21066233, 280.39626878],\n",
        "       [261.59126541, 261.77711521, 261.96299245, ..., 280.03700755,\n",
        "        280.22288479, 280.40873459],\n",
@@ -730,7 +738,7 @@
        "       [260.49750547, 260.70462681, 260.91179852, ..., 281.08820148,\n",
        "        281.29537319, 281.50249453],\n",
        "       [260.48196421, 260.68938659, 260.89685975, ..., 281.10314025,\n",
-       "        281.31061341, 281.51803579]], shape=(82, 102))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_u</span></div><div class='xr-var-dims'>(eta_rho, xi_u)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>16.44 16.45 16.46 ... 30.95 30.93</div><input id='attrs-08c562d2-d636-429d-8699-8cd67e58c983' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08c562d2-d636-429d-8699-8cd67e58c983' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21cb6762-8ace-4279-8641-1d6da65839fc' class='xr-var-data-in' type='checkbox'><label for='data-21cb6762-8ace-4279-8641-1d6da65839fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of u-points</dd><dt><span>units :</span></dt><dd>degrees North</dd></dl></div><div class='xr-var-data'><pre>array([[16.43888357, 16.45059292, 16.46206825, ..., 16.46206825,\n",
+       "        281.31061341, 281.51803579]], shape=(82, 102))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_u</span></div><div class='xr-var-dims'>(eta_rho, xi_u)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>16.44 16.45 16.46 ... 30.95 30.93</div><input id='attrs-e1c0900a-0c36-4948-ac18-44b693c7099e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e1c0900a-0c36-4948-ac18-44b693c7099e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4facdf13-6e59-46b2-bb91-a73b84e2ec1c' class='xr-var-data-in' type='checkbox'><label for='data-4facdf13-6e59-46b2-bb91-a73b84e2ec1c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of u-points</dd><dt><span>units :</span></dt><dd>degrees North</dd></dl></div><div class='xr-var-data'><pre>array([[16.43888357, 16.45059292, 16.46206825, ..., 16.46206825,\n",
        "        16.45059292, 16.43888357],\n",
        "       [16.61693806, 16.62866281, 16.64015324, ..., 16.64015324,\n",
        "        16.62866281, 16.61693806],\n",
@@ -742,7 +750,7 @@
        "       [30.75481582, 30.76789006, 30.78070396, ..., 30.78070396,\n",
        "        30.76789006, 30.75481582],\n",
        "       [30.93277322, 30.9458666 , 30.95869928, ..., 30.95869928,\n",
-       "        30.9458666 , 30.93277322]], shape=(82, 101))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_u</span></div><div class='xr-var-dims'>(eta_rho, xi_u)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>261.7 261.9 262.1 ... 281.2 281.4</div><input id='attrs-6fa87d0c-1372-4bc4-977d-a56cdf43cb18' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6fa87d0c-1372-4bc4-977d-a56cdf43cb18' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f75f3d25-7bf3-4610-9499-0f868df8ff62' class='xr-var-data-in' type='checkbox'><label for='data-f75f3d25-7bf3-4610-9499-0f868df8ff62' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of u-points</dd><dt><span>units :</span></dt><dd>degrees East</dd></dl></div><div class='xr-var-data'><pre>array([[261.69653101, 261.88215115, 262.06779827, ..., 279.93220173,\n",
+       "        30.9458666 , 30.93277322]], shape=(82, 101))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_u</span></div><div class='xr-var-dims'>(eta_rho, xi_u)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>261.7 261.9 262.1 ... 281.2 281.4</div><input id='attrs-bbef0fe5-a790-4a9e-9a1c-89f61e5b2773' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bbef0fe5-a790-4a9e-9a1c-89f61e5b2773' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e89ae3d-851e-440f-b7bf-5c351d2d8e9d' class='xr-var-data-in' type='checkbox'><label for='data-4e89ae3d-851e-440f-b7bf-5c351d2d8e9d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of u-points</dd><dt><span>units :</span></dt><dd>degrees East</dd></dl></div><div class='xr-var-data'><pre>array([[261.69653101, 261.88215115, 262.06779827, ..., 279.93220173,\n",
        "        280.11784885, 280.30346899],\n",
        "       [261.68418684, 261.87005043, 262.0559412 , ..., 279.9440588 ,\n",
        "        280.12994957, 280.31581316],\n",
@@ -754,7 +762,7 @@
        "       [260.60105978, 260.80820642, 261.01540297, ..., 280.98459703,\n",
        "        281.19179358, 281.39894022],\n",
        "       [260.585669  , 260.79311688, 261.00061506, ..., 280.99938494,\n",
-       "        281.20688312, 281.414331  ]], shape=(82, 101))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_v</span></div><div class='xr-var-dims'>(eta_v, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>16.52 16.53 16.55 ... 30.85 30.84</div><input id='attrs-1c248ff0-eb88-444a-8846-c6751bf931e9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c248ff0-eb88-444a-8846-c6751bf931e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-552b6854-84ee-4454-8cd7-b2e75a43ffa8' class='xr-var-data-in' type='checkbox'><label for='data-552b6854-84ee-4454-8cd7-b2e75a43ffa8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of v-points</dd><dt><span>units :</span></dt><dd>degrees North</dd></dl></div><div class='xr-var-data'><pre>array([[16.52196462, 16.53379869, 16.54539867, ..., 16.54539867,\n",
+       "        281.20688312, 281.414331  ]], shape=(82, 101))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_v</span></div><div class='xr-var-dims'>(eta_v, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>16.52 16.53 16.55 ... 30.85 30.84</div><input id='attrs-af6f216b-e39c-40c6-8f6b-0f22431d2fca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-af6f216b-e39c-40c6-8f6b-0f22431d2fca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3f1fc5e-4b24-4867-b305-75942987aa66' class='xr-var-data-in' type='checkbox'><label for='data-e3f1fc5e-4b24-4867-b305-75942987aa66' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of v-points</dd><dt><span>units :</span></dt><dd>degrees North</dd></dl></div><div class='xr-var-data'><pre>array([[16.52196462, 16.53379869, 16.54539867, ..., 16.54539867,\n",
        "        16.53379869, 16.52196462],\n",
        "       [16.7000454 , 16.71189504, 16.72351029, ..., 16.72351029,\n",
        "        16.71189504, 16.7000454 ],\n",
@@ -766,7 +774,7 @@
        "       [30.65917215, 30.67236683, 30.68530147, ..., 30.68530147,\n",
        "        30.67236683, 30.65917215],\n",
        "       [30.83715522, 30.8503692 , 30.86332277, ..., 30.86332277,\n",
-       "        30.8503692 , 30.83715522]], shape=(81, 102))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_v</span></div><div class='xr-var-dims'>(eta_v, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>261.6 261.8 262.0 ... 281.3 281.5</div><input id='attrs-3036ab51-babf-44cb-8472-cb04188f92fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3036ab51-babf-44cb-8472-cb04188f92fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d18d75ce-8139-449e-9847-506700ae37c6' class='xr-var-data-in' type='checkbox'><label for='data-d18d75ce-8139-449e-9847-506700ae37c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of v-points</dd><dt><span>units :</span></dt><dd>degrees East</dd></dl></div><div class='xr-var-data'><pre>array([[261.59750119, 261.78322926, 261.96898467, ..., 280.03101533,\n",
+       "        30.8503692 , 30.83715522]], shape=(81, 102))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_v</span></div><div class='xr-var-dims'>(eta_v, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>261.6 261.8 262.0 ... 281.3 281.5</div><input id='attrs-24b14456-d79b-4d71-b289-7e80b60bad96' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-24b14456-d79b-4d71-b289-7e80b60bad96' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2998a5e5-7505-4be7-8f8a-f5befaf5a275' class='xr-var-data-in' type='checkbox'><label for='data-2998a5e5-7505-4be7-8f8a-f5befaf5a275' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of v-points</dd><dt><span>units :</span></dt><dd>degrees East</dd></dl></div><div class='xr-var-data'><pre>array([[261.59750119, 261.78322926, 261.96898467, ..., 280.03101533,\n",
        "        280.21677074, 280.40249881],\n",
        "       [261.58502143, 261.77099311, 261.95699233, ..., 280.04300767,\n",
        "        280.22900689, 280.41497857],\n",
@@ -778,7 +786,7 @@
        "       [260.50525756, 260.71222872, 260.91925006, ..., 281.08074994,\n",
        "        281.28777128, 281.49474244],\n",
        "       [260.48974204, 260.69701377, 260.90433607, ..., 281.09566393,\n",
-       "        281.30298623, 281.51025796]], shape=(81, 102))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_coarse</span></div><div class='xr-var-dims'>(eta_coarse, xi_coarse)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>16.34 16.36 16.38 ... 31.03 31.01</div><input id='attrs-4bf3d828-53ef-4d78-a945-b551b1620024' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4bf3d828-53ef-4d78-a945-b551b1620024' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c82354b1-6617-4832-8f23-3697d7ac957b' class='xr-var-data-in' type='checkbox'><label for='data-c82354b1-6617-4832-8f23-3697d7ac957b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of rho-points on coarsened grid</dd><dt><span>units :</span></dt><dd>degrees North</dd></dl></div><div class='xr-var-data'><pre>array([[16.3380086 , 16.36152874, 16.38423024, ..., 16.38423024,\n",
+       "        281.30298623, 281.51025796]], shape=(81, 102))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_coarse</span></div><div class='xr-var-dims'>(eta_coarse, xi_coarse)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>16.34 16.36 16.38 ... 31.03 31.01</div><input id='attrs-f385baf7-35ba-4b21-bbcb-13d5d9a5442c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f385baf7-35ba-4b21-bbcb-13d5d9a5442c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87385d4a-29fc-4938-8544-a026ec381acc' class='xr-var-data-in' type='checkbox'><label for='data-87385d4a-29fc-4938-8544-a026ec381acc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of rho-points on coarsened grid</dd><dt><span>units :</span></dt><dd>degrees North</dd></dl></div><div class='xr-var-data'><pre>array([[16.3380086 , 16.36152874, 16.38423024, ..., 16.38423024,\n",
        "        16.36152874, 16.3380086 ],\n",
        "       [16.69412048, 16.71770257, 16.74046392, ..., 16.74046392,\n",
        "        16.71770257, 16.69412048],\n",
@@ -790,7 +798,7 @@
        "       [30.65257458, 30.67883393, 30.70418258, ..., 30.70418258,\n",
        "        30.67883393, 30.65257458],\n",
        "       [31.00848602, 31.03482226, 31.06024522, ..., 31.06024522,\n",
-       "        31.03482226, 31.00848602]], shape=(42, 52))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_coarse</span></div><div class='xr-var-dims'>(eta_coarse, xi_coarse)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>261.5 261.9 262.3 ... 281.2 281.6</div><input id='attrs-57bd105a-2720-443b-91c9-8aed91b06f47' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-57bd105a-2720-443b-91c9-8aed91b06f47' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4239f513-eda8-48d0-9b97-dc998f7daa1f' class='xr-var-data-in' type='checkbox'><label for='data-4239f513-eda8-48d0-9b97-dc998f7daa1f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of rho-points on coarsened grid</dd><dt><span>units :</span></dt><dd>degrees East</dd></dl></div><div class='xr-var-data'><pre>array([[261.51722173, 261.88820487, 262.25928167, ..., 279.74071833,\n",
+       "        31.03482226, 31.00848602]], shape=(42, 52))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_coarse</span></div><div class='xr-var-dims'>(eta_coarse, xi_coarse)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>261.5 261.9 262.3 ... 281.2 281.6</div><input id='attrs-a9ad6793-e9e5-4c83-9083-fe4f399e1a45' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a9ad6793-e9e5-4c83-9083-fe4f399e1a45' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87862e58-fe88-4d5a-b7be-f442ae6f376e' class='xr-var-data-in' type='checkbox'><label for='data-87862e58-fe88-4d5a-b7be-f442ae6f376e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of rho-points on coarsened grid</dd><dt><span>units :</span></dt><dd>degrees East</dd></dl></div><div class='xr-var-data'><pre>array([[261.51722173, 261.88820487, 262.25928167, ..., 279.74071833,\n",
        "        280.11179513, 280.48277827],\n",
        "       [261.49203265, 261.86398989, 262.23604223, ..., 279.76395777,\n",
        "        280.13601011, 280.50796735],\n",
@@ -802,7 +810,7 @@
        "       [260.40176478, 260.81573246, 261.22987337, ..., 280.77012663,\n",
        "        281.18426754, 281.59823522],\n",
        "       [260.37040713, 260.78557842, 261.20092568, ..., 280.79907432,\n",
-       "        281.21442158, 281.62959287]], shape=(42, 52))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ba94192a-837a-4979-b375-4e7082dcba3b' class='xr-section-summary-in' type='checkbox'  ><label for='section-ba94192a-837a-4979-b375-4e7082dcba3b' class='xr-section-summary' >Data variables: <span>(15)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>angle</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.06568 0.06568 ... -0.07345</div><input id='attrs-a51d6f7b-cf19-48c7-b286-a6e377f297f3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a51d6f7b-cf19-48c7-b286-a6e377f297f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8a9e958-8f47-47d5-93f5-0ca2c19c98ab' class='xr-var-data-in' type='checkbox'><label for='data-e8a9e958-8f47-47d5-93f5-0ca2c19c98ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Angle between xi axis and east</dd><dt><span>units :</span></dt><dd>radians</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.06567826,  0.06567826,  0.06436381, ..., -0.06436381,\n",
+       "        281.21442158, 281.62959287]], shape=(42, 52))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-32b07a51-14db-43e7-ac0e-2f3e374cf1ca' class='xr-section-summary-in' type='checkbox'  ><label for='section-32b07a51-14db-43e7-ac0e-2f3e374cf1ca' class='xr-section-summary' >Data variables: <span>(15)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>angle</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.06568 0.06568 ... -0.07345</div><input id='attrs-a4cc9ad8-d09b-445c-a94f-8ddffad37da9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a4cc9ad8-d09b-445c-a94f-8ddffad37da9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02c3fe47-ac85-41e5-922a-3f6ec6c01085' class='xr-var-data-in' type='checkbox'><label for='data-02c3fe47-ac85-41e5-922a-3f6ec6c01085' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Angle between xi axis and east</dd><dt><span>units :</span></dt><dd>radians</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.06567826,  0.06567826,  0.06436381, ..., -0.06436381,\n",
        "        -0.06567826, -0.06567826],\n",
        "       [ 0.06573897,  0.06573897,  0.06442335, ..., -0.06442335,\n",
        "        -0.06573897, -0.06573897],\n",
@@ -814,7 +822,7 @@
        "       [ 0.07331822,  0.07331822,  0.07185575, ..., -0.07185575,\n",
        "        -0.07331822, -0.07331822],\n",
        "       [ 0.07345462,  0.07345462,  0.0719895 , ..., -0.0719895 ,\n",
-       "        -0.07345462, -0.07345462]], shape=(82, 102))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>f</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.115e-05 4.117e-05 ... 7.475e-05</div><input id='attrs-3fd6dcd6-eb73-4c99-805a-6bf764f9f652' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3fd6dcd6-eb73-4c99-805a-6bf764f9f652' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7684d380-49d4-4c30-a770-34de38ca73de' class='xr-var-data-in' type='checkbox'><label for='data-7684d380-49d4-4c30-a770-34de38ca73de' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Coriolis parameter at rho-points</dd><dt><span>units :</span></dt><dd>second-1</dd></dl></div><div class='xr-var-data'><pre>array([[4.11451118e-05, 4.11739055e-05, 4.12021278e-05, ...,\n",
+       "        -0.07345462, -0.07345462]], shape=(82, 102))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>f</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.115e-05 4.117e-05 ... 7.475e-05</div><input id='attrs-835da051-a93b-43ed-a169-dd0b6c547293' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-835da051-a93b-43ed-a169-dd0b6c547293' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35b852cd-0f2c-44e2-98fe-04ad118bcedc' class='xr-var-data-in' type='checkbox'><label for='data-35b852cd-0f2c-44e2-98fe-04ad118bcedc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Coriolis parameter at rho-points</dd><dt><span>units :</span></dt><dd>second-1</dd></dl></div><div class='xr-var-data'><pre>array([[4.11451118e-05, 4.11739055e-05, 4.12021278e-05, ...,\n",
        "        4.12021278e-05, 4.11739055e-05, 4.11451118e-05],\n",
        "       [4.15784179e-05, 4.16072228e-05, 4.16354562e-05, ...,\n",
        "        4.16354562e-05, 4.16072228e-05, 4.15784179e-05],\n",
@@ -826,7 +834,7 @@
        "       [7.43605969e-05, 7.43894019e-05, 7.44176353e-05, ...,\n",
        "        7.44176353e-05, 7.43894019e-05, 7.43605969e-05],\n",
        "       [7.47484530e-05, 7.47772466e-05, 7.48054689e-05, ...,\n",
-       "        7.48054689e-05, 7.47772466e-05, 7.47484530e-05]], shape=(82, 102))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pm</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.038e-05 5.038e-05 ... 5.038e-05</div><input id='attrs-5fa8a0b1-6998-45a8-a7c9-60a7cd1253ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5fa8a0b1-6998-45a8-a7c9-60a7cd1253ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea08eb9d-4135-48c0-b09c-fa1ec429dd3c' class='xr-var-data-in' type='checkbox'><label for='data-ea08eb9d-4135-48c0-b09c-fa1ec429dd3c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Curvilinear coordinate metric in xi-direction</dd><dt><span>units :</span></dt><dd>meter-1</dd></dl></div><div class='xr-var-data'><pre>array([[5.03847884e-05, 5.03847884e-05, 5.03847884e-05, ...,\n",
+       "        7.48054689e-05, 7.47772466e-05, 7.47484530e-05]], shape=(82, 102))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pm</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.038e-05 5.038e-05 ... 5.038e-05</div><input id='attrs-ab67b04b-07b4-4ff6-aa63-ebeb95feae98' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab67b04b-07b4-4ff6-aa63-ebeb95feae98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d79872e-4cd5-4f5d-ae13-561363f6a1fb' class='xr-var-data-in' type='checkbox'><label for='data-1d79872e-4cd5-4f5d-ae13-561363f6a1fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Curvilinear coordinate metric in xi-direction</dd><dt><span>units :</span></dt><dd>meter-1</dd></dl></div><div class='xr-var-data'><pre>array([[5.03847884e-05, 5.03847884e-05, 5.03847884e-05, ...,\n",
        "        5.03847884e-05, 5.03847884e-05, 5.03847884e-05],\n",
        "       [5.03847884e-05, 5.03847884e-05, 5.03847884e-05, ...,\n",
        "        5.03847884e-05, 5.03847884e-05, 5.03847884e-05],\n",
@@ -838,7 +846,7 @@
        "       [5.03847884e-05, 5.03847884e-05, 5.03847884e-05, ...,\n",
        "        5.03847884e-05, 5.03847884e-05, 5.03847884e-05],\n",
        "       [5.03847884e-05, 5.03847884e-05, 5.03847884e-05, ...,\n",
-       "        5.03847884e-05, 5.03847884e-05, 5.03847884e-05]], shape=(82, 102))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pn</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.04e-05 5.04e-05 ... 5.04e-05</div><input id='attrs-b47d0c1f-3ca9-46a2-bd36-8159862d97c7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b47d0c1f-3ca9-46a2-bd36-8159862d97c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2dc8be85-878e-4677-85a5-f8700e6d4f48' class='xr-var-data-in' type='checkbox'><label for='data-2dc8be85-878e-4677-85a5-f8700e6d4f48' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Curvilinear coordinate metric in eta-direction</dd><dt><span>units :</span></dt><dd>meter-1</dd></dl></div><div class='xr-var-data'><pre>array([[5.04046128e-05, 5.04046128e-05, 5.04046128e-05, ...,\n",
+       "        5.03847884e-05, 5.03847884e-05, 5.03847884e-05]], shape=(82, 102))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pn</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.04e-05 5.04e-05 ... 5.04e-05</div><input id='attrs-d1ed7a58-1874-4fd8-b0a6-ed32d8fbb1c6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d1ed7a58-1874-4fd8-b0a6-ed32d8fbb1c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae31bace-d4c7-4d52-95b9-4a2035863eaf' class='xr-var-data-in' type='checkbox'><label for='data-ae31bace-d4c7-4d52-95b9-4a2035863eaf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Curvilinear coordinate metric in eta-direction</dd><dt><span>units :</span></dt><dd>meter-1</dd></dl></div><div class='xr-var-data'><pre>array([[5.04046128e-05, 5.04046128e-05, 5.04046128e-05, ...,\n",
        "        5.04046128e-05, 5.04046128e-05, 5.04046128e-05],\n",
        "       [5.03848533e-05, 5.03848533e-05, 5.03848533e-05, ...,\n",
        "        5.03848533e-05, 5.03848533e-05, 5.03848533e-05],\n",
@@ -850,25 +858,25 @@
        "       [5.03848533e-05, 5.03848533e-05, 5.03848533e-05, ...,\n",
        "        5.03848533e-05, 5.03848533e-05, 5.03848533e-05],\n",
        "       [5.04046128e-05, 5.04046128e-05, 5.04046128e-05, ...,\n",
-       "        5.04046128e-05, 5.04046128e-05, 5.04046128e-05]], shape=(82, 102))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spherical</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>|S1</div><div class='xr-var-preview xr-preview'>b&#x27;T&#x27;</div><input id='attrs-66ce3175-f453-4bda-81ac-8fd52d033a6a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66ce3175-f453-4bda-81ac-8fd52d033a6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec588cc4-0719-40b1-b40f-68e52738a154' class='xr-var-data-in' type='checkbox'><label for='data-ec588cc4-0719-40b1-b40f-68e52738a154' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>Long_name :</span></dt><dd>Grid type logical switch</dd><dt><span>option_T :</span></dt><dd>spherical</dd></dl></div><div class='xr-var-data'><pre>array(b&#x27;T&#x27;, dtype=&#x27;|S1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mask_rho</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 1 1 1 1 1 1 1 1</div><input id='attrs-87fda0c8-4749-4417-b6a0-c0926f2f0454' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-87fda0c8-4749-4417-b6a0-c0926f2f0454' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18ccec86-e779-4142-a535-62af848f53b4' class='xr-var-data-in' type='checkbox'><label for='data-18ccec86-e779-4142-a535-62af848f53b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mask at rho-points</dd><dt><span>units :</span></dt><dd>land/water (0/1)</dd></dl></div><div class='xr-var-data'><pre>array([[0, 0, 0, ..., 1, 1, 1],\n",
+       "        5.04046128e-05, 5.04046128e-05, 5.04046128e-05]], shape=(82, 102))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spherical</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>|S1</div><div class='xr-var-preview xr-preview'>b&#x27;T&#x27;</div><input id='attrs-ebba9649-0978-49c2-a8a1-845acaa2aa6b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ebba9649-0978-49c2-a8a1-845acaa2aa6b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76e322b2-c6c6-474c-ac08-a7e81e6fd08a' class='xr-var-data-in' type='checkbox'><label for='data-76e322b2-c6c6-474c-ac08-a7e81e6fd08a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>Long_name :</span></dt><dd>Grid type logical switch</dd><dt><span>option_T :</span></dt><dd>spherical</dd></dl></div><div class='xr-var-data'><pre>array(b&#x27;T&#x27;, dtype=&#x27;|S1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mask_rho</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 1 1 1 1 1 1 1 1</div><input id='attrs-23836bdd-ef8b-47a0-a8fd-f2d4a8598edc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-23836bdd-ef8b-47a0-a8fd-f2d4a8598edc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a305b48-cf41-42c7-af95-2f1c4c577294' class='xr-var-data-in' type='checkbox'><label for='data-3a305b48-cf41-42c7-af95-2f1c4c577294' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mask at rho-points</dd><dt><span>units :</span></dt><dd>land/water (0/1)</dd></dl></div><div class='xr-var-data'><pre>array([[0, 0, 0, ..., 1, 1, 1],\n",
        "       [0, 0, 0, ..., 1, 1, 1],\n",
        "       [0, 0, 0, ..., 1, 1, 1],\n",
        "       ...,\n",
        "       [0, 0, 0, ..., 1, 1, 1],\n",
        "       [0, 0, 0, ..., 1, 1, 1],\n",
-       "       [0, 0, 0, ..., 1, 1, 1]], shape=(82, 102), dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mask_u</span></div><div class='xr-var-dims'>(eta_rho, xi_u)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 1 1 1 1 1 1 1 1</div><input id='attrs-43fa7f62-9d40-4626-9641-648fcfbdfd61' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-43fa7f62-9d40-4626-9641-648fcfbdfd61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-817337d5-dec2-4c2d-b6fd-6d77cab4769e' class='xr-var-data-in' type='checkbox'><label for='data-817337d5-dec2-4c2d-b6fd-6d77cab4769e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mask at u-points</dd><dt><span>units :</span></dt><dd>land/water (0/1)</dd></dl></div><div class='xr-var-data'><pre>array([[0, 0, 0, ..., 1, 1, 1],\n",
+       "       [0, 0, 0, ..., 1, 1, 1]], shape=(82, 102), dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mask_u</span></div><div class='xr-var-dims'>(eta_rho, xi_u)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 1 1 1 1 1 1 1 1</div><input id='attrs-fc9c0946-b233-4eff-8a58-9e270dc3790d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc9c0946-b233-4eff-8a58-9e270dc3790d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81f53a41-d1bf-469f-9b48-ba6944df1d47' class='xr-var-data-in' type='checkbox'><label for='data-81f53a41-d1bf-469f-9b48-ba6944df1d47' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mask at u-points</dd><dt><span>units :</span></dt><dd>land/water (0/1)</dd></dl></div><div class='xr-var-data'><pre>array([[0, 0, 0, ..., 1, 1, 1],\n",
        "       [0, 0, 0, ..., 1, 1, 1],\n",
        "       [0, 0, 0, ..., 1, 1, 1],\n",
        "       ...,\n",
        "       [0, 0, 0, ..., 1, 1, 1],\n",
        "       [0, 0, 0, ..., 1, 1, 1],\n",
-       "       [0, 0, 0, ..., 1, 1, 1]], shape=(82, 101), dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mask_v</span></div><div class='xr-var-dims'>(eta_v, xi_rho)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 1 1 1 1 1 1 1 1</div><input id='attrs-0434ed35-4d49-42ad-961f-8f4e67022656' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0434ed35-4d49-42ad-961f-8f4e67022656' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52283fcc-71b6-479c-8080-77116e8dd997' class='xr-var-data-in' type='checkbox'><label for='data-52283fcc-71b6-479c-8080-77116e8dd997' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mask at v-points</dd><dt><span>units :</span></dt><dd>land/water (0/1)</dd></dl></div><div class='xr-var-data'><pre>array([[0, 0, 0, ..., 1, 1, 1],\n",
+       "       [0, 0, 0, ..., 1, 1, 1]], shape=(82, 101), dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mask_v</span></div><div class='xr-var-dims'>(eta_v, xi_rho)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 1 1 1 1 1 1 1 1</div><input id='attrs-9fbf276c-e10c-4e42-98f2-afc28a6d9ff2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9fbf276c-e10c-4e42-98f2-afc28a6d9ff2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22516726-cdac-4546-8ff4-08d1f5c29f80' class='xr-var-data-in' type='checkbox'><label for='data-22516726-cdac-4546-8ff4-08d1f5c29f80' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mask at v-points</dd><dt><span>units :</span></dt><dd>land/water (0/1)</dd></dl></div><div class='xr-var-data'><pre>array([[0, 0, 0, ..., 1, 1, 1],\n",
        "       [0, 0, 0, ..., 1, 1, 1],\n",
        "       [0, 0, 0, ..., 1, 1, 1],\n",
        "       ...,\n",
        "       [0, 0, 0, ..., 1, 1, 1],\n",
        "       [0, 0, 0, ..., 1, 1, 1],\n",
-       "       [0, 0, 0, ..., 1, 1, 1]], shape=(81, 102), dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angle_coarse</span></div><div class='xr-var-dims'>(eta_coarse, xi_coarse)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.06565 0.06499 ... -0.07352</div><input id='attrs-bdf477e8-5936-4a9f-a90e-289653b59c6d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bdf477e8-5936-4a9f-a90e-289653b59c6d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35c402c6-c7f3-43b3-9522-e8440c8608fe' class='xr-var-data-in' type='checkbox'><label for='data-35c402c6-c7f3-43b3-9522-e8440c8608fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Angle between xi axis and east on coarsened grid</dd><dt><span>units :</span></dt><dd>radians</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.06564791,  0.06499098,  0.06236175, ..., -0.06236175,\n",
+       "       [0, 0, 0, ..., 1, 1, 1]], shape=(81, 102), dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angle_coarse</span></div><div class='xr-var-dims'>(eta_coarse, xi_coarse)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.06565 0.06499 ... -0.07352</div><input id='attrs-1671df06-75ba-4367-98d3-ac63887a5cd0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1671df06-75ba-4367-98d3-ac63887a5cd0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-acc9752b-408f-46db-a419-b013831998d7' class='xr-var-data-in' type='checkbox'><label for='data-acc9752b-408f-46db-a419-b013831998d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Angle between xi axis and east on coarsened grid</dd><dt><span>units :</span></dt><dd>radians</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.06564791,  0.06499098,  0.06236175, ..., -0.06236175,\n",
        "        -0.06499098, -0.06564791],\n",
        "       [ 0.06576971,  0.06511161,  0.06247767, ..., -0.06247767,\n",
        "        -0.06511161, -0.06576971],\n",
@@ -880,39 +888,39 @@
        "       [ 0.0732506 ,  0.07252002,  0.06959544, ..., -0.06959544,\n",
        "        -0.07252002, -0.0732506 ],\n",
        "       [ 0.07352282,  0.0727896 ,  0.06985443, ..., -0.06985443,\n",
-       "        -0.0727896 , -0.07352282]], shape=(42, 52))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mask_coarse</span></div><div class='xr-var-dims'>(eta_coarse, xi_coarse)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 1 1 1 1 1 1 1 1</div><input id='attrs-939bea3f-af1c-4b08-bff8-3b27d7d42930' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-939bea3f-af1c-4b08-bff8-3b27d7d42930' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1cfcc415-3dfa-49c0-a756-5c0cad162fee' class='xr-var-data-in' type='checkbox'><label for='data-1cfcc415-3dfa-49c0-a756-5c0cad162fee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mask at rho-points on coarsened grid</dd><dt><span>units :</span></dt><dd>land/water (0/1)</dd></dl></div><div class='xr-var-data'><pre>array([[0, 0, 0, ..., 1, 1, 1],\n",
+       "        -0.0727896 , -0.07352282]], shape=(42, 52))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mask_coarse</span></div><div class='xr-var-dims'>(eta_coarse, xi_coarse)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 1 1 1 1 1 1 1 1</div><input id='attrs-3a53dcb4-8b95-4c7a-8de2-431a30c22214' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a53dcb4-8b95-4c7a-8de2-431a30c22214' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9cc5535-4bd7-4ced-932d-120869ee69c8' class='xr-var-data-in' type='checkbox'><label for='data-a9cc5535-4bd7-4ced-932d-120869ee69c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mask at rho-points on coarsened grid</dd><dt><span>units :</span></dt><dd>land/water (0/1)</dd></dl></div><div class='xr-var-data'><pre>array([[0, 0, 0, ..., 1, 1, 1],\n",
        "       [0, 0, 0, ..., 1, 1, 1],\n",
        "       [0, 0, 0, ..., 1, 1, 1],\n",
        "       ...,\n",
        "       [0, 0, 0, ..., 1, 1, 1],\n",
        "       [0, 0, 0, ..., 1, 1, 1],\n",
-       "       [0, 0, 0, ..., 1, 1, 1]], shape=(42, 52), dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>h</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.0 5.0 5.0 ... 786.3 803.4 803.4</div><input id='attrs-dcbee602-ef7f-423f-995f-8c4cf91f5d07' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dcbee602-ef7f-423f-995f-8c4cf91f5d07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-543a7d20-7235-40d6-9065-dbe1f45b1aab' class='xr-var-data-in' type='checkbox'><label for='data-543a7d20-7235-40d6-9065-dbe1f45b1aab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Bathymetry at rho-points</dd><dt><span>units :</span></dt><dd>meter</dd></dl></div><div class='xr-var-data'><pre>array([[   5.        ,    5.        ,    5.        , ...,  849.10386768,\n",
-       "         986.22578069,  986.22578069],\n",
-       "       [   5.        ,    5.        ,    5.        , ...,  849.10386768,\n",
-       "         986.22578069,  986.22578069],\n",
-       "       [   5.        ,    5.        ,    5.        , ...,  922.08775784,\n",
-       "        1041.29448154, 1041.29448154],\n",
+       "       [0, 0, 0, ..., 1, 1, 1]], shape=(42, 52), dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>h</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.0 5.0 5.0 ... 791.4 807.4 807.4</div><input id='attrs-1871c3d5-99e4-471f-b8ff-1bf50c34de0b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1871c3d5-99e4-471f-b8ff-1bf50c34de0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d229d656-f365-4449-b044-1a7714c9e6ce' class='xr-var-data-in' type='checkbox'><label for='data-d229d656-f365-4449-b044-1a7714c9e6ce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Bathymetry at rho-points</dd><dt><span>units :</span></dt><dd>meter</dd></dl></div><div class='xr-var-data'><pre>array([[   5.        ,    5.        ,    5.        , ...,  844.8145955 ,\n",
+       "        1001.90075266, 1001.90075266],\n",
+       "       [   5.        ,    5.        ,    5.        , ...,  844.8145955 ,\n",
+       "        1001.90075266, 1001.90075266],\n",
+       "       [   5.        ,    5.        ,    5.        , ...,  906.81936458,\n",
+       "        1047.14134622, 1047.14134622],\n",
        "       ...,\n",
-       "       [   5.        ,    5.        ,    5.        , ...,  793.7244742 ,\n",
-       "         806.86825912,  806.86825912],\n",
-       "       [   5.        ,    5.        ,    5.        , ...,  786.25173909,\n",
-       "         803.35349823,  803.35349823],\n",
-       "       [   5.        ,    5.        ,    5.        , ...,  786.25173909,\n",
-       "         803.35349823,  803.35349823]], shape=(82, 102))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma_r</span></div><div class='xr-var-dims'>(s_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.975 -0.925 ... -0.075 -0.025</div><input id='attrs-e2115d5c-b216-402f-85ab-9fe8cd571245' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2115d5c-b216-402f-85ab-9fe8cd571245' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-785b16a2-d92a-4d5d-89aa-6d808718c002' class='xr-var-data-in' type='checkbox'><label for='data-785b16a2-d92a-4d5d-89aa-6d808718c002' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Fractional vertical stretching coordinate at rho-points</dd><dt><span>units :</span></dt><dd>nondimensional</dd></dl></div><div class='xr-var-data'><pre>array([-0.975, -0.925, -0.875, -0.825, -0.775, -0.725, -0.675, -0.625,\n",
+       "       [   5.        ,    5.        ,    5.        , ...,  799.96646826,\n",
+       "         811.05492641,  811.05492641],\n",
+       "       [   5.        ,    5.        ,    5.        , ...,  791.42439919,\n",
+       "         807.38153536,  807.38153536],\n",
+       "       [   5.        ,    5.        ,    5.        , ...,  791.42439919,\n",
+       "         807.38153536,  807.38153536]], shape=(82, 102))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma_r</span></div><div class='xr-var-dims'>(s_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.975 -0.925 ... -0.075 -0.025</div><input id='attrs-b11e067e-0edf-4d9c-9312-a9027c1524c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b11e067e-0edf-4d9c-9312-a9027c1524c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-173f8fa6-7097-4164-a8a9-2842596a0cec' class='xr-var-data-in' type='checkbox'><label for='data-173f8fa6-7097-4164-a8a9-2842596a0cec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Fractional vertical stretching coordinate at rho-points</dd><dt><span>units :</span></dt><dd>nondimensional</dd></dl></div><div class='xr-var-data'><pre>array([-0.975, -0.925, -0.875, -0.825, -0.775, -0.725, -0.675, -0.625,\n",
        "       -0.575, -0.525, -0.475, -0.425, -0.375, -0.325, -0.275, -0.225,\n",
-       "       -0.175, -0.125, -0.075, -0.025], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Cs_r</span></div><div class='xr-var-dims'>(s_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.9579 -0.8615 ... -0.0002471</div><input id='attrs-9051f7f0-5d91-4923-a3b5-90d0850bb084' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9051f7f0-5d91-4923-a3b5-90d0850bb084' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19d53d69-e86b-41ef-bf59-6655becf7d18' class='xr-var-data-in' type='checkbox'><label for='data-19d53d69-e86b-41ef-bf59-6655becf7d18' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Vertical stretching function at rho-points</dd><dt><span>units :</span></dt><dd>nondimensional</dd></dl></div><div class='xr-var-data'><pre>array([-9.5790392e-01, -8.6149198e-01, -7.5501293e-01, -6.4611858e-01,\n",
+       "       -0.175, -0.125, -0.075, -0.025], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Cs_r</span></div><div class='xr-var-dims'>(s_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.9579 -0.8615 ... -0.0002471</div><input id='attrs-df9f1022-b3b2-4e08-9fae-d989a33d4e5a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-df9f1022-b3b2-4e08-9fae-d989a33d4e5a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a9d302f-aeaa-440c-9a37-4b589f5af915' class='xr-var-data-in' type='checkbox'><label for='data-8a9d302f-aeaa-440c-9a37-4b589f5af915' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Vertical stretching function at rho-points</dd><dt><span>units :</span></dt><dd>nondimensional</dd></dl></div><div class='xr-var-data'><pre>array([-9.5790392e-01, -8.6149198e-01, -7.5501293e-01, -6.4611858e-01,\n",
        "       -5.4124850e-01, -4.4488612e-01, -3.5953298e-01, -2.8607488e-01,\n",
        "       -2.2426941e-01, -1.7319578e-01, -1.3160250e-01, -9.8145016e-02,\n",
        "       -7.1531408e-02, -5.0600942e-02, -3.4358352e-02, -2.1981522e-02,\n",
        "       -1.2814899e-02, -6.3567827e-03, -2.2454644e-03, -2.4712758e-04],\n",
-       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma_w</span></div><div class='xr-var-dims'>(s_w)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.0 -0.95 -0.9 ... -0.1 -0.05 0.0</div><input id='attrs-c6c7a0ce-bbe6-46a1-9c63-4c06b05baaa3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c6c7a0ce-bbe6-46a1-9c63-4c06b05baaa3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c96f27a-20aa-4a8f-8f14-f5debb81c875' class='xr-var-data-in' type='checkbox'><label for='data-9c96f27a-20aa-4a8f-8f14-f5debb81c875' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Fractional vertical stretching coordinate at w-points</dd><dt><span>units :</span></dt><dd>nondimensional</dd></dl></div><div class='xr-var-data'><pre>array([-1.  , -0.95, -0.9 , -0.85, -0.8 , -0.75, -0.7 , -0.65, -0.6 ,\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma_w</span></div><div class='xr-var-dims'>(s_w)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.0 -0.95 -0.9 ... -0.1 -0.05 0.0</div><input id='attrs-556767ae-3116-45f9-8bae-79e47b4cfade' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-556767ae-3116-45f9-8bae-79e47b4cfade' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d524f1b6-aa16-4a6e-83d7-8c1ba8e9d3c3' class='xr-var-data-in' type='checkbox'><label for='data-d524f1b6-aa16-4a6e-83d7-8c1ba8e9d3c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Fractional vertical stretching coordinate at w-points</dd><dt><span>units :</span></dt><dd>nondimensional</dd></dl></div><div class='xr-var-data'><pre>array([-1.  , -0.95, -0.9 , -0.85, -0.8 , -0.75, -0.7 , -0.65, -0.6 ,\n",
        "       -0.55, -0.5 , -0.45, -0.4 , -0.35, -0.3 , -0.25, -0.2 , -0.15,\n",
-       "       -0.1 , -0.05,  0.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Cs_w</span></div><div class='xr-var-dims'>(s_w)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.0 -0.9114 ... -0.0009921 0.0</div><input id='attrs-e4be5f00-c1c1-48b6-9266-1a6b80beffc3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e4be5f00-c1c1-48b6-9266-1a6b80beffc3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55a429e0-1c96-4ecf-9baf-d34e53b4938b' class='xr-var-data-in' type='checkbox'><label for='data-55a429e0-1c96-4ecf-9baf-d34e53b4938b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Vertical stretching function at w-points</dd><dt><span>units :</span></dt><dd>nondimensional</dd></dl></div><div class='xr-var-data'><pre>array([-1.0000000e+00, -9.1144282e-01, -8.0901909e-01, -7.0042384e-01,\n",
+       "       -0.1 , -0.05,  0.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Cs_w</span></div><div class='xr-var-dims'>(s_w)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.0 -0.9114 ... -0.0009921 0.0</div><input id='attrs-b6fd6086-1b60-4620-bb3a-d4a6733bb30a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b6fd6086-1b60-4620-bb3a-d4a6733bb30a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-645e5dfe-1dab-44bb-a0ed-124a1903bf2c' class='xr-var-data-in' type='checkbox'><label for='data-645e5dfe-1dab-44bb-a0ed-124a1903bf2c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Vertical stretching function at w-points</dd><dt><span>units :</span></dt><dd>nondimensional</dd></dl></div><div class='xr-var-data'><pre>array([-1.0000000e+00, -9.1144282e-01, -8.0901909e-01, -7.0042384e-01,\n",
        "       -5.9285146e-01, -4.9180421e-01, -4.0074578e-01, -3.2131144e-01,\n",
        "       -2.5376186e-01, -1.9746487e-01, -1.5129805e-01, -1.1393995e-01,\n",
        "       -8.4058918e-02, -6.0422052e-02, -4.1949075e-02, -2.7731461e-02,\n",
        "       -1.7031731e-02, -9.2730094e-03, -4.0252637e-03, -9.9205703e-04,\n",
-       "        0.0000000e+00], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ba9b22ec-fb8f-4a13-b496-4bc7008785b1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ba9b22ec-fb8f-4a13-b496-4bc7008785b1' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-fb80fbb0-fdb9-4934-b7c7-2d6bd7af6907' class='xr-section-summary-in' type='checkbox'  ><label for='section-fb80fbb0-fdb9-4934-b7c7-2d6bd7af6907' class='xr-section-summary' >Attributes: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>ROMS grid created by ROMS-Tools</dd><dt><span>roms_tools_version :</span></dt><dd>3.1.3.dev32+g91d580e67.d20250929</dd><dt><span>size_x :</span></dt><dd>2000</dd><dt><span>size_y :</span></dt><dd>1600</dd><dt><span>center_lon :</span></dt><dd>-89</dd><dt><span>center_lat :</span></dt><dd>24</dd><dt><span>rot :</span></dt><dd>0</dd><dt><span>straddle :</span></dt><dd>False</dd><dt><span>topography_source_name :</span></dt><dd>ETOPO5</dd><dt><span>hmin :</span></dt><dd>5.0</dd><dt><span>theta_s :</span></dt><dd>5.0</dd><dt><span>theta_b :</span></dt><dd>2.0</dd><dt><span>hc :</span></dt><dd>300.0</dd></dl></div></li></ul></div></div>"
+       "        0.0000000e+00], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9c9d0590-7ff4-4838-9b35-001aed0f30c7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9c9d0590-7ff4-4838-9b35-001aed0f30c7' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-990bd706-2e4f-4f37-b1af-27927cad8f6b' class='xr-section-summary-in' type='checkbox'  ><label for='section-990bd706-2e4f-4f37-b1af-27927cad8f6b' class='xr-section-summary' >Attributes: <span>(14)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>ROMS grid created by ROMS-Tools</dd><dt><span>roms_tools_version :</span></dt><dd>10000.dev364+g785fa77b7.d20260612</dd><dt><span>size_x :</span></dt><dd>2000</dd><dt><span>size_y :</span></dt><dd>1600</dd><dt><span>center_lon :</span></dt><dd>-89</dd><dt><span>center_lat :</span></dt><dd>24</dd><dt><span>rot :</span></dt><dd>0</dd><dt><span>straddle :</span></dt><dd>False</dd><dt><span>close_narrow_channels :</span></dt><dd>False</dd><dt><span>topography_source_name :</span></dt><dd>ETOPO5</dd><dt><span>hmin :</span></dt><dd>5.0</dd><dt><span>theta_s :</span></dt><dd>5.0</dd><dt><span>theta_b :</span></dt><dd>2.0</dd><dt><span>hc :</span></dt><dd>300.0</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset> Size: 894kB\n",
@@ -938,20 +946,20 @@
        "    mask_rho      (eta_rho, xi_rho) int32 33kB 0 0 0 0 0 0 0 0 ... 1 1 1 1 1 1 1\n",
        "    ...            ...\n",
        "    mask_coarse   (eta_coarse, xi_coarse) int32 9kB 0 0 0 0 0 0 ... 1 1 1 1 1 1\n",
-       "    h             (eta_rho, xi_rho) float64 67kB 5.0 5.0 5.0 ... 803.4 803.4\n",
+       "    h             (eta_rho, xi_rho) float64 67kB 5.0 5.0 5.0 ... 807.4 807.4\n",
        "    sigma_r       (s_rho) float32 80B -0.975 -0.925 -0.875 ... -0.075 -0.025\n",
        "    Cs_r          (s_rho) float32 80B -0.9579 -0.8615 ... -0.002245 -0.0002471\n",
        "    sigma_w       (s_w) float32 84B -1.0 -0.95 -0.9 -0.85 ... -0.1 -0.05 0.0\n",
        "    Cs_w          (s_w) float32 84B -1.0 -0.9114 -0.809 ... -0.0009921 0.0\n",
-       "Attributes: (12/13)\n",
+       "Attributes: (12/14)\n",
        "    title:                   ROMS grid created by ROMS-Tools\n",
-       "    roms_tools_version:      3.1.3.dev32+g91d580e67.d20250929\n",
+       "    roms_tools_version:      10000.dev364+g785fa77b7.d20260612\n",
        "    size_x:                  2000\n",
        "    size_y:                  1600\n",
        "    center_lon:              -89\n",
        "    center_lat:              24\n",
        "    ...                      ...\n",
-       "    straddle:                False\n",
+       "    close_narrow_channels:   False\n",
        "    topography_source_name:  ETOPO5\n",
        "    hmin:                    5.0\n",
        "    theta_s:                 5.0\n",
@@ -959,7 +967,7 @@
        "    hc:                      300.0"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -978,7 +986,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "9d3ad4a7-b383-490b-b38b-6824464f599b",
    "metadata": {
     "tags": []
@@ -986,7 +994,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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dQH/fT4s6Zhs3bsRvf/vb2c92+5wJ8653vUvUDv3BD36AYDCIt73tbbj88stFuRoilUrhpS99qVBCfuCBB0St0auuukqUsfnEJz4xW96Glrn22mvx3//93/jd736HN73pTWhra8Nll10Go7CUoZaFjDSasid/bGwMkUgELpdr9sRmxS7pb67wZe735fhb6rxStlvM/GLaoLftM22b3r99Pp84X+Pj40WvW8r+yrFOudddzHVk39NDKRQKiZdPKeubZZ3FXq7c63i9XthsNkxNTRW9LauiKMqir0PLUwjOQsJ9Frpt2XJ665dznWK2V8x+F/LdQrZP36mqip6eHhw8eLDo7cr+fSbrGrFOoX8vdJv/+Z9O0Ef6LnfdUz/nrKsAyaQm/u1wqKesk10+fztawf1kvj91vzSRoXbqdhVoWqHvaUvpU/ab/ZxGxoib37YUogW/pymWShX+Lbm/I2+e+DUF18l+ng5FEQ5H0dxaj9a2jJFb46uBFVFsLiiLbKhpSvESGXa7vWDJmYmJCdxxxx34zne+g+c///niuzvvvBPr16/HQw89hPPOOw+//vWv8eyzzwpDr6WlBdu2bcNHP/pRfOADH8BNN90k3gFf/vKXxTPpM5/5jNgGrX///ffjtttuY0Ntsfnud7+LwcHBM9rGhRdeOGuZM5WBbphkMik8pEzlIAOCPNMjIyNGN6WqoIEJ6hCQkcxUDnpBZw0GprLQs31gYMDoZixJfvGLX5S0Hg1gezweMYDNFM8LXnQ2vvnDG41uhmXIHRAmyKFCUyH279+P9vZ2cQ2ff/75ImqOaoY++uijSCQSeOELXzi77Lp168S8Bx98UBhq9Hfz5s3CSMtCxtdb3/pWPPPMM9i+fbtYJncb2WXe+c53wkiqwlDLjlwXHm06dRRr/nf0ly4MGvEuNF+2zkL+lrpsqdsuZbuLvY7s3+SSJhoaGkraTrnXKeeyhT6X2sZitrmQ7dG1vnz5cqTThUNbFtqWYtt5JuuUe7mFbuNM18mFni9i5HZ6+oy3tVQodxnPUrZHx7q+vh4bNmwoeXsLWa6SnuRSvPBnum4p31HkC42GL3T9Yj6Xa9nFisY4XQTK6ZbfsKET9NX86KBTP8+tQ5PD4UIgEMTwcH+BdfS2c/r9ZP+dprDIAm1LS9dLF9xOZtnC39M+cr9Hzr/TM3GMedvM2U5m2fnbzN/+/OWSyZT4dyolD/m0Cor4b7FF4TPvOAoZzWX37t3CwzWfc889F9/85jexdu1aEbZ488034+KLL8bTTz8tBpZpwK22tjZvHTLKaB5Bf3ONtOz87Dy9ZciYpKg8GuAwgqow1JYtW4ahoSG84AUvwEUXXVTyduiiYBiGYaz5nmCYpcLu3VcWvQ4ZK5FIDE8/dQzn7Fy9RHLUMiGO80mkp6XrRBYhR+1D7/gS/uuOX2LHOWv1F2SKIqspkUXmTXvxizP5gMSWLVuE4dbd3Y3vf//7hhlQlaIq6qhREiFB+R8MwzAMwzDViNvtxMZNXUY3Y8nhdGYifOLxwkaglVAUtSJTrqZEdpIZavMh79maNWtw4MABkbdGOcakZ5ALhVVnc9ro7/ww6+zn0y1D7TLSGGRDjWEYhmEYpgqgUN+DB/pgJlSleOEKh+pblLZI9+fMBKAlEtY31JYCoVBI5BWT+NlZZ50lUmVIpTHL3r17cezYMZHLRtDfp556Kk+v4je/+Y0wwrJh77RM7jayy2S3YRRsqDEMwzAMw1QJY2MsWFQsDseMocYetbJ61BbKe9/7Xtx33304cuSIkNf/u7/7O9Gnv/LKK4Uc/xvf+Ea8+93vxj333CPERa655hphYJGQCHHppZcKg+x1r3sdnnjiCfzqV7/CRz7yEVx33XWzXjyS5T906BDe//73Y8+ePfjSl74kQitJ+t9IqiJHjQ01hmEYhmEYEi5aWHgZM4dzxqMWj8vz6ZjF48SJE8IoIzXqpqYmoTdB0vv0b4Ik9KnkxxVXXIFYLCbUGsnQykL9/7vvvluoPJIBRyrLV199NW655ZbZZUj9l2qxkWH2+c9/Hh0dHfj6179uqDQ/wYYawzAMwzBMlbBt+wqhYFgt6rLlDH1kQ80Yvve97+nOJ7XqL37xi2KSQeIjP//5z3W387znPQ+PPfYYzASHPjIMwzAMw1QJD/95X1XIzC+GoZZMFFautBKFipUvxsQsDDbUGIZhGIZhqgSny4FYjD1DxeB0VI/qI2MuOPSRYRiGYRimSujoaIDNVhXj9GXD7rBVjZhIxoez2NcHX38LhQ01hmEYhmGYKhITqcbQRwq2S52msLUMludnjIINNYZhGIZhmCphdDQEShHy+dxGN2XJqT4mqkBMpBT5/OJ3wh61haJWk6FG0p0MwzAMwzDVistl5xy1IqGCygTnqDGVpqo8anZ7VfxchmEYhmGYgjQ1BVFfHzC6GUtT9TFZDaqP7FEzE1VxpNijxjAMwzAMkzE2jh4dMroZSwrHjJgIe9SYSlMVlgvnqDEMwzAMw1BfSMXw8KTRzVhSOKopRw1qRSZmYVRFLGA6nVE3YkONYRiGYZhqxm63zXqIljKK0HE8lRKFHReUo1YNBa8Zc1EVhhp71BiGYRiGYSgHScGOHSuMbsbSDH2sAnl+zlEzF2o1eNM0LTO+woYawzAMwzDVzp/uf87oJizR0EfrG2qMubBXizeNYDERhmEYhmEYphiqqo4a5ZAtuseL++MLRa0mQ409agzDMAzDVDudXU2z0UbM6cmWd0pUgTw/Yy6qyqPGhhrDMAzDMNVOIOBBKpUWwiLM6amq0EdFWXSPmqYUFoJhqtBQyyo+UvIshz4yDMMYz+7dVxb8/uabv2tIG+6//1mcf/5aMZhXyTYwTKnoXaey+yuXEyeGhUBGMOgraf/Ukde0dLFrYanicMwUvK4CMRHGXFjeUGPFR4ZhGOZ0+SfptAZ+TTDVgtPpWBLFm2US/EZ51OiYUcgoDf5bFTrmi33czXJelwJsqDEMwzBVzc6da4xuAsNUlBUrWkTha6Y4jxrBIaNMJVGrxVDjsEeGYRimEI88sh/RaNzoZjBMxZiejmJoaNLoZiwZcguEV0WeGmMaqiZHjT1qDMMwjAwKfWSYaoG8QpMTYbS31xvdlCWBPcejlkgk4YELVqUSBa8XX/7fOlj+SHHoI8MwDKNHc3OQw8CYqsLtdhrdhCUFe9QYoyjqzXT77bdjy5YtqKmpEdP555+PX/ziF7Pzo9EorrvuOjQ0NMDv9+OKK67AwMBA3jZ++tOfYs2aNVi7di3uvvvu2e+PHDkikjObm5sxNTWVt862bdtw0003lfQDOfSRYRiG0aOuzs+GGlN18vyr17Qb3YwlAw32Z/uRiaS1DbWsR22xJ2YRQh87OjrwyU9+EqtXrxaqN9/61rfwyle+Eo899hg2btyId73rXfjZz36GH/zgBwgGg3jb296Gyy+/HH/605/E+rFYTBhyd955p1j/DW94Ay699FI4nXMjO2SkffrTn8bNN9+MMyG7Poc+MgxTbejJc8tkvUtZp9Q2VKrdepR7e8Xuh2GMDvV98IE9uOjiDYuw9cKdcFVRkdZkRk5hFUCb4kZKixa1jrKIyo+xaBzJBBe9ZkxqqL385S/P+/zxj39ceNkeeughYcTdcccd+M53voPnP//5Yj4ZZOvXrxfzzzvvPGGokcFEHjKxc7tdfJdrqF1//fX47Gc/Kww68q6dKRz6yDAMwzAMMwd5kLMD2czCwx9jUeuHPnKOmrlQz8QA+t73vofp6WkRAvnoo48ikUjghS984ewy69atQ1dXFx588EHxmcIlr7nmGrS1taG9vR1vfetbEQgE8rZ75ZVXYtWqVbjlllvO5HfltZPg0EeGYRiGYZgMHZ2NRjdhSQqKkJgIw1SKoq2Xp556SuSfuVwuXHvttfjxj3+MDRs2oL+/X3jGamtr85ZvaWkR87Ls3r0bw8PDGBkZwfvf//5Ttk95ahRe+dWvfhUHDx4s6UdRWGUW9qgxDMMwDMPk4/O5jW7CksJZwFCj/iZFhlnPNKjExCyKPD+JgDz++OOYmJjAD3/4Q1x99dW47777itoG5a/pcdlll+Giiy7CDTfcIEIpi+WBBx6Y/TcJm+zatQtjY2NFb4dhGIZhGMaK7NlzQiie0gB5tUMGVyKRht2mIhJOIDKdRCqdRku7Hwf3jCIRS4sIsVRSxbH9YcSn++A7awyDg4M4fvy40c1nLEzRhhp5zSg0kTjrrLPwyCOP4POf/zxe85rXIB6PY3x8PM+rRqqPra2tRTeMvGoUUvm+972v6HUvvPBCPPzww+Lf5M37wx/+gBUrVhS9HYZhGIZhGCtit9lEPTW73WYZYyuZTCORSIHiqqbGY8L4qm/0YGwkionRKKhc4qqN9Xj8oX7QQk1tXviDLhzeOwaH04aeNbWYDiUQiyThdGWOS1OrD3aHitGxTDRY0zIbtp/XhjpfHerq6kQ6j5XgHDWLFbymZFRy+5LR5nA48Lvf/U7I8hN79+7FsWPHhMFVLDt37hSKkR/84AfPuH0E56gxDGM1yq2sWM519NBEN6o4btz9j2Xb1mJsTwarSDJmZeu2HqjqmXjTZOtqZ9Rni8dTwoD0ep0Y6J9ELB6Bz+8UbT1+bFzMX7O2CUePjWB0JAKn04bt57Ti4T+dhM2uor7VDX+NE1MTcWFgEf6AA16fHTaHTXgQt53XmudJ3H7+nLHlC+TXmKupzRS3zh4rMgQZxpSG2oc+9CG8+MUvFu5fktGnsMR7770Xv/rVr0Q44xvf+Ea8+93vRn19vRAOIQVHMtJI8bEUSFWSZP9JHbJUOEeNYRiG0ePQoX54PC60tdUZ3RSGqRgjI5PQtICoqVYujxZJBCRTKYTDMaGOWFvnw8hICFOTESiqgq7uOjz+2FExr7W9Fl6PE/v29iOtpbBteydOnBhDaCoGn8+JdRvaMDUVhd0BqDYbPG4HOrvr4XTY4HI7sHZDU97+z7u4a/bfkWQENTlpNm73TA20dMbYKhTueTrz0j5T9DqeSIhlj0//Hzp9+WroVoA9auaiKAuIYnGvuuoq9PX1CcOMil+Tkfa3f/u3Yv5tt90mPFfkUSMvG+WafelLXyq5cVQYm2qtkbBIqbDqI8MwDKMHdS6z7wqGqRaikbgwqLKGWjyeFBMZXOTN6u8fF2rewaBX1F07cXxECGls3NSF/ft6MTY+DZ/Pha1bl+OBB/YK/1r38mZ4vHacPDkmDCp/wAOyibw+F9wuu/BKrd/QDofDDrudDAIFzS1BpLWEaMO69flhhKvXtCClxWc/O11z3VYKY6wkDqdD/OU6aoxpDTWqk6aH2+3GF7/4RTEVy/Lly/PUGrN85StfEVOpcMFrhmEYRo9AwC3qSjGMFTh+fEiE53V1NeHo0UFMTUXgcjnQs6IFf/7zPjEy0bOiFaHpKPYf6BOG2rnnrsGhg/2IROMIBLxYsaIFkUgcDocq7g2Px47u5U2ilpjLZcfmLd15+9y1a65wtoY0GhtrZj+73XOeLSp47fVmQgmXGvTbiaTF5fkVqGJa7H0wFcpRMzvsUWMYhmH0qKnxGt0EhikbZKSRUUGeLDKYaHI6HXA67bj44g2zYX8dHQ1igDzbP9qwcS50kFi1irxbc0Wx3e783K1Cg+tWJiu6wjlqTCWxvKHGHjWGYRhGj8HBCSFe0NPTYnRTGOaMWbFiTmk7GPRJhXKGhyfFtb9xnoHG6Be8trxHTVEqkKPGJSEWiuXdTOxRYxiGYfSgvBnKwWGYaoLCISknjSku9DGRZI8aUznYo8YwDLNEkUm1m0FOfynR0BBAKrU4v7GUY6dIZc/l59bMsv2vec3F4l1Mnp6TJ0cwPj4txCTIk/PAA8/h8OF+XHTRRiFMsWfPSbHOjh0rcOzYsPD6UE7UueeuxYMP7hHv8l/+8lGjf5KpkV0L80tSeDxOce2bAV0Pi/QWkq8jqzpwJnd5dXnUFtfjxR61hWN5Q409agzDMIwelHMSjcbh97sNbQfl/FAHJpnM1JGif9tsCsLhuDB0qIQAqe6RUh95AJubgxgYIGU+c3cc6+p84h1MOT5tbfVoaakV/yZP5gUXrEdHR+Ns2GlLy1yJhPkhebQswYZaeSBj2e3OKBkyp8eRzVFjLyRTQarGUGOPGsMw1QJ16qmTn032nz96mTvvdNCy2ciE7HZyt5upnZT5TJ1xMjCyBgepxWUS7zXxDKbvs/Mp7CoWSwiDgzrsJHRAKnM0j4QP6C/Np7+kSjc9HRMGDG2TDKrR0ZDYZ1ZBjpTtaFkSTiCPDa1LxkBTUw1OnBgR80hmnNoxNDQhPpOBQB4bmh+NxMS65NHR0hoaGgOijceODiGtaVizpl3IlY+OTontbt++Qnh4qP2trbUiF+jZZ46Jtmzeslzk/gz0j4n9XXjRevzpT8+J305GChlYTz11VKjvkYDD2FgIfb2j4vhddPEGPPbYIfHuova0ttZh//5ecYy6u5vF7xoZmRLHgbZDn2m7ZibX+MoKMmQhQ5Q8O4wxPPfcCTQ31xrdjCVB1qNmdTERrqNmLixvqGU7GOxRYximWjh8eEAYDs89d1x83ry5W3heBgYmRO0i8kxQyFnGcKibMRyOiGU3buwWxkhf36gwti6+eCMefni/8No0NQbRvqweTz6RWXbtumUIhaLoPTkqPp9/wVo88cRhxKIJ1NcH0NnViCefzCy7cmUrYrEkek+O0FsaZ521Uhgg0WgCNQGPqL+0Z88JEfbX0dkoDKneXmpDxrPS3z8m6j55vC7RsaffQ5DhQ0bL+FhIbJf2S9skDxkZWpkaaWS4Zo4N/SbyJGTDe2hboo7UjHFJhh0ZReS9IqNieU+z+J48D8uW1Yui2DSfOOusVeLfNNEyuy7ZNHsO6ur8WLt22eznCy/MeIOy7Nq1cfbfZJCtXt0++/mcc1bnLbtjx8q8z2S8ZSHjbamzd+/JvN/EMGbE4ZwJfUyyR42pHJY31NijxjBMtbFyZaZoLBlZWWpryXDomP184YVzdY+IXbvmjAzKW8k1HM47b23esuT1ydLUFMxTSyTjJZfzz1+X97mzs3H235s3L8+bN39dMsKy5LanUFgcGUZZSHY8l/lqjrkhjmTYZdfNGGpzNZ8I8vRlmf8eyZ3HMEsR8gwzC4Pl+RkjsLybiT1qDMMwjB4TE9PYtzcjYsFUHvKuMsZw4vhw1dVDO1NDLWVx1cdswevFnpiFYfnhQPaoMQzDGKHgaHzelF4HNDdvT9PSiMUTFW6zWtZzpKf0WYoiZLmVQ08Xqjvfu8qUn0JqouPjYaSSadhnpOcZORQyTbBHjakkljfU2KPGMAzD6EEhjCJPjTEEElPJCtAwlYXCgEmkp1KGmqz0hFbCOnplLGyLcCllj5HVc9RYTMRcWN5QY48awzAMowcJhTQ31xjdjKrF7/cIwZf5ipDM4rNlC3syi/WoJdmjxlQQy5u07FFjGIZh9IhEEtg7U2iZqTzbtvUI5U6m8uzbd3JWQZVZmEfN7HULy+VRW+yJWRiWP1LsUWMYhmH0IHl9qofGGMPjjx8WZR6YykM1C+NcwHlB2O3sUWMqj+VDH9mjxjAMw5wuR43qpTHGQLX9KE+KqTx1dT4kk8YL/ywFHNXiUauAKiOrPi4ctVoMNfaoMQzDMDLC4ZjRTahaqF4e16QzBq/XLQxl5vTYZnIoeVCBqST2agl9ZI8aUw0y12fCsWNDotivx+PUXa6UY1Cp41Puti1Vyi/BX/yIe2m1mSoXfpjbPnpPnDwxgs7O/ELZp1JOKblUiSqHatHn3OzPIK/XVdSxKPczulqgYzP/2EWjcRw6NIDtO1boXG/pErwlha9H2VWqwoE0CnuqZNeGQ/EimS48wKIqhbu3ClIlP32yHrWkxT1qoPyxxc4h4xy1BWP5I8UeNYZZGDU1Xh7VZqoSzlEzlqGhSYyMTBrdjKrE5aIcNaohyCw0R43rqDGVxPKGGnvUGGZh7NlznEM6mKqEFAfPPW+10c2oWkiWn/OkjIEG5zZs6DK6GUuCavGoseqjubD8kWIxEYZZGNRRYolsplp57LHDRjehamltrUVbW53RzahKKKzwxIlho5uxpHLUEjygyVQQy/fKOPSRYRbGhg2dbKgxVdtZZTER44hE4iL8kTEGPvbFFbxOWdxQo+dhJSZmYVi+V8YeNYZZGMPDk/zwZKqWujq/0U2oWlKpNMbHQ0Y3o2pxuTg3ubiC19Y21BhzYfm7k3PUmHJidvW0M6Gvbwxr1ixb0seg2tTgbtz9jyZQcJSpu5UizmFcnlLPikZoOopwGYp/jyg6SpGyw6o/YJIuum3lVgHV+02leirK9Y6WPQOseP+Xguw47Dx3DQ/ULVBMxPIeNfHfYtdR42ttoVjeeuHQR4ZZuPIdw1Qrj/6Fc9SMIhDwiNBrxlivZiXQDYebKbR86mSTTzKxCsm2Ag4H7KpWcKKBE70pmxpABa+z3zHMYlM1hhp71BhGn4sv3mh0ExjGMNiZYBwkD//QQ3uNbkZVE4uxRP+Cc9QqZNQaRiUUH1n1ccFY/khx6CPDLIwHHnjO6CYwjGGsWtVidBOqXJ7f2uFkZocjKorJUbO2PD9jLixvvXDoI8MsDE6QZqoZvv6NgwZS29vrjW4Gw+hirxLVR8ZcVI2hxh41htGnuTlodBMYxjCOHuVaUkZSU+M1uglVDUv0L8zzWxWDOhQHXomJWRCWt1449JFhFgYXnGWqGRYGMJZnnz1udBOqmnicw/kWaqhl+5UMUwksLc9PktJZWWk21JilIjFvlOf5ySePYtcu6wqKlFu6v9wy4G9449+io6OhqHVuufl7Re/nhhv/QTJHK1HePV20pL98e8ZZSzvP60FaO10HLF281LTuyLHkvaTprVN4nqKUInBQ2nuxFLl/o+W4S33mV4usv37oqVr0Nadf0kOG7BpRir6uFEWe7lJqOl7WUEta3qNWATeOxfVYyolaDWGPBBtqDCOHVKyy0sOMUbBLx0ieefqk0U2oarZt6zG6CVXNwMC40U0wPbasocY5akwFqRpDjcVEGEYOGWnr1nUY3Yyq5tDBfqObUNVMh6JGN6Gq6e0dNboJVc3g4ITRTTA9jhnVx6TVVR85R81UWNpQy40jZo8aw8ih5Ojpae6oMtVLTdBjdBNQ7YZCaeFyTDngY396sgP+CfaoMRXE0tYLhz4yzMKLnY6MsOqXkew8d43RTahq1qxtM7oJVY3b7UA6zcaCUZzLz58Fe9TSli94zR41M2Fp64UNNYYpJkeNw4ONZM+eE0Y3oap55OFDRjehqjn33LWcJ2sg99//rNFNWDI5alzwmqkk9mqpoaaw9c4wUurq/Kit9aFauXH3P5ZN+a6kbWkp3P/H54qWwpIrOMr56C0/KPj9R278O5326Sk4SlQfdX6LpsnmFb+fcqEhhZQWg6I7filTmJOvo2jyARBF9nt1FOtkb7JSItdKU4ok1LLdL1nVvkcfPSDyZH0+N8xEudVdzQpHPhYhz5+0uEeNbu/FHjPhMZkFUzWGGsMwcoaGJjA5GcbKlRz+ZQRkCPkD5uqgVhs9K4orjcCUF/KmsZqecdx7z1NiKtfgk3xIQS2+/IXOQLtsIERvwMVe4sC93THnUaNnNjsAmErAhhrDMKLYKSeTGwe98Ddu7DK6GVVPpvNldCuqk6am4KzHgmHMiD0nPYD6l5ZNF1AUaIv9IOQH7YKpCtVHNtQYRh+Xy4FAgFXvjIJe+o88csDoZlQ1x46MspiFgQSDXs5RY5ZEjlpWKZlhKgF71BiGgd/vhqryCJdRkDOTj76xKKrCXmUD6esbg9frwrJlHILKmJNcj2/KymG69DJa7BcSv/AWjFoNhppl3dMMUyZOnBjG0BDL8xsZ+tjV3WR0M6qabds72KNjcCeY1GcZxqxkc9QIzqdkKgV71BiGQTJJ8fZ8n5ihRg9jDIcPjWDFqka4nHwejKC1tU5X9ZNhzOVRs/CgAkXXLHaEDUfwVLehduutt8LtdrOhViXI5JPLjUxe2gq0ttWKPDUr/8bKUfwLnFTEDh8aQHNzsMg1C58vvfP4Lze8quD3aS2lK11frNS+3jrpEtbRle4vIWRxvmJbaDqMeDIKm0N+/hQUNuJUyfdinqLXNsl6WilJ+DolAiRxRpnjplRI1l/VvVZj8TjC4TjcHufcfjg+yhTccvP3Cn5/4+5Xl3U/0utU5zqQKS/qlcVQJeUvVEXfS6baMqWe6L5Jp1NsazAVwdIWDBtqDLMwwuEY5+cYDEs9G4vX52CzwGDl2bGxkNHNYJgFRT5YO0dNqczELAhLWzBsqDHMwujvH2cVKwNxOOzYvKXb6GZUNStWNcDpsmSQyZLpAGususksEeVHzlFjKkVVGGo8Us0w+qRTnKNmJBT6eOzYkNHNqGr2PDOI6VDc6GZULbW1fmzcxLUEmaWRp5ZMVIHq42JPzIKwdM+MVR8ZZmFs37FCSGMzxkBqd1OTEaObUdWwPL+xRKNxPPoo1xJkzA171JhKUxWGGoc+Mow+jz9+mKWxDYS8/rkiCkzlaWn1w+niQT2jII9+PJY0uhkMo4t9JvKEDTWmUlg6IJ8NNYYhTm+ATU9HoKrkTWBj7UwpxSvjdtuxfv2ygnJ/+kqc6aJUFTPbSxWl3phZR96BTmkJyfZKWEfn+tN0VSlLUH2cF3ujqVEkUgnY0nLPsqo4ZA2ATXEWfVxVpfArWFF0FBwlanaK3jHQDf8vvC89xUXZJa6fZpDW3T+FlDU0BvL3o/ObWBGSMdKjlkpZ2FBjeX5TwYYaYwrZ/N7eUbS310vn672wWVL+zKmr9fN9YiDT0zHs29eLHTtWGN2UqmWwP4ymZi/cHg4BNgIy8pqaii1PwVSCG3f/o87c4gb3FGGYSy39gl/b4JCWD5GVzNAf7Cg8z64mF15LLZWCXQxuMsziYumeGRtqSwMKIcieK8YYelY0G90EVLsXjj0ExqKqCtKsOmgoTz15xOgmMMzCxESs7FFjeX5TYWkLhg21pUEslkBf35jRzahqHv3LQaObUNU4nXa0tLI3wUi6ltcgWMfeNEPhzhtjcux2zlFjKoulLRg21JaONDl1VBmmqsVE3CwmYiSjI1GEJlme30g2buw0ugkMszDVR5bnZ3n+CmFpC4YNtaVBTY0XGzdy/RwjWb2m3egmVDWRSBzHjw8b3YyqJhpJIhazcOdrCTA6GjK6CQyzoNBHVklmKoWlLRg21JYGQ0OTHPpoMPE4y2IbnqPGYV+G4nTaYLPxOTASEpViGDOTrctr6dDHrOrjYk/MgrB0vBkbauZSdpSpNE6HoyKRv7LqjdUzGrYQufgjhwfR1dVYkfZYh+KvV9k17vM7sHJVU0HpfL3zJ5Ozl0nwE2mJbL5MVS0zLyadl9LiRX1PxCW3XyItf3nrDWCXcjfblPzj6m32ir/hpHzQwm0rXFaA0JTCx88mk/QX56nwPFXn1axIfm0pkv6Z7ckU+PTWKYzeo2YhAxGqkik6zoMW5uKWm79XtCKkouiV2pBdKHp9Ndn2Cl8rdsUjfQYpkrIYXrsdiVRYpw2AYyZHjVQfHQowEv0pHHie7joMcyZY2oJhQ21p4HDY4fO5jW5GdcP9IkOJRhMYGpoyuhlVzcDJEAZ7OfTOSC7etZGNNGZphD5a2aPGOWqmwtIWDBtqS4OmphrU1fmNbkZVc/75a41uAqo99HRqKmJ0M6qajDfH6FZUN48+ekCoADOM+QteV09UDmMsVWGo8QiduXnu2eMIhbiTauR98vTTx4xuRlVDzyinw9KR6KbHV+OAP8DKm0aipTVr5/4wlvGoJSys+khVPTVlkSd2qS0YezUYatnkT8ac0AOP5fmNI5lMIxblUWwjqa31iokxtgPGo+TG0tBYI/KVGcas2GwZ/0Zqpn/JMItNVXjUOPTR3HR0NsLplCfcM4tPkI0EQxkZnsKe53qNbkZVE5qKY2yIPftG0tJSO+uxYBgzUhU5aqz6aCos7cbg0MelAYW6ZEepmMpjt6tYsaIFVkemkFg6EsVFXZXGwvNSWlq0r5Dyop6Co6ali1J2FPOQLFqlMZWOSufFJCPL4aT8uRuRzIul5OskdE5fsoTcMtc8e2AiriIaUzAZl7fB7eEktsXk2NEh1Df40dxca3RTGKYgthnVRw7RZSpFUb3jW2+9Feeccw4CgQCam5vxqle9Cnv37s1bpr+/H6973evQ2toKn8+HHTt24Ec/+lHeMg8++CC2bduG5cuX44477sibR0aV2+3G0aNH876nfb3+9a8v6sdlO0zsUVs8tBL+m8/hQ/05n9IVmejaqMyUNnzKyMjLJxKx2LPnxGmXW/qTefG4Haiv9xndjKrGX+tCfbu+qFEooUinWDpWcEqkw9IpqUULT+mYdEpphae0ltCZkvIJhSdNS8kn6bNdPsmekbnPZZtdQVKUR8h+J2eh7xfGjBQvA6jI/lN0Jsl/qt5/iv5kn0ml0VLa7HeWg1UfTUVRV9h9992H6667Dg899BB+85vfIJFI4NJLL8X09PTsMldddZUw3n7605/iqaeewuWXX45Xv/rVeOyxx2aXeeMb34gbbrgB3/nOd4Txd/z48bz90A124403nvGP49DHJQJ7PA2F8nL4HjEWu8MGj9dldDOqmng0halRueeQWXza2upQW8sDFoz5VR+TnM/KVIiieme//OUvhVdr48aN2Lp1K775zW/i2LFjePTRR2eXeeCBB3D99ddj586dWLFiBT7ykY+gtrY2bxky7MjTRtuoq6vD1FR+/aC3ve1t+K//+i88/fTTZ/Tj2FBbGpx77hqjm1DVeDxOtLfXGd2MqmZiPIy+3nGjm1HVpJJpREPy8E9m8aFwsnBYXlydYYwm61GzdOgjDZ5XYjoDPvnJTwqnzjvf+c7Z76LRqHAmNTQ0wO/344orrsDAwEDeemSzvPSlL4XX6xWRge973/tmvPhz3HvvvcJGcblcWLVqlbB1jOSMLJiJiQnxt76+fva7Cy64AP/zP/+D0dFRYSh973vfEwfvec+bq9xO3rL169cjGAzivPPOw4YNG/K2e+GFF+JlL3sZPvjBD55J89hQWyI8+2y+R5WpvEeN8ziNh0+Bsag2RUyMcZCRNjY2F6HDMGYjm09vaUPN5DzyyCP4yle+gi1btuR9/653vQv/93//hx/84AciArC3t1dE9WVJpVLCSIvH48Kp9K1vfUsYYbkRfIcPHxbL/M3f/A0ef/xxYQi+6U1vwq9+9SsYhXomRhD9ADKqNm3aNPv997//fRESSRYtWaNvectb8OMf/1hYpbmhjyMjIxgaGsIXvvCFgtunkEjy4P3xj38stYlsqC0B6BxxDTVjmZgIY3Q0ZHQzqpq6eh+WdcwNeDGVxxNwoqWHRSyMVtTjDjBjdvEtIs2hj4YQCoXwT//0T/ja174mIvJyHUekefHZz34Wz3/+83HWWWfhzjvvFAYZpWsRv/71r/Hss8+KiD3SyXjxi1+Mj370o/jiF78ojDfiy1/+Mnp6evCZz3xGOJQowu/v//7vcdtttxn2m0u2YMi9SKGJ5DHLhXLPxsfH8dvf/hZ/+ctf8O53v1vkqFG+Wi4kNJJ7kOdDXjbKdzsTr5rD4RDhl+TeZMzrzamr00/gZxb/HNhZddNQpqdjmJrkAQsjiYYSOLFnxOhmVDVNTTVYt26Z0c1gqhwSuYnHU5gOJRCLZv6ePDaFwwcmYHc4sHbtWqQSfjz9xDAS8RTuv/9+WIoKyvNPTk7mTbFY7LS2x0tf+lK88IUvzPue0qvISZT7/bp169DV1SUEDAn6u3nzZrS0zKlcX3bZZWK/zzzzzOwy87dNy2S3sWTk+cnCvPvuu/GHP/wBHR0ds98fPHgQ//Ef/yEMOMpjIygPjbxiZLGSpVoMN998M9asWYO77rqrlGYiEokIFcpwOFzS+lZl9+4ri16nvGpacyNRFO69Zk3b6RW+dCTPddYqYY1KqoaZY0SufVkwo8CmIwNvBKWd8zK3Qfd60EooA1B4HXpGRSIJNGmn1rPTOy8yGX5S+pMhk+FPpuX5WREd2fxwsrCRP5mQrzMQKbxOf1g+YDASk8+LS9rnc8jPX6cv/7gmwjaEIgrGdOT54zqn1iuZ57LJ2+CyFTbOHYq8s2JTJaIzGmBTnAVnqTrXsYrCNSw1RefHyjanyGugkdpewU1p2mzo9fR0FEePDGHjpi75vhlTccvN+YP1WW7c/eqityW7RjIzJfM0+TqpFBCPJ6GlNbg9DvT3TQrDKlBLnts0ThwbFwbZ1u3t2L93CJOTMXh9KtZsaMCzT4zA4VDR1hGA02mDotgRCNhgU21ClTylTWLz1lZx7V500flF/1YmQ2dnZ97n3bt346abbiq47Pe+9z389a9/FaGP86G+vtPpFJoYuZBRRvOyy+Qaadn52Xl6y5AxRzaFx+OBqQ01eqCSUAiFMlKyHbkHc8kaRPNDDW0222wYYrEnkIzCD3/4w1i5cmXR69M+qU2ns9AZ4xgfD+PkiRFs3tJtdFOqlt7eMSEP39hUY3RTqhZ62XOItrGodhWu2sq/hJk56B6IRFjQhZmDQmGj0QRi8Rhqa70YHJwUEQgOuw0trUE8+eQRYWx1ddcjrQFHDg2J9c45twf79vYjFksiGPSgu6deGG0Oh01MXq8Da9Y1weG0iXDGTVvbZgasMsqvO3ZmPmepCWYGSGhZ0l2gdlk2t7sS8vkz2yfV95qaub4HpUwV4vjx43jHO94hFOephFc1UZShRi5HktT/yU9+ImqpZS1QEgUhK5PcjJSLRnlpn/70p0WeGnnD6MCSB64UPvShD4lYVErwe81rXlPUulxHzfwkEkk4nJauu256IuE4nA4+B0bS2hY0uglVj2JTYXcX9i4xlYE60IGaU73KzNINq6fJZlPEoCy97wMBjzDGh4YmheG0cWMnnn76GCYnwgjWerF6dRseeeSAWH/VylbRoe/vHxferZoaD9IpDW6XAx6vU1wv6za0C48XiXyQ4dTRMZdSs3Fze157elY0ZtqlZQbvna7S5fnT5K5jzhgy0nINNRmPPvooBgcHhRpjrjgIRfZRJB+JfVCeGaVe5XrVSPWR6joT9Pfhhx/O225WFTJ3mflKkfSZ2miEN40oqnd2++23i7+5Co4EJeyRbD/lhP385z8XeWUvf/nLRdIfGW6krPKSl7ykpAaSouQHPvAB4VUrlqwXz7KjHmdANBoXidvZh5tR+HxuONlQMxQ6/k4XnwMjOXliTITidC9vMLopVUs6mcbUyXGgtcnoplQt9E7iUiHWgIytWDSBxsYAlvc0o69vDA6HHR6PS7xzKDc9E1KoCGMt0xfJrLtrV74SeHt7/WwIePuy/OvD6y0c7rv4qo/mSF1YFMogn7+gfRTBC17wglO0Lq655hrhICIbgSLwyAb53e9+J2T5CarpTHL855+fCU2lvx//+MeFwZfVriBHEhlhWfV5WobsmFxomew2jKDo0MfTsXr1avzoRz8quUGF9kFeNZqKhVUf9SXxaVSrvt4vHoKPP35YfL9hQycmJ8OzD9WdO1dj796TIlcnGPSioSGA4eEpMZJVU+MVD1k6Z1mjr1gofIDWZYyDXnxZJSvGGNIUs8MYiug38GkwnMf+egi7LsnkuDNLl3POmVP6Jjbl5B1Sn4E8a1lUNdsHMP8NmO2vkDeHqRyBQCBPYT4rSkiRe9nvSVGeBAzJwUPGF6VqkYFFZcCISy+9VBhkr3vd6/CpT31KRAVSrWeKFsyGXF577bXCQ/f+978fb3jDG/D73/9eqNn/7Gc/g1FYehidDTU5O3bk5/xdcsncDdDYWIPly5uRSGRisFtaahGLJ8QDijqUlPBN88hYC4WiOHFiBMlEChdcuA6P/uUgIpEY6utpFK0Fjz56ADZVxZq17WIEamhoAg67irXr2tHXN46jR4fQ0OAX4Q6TkxGxTbfbCVVVxL7ZG7r4PPvsSfT0NAlDnDEGn99lCvGUaka121DTzSUSjISf94zZYY9aGfdRZm677TbR3yePGmlTkFrjl770pTy9DErDeutb3yoMODL0rr76atxyyy2zy5D2BhllVJPt85//vBBM/PrXvy62ZRSWNtQ4R630lyVd0DQRFKKQq363evVc3HdtrR8dHZm4b+KcnatPMQAzBZUpHy0Fr9eFZDIxsw8VsVhCGH807/ixYeFh6+hsFN8fPpSJEz7v/LV4+qmjmApFEazxCiPvL385KJKJu5c3i+0MDIwLQ3LlyhaMjISE4ej22IXHcGIiIjxGbrdjNtSTOwRzUD0Y9moai8fDuVFmeF9ER6YBFtUxlLUmk+en64LeT+RByfYlKOqEqU6yOWpc78947r333rzPJDJCCvM0yeju7j4ltHE+lN712GOPwSxY2lDjHLXipPlLk6Y//ajSjL0Hl8sGl8sDTcso9rS21gpDiownmjLKj3Nt6MgpALxtx/K5dmoazjt/tXhQ0no0sqWqQSSSaShqRh49Fo8jpSVQE3Tj2PFBpJJptLbVCqGhAwcGxLVx1tkrcPBAv0hy9vpc2Lq1Cw8+sF9cL93djfB4nDh+fETsY/WaVoyMTiEaScDlcmBZRx36+yeEt5C8IeQJjMeSsJFynMj3ImPwdNeeZhohC7tDQVo7sxePviy9GSihXINWXnl+TSs8r39gFColwnedWnBZ77ykUViGP5WWK93GJQq8UR0J/umkfN6ERM7+eEhu/B+RzDswKTdYT4blrytZ25d5C5cvIMbj844dHZe+GBLN8v20yTT4ATS7C8/z2OXXkFeyK7eOpL9HIulPaGrha8WmlPdZoyqFz5Oi+wyQX0O5t1l4OjZ73yl6JQKgzohVpMT69AyemAiL72hAkJ7xo6Mh8Z7o7GxEb+8oQlNREdK/cmUrHn30YGZgsKNB5Ezt29crtnr22atw+PAARkamxDvgF794VNqCD3zgChFVQgOLtI2mpqAw4m677Sc67a4W5APksvNaSlSBorcfFH7OKDpdX1XR7xbbbZn5NMCcXdYcb/IyQod0sf0b7D9ZMFVhqLFHzbwcONCHVavaispvy3jjMh45grxB5C3LQgZghsz537IlvyYPGWzZF8LmefMu3rVuJl9IE39dbofoCNBLmF7+WW8crU4dikz4gyaUK48eyXgE161vx4njoxgemhIiHefsXIE/3b9PPM3b2muFstVzz1KnQMOWLZ0YHg5hbGxaJFZv2dqFZ585KfZRX+9DoMaNwYFJ8VsbmwJiZJcStMkgJCOXOgkUJkrHgDoq2ZpExQxOkIFOx5MxELrk+DFlLMpcEVamOOi5Q88+LU3PoszgGQlWkUJfXb1P5DUnYmnxnKQQa4qeSKU1NDcHEY8nRBg8GVhbty7Hww8fwImTIyKHae3adjzycEYBcMXKVrGfQ9lIi/PWYe+eEwiH40INcPWadhw/NiQ8HhSuT89D2iYZZvQ8pP0G/N5ZhUDaV66gVlvb3MDgpk1z5WL0DDUK06cpF8rdZqzLbI4ae9SYCmFpQ41DH80PGSiliJAsJmT4iHE6lRJY50bk6up8YsqyclV+UcTceWSsYf3cvIsuXpu37MW71s56V8gj176sdlZQgow58gCSkUfXLhllqZQmrmeS0qdjRpc2GWqHDw0JiWPaN6lrPf3UCTFAQfunnL/ek2NimxdevAaPPHwY8VgC9Q1+dHbW44nHj4ntU02a+LpWYVjS5y1bO3Ho4KAwCgMBN5pbgjhxfETMa2ykMFhgaioKm6qguaUGkxMR0Xa7U4XP50R4Og7Vps4qelG7OedQn6Zm/+LXrWH0oWtzJYXdmbMDJorSa+Q105CIp/Puq0gkKe7BYMAuCqdHoynhpmpq8aH3xBS0VAQ+v1OE2PaenBQFgNs6ajA5HsX4aFxsd8u2ZXj8rydE3an6Bi9aW+rF84RYu75N3Oe9J8fFYbro4vV45OFDYlnKMe7qasDTT5+A3Ubh583imTQ0PCWe7bV1XvGMSacyYWPUZhr0omcEDbBRm9xulzDwaPnm5hpccMHa2dqC84VFyDuWgZ5V+bVc538OBn15Yfq5BZXnG1gMsxCy/RUaBLAsogu02Dlqi7t5K2FpQ41DH82P15MRDqlGstcljdDl5ojlGnxEZ+ecZDt1cKjIZ5bNWzrzlr3gorkcQfLArViZkaAldp67Im9ZMt6os/enP+5De3stmppqxD1D56OlJSgMNeo8ZTtWqTSFlioi/4+8evSiamquEYZjOJyAx2uH01mHA/sptFTDso56sb2jR0bE5x1nd2Pfnn5hQPoDbmzY2I6HHzok2tK9vFHs6+CBQXFctp/VjUMHBjNhqV4ntmzrxMMPHRbGM22XOndHDg+LtlEdnYH+CWE8UtjpmrWt2PNsn3jPkFFK9XZoPn0mhUsKfwqHY6LTSDV3Ts4Ysz6fS3QcJ8YjYlnyfJJhS51O6jBSh5M6qwQVSSVDlDrFtCztlzq74uWtaOIzHT+C2khT1lNbKCSWvALUAeBctXnkhkKRt1h81qCR0a/RoU7PxB3ZYUslxXxNUZFWVTiSCdjUBNIOBxS6rpOZ+QmPB/YoFbWNA9TpcjmBKco50oC+UaTWNyElwu8AZ4MfifFpaCSsRGJK7W5MnpwUy7pnimOHh2cGTtYEMdo3hdh0Ag6XDU3dtTj+zBDsiob6Np8YcOk/PCHW6dnUiMHjU4hPReF027F6cyOeeSRTl7Sp3YcavwNHD4yLz2s2NWCwdxrjo3R927Dj7EY8++SIuBebWjzwBxw4eSwkrjHPCjemQwlMh6j8SqZDSdcdDaqIEHO7KsLBaVmKEqit96I2GJh9Bq/f2Cr+TR52u82FCy/OeZ40+rFiZaZ0AV2/O8/NF6Q67/xVUCn2fIa2HJn95cub8sLQ2pfli7a43XP3w8W7NvDgKmNasu9qzlFjKkVVGGr80DcvGzd1sSFtEFkP19k7V4jwIEeOjVATzC/sSF6+XHKNyZ4VzXm5Wdt2dEvXJYMrl4t2rZEuu2lLR35e4gUrRKcze71klBKpk2cXxiuN7GchTx+t4/ZkBGT8fjfSWsYIzUzqrBGQiKfEdqnoN4VLkTFG65LhRsbf+FhYjC6SoXbixJjwdpIRV1fvwcH9g2IzVEh1cjIqwlQplPTsnd145qnejHei3ofW9ho8/WQvtHQaq9Y0i7DZkycynfZzL1iOv/7lhBB16eyuQ3dPPZ7460kxb8WqBhEadvzouGjD2Ts78Nwzg5iepnAvF3rWBPH4I31idLK7J3Psjh4eh6alsPWsZhzcN4bQVAJevwNr19fhsUcGkdI0tHf5RUf+2CEyOoAN2xpw6FAIofEYXB4yHBrw9MOZELPmZX6kHXb0HpoxMjY2YLg3hKmxGBxOFU0bWnDo0T4xL9jsgzvgxMDBMUzEVQRXNCI6Oo3YeASqTUHDxnYMP92LRBRQgz4oAQ9SJ4bFuraORriGJ+GcCglDLLS8C/5jJ4SRlfD74LTXomG4n0xdjNc3whGPwxueyvzmpi40jA/Blkoj6nJjyleD+vFheF1pRIJBqBS+HM4IQCTcbjgiESAZA8irQnUcY/HMEG8646kij1N2RJkKYYu/VBReUeAMUAg0YKdcVEWBr4m8oQpUu4raZp+4Hui30tSxvhFke9O/hfHf6Jm9fjvX1sObk7+2+by22X+7bUB909w92LN2zvujKBq2nTM3AEPUbM5IS9tVB7y+fGO/o6sGNsWdc2/M3SciDFCZ23Ymv9ZYHnvsMM46awW/FxhTkhVZs75HrQL7YBaE8U/lRYRDH83PQw/tO6W4JVNZDuwbwOat+QaUWY3K3FuZEv1lRU8bGuc6o3PLZp4HLpcfuWWll/fMqZYSq9fOhbR6fc4845G8gHNo2LZjLseRDLmu7vocgzX/mJ5/4YocMRE/unvmWrF6TZMwOMmgIy64uCdPTCRXZGTL9rY8MZHzLs7fT9uywKyYyIYt+b9t54VteWIiTa1z+TQr1+d7Obae35YnJlLbNLds55r6PDGRlWfnHhfAf7ZnVkzE6XcBOamgjZvaEcoRE1HXzbU/lq5BrHHuuIS65+bFw3b0LZsTFYp6fJgKznhtUgoGG/Pb0N+8DLYcMZF4IDD770h9PRDIERNpyf4eDXafW0xZHLVzgxKqLQ1vfX4OkjDYxHsmDec8j6jdSd5yy0kNLCrkwaZOMCvRMmaEQnYtb6gxpsLShlo1e9Rk6o7lVnYsTRVPm/cpd/vFq0HJf5PeOrLfVFqnqrQaWJXpwJ1OjXFsfBIpTa4SaBzlPT6yc6R/T+hc+7LrW9Nbp3C4zLLlGcMgkT5V9jutFVZ2JJKSfcV0FBxlCol6yo7jMfkz9Nh04Q71vgl5R/vgVOEQz95p+SspEZefJxKmKMReSduIgeip89zxWvQNy3OX2nRUJNs8hc9Fm1ceItUqWcfv0PmtOtGxXjt5Bk/FqRNeruSEK+Z9r6Nuo0nmaZrOMLmO8mRu7hh5xs1UAF5PKZmRc8vN35POu3H3q5esm2U2R83CoY+aqohpsffBLIyqMNQ4hMK8dC/P5DwwxkEhhIyxULii1+sQ4g+McTQODwAN5vYuW52uribTCUwxZkB2TcgHxmR9P1V3EEK/W+ywO2YHh7Ly/NY12RgzYOmnIYc+mhsaNaWi1YyxbNycHzLGVJ5UMqPoxzDVDtUzm5qS14pjGDPkqFFOsWXJKF4t/sQsCEtbMNUc+rgUSCSSOHYsIyTAGMcjDx0xuglVjz/ghIsUJBhDGW7ML7nBVB7KTcvUp2QY85H19rLqI1MpOPSRMQySLyeVPYapdgI1zlk5dcY4PJFpyugyuhlVTXt7vSiTwTDmrqNmYUONVR9NhaV7Bhz6aG5IqW/DRs4HMZrOrrl6R4wxnDwxhYkJMwq6VBe+6ZDRTWCgiWgLhjEjVVHwmjEVlrZg2KNmbiYmwhgazNRmYowbzHCaoHZS1UNlu4xuAyPqtzHGMjEextgYeTYZxnzYZvLq2VBjKoWle2ico1YspTx4ipc8p2K8xPR0BLF4Yvaz3noyWfPM9oqX2pdtr7QyACirvHtplLYtygU5eHAAjS2uRS5FUJnrsZRjupBrVbZmIdKa3BtARbcLUdvigMNJ0vmnetXiabnxEJdI7Ud05PnDEhn+oaj8OZmtiVaIQ1OFXyNHdKT2qRh2IVIp+bnQSwlJJrSi1+kroFfR5+iG/Zh8nbo6eUheT25dthzawvLrYUWgcAO7/fKGp3TKP8jOus0h99YqWuFzq0B+zmXzUojPKuGduo6eETw3j0KAqZaauDN1bmdFkR0Hft8vVWhQvdj3C11vsue0JrnmVMUpLXuiV5aCsNvss2Iip1t2yUIDVostn8+DYgvGoldZhuwNzx41c0LnxetlaXgjoZeN3c73h9FEw0mkWEDBcNaMHzW6CVVPW3sdl21hTAuHPjKVhj1qjGG0tATZiDYYu8OG9ZtY6c5oxsdiwpPg8bGIgpGoOt4qpjJMTkYwNhrCipX8XGLMR1UYapWQz+e+34KxtAXDHjVzs3dPH0ZHOXnfSGLRJAb6+BwYDj2q+DFlOJNOv9FNYKBhOszCOoy566hZWvWRMRXsUWMMI5FMwsny/IYST6QQno4b3YyqZ+W6WqObwJBnkw01w3E67SzPz5gWtSo8aizPbyYsbcGwR83cNDcH4fZwzSKjcbstPV6zJDhyYAKhqcLJ7Uzl6Ar1G92Eqofyljs7G41uBsNUb+gjYyos3UNjj5q5Ifu5HAWvU1q8bEqRuuvIFCl1lQHLq5hZ7LZOh8evocPnRiI9vUAlzdKo1G/SEwwrXhuUVBr15hUeANLTBElIFBzHwxocMQXJ2KnPqpjO9qISBceJhHxwaiBS+J47HpI/J/sj8lfFZKLwegkd5cmk5DikdVQfZx7nhbcnuQVTOuW40gVObjoFTIflbZiakt/rI/7Cx3VZk/zYTScLP7tiOsfOocoNeoda3PeEqhTenqbIvVqajiKkJhn/1TSdRihzxzyeSOKxxw7hvPPXnEYpsrzPoVL2dfPN3z3DFjFLjaoIfVQroPq42Nu3EJY21KziUdu9+8qi1ymn9HspkvV6xksamd7Tvn29aGrx5b0g5QYUF0BdDIYHI5iajGPFag69MxKPzwmbnQeUjOa4l9UGzeCxYG8FU5QhLenjKTqDA7ISE2rOoEEhHDbH7ECPrBQFw5QTS19l7FEzP0vdiF7qkCS8zcbnwGhqGz2wO/k5ZTTeVAzjqpcVyQxEVRUs72k2uhkMU705auxRMxWW7hlYxaNmVbaf1W10E6oer9+BYC3XsjOak4fGEdMpjMxUhrr4FNSyFqJnioXe14XCUhnGTKGPySQ/r5nKUBWGGnvUzMmRQ8NGN6HqIW+ay83Km6aAx5MMRxMBVmwkGM3hQ4NGN4FhdMVErDyYQOnXlZiYhaFaPeyRYI+aORkbyxewYCrP0EAYE+Ncs8homtr9cLosHYm+JNgb6ERK4YELo+FXNmNWWPWRqTSWNdRyBTDYo2bO8+PxsjS/0aRS2uyLhzGOeCylK9rDVIae6X7Y0xzSZDQ7z11ldBMY5jSGWhWoPi72xCwIyw7hWsmjJpMALkUNsjR0Bcwla+goRWpp0Snduq0jT+VRJrMv9qLJJanTKDwvrck7XLJ5ep1l2WNZLwJC0/Hva6VsTz5Lpw3ybQVavSI5eiK+8JdOKfZEKWOPpRy7UtbRUYTXPRdJybyURHpeT2r/8MkI6m1uOL2nGs1TOlL7owXk/ImBiNz4Ho0V9hhFddodTurMk0jJx3W2J5PT17u2tBJCjXTv5wLnQkmnoCU1pCSHTy/caXys8LxwWH4c4p2F80PtOq+sGqf8bnLbCh9Yl03ebqdE7l8m208oOl0HmZqepqOmN//nPvfsSWzbvvw0Tzzj3+uydzDL9utzy83fl867cferC35f2kCWUvZ1bLbMtZ8WDxDjr0HG+lh2KJ09auYmEkngiceOG92MqmdsOIJEEUYaszjQ42qJjydZgpDdI/LUGGOZmooY3QSGkaqSWj70kV5GlZiYBWFZC8ZKHjUrkkykylLsmjkzpsbjukWGmcrQsqYRdrdlAxyWDKOOAFKKZV+LS4ZAwMOhwIwpqYqC14ypsOwbiT1q5oaMtJbWGqObUfWoNgWqXpwVUxFGDo8hleAXv9Esiw7DlZaH/DGVYcPGDqObwDDVKybCOWqmoioMNfaomQ86Ox4Pi4kYzYp1dfB4HUY3o+pJxJKlJSAyZYXl+c3B448dRjzOoi6M+agGeX7GXFjWUOPQR3MzMhzC6CjL8xvNs48Oco6aCXD5nFB4hNFwBl21iKs8cGE0NrsNSZniDMMYSDZCi0MfmUpht7pHjYw0NtTMRyKRgtvNHSKjoVFBCn9kjKWmNQDVbtlxsyWDQ0vBpqWQ5FpqhtLR0QC7nc8BYz5IJdnyHjX6iYv9OuLX3YKxW92jxkbafORx1aUkb8vW0aRi9pl57csCwoOQu5yeBH9Sk6uAJSQ5JQkdeXCZEympI++elGxPT4ujVOn3YtHbVErym+jUqUEfhmO2U+4Tve3JLhO9iP1SfqveOmnJb0roNEI2q5TrhIhJZOkjku+JkETmfuzZYSjdrVAKdE4nE/I32nhcLap0gN6x01tHT2o/IvlNUZ3jIOvk6KV96Er3l+leqkmEkVBVxFRn0fvJCeLIIxqWr3TiZOELrMYhH8SqccgPUr2r8L68dnkb3BJ7yCaR7Sc0Rd4+TSLPr9/dyP9NDruCdDpV0rtHUfSeRPJ7SVZShoJhi0WvdA5L9y8GajkLwxRRR83CxhpjGtRq8Kgx5qO/f5JD7kyAx+/ke4RhZsjYsNz5MprBoUlMTUWNbgbDVKeYCMvzmwrLG2qs+GhOxkbDZRsFZ0qn7+CY0U1g6L3YWEsPK6ObUfWccDdi0uY1uhlVj92mIsU5aoyJ66hRH5NLSDCVgEMfGcNGpRxOzkFgGAEnppuC5vgEQpoTk3af0U2parq6G2c7xAxjxjpq2X5m7mfLUAn5fL6/F4xlDTUOfTQ3Gze384vYBHSsazC6CQw9r0YmodT6RUYMYxwkJGLXLBzStEQYHw8Lj1p7e6PRTWGYgqGP2VxbK9ppTHG8+93vLnIN4CMf+Qjq6+sXtCwbaowhPHj/QVy4a5XRzah6JobC8ARcRjeDYUxBVHUimeael9EkEylEInGjm8Ewp5CbTkOCIg6H3ZL1JLVF7jvTPqzC5z73OZx//vlwOhdWG/j+++/H2972NjbUqiFHTU896sbd/1i2/cjUsE63lnSOlhbbpL+5pJEsWtlxKkE3u1KUGp2eYt+0To1VmZqfTEWPsCuFj4OeM7EUtXw9dUmZmh+pHfYPRqG2q2VRO4zpKAMm08VtS7RP5zfJ1pPtRw+986fXBpmq4XRS/swJSRQcY/Xd0CbUggnWeqqUekqN5URPgTOakihP6lxDMkE/mXIiUYl0kImZkEfJbVv2/PfpUOEdPfK0hpa2wgZji0duSPaFCx/YBpf8wPokipBum47qo6ZzcpUSlIC1/GvI6bIjFk/ovntkaoz07jfzAK1MEZLVIDPccvP3C35/w43/oLNW+R4O6dN41JWcSzWZSp12eaY6+PGPf4zm5uYFLRsIBIratmUNNc5RMzfLltUa3QQmpyYMYyw1Q4OYaGk1uhlVT2N8AilFxaCdn09GUlfnQzDoMboZDHMaj5pFjTSuo1YUd955J4LB4IKX/8pXvoKWlpZqPFT5cOijufH5OdzODLRubjO6CQzVjYrFjG4CI+T5S6maxZSbqckInn3mpNHNYBjdHLVMLTWm2rn66qvhcrnE9fCHP/wB4+Pjusu/9rWvhc+3cMEqNtQYQ9i3d8DoJlQ9dI/0PdFrdDMYCm90ubiujAmYtHsRsrMnx2hsdhVJq3orGAsZaha9RrOqj4s9WQybzYZLL70UY2PlLXtkeUPNyjlqSxVSSlK5U2o8XALGNEw1NhndBGYm21XlnBPDcbkcaG1deCgRw1SK3D5lNsWGYbJs2rQJhw4dQjmxrBXDOWrmhU7Jpq3tRjej6qHBDG8DF/c1A/UnTxjdBAaANxVFIBkxuhlVD3ktuHwLY0aoT5ntV9KgsyWh31eJyYJ87GMfw3vf+17cfffd6Ovrw+TkZN5UCpYVE+HQR/MSiyYxMjyNQMBtdFOqHncth3kxTK5ktMKuZsOh1/aB/QNoa+M6j4w5vWqUj8QeNWY+L3nJS8TfV7ziFXn2R1aNtpS8RjbUljDllODPUHwHRSafnD3+hYjGYgiFIqfINetJPifTxUvwD0TkDuNByTyZBL8eegO/DrXwcbCX+bLUG9hLSOTnY+EEwscnEFh9qtpgQuf9I5Phl50jve3FS5Tnj0v2lSpByl6v3TIJfiIsk+eXSPDrrePyNGAyXFh2vZS+gN5jTzavVAFQWZpGSqdmhGwdTedC1rvGZY+bYiX9xxz+jJiIpH167xNVcq/LShFkZsobODxUeMWjtQ7pOu2ewut0+uSNCDgK/yaPTtvsqnx7aa1wjRNFp7uhzZf0V2g79J3exa+UMC9t2gAjmWw/wdL95oK8vdTfTls9R22x92FB7rnnnrJv0/KGGueomQ8KF3C75Z0NpjJQp1jh+8MkWPOltdTwp6LwpGLot9cZ3ZSqZ8dZy41uAsNIQ3MTCc5RY07lkksuQbmxbC+tGjxqS5XaOg9Wrm40uhlVj81lh7uN60WZgfrJEaObwAghEQ2utLzQM1M5jhweMroJDFOQrAPAsqqPSoUmi/LHP/4R/+///T9ccMEFOHkyU2bkP//zP3H//feXtD021JiKc+L4OI4fK698KVM86XgSqUjc6GYwLMBpGpKKipjK3n4zMDY6bXQTGEZXot+yhhpTMj/60Y9w2WWXwePx4K9//StiMzVSJyYm8IlPfKKkbbKhxlScRDwNu71wPg5TOVKxJFJhNtTMwMnmTqObwFAOoc2FUUfA6GYwJHTkYYOZMbdHTS8XfymjqUpFJquqPn75y1/G1772NTgcc8+wCy+8UBhupWBZQ43l+c2LP+CC3+8yuhkMqRDZ+P4wA43jHOZlBrypGDqiw0Y3gwGwfQfnqDHmJFs6gj1qzHz27t2LXbt2nfJ9MBjE+Pg4SoHFRJgFoPcwkqg+zlN0zMXpUuDyqNDmbVdvHZlqYExHla9fR/XxxLStaBVCWev0fIN2iRKcQ+ey1Btokgmx6QjsybflDgBuYDJSHhXJpM5lktTKp9Kop/qod/5k6+gpO07rqIqGJOqOCR25SkpAL0RDKIppj+ReKqFWj6pjgMseiXadt4HeeJdsUDlZWPxPIFMo1svN15snq1GtN+BdaJ74TtNTkazcCHoqXnhfgzrv+sFA4afRcFT+wGl0Fz54fWEVje7CbXBJlB0JTSm8Pb3ne6GuyIN/2oddl2xAKcjOUykDtzJlY7G9MifayNQdWRHSXNhsmfuMxUSY+bS2tuLAgQNYvjx/oIny01asWIFSsLyhxh4183Hk0BhWrW2Ew8Hhj0aSHskUX1QbaoxuStUTszuNbgJD50F1cOijWVCU2dpDTPVw4+5XF/zeTGGGimrxgtcsz18yb37zm/GOd7wD3/jGN8Szq7e3Fw8++KAogn3DDTeUtE021JiKk0ym4LCzp9NwaDSQPc6m4ETw1Fp2TOXhgtfmobOrng01xpRkI7XYo8bM54Mf/KC4Ll7wghcgHA6LMEiXyyUMteuvvx6lwIYaU3FWr2uCw8neNMNxOqCwqIspWDlyDPubeoxuRtXj0FJojE9i1O03uilVj9/nFh4LHktizIblc9So37zYfWeL9s0VRcG//Mu/4H3ve58IgQyFQtiwYQP8/tLfKZZ9BLKhZl4Geqf4vJgAxe0EXKysxjBZ6K3BHjVzcOzYMKJRrmnHmFeeX2OPGjOPN7zhDZiamoLT6RQG2s6dO4WRNj09LeaVAhtqTMUZGuL6OGYgPTwBLZKp8cEYy4iPC4+bgbjqwGFPi9HNYGY6w5b1WDAWCX206KCOWqHJgnzrW99CJHKqQht99+1vf7ukbXLoI1NxHHqSh0zlSGtQOK7IFCQVDkE1A6qWRntsFEeczUY3pepZtboVbheL7DDmDX3kHDUmy+TkpLA7aCKPmtvtnp2XSqXw85//HM3Npb1X2FBbwtxy8/eKUk0qVTlJb535Evu5c2Scfe4yaNqpUs1pHcnnhERaPaKj+DykI0ndHyncMQ4ni5d31xMvsiuFj4Oeloqe6VTKa0HWBpu/DppmB6K2srRBb3AxWUZJfz0Z/qiO3H9Ecm5lMvtiPzH5j4rFCjc+oSdLL5nXNT6M/rS/bDL8qqQsBDETtXNq23TKFJTyGNV71KQktRf0HChpnXtdNrKtd00WlPRPa/Amo9AkEcEVFZ6T7Gx0RH6QBpsKP9eGYvJrvEe3pIXsuMpDEgs92wWS55BMun90bBIBnw+1tb6it1du2fxKoSfDz5jHe2V5jxrdPoueowZLUVtbK+wNmtasWXPKfPr+5ptvLmnbRQ2n33rrrTjnnHMQCASEZfiqV71KFHebD0lRPv/5z4fP50NNTY1QPcl1BdL8bdu2iToDd9xxxyk/hizRo0eP5n1P+3r961+/4LZWg6G2VPnzA8eNbgJDN380aqqXXzXDZ8EcpBUFUZW9OGYgEk4gEuEcNcZ8sEeNmc8999yD3/3ud8L2+OEPf4jf//73sxPVUDt27JgQGVl0j9p9992H6667ThhryWQSH/7wh3HppZfi2WefFUZZ1gh70YtehA996EP4whe+ALvdjieeeCKv8PQb3/hGfPSjH0VbWxuuuuoqsY3Ozs7Z+WRc3XjjjSLWs1SyNxAbauYi6xpmjMc2OY20z8tGggk45G83ugmM8BarOOZqMroZDBXVdlk24IdZ4mT7symrGmrUb17sOmcW65tfcskl4u/hw4fR1dVVVtujqCfhL3/5y7zP3/zmN4Vn7dFHHxVeM+Jd73oX3v72t4taAlnWrl2btx6pn+zYsUOsW1dXJ+I5c3nb296Gz372s0LectOmTaX8LvaomRQKFWht54KypoCMZosWnVxqtETHcMzHIhZGQ3fDuvBxPOVdbnRTqp6OzjqoYFVaxsSGGovdMPPo7u7GH//4R3zlK1/BoUOH8IMf/ADLli3Df/7nf6KnpwcXXXQRiuWMlAQmJibE3/r6evF3cHAQf/7zn4UBdsEFF6ClpUVYmeT2y4W8ZevXr0cwGMR5550nJCxzufDCC/Gyl70sz9grFjbUzAmdl/p6j9HNYCj/pLudpNWMbgZDNaOSp6pEMZWH3hosr2MO+vomcOjgoNHNYJhTyHYrLRsdRAO4lZgsyI9+9CNcdtll8Hg8+Otf/4pYLDZrL33iE58oaZvqmYQWvvOd7xRGVdbrRdYjcdNNN+HNb36z8MCR54wqdO/fvz8v9HFkZARDQ0MiPFKWD0frk2VaCmyomZPpUBzHj2UMfMZYnEd7jW4CM0Nc5TAvU6Ao6HdyqQQzYLepSLLHgjEhipIVE+Hrk8nnYx/7GL785S/ja1/7GhyOuYgAspXIcKuooUa5ak8//TS+97055cHsRfuWt7wF11xzDbZv347bbrtNhD5+4xvfyFufctoo7FEGedkof61Ur5rL5RJF5nJz4xjjSSRTsOtJHjIVQ0mmLBcnvlQ5yDlqpiHFpRJMgc/nQjDI0ReM+dI3SEyE+rCRcApDgyHLGWyaolRksiJ79+6dTQXLhSIIx8fHS9pmScO4lEN299134w9/+AM6OjpmvydxEGJ+KCOFOZLiSbGQlCXJXN51111Fr0tuR1rXajfQYiGX2T/9mgW/lYQE+P0u2F0BpAvsT6LYrSvPP6UjrT4skeffOylXddPbXlQi764X/SCTQtfz+qs6stOy9fRMX6dEqr3R4cdUuPAjwC5Zp1QTuxRJf1k5BCImkRQPJ+UtjMUL7ywakV/7sbhO+yTbk0nw610rW6aP4Sl/8XlRqk1SekGv/INknk1H/r7c71TZYzmdkl8Qeo9ymeOlFEn/tsgwBr3+ou/1kl41JYROJXRqkvSHnUU9C4mphPzkBp2Ssho67bYXkNon0gXrIWRQCrxHHE4bXG6aU3hfiu47q/hntaLItic/dvK2WbMTal4KnztVUQted1lRs2QyjenpGBLxFOrqvRganMLk5DRUm4rlPQ149OGjYpnOrjq43HYc2D8korSof0mpPZHpFGLRJGLuRyrwG5mlQGtrKw4cOCBU7XOhFLAVK1YsvqFGF/b111+PH//4x7j33ntFYlwu1LD29vZTJPv37duHF7/4xUU3jpQgySgkdcmVK1cWte7o6KhwM5KRyJiH0FQMSSTh9bEEttFEaoJGN4FhGKYgsWgCRw6PYtv2bqObwpgQ6o/SYAsZThOT04jHU/B6ncKwGhgYF5/XrGnF/v39GB0NCQ/tps0d+PODB8X6nd218Hid6Osdh9NpR03QI/6SweZyOWCzKThrZ7f4m02haWmtEX+TyYRI9QnW2tHVXS+MO0tBYxOLHfhk0cCqN7/5zXjHO94hogjpuunt7RVq+O9973txww03LL6hRuGO3/nOd/CTn/xE1FLr7++fdenRCAM1ipQad+/eja1bt4paaSSxv2fPHlFXoBRI5p9iPUny8jWvec2C1+McNXMyORGBzaUzzM1UjNr+Xgx35w+2MMYw5Mh0ABjj2evhMFQzYLOrSFJ4NlNV9PePo6HBj337+pCIJ9HaVivUFQ/s7xPzd567Cnv39GJiMoxg0IsNGzpx7OgInE4b7PagMLJqgz7hkbXbbVizthU2mzrbF7xo19q8ou2NjXPec5fLDw1zIRF2e+H+o8J11BgJlK5F1wVpc4TDYREGSalYZKiRo2vRDbXbb79d/H3e856X9/2dd945W4yaBEai0aiQ6SevFhlsv/nNb4r2iGUhRckPfOADwqtWDNVsqE1OhsXDikaHzPb7aQTM7bDoUArDlEjUxh5ms9AeH8MhF5dKMEMdtbVrM+kUTPVAnVzSFmhpCYo+jMfjFAZXe/ucpsHWbflhZVu2duV9Ju9Zlhndj7JCIZWWr6O22H1Hk/VNywX1uamwNTmtKAQyFAqJdDDSzCiVokMfF2pRlioCUmgf5FWjqZTtmM1QqQQHD/QjUONBU1MQTzxxRATmr1vfgampCE6cGBHR8xddvB5PP31MGE51dX40twTQ1zsGh8OO+obMBUWjmQ6HDU7n3GjUmbJydRMSWrgs22LOjKlGLuxrFjqjwxg/gwc5Uz4CKS6VYAYyYUPjWFvDgiLVRHt7ptxTY2N+lIGZpPBJTMRsbWLMhdPpPEWvo1QsqwldzYba9h1zCYu7ds1dKM3NQaxY0SKODR2XlStbkUikxL9pogTaaCyBRCKJ8HQcff1jSCZS2L6jG489dhiRcBx1dT70rGjGXx89LLa5cnWjMPYGBiaFmuOGje3oPTmORDIu4sXrG7wYH48Ig8/jceC5Z/rR0eNFoGZuxIsxBls8DviMbgXDmIuYykWWzQC9k4YGJ7F2XXV71cjDRO9pipKZmJgW/yYPk9vtQG/vqNHNq0qyauIyQSKmeolGo6Ls2D333CNqS88Pjy1Fop8NtSoke0y8Xlee6mNXV+Ps55oar4gNJ9JaEmednZ/LlI3zTqXjIn68psYtpPdp06SOlIrEkEolRdLt4MCESPBtbQtgaGgKo+MTYkRqxzlt2PfcCEKhOAI1TixbGcAzjw2J5N32roD4OzIUEQagq7UGofGYUIVzuGzwBV2IhuIYnXZCddpmY8Zzz/dYvLDMdn9ILr8dnpaHMiQkspR60Q+yy6/Uy1K2np7Kn91ReKXAyCRO2hsLryOJzXdKVAaJUupXJnWUHZOS45rUkQeNxeTzolGt6HUSEmXHTDskM3SUC2U8Z2tFXNIOVUctXpGk8KR0ToZM9VHVaXe5a5Pq9W9kSo36iovF35uyec/Z2+RqkXqntsydNkWyPU3nZExOFP5REw3yB8R4XD6v2ZMu+r5Nq4VvDE12sYrDWniezaEVrSwsUCqvxkjtoXchGVFToQhSohRNJk9qaGhSRKi0tNRifHwaIyOT4prdurUHf/7zPsRicTQ314r5Tz55RGxv48YujI2F0N+fiXQ577y1IiKGoAFTMtRo2zJ2775SOu/mm78LsyI/r3TuSrnHiltHEd1ibUGGGglKKrBBsZoyRiUKUlu04PUb3/hG/PrXv8bf//3fY+fOnWWxQdhQY84IOr7iZeSfe2E0NQWQ0uZGpddtmMv3WN5Tj/Zul0juJTZta56dF07GsXVnC1LJtDC8yChTbQqSiTSSMzd1Mp5COpWGN+DE8MkQpqYUuBu84vE6fXJcPF7r1jRjuncC3v4k0k4Hou2t8B49Dk1RkagLwhP1IDg9gbRqw2igHu54BPZUEimbHWHNB388jLSiIGFzIKnaoGpppCkmXatA3DZTlTQnJ3DEOXcvMMaxJXoMj3uKL5XAlJ+d55X3PJAhRR4p6h+QoTM2Ni0MKMqDonfS4OAkUqkUOjoaMDg4gbHRadEp37S5Gw88sEesT6F5tbU+PPtMpuTQlq096O8bw+DQBNwuB3aeuwb79/fCpqoifI+WjccTwtiiAcpAwJ1nZO3YsUL8O9tXueSSTbPtpfVXr54TtyHjLZeuLg5fN4LsuWKPGjMfKl3285//XBS4LheWN9QYc0GhkDY9r4yqCA+ZwA44XZl/D0UV1DaRQTZH1/oGhMfnLmFPw1wcX3BFI8I5CcXhnq7MiLdCHhQHxv1qxgBTVWGw0RC6OjPEHohPi3khpxdJ1Y7m6REx74ivFfXRCdTEp5FQ7TgY7MDa8SNiVG3MVYNpuxvt4WFh1J3wNcOfCMObigljr8/XhObwqLD1InY3YnYnfImIWDZsd4v9qTQiq6hI2uxQtDQ0LK5heKSVO6NmoTbNeZtMtcqrU4c3hXgsKQwhf8CFyYkoYrEknnriJF7wws04fnxUDNCRJ4neHr0nx8RA3rr1y3D40KAwuNweJ7Zs6cSDD+yHoqgiQsTldmD/voxa4PYdPThxfBRTkxF4vC5s3NgpQgfJQCODiCJMKIrD6XQJ44xytymyxOFwiI75ueeuyVMP3JVjUJExRnngWbZvX5HnvVu5ci580+3OFw4iA45ZWsx61Kzaz2SPWsksW7ZMqOKXE8s+IdijZk6efbof51xogKIaXQe2mVEwmw1R21yCetjtQzibrDWdRl8g37Nx2JkxEBNJoNffjN6ceXvreuZCqDQNB2o6oFIgj6IipfgR1dyzxVzjNjtsZNVpGmzpNLzJKBRNE0YfGW2BxLT4fDjYgRUTJ+FIJzHp9GPM5Uf3VKYURm+gGZ5EFHWxyZn9L0cPLYuMYTnmrkHrNBmLCkY9tbClUwhEo0hDxYi/DjXRUGafig0tE2PCo0gGYdyR6TyQQaqJY+UQy1FfY7ENRgZIQCe+kakoA3aL1xek+1pEFNL9rSCdSCGdTIt3psPrRHwqKowi1W5D0gNMjESgpTX4a92iMHBoLIpJWxrLV9fi2MEJRCNUF9OB5jYf9j09IgLBulcExbInjk2JXe44txXPPtMrcp8DQRdWrW7EY4+eFANz3T31sKsO9PVSSLwqIjAmJiKiDlY2h5rC320uO5wuu/A+dXbWw2azC0n2VatbM4/3GSPq4l3roChz91NrayaEn1ifY0wRmzbNeahoXb/fPfuZFAczZDrlemGGTHWRFRMh7yvD5PKZz3xGKNV/+ctfRnd3eWpAsqHGMOWCOj2KDVm7LWFTkVTmQkDHXXMqVnRZ9tnnwlaidhdGPHMdioO1nXmb3lPfM7velNOHQV/D7LwjwXY4ZnIyyEAb8DUID13c5oBdURGhwTFNyxhcmgZ7OglFSYtwT38kJAzJSdTAnk6hJjwhDLTBlmVoHh2AMx5DzOnCSG0Tlg0eF7bueLBerBOcGBP77GvtRMPoIFyJmDD4Rhpb0Np/QuxvsqZWeCxrpsaFATjS0AJ/aAL2RAJJu0MYinUTI2JexO1FSrXBG5kWnyc8ATgTcdhSSbGNsNsLTzQMKq1E66ZUFY5kQixLYarUbntKE59pO7NeyeyBMzHPuPPPN2Mc0Zx7tiAkxjRzZdFdR3/prqfrLQE7HEjCLq49IKI4UZOOiGViil3cn4F0VKw/rvrgT0Xg0RK0NgZsNehIjYl7d0p1I6HZ0ZKeEPfuSVsdarUwgmkKy1ax19mGjfETYuBnzObDuOrFysQgvCMqTgZaRPh2MDYl7oX99cvhOnwCSjqNlN+LVI0fzr6hzH3T3IDIaBixiYi4RWpXNSE2njHM7D4nNM2BVCItQtBpvt2hirDzOk/mlmpq8wrPEX3vcKrYsL0Jfhd5oDLv3mVdc8+8rTvy69Odf9GcR9+mOGeLCRPdyxtmO8RkgHV2zj3vCAodzOYFyXIuGWbxPWqwJvTAWuxXprlfySVz9tlnC0GRFStWwOvNeORzobJlxcKGGlNR1m/k+kTlhnLvUjmdlZh9LuQzpdqRcsyNEk94M94C8rR51CSGg/k5DlPejMverirob8zvWB1tX5EnJjIVmDMsB5vb5yIZNA297V0Zj9xMdzbhdIrPKZuKsNcPlUYiFUV0FmNOap8mwlCz32XWhTDSyAAUIaJurzAsabQ/7PIK46w+NCaWHQo2IhAJwRmaFsseq+9Az8hx0emdcAcw7fRi+VjGK3nc3yrCV4PxKbHsU54urJ86JpYdd/gw7vCjJ0zLKjhob4I/HUFjIuMZeNK3HOvDx+HQUhhVvBi0B7Em1ic62secjXCnE2hMZLydzzg7sDIxAKeWREh1Y8AWRE9ySHTg+21B2JFGQyokPvu1KEbsNWJZ6twP2mvQlRgR80admXPSkMwse8LZgMbkJFzpBOKqQxTLXhbPPPzHbT5hDDQkpsXnPkcd6lLTYlnyog46glgWzSw7ZfcgqdhQlwiJz8OeWgSSYbhTceERHnDVYVk0I14QdriFGmJ9PHMchp1B+FJReFMZb22fpwHLIkNi3rTNLerCNcYmxNkfcgVFCLAvSbL3Co57m9EZGRRGBi07ZfeibWY/g65auBJx1CYz7T/gaUNPdAB2LSWO4agjgOXRwcw15qoXv6spMSme9895OrAq2g93Oo4pmwe9zjqsCWf833TMbFoKbYnxzLnxdGJ5bADedBzTqgtHnE3YGD0h5vnSUexDO5YlMsfpWdcycS786ag4NwedLdgYywxE9NuD4ri2JcdFp+2goxnNyUl4tDjiigPH7A1oTIfE9TGhehGDXeyTzlHW2KN5dLzp2o8oDjGPjMUkVAypgYzHW7FjWPFjVPWJAQriOUe7mCfuFEXB465uNDZkXul0vQ/454SCYj35nqToirmBAV+LDb6WuVCdQOdcvSqHK4nmHGOL8PidqHNn7k+f33mKcFHW21AO6urzw90Zxlw5ahato8aUzJVXXomTJ0/iE5/4BFpaWlhMZCGwoWYuxkbD8Nfyy9doqDM47psztMqKuOfI4Jr7ivL0siScOaUZ0gqmffnx3ImZMMxUGgj5apAxIzIM1bfkqT72uuZCWEccLsRccyPvhxrzww721M0pl047POjzzXRkYxqeC+Qn6T9d0zOr+jgJL3qdc9t9zts5p/qoaXjc3S26y9RpVlQNQ4p/1tty1DHXWU7ChsP2JrEsde4pRDasOMWyaxJ9GLIFYBP6d4ropA/aMh3khJJ5TI/YM3XWaD4ZLWQ0iBxL8lzOhPLGVbtoBxkqmcOrIKI6xb5TM+clbHPNbpcMhOxn2g4ZY7Q+rSeOU3ZZ1TGzbMbop/m0LK0jQmWF4UfeFU18TwbgpCMTTkz/pn3Q/rLXBBlc2XkJ1SaMQnEqVAeidocw4LKcdDWI7SY1CidWcdDdOrOuipDNgxFHQOQ6UVv2u9tmDRfiCW9+HuagY+6a3+fKH4jICohsixzBkL1GTFkOuDL7zIrBPeHOv7bGbP5Z1ccTjnzvzyFHfih1WJ27/skblsuILZCn+jg1c+6J1IwHKXu8hcCRxaFyLo0X1/F7nDEVVq+jRsqyeuqy5dqHFXnggQfw4IMPYuvWrWXbpmUNNfaoyZA9WPRGhvQeRhLp5ALf0znp75tCx4q5TlguOqrrUjno8bj8/I7GCndkJiflv3U6pCfHXnhepcLU9UJ81CLl+T2pKBoSYzjiL1yjyOEsfFwdEqn/07VBjvx4pyTHm3IFS5HTl8nzJ3Xk+ZV4asHy6dkjk726FKSQ39QU4jkHiQyL5MwjeBh+RJO5j2MNITjzNhhDtoOvYWpmnkbqqSlgJJtjObNsOFsLLE3rzdxv1Nw00K/485adQuazLQFEcpeNA4Mz85SZHxLKvjISwLTIq3Nllo1pGII3b7vTeZ9z8ntS2lybModl7rels7L5M8unaVeZfWYHr2On5POJRMrC6KmySTpZ+2wt8toQZUYmwV/qOlNTheeNS0qVEBM6z9C45Lmb0GkDlXMphCb5XiAJN6XbJZVOzqoEz1tJvjnpHHm7ZX1uChOXI3/oFXoHZtpW3j6Jngx/seuYWbZf75iWZjDpXAu6/aE5Q41EcGhZWbuY6mPdunWIRCh6pHxYdkjOqiMdS5kU1UBzWvaSW1LYSHGNBzHMgUaek8KDF0zlaUllQlcZ49mwsY0HWxnTkb0mLdvNzCSZLv5kQT75yU/iPe95D+69916MjIxgcnIybyoF9qgxFYOUu84+twuJdCb/hDEOCjEbsbq63RKiJzWMJ9T88EvGGEiwgzEH4+MRkZDvclm2q8IsQeY8apyjxuTzohe9SPx9wQtecIpNQvZIKUqhln36saFmPkJTMRw7OobVG8pbY4IpHhJyIPEMxniy+WyMOSDhDsYcjI+F0dQYZEONMaXqo2ULXnMdtZK55557UG4s+/RjQ818ZGviMMbjScXgT8cwNSP4wBgHPakO2fIFJxjjeNbRPltjjDEWh8OGlFU7w4wFQh/52mTyueSSS1BuLJ8wxIaBuUIfa6kAD2M4pBVIsuqM8dATimpkMeZgS/y4VUv8LDk2bGpDMMjvDMZcKBZXfZyto7bYk0V48skniwqDfeaZZ5AUstELw/IetWqklN+ut45MASmtxXW2d+pFSHX/auucSGvRguvEU/I7NyIJ6x2Py42NkVhhpbPQlPyGSoR0bh6JAqCaKuFa0xslLiEkIEWVqGXzHKcehz7NC82uQIsVPhY2ieqj3S7fj13naVLKeAnJ8xf8Xu8U6ag+KrFUUd+LebJG6J1DvfNXYB6FodYihBOqJGdQ59wqqRLaoJSg+6q7vTK/cSXPolIUEkuKKdXonk6XT/5e7/CU+TUVCxc+i0NR+c05IlHHJSKSe03vWZ20F26DneQ9i1SKPHRgGLXBGjQ1nxouryh6B69wGzSdwalyqzEy1oXrqDG5bN++Hf39/Whqyq9LK+P888/H448/LopiLwTLG2rsUTMPA/0hqDYFzcvkUtFMZWhMTyGZtmOU6j8xhsPeTfMwLIpMM+ZAQTJZofonTEW5cferC36vlZA7rSePLxto1pfg14qoo5adLJaittivJAt1zTVNww033CCEjxZCPC53chSCDTWmYiSTafhchevlMJWFSiCzcWAOqIjzs0rhenZM5ZlWcgqyM4ZSE3TBYed3BmNWj5q1DDSmNHbt2oW9e/cueHnyqHk8Cw/ptqyhxpiP5hafSA4HCoc+MpWDigjH+fY3BXYthVXaEJ5T2VgzAx2pUey3tyDBAxmGEwi4oWhsqDFmVX20ZuhjJcqcWcmHcu+99y7q9i37JmKPmvmYmoxVde6gmRhVvQipXGTZLCgWC51ZymgiW4nPhxkYGw2jt3fC6GYwTB7ZbiX3Z5hKYHlDzWbjfCizMDgwzaECJqErNQqfFjO6GcyMYTCuLCy2nVl8jtgakAC/N8yAza5yjhpjOqwuz5/1qC32xCwMy8c+OZ1Oo5vA5OSokUQ/YzyqSL/mJ6UZoOCZCbAEuVmo1SIYUWyIW3ccc8kQDLrh83KtR8acoY8WtdMYk2FZQ60aPGpy1SQ9BSStaNUkmaJRWkedKV1AinnbOS3ib1QyQBpPyw0HmQy/nrT0RKh4CX5lOlG0PL+e1H5JkuKloGcAK6f+3mG4EI+TnEhhr1raUXh78QJS/1liem2QybuXIF2uJHTk9BPya1KRnT+ddaQ1AnR/k548/6lfOZBEtzKC59AqaUNxcv+nQ5O1T+8S0v1NJjD4y3if+VMRTKguxAvJ85fyW0s9PiX8Jlk5iTHJs/B0z1DZc7fOJb8Hk5LneFqVP3c1pXC7k+kUJiai8AccRUntp7XCbVB1Sy5I3o06p0GRtHtmb5K9cO+euOXm7xf8/iM3XqGzlux46/R5JP0UPXXJhZ6jVCol+kF6fSGGOVMsO2SYNVYikYjRTWFmeOyRPs4ZNAl2pNmjZiK462YeUtZ9LS45Usk0hod0rEyGMYBZeX6LPrmpn1aJiVkYljfUamtrjW4KM0Nc5tFgKk6LOgVVt44MUymSUHECdUY3g5nhkK0JYZboN02OWkrPq80wBsDy/IyMb33rW/jZz342+/n973+/sEMuuOACHD16FKVgWUMti8PB0r5mob6B83DMlKPGddTMcy58khBUpvL0pIYQ0LiEiBnweBzYcXaX0c1gmDxYTITFRGR84hOfmK2R9uCDD+KLX/wiPvWpT6GxsRHvete7UAqW7allb6BkUh4Xz1SWtmUBo5vAzLAn3YwUhz6aJgw1yLUFTQXL85sDqlP1l4dLG4VmmMXC6oaaGbn99tuxZcsW1NTUiImKRv/iF7+YnR+NRnHdddehoaEBfr8fV1xxBQYGBvK2cezYMbz0pS+F1+tFc3Mz3ve+951iI1BNtB07dsDlcmHVqlX45je/WVQ7jx8/LtYj7rrrLtGOf/7nf8att96KP/7xjyX9dssaarknjzEHzzwxaHQTmBl6lNHTKHkwlYRf9+ZhUnGzPL+JOsTxGIfMMybNUbOooWZGj1pHRwc++clP4tFHH8Vf/vIXPP/5z8crX/lKPPPMM2I+eav+7//+Dz/4wQ9w3333obe3F5dffnme8AsZafF4HA888IAIUSQj7MYbb5xd5vDhw2KZv/mbv8Hjjz+Od77znXjTm96EX/3qVwtuJxmJIyMj4t+//vWv8bd/+7fi3263u2TNDMurPra0ZJQGGePPh1Vd3UsRrxI3ugnMDFE4sB/NRjeDmWFacYm8QcYchlpDI8vzMyb1qHGOWsV4+ctfnvf54x//uPCyPfTQQ8KIu+OOO/Cd73xHGHDEnXfeifXr14v55513njCann32Wfz2t78VdsG2bdvw0Y9+FB/4wAdw0003iVJeX/7yl9HT04PPfOYzYhu0/v3334/bbrsNl1122YLaSYYZGXfbt2/Hvn378JKXvER8Twbl8uXLS/rtljfUsvUulio37v7HskrVllXetoAE/+xetOQp52Pl2lrxvUyGXybbT4xKJKTHYvKR76nJdNES/EpYR54/JTlGJkh2V5I6bShgIYftNiiazm+VSe3bdaTxdeT5S5GEl12qit4opo50v+z86Un66yK7HnQGJLQCUu0exNFmD+FQuqH4NpQw+iFVKNfbVinn1lZBSf8ybq85FcKU5sKYUuD1qNcxk7Wh1M5cGTuBk5Pybek9Q4ejhc97q0d+z0TthfflUuXPG1WRz2tpDxSUP1d0xJAURSa1r/N8UAofB6XkyINSniuqKUv+ZLnvvmdwySUbF7w9fY+TVkK/QtYX0ZPaTxW9n9ORfeRl+kbZyUKoOu+JMu6DmJyczPuaQg5p0iOVSgnP2fT0tAiBJC9bIpHAC1/4wtll1q1bh66uLpEnRoYa/d28eXOe84aMr7e+9a3CiCLDipbJ3UZ2GfKsLRTKSfvIRz4iQiB/9KMfiVBMgtp45ZVXohQsa6hlGR4eLtmKZcpHIpFGLMr5gmbhSLIOHN1lDuidzwqc5oG6j5yjZh6efqIXF1y8wuhmMBwsPwvnqJWPzs7OvM+7d+8WHq5CPPXUU8Iwo5QmCjH88Y9/jA0bNogwRfKIzVd5J6Osv79f/Jv+zo+wy34+3TJkTFLYYlYkRA9a9t///d9PcRLRbyLjrRQsa6hlb6DTWeZMZYhFUxgdiaKtgwVFzMAG5yCeTLUb3Qxmpm4XeXAYczCkBpCSeP0Zppo5+5yMSEK1M+dRgyWphCpjdvtkvJA4SBa9PvvatWuFUTYxMYEf/vCHuPrqq0U+mpmg0Mm+vj4hVpLL6OiomEfewGKxrKGWJfcCYIwjmUzD7ljaYagMsxhQPtSExqUrzIJbS4hzkrD+63FJsGYd52+ahWNHh7BiZQuczuoue5T1lpAqKXNmZFUcFwJ5zbKKimeddRYeeeQRfP7zn8drXvMaIRIyPj6e51Uj1cfW1lbxb/r78MMP520vqwqZu8x8pUj6TO1biDdNz8saCoWEoEgpWPZNRAfLbrfzjWQSautcCNay18As9CUDHMdiEjxKAi1qCAfTjUY3hSGhHS2OhGZDSCntpcqUl9BUDI1NfqObwVBucySOWCxZ9Yaa1UMfKd223GnE89HKsP10Oo1YLCaMNqqZ/Lvf/U7I4RN79+4VcvwUKknQXxIgGRwcnPV2/eY3vxFGGIVPZpf5+c9/nrcPWia7DT3e/e53z14bpCRJJQCykBftz3/+sxAwKQVLG2pUH4EkOsndyBjL0EBYeNXaOfTRFKTZSjMV1nzdL+V7g8+IWeg9OYHlK0oQ2mHKjt/vRpqVDi0f+mhGPvShD+HFL36xEAiZmpoSCo9U84yk84PBIN74xjcKY6m+vl4YX9dff70wsEhIhLj00kuFQfa6171OFKCmfDQS/aDaa9lwy2uvvRb/8R//gfe///14wxvegN///vf4/ve/j5/97Genbd9jjz02a3tQLh15/7LQv7du3Yr3vve9Jf12yxpqBJ08OmlLGx01I8lTQtNVcNTKp5o0T9kxl1Q6X8UrEolBtSni+3iqsJEQTsqNh8FI4bDJoaiO6uNY4fapeqImeuqJSYlqoBme1jIFQokq3zL3BEbicle+ItuezvEppGg4uz3ZvFIC4XWOt676pew36Z2/UlT+FjASm0sMKgYVLxSZIl2ZhzY1mRqjTSc0WdU5Dg6leKVInXm6apHFoiuAW3hmHxqgyJRcS9heyeqNsusyXfw6kxPylQZ0nqEDkudup09+jvyS6yGuI15k01GEpB+cSsdPuXdUHVk6TaqeqPOMkhxYejcqEuWlcj/6FcX4CCA9L9HKlS1FepJ03ksy1Uedbcv7IqmiVavTOusop5M8nLkWSYWSfoden2spUskctYUyODiIq666SuR/Ud+eil+TkZatU0YS+hSSSh418rKRWuOXvvSl2fVtNhvuvvtuofJIBpzP5xM5brfccsvsMuTUIaOMarJRSCXJ/n/9619fkDT/PffcI/5ec801Yt1ypl1Z1lCjm73UeFCm/DhdNjidnKPGMPOxKRpcSgrT1nrXL1makpOwpVMYsAWNbgoDYOcFXUY3gZnhyJEhOBw2dHZWd5g2e9Qqzx133KE7n/r7JI1Pk4zu7u5TQhvn87znPW/WO1YKVL+t3FjWUCMo9JEsa8Z4aoJOOFhMxDTsiTVyjppJoPLKNWoUo+m5mHbGOEian8slmIfnnh7AqjWNcLkt3V1ZEpCRltCrVVklWD1HzYwetaUC1Xb75Cc/KfLlyAs4Xyfj0KFDRW/Tsk8+uoHogLGhZg6OHJxEe6cPNUEWFDEa6oQuc0zhMNVSY0yCRd9aS5Co4oB90au9MguFcptTslBUpqLU1voQj3M9VKsbakzpvOlNbxIlAygXrq2trWC6Q7FY1lAjVq5cmZfQxxgsz6+Xl8JUDBWaUBpkzMGk5sJkkgcwzEJEdULVyV9hKkttnce6w+9LDKfTzoZabq6xRQ01+n3lMDBOtw8r8otf/ELkuV144YVl26Zle8400kFKLizPbw7aO/xwuXWyyZmKQY/HiFbd8spmIqDEsMw2aXQzmBlq0hE0p6aMbgYzQ0urH04nvzvMABlpVEut2pnzqBndEsZs1NXVlV3E0LKGGnH06FGRp8YYT3g6AXWxC3MwCyIBFUfjLJRgJjERmwnU3pgMGhSRp8aYg2NHxzExFjG6GYzIUbNzjlqemAg/J5h8PvrRj4o6auFwGOXC0qGP5ZTHtApySVx5RzEtkeFPafLwuXg63yg7cXwazZ01Qq03IpHnH47Kxw36woVHVAcm5cafOh4t+L2SkP9W6Tw9ie1KPaxLDBWY3+n0qnG0O0I4kJDXJtJknmidd7RUgl/MrJCRXorUfqk2Uimy6wVk7hNpBRHFrl8aouj9yGcp6cIzdX+NS+7R0CQiDw6PfB29iHSbRJ5ftRV/2lOSkhpEUvIcSqS9GIs7hZzzKevojftJzp/e8wZ6uVeSWbrlQGT353Rcukr/uLxMR6+v8LkdjMofBEFn4fa5bPJ223Xk+RVbGolUHGnkXzQpTW+cWXIN6TyHNL0KvIrs98rbQOZ+sZi94+9y2bBpc+cpTwu5PH0p5YXSJUjty2/MNBJFfU/Y5l1r8rA9LWeyDpSiu9hpulZNA/7MZz6DgwcPoqWlBcuXLxeFuHP561//WvQ2LWuo0UOgs5MeKAzDzM9R44LX5iGq2RFPc2iXWbBpKTi0OCLgvEEz0NLqg02vxh9TMchAOXRoAJs2cckEwuR2NWMAr3rVq8q+Tcsaaoy52HpuplAmYzwJzYbRpHwUnaksNWoMfiWO4wkORzUDDi2FmmQEY/aA0U1hyHOW0pBMpuDxcl6tGRgdDaHasXroI8vzl87u3btRbiw7TEU30P33329ZZZmlxt4nR4xuAjMDvVoSumFDTCVRFHngEGOc15kxT37zhCSMnak8Hg8rabM8P1NJLO1RO+uss4xuAjNDNMqiLmbBb4vDqyYxzZLwpmA67RThj4w5mLJ5MOZyG90MZgbVpiBVzvxN5ozYsWMFqh2rG2rsUSsOUnnct28fGhsbheqjnoNodHS0yK1b2FCjRHC73c4eNZMQ4ELXpoF8aWm9xHmmotiQhr1kRROm3HjSMdTHJ3DU1Wx0UxgATc0+MTHm4E9/2oNduzagmmF5fiaX2267DYFAJlT+c5/7HMqN3co30tDQUNnrGSx1NIksmOx7Ii0p/prS5EpisZxVaNSpuatm9rtwsrCRcHJaLqgwGC08b3hER8ExtxG56KmwyZT8Sn0il7P/XUAxsBTGEy6SjtNVqpLKk+sZeCn59rQKDZjoKuKZgcSp7fPa43CrSUylJCFFpfwmPXW7Mio7itV8hderqZG3weuTz3M7C8+z61z/yXkqs1liOnXdY7HC23MnVPjTGgI1p4YHxyXrZLZXOJw4qXMulKjOPShT4tVTG5XMUyTHhxgekj+keusLn9sjU/Jrpd5VuA0eu7zdDp1I7PD0FIaHIuhZlZ/Dqah6iouF26foZHvoHCIommx7OueirM+8coeqF/9iyh5TVVWRSqWrWuDF6uP/9PMW3aMG63D11VcX/He5sKyhRoWujx8/jrVr12IpcOPuVxf8Xt+1XrwkrhEkYikcfm4U63fwCLUZCNhjwqM2kbbs7b/k0JUGZypKSrEhYucIADMRjXDovNFkB3M7u+qEHL6aYwzLpfa14geNdUoFaSh8HaQl3+uVEdLbz0KtCKuGPjJnRiqVwl133YXnnntOfN64cSNe8YpXFCz5shAs21MjN2RXVxeHPpqAZFKDTW/IlKkoHjXJcvAmghU4zUXSZsOk0290M5gZHE4VHq9luypLDr/fjXQpNSQthNVz1Kgkql5Z1HJg1bHJAwcO4CUveQlOnjw56yi69dZbRbmwn/3sZ1i5cmXR27Rs75mKzMViMaObwcy8aFuWccfHLJA3Lcmqj6ah1hZFoz1sdDOYGRypJNqnh4xuBjODy21DazvnqJmF3t4xhELVrcJpdUONKZ23v/3twhijiD4qbk3TsWPH0NPTI+aVgmWHqaampjA8PMweNROQ1MsJYyrOQMKXCUjhW8MU2JU0bArfI2ZBg6Kfe8RUPCLj2SdHsINrcZoCh8OGRFye014VWLxfyaqPpXPffffhoYceytPHaGhowCc/+UlceOGFJW3TssPqLpdL5KkxxjM9FUdogr2bZqHbNYEam1wIhql8AfI4y/ObhqRqw6CnzuhmMDPYSJ5fR6iIqSzd3U2oq2cPJ8EONaaQ7UGOovmEQiE4naXVILSsoUYWbKmJe0x5SSbTsNkte6ktyWK+LM9vHkjtcYJr2pkGujNcKR25SKaiqKqC7hU1RjeDmWE6FMXoSAjVjFW9QfM9aos9WZGXvexl+Od//mf8+c9/FqGxNJGH7dprrxWCIqVg2WFcqqFm9Rw1maJSKUqRMgl+PUWlhE5CcSQ1Z5gFmgOirZGUgqMhufF8Miyfd3Sq8KUaG5Dn9qiSUdiyS7iX4rgtKRm7PE+28YQbsZRNfzhQZleXOLJdVSHIRV5fjY6wWGcg7i/btZJukQuUODsLj4Y3NsgHUxoC8jbUOQvfAH6H/Jnitsm3Z5dcKg4def6ERFs9qXPoZGVClFQKamwCYx2nGgcjMfkzamSs8M7Gx+WNiEtk3/WuI0XvepBdezr3bWowIp13rFFyrbjkXYdGd+HrIeCQPyidOuN4DjWNSDyF+LzfrSrxouX59VB1HuSKZJ6i6JQIKCkPWPacrGR0kP7zJp5IYGIigubWQM4asvbpqD5KVBf1SwVJVB8lyo5681TFcQbv2twcNWUByzPVwr//+78Lif7zzz9faGUQyWRSGGmf//znS9qmZQ21vXv3ioNTVR1EkzJ0cgreGicCtW6jm8KIUDsVKfaomQsOoTFVjhqfEHPRdyyEtg4WpDIDTpfdst6QhWJ1MRFFVcS02PuwIrW1tfjJT36C/fv3C3l+ulbWr1+PVatWlbxNyxpqpLpCxhpjPLFIUhhqjDlod4VwOBJEnJUfTYEodM3ptOZBVTC2rMPoVjCMKamt9SIY9KKaqXZDlTk9q1evnjXOztRhpFo5oY8xBw6XDQ4n5wuaBZsIVOE3jVlghUHzEezvM7oJTA6bz24yugnMDLFYEo/+5bDRzTAFFnWocY7aGXLHHXdg06ZNcLvdYqJ/f/3rXy95e5b1qJHCCsGhj8bT0OZjQ81EHI4GuY6aiQjY4kgpCsJpvZwJppLYEywmYiYOPDeG9VsbjW4GMyPPn0xUtzw/9ysZGTfeeCM++9nP4vrrrxd5asSDDz6Id73rXaKe2i233IJisayh1tvba3QTmBkOPT2MVVubYZepBDAVpdkRxtEYq6iZBb4rTIaiIO6Wi7EwlSc8XVhEgjFGhbO1LWh0M0yBVXPUmNK5/fbb8bWvfQ1XXnnl7HckJLJlyxZhvJViqFl2WJ2qgDPmIJXURC0cxhwE7VE2D0zEQMKLkQQbBmYiXMd11MyEP8DeZjN5k3z+6hYGs7pHjUMfSyeRSODss88+5fuzzjpLCByWgmU9anSwrIHeiI1M3jZdZkncwscyLpHEJiI50td1HTWIzsj194XlYwNDUR3p62GJPHJU58IvZbRLtk6pYg9pE7RhHom0uoB2lfspaoKRx0oNSxV5nuodUSShYDxZuPOjBeX5tjZJfall7fIf2xYo3MB2j1zuvNUjfz7Uuwpvr8YhP+ceu3yeS/IYUJXi5fnjOhFaVC5ExsnHeuHbcKqgyEBUflxP+gq/Tvd45K/ZPp3Aj6RMUl+vRIbsmaz3LEzJL9j+vsIH8GBALg5VJ7keap0614NNfqLsqoKWFXWYTuR3kFUlUYKinN7N6Shenl/noaI3T46Ze69z52/PcyfQ2LT6jOT509K+SLJoeX69Po9Mhl8twwuBPWrMfF73utcJrxqFP+by1a9+Ff/0T/+EUrCsoTYwMFAVIx9LAcnzmDGI56YbjG4Ck4NTSUHTqcfEMNXO/ieHsG57M2wcPm8Kqr1fZfnfXwmPl8XFRH7961/jvPPOE5+p+DXlp1111VV497vfPbvcfGNORlG9g1tvvRXnnHMOAoEAmpub8apXvUoqgU8jDS9+8YvFBX3XXXflzfvpT3+KNWvWYO3atbj77rtnvz9y5IhYnrY9NTWVt862bdtw0003FdNcxiQMn5g0uglMDlv8Q0Y3gZlX147FXcyFs63W6CYwOag2FSk9TyJTUTZvXWZ0ExjGlDz99NPYsWMHmpqacPDgQTE1NjaK72jeY489JqbHH398cTxq9913H6677jphrFGs5Yc//GFceumlePbZZ+Hz+fKW/dznPldw1CEWi4lt3HnnncKYe8Mb3iC24XTOhVKQkfbpT38aN998M0qFJDEZhmHMjshPKyVEllk0tCSHAZiJlk4/5zmbiL7eCfj9biEsUs1YNfSRTutin1qrXjr33HNP2bdZ1DDuL3/5S7z+9a/Hxo0bsXXrVnzzm98U7rxHH300bzmyFD/zmc/gG9/4RkFDzWazCQ/Z9u3bYbfbxXe5kDIKuQQHBwdL/V0lJ+0x5WfF1hajm8DkMMzCFaYrQF5rz38GMsaSGOIoADNBg75pHswwDRMTESST1SvRb/XIR6Y8fPe738X09PQZb+eM4m0mJibE3/r6+tnvwuEwXvva1+KLX/wiWltbT1mnpqYG11xzDdra2tDe3o63vvWtIpQyF5K1pIrepchYZhkfHy95Xaa8I069B8aMbgaTQyglFwJgjIG7oAwjZ2wwjGiYB1/NgtfjRIq9zpaFVR/Lw1ve8pZZvYwzoWQxkXQ6jXe+85248MILRdXtLFTU7YILLsArX/lK6bq7d+8W66qqeoqRlh09++QnP4mXv/zlYnsrV64sun0Oh8PyLnTZOrqqjxJ1R02ipqSn+hjTUU3Llr0haf7pSHL280BEPjZwPCy/HCdHC7fBFJk9eiO9pYRGyLZXph+73D2OJ6ea9Z+Ulhy9LuHNIDvmpfZRCoRvTWhuRHrqkFQLG9CtnfJnWWd94fPU6ZMrOHb7Cz8D2nSUHRvd8h8ckKg7Om3ya8ihcy3LItz0zp6sdXp9yaSOam3HeY2wOU595gzqPL9kKpd6t1IkrKN0m7IVHZYp/UUldqrVMSrlcSq9fXKv/FF34ed40CFvQ42OIiRdR0momI4Dthw1YT1dEcVe+Pp3qPL9qDrKxyoKnwtF8n12buFvS+mhVvJNd/prZd0mEqRKI6VlIgE0yVCTTGVaty+iM2ylSrqsqmIvup+kJwhyunM0O1/L/Lu0c8pYHa1MobElG2qUZ0aJcffff3+eSMjvf/97kSh3OoJB/YKJl112GS666CLccMMN+M53vlNSG0mwpLaWk8KNREtrcPvZg8MwMuhZrvGL3lT0HxjFsvVNRjeDmaG1JygGdhlzsH/vMOobPGhs8sPqnex4PIVIOIF4PImmZj9OHBuHonixYsUKqKodD95/BKFQGFaCRIgXW4iYhY4X2VB729veJtQa//CHP6CjY67WDBlppHAy3zi64oorcPHFF+Pee+8taj/kVTv//PPxvve9r+g2xuNx4XKMRguPCDKVQbWraOjSN8qZynIwzIMXZqLeHsWYFkdMp44TU1liYavU4bQGkyNR2B0qaho4v9YMOBwqEon0kjG4KL9xOhRHIp6Cz+9EOJzA2GhEGGHrNzbjqcf7xPy6eg86umrx1OP9Yt3Va5uQTKYxNhqGw2FDY5MPbg89pxOif5lOJ3HuBd2WM9SY8vCLX/xCpHhV1FCjC56EPn784x8Lo6unpydv/gc/+EG86U1vyvtu8+bNuO2220QYY7Hs3LkTl19+udhusVBbQ6HQKUIlTGWJTsUwOTSN1lVcu8scaKLA8jTnqZkKKwaaLmXcPr4/zEQykWIxERMRrPVUTPGR+nLJRBoOpw0T41Hh2bLbbXB77DhxbEJcG+2dQUyOR3HyeEY34byLuvHYX04iFkuivsGLjs4gjh8dF8aW022H3a6ipsYFpyvTBd6wuQU2mzobDnnBxfl927b2mtl/k1cNSAqRCGqbFZUvK5FDVg05ahdeeGFZtmMvNtyRwhB/8pOfiNyy/v7+2TBGj8cjxEMKCYh0dXWdYtQtlI9//ONCZZLUIa1MOBzDsWPDiIRj8PndWLWqFUeODMHpsCFY64XL5UAsloDTaRfTUim4mE6mxQOQMQcqNATtMRw3uiHMLL1xP+IqewrMRHMPe53NBHWurSqFvhTx+ZxIJBam+kgGNnmyEskUXC67CCOcmoohmUiiq6cW+54bRjSaRE3QJTxazz41IHrxa9Y1YHIihoG+EBxOFdvPacfgQEh08GuCbuEZq61zC+OL2hMIuNDZXTvbN9pxTibaK3vdbNyS3zdVanPyHe16uYanskS6X4xBfPvb38a//du/Yf/+/bNpWBQZ+LrXva6k7RVl/dx+++3i7/Oe97y876kmGsn2Lwb0A6nW2le/+lVYGa/XhRUrWoQxlh05pAdQPJEUo0I0inT06JB44G3a3IVDhwYwOjoJj8eJ7TuW44E/7RMGUWdXA7x+O3pPZkaPurobMB2KCSldGj2qCdrFQ5FCF3JHkBYLp8cBm6O4hyCzeNDppgLLjHlocEQwrvkwrXDtR7Nw7KlBrDj7zENWmPJAIY+U78xUDuHJSqaFuiOF+42OTItwRzK2QqEYDuwbRnOLH13ddRgaCmGgb0rop1Ao4J8fOIp0SkNjs1d4oA7uGxYeseUr6kV/hjxkdmfmPdTS5hceLqfLJvos513cNduG+kYvlq+sm/28em1jXhsz3i19YRCmeOh4LvYxteo5++xnPyu0NShFLOtRIy2Pa6+9FsPDw0IgcdFDH4ulmHWWL19ecPmvfOUrYrI6WW9Zls5OeijNHY+GhjmFzM2bu5DOUWq8eNc6pFLpzMM1nUBbW1A8VMktT0ba5ERE9NJrgrV47pk+MRrW3FIDf8CFfc8NII0kNm5pQf/JSYyPR+HxOLB5Wyv2PDMIzUax2y54fQ5MjMfhcCgIBF1IxDKts9kVqdeMjE6VC5WahpSmYu80h6GaCbeahL1kGUlmUeBHlqmYHIkgPBVH+wr2dC4kH4sMISHWoFEZpajoAzQ0+ESuFX3O5l/99S/HxbJkcNUEPdj7HEmJa9i4uVV4r2h5MtK2bGvH0OC0eM/b61W4nDa4XDa0LasRIYgdXUHhzcr2A8hYy7Qn81zbsXNOyyBQ48pTfQzW8gAVYx2+8IUvCKfWVVddNfvdK17xChEZeNNNNy2+ocZUHj2p2vlkH5Iq0qhv8M1+39JaIyYimY5h21lzD03i/It7kNQyybArA3WZBz2NpGlJNLd5EYopgN2GeFLDVCghJPftLjv6+2KYGImI9dZsb8GeR/rFurVNHgzbfRg/PIL4VAw1XbWwOeOIh2IYn3Yj2doI2+i4MBzTLic0lxNKLI6pIRVxe0ZQQajg5Yy4KKkSJOvNPGJTyuhwqSPKOTH0LjWJTvcUDoTr9EsHVOrYlRLOVGrbpGUPyijbT7tplSuhBbpPDXG0hRWsbHci5i4sJtLtkwtbLPcXLquxXCLBTzR7ChuFdS65sejWcYo7JZLnehL8dp15qiKR05avIn1K6knwJ3Xup56eABpcp8532fQM6sLnYjIhb0Nfg/wVHA4XXm86pSM6E5WUWdEbLNP7SZLzFBqRl3M5GCic31ebMwg5nxbJNUnUOdOIaSoicQ3TOceylPG/tE1+LzlU+TxFciAURX5jyCXblTzDih6B9N6MRpJIpciQcWJiPCZCBOlRRx6nA3tHxTu5odEjvE+H9o+JdTZtb0HvsSkhjEERM9vOasNf/nxSLNPa5hfhgKOjIfG5ts4Ohyud+et0gIoebNjUJERaaECXPBznXdQJZUaOrybHgKJ+yNr1zbOfqS2bvU4x0KuHougJJFVGPEn+uuDBMRmco1Y6fX19okTZfOg7mlcKbKgxedDD2mab85DV1nvgynknd6+cU3Bs6XKhpWsuyXbdOXMx4FPjNrRsXSaMNaffNTulBmcMMVWFkkqLCckUbONTaA6FMOyrhy8eRm10Uix3oLEHPSPH0JaIY1zxYlgNYGVqECmoOG6rhwthBJQYUpqCY6hDE6ahQEMEDkSgwqMkkIaCmEY+C4s+GYrAxrIVpmPQUweHi8NRzYRVw3KWKhRC75uLgKsoGWMq89ykMMBYLIVkUkNN0ImJsYhIJaD3JRlRRw6NCyOGQv7IDjh+dFJ83rK9RRhXU5NxEZmyblMjHv1zn+isdnYH4XLb0HeSDCobPJ46xGNJETpI35MR1dTiE8aWm4QwHKoICcyKWKzdkF9GYuf5HXnGIuVzZXHN1rhT530uDmr3QP/UaQ01hqk2Vq1ahe9///v48Ic/nPf9//zP/2D16tUlbZMNNWZRqemoFR4L1abC5rRDm8482NO1cwYekWxvxvFEZiQ24vRg2D8Xnne4oQuDobkCpk8pHbAhhTRUxJBGXLMLLyJBr1MyyZxIgdZotk3DpqQxkPSLELMWWwiqLY2nI01Y7RqFS01hKuVEbyKAte4R4XU5Hg8I0Q2STSfj7nA0iGZnGDakEU3bMZZ0o25mHo1ua+RwVNIZgzBtE+Zg5rVuvs5eUlMwkeSXq5nomB6EMxZEzO01uinMDP1HJ9HQOheVwBgLhc8Xsp2zHikiEUuJ8H/KdYpQ7SuK/qA8qVYvBnunEY+l4PeqqGtw48iBCREZ0trhQyyaQu+xkHjmbz+3BXufGUU4lEAg6ETPqiCe/OuwMIq6e4KiDYP9YRHu7/PXCSONDDHK+SZHVG29WxhUHq8Ddht5sdyw2TMeqw1bsgZV5oecd1F+ZEtDo3fWm9XaPpfmQFDNMjNBv4dCI1euzs8ZY6wBe9RK5+abb8ZrXvMaUb4sm6P2pz/9Cb/73e+EAVcKbKgxi0qofxLeJr8w1MqFpiigrB4iDruYsgxjLuyMljqUrJ/9PJkCBlN+KPFMWNjeWP5L5slICyj2hJ4f9NImI8ymUNBHxiBzzBhjNJ8MPJqX1FRhCDYKQ07DwUgtutyT8NsSCKftOBKuwQb/iNh+X8yPtKZgmXtKWHJ7QvVoc4XgsSURSdlxPFKDbu+kWGY84RKiHzX2mPg8mvBkcpnUNJJpFaGUA15bUsyj5eivODYzv1wGGbehJNfrMh8WfWsx1QHJlIsAOQW2dEpMiqYh5nDBGwvDpqWhRjRodhvsoWkopAQY8MEWi8M2HcH0dAK+7kaEDg1CS6Rg97vhqPVi+vAQDrjTaFgWQN+BUeFZI1ae1YZ9jw8iGU+hpt6NxnY/ThwYFwZdc0cAaS2F0GRcGElkyJEXiv7t9ari38u6AiJyhIw6RVXQuswH50ws5aZt+e+FnRe25oU+NrXMDai0d+QbVI1N3gWEPlrD40wGabXDQqTMfKhu9J///GdRluyuu+4S361fvx4PP/wwtm/fjlKwvKHGISzGEp+Ow9u8lM4BBU4CZK6R8EY2UnAqle+F6o3nvKDTGsaTc+ElhyI5MTqahien5mL7iZGEZyZfSsPxSEAYfHSZ0jdDMY/I0YmnM7kP1IZMzo4Gty0pjDMK8yTDsd2dGQUeS7jF8p2eKbHsgelaNDkjCDpiSKRVPBduxJbAkNgGGYTkGXTaSHhGwaFIEEF7HAF7TBidB8N1WO6ZmP3NZBA2OiLCQB1NuOFUUsJIzXz2oMaWqVMY12yIp9XZebTf2UMwc0zZGCnMpMOHWouXH1lqrN6WH05mGjQNChk9M0YR3VkOjQLBNUQUBzxaTHymeSHFjXot84wIaw5xX9ZpYfF5QAmgXgvDi7h4nhxHPdZoA2LeqOJDFHYs10aFsbVPbUazNoVaLYIEbHjG1o6t8WOiOSOqH5OqGz3JYbHPg/Ym1Ian4Y2HkVJt6K1tQ00sJO5/NelAakYGXbPZQC6otMMBLaDC1ZCJr/csq8soztHAnqoguKkDq2qTSMSS8AbdWL6lZfZQrNmW/1xdNeuxAnx2DXWNc16oBgpFFDmGmSeRv4br5J0p55w3p85YbVi9X8ketTPjrLPOwn/913+hXHDvgFlUSJpf5ZE3CYroPM3YbILQvELU0XiOtzCeHxp3YDo/aWNias6YPBpxAJG5dIQnpzKdGArZ9Nvj2BMOzIZoRlN2DMfds5oaQ3EyFilMMuOpi6QptDQTYkSROQ6VWp1pMHn8yNAk7+FU0okO95TYbl/MB68tIYw8eiA/HWrEOt+IMPQmky4MxL1Y7R2bMVZrhIHX7JoW23x6qhGrfeNC+IS22RvzY413TOzxZMwv2kaGKLXnQKQO7a6Q2C61czDuQ4drSixLhiV1HmvtUfG3P+4TNeRoWTIsycCl9mlqxvCl30ptpmUnk86MB9OmZQzjtAM+leZReQObWNahpsi9i5hmE17V7PGkkFu76CyTB5OOVGZsXRwxcRDz31DUoSUvMSMnN8SNDhUJKZCgHH1FYWcU1pYtPktelXiUPkOEW5OqHSngZjvok+OxjBqt3SbqM4Um4mJdf9CFeDSJSDiJwwcmsPn8doz0T4sQOZfHDm/AgcETYbFssNEr8oimx6JiP22r6jBxdBSpRAoOjxOeBi/Gj4xmzre3AUgkgdHJzOcVy4Djg0A8gcaUH2O1jWgdOCHaN17bIAyx4MQYfJMa9vo70BkZgjcVQ9jmwnFPEzbHTgjPeL89iJSiojk5Ka6xw84m1KYjcCOBGOwIKS54tEyOrgq7GHwiQytNIk7CkHOJXF66z+k4HlXqxfcpEViu4EllGbQZL9MRJX+g6glnl/TziN+JEcxFMvTXZAyqjkBGrCNRN6fcqDkyzze7PxPabnMV9vbbnTa0r2G1WjPx0J+O4LwLlxvdDIYxHel0GgcOHMDg4KD4dy67du0qentsqDGLSrCrrqxhj8yZQR4yMmiynkMU8B5Ok7GYYziQ5yxLPGkXhlaWE7H8XMP94bkOGnnkBuJzoah75pUFeDLUnBc7QoZUlr3Tc9uhhpGhRy0WRo8CTCVdUJSMETUU94ocRZpHn8lAo3lkjBHhlCPjsSR1NU0RHVWCtke5hfQxppCfUREhq/Q9GW4BWwIee0J0ZMNxB1qcJFQDjCddYtutjoxwzbFYEA2OMAK2uFh2H4JYiwGx7Ah8mIYT3RgVyw4kOxCMh1AbD4lO9r7abmwcPQhbyo3JmjpEvD40DpNENjDS0AJ3NAz/wLgw5KZ7uuA7egJKKoWk34d4jR/JExkVKbW9gax6pEenMkdsRxvG9w0gFU/BWeOGtzmA8QPkVQW8q4JIxpKY7A+Jzx1bWtG/bxiDyQS8ASdauoM4/HRm2eauGmHUjJ/MbHfN9mYc3zeGyHQcHr8TnavqsOeJTHvbugIijKz3SEYIaNP2Jhw/NCFC0NxeO1ZtbMDTj2SWXdblh9Npw9GDGe/tuq2N6Ds2hYmxGNweGzZsa8RjD2WWbWn3wut34Mj+CXGu1mysx3B/BONjUVF3aeP2Rux9ekSYw/VNbpFbdPxQpqbTsp4gpibimBqPQbUr8NfUY2SAjC3AW+OEb8ZwowEIj88h8o3I6ItOzykAZo0/GkV3euwZwSU7eY8ds2p54rg2+sUVms3HrV1eL47HicmZ+6XWP3dfLc8ILw0PZzzxJzt68u6NUKAWR2akzQ/72vLvG3dG+jzLmG3uHuuz5cvXn7DN3Eck2kSDMsrcQNA0ZhQLZ5oURf4gkdmiuoaPTbCxZiKy9V4ZhpnjoYcewmtf+1ocPXr0lHJj9K5IkbRrkbChZkHpfv3adYUlaTVNfvEkJBLXMZ3rbTSWMc4mn+5Fzaa5pOn+qFzOeHJSRy63jA4HPe+FPKTB5C+lBUrMu9WUMGJIFMUUsQcLakPGqMyeAeG1yvlC/JYcJueFqcay81UF42kKO52ZoQJ9yUCeBPn0jKANXW8DKR8058wIvwc4iJlQKwegrgziQM4+enP+3blMxSRWiX/TnkkndRyZDubqIN001L7M561Iw+GsR/2yGjR6Ztrdnim0nAnyCmBVTdZQjlOF17zftmxd6zy564yx20hS++fOC99ryyzrtc8cuFVzHfyOsxvhyX5PYgYX5W4XcK2c8+Y2n5VrRAPLduUvu6ors6yqqGicl+9z8a72PFnzzmVzhn7jZk9ebs9FOYVvxX5a5oyQxtr83KCdOXWaRBvrM/M1pNFSny/EsHlTpk2pdCbcrjk4p2QrzlirGzHKmbJrCHTlC4o4nPMFX3LqXjblXoeiwqT412hehzb/OdLplcvcDwcKv55T80Zoc0nOeKhO+V6+G4qvls+TvEscHvlz3OUqfE/r9ev1ShgMz7xLhsficM78W7RBlR+H0kp3yhso25eSNXYLzVMKnwtVp7aHTO5fVh4gu0Xp9iQvTv3QPck68/bT3l4P26yn1QTvkqIp/Z2uzNz3JP5Cx8CmyEs7LEWoO1FK1Zpi92FFrr32Wpx99tn42c9+hra2trKEybKhxjBVBIX2EZPCWGDMwHRjI+oL1OxijGPFOsqVMroVTC52SZ1Bxhjq632ZEGKr9rgZpgT279+PH/7wh0Kmv1xwTBqzqLia80PjGGOhVyrXkzMX7qkpUUuQMQ/PPT4kctsY89C4iqXgzcTBA0OIx/VctdYlG7VkVdVHNcertmgTrMm5554r8tPKCXvUmEVFYSERU5GbB8aYA3s0CqTnVEMZ46HcMov2wZYs/U/3oX3bMqObwcxA+aHJRAqoYk8ne90Z4sknn0SW66+/Hu95z3vQ39+PzZs3w+HIvz+2bNmCYmFDjVlUor3jcNbP5cIwxtLumhLiGrnlBBhjSZNUOYcPmYqWdh+HdDGMDitWNcE9U9eOsRZU5idTFmhx92EVtm3bJnLRcvUh3vCGN8z+OzuPxUQY06EvasIYgd1CD0erEGpuRoOjOkOIzAoVRWbMhb8lX0CGMZbwdFzkqAWD+WI9DFNtHD58eFG3z4ZaBblx96ul87QZKeZT0cuTkHW65Z1xUkEruBdN3lFMSpoQT8lHnEdiasZQ62oT/84yEJFfcukSRhp0Yw/MMCIua59qTBvCaQdi2gIUH81w7EqRf9Zrt0QKLtWdq/qXT2NH4RHjLp16yN0zNaEK0RM49T6LHezHyrZaqVhCq0f+DPA7Ch8jUiyU4ZbMc+ocO1uOrPtiqtvNzEXxyBRw5c8URZUrtY30DqEuYIdnnoKh3iUZk5ymNq/8/C3zyZ+7fXWyYyQ/rlTzrRB6KZDJhPxHyR4T7W3y87exNrbgaz/Lqhp5A9tmrn9vQEUw514ISK59wieZ51Ll69hUuXdIVQrPo/p0MkgRsPh1ZIqLxd8vpSg76jFf9TESTiGdVlFXq+dVM8F7ZDFUH2eeX3RM6NqQXR9LFVZ9LI7u7kzJlEQigXXr1uHuu+/G+vXrUS542JBZPJJppEcy9ZcYc0AFtWNpvZc+wzDzw1gY4xk4Mm50E5gcnE47UjP1+RiGgchHi1LOeZlhQ41ZPFIpaDFr1RdZ6nS4pkQtNcY82II+KFwU3lSsWlcLt4cDTkyFKFrPxrNZ6OisR1cXFyC3IkpW+XERJws51PK47rrr8K//+q9I6havLA7Lv4n4wW4glDxZxYpQZoSKXbM8v7lQXHahMsiYh8G+MJpaPPB4+fllFro36MQbMxVndCSEoaEprF3XhmqD+5WMjEceeQS/+93v8Otf/1qoPvp8+Urb//u//4tisayhVo5q4MwZ4nJCbZHntjCVpy/mRzzN3hszkRwYh9ZQT0kyRjeFmWE6lEBdAyujmomxgWk0dgRgd3Dothmw2VXEqjxixqr9TFZ9LJ3a2lpcccUVKCeWNdQY49EmpkXoo621zuimMDP4bHGEU3zbmwprvq+WNC63jf3OJiMaiiOd0gB2cpoCt9uBYNBrdDMYxlTceeedZd8m99iYRUOjRGP2EpiKRmcEQ3F+uZoJR0cDVC4MbyqWrwxyMVuT4XDboZWiBMssmphIsLY63yVWD31k1UdzwYbaEkYmtS/7Xk+uWm+dpFb4jppOyu+0qYQC2F2ZjFH6d/b76dIecJrkrq7Yva7bj9ZphaxjUcleYM6xo39q6gJSeStlN6RLeJJLZPaJVGeNdF7tssJhuO3N8u11egsnBHfpyKqvqJHPW1ZAqn3k2CS6fH4RSlS01L4kCsxjk69jVwsfBz2Jab15MulwVSJPnl1LPketyLNQ7yI/vG8YzS1e1DXk14hy2eSlF7yS67LZLW9Dl08u7DMmDSuTn4u05Fkd1SmlEovLr5UWf+F5KwOFJfiJFRIZ/lU690WjzjHKyvDXbMiU0VCUGbl+nd6LS3Jf2BSXdB1bCde/nmy+TJ5ffx2ljPeL3jolyPPPa5sGDc88dRIX71qH0ijnS6ay6pPZ40fPOFWxiYlhFgsexmUWD3qW2XkswEw8Nd0EjYO6TEV4PGr5EdqlBvVJ+YyYi6P7xjA1LjcQmcpi1fwsZvEVH7MTszD4WDGLhjIeAuLyEWim8qzzjhjdBGYeFPbInR5zUd/ogdvNg0xmQrWpmRw1xjRs3NhhdBMYxvJY3lDjkWoDSacBEWbHmAXnTNgQYx6WbWiWhj0yxuBy2aByEoWpqKl1wSmL9WUMYWQkhGrE6t3KbI7aYk9W5Nvf/jZisVM9//F4XMwrBcv2DniE2ni05jrAzfL8ZmIixefDbBx7og8pEt5hTMPQQBiTkxxmZyZcHjsbzyajr28c1Qz3M5n5XHPNNZiYmDjl+6mpKTGvFCxrqDHGo4xOWn/oaYkxGM8vvsiYAK2CojjMwnPU2HY2FZNjUUyMRo1uBpODTUfYiWGqEU3TChrwJ06cQDCYEUQqFg7CN3kYZmmhm/J1SKmpEHqh/8l04YdxREdJbCqhwjURRazOBuQUWI5GSzPclKUqyywbAS7371ngSPNqzxienG6eWQeGoznloUzpzkDB7+va5OpsbU3y47BMouAo+55Y7i+syrdMR62v3iU/t/4CCo7pdjfqXBQlXHg9t47qo1Mt/AhXFWfR6naqjpqgTMFOT8VOf7S5eBU7PQXHUtbRID+HHZ31wnszX83NocoL/MrOk0/H4NO7jiISVV27nsis5PtQQn7+JnXmra8tnGO8IpAqWsmyziU/EDUzyo6F8Mwc15BTQSKRgn9mWZdN3n2RXf+6yo4lXP+lqT4Wr4aqp9JYmlen+Id/oTZcdNEG8fejt/yw4Do33Pj3Re9Hti397VU4JFabO8d0DehdB0sRRSFjY3H7XIu9/Uqzfft2cS/S9IIXvAD2HCG9VCqFw4cP40UvelFJ27a8ocY5agZis1VWhp5ZAHw/mA1/jYtvE5MxOREVeYP1DdVZJ8qM1De6+X1uMv766CFs3NRpdDMYxnBe9apXib+PP/44LrvsMvj9/tl5TqcTy5cvxxVXXFHSti1rqHHssPHEOtvYUDMZx2PyOmOMMRzfP4bmxhajm8HkEAknYXeYwOXMzBKPpzA9lUBLu2W7LUsPBcLLWa1YtZ/JBa+LZ/fu3eIvGWSvec1r4Ha7US74TcQsGu7Dx41uAjMPl1q9L1Wzwj4C80FGGgtXmItkIo2pCS73YiYaG2uq8j5hzy4j4+qrry6rkUbw0BTDVBHNjmn0x+dc8ozxdK+tt+zI7FKlbVnh/EjGOOxUb5CHlk1FY2MANlv1nhSrPrcrUZDaqldNKpXCbbfdhu9///s4duyYkOXPZXR0tOhtWvVYzcIjH8aRrOXODsOcjomRiNFNYObRe2ISx4+eKrHMGIe/xomVa+uMbgaTQ2/vGEaGp1BtcLeSkXHzzTfjs5/9rAh/JJn+d7/73bj88suhqipuuukmlIJlDTWrjnQsJdJOrtllNp6ebjK6Ccw8xofCRjeBKfD+4M6YuYhGknjmsSGjm8Hk4HDYqjJHLesAsGo/U1W0ikxW5L//+7/xta99De95z3uE8uOVV16Jr3/967jxxhvx0EMPlbRNDn20ICWVAShhXlzn+RxOKAj0jyLsyfeq7exO4aHDhaVsl2zHSG+4Q6ZIXcm4/pn2qUhjpXsc+6P1i7MfnRCY1P/f3nuASXJVd9//Sp27J+fZmc1Bq1VAYSUUQCCLjAjGtkwQ4f0wWOBXyCYZlFYkG5CMbaINkjHJCBshwC8mGYRQlpC0yhu1aXLqmc4Vvufenp7unq1bO907011TfX56Wjtdt27V7Yr33HPu/wzYi5i09TpI7bdLFcvp94Url9rvCoplw9sEkuJhBznxkGJVJOEe9kuIiA8DFMlfudS+kwy5ZP/YlxxeB9XI8ztRjdy4ZckVpx2BJL4eZIffG40GYRisM1G+juKQ9iCg2CfIzjm8ZTsDTsna9IofHRnB5iYz4mPX4pBmZVPMvg3rHOT5RVL7YYc0EyGH+8kn569lSZMhmTL8Sv676nBfiK5/0bV/Yql9QQoKh4e/qOyTN92BRsFJan85t3fd9X9Sl6iq/LOq8CEIYHh4GDt27OB/M+XHQvLrV7/61bj22mur2qZnPWoFKPSxTpgmLNnzl9eqgo1gaRJl8XUbZ+wkxUe34fer8Dnk+CNqj6JKaOsI1rsZBOH5fmVB9XGlP16kv78fQ0ND/O8NGzbg5z//Of/7wQcfhN8vHlxywrM9aa+6pFcNkoxk93xiZcI1zBoUjuo2Hr1/pN5NIBYxPZ3GyPBcvZtBlMBEK6Ixen4R7oH6mcRiXv/61+NXv/oV//v9738/96Jt2rQJb3vb2/DOd74T1UChj8SKIBs61EQS2SpHEIjlx7BkjOTC9W4GsQgWYke4Cz7a6/FR89XIc09PYueFffVuBtHgeP3RINXAi+NVE/czn/nMwt9MUGRwcBD33HMPN9Ze85rXVLVNzxtqXndRuxUpp0NZJEtK1JegrKPXN4u9KzVHjaiKto7lzblCnDzNrUFEYjTIRBCEk5hIvVtCuJ3zzjuPf04Gzxpq5JKuNxZM1bOX16pEYufEs+NYq5e2Tpp34zYyaQNzcxmEQhRq5ya2ntpe7yYQRAOoPq78HDKvzlFbCTw7R42oL0YwiEx7W72bQZSQtRSM5UL1bgaxiOeenKp3E4hFZLM64jPpejeDWMT4KKWyINyDVw01wl143uXh7dDHamT4l0+6P2eKH1LKdBywTGRajk9QKjolnjxVLhoKUWBxr9rJYHaK57hF14mNwP5u+2tljYPU/oBAan/QQRq8Kygua/VXLhserFBqn+FzGCq0k9rXJAU+WXxcJQdJcZE8v7PUuFx5HScZcmFnpbqLXyjd75R3xxKdd6ffJN6eKvugSFnIKD++ikMbNNleUTWk5oR1TIefZAoeiKmQ+BqP5+SKtsVQBe12Sh8QdZDTjwjKQg73mTYvwW9HaUqEidEMNm/Jf5cd5fmViuX5P33TT4VlhLvZdeP3K67jJOl/IhauZEnizz+vGWy1yHPm1TxqK4HnDTWiPkimAVMheWs3EVRyiMg5xA2aE+UmNm0jz7PbaGkN8Q/hLlTVRSNfRMNCc9SIWuLZp15hhMPbHjX3Yvj8MEjx0VWwO4KyqLmPmWkKsXMbM9MpPPVkPhcO4R7OOb+/3k0giIaZo0Z51CrnyiuvxF133YXlxLOGGlFfTFWBqYrDWYjaM6UHMJSN1LsZxCJGhhL1bgKxGAkwKW2C63jg3iP1bgJBeN5QI6pnZmYGl156KZfj/9SnPoWjR4/iZPGsoUY3UH0JTE1DzonnZhC1p0VN8w/hLuhR5T58PhXNLRT66DZMw6QoGcI1eLWfKdfo40XuuOMObpy9973vxX/8x39g7dq1eMUrXoEf/OAHyFXZJ/bqsVqAHur1QTJNWIrnL69VhU8yHMUQiPqw8wIK53IbmqYg1kRzOd1GZ3fEm6JTxKqC+pWEEx0dHbjmmmvw2GOP4f7778fGjRvx1re+Fb29vfjABz6APXv2oBJITITgLPdzJ9HWDkPRAOv4ESdrOSdKOQU6VyNGZ9PehY2JJNrc8NBewsheDgp0Sy6u63Ds9A3Hq3Uy1gyIBWLWtYrV6NZF7EeS1jsoOPaFjYrUGxkRJwVHQZnfQdmxUgXH/HKtIgXHh+4/jHN3rhPWcVKqq0bBUXRjyIJtzZc6bK2aUWVpeWsIrn9LeD+Ljx0jl7FwcN8kznjBQPn2HJQiFdhfr35ZfI2bDtdrTvC8afaJ66R0q2KFNaftNQvutYCTGqqgzEnZUXVUcCzW6+xs4vcXu1Y/+8lfwmtce92bbJfftOv2itULq1FCbCScjs911/+ZY93CKz//DPGyf4g4GYaGhvCLX/yCfxRFwStf+Urs3r0bp5xyCv7+7/+eG21LwbNXF4mJ1Bf/7CzFdLmMGT2AWYMS+LqNXFbckSfqA3t0OUnaE/Vh33MTyGTofiHqi9fnqBXk+Vf640VyuRz+8z//E69+9asxODiI22+/HVdffTWOHTuGf/u3f8Mvf/lLfP/738euXbuWvE3yqBErgm92Dsk2kh13Ez2+WW6oTRvBejeFKKG1neZCuQ1/QMOGDZ31bgaxCEWRSOSFqDtFj1q9W0K4jZ6eHpimiSuuuAIPPPAAzjjjjOPWueSSS9Dc3LzkbXrWUCOPWn3h89PoKeYqZFgwqwpTI1aS3r6mejeBWIRpWpiaSqCpmQY13MS6jW3wByg/J1FfvO9RW3n5fK/K899yyy1405vehEBAPMeZGWkHDhxY8jY9G/pI1JeZgfK5HUT9mTICSJueHZtZtTzxGOXrchtsRHRsdLbezSAWkZjLUugjUXe8bqgR1Yc9vuMd78DevXuxnHjWUCOPWn1pev75ejeBsLnZyaNGEEt7f1AnzH3EZ9JIJSntC1FfvG6oUcLr6tA0DQMDAzCM5R1M8qyhRtQXiQxk19GhJaBJyym5SSwHG7e017sJhE0etbPPXVvvZhCLUFWZh6USRD3xuqFGVM/HPvYx/O3f/i0mJyexXHg2DqoxPGrL95Co5nnjNCKSi0YqVvVxaoMl2Jnk0AhLFUiX62JjxSl1gCSS5nZseBV1RFQ5BGXN74sdK0NWYM3Lkstb7SX4GVvW2c8D2dKUFdbZFBOPdItk+LuC4gMe1eyPXchB0txJNlwTHD+RzH6+TFuSbPiS5fRtZPMzyTkosliNs5ZS++K9SMsrte9w/Vf3zJYrvmWc+vuGaeKRh5/HOeeWp02Q4fDsgP31oMjiOpqVEpb55Mqv8WqGYSKquJZPFqW0EG9Pk6tJMyEu++wnfw23cu11f1yT97NItt/pfnGq4yT3TzDp/u85lj9w/3P839/d9SRfN51Ow0vUIuGAV71E//zP/8xDH1nONKb6GA6Hy8ofeeSRirfpWUONqB/sxZEL0SR8t7Ev0wLDs4/H1cvRI9NYt54UBt0EsyMpbQJBEE6QR41YzOWXX77s14VnDTW6geqHZVoIjY0jPkiCIm5ijS+OQ9kmGDRPjSBO+P5ob4/UuxkEQbgQb0dqseffyuc5Y/vwIjfccMOyb9Ozw+uNEfroTizDhCV79tJatYTlrCh4k6gj51+wvt5NIGzeH109lDaBIAiCWDrr16/HxMTEccunp6d5WTV4vjdNhlrtkRQZyQ4SSHAbTJqfVB/dxx8eOVzvJhA27H6MzgtBEI0nJkKqj9Vz8OBBW9XHTCaDI0eOVLVNzxpqXr2BVgNmzoCSydS7GcQiDmabl3WCO7E8pFMkN04QBLFa8Lqh5kY+/elP45xzzkE0GkVnZyde97rX4dlnny1bh4m6XHXVVWhra0MkEsEb3/hGjIyMlK1z6NAhvOpVr0IoFOLb+eAHPwhd18vW+c1vfoMXvOAF8Pv92LhxI2677bYTtu/OO+/kH8b//M//LHxnnx/+8Ie46aabsG5duTgVGn2OWgHyqFWi3iaqY48mUAQzcjkEszpkB3Uy2/04NU2xH1OwFHEl4UNUsC2O6SAgYEmVy8eJ2uCkVimsI96N028yBmL83x2pg3g0WJQc3yhQduTrttgb2tubxUbFOoGyI6MtYFak7MgICprnpDjnrOBoXyZL4g06Kzjal0kOiouSzUlsbY1CFigG5rfndGMIlE2rMsir63Qsd2dFtD2nR7n494rbdiJFyG3b+o9T3HSat2EJJGMtlHcCSlEdrhXR81WkqsjImfY/yhQ9u5iyo8P9JBDOhepw7ERqqLd86h6sRj5+3RuFZZZjMHlt+h6f2PWfNdkP0Ti4UfXxt7/9LTfCmLHGDCsmgX/ZZZfhqaeeWlBW/MAHPoCf/vSnuP3229HU1IT3ve99eMMb3oDf//73vJx5upiR1t3djXvuuQdDQ0N429vexvOffepTn+LrHDhwgK/znve8B9/+9rfxq1/9Cv/n//wf9PT04GUve5mwfcxwLLy/rrzyyrIytv21a9fi85//PKrBs4YajXTUV0zEcjKGCIJYYO26jno3gbAhHk+hvSNa72YQBOEyyKNWe372s5+VfWdeLuYRe/jhh3HxxRdjZmYGX//61/Gd73wHL3nJS/g6t956K7Zt24b77rsP5513Hn7+859zw+6Xv/wlurq6cMYZZ3BP14c//GEuAuLz+fCVr3yFe74KRhWrf/fdd+OWW25xNNRMMz9Qx+o++OCDaG9fvuk/nu1Nk5hI/Qi2hJDtaKt3M4hFDGks9JFwG394+GC9m0DYcOzoVL2bQBCEi/GqocY9ais9R21+X/F4vOzD5nIthZmZGf5va2sr/5cZbLlcDpdeeunCOlu3bsXAwADuvfde/p39u2PHDm6kFWDGF9vvk08+ubBO6TYK6xS2cSKYR65gpC1Xfj15OWNEWSbu97///diyZQuCwSA/QH/1V3+1cEALsJjNzZs38/V+8pOflE3CYxc+2/bs7GxZHWb5ViN7SYZa7ZkbmYU2Ha93M4hFUA41giAIgjg5qF+5fKxZs4aHKRY+zM44EaZp4uqrr8YFF1yAU089lS8bHh7mHrHm5vIBaWaUsbLCOqVGWqG8UOa0DjPmUqnUktrGvHR9fX18ntz+/fv58muvvZZ7/KpBriZGlLkRf/GLX3DrlcWIJhIJXn7s2DH++dznPocnnniCuyaZu/Jd73rXwjaYtcy28aUvfYln8H7ve9+LbDZbth9mpLFtnAxeHelYLWIiwnlWRN3oz03WuwmEDRs3dde7CYQNF1y0ud5NIAjCxXi1n8lynNXiwzh8+DB35hQ+H/3oR0/YvquuuorbGN/73vfgNj7xiU9w2+fv//7vueFYgBmU//qv/7ryc9ROFCPKGvKf/1mc2LphwwZ88pOfxFve8hY++U9VVW6oKYrCPWS8AfPLSn8Q88rdfPPN/GSw7VcDhT7WDzWowcyIxREIgiiSzYrFJoj68dCD+3HOuRvq3QyCIFwGzVFbPmKxGP8slfe97308Eu+uu+5Cf3//wnImEMKcPixfWalXjak+srLCOg888EDZ9gqqkKXrLFaKZN9ZG1mk4In45je/ia997Wt46UtfygVJCpx++ul45plnUA0nFQu1OEZUtA77gcwgY7C/3/GOd3AFld7eXu5RY6GUpVxxxRVcEnPXrl04WchQqz0aM9S0ouFNuIOn/b31bgJhw6Hnx+vdBMKGdJoMaIIgjof6lfU55u973/u41P2vf/3r46TuzzrrLK6uyFQaC7CpWUyO//zzz+ff2b+7d+/G6OjowjosOpDZJaeccsrCOqXbKKxT2MaJOHr0KLdf7EIiWRRiTVUf7WJEFzM+Ps5jNd/97neXLb/++ut5XVmWjzPSCqMUn/nMZ/Ca17yGy20yz1yleGWkw/F3WJXXkQQyzQ4KzRAp4Itk0uNDM4gqPlh+ezn0QMC+XtbpGhb9JCd1SVHDHdXOHWTzRQVO50iuQhtcmFbAoW0OmvW9ffmy3ulpHGvuWVi+KVYecrwUGf7NTeLOa1tA/PISyfCHHFI4qLK9sa9IvoqlwfNlAjl9h8egs9S+UrE0vt29yfYhOaQIcJbal1zwbFzuuY/msrXbuT/ldG4ttLdFjzv2luO5FaVKcDi3jqkh7O81VSDb7yTp75RWQKnq8WVVlIKC8Tcfu0RY53Of/F/Um7+99nLb5aZVncFeXYqMyvnYtXlZ8OXgkzfdISz7+HWvF5Z9YtcP0Yh4pZ+5mFokpK50+1dddRVXdPzRj37E7YbCnDI2r415uti/bJrVNddcw51HzPhi0XnMwGKKjww2VYsZZG9961t5aCLbxsc//nG+bZYzjcG8YGxa1oc+9CG8853v5Ebh97//fS77vxTY9n/3u99hcHCwbPkPfvADnHnmmaipoVaIEWWylXawiXcsFwFrtJ0ICDuoTjCVlQsvvJBPwGMnp1Io9LF+mIYJy0fCFW4jmF0eBSJieTnv/E31bgJhw5oBUq4lCOJ4KPSx9nz5y1/m/774xS8uW84k+N/+9rfzv5mEPnMAsUTXbEoVsyOYHkYBNu2KhU2ySD5mwLH8ayznWWn0HvPUMaOMOYm+8IUv8PBKNrfMSZq/lOuuu45vk3nWmEPrv/7rv7hnj4VEloonrrihJooRLRUDefnLX86tXuamZO7IamBeNXYwWebwSmFeOGYhs5NG1JamviYg7uSjI+pBTqF5g25k9+OH8IKzaC6U23j0Dwdx0cVb690MgiBcButXvuAFL+DKfl7EjQmvrSU4XQKBAL74xS/yjwjm6frv//5vx+0wY/APf/gDquHyyy/Hj3/8Y278MUOQGW7sWmHL/uiP/mjlDTV2oJgrkRlfv/nNb46LES140pjlyYwkJsPPDly1nHvuuTyr+Ec+8pGK6zJJTKZOyUIzidqSjqcBaemTQ4kaYFk40kJz1NxIIrG0vDFEbaHRcoIg7DAMA4899hgXu2NpqAiilIsuuojPa1su1OWMEWVGGosBTSaT+Na3vrWQwI7R0dHB3Y6VwlQjt2/fviBGsppfso89dhDhkB8bNnbj8OEJaJqCWCwIv1/jqpiapkJxmnO1SpgbSwBd7jv+jYxkWVg7cQj7O9bWuynEIpqaQvVuAmHDps2UNoEgiMYLfWRzUJ3moS7XPrzO3NwcD38spRKFywLqcsaIPvLII7j//vv5ssWqJyxb99q1lXcSWWJsNqGPyV1Wg5vmqA0OdsA0LT7Bnc3jmk1nuWGWTuewb98wclkdp50+iEOHxjExPotAQMPZ567Dvffs4UZdb28LorEghoem4fOp6OwJI5PRYRgm/x4O+/n2azSHWYg3H12rGyaOQLnt3AkZBO4klRKL7RAE0bh43VAjqofZOmx6GIs6TKfTZdcMu16YN3bFQx+dYAbcyRhGzJCzq//Vr36Vf1a7mEhzc3jh77XryvPDtbVFFv7evn1NmeLUzvM28lxLsixxI4954bI5nf89O5vG1GQ+4fj2U/tw3z37YJg59PSyLO9BPPN03ut56o5eTE4kMDUdRyCgYvO2Djx/YIobitGYH4rfQCrFct3J8AdUsKl91T6E+s7sw7PHqqpKrBgSpkLOAj5E/fJ1XXRxXhqYcA+HD01g7dqOejeDIFYMNrDL+hUs/JoNFDvx4AP7eF5BNnCcyxno7Iqhu7uZz7FtNNzUr2wU1cfVAssbza6Pb3zjG+jq6loWY75q1Ue348aRDqc2iW58VkdRJASDRUny7p58Mj/TMtDf50d/X1Gd7IILN8OwinNeLrho48L2gyEfWjv83APHpnKykMtcjuV2sGBIJoaOJKDnTAyub8L4aBJjoym+7w0v6MaTDw7DMCy0dATR2hHCkf0zUFQJUkcz9IyObCoHWZXR3B3F3vsPI9rWD2gqoCl5jWxJWvj9kah9eGciIR5pEEXNmk7a0qr9fizBcoajN15UqFT+NLKc2i0Kf9XE7ZY7xHNB+1tMSKYBLa0iEiq64ddGxC/mwYj9uWj1Vy7BzwgLnjSqLA77E8nwyw6PLZEEv5MUuqN8eoVS+4UWVrY9tlSu4XPO7eHV8rJJ+lctnz7/vFp8fCXL6dyK5Pkrr+Mkwy87nHNVUGQK0rJU25lyekxagnNhWtXlEBLx4Y9f5lAqVXy8q2mf4zsdyw97j+ff3/nX6txcBnrOQFNzEPGZFOLxNF9nw8ZOPP7YET7AywaB2aDtU0/mR0+37+jD+OgsRkbiPFJn5/kbsGfPMUiyhCvfdSFaWkIYG52FqilobQ3z7ZmGxUXhWJ9j5/nry9p0wUWbcNoZazA+Not16ztOPn2IQx2n9AH1wI39TKK+sLmLDz/8MLZs2bJs2/SsodYoIx+VPFBYeKSslhh8vcVY2axpYuupRYMv2uTDuk15g3A6C2w/p3vheLJDunZLC3TdxISR7+Qauskf5ozMXA6WFIfEjMtwENaRMfYWBHrbgFQGa48N8fUO9qxD3+hhyJaFRCCMKSOMnswUdEnGpBaFDAsBIwtDkjFshRE0s/y5n4MC3SH/EHE8iq4jPDONbIjmQ7mNDeu76t0EwobzX0hpE4jqyb8rLe61Sqdy/H0Zifq5MZVMZPg7uae3Gc89O8zLWtvC8PtU7HluhH8//Yw1OHpkCuPjc/D5VZxz7lrcc/deHgXD6jHjjJVrqoxwxM+3x6JhfGyAlMmMr+/gETLMGFNVBRdevHmhbS0tYWzaUgy5ZoZWKQODbRUZuWzaBdtPo+D10Ef2s1ba4+XRQ4dzzjkHhw8fJkNttYY+eoH8KDPgD6pg6QGnEzIUTUEgUjQAO9a1YDxYDO2UNpWkcIiGcFAPLnw92rkGsmXy4Uc9ZWLCF4NiGTBkBbKpQ7UM+M0cJITRoccRsHJISj6MazFsTh+DlbNwWGmFDzq6jRm+zd3aGmzUR+CzcojDjyE0YQBTMCBjHCz81EBYysKAhEkrhCDY9pkBKCPL/TUmX9cpue2qY96rSbgPk55RruSxR5/HWWeXew8I7xlSzChi4X9s6kgqmYVumNyrxKYKsHmKzMjp7Ipiz3OjfECyo4slQgcO7B/n3q0zzxrE/r2jmJ5OIRTy4bQz+nHfPfv545YZPMGghiOHp6CoMjZu6uJz0tk+mfHF1mlvj0BRFb4eM3bOPnctN8bYu3brKT3Hea9KaW4uDryxfZcSjVavuF0pzHP3+GOHce7OxrhfqF9JiGA511jSbJZH7dRTTz0uPdlpp52GSiFDjVhW2PGOdYaB2SVWkCSY894xUwLm1KIRl5U1JOa/W2kLh/zlYRWPh9bCTBVD9IaVvAeQ8ZzaDSWX5S9UZpCNIAoFFvfGKTw0x4JvPkSnSUojKOWQtVQMI4zN6jgUycSwEeXGWq+SVy59JtOONeoMwnIWKUvDgVwLNmvjMCFj1AgjZ8loU5L8+zE9ipic4eFLbPmUFeT7YWUZS4E+3w7WNmYUrrQEi6H5EG9rX9F9ENXdLwcOjGLNGpoL5TaSSRITcTMH909gYG0LDh2cQjKV5dMDenpieOrJIei6hcG1rchlDTz//CRf/5ydg3jmyREeLsiMmM1bu/CHhw9xo2hgbStXpR4djnODKhYLQNfZu0WCqsnckGPGGluX7Yd5qlpaw9ywYn2NU08rzyd74cXlBlV7R3Th72CwWVi2WmHHIX+8GgOve9RYj2yl/aNe9b+OjY1h3759eMc73rGwjF0nNRMTWU149QZyO5ZpYWTvBNBV35cPUzg0SsIj51A6uighaRVHH49ZTQsTCiSYeEovD0UbNwsiMBb26aUhIRaey7Vz35s5H1k/aYS4kccMPLZMhglNMvn0tmY5zY2zaSvIwzt75Tivu99oQ7c8i5iURhYKnkY3Tpfz8wnGrAgSlg9rZdbZkLAHHWhFEhFkuNG5Dx1Yh3Eg40NcCSIl+9Giz3GDcEoNQ7N0BOZMyHqOG8W5QCDfNkXh5wpMOpbFONjMxyFqAx13d1LqrWgkmCIxezbwToVuIpPWeVh7IKQhm9GRSerIqCY6ukMYOjzHvUrBkIZQWMWxw3NQLBl9A1HEZ7J8rjOre+a53XjkgaPIZg20toXQ0xfFE4+N8P1t2nJ8mN1SYHOoGLHmAGJNAe6h8vkVbNveww0q5gljBlb/QMtCndPPLA/xO/+C0kTzEjpKjCYWXigS/CrMm6Z7Nw873mvXNd5AIJ1/YjFMpf7MM8/Ed7/7XRITORHkUasPJgsj8UAuuKUhcc9YKTmLiafk/542mTeweJM+b7SUGZIzRtF7eMBsLW5EkfCY2Ve2Xf59XoAkA5V7B5nkPmMcEaiKhpzEgjYlPn+Pzftj+CwDWiYDLZ2G7vfxv2XTQDYQhBVTYBydYNY1lN42mHNpmNNz+R1e0I2ju4f5+Qw2BxDtjGD0uXFMaRb6NjQjnchhaiTBJ6BvPrMLh56dhGYZiDT5eQfuyIE4JBno6AnD0C2Mzma4kmj/YIx33tiyQJBNVvdjciLJBRIi0bzxnE4bvIPVFFORyRj8HlYVGZqPjdqaUCSZi9x45SV5zjnlqUwId7Bh48nNHSy8e9g1y+YpsWuWLWPGCvseCmlIzGW5gBO7j9j1PzGeZLcjwhENaSOHmekMN5rYPTU1kUYqoSNhKWjvjeLovileFmsNQtFkjB6exWRCQmywDcmxWWRnUpBVBS1bu5B5+jBvi9Ia5XOHdXbfM0/IYCcmp2eQmmLryujZ0YORp0b4wE2oNYS0omH4cJzfj90DMX7fpxI5KPPjXuwe1xQFmk/mnqfWjiBCmoYAC433q2hrD0Kef26deU5v2T17/kUnlyi4f03ekGKCF6UEgysfoUAcT2GOeiPgdY8a5VGrnueffx533nnncSnKTgbPGmoFVo+hVrmCnEjpzFFljL1ZK1TL02SxOmBwUTVTktG3rgnxnFiFrTtk7/odd1BCZJ18OwynGa+Ch6jksB/L6XgzD5Rt45wUHAVlDsas5aDuyFQ02dkoPSNxaOjo1BZMRgN+sCOcH//1I9qvQ5lLQs5kkGvLG4vMRFzbkQE6SjqjXcxQygvMtPoNtJ5d3lHtOa8Hrf6C4pgfa9dGeEdRVixs3NyEkGzwDqemSRhYE+JlwbDCO6IZKb+upgShyAZXD7MMFbLlQzKegGWZ8PvkvFE3kuId2aZTmnHs0AyXjmbhRus2tOPxPwzBMiUMDLbyfe3fO8bbc9Y5g9j73CjiM2k+sf7UHWtw/717edmawTYE/BqfpM848wVr8fzBcUxOziEQ9OHss9fh93c/x8v6+1t5WNSePcP8Jbz91AGMDM9gaopN6Ndw+umD+MMjB/i6HR0xxGIhHDw4xi+19eu7MDOdwPQMU0uVsXVrH5579hifg8aSWrPP0aOTfLssJyILrZuZSeDgwVFceunpOHRojCurson5bN3h4Wm+3ba2KJfDnp1NzbexDSMj09wACATy646O5udntrREuIeDrcvqdnU189/JwpI0TeXrjo9P8XWj0fxAwdxcPtdLe3sU09NJvi7zRrB0ImNj+e3GoiHe7ng8Ob9uDDMzSWQyOR72xNo4PDzFp0Ky7bL6k5P5+OfOzmZeL5XK8PCynp4W/lsLbWDqs6z97Fnd29vKjwlrkyzLWLeuC3v3HuPXQ6wpxI/NkcMTLCsgDxdl605PJfhvPWX7AJ584hBvf3NLGK2tUezZc5Rvl52beDyFkeFpXnfnzs14+KH9SGdyaG2N8P0+9ugBPvSxZUsfEokUfn/3MxgYbMcFF27l55zNV2puDmLt+g784eGDvP1szhGbn3PoedYmA+fsXIennjjGr1mWRmXTli488sgRbugMMM+OJOHY0Rn+yNh2ajdX1k2ndG60ses2Pp3l1zUzcizJgmXk5dO5UIRPgcJStJj59CktnSE+wKEFFH69rdnUgvQsE4xSEOtvhrSG7W++M7k570laePJuznsLmc5ha28rUJLilBlrBXxhAwPbip6SaFBDtAP4wT8+LHxEvffDZ+aVH5XC28viYlVf/rsHhHWu/uhOYZm06N2UTuvQNBm6mXKoIwqsclBxreId/NlP/BKV8qGP/RFqQXVGRDUqjfnjc+DACHr7oye/PYd2f/TaV1fdvkr41E13NrShRlTPS17yEq78SIbaEiCPWn1gnXI9a9CApsswwkH+WW5RmYKRykKOgiUGZlMzk5rJ4/craG4qhpF19xRf5qqkYOPm8pCZ9o78CLksKXweSSnnnre2bFChtyQ8acfp/WVS+xdeXK661N1d9GhuP7W/7LdcdPHWhWcF++ecljA3DlRVw8Cghr7+osdz0+Yevq5P03gHeXCwnddhBkdTcxiBYPG3t7RG+HZY8nqeszAa5HXZ38zAYUqsLJyMwYwYPqNyPp69oLDKYMYXM9YKMOOAeWZkWeHbZ0YEWz8Syc+tKRhfnZ0W/5sZVKGQn++fGVj586LxutPT+TyMzGCZm0txsQPWXmbUFdbN/1Zp4TtblxleyWSGb4cZasw4YQQCBc9ojl8jecEGo6z9bL+FZ3Q+BYm88LeqMm9M/ngxwuG8+4a1iRmb7JjmVWwVbiiz5YVt9fXnw+jYcWWfTZt6eBlbh22ns7NpYfxmx2mDeXWzeUPowou2Fd8biHKj+oUXbOHlZ5+zYSGvpd21xQy6grz7GS8o9xTtPL/ECmLjId3F63/dhuI1ydhYEgaY0lkqk2KIdnPbvBsrnT8u0eZysQg2t0rJqCdOPbDK2b9nEn1rYmhp9mz3hXA5Xu9XUh616nnNa16DD3zgA9i9ezd27NhxnJjIa1/72oq36dknHY101IdsSkdiJgOUh/cTdUadmYVkGAseNeJ4ioYD67wXDL58WotSmDGUL5GOS2TPjK9IpPjsYV63Upg3qQAzaJjhNDWVN5T6+krCX5nHc21ROZUZGcwYKjAwUBAfyXfaN24sV4dra4vZbgfzHqPydYvbHRy0X7f4W4vzdPr7yw3sTZt6y74zI8rOSGYwT1kpxd+TNyCBkrlCPfnjwjxhDOYlLODzsXWLAxCl56LUa2jHieTEWegjvUbcBxswYIY+4S6YWmWjQB41QgRTfGTs2rXruLJqxUQ8O5mIPGr1m4hemJNAuAjmnaGXiutg3ppt28oV4wh3wLyY9P5wH109US5lT7iLZ57K50dtBLxuqBU8aiv98SKmaQo/1RhpDDLUiGUl2hZE1zpyp7kN0++DGSiG5BHugIUEPvRQfi4d4S4OH5rgxhrhLngoLZ0X18Hm3Dba/eJVQ42onv3792O5IUONWFamRxL8Q7gLS5FhaZ6NdF7V0MveneTn19W7FcRi4tNpzLLwesJVNDUHGyYk1eseNRYUVYuPF9m4cSMuueQSfOtb30I6nZ8rfrKQoUYsK1xIhHAdajwBObU8Dw1i+cirATZe/qHVwAvOWn/CeWxEfe6ZRjEIVhMbN3Xy+YONgNcNNaJ6HnnkEZx22mm45ppr0N3djb/4i7/AAw+IFW+XgmeH2N1oqO268fvCsmuve9Oy7Uckwc/LWJ6viuX582pmdgSU8uMbDinwBVS0s2RAAvpD9nL/ByJFhbPFZDKC8ygtrzQ+z0xdKaqD1L6ozEGCn3m/RMia/W9qahLX6QwYsFQd8KuQAkVDuj0gPkdB1f44BATLGaosPn+KZF8mS+K5JqKyUmXH4+s4jT3Zlzkr5DnIeVf1kl7cBqYAqS77mJmXVf9qxd49w1xQJC9ucuLjKrweHB8p1ciXi8sUwfNrMiO+Z+IOg2upgP3O3vR/zxLWmc3at+Gdf/MC8X6MrMMjvLysrZfdLxKy5nzOx4reZ07y/ILng+MzpXJyVrI2963DdVdNKgLhtuYvyKefGUb/mibuWSvuR7Q9pz6KVMXztvq0AnZ85OOvcKz7i1/ewfNlveFNL8Dr3/ASxONz+MxnPlNxGwjvccYZZ+ALX/gCPv/5z/N8arfddhsuvPBCbN68mSfDfutb34qOjqJ41lIgjxqxrIRjfgTDNNHbdTAVwHmZc8I9sFxoLI8a4T5Y6oFGm3OzGpiZymB0iMLr3Yaqyvx51ggwYQgGy/XoRUhM5ORhaWbe8IY34Pbbb8ff/d3fYe/evfibv/kbrFmzBm9729swNLR08R1vXmUlkKFWW4YPziCdFHvgiDrBkiXrFJbqxucThc+4k0g0wMPsCHfBwh4zaXqWuY3OrkjDqHFa86G39OwmRDz00EP4y7/8S/T09ODmm2/mRtq+ffvwi1/8AseOHcPll1+OpUKhj8SywkagWeLVRdEqRJ2xkmlIQXFoIlEfWI62004brHczCBvWr+/iXgLCXfj8Cnz+xvDcrCZUVYFhWA02Rw2eRJYs/lnpfXiRm2++GbfeeiueffZZvPKVr8Q3v/lN/m/B+7pu3ToeDrl27dLzDnr2LUQjHfWhoy8KzUcT8F1HI8QarEJyOQOHnh+rdzMIG5584jASCVIXdBuRqA/tneJE5kR9iMfTmJxojJDUgpiNIlNfhyjny1/+Mv78z/+cz2G844478OpXv/q4ENnOzk58/etfx1IhjxqxrKQTOYSbKF+X25D7Kpu8StRurkM8nqp3MwjBO4TeH+4jMZvD0UOz2H5aW72bQpSgqXLDTHsw58XSvBoazbrPK/3TvOpL2bNnzwnX8fl8uPLKK5e8TTLUXI6jypigzHKoI1LF80kR6KZ9h9Hn4HddrPo4PTSL/oEwuoLiOQSdgrL2mHg/09OCAic5YMGTxgqogEgkwCmiRrQvJwVHgXdRdbjznCSORQN4HSVqjoth6o7JvcMIrGmDXKJgF/OJ743govNaIK1LwnqOCo6CR42T2qizuqO6bAqO1XvfTz4gge07GPI1nILjjTd+t+I6119/hbDMEkjcnYzSbm9fS5niY7XU8vwZAsW86az4Wh1JV34dNzuEHjZp9ufCL3imMDTBs9ru0ZoyFMxlJUxnxcdVleyfh06dT9FjV6oiROu9Hz5TWJY2nFQflw+nsyp+5FXe3yioYsbagEizDzkzsQQlTSdPlP1+ZKsKRd9lVoosYBj568uSdehWGn7l7Kq3RXiP6elpLsk/Ojq6IDxTgAmJVAoZasSyQ2GnLp387NHRv9UMMwS2beuvdzMIG2TuUat3K4jFaH4FrV2hejeDWAQTeBkZSmDdxmZ4Ha+rPjIzeqWDOr0aNPrjH/8Yb37zmzE3N4dYLFbWH2Z/V2OoefMqI0OtbpxyTle9m0DYoDYFIXn0pbLaJeAf/cOBejeDsGFoeJqfH8JdsFd7QXWPcBez8WxDzVHzaugjUT1//dd/zfOlMUONedampqYWPpOTk1Vt0/OG2mK3I7Gy7Htiot5NIGxQQn7yqLkVOi2u9aiRS82FWMDI4dl6t4JYhKbJ8DWIkNhCv9Kj0UOUR616jh49ir/6q79CKLR8Xn/PG2pEbcmk9Ho3gbAhfZgMaLdKWnd3t9S7GYQNmzb3oKk5XO9mEIuQFalhZOBXE0ztecNm74c9Msz5ARxF8WwXmqiSl73sZTyH2nJCc9SIZSVCio/uxKLBCzfCQmcCgcZIErvaGDo2habmEJrJWHMV7Dm2+YzOejeDsOGRB4ax88I+eB2vz1GjPGqVceeddy78/apXvQof/OAH8dRTT2HHjh3QtPL3+2tf+9oKt94AhhqFPtaWnsFovZtA2BAYbK93Ewgb0ukcz6PW1uYgeUrUhVQ6i2CWksS7kSN7p7B5B8nzu4/GGAw051WjeXg00fC87nWvO27Zrl27bO2SgmJoJXjeUFstHrWbdt1uu/za6/5YWEfkIVGgwbSMiiXPJYHkuZPsul8phjqyY/30E+M44/weRAQSzYyuoL3xPBgR52AZCtm3LZsR78dwkM0XPl41qXKpfQeHiOaz357qEMqvqA6y04J6nQFxyGmzz8RcMoFIq2/Jctmqw0Ch6HpwlNoXpIVwluB3On9S5fLSwpdq7UZFj2v3vKfTDRL81Ujmr+b2iZ65Tnzs2uNfyCdClDqgUFrx9hyqiDQ2nIYrHxwTR0G0+u3fIy0O8vytgrKYwzshptnXiQrqDE3qaEqJ71vRI1STxW0QzZlRHDrjb/q/Z9kun0gLqyz7nS5qt6MwvcCb4VRHEdSRS1IhtPaGEc8Wv6uyKE1CtuLf4/BaFEv6O6YBqGxbpRQ626aU4ekIvOZXY6kqnDIfLdc+vIK5wg4hr11fq9ZQ8wKGblHMtktJT4pz9xD1Ixz2Y8sWkucniErQghQu7EYUh8FRb6o+NsbvJZbON7/5TWQyx6sFZ7NZXlYNnr3KyFCrPcwB0reOQrjciOSl4SuPhT4OD0/VuxkEsaro2NBa7yYQNhzZP4NGwLQKc9S8+V4l1cfqecc73oGZmePvg9nZWV5WDWSoEcuGaVh0vF1K2/beejeBsCGXMxCPk7eTICrh2BMj9W4C0cAUPGok0EUshvWB7a6LI0eOoKmpCdVAc9SIZSOd0jE1lkJb5/LljyCWh8knj6GVjDXXwUZkfT4K4yIIYvWz9fQOYUfVS1jzc5K8OtWjFh4vr3nUzjzzzPx8c0nCS1/6UqiqWjan8cCBA3j5y19e1bbJUCOWdY6a7NEHlxe8nYT7iEaD2LaN5qgRRCU09ZC6sBsZOTqH3rVRaJrSIHPUPGZtECet/Pjoo4/yXGqRSGShzOfzYe3atXjjG99Y1bY9a6gVJnmSoVY7Yi1+RJtIztqN+FuC9W4CYcP0dAKHD4/jtNPW1rspBLFqkGhA0JWkkjnoObNxDDW6Dol5rr/+ev4vM8j+9E//FIFAAMuFZw01r3jUbtr1A1x73ZsEpU7y5QL5XQfJc9mqRp6/KKk/MZFCLmeiuy+MoIN4RU/IXrK3KyjOL9HSYm8Azsw4yPM7PEQtUWiGQ7v9fvsyn4NtKqrjJMGvOLzjfAK5/yaf+Ni1BkyEuoMIBMolZH0O7xhnGWv760RyeJyIZfidRiQrfwk6h9ws70t1OST182FC7pfGJ2qP6A50eqPpgsKsIb5W731G8ExpkoT3TCgsvpeaFj1nCrQ5SvrbP7/Cqn0d6bkRBNEkvN9FqQCCqvjoidKVOD0nxZL14jrVOGGc6oheWU4JhUU/qZqULaWvsozqw2hSQmC+nyF6j6gOP0j0alQd3kmiOqWpA5bKUqI2dT2/3ZRhYTano7qZR+6Fnb0VD32EN7nyyiuXfZtePVaeMdRWE5m0vjDSRLiL8b0T9W4CYUMg4EN7OymlEkRFsF4kvdtdR+faZvgaIHWCOX/tUegjsRg2H+1zn/sczj33XHR3d6O1tbXsUw2eN9RWOhEdUUTVFAQC3g55WI2wwQoasHAnbDJ6KCROOEwQxPFYA11Lc30QNWXs0AzmJlPwOoV+pVcNNeaNVVb44+TxXc3ceOONuPnmm3n4I5Ppv+aaa/CGN7yBT8e64YYbqtqm5w01ona0tPnR1EqdTjfSvp7yDrmR2dkUjhwhbydBVII0GQd0GoR148CT0QDnxZqPHPKq6iNRPd/+9rfxL//yL/jrv/5rrvx4xRVX4F//9V9x3XXX4b777qtqm569ysijVnue3xdHfDpb72YQNi+VbKI4l5AgCGJVk8myl3u9W0EsItTkhz/k/dBHw8hfe9K8aB28OEetBh8vMjw8jB07dvC/mfJjIfn1q1/9avz0pz+taptePVY0R61ODy9VNOOYqKuhlpikpMpupKkphLVrO+vdDIJYXfDcg/RudxuqT2mIiNSCA4A8asRi+vv7MTQ0xP/esGEDfv7zn/O/H3zwQfj91UWceVb1sRHk+R1V5wRPS8kSzyETKUKK1CAZWkmdnu4IoiGNL3NS12r125f1C9QgGeui9h6hsYj498zNOp17gRqjg+pjIGhfFgg4KUXaL1cdVB+dlI0jAhU00TFlhBUTQU1CdJESms9BRUtzeP/IAgVH2UFRVHRtVa+cWJveQDXtIwVH72I5GAfid43Y82NZTmX2155TSsSMQN0x6aD6mJ3I2C6f+v2ksM5Ehzjdx/OdYdvl/laxpyUoEKAQPVslqxeBUVn4nov67I9r2OG9FBCqPjop4C6f6qNTl99RwbGK7WlV/NagoE7pu96I52DEM/D1RxzVN53OQ8wn2I9S+blzOg8nY1AWPGoTWQVaSobXshJRwuvqef3rX49f/epX2LlzJ97//vfjLW95C77+9a/j0KFD+MAHPlDVNj1rqJFHrfawh5fsYOgQ9UHzqxjYQV4bgiC8QXd8DBk5ilTA3igk6gQbIJ83YrxMQd2aPGrEYj7zmc8s/M0ERQYGBnDvvfdi06ZNeM1rXoNq8LyhRnPUasfh52fR2k6Jld1GJpnD5JE4eja31bspBEEQJ40pSZBpENZ1yCEfJF8zvI5ZmKPmEEWymiGP2vJx/vnn88/J4HlDjTxqtcPQLcfQQaJ+c9Qovx1BEF5hzh+GqnhftGLVYVowxmch97Y2RLobr8rzE9UzMTGBtrb8oPjhw4e5AmQqlcJrX/taXHTRRVVt05vDAWSo1YXTz+6ktAguRJIlBGOUNoEgCG+gyyosete4D0mCMevtPGqlUVqyR0Mf2Xj7SudR89qY/u7du7F27Vp0dnZi69atePTRR3HOOefglltuwde+9jVccskluOOOO6ratjevsgYRE3Ebe5+ZqncTCBtUTUaklUJSCYLwBuFsAqE0Kdm6DlmCpDioYXkoh1ppP5MgPvShD3FZ/rvuugsvfvGLuRz/q171Ki7PPzU1hb/4i78om79WCRT6SCwbiTnK1eVGUrNZJKbT6FrfUu+mEARBnDQWZEgOiplE/fpdvvVd8DIFxUcve9RojlrlMPn9X//61zjttNNw+umncy/aX/7lXy4Y80wB8rzzzqtiyw1gqHlBTOSmXbfbLr/2ujc51BI9QMTHQxLKrovnAihyMaQuGgsufA8o4vCHkECaty8sbltfQrdd/vrtOu4esfcWpdNiI10UNeP3i58eQYE8v2g5IyyQJw4o4t+qOUgkt/rtUxi0+h22Z5rwKcdLGLNlImRJ/GiQJFFF8XGQBNejU6hs9dL9lVGr/RCNiaOkv+B5PJuzf94xDFN8vaZ0+7KE7pA6I2E/wCYfmBbWUfaIpfshGBy1ouLw64QgNHuu2X75tGTCbPJBx8Tx+3EI87bC4neZJXggOs27FnlUnKIyRWVOuhRO2xPZCopDChiR08vnc0hPI0iVEF2UGqb38H4cW7Oe/93ks39fNQvSJ+TrCFIrOMjzi9IBqY6pFVAV2VTxNz007oM/pWFzf3XbIrzD5OQkuru7FxJdh8NhtLQUB8fZ37Ozs1Vt2/OGGnnUasfmre31bgJhQzDigy/g2VudIIgGIyDr8Ckmxg2S53cdHp87WDr4L5FH7aT24TWkRdf+cmk2eLb3RnPUas8jDxzDeRetqXcziEXoWYNUHwmC8AyqbCIk2XtriPoyF2lCI0jzF4S6CKLA29/+dvj9eY9+Op3Ge97zHu5ZY2QyGVSLZw018qjVFjrO7iU1l+MToCPNgXo3hSAI4qTRTRmmQxg/UT9yPh+8jFWq+kh51E5qH17iyiuvLPv+lre85bh13va2t1W1bc8bal6Yo7ZaGFjn/USXqxFmpNHIH0EQXmHO0DCrU8oRN9IyOYZUMOzZEMhGCH0kKufWW2/FSuFZQ41CH2uLYVjQNHpouZG2vki9m0AQBLFsBBUdPVoCe3P5xLKEezClvCKnJRSdWt00gjw/G9dd6TxnNHa8dDxrqDV66KNIyc7JVc8kj23rCNQgeZmVV9HSszkMHUmiqyvvVdPktLBOSKDe1OagXDgQFs9H2BTL2i6PN4kVvrJZq2IFx2jYvkykbMWICNSonJQffQ7P/s6A/b7aA+JjNzM0B39ARVN7cMnqkiIF0HyZXHEdsSJkdS868STd2r04b7zxuzXbF1E7Pn7d6/m/mUwOIyMzGBgoiiSZltO8KNH95KD6KNheyTSY48g4lMVz9vfFnPhxDHnavlBKi5UnTUtcJmTKIX3L1JztYuWQfRdF8pnQerPQDtuoCzt1nh2KRAm0Jac+RBUBO5agB2w5CD6ZEft3GVO4FJ0J0yHU3RJsz0kK2BIMxC5u916zDelJA5ZkQgvaby8cEf/WUEiqWJFSdMqdHF4iQ0GWnPuM6Yl8/j5JVvCTwyH+95Wk+kisIN4cDqDQx5pj6KajHDBRXzGRRh2wIIhqUVUF0Sglincjui5hZtaz48yrmmYzCU1oPnqAQp+SXEJEjfCsoUahj7UlHPFh89aOejeDsMEfVKH5vRmGQhArRTarY9/ekXo3g7CBRZ9lBN5Dor6ErSx81XhcV5mYiOTRsMeCV7EWH2JpePZKKw2NIq/ayhOfSWNsxD58hagv0ZYAAiFxGChBEPbvEEpr4U7Y272/yz7knagvGUn1bseyzFCjwU+iNng2dqB0kid51VaeTFpHNkt5bdzI8MEZtPZEEGkilTSCWCo+n4Kt23rr3QzCBmY/04i8OzmstMLLWKbhfY9aDbw43j16y49njxV51GqLrEgIBMlr49ZOjUeVkglixWDetNGRmXo3g7BFwv6jlBfSjfQY0+g04vD+HDXPdp8Jl6E2gqFGHrWVp7Ut5KDER9STtu4wn6dGEMTSYa+NsbE4Nm7qrndTCBvamnQk0xR+5jYMSYZieXdwvDHmqFHCazfh2d5bI4Q+3rTrdmHZtde9SVAifriI5NUlBw1iWcp70Q4eHEcwpKGnt4l/V2XxaGdQTdlL8EcMTGbs27cmIg6rHBPISE92iF/iR6ft9xPJq+3a0ua330+zz6w4FQHDLyjrDIp/a1/Ifl8dDvL8VkpHQFGhLtqf5vCglCW18uvEwVAXpYtwhp7kxMpx7XV/7Cg9zy5n9h4pfX9YDs9CC0ZFEvz5fdlvL2uKn9OzDiIaU1n7esmE+Dkkj9s/jw3TQU5/mRE+OxyeKU1RA4fHjv+9llN+E9WhTLAvx/6DUUXfQtAGKyyOSGEy/HaYHeIXltlq/w62HCJfZIeXgiZQdfb5Fi3QQ4BlIKIpCATs64QEqW4Y4UBl6WycyJri/WQN+zLdoQ6vp8+fc1kWboMglhPPGmoU+lhbDMPkctaE+5gYTiLa7IdKkakEsWQURca5OzfUuxmEAJ06ya4kK6vQPNzlsgzve9SUGiS8XuntewnPXmkU+lhbWlpDXKKfcOdcG5niDAiiIth749579tS7GYSAZ54P17sJhA1+M4fOzDQ8y4KYCA1ME7WhIUIfyaO28jBDgDxq7mTzae1c7IUgiMrQdVKydSvb18/hyf2RejeDaNA5al4WE6lFnjNSbV06FV1pn/70p3HOOecgGo2is7MTr3vd6/Dss8+WrZNOp3HVVVehra0NkUgEb3zjGzEyUp409M4778TmzZuxZcsW/OQnP1lYfvDgQe4JY9uenZ0tq3PGGWfghhtuWHJbyaNWWw4/P4Vcjjo1bmT/05NcGIEgCFT0Dunqzs+5JdwHDT25k4ys4ViwDV6lEcRECHdR0ZX229/+lhth9913H37xi18gl8vhsssuQyKRWFjnAx/4AH784x/j9ttv5+sfO3YMb3jDGxbKM5kM38aXvvQl/PM//zPe+973IpstT1zJjLTPfe5zJ//j5m8k8qitPDqbo6bQg8uNpJM5kucniCro6IjVuwmEgMk4Tbp1K63Z8oF2L9EQedSk2nyIFQh9/NnPflb2/bbbbuPer4cffhgXX3wxZmZm8PWvfx3f+c538JKXvISvc+utt2Lbtm3cuDvvvPO4oaYoCveQ8QaoKl/mK5EOev/734+bb76ZG3Rs+9VS8KqRR23l2ba9Gz4/hT66kXDUR6kTCKIKnth9GBddvLXezSBsmE3S+8aNWJDQnJ3DsWD7sm43WoVCiZOCo17lxB9TzavCqqqMtgBFEREun6PGDDNGa2s+Ez0z2JiX7dJLL11YZ+vWrRgYGMC9997LDbVYLIZ3vOMd6Onp4Z3HT3ziEzyUspQrrriCe+x27drFvW4n41EzDKMhPWoi6X6RJDVDlgSywQ6OV3leqv3QgWlurBUMAtkSj3b65IywLKwKJOsd5Oc3xuwflrIk3s9AyP4ln3Z4sKuCmOomB3n+dod2d/jty3oEEvyMqGbfhpDguDHaNjbBzoZWZLU6eX7BdSKS7c/XkZZRtr86arkvwv1YsL9nPnnTHWXf7/rtMwt/f/TaV1UstW9YuYrlw9MO/b/xtPh5PCbIKzY7J34+SFPpin5PtYieG7xM9I5xmFs72JfB7ufZe2bROgHxs8vSHLwg1USEmILj6jQ4LJLnD4nfmWZzoKLlDClmL+7l9ztJ8NsvVwTS/Azfcc2WIGcUhEISAkGpIgn+ag2y+oQ+eneggPKouYuqfbfM+Ln66qtxwQUX4NRTT+XLhoeHuWesubm5bN2uri5eVuD666/H+Pg4JiYm8KEPfci2U/eZz3wGX/va17Bv375qm0getRoyNZUkr41LeerRsXo3gSAIYlkxLYlCul3K/tYBeF5MhC4+wu2GGgtLfOKJJ/C9732vqvpNTU3HedJKednLXoYLL7wQ1157bbVN5CGWg4OD/F9iZQk6JNEkCIIgiOXk4GiARJJcysaJg/AE7AKzLCi5HHzJJAJzs9xQ27BhA7ZuWI/w9BS0TBp33303vGYY1OJDrGDo4/ve9z6u1njXXXehv79/YXl3dzcXBpmeni7zqjHVR1ZWKcyrdv755+ODH/wgqiWVSpFHrQacfmbxOiDcRf9aEkQgCMJbRIMG0lkF8/mHCRchuanPNW9sSbCgZrKQDQO5QAD+dBr+VAqyaWC6sxttRw9D0XWkwxGkIhG0jI7w4OiZjs68oZZJw1RUWIbBBe+MqSmsDUdgqCouOHtnvX8l4WEqMtSYwcOEPn74wx/iN7/5DdatW1dWftZZZ0HTNPzqV7/isvwMJt9/6NAhbnBVyrnnnssVIz/ykY+g2jlqo6OjXKyEWFnu+/1+XHDxxno3gxAkvCYIgvASsaCOyVkNhsO8YqI+zATE0VJVYZmQdAOWqkJLJrmxZaoqTEVBcHoakmki1dwMXyKBwOwsLEnC1OAgWg8/D5mVRWPIhCMIxmd4Hd3n4/9mgkFufDEme/p4vUJI4+jA2uL+g0AK+VQdzKPG+pWtbe0wtHwkkeemfRQPw4rug1gBQ42FOzJFxx/96Ec8bLEw74yFMQaDQf7vu971LlxzzTVcYIQJhzDDjhlpTEikGj75yU9i+/btXB2yUmiOWg3x2oPKQxw7NIuuPkoMSxCEd2CKfpQ0150kfKGy75JlQjEMKKaBnKrBn87Cl01zz1u8qRXtY0O8PB0MQQ350DyWz7071dWNQGIOgUQCsk/FTF8f/HNzsGQZOVmGpWnIhsPc6DJ8PiTZh4nbzfdHJtcMlrUj3lWM7NIXXTpsm0uhEeT5CXdRkfXz5S9/mf/74he/uGw5k+B/+9vfzv++5ZZbuCeLedSYJ4vNNWM506qFJcZ+5zvfyYVFKoXyqB3PTbt+ICz7+HV5L2glan6FF+WaNW1lSoE+JQLdtPdkKrJY8imoJm2XRx1O4YZYXi53MV1BsXRaQrd/yOaqvFREYmJOClZBwWENOig4BhX7MqfMCKxtIZs73bQMqHLQto7k8GgQXw9OxnrlLzXnUUp6SRInxknl1oL98+Fvr33twt/337cPO8/bsPDddFBwtKBXtB9GVvB4SBvia38kJb7Zjybt79vssP1zlaFNistqhVAR0uEZsH98fvBp0U+2fA4PQ4EipKMa5HLL0wm2ZzmpVdo9wBkB8W9VBUqNImVHXuZbVIfNz7JM+FULOVlFJJvkxlZW0bj3qTkVhz9nYDzahubEDCLpORiywtfRMhasOQnxcBPS/gBa45O8bKK5HX5T528RFjbImplsaoYpKwgEJOiygvFwMVorEQwi0d6BgJzXn04uStlkzv8guyMhy5ZQbVUkZHmiLgDz3vF/ZZmUCwl3hj6eiEAggC9+8Yv8Uylr16613cdXv/pV/qnWUCOP2tL43V1P4wVnrceRIxNIJDIIh/3YtKkb+/aNQNUUtDSH4fOrSCWz0DQFobAPFkzeoQ6F7KWAifpzxtnV5yIkiEYml6M8SW5loD2FyTkNc+mTyjLkPZhxBQs+M8feznxuVtjIQIWBKTWC1swcQkYGBmSMBFuxYfYoN8Ym/DGYloaexDjfzMFYL9pT0wjqGeh+P47GuhDN5A0xXVGRlVXuOUv5Feiyyo21sVg7N64Hxg5huLsPxnxYIeNoILzwd1ItFx/L+vMDhpbs/kH1BXn+atI5rBKY/UmRj+7B00+4wog8edSWxkUXb+NG7bp1nchm9YW5TcGQD3rOgM7iwtMWjh6dhK6b2LylG88fHMX4+CyGjs3gjX9yNu65ey+v09ffjFBEwYF941BUBZu3dmJ6KolUMgfVZ6B/sAkjQ3NQVBmhkMaTZbN9GJLJc7Z4Lua7jux9ZgqnnL68yUcJohFob6eQYTfjjdBHJnTBfosJv2RAkUzMGT40qRn4ZQOGJWECGgbkGV42aYa48dUnzwBZDU9rPRjQJxG2MkhLGvaqnTgldwxWWsOY1oSspCJqpKBLMg81ZN4wZljlJIUbcgciPTAkGZYkc49a3F+cX3Y02sX/1bT8+3golv9eIKdoNnnUgJGmTuZyghdZCH0kNXGiRnjaUKPQx8phBpLPp/JPgd7elrJ1mpuL8edbT+lBNtuBPzx8CLIs4cKLNy2UZfU0otE+btT5AypisQD3xOXMNDcIk4ksdN2CoZsIBFUc2DuFdDaNjVtbMDaSxORYmhttZ+7swhMPDHPDsbUzhOa2AI4djENWJPSujWEmbiCdyEHRZLT3RTEznuRtYS8R1a/AyJl8XVmVG9IAnJsTh2sRBCFm8bOPcA9zaQW6IdfUmGIokgVNZgaVhTldQ7MvC002+Zy52ZwPvaEEL5vIBPh6ncEUVMnC09MtWBeNI6AaSOgajiQj2BKb4ts8mozweVZtWhoGJCTTGlSJ9Vss6BYzqGRMWCEYpowMFO4NmzRCMOfngu3Vyg2o3b410ErCJefUYoj73KLYR71kysJyEciloRsKcrL3Im1MozBHzbuGWommyorug1ganjbUSEykNqiqjFN39B23nBlLLFTS589/j8YC/KNb+ctu3cbWsvXPOLsHGSM/XyLa5MP6zcWyU8/tXjiXlgkMbG7hBp4voMKXlWCZ1oIHMJPIQc+ZsPwmAlEfxg5OwzRMtA82IzWTQXwsgZwpofeMXgw/OQIjZyDYHES0M4LRvROQFAmxvmaYOQPpeJobeU0DLUiMzbH3JlS2z7Af2USGx6mz74oi8baxuuy6c5NBGIlQjjuCqIbHHzuECy/eUu9mEDYk0gpPel1qRGmqCVmTkNVl+BQTfs3kXrfJpB+dkTTUgISMrmAup6EvkoAiWxhOBBEKmOgIpvg2nphsxZbmaW58zWZ9GMmEsCk2zb1PRxIR7tVq8WW5pysxp8EnG3wfOah8ftNMzseVKNMG+65iJuvn67JW7ok3l81R2z1djHSwZBVTetGgGs8VB0RZ3bglntvtNoK5NFKGHznNe4aaNZ8PgsREiFrhaUONPGq1IZPRMTo6i3WReYtsBeFGkALugSsQjCoIRosvhM7BvIzuXC7/QuzfXpyjFWoKoG2gaUFMpOfUogoUM7Q6tnVxo05W5Py/mlKMSZclmFkTZs7kZanJJEzDQrgjglxOx9wIS4ZpofvUbow+Nwo9raOp2Ye2NTEceWqMG3ntA0287tTQHPyahMHtHRg7HIeeMeAPa2jtieDooTg3cmOtAd6O5GyWG4vseyKZ4+1kxrE/qCKXNfNGomxvHLJ1mYeSIAhiZbH4KDkbF1UVQFHyBlQ6KyEatvj3bGb5GEgAAEe5SURBVE4Ce5w2RUyw1/PkjIJo2EAsbPJ6h4f92LAmzdedmVWQ1DUM9qT5s/PgUABNEQMtsRxMU8JExo/+1jQyORmTCQ1Tcxr62tJctv1YPAi/aiDsM1DITsI8XrquIM08PabEDTRm6GVNBbOWjOEUM5Lyz9BnpkuembKEx6fKQ8cnMkWDajRdrnDIDLNSGrH3wUIrmSy+Fyn2B7xrqNUiIbV3j97y0xCGGnnUVt5Qm5tLY7XDDB1ZzYdIMti/qr94i4QXzVdpWde28DcTDWMeuQJd27rKVB/Xn9W7UMaux1hnGH7J4nP0mHFmMq1gKe+FDEd93ABkf7N/s2mdG4DRZj/ik2mkEjloPgW9gzHsfXKCr9O3JgxFkXF4/wzfxylndODwwTjiUxlMjiXxitetwyP35RN49vZHEAypOHQgDlXxYcsp7RgbTWJuNgu/X8GGza3Y89wo339zSwiBoIbJiQT/3tERQTZt8DmMvO2tYczMpCBLEgIB5l1VoOsGX5f9XTiuBLEa2bKteN+6Cf5O4++1ecvIMPgzAlYAWi7LVfdMSeYejVAqgXYriQT8kGEhhjRkmBhGDO1IINKTgW5IGB7XsL4/w42tyWkVOcNAX5fOHUDPHtTQ22kgGjKRyUrY87yGUzdleVtGJxW+bE23zo2ifYc0tDWbCAdNZHISDh5V0dZswDRlzCZkpDNsUIk3mTc9nZFhmgqMeaXLIyM+bswxrxQLB3xiHxOhyJfNpVQcHcsbQp09Bg6NBzEaLxpGe4YisEL5CIJkTsVU3knGYV41mMXnebJE+bfokyOWg/FYO3x+bx7TRjDUCHfhaUONxEQqg+lD2aFImlBi2rQUFrOBYMB/nGw7q1epXLVPtpe4jvqy4jqCUPGYJjbQDYHxXm1uaJFMr9Nkd41Xso7T6fdFyyXz+9qLx7F3Y3ki0Z07O/i/ipxfZ6A/tlDWclqAzw98/KFxBLQIXnhRpBg+agFtLc2wLAU+nwK1x49cm8ENOUXyobU1AsOw4PdpUGQFEmQYzJi0ZKTSWcRnUvz+amuL4vChKX6PtbXGEIsF8dyzQ7zutm29GB2LY2R4Boqi4oILt+Dee57lbersjKGruxm7Hz/M23TKKf2Ynk7i2LFJ/v2ii7bhgfuf46p77e1R9PW34aknD3Ov5oYN3UinmRd3hhuIO04bxN69Q3zdaDSIrq5mHDyYNzQ7O5t4SOz0dAKyJGPNmnaMj8/wdQMBH5qbwxgfj/PfwuqyR0YyyTqrEi9jfxuGyY3OYNCHVCrLy9hcS8L9sPMXCGh8YIGdc3bumJrtzEwShqnD72dhyzJm4/l5s03NISSSSSTZeYbEz3cmm+WDIUzZNhAEhodn+bpd3VEk5rKYnk5xi2Pd5ij2PDOOXM5ErMmPltYg9u2ZRNZIY83aGB8IGRtOciPkzJ2d2P3wGKYTFqItfnT0RbH/iTH+FO5d34SDUxb30DO6d/Rg/Lkx6Bkd/qgfQ2ontENDfJKH0dHCH1ryzCz/PpUNoz0Zh2zqyCk+TAVVKMk0giNxZHM+Hr5nKTnk2B2djSOt6MjMgM8Vzuk5HBqS+DMwpzMPl4zpWV/eHmSiE0fKO6aPP1fuOZqKKwsy+0dHi8vZPfX8MVZWfEgy466AbjCv2/wzTgIyuWKZ5aCsN5Xx5d9Y8wNrCzjcm1ZJJEbZcofwcGt+wMkWUfMcuhxM0MN2P4pU+QvG4X0lGp9m0RkiRD/VSdKfzSNfTNPcNJQsMBNttq2zOIdZgUROfL5TgndpPvy1sjQX2Zz4wGXFXQ3OxGy+bkpX8PyEN401SWLe8ZV1cKz09r2Epw01Cn2sDUxcpFRghHAPrG+0Zj4UtLgsP6It+xTI88Z0MCgjGCx2Vjo6yw3CgcHifMJQKISenuILeMdp/fxfef5xcu7OjQtlzS1hbN7cA4nFqwI4/4Xl830uvviUhb/b22PYuLEYinruzk0LRiVr7xlnruNGFzOSIhFww4rPC5TyBhkzAFkZ63g3NYX4ugUPH1tW6NSwDnsmkxdYMc0QZmYSfB+sLqszPDzFtxuJBDE0NMW9xcxIYwbiM88c4ev09RW9qYR7efLJw3wQgA0WTM8k4PNp2L69H88/zyTITbS1RxCJBDA2xox1IBT2I5XKITGX4dfMsaPT6F9TDINjAwVsYIN7jBWZG4EtLfnljM7u/GAIW4cp2W7Y1IqslYLPLyMc1dDZHeJ1GTvO6sBkptgz3r6z6L2bDCiI9RQHXTrnPfScIQ25jQNlv9Nszt+v5n4Fo5HyML2RSDum0sUBsJRRvM+ZuqA1V+xspjKrxwuiyRZUxeRzzgh3wXKsscECL2IVxERI9ZGoEQ1hqFHo48oydGyad242bKR8XW6DXfosRHE1sliUxe8v/g4+R89f7HA2N5eHpXZ3l8/La2oKL3iM+/vLO7JbtuQNzVKDscCmTeWhb2edVTRCCfdzzjn587V+Q7kQw+mnD8K0ih3J2ClFMaRgmHXA8oMbR49MYXBt8XoxrSxPPVLAH2DXYH7bOTOJpuby/YQjPigGqa6uBD7VRMhvYKYkvJFwB1mVeTtX53vnhJgNoPpIedRchUfvpDwU+lgbdB4a5ulLadWSyxo4djhe72YQxKrkgouK6UYId8FC3piWIuE+spqPG2tenqPG1XAIogZ4+kojj1ptiIT9iDWVz6si3AEL03Oak0AQhJiHHjxY7yYQAmZSGo5MUci9G9H0HFriE/AijRD6KJXkUluxT71/5CqiIQw18qitLJpP5XN4CPcRDGnYvLU81I8giKWRSVPYolsJ+XQMtObzbhLugimOelWen6sBkeojUUM8PUeNQh8rxWmMw/6hxEJPjh2Z5pPymfJjWdm8gMRiFDgkYJbtPXOSJL5UfbJ9Z8pUxZOZLZY1264OlhcnZ5Yohl92+K0F8Y+l1plLMHn+DNauj1ZUr6DeZlu2SN2zWEepWFE0n3tJtK/KX4Ti/VTHjTd+d1m3R6wM1173JtvllsMdbQnC5kqfDa2tobLvpfPaFmNa9s8hlrNLRFoggDucFF/7BxPi58PkpP3vVQ6Jw58NwbPQEcE965SKw+m5dpxyYwG/g4KjJkH1A9Yi2V8rJN6PVZJvsxRTsNxRVlikxMiYT4psS0Zw0qsJ/NEdrm9BJJHTObJTcGQ4Tcey209OVjASaEUmbd+GhOAQ6FWoMWYy4mOQSdqXyfGMsI406yz7OPf8HP83NaZj9L6p/MLL4Clojpq78PSQAIU+1gYmX85V9QjXwRQOmdALQRCVs2agqHZKuAtmACeynh5rXtWqj+GMN72dhYEb8qgRtcLTVxqFPtaGtes6eP4swn0w2XCWsJogiMp59A/5PH+E+9ANmc9TI9xJU3IGXlZ9hEMEyWqHOYtr8SGWBhlqxEkzMjLDvWqE+4jF/BgYLJeqJwhiabAoMYrIcCeKbGGwNVHvZhB2SBL3qnkR8qgRtcbTVxrNUasN42Oz9W4CIWBsNIED+72pvkUQK83GzSWJpglXYVo0Ku9m9neug7c9at7tPks1+hBLw7tXGs1Rq2l4Hc1Rc7E8P/VmCKIqMmmdJ40n3JlH7cgMhdy7lcGxQ/C2R827oY+Eu/B075pCH2vDOeesh6rSQ8uNRCI+tLRSriGCqIZjR6dooM+1SAhrAvlAou4oBc+T1yj8Lg8baiueQ23+QywNT0smkaFWGTftur1i+Wvmv/793c/gggu3Hl8k6OCIJOYXNmi71EFyWfJVJMHPy1Cbl4iTXLxIzl4k2+8sjS8LVa9ZLH2lsv6ObRA8YZ2l8empTKwkImPK6dlvnfjZIFlckl+ev+9MS/zcMATy/CIJfsZszv6+OJIUdwKPTtvfm8fuEoc4G+PzMuKV4BDaJQs6qY4S/AKpfSvg8GyP+Byl9tta5nBMLk8LY7UEhHXM5vJ1F+qEfZXnWHGy3wUS/BLz0GqCZ3jOcI7ztG3D8g4iiDaXcxAOzmTsKw3lQhifsL//MjP2aS6kqbRwP/KkfZk0La7jE0jtSw7y/Eg6y/NLR/IiKcrzcai5g/mF173IsQ5x8tx111347Gc/i4cffhhDQ0P44Q9/iNe97nUL5WxA7frrr8e//Mu/YHp6GhdccAG+/OUvY9OmTQvrTE5O4v3vfz9+/OMfc/vgjW98I77whS8gEoksrPP444/jqquuwoMPPoiOjg6+/oc+9CHUE/KoEcsSXke4k4mJOUxNeVMmmSBWmrPOGaSwboKoggmffe7O1Y/p+TlqbiSRSOD000/HF7/4Rdvyv//7v8c//uM/4itf+Qruv/9+hMNhvOxlL0M6XTTk3/zmN+PJJ5/EL37xC/zkJz/hxt+73/3uhfJ4PI7LLrsMg4OD3CBkhuENN9yAr33ta6gn5FEjTpqenuZ6N4FwMKI1QRJTgiCceeapIWze2gWfz9OvylXLE1OU586trEuO4KnIGlgeM2gW5qh57He5PeH1K17xCv6xg3nT/uEf/gEf//jHcfnll/Nl3/zmN9HV1YU77rgDf/Znf4ann34aP/vZz7in7Oyzz+br/NM//RNe+cpX4nOf+xx6e3vx7W9/G9lsFt/4xjfg8/mwfft2PProo7j55pvLDLpa490rjVQfa0Zra9FtTLiL3r5mdHR6dWSTIFaWNAtVo4gB17KlabreTSAEGJIMxWH6wapl4Td5uvu8qjhw4ACGh4dx6aWXLixramrCzp07ce+99/Lv7N/m5uYFI43B1mcOHeaBK6xz8cUXcyOtAPPKPfvss5iaqiJ8fJnw9JVGqo+14YknjtS7CYSAifE5JBPOMfcEQdgTiwW4qi3hTnyyRwUrPMDRQBtMD3qdLDSOR60W8vws3LD0k8k4zB8UwIw0BvOglcK+F8rYv52dnWXlqqqitbW1bB27bZTuox5490qj0MeaQeo97mVuNoOc0yR1giCEDAy2QmWKPIQrieccRECIuqKZOlQH8Z1V71HzsKFWS9asWcO9X4XPpz/96Xo3yXV4OvCeDLXlQ6T0B0vCjh0Dtop/IgUwy6pcCVGWxA98kbqjJVWu+FatB1Z4fBwwrJX3dCmqTB1NgqiSp58awpZt3QiFyCCoB2a7OE+a1RbEEPwwY+WKtmazWPXRCtur32qaVPH7QHdQQpT0KvochuC9I0uQRGUCdUmGnrZvQ9LBQyxScHT6rfq0/Xtsw+Q4knoIOfN4pU11NGFbR55ICfcjUoRMZcRqp5lsXqFxMVndfv8MXRe3gZGe3188cQSp8Yfml14JL8EukZUOJChs//Dhw4jFYgvL/X57ZVYnuru7+b8jIyPo6elZWM6+n3HGGQvrjI6OltXTdZ0rQRbqs39ZnVIK3wvr1AMy1IiTgr3ExsbiaGmheWqVokj+iiT4T2QQ2tXbuKGXh2iI6snCUUGnFAGiNjgZ4E5P/coNSedUAARx4kGXQgjT8XXKl7P3R2GZCXtZcYYu2FfGEF/f42n7siEHef6pKft2KwfsO6UMwyFNgSgVh1Nol1CGXyDBz7BC9kaSFa28Y1ZgkzqOx/Vix4xwD4YlQXEcLF2lLDwf6B20HDAjrdRQq4Z169ZxQ+pXv/rVgmHGwijZ3LP3vve9/Pv555/PZfuZmuNZZ53Fl/3617/mz3c2l62wzsc+9jHkcjloWv55xRQit2zZgpaWFtQLTw+1k5jIymMYJsbHZ+vdDELAk7uPYmbGeYSQIAh7+tc0QxPkvSIIQsyhXBOmDbF3c/VieX7ORy3nqC2Vubk5rsDIPgUBEfb3oUOHeF//6quvxic+8Qnceeed2L17N972trdxJcdCrrVt27bh5S9/Of6//+//wwMPPIDf//73eN/73scVIdl6jD//8z/nQiLvete7uIz/f/zHf/A8a9dccw3qCXnUiJNC102SrnYxpmVBITEEgqg6vQWJUbmXw0ZTvZtACOhQktykGTfC8BSk+lgXHnroIVxyySUL3wvG05VXXonbbruNJ6VmudaYjD7znF144YVcjj8QKA4WMPl9Zpy99KUvXUh4zXKvFWBz5H7+85/zhNfM69be3o7rrruurtL8DE/3sEn1ceUJBDScffaGejeDENDeEYXP7+nbnCBWjJHhOAJBDX66h1yJTzIgWRYsCkNzHxKgOYTcrlbY1VbtvPRVg2RBkla431zh9l/84hc79uXZ+di1axf/iGAKj9/5zncc93Paaafhd7/7HdyEp4cEyKO28sTjSezefajezSAcDGlVpdAtgqgG9vKngT730i4nIDsIQxH1I22qyFoefPcsPA883X0mXISnrzQy1FYeJv3u5YGl1c7+faMkz08QVbJlWxeiJyF2QawszJMmkaHmSpKmhlnTi2qpBXl+73Z83DhHrZHxdDwHGWrLx64bv2+7/OoPvBqtLVHb2054Iwok+PN17GuZjpL+lSu7OVKjJ4hIbc1RPbFCZUUWTq8qmlClrRoFx0r2v1qUHW+88bs12xex/Ny06we2y//22ssrT+uB4sDG4UMTaO+IIBrLG2uW5aD6aNpfr2mHcZIxgerj0aT41ZwYtpcp9x8VizqZTiqusv2+FNlepTG/M6UiZUdeJpDNNztDwjpGt1hNWGv3Y48V4caaUvLsiYbFz435LsHxbXB4TeSy4vmLIkReWMlh0ExKC64tkTS/Qx0roEJJ2ZfpPvH7VzfsD4Q8K04lowok9aNzs2iPZXFwLLRkqf1Eqlw+vZR0dsp2eTbncN2b9sfAdLiPT8SCAqxlwjBXPsUOQZBHjTgpNE1Fc4vHJgt7iO2n9pNqHUFUSTqdg66TR9qt9GfG4bccknwRdZXnV2UPejsbQJ6fjXvU4kMsDTLUiJNifCyO4eHpejeDEHD0yGS9m0AQq5ZgyAdF8fRrclWjWQYUgWeUqC+ZnIyRGS+GPnpfnp9wFw1hqBkGjYiuFLphQKWOjGsZHaUcdwRRLX39TQhHvNjZ9AYJ2Q/Tw56N1QyL/gz6vGhEF7yE3r3u5Bp9iKXh6WNF8vwrT2dnEzo6KZeNW/H5FG/LCBPECrJ/3wSmJpP1bgYhYFyLIes0l46oK52xDDzHfH9SPMecIJYXT19pFPq48sTjKVIVdDHnnb+p3k0giFUL1xSkcT7X0pOdQswgQ9qNeDe3XUH10bvdZ5qj5i5I9ZE4KSYn5+bncATr3RTChrt/9ywuvGhbvZtBEKuSjs4wJbt2MZYk8YTXxMoi77efhy45qEEac7PYPW7BtMrVGlMC9UYnBUfdsFeJ5G0QqEhLkgxZ4G1VZJ+wzonIzmfuC/hboAY6MPHcPyIej5+wHkFUi6ffQGSorTyWaXFVQfvwOsGQieOL1b6OIimwhPlynM6vQEbaBS/36kISHWS2bY4d+5nVhGgsd9sc91WjkVeS4PcuH7/u9bbLTcvJ22+dOKWHzL6ZC8tEst4zWfHzJG2Ir+/RlP3zaWJWXEc5OGO7XNftZdKdJPj59gSdWSbxLiSoViTB7yTDb6yJCetEu+w71C2t+eOjZaOISgoCWvHZE3JIe5cVnItUSnz+5ozK5fllgWy+NCc2bKS4fZmUqTxixVKqSK+iL38/adNaHc8e8FpoauE4edejRrgLMtSIk+LUHQP1bgLhQG9PS72bQBCrlomxJCIRH8JhEhRxJRbrLtP73a34ffUfEF1uFvIvejn0sQZSKRT5uHS8e6WR6mNNePTRA0ilPDhh2CM0t4gTxhIE4QxzLLvB+07YE8ilEcyIPYlEfZlNeLGL6f08aoS78OJdtACpPtYmIWzhOBPu46mnDte7CQSxaukfaEZ7Z7jezSAcBCtker+7lqFR+9BeT6g+etmjRmIirsK7VxqFPtaElpYIVNXTlxFBEA3K5EQS01NiIQOivsRDMUxFmuvdDELA5nU54VzQ1UojhD4S7oLmqBEnRXd3MxTFg6NmHmHHjsF6N4EgVi2ZtA6jRKiCcBeBbBr+XAbTUZqL60ZY1ysfPgwP4X0xEZqj5i48aah99KMfxS233EJz1GrAF/7hJ8Ky667/k4oVBZ0e6GJ1wCoUrlbtU6Kyho+OzqC1NVrFfpb3JbTcyo6k4EgshTIFx+PK7N8LVolSpM8n8YHzwjKzMJq+iKwpvl8mM+KycUHZ7JyDouCxOdvlkiKWO5RUgfqtj6npCgiJ1fqsmP2+zDZxmhazyz6ENNQu3k9rm/3x6Qznz4dfMuBHFv5I8byoDo+a2Zz99pIO751Mxr5QnhHPzZYn7b2wkmC5k9R9NaqPVb3fHFQfpax9G8y0+BjkjCSeP2YhZ+RgWcUGWQLlVIYskNr3qeLwY0WxF/pRFXuVUYam2l+nskC2v5TJ+U5KU2QtVF/TCdcniJPFk4ZaAfKoEY3OyMgMtm7tr3czCGJV0iEwLgh3YKoKDIrocC1skIOlWdU9Mlae1zswPT9HTZbyn5XeB7E0vHul8ZGW/AOcDDWiUVF5MnKCIKph6Ogsjh6mZLZuJefzIRUT52Ej6ktrE5Poh4co6UsKvH8Esdx4uhdHHjWi0XnhBVvr3QSCWL3QqK+r0bJZNI2N1bsZhAA268RLDs/SsGgve9SkGn2IpeHdK40MNaLBYWEa9/z+mXo3gyBWLbGmAGJN4rlfRH2x8hMI690MQsDBY2y+JbxD6RxVDxtqhLugOWoE4VFM04JJnRiCqBpVkWB4ezxzVcPmp6Wi1YglEbWgowXI5oCpuBc9ah5yFS5Ckiz+Wel9EEuDDDWC8DBdXZRjiCCqJT6TQSqVQ1SgckjUmdUr39sQsCnSPrGo54oSinYJy6yQfdfXCpygS1yi0ir1tDoqWBPEckGGGrFi7Lrx+zXZjygNQFGlyZ2s9ENeliV0dTH54OX1COy68XsV17n++isqltp3qiMqI9n+xkR8n1sV1zFLZPstyeSS/IVlOcHmsg6vmJms+D4fz9iPys9MizfoF8jCSw49YssvkOcPOnQBImIVCLNZIM/fGhDXEYSQ+vzi4yN6RKb0/DNNMizEJqYxFSjKpGdN+0pxh5QHE+Pi460PJW2Xy8MJYR15MlWRBD8nLZCtNxxz1ggaUMW7xXTYj+DwSA7vFkX2IZsz+TlU5vtiDEsR70eR7a8fWRZ7rzQ1gppR8KhJsqeNNMqj5i48HdNBedSIRiab1fHMM0fr3QyCWLW0tQfRt4ZUBd2KJbMMje4djGt0ZmYlxOe80yW3TMPzQiKE+2gIQ41BXjWiEeeolY5kEgRRGXNzOUyM23tUCDcgYbSrr96NIATEIhZ6O03viYl4eH4agzkLa/EhlkZD5FFjkKFGNBo+n4qNm3rq3QyCWLXkcgaSiVy9m0GIkCS0TI7XuxWEAMOUPCnP72UhEcJ9eNpQI48a0eidzNlZ+7kSBEEsLWG8qnr6Nbnq8WXT9W4CISCVASZnvOM6WVB99HikCuVRcxcNY6jRPDWi0chkcpic9FISG4KoLc2tAaxZWxSqINxHThOLnhD1jxT01DjH/IC/JHlah49wGZ6+2sij1hhUoy7ppBRZO1b2DWZZEhQHtSwGm4pfC/VEp+05qTtWsz2i8bAEghJOqq9WibpjeZ3i8smJFMZHk9i8rY1/zwkUBdOGeHx4okTSezFjafv7Ux6aEdaBIlWs4CiUHXeq46D6aAW1itrGkLL2xzvhIDaRE9QZL6ly2OyEcbT4fk8LHGz6lL1aJkMRKDhKY0mInqBOCo5SUhAumxP3Q5iCpR2WwzHl+ve2GxNXqUYRUqjU6NA2X0aBqpjo7UpjNhVaWK6alYcSy6pW8bVoRn0VX9uWQx2+TW1euVRVYfZ5N38fu6pW2r72kv2+0nj6WJGhRjQyLS0RnLpjsN7NIIhVC+uGujjDBwFg8+zhejeBEGB6dY7aCQZACWI58bShViooQoYa0WiMj8fx3HPH6t0Mgli1BIIqWtrEucGI+kNzXdwLS8323EEP3T8FeX4y1Iga4nlDjXKpEY2KYZiuTvhNEG5HUWX4/Z6eIbDqGfU317sJhBAJAz3ikNPVmkcNXjfUaiHNTyMsS6ZhDDXyqBGNKM8fiwXr3QyCWLWkkjqOHY7XuxmEA4bXO82rnFDA9F7Ca7rmCDcbanfddRde85rXoLe3F5Ik4Y477igrn5ubw/ve9z709/cjGAzilFNOwVe+8pWydZ599llccMEFfJ1PfOITZWVr167l273vvvvKll999dV48YtfXGlzKfSRaFhCIT+fp0YQRHWwkV9ySrubntREvZtAOJBMe8gfsCDP73VDjQT63UTFd1AikcDpp5+OL37xi7bl11xzDX72s5/hW9/6Fp5++mluYDHD7c4771xYh31/y1vegh/96Ef8c88995RtIxAI4MMf/jCWAwp9JBqV0dEZHDs2We9mEMSqJRTWsHYDhdYRRLUcGp5XSvQA5FEj6kHFwfeveMUr+EcEM7quvPLKBe/Xu9/9bnz1q1/FAw88gNe+9rV82dTUFM466yycdtpp3DM3PT1dtg1Wh3nh/vu//xuvfOUrcTJQ6CNhz/KO8u268XvLur1GgqT2iQLXXvfHFcvpm3yUW+T2cpLnt38nmJa+8Hc2q2NsNIk1a2OO6uqpYpXjmHSQ559L2i9Xjs1VLpvvE3ceLb9ScR3hthQJUipXkQQ/Z9p+rpLhIPGeEhzv0v08IQWQscaLZQn7tmmz4rlS0pzg9wi2xcmJ0js4SPBLDu8d0XFQlSrqiPdjieT5nRKeieoY4t9qzacb2NI3g91Hi7kIJbNycRFTc2hbSCDP32RvIJpt4ukAlkMZr5uab3s4AGNTC7wKS9sjSt2znPsglsay+6Rf+MIXcu/Z0aNHuZDB//7v/+K5557DZZddtrDOrl27cOmllyIUCnFD6mUve1nZNtatW4f3vOc9+OhHP3rSBhaFPhIEQRDVYBgWZmbE+bKI+tPjn4PkYJATdcZDp8YqRGaRR41YzYbaP/3TP/F5aWz+mc/nw8tf/nIeJnnxxRcvrMO8ZGNjYzh27Bh++MMfLhhTpXz84x/HgQMH8O1vf/uk2kOhjwRBEEQ1MAeI4pRwmKg7YSVHhpqLGY57R57fMvSGCH1knt9afIg6GmpMCIR51R5++GF8/vOfx1VXXYVf/vKXZev5/X50dHQIt8PK/uZv/gbXXXcdstnKRjSfeeaZhb/b29vR3NxMHjWCIAiiIkIhDdtObat3MwgH0qYqjMwj6o9hSqvfkLYsKJYJxTK4SF5HazM6jRlIlom9e/fiscceq3cLCQ+zrAliUqkU/vZv/5Z7yV71qlfxZWwe2qOPPorPfe5zPNyxEpgwyZe+9CX+qQRmnBVoaWnhRiHlkyIIgiAqIZXSsefpKZz2AvGgIlFf9qeaV7sZ4Gk6o2nMZlTkjPpb08xgVGDy6yUqZaDCRMLywS/paJVSUHMK9qqd2KSPIGhlMSsFcERtxdbcEAzIeE41gUgEzdEIFFh8lhXrbzIBPG9RC1XG+l8PDWmo5XI5/imEGxZgoY3VeLQikQiuvfZa3HDDDQtCJJUaaocOHeIhltu3b694/wRBEESjy/OTGeBmNgUncSjTxD1rhPswLAmytFz3kMW9p6psImvIiPp1+AIG30cip6IvkuRlo8kgAj4dnf4Er/XEbDs2hafhkwxMyVEMmVG0SCnokJGyNGQsFRMIIauEYULCs2r3fFbmPI/5Bvi/s8mHMDU2hsTIFNqVvBosi9ha3OcliOWk4icby5PGXL0F2Dwy5jFrbW3FwMAAXvSiF+GDH/wgdw8PDg7it7/9Lb75zW/i5ptvrqqBTAHylltuwXe+8x3s3Lmz4vokJkLYQSqNBOENROqN+TLx3GSrkBNpEXqpap8MNLf7F5axzqEd01lxR20ibT+f5dfPstevfQdWSuYqVrmznJTxRLGBuoNCYVqvXNlRJIvJ6okUAp368PPKgcfvp6QNPUmoEzqUrOLYbj2bFu5GN+zlNw0jV/F1p8j254ehKvaeF00Ni3+r6NwFxd03K6BWrgwqqCPcFkM9sVdkvxSBHpbn/U95rxYzmFTJRMZS4JcMRJQsVNnC0WwUg/4ZBCQdSVPDuB7CpsAkV7d8Xm9GUNLRpiShWzKezbUiGEryrWYtH7IIYBQR5KCA+cqMpgCOFoytHuAp5AfwA80qn/MzhHLVRqYJ2tQkI6/vKjhGuy1MsW3EfOgaFKivesafttKqj8SKGWoPPfQQLrnkkrLwRAaT5L/tttvwve99j6s1vvnNb8bk5CQ31j75yU9yFcdq0DQNN910E/78z/+8qvokz08QBEFUg6xIiES92yHzAlOzGnSTun0rjwVVsqAwA8tUEVMyUFULOUtG1lTQ5Utw42skG0ZUyaJTyxu/o7kgurQEN9RmjABGcyEM+OPc2BrOhqFwb5uElJk3IkdzYe4hY+UmZDye7FpIL8ESOQ0Z0YUWHbPKcxzGUSKvX+IRW7YjQHnUiNVgqLH8aE6hIN3d3bj11lurbtDBgwePW3bFFVfwTzWQ6iNBEARRDYZu4sCeGZxxbme9m0IIyOgsPrXerXAvfF6WPH+AJAlhTYcim5jNavzvqI+pZgLP55qxOToFTTYxk/NhOuvHukic51c7kGpCTM2iSc1ww2xPshVNWgamLME0fDAgIa4zz7PMQ1BTpoqRXJgbZwO+GTyfaca0UfQmPpsqF+iJG/4Fwyplij2S9cbS51UfnXLbeQKao+YmPB/UTaGPBEEQRFXwziNZAW6mLZrDdEJFLumVeUL5eVialjewUpKKkN9AwJfvw4zHNazpSEMNyIinVWR1GWtaU9wztW8sjLZwFk0xA7op4+mxJpzSOcM9VJNJH2atAJr8WeiWhLmchpwpc4ONrQsFODAX48ZWIUxx93T7QujjrOHH0UzRm3U4HSsLfZw27I0XNv/L8lgeNUnxfNeZcBGev9rIo0YQBEFUA8uhtmFL+VwWwl1YXKyiPvvmBpXKrhMgnQHCQSAYsJiaO6biQF8Xd0hhZo4NFgNrunUoioV9h1T0dBiIRixksxL2H7GwfWOKb3NsUkUyLaO7LcdDOo/O+eDTTKiKNa+cKCGeUGHkNGR0Gboh4ZmhKExuDUlIZlUcShW7dk+OloQH+hQcmo0sfM2WGlcKkLOW31PEQh1XvTz/4jxqHjfUapHnjPKoLR1vX23kUSMIgiBOgonxFKJNNE/NrQxP+2AeN0fNAhujlWWLlzHPVEA1+b/xhIzmqAmfZnEjZ2ZWRn933ns1Mc3WBXo68vWf2AOs6wPCISCZBvYdAk7bnPexHh0FcjrQ0y5BN4ADRyxuqAX8QIalfrWAVJoNEgOpDMCi5vYfUcA0VdiyA0eL3S9NlfDEnlDZL5hN5MutsIxMrvz6m0lqgFWs72Zh0g4tCeajG84VDcTVSjH00fNdZ8JFeP5qI48aQRAEUS0Toyms3dBU72asYiweQcrmRRmmDL9qcLl2kwlGmBKXWGeeqbmMAr9qIuLPlx+dDqC/NQWfaiKZVTCV0LC+I8mNr6OjPvg1E51NWR4S+NzREDqbswhoJuZmJQyN+bChP82NouEJH99/NGBwr9ZsUuYeMAbrFrBlUzP5xMzMwGLfZ+YkXpf1y597vvzXPPJ04VflmZwpWknDE+x3Fo3GCaZ+UUI213jzcljYJZPM9wKN4lGjOWruwutXG3nUCIIgPIBYxErsTnASvjIF0v2lKuksnEySpYVlumBzsw4d8PGMfYhPfEo8eKixeDkRIjW7fOzbPPm/mcHD5i7JWRNZU0ZIY2l7LT43ia0esTJ8nZm0hrBPR1Az+PykkdkABkKzvGwurSKVk9Hbkl/36JiG5oiOlliOhx0+dSCMU9YmuAE1OQVMx2Ws68/L2j9/TEMkbKI5muOeraf2+dDdz3KtMo+RhOm4gnC7yY2jxIwCFomX8ZnciDNnsphMWLw9um7wHK3PTbB3OTOimMtKwtBhBd3tFlQzhWd5ugMFhsFk+LN4dN6gAvKd66N6PrSQ/71IqX82aS/Pb5r2Uv/82Mr23SeJi7/bI/TEzKsa2mEJ0jHAQY3UFJRZzeLEzKagzHLYj+azvxZL7ZiMkYFpGVDVvMeQeSztcEpFxtRXbffv0IMNBu3rxMLiOh1+5wH9uC+HEbZeTMbWThr8J2qD5w018qgRBEEQdkacJEkwTRYel/f6sO/ZjMHLVE2GaVjYeEorErNZhCIakrNZvkxRJfgCKuZmMrBMC0YuyP/NJTLcRgq0h5GZTMLMGQjEc8iEwwhPsQxMFjKhMCTLwobsLJ+7c1DrQJ8+hZCVQVrScFhtw/bWKV42kgpyo6U3zBL3Snhuugm9kSQiWg4ZXcHeeAyntk5xs2w0GeBzjvojc3zdPdMxdATTXNmPzYM6MBNBdzjJjavpjA9pXUFY0fn2mQHGDCLmVeLCEqxTmlJ5SF0mJyNnyDg8EeB19YyBVMaHY+OsA5/vDD91MN/7NbMsGxWwe0/R8IgnFBwZLvbC9x8pNzwOD5d/nysRBUmkynvvLMRwMTwajQbnXYsuKdAEOQtXG2Yh9LHgkiWIGtAwhhp51AiCINzNXXc9xQ2d089Yi2PHpjA+FoemKTjv/M34/d3PcQOqu7sJra0RPPnkEV7nlB09GB+bxchwHIoi4/wLNuC+e/ZB1020dwTR2RXFk7uH+LpbtnVhZiaFoWMzMJHDeReuweOPDCOXM9DaFkJXbwTPPjHBDbb+tVFkMgYee2AEA+ubsPnUVkyPJvl2g2ENzR0K5qbTPDGsqfl520zdXBCKZJ44SZVhKgosSUIuEIAlAYamccNiRI3ljSPIGFKbwNJJmZIEHTKenmriRlHeLyZhIl30dhyIl6fkfXxiXup83ns4lfEvlB2ey88LkuYTW++fLq97ZC60SFhCK58HVUImN59QujQhuAsYn6ZOs5vxmzraszOYnfeorWbM+QF/2eNz1NgzbeUTXtPoylJpmNBH8qgRBEG4m4svPmXh76amELCtb+H7BRduLlv3oou38n+ZwdXSEsKmzV0LZee9cAP/Nx8iB7zwovULZW3tYazf0I6cxbxUwBln95Rtd8fZ5TnT2jpD2Hpa3hjq3VCeYLdvQ14Rcnwy/yr1RYsGlb8172XKpvNJeDORcjGFuZLQuSwzkEr6LaZFimhLpa3ZgF+zcGzMvfm3GhlDkqG4We2kCjERrxtqhLvw/NVGHjWCIAiiWto68oYW4U7YnDU2P45wJxlZxaFgB7wU+ih7PPSRPGruwvPDduRRIwiCIKqltUMswEDUn3RGwtyiuWyEe2Dd8e4Mm5/pIUONhS8TRI3wvEeNVB8JgiCIxVgCgQMmGV/Ks09O4czz8+GRWZ5w+HimBcqOjImM/ei7PDojrCOVKTguQvQuy4gHI6WUvXqhlMqJd8MyONuQzSXEdSy94uS2mmqfX0vxiw1ky1fcnqGY0FULcjR/nGXT3gPqy5bPzytFz9irPhZCZ23bJ9urIapBcb4ws9teclDvEUsRmt3225PbinMRFxON2R/vaFTsxQgLDndIEV+LmqBMZRMuC1hA66EEOgfzy0pOXQUI9uPgSRW1O6qJ+4JOZYyH5Pz90hcFTmkWXxurH3aSVnrwgwZXlornDTVSfSQIgnAX1173JtvlFsQdJaZLaLvcYf6LJZDgJ7wDy53W3pTDwREKUXUlTFlV9YYHymAZznn6Ac93nQkX4fmrjTxqBEEQRLWs35IXDCHcSSG9AOFepnvKBXtWfeij6vE5apLEPyu9D2JpeN73SB41giAIolri0/YhgIQ7SGdlHJsUhwES9aft8CF4AWM+kR951Iha4nlDjTxqBEEQRLWMj9jPXyLcAVN8bA7bz40jiJXwqCma0gASMLX4EEvB84YaedQIgiAIwpvIEtAUIkPNzaRiTfCSR43yqBG1xPNXG8nzEwRBENVSUHwk3Ilhsnlq9W4F4YThETl7I5driNBHyqPmLrx9tVHoI0EQRMMiUorMl9m/E4xFVZ58aATbz+7if+cEr5F4TtzpmBNETiqjDiGVIjn9rMOAY1IstS+lBakI9LSwjmGIy0RoSsh2ueq3X86wmu114Y1WsYqj1VSck8Z+2TPsj4LTRhGci7TY6yYl7cu0nPh4m4L2ZQbFaQACnfZz6VoEcvqMmEBSvy0gblurz/73tPjF/aAWn31ZUK1cAl9d9HOmnxhGc18//1urIjm5Ydkfg2oMdL+8tDbbocynnxhokrExZuCP17288gYQRIVQ6CNBEARBCNBF1hnhCiRYODU8Vu9mECfAKY3GasGYN9wVj6s+FvOorfSHWAqeP1IU+kgQBEFUS1ObOPEyUX9Y9192yL9H1J/Ihk54AX1eTETVPB+MRriIhjHUKPSRIAiCqJSOnnC9m0A4ImFCp2TXbiY7mYClr/4+mJ5rDENNqtF/xNLwvKFGoY8EQRBEtex9YqLeTSBOQFynPGpuxkhmYTHVl1WOUZDn97iYCOEuPG+okUeNIAiCILzLusB0vZtAOCD7VW/NUfO6R02SavIhloZnr7brr78eN954I81RIwiCIKpmYFMzVjt6rvKk3apqr9SoaD5hHSviq0ghkWH0ReyXrxGrJ0aay8Uc1JlZBJraoWrizp+h2xsK2aywCpzGd5ub7PcVaxKPf3eH7Psh3UFxI0RKja0OCo7NPvvfGtPEdaKafZ2wg+qjIvipqlRex2qJ8n8lKQdR/1x05pzMO5HtZwpUIhkibSDB5WErz98bldAjOJcEsdx41lArQKGPBEEQXsaxKycusZYmz5+YyyHWljc0soJOXkIXdwzTafs2SFNi+XtpLluRzD7Dyorl+b3OoVA+fQLhTob2TyPSHFi4j1Yr+nzCa6/PUcubzSvt8SKP2lKh0EeCIAiCEDA2lKh3E4gTENUr9xgStUNWZJiLR0BWGSx0syAmonneUCPcRMMYauRRIwiCIAjv0ZKdq3cTCAfCTX74Aqs795gx701rhDlqhLvw/NVGhhpBEARRLaee213vJhAnwCRhAlfjC6jiCWWrhII3rRE8annx/JX145A8/9LxvEeN5qgRBEEQ1fLcY2P1bgJxAp6JDda7CYQDyXgGM2OrOzw1V2Koqdrq9g4SqwvPG2o0R40gCIKolmyGBvnczpb4oXo3gXBAViQY5mr3qBWfA40jJrLSH2IpeP1qo9BHgiCIVYPTgFrlHT2n3E2WYHuLq0Sb/QvLMoZ95yJjiMc8M5nKlB2d1B2dlB1Ny74Oy1ckK5ptmazaL3eU2o+Jk0ubHaGKpfbDvfbba+8Qey06QuXXSechE/KAhKAqvn7MKqTcVdm+UkgRX1dtAfvz2hkQt609UI0Ev32Z38HZ4xP8Hs1hyF6roo686JDGOn0w2jQEA9aS6ywFkT5J1kG4JC24h0Wy/QUSVv7eU1UFEX7brG7Dk1g9qI3kUWMvbUqyRxAEQSyVrv58DijCvaRDkVU/B8rL6LqJmckMgv32OfNW0xw11ef5bnNNElJTX3zpNMwcNQZ51QiCIIhK2LN7vN5NIE5AKkLGtJth0vxxh7yBq4Fcdt5QU71vqBHuQm4UjxqDDDWCIAiC8BbN4yOQBAnMifqjqqyrKXlCTERrAI8azVFzFw1lqJGgCEEQBFEJ/eub6t0E4gRYkgxplYtVeBl/UMXmHW1YzVCya6JeeN5Qo9BHgiAIolpyWXpvuJ14axusknc94S4Mw8TuB0bgBY9aI0jzsxxqtfgQS8PzQwNc8UqWuTeNDDWCIAiiEkaPzqGzL9Jwk9/NPvt5X4aDuIrcF7Zd3uWg4NjTYu8J6wuJVTFbFikeSkYSVhQIOkgeypL9fnhUnoCgal8nIljupMbY5BPXiWr2ZX4HdcmAUplSpZNSo+pwaSuC7TnVkaXyrqWlymA/JeLgjRIlQBapszLMeSXGxSiO96r99k54f+t5Q83n0+Dzvq1GuAjPG2qF8Ecy1AiCINzC8oWpWY6S/vZkzbjD9iRha0V70h1+Tk5UmC4m0D1+g/Z7kiSxVaGo9r1HKyB+zTtJ7a8mpLkkrJDfWZueqBvMCOrstk/dsFrI6Y0U+liLOWSNNfB1MjSE75FyqREEQRDVsO2srno3gTgRLAkXyfO7mubWALyg+tgYYiKEmyBDjSAIgiAEHHxmEiYJVbgas68TCK5uQ8DrPPfUJDwhz98AHjWpRv8RS4MMNYIgCIIQkEkbyxmpSawA0ugUkEjVuxmEh1mQ528AQ41wF2ojGWokz08QBEFUQiiq1bsJxIkwTUj0fnc1W7e3whPy/A0Q+sjmFK60eFKjiTOdDORRIwiCIAgBvYMxyAp1KlxN0A+rJGcq4T4mxtPwhDy/QLSHIFYK7w8NkKFGEARBLBHLKjfK9j05iQ2ntkPzyTAXlRVwmsIm0riQnKQiBYah5ZTDKWzv+bMclB3NDnslPkMgzc+I9oi3195hP/a7NipWuOwJ2Zd1BsQesrBaXmb6NDZED79f/I4XyfD7HeTswwLZ/Jjm1Db75QEHSX+foA0+h6F0odS+w+8RydYvltMvRZTvSpIcUiHY1JkYzWLLFiflR8E1b4mvHUNQR5PtUzukdEl43IwThDebuaI8v1NqAm8g18CP0xB+omWhIY4UGWoEQRBEVZCgoOvJTc5Bn0nWuxmEA4oiwVrFNxKpPhL1ggw1giAIghDQ1R/lnUzCxTBP0So2AhqBc89fs6rnJeWy+eTampNXmyBWADLUCIIgCEIA6/+TCeBu1KYQlGiw3s0gHHjskSFkMg5J3l1OtiT00euQPL+7aAhDTZbzP5MMNYIgCKISJoYS0HOkKOhqTBPWfEeacC+Gvnrvo0bKo0a4i4a44sijRhAEQVQFG/ilsDpXY6RzMNM5oIm8am6lrT0EWV69XpRGkufPP/RW+lyt3muh1jTCFUeGGkEQBFEV67a1QlYaIvjkpBApPtYCPveJbGlX09IadLiPKu+0G5a9suNKQQmviXrREG8fMtQIgiCIahg+NItsht4dbkZtDsHf01zvZhAOHD08g9n46s2l1kiqj4WE1yv9IZaG2hBzDCYm+L/33XcfnnrqqYULZPG/or9PZtlSyk92u7X+OxgMIpvNwjTNqupX+n2p26xmP26qI1q21Lqly9ngBDs/BTnkk93uicoqaVu12z7Z/Ve73krVYXNnw+EwZmdnK97mcrWhGpZDYvvuux+x3ZYF8RwWyyoaS6X1TCuvxma3vdJR99KinJkQ1oln5bKy2QkTwQMSVE3CsVS+bPEhODRafJVai1w7iQPzKnGLD9v086IfWr5y6Z+iNzarkygtLFay5krEDxY13JwtzYlWKLNgTgftwz0tC+aRUjGFkvMQtdtU/g8raBQ3V7ZdC5M+0+Y8WIhpVnFZYTvzX33y/HUyv0C1JGiWjIzMOtPl6xa2IRcaVXo8LYvnHVvctkKdQn6z0raxvwOKuI62UKe8XJGKv6f4u/J/58vKr5388mJ7S+uyP1kUIV+26NhIYM99+zr8MTD/d3k7itL5+U2W18tfkuVtt1Bap3ybTCgiv6xYHgw0Q9ezyGaTJdstXWf+OxZt0yr/PQuf+d9ZXFZcx7SK11tpuV5IeFiy/8I6hln6vXhsC3Wf33+Ml+179nDxAiKIGtAQhhozKhjT09P8Q5wcZ511Fp555hkkEsXODuEu1q9fj2PHjiGdXr0jmF6HDXiceuqpePDBB9Fo3HrrrVgtbN68GUePHqXn3Unw9Apvv6WlBf39/di9e/cK74moFjYoxaKaVvs7KZlY3e1fGpTw2k00hKF28cUXY3R0FLFYDNFoftjvuJEtwd8ns+xk6yzX30tZt5JtDgwM8E6mrusVt6mSskq2uZz7qWb7ojpL3Va1bRCVbdy4EZqmIZVKLXkbS932ydapZr3l2I/b6jAvFzOm29raKt5upfuqFUv13LW1lbpfSrGE21v864pFDnUWHZNC0WLPXWkdY9GOFFVH/9oOWFY7sqURkCV1ZnNiz3Q8XeyQlB2eiUUdPsdjN1923ColC0TzfxYvL92PICeU5VMWncvC/iUovuLfpWUac7QJIh6YB6q8rLhdf0lZ6S5987nriqsWC9WSn8T249d8iEWiOL09ZFsnH2plf30q8wIXxZ9TrKNKVrFOyXLmNRNFv7C2lf6+wnJlvs7xET35Lmvp94VjPd9mu7YVTmvZNiFBnm+zfZ3y41EolyCXLStto1zy+8uX5+sUdlHaDlZW+PnFtmmQZAnyvDe0/JwUxdrLwuL43+ai45Zfx0L+ZiyG0hX3ZcCwrZMzS38/W1iso1vlIXml5exz9PkRjI9O4ZwLdhx3DRHEStIQhtoZZ5xR7yZ4jlNOOaXeTSBOwAtf+MJ6N4EgbLn2uj+2XV7ofNnBwpnsMKyMsI5u2o9+Z824sM5EiWHFOPD0BDp6I4g0+bFv1t6w+dmR0hDCcv57t+A1e+tTwjoilUlLcxiFDtvnd7Ji4raZHXnDZjFGn8iQBqI9/orFRNZGxdL5PSH7ss6AOAw2rJaXWaYFK2fAHxR3aUqNu1L886GKtvvR7MtimlPb7JcHVPF+CiGWxy8XVoHoUmChnCIUURi2JD5uzIizXS6JEz/LNnWOHonD0IHBdS22exFhWXrFYiJZ074spYv3kzaWLzx8tVOLPGeUR63BDbV4PP8SXu0udoIgCC8Sj88to6GWrcJQSwrrzC0y1BKJFIKzzE1iIDFn3znNJMSGiJESvGb1473dC4i8owVPhe327NvADBgRgv4szEXHoBQjZb8fPSmuk+Vzx+zJmPZlaYecW/IiQ81I68gOTSG2oUNYRxX0Cw0Hw0YS5DZWHAw10TxCnXkOBRTmta20obbgHavIUJOWxVBLJVNIpw3E4z7bvSynoZYzi/NWl2qoZaow1OJSvKzP6fXn82rbh1eQLDfEyywTzDBbt24dhoeH690UgiAIgiAIogHo7u7GgQMHEAgEsFqpdR/aC8esFnjKUCtcaAXxEIIgCIIgCIJYSXw+nycMjlr2ob1yzFYazxlqBEEQBEEQBEEQqx3SxyQIgiAIgiAIgnAZZKgRBEEQBEEQBEG4DDLUCIIgCIIgCIIgXAYZagRBEARBEARBEC6DDDWCIAiCIAiCIAiXQYZagzE7O4urr74ag4ODCAaDeOELX4gHH3ywbJ2nn34ar33ta9HU1IRwOIxzzjkHhw4dWih/9tlnccEFF6C/vx+f+MQnFpb/2Z/9GV7+8peXbetnP/sZJEnCDTfcULacfR8YGFix3+nlc8SO3datW/m5aWlpwaWXXor777+/bBv33nsvzjjjDKxduxZf//rXF5afd955eM973lO27le+8hV+jm677bay5W9/+9tx0UUXrdjv9Pp5YoK61113HXp6eng5O0979uwp2wadp5XFMAxce+21PDcQOwcbNmzATTfdxM9NAXZM7T6f/exnF9ah81Tfc1QKO97s+P7DP/xD2XI6R/U9R//1X/+Fyy67DG1tbfzYPvroo8dth/oOBFEFTJ6faBz+5E/+xDrllFOs3/72t9aePXus66+/3orFYtaRI0d4+d69e63W1lbrgx/8oPXII4/w7z/60Y+skZGRhW1ceuml1pe+9CXroYcess4++2zr97//PV/+la98xYpEIlYul1tY90Mf+pC1Zs0a60UvelFZOy6++GLrbW97W81+t5fO0be//W3rF7/4hbVv3z7riSeesN71rnfx8tHR0YVtbNu2zfrBD37Az82GDRusQ4cO8eUf+chHrC1bthy3P3aOrrzyyrLlg4OD1nXXXVeT3+zF8/SZz3zGampqsu644w7rscces1772tda69ats1Kp1MI26DytLJ/85CettrY26yc/+Yl14MAB6/bbb+fPqC984QsL6wwNDZV9vvGNb1iSJPH7qwCdp/qeowL/9V//ZZ1++ulWb2+vdcstt5SV0Tmq7zn65je/ad14443Wv/zLvzDrzfrDH/5w3Hao70AQlUOGWgORTCYtRVH4w7aUF7zgBdbHPvYx/vef/umfWm95y1sct3PWWWdZ999/v5XNZnnn86c//Slf/uyzz/IH9L333ruw7rnnnmt98YtftAKBwEIHlf3r9/utW2+9dQV+pffP0WJmZmb4cf/lL3+5sGxgYMDav3+/NTc3x1+ITz75JF/+P//zP3xd1iEt0NXVxc8R66QUYHXZev/7v/+7Ar/S++fJNE2ru7vb+uxnP7tQNj09za/77373uwvL6DytLK961ausd77znWXL3vCGN1hvfvObhXUuv/xy6yUveUnZMjpP9T9HbACkr6+PD06xY7vYUKNz5I77iBlyIkON+g4EUTkU+thA6LrOQxgWZ4JnoQx33303TNPET3/6U2zevBkve9nL0NnZiZ07d+KOO+4oW3/Xrl08jCsUCkGWZb4ug9Xr7e3F//7v/y6Ehj3yyCN405vexMNRWGgK45577kEmk8Ell1xSs9/ulXO0mGw2i6997Ws8TPX0009fWM5C7rZt28aXs9CfU045hS9nYSeapi2co6eeegqpVArvete7MDExgQMHDvDlrJy14fzzz1/hX+zN88SO4/DwML9PCrBzwe6nwn3AoPO0srBw1F/96ld47rnn+PfHHnuMn59XvOIVtuuPjIzwZyA7zqXQearvOWLvpre+9a344Ac/iO3bt9tuh86Re+4jEdR3IIgqqMK4I1Yx559/Pg8lOHr0qKXruvXv//7vlizL1ubNm/mII7skQqGQdfPNN/MRsU9/+tM8DOg3v/lN2XbS6XRZqF0BNsJ22WWX8b/ZaBkLDWO8+93vXggpufbaa3kIGFH5OSrw4x//2AqHw/zcsDCgBx544LjtsJHlycnJ45ZfcMEF/Hww2IjlK1/5Sv43O28s7Ivx1re+1brkkktW8Fd6+zyxkB52Lx07dqyszpve9CYedlUKnaeVwzAM68Mf/jC/T1RV5f9+6lOfEq7/d3/3d1ZLS0tZeGoBOk/1O0fs+x/90R9xTzXDzqPGoHNU//vIyaPGoL4DQVQGedQajH//93/nE4D7+vrg9/vxj//4j7jiiiv46BYbtWRcfvnl+MAHPsAnZn/kIx/Bq1/9aj75uhRWt6Oj47jtv/jFL8bvf/975HI5/OY3v+HfGS960Yv4dwb7l0bEqjtHBdjxY5O12Qgjm4T9J3/yJxgdHS3bTkFsZDHsnJSeCzpHK3eelgKdp5Xj+9//Pr797W/jO9/5Dh+h/7d/+zd87nOf4//a8Y1vfANvfvObj/OUMug81eccPfzww/jCF77AhT+YuIQTdI7ccR85QX0HgqiQCg07wiOwkcfCaD8b4WcjjJlMho+W3XTTTWXrskm9L3zhC5e0XSY+wi4r5lFg8wT+4z/+Y2F+AYstn5iYsHw+n/Wtb31rBX6V98+RiI0bNzp6Ckphc9nYOWLnpKOjg88ZYNx99918nkfhHLLvRHXniQlR2I0qs4nwf/VXf7Wk7dJ5Onn6+/utf/7nfy5bxp5vi8UlGHfddRc/no8++mhF+6DztLLniHnOmAeHzQktfNjxZN7r0jlmTtA5qt19dCKPmgjqOxCEPeRRa1DYyCOTDZ+amsL//M//cC+az+fjUvxMQrcUFpfOJMiXApPtXbNmDe68807u8WGjYQzmdWCfz3/+83xeFY2KVXeORDBvKIvdX+p8A3auv/SlLyGdTuOss87iy9m5Hxsb414Ftu9zzz132X5Lo50nJmPd3d3N53UUiMfjPI3CUufA0Hk6eZLJ5HEeTkVRFqIHSmGS7uwYl871XAp0nlb2HLG5aY8//jh/nxQ+bD4Tm6/G7relQOeodvdRtVDfgSAECAw4wqP87Gc/s/7f//t/XOHq5z//OZc63rlzJ1dhKsgfa5pmfe1rX+OS4//0T//ERzB/97vfLXkfTDo3Go1aW7duLVv+jne8gy8vnWtFVHaOmPfmox/9KFfHOnjwIJc5ZseVjTgyNbSlwjw77Fy8/OUvL1vO5miw5YW5AkT19xKT529ububpLR5//HGuJrhYnv9E0Hk6OZj8OlMKLMiKs+dbe3s7jxJYrJzK5uZ++ctfrmo/dJ5W/hyVIpqj5gSdo5U9R8zjxbxobH4Z61p+73vf499L1TZPBPUdCOJ4yFBrMFg4wfr163kIAZMPv+qqq7hseClf//rXeSgdk8VlnU+WB6oSmHQue1C/5z3vKVt+22238eV/8Rd/sSy/pRHPEevkv/71r+cCIqy8p6eHyxzbiYk4wXJ+sXPBjIlSbrjhBr6cicgQJ3cvMeEDNvmdSYEzQ/qlL30pl6GuBDpPJ0c8Hrf+7//9vzy8jT3P2Pli6RNYmHcpX/3qV61gMHjcs3Cp0Hla+XN0soYanaOVPUeF9/7iDzvuS4X6DgRxPBL7n8jbRhAEQRAEQRAEQdQemqNGEARBEARBEAThMshQIwiCIAiCIAiCcBlkqBEEQRAEQRAEQbgMMtQIgiAIgiAIgiBcBhlqBEEQBEEQBEEQLoMMNYIgCIIgCIIgCJdBhhpBEARBEARBEITLIEONIAiCIAiCIAjCZZChRhAEQRAEQRAE4TLIUCMIgiAIgiAIgnAZZKgRBEEQBEEQBEG4DDLUCIIgCIIgCIIg4C7+f2gtd/UizC3wAAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 1300x700 with 2 Axes>"
       ]
@@ -1009,7 +1017,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "cee59642-7ada-46e0-8b53-4d36bb451dc1",
    "metadata": {
     "tags": []
@@ -1019,7 +1027,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-09-29 13:35:41 - INFO - Writing the following NetCDF files:\n",
+      "2026-06-23 09:14:09 - INFO - Writing the following NetCDF files:\n",
       "ROMS_TOOLS_INPUT_DATA/roms_grd.nc\n"
      ]
     },
@@ -1027,8 +1035,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 7.93 ms, sys: 2.48 ms, total: 10.4 ms\n",
-      "Wall time: 11 ms\n"
+      "CPU times: user 79.1 ms, sys: 28.7 ms, total: 108 ms\n",
+      "Wall time: 252 ms\n"
      ]
     },
     {
@@ -1037,7 +1045,7 @@
        "[PosixPath('ROMS_TOOLS_INPUT_DATA/roms_grd.nc')]"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1058,7 +1066,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "10905992-f199-4910-83d8-14abb83ccad2",
    "metadata": {
     "tags": []
@@ -1079,7 +1087,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "62ee49ff-8359-4247-b92b-65ef036866c8",
    "metadata": {},
    "outputs": [
@@ -1088,7 +1096,7 @@
      "output_type": "stream",
      "text": [
       "---\n",
-      "roms_tools_version: 3.1.3.dev32+g91d580e67.d20250929\n",
+      "roms_tools_version: 10000.dev364+g785fa77b7.d20260612\n",
       "---\n",
       "Grid:\n",
       "  nx: 100\n",
@@ -1104,7 +1112,10 @@
       "  hc: 300.0\n",
       "  topography_source:\n",
       "    name: ETOPO5\n",
+      "  mask_shapefile: null\n",
+      "  close_narrow_channels: false\n",
       "  hmin: 5.0\n",
+      "  parent_info: null\n",
       "\n"
      ]
     }
@@ -1148,7 +1159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "0624ac2d-7f81-477a-a31c-e572109defeb",
    "metadata": {},
    "outputs": [],
@@ -1158,7 +1169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "b3319391-16d9-4273-b3d8-230c854cba86",
    "metadata": {},
    "outputs": [],
@@ -1176,7 +1187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "71ef4d75-53f3-4ab6-8edf-bc351a671586",
    "metadata": {},
    "outputs": [],
@@ -1186,7 +1197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "eec0ae43-4118-40c2-95a8-baeb8c02c24b",
    "metadata": {
     "tags": []
@@ -1196,15 +1207,16 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-09-29 13:35:43 - INFO - Sea surface height will NOT be used to adjust depth coordinates.\n"
+      "2026-06-23 09:14:10 - WARNING - Optional variables missing (but not critical): ['Lig', 'DIC_ALT_CO2', 'Alk_ALT_CO2', 'temp_WOA', 'salt_WOA']\n",
+      "2026-06-23 09:14:10 - INFO - Density-space interpolation requested but the BGC source has no temperature/salinity; falling back to depth-space interpolation.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 1.64 s, sys: 319 ms, total: 1.96 s\n",
-      "Wall time: 1.57 s\n"
+      "CPU times: user 738 ms, sys: 144 ms, total: 882 ms\n",
+      "Wall time: 893 ms\n"
      ]
     }
    ],
@@ -1248,7 +1260,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "a67cdb30-8148-4942-8dc6-173de225cc3a",
    "metadata": {},
    "outputs": [
@@ -1706,22 +1718,22 @@
        "  * ocean_time   (ocean_time) float64 8B 3.787e+08\n",
        "Dimensions without coordinates: s_rho, eta_rho, xi_rho, xi_u, eta_v, s_w\n",
        "Data variables: (12/42)\n",
-       "    temp         (ocean_time, s_rho, eta_rho, xi_rho) float32 669kB dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;\n",
-       "    salt         (ocean_time, s_rho, eta_rho, xi_rho) float32 669kB dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;\n",
-       "    u            (ocean_time, s_rho, eta_rho, xi_u) float32 663kB dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;\n",
-       "    v            (ocean_time, s_rho, eta_v, xi_rho) float32 661kB dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;\n",
+       "    temp         (ocean_time, s_rho, eta_rho, xi_rho) float32 669kB dask.array&lt;chunksize=(1, 20, 82, 102), meta=np.ndarray&gt;\n",
+       "    salt         (ocean_time, s_rho, eta_rho, xi_rho) float32 669kB dask.array&lt;chunksize=(1, 20, 82, 102), meta=np.ndarray&gt;\n",
+       "    u            (ocean_time, s_rho, eta_rho, xi_u) float32 663kB dask.array&lt;chunksize=(1, 20, 82, 101), meta=np.ndarray&gt;\n",
+       "    v            (ocean_time, s_rho, eta_v, xi_rho) float32 661kB dask.array&lt;chunksize=(1, 20, 81, 102), meta=np.ndarray&gt;\n",
        "    zeta         (ocean_time, eta_rho, xi_rho) float32 33kB 0.2544 ... 0.2846\n",
-       "    ubar         (ocean_time, eta_rho, xi_u) float32 33kB dask.array&lt;chunksize=(1, 50, 50), meta=np.ndarray&gt;\n",
+       "    ubar         (ocean_time, eta_rho, xi_u) float32 33kB dask.array&lt;chunksize=(1, 82, 101), meta=np.ndarray&gt;\n",
        "    ...           ...\n",
-       "    Lig          (ocean_time, s_rho, eta_rho, xi_rho) float32 669kB dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;\n",
-       "    DIC_ALT_CO2  (ocean_time, s_rho, eta_rho, xi_rho) float32 669kB dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;\n",
-       "    ALK_ALT_CO2  (ocean_time, s_rho, eta_rho, xi_rho) float32 669kB dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;\n",
+       "    Lig          (ocean_time, s_rho, eta_rho, xi_rho) float32 669kB dask.array&lt;chunksize=(1, 20, 82, 102), meta=np.ndarray&gt;\n",
+       "    DIC_ALT_CO2  (ocean_time, s_rho, eta_rho, xi_rho) float32 669kB dask.array&lt;chunksize=(1, 20, 82, 102), meta=np.ndarray&gt;\n",
+       "    ALK_ALT_CO2  (ocean_time, s_rho, eta_rho, xi_rho) float32 669kB dask.array&lt;chunksize=(1, 20, 82, 102), meta=np.ndarray&gt;\n",
        "    w            (ocean_time, s_w, eta_rho, xi_rho) float32 703kB 0.0 ... 0.0\n",
        "    Cs_r         (s_rho) float32 80B -0.9579 -0.8615 ... -0.002245 -0.0002471\n",
        "    Cs_w         (s_w) float32 84B -1.0 -0.9114 -0.809 ... -0.0009921 0.0\n",
        "Attributes:\n",
        "    title:                                ROMS initial conditions file create...\n",
-       "    roms_tools_version:                   3.1.3.dev32+g91d580e67.d20250929\n",
+       "    roms_tools_version:                   10000.dev364+g785fa77b7.d20260612\n",
        "    ini_time:                             2012-01-01 00:00:00\n",
        "    model_reference_date:                 2000-01-01 00:00:00\n",
        "    adjust_depth_for_sea_surface_height:  False\n",
@@ -1729,7 +1741,7 @@
        "    bgc_source:                           UNIFIED\n",
        "    theta_s:                              5.0\n",
        "    theta_b:                              2.0\n",
-       "    hc:                                   300.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-79d8efb1-32bc-41e4-9544-081fff9b6b4c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-79d8efb1-32bc-41e4-9544-081fff9b6b4c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>ocean_time</span>: 1</li><li><span>s_rho</span>: 20</li><li><span>eta_rho</span>: 82</li><li><span>xi_rho</span>: 102</li><li><span>xi_u</span>: 101</li><li><span>eta_v</span>: 81</li><li><span>s_w</span>: 21</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-16c21d6c-2f6f-4d4f-b92c-4fb730ad0715' class='xr-section-summary-in' type='checkbox'  checked><label for='section-16c21d6c-2f6f-4d4f-b92c-4fb730ad0715' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(ocean_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-01-01</div><input id='attrs-3d9d336c-b688-453f-9ff2-3cd11c2a4986' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3d9d336c-b688-453f-9ff2-3cd11c2a4986' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-752a918e-35d5-427d-8fbb-7bf23fa15f25' class='xr-var-data-in' type='checkbox'><label for='data-752a918e-35d5-427d-8fbb-7bf23fa15f25' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>absolute time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-01-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ocean_time</span></div><div class='xr-var-dims'>(ocean_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.787e+08</div><input id='attrs-abdd622e-a8d9-446a-831d-2d386b3e8a05' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-abdd622e-a8d9-446a-831d-2d386b3e8a05' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb55a10d-bd9f-4bb9-a38f-653a5a3e3751' class='xr-var-data-in' type='checkbox'><label for='data-fb55a10d-bd9f-4bb9-a38f-653a5a3e3751' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>relative time: seconds since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>seconds</dd></dl></div><div class='xr-var-data'><pre>array([3.786912e+08])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-de6ae638-a206-42c3-8c72-49aefd7e255a' class='xr-section-summary-in' type='checkbox'  ><label for='section-de6ae638-a206-42c3-8c72-49aefd7e255a' class='xr-section-summary' >Data variables: <span>(42)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;</div><input id='attrs-66c2b416-1cbc-4d64-b73e-53401cbc317d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66c2b416-1cbc-4d64-b73e-53401cbc317d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4ef739c-51ee-4926-8ab1-32feb338b9d1' class='xr-var-data-in' type='checkbox'><label for='data-c4ef739c-51ee-4926-8ab1-32feb338b9d1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>potential temperature</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    hc:                                   300.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-567ce39f-3f3f-46f1-a350-b425f180c118' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-567ce39f-3f3f-46f1-a350-b425f180c118' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>ocean_time</span>: 1</li><li><span>s_rho</span>: 20</li><li><span>eta_rho</span>: 82</li><li><span>xi_rho</span>: 102</li><li><span>xi_u</span>: 101</li><li><span>eta_v</span>: 81</li><li><span>s_w</span>: 21</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a319218e-0a70-4932-b3d5-e93b6f9ddbcf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a319218e-0a70-4932-b3d5-e93b6f9ddbcf' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(ocean_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-01-01</div><input id='attrs-afb3b5a5-4c8a-463e-a239-0031108434f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-afb3b5a5-4c8a-463e-a239-0031108434f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-826f8c6b-0801-4ee0-908d-248f137205c0' class='xr-var-data-in' type='checkbox'><label for='data-826f8c6b-0801-4ee0-908d-248f137205c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>absolute time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-01-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ocean_time</span></div><div class='xr-var-dims'>(ocean_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.787e+08</div><input id='attrs-4b8ef9a7-d6b4-4883-80bc-e81b6dc89fe6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b8ef9a7-d6b4-4883-80bc-e81b6dc89fe6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9de350f4-4b64-4498-9cd2-dbea18964c70' class='xr-var-data-in' type='checkbox'><label for='data-9de350f4-4b64-4498-9cd2-dbea18964c70' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>relative time: seconds since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>seconds</dd></dl></div><div class='xr-var-data'><pre>array([3.786912e+08])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c3c85ce6-819b-45bc-bb35-0c0170047fde' class='xr-section-summary-in' type='checkbox'  ><label for='section-c3c85ce6-819b-45bc-bb35-0c0170047fde' class='xr-section-summary' >Data variables: <span>(42)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-6b233c9a-fe9c-49f1-b917-92246c7e7230' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b233c9a-fe9c-49f1-b917-92246c7e7230' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a468d1d1-7f30-47bf-a7db-a1d0ae9ccf29' class='xr-var-data-in' type='checkbox'><label for='data-a468d1d1-7f30-47bf-a7db-a1d0ae9ccf29' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>potential temperature</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1745,17 +1757,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 653.44 kiB </td>\n",
-       "                        <td> 195.31 kiB </td>\n",
+       "                        <td> 653.44 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (1, 20, 82, 102) </td>\n",
-       "                        <td> (1, 20, 50, 50) </td>\n",
+       "                        <td> (1, 20, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 139 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 63 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -1779,13 +1791,12 @@
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"12.706308\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"45.412617\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,45.412617,12.706308)\">1</text>\n",
+       "  <text x=\"12.706308257291242\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+       "  <text x=\"45.41261651458248\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,45.41261651458248,12.706308257291242)\">1</text>\n",
        "\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"95\" y1=\"0\" x2=\"119\" y2=\"24\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"95\" y1=\"58\" x2=\"119\" y2=\"83\" />\n",
        "  <line x1=\"95\" y1=\"96\" x2=\"119\" y2=\"121\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -1801,8 +1812,6 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"95\" y1=\"0\" x2=\"119\" y2=\"24\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"153\" y1=\"0\" x2=\"178\" y2=\"24\" />\n",
-       "  <line x1=\"212\" y1=\"0\" x2=\"237\" y2=\"24\" />\n",
        "  <line x1=\"215\" y1=\"0\" x2=\"239\" y2=\"24\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -1810,26 +1819,23 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"119\" y1=\"24\" x2=\"239\" y2=\"24\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"119\" y1=\"83\" x2=\"239\" y2=\"83\" />\n",
        "  <line x1=\"119\" y1=\"121\" x2=\"239\" y2=\"121\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"119\" y1=\"24\" x2=\"119\" y2=\"121\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"178\" y1=\"24\" x2=\"178\" y2=\"121\" />\n",
-       "  <line x1=\"237\" y1=\"24\" x2=\"237\" y2=\"121\" />\n",
        "  <line x1=\"239\" y1=\"24\" x2=\"239\" y2=\"121\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
        "  <polygon points=\"119.66222261492523,24.662222614925227 239.6622226149252,24.662222614925227 239.6622226149252,121.13281085021934 119.66222261492523,121.13281085021934\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"179.662223\" y=\"141.132811\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
-       "  <text x=\"259.662223\" y=\"72.897517\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.662223,72.897517)\">82</text>\n",
-       "  <text x=\"97.331111\" y=\"128.801700\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.331111,128.801700)\">20</text>\n",
+       "  <text x=\"179.6622226149252\" y=\"141.13281085021936\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
+       "  <text x=\"259.6622226149252\" y=\"72.89751673257229\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.6622226149252,72.89751673257229)\">82</text>\n",
+       "  <text x=\"97.3311113074626\" y=\"128.80169954275672\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.3311113074626,128.80169954275672)\">20</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salt</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;</div><input id='attrs-b1d7b765-d6d1-4aec-a272-5a6b69d50cd3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b1d7b765-d6d1-4aec-a272-5a6b69d50cd3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a804d00d-fb31-4f5c-aae2-4029d8b4b56d' class='xr-var-data-in' type='checkbox'><label for='data-a804d00d-fb31-4f5c-aae2-4029d8b4b56d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>salinity</dd><dt><span>units :</span></dt><dd>PSU</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salt</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-08f0bb83-6c9e-4f94-ae41-87c852f7749c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08f0bb83-6c9e-4f94-ae41-87c852f7749c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80a504eb-731c-416d-9212-63675e70c16f' class='xr-var-data-in' type='checkbox'><label for='data-80a504eb-731c-416d-9212-63675e70c16f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>salinity</dd><dt><span>units :</span></dt><dd>PSU</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1845,17 +1851,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 653.44 kiB </td>\n",
-       "                        <td> 195.31 kiB </td>\n",
+       "                        <td> 653.44 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (1, 20, 82, 102) </td>\n",
-       "                        <td> (1, 20, 50, 50) </td>\n",
+       "                        <td> (1, 20, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 139 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 63 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -1879,13 +1885,12 @@
        "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"12.706308\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"45.412617\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,45.412617,12.706308)\">1</text>\n",
+       "  <text x=\"12.706308257291242\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+       "  <text x=\"45.41261651458248\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,45.41261651458248,12.706308257291242)\">1</text>\n",
        "\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"95\" y1=\"0\" x2=\"119\" y2=\"24\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"95\" y1=\"58\" x2=\"119\" y2=\"83\" />\n",
        "  <line x1=\"95\" y1=\"96\" x2=\"119\" y2=\"121\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -1901,8 +1906,6 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"95\" y1=\"0\" x2=\"119\" y2=\"24\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"153\" y1=\"0\" x2=\"178\" y2=\"24\" />\n",
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        "  <line x1=\"215\" y1=\"0\" x2=\"239\" y2=\"24\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -1910,26 +1913,23 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"119\" y1=\"24\" x2=\"239\" y2=\"24\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"119\" y1=\"83\" x2=\"239\" y2=\"83\" />\n",
        "  <line x1=\"119\" y1=\"121\" x2=\"239\" y2=\"121\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"119\" y1=\"24\" x2=\"119\" y2=\"121\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"178\" y1=\"24\" x2=\"178\" y2=\"121\" />\n",
-       "  <line x1=\"237\" y1=\"24\" x2=\"237\" y2=\"121\" />\n",
        "  <line x1=\"239\" y1=\"24\" x2=\"239\" y2=\"121\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
        "  <polygon points=\"119.66222261492523,24.662222614925227 239.6622226149252,24.662222614925227 239.6622226149252,121.13281085021934 119.66222261492523,121.13281085021934\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"179.662223\" y=\"141.132811\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
-       "  <text x=\"259.662223\" y=\"72.897517\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.662223,72.897517)\">82</text>\n",
-       "  <text x=\"97.331111\" y=\"128.801700\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.331111,128.801700)\">20</text>\n",
+       "  <text x=\"179.6622226149252\" y=\"141.13281085021936\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
+       "  <text x=\"259.6622226149252\" y=\"72.89751673257229\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.6622226149252,72.89751673257229)\">82</text>\n",
+       "  <text x=\"97.3311113074626\" y=\"128.80169954275672\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.3311113074626,128.80169954275672)\">20</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;</div><input id='attrs-a3829354-be6b-4974-881b-6a967e14d4f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3829354-be6b-4974-881b-6a967e14d4f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf9449b7-ebbf-46b1-a817-37da5bc4c8c8' class='xr-var-data-in' type='checkbox'><label for='data-bf9449b7-ebbf-46b1-a817-37da5bc4c8c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82, 101), meta=np.ndarray&gt;</div><input id='attrs-2d955883-aa4e-42c9-871d-c9d510b831c6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d955883-aa4e-42c9-871d-c9d510b831c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b294d3b5-5ec5-48d1-88d9-d7ae1c2c6adb' class='xr-var-data-in' type='checkbox'><label for='data-b294d3b5-5ec5-48d1-88d9-d7ae1c2c6adb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1945,17 +1945,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 647.03 kiB </td>\n",
-       "                        <td> 195.31 kiB </td>\n",
+       "                        <td> 647.03 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (1, 20, 82, 101) </td>\n",
-       "                        <td> (1, 20, 50, 50) </td>\n",
+       "                        <td> (1, 20, 82, 101) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 178 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 102 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -1979,13 +1979,12 @@
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"12.706308\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"45.412617\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,45.412617,12.706308)\">1</text>\n",
+       "  <text x=\"12.706308257291242\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+       "  <text x=\"45.41261651458248\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,45.41261651458248,12.706308257291242)\">1</text>\n",
        "\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"95\" y1=\"0\" x2=\"119\" y2=\"24\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"95\" y1=\"59\" x2=\"119\" y2=\"84\" />\n",
        "  <line x1=\"95\" y1=\"97\" x2=\"119\" y2=\"122\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -2001,8 +2000,6 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"95\" y1=\"0\" x2=\"119\" y2=\"24\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"154\" y1=\"0\" x2=\"179\" y2=\"24\" />\n",
-       "  <line x1=\"213\" y1=\"0\" x2=\"238\" y2=\"24\" />\n",
        "  <line x1=\"215\" y1=\"0\" x2=\"239\" y2=\"24\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -2010,26 +2007,23 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"119\" y1=\"24\" x2=\"239\" y2=\"24\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"119\" y1=\"84\" x2=\"239\" y2=\"84\" />\n",
        "  <line x1=\"119\" y1=\"122\" x2=\"239\" y2=\"122\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"119\" y1=\"24\" x2=\"119\" y2=\"122\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"179\" y1=\"24\" x2=\"179\" y2=\"122\" />\n",
-       "  <line x1=\"238\" y1=\"24\" x2=\"238\" y2=\"122\" />\n",
        "  <line x1=\"239\" y1=\"24\" x2=\"239\" y2=\"122\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
        "  <polygon points=\"119.68690116177123,24.68690116177124 239.68690116177123,24.68690116177124 239.68690116177123,122.11264373602867 119.68690116177123,122.11264373602867\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"179.686901\" y=\"142.112644\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >101</text>\n",
-       "  <text x=\"259.686901\" y=\"73.399772\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.686901,73.399772)\">82</text>\n",
-       "  <text x=\"97.343451\" y=\"129.769193\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.343451,129.769193)\">20</text>\n",
+       "  <text x=\"179.68690116177123\" y=\"142.11264373602867\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >101</text>\n",
+       "  <text x=\"259.68690116177123\" y=\"73.39977244889995\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.68690116177123,73.39977244889995)\">82</text>\n",
+       "  <text x=\"97.34345058088562\" y=\"129.76919315514306\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.34345058088562,129.76919315514306)\">20</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_v, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;</div><input id='attrs-125fc360-b6dc-4681-8426-2c40c27abfb7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-125fc360-b6dc-4681-8426-2c40c27abfb7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f7c034b-ae09-419c-9913-905c14fc950e' class='xr-var-data-in' type='checkbox'><label for='data-1f7c034b-ae09-419c-9913-905c14fc950e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_v, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 81, 102), meta=np.ndarray&gt;</div><input id='attrs-eefb3b49-5f8a-41fb-896a-e23d2690e373' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eefb3b49-5f8a-41fb-896a-e23d2690e373' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a63a02ba-b8f5-431d-a828-e6c50cca4051' class='xr-var-data-in' type='checkbox'><label for='data-a63a02ba-b8f5-431d-a828-e6c50cca4051' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2045,17 +2039,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 645.47 kiB </td>\n",
-       "                        <td> 195.31 kiB </td>\n",
+       "                        <td> 645.47 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (1, 20, 81, 102) </td>\n",
-       "                        <td> (1, 20, 50, 50) </td>\n",
+       "                        <td> (1, 20, 81, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 178 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 102 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -2079,13 +2073,12 @@
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"12.706308\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"45.412617\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,45.412617,12.706308)\">1</text>\n",
+       "  <text x=\"12.706308257291242\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+       "  <text x=\"45.41261651458248\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,45.41261651458248,12.706308257291242)\">1</text>\n",
        "\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"95\" y1=\"0\" x2=\"119\" y2=\"24\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"95\" y1=\"58\" x2=\"119\" y2=\"83\" />\n",
        "  <line x1=\"95\" y1=\"95\" x2=\"119\" y2=\"119\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -2101,8 +2094,6 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"95\" y1=\"0\" x2=\"119\" y2=\"24\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"153\" y1=\"0\" x2=\"178\" y2=\"24\" />\n",
-       "  <line x1=\"212\" y1=\"0\" x2=\"237\" y2=\"24\" />\n",
        "  <line x1=\"215\" y1=\"0\" x2=\"239\" y2=\"24\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -2110,26 +2101,23 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"119\" y1=\"24\" x2=\"239\" y2=\"24\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"119\" y1=\"83\" x2=\"239\" y2=\"83\" />\n",
        "  <line x1=\"119\" y1=\"119\" x2=\"239\" y2=\"119\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"119\" y1=\"24\" x2=\"119\" y2=\"119\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"178\" y1=\"24\" x2=\"178\" y2=\"119\" />\n",
-       "  <line x1=\"237\" y1=\"24\" x2=\"237\" y2=\"119\" />\n",
        "  <line x1=\"239\" y1=\"24\" x2=\"239\" y2=\"119\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"179.662223\" y=\"139.956340\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
-       "  <text x=\"259.662223\" y=\"72.309281\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.662223,72.309281)\">81</text>\n",
-       "  <text x=\"97.331111\" y=\"127.625229\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.331111,127.625229)\">20</text>\n",
+       "  <text x=\"179.6622226149252\" y=\"139.95634026198405\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
+       "  <text x=\"259.6622226149252\" y=\"72.30928143845463\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.6622226149252,72.30928143845463)\">81</text>\n",
+       "  <text x=\"97.3311113074626\" y=\"127.62522895452145\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.3311113074626,127.62522895452145)\">20</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zeta</span></div><div class='xr-var-dims'>(ocean_time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.2544 0.2465 ... 0.2423 0.2846</div><input id='attrs-b91d5661-dbd8-4ab2-bde6-3c3ef9da9ea4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b91d5661-dbd8-4ab2-bde6-3c3ef9da9ea4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7fde52b-73ff-48d0-81a9-31c881f92cb5' class='xr-var-data-in' type='checkbox'><label for='data-d7fde52b-73ff-48d0-81a9-31c881f92cb5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>sea surface height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([[[0.25443825, 0.24653858, 0.23852521, ..., 0.27640378,\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zeta</span></div><div class='xr-var-dims'>(ocean_time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.2544 0.2465 ... 0.2423 0.2846</div><input id='attrs-8b21c2bb-c87a-4389-9a41-399236ef9a29' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b21c2bb-c87a-4389-9a41-399236ef9a29' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a3613ab-6067-45a0-8293-9f440b7fb096' class='xr-var-data-in' type='checkbox'><label for='data-2a3613ab-6067-45a0-8293-9f440b7fb096' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>sea surface height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([[[0.25443825, 0.24653858, 0.23852521, ..., 0.27640378,\n",
        "         0.2786252 , 0.26972738],\n",
        "        [0.24199636, 0.23329866, 0.22517073, ..., 0.28287753,\n",
        "         0.28147155, 0.2703949 ],\n",
@@ -2141,7 +2129,7 @@
        "        [0.02855656, 0.0252695 , 0.02208067, ..., 0.22117819,\n",
        "         0.26091924, 0.30883563],\n",
        "        [0.03121813, 0.02821547, 0.02531431, ..., 0.19237545,\n",
-       "         0.24229479, 0.28464696]]], shape=(1, 82, 102), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ubar</span></div><div class='xr-var-dims'>(ocean_time, eta_rho, xi_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 50, 50), meta=np.ndarray&gt;</div><input id='attrs-52fab033-e714-406d-8956-6996590d0f96' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-52fab033-e714-406d-8956-6996590d0f96' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b212ffb-b6bc-4e2f-81e3-d76f5d3fc92f' class='xr-var-data-in' type='checkbox'><label for='data-3b212ffb-b6bc-4e2f-81e3-d76f5d3fc92f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>vertically integrated u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "         0.24229479, 0.28464696]]], shape=(1, 82, 102), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ubar</span></div><div class='xr-var-dims'>(ocean_time, eta_rho, xi_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 82, 101), meta=np.ndarray&gt;</div><input id='attrs-d6a3faa4-b8ff-4811-a809-fcf852a18e0a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6a3faa4-b8ff-4811-a809-fcf852a18e0a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58e598ee-1ef4-4bb3-a95e-568d66c10f1f' class='xr-var-data-in' type='checkbox'><label for='data-58e598ee-1ef4-4bb3-a95e-568d66c10f1f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>vertically integrated u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2157,17 +2145,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 32.35 kiB </td>\n",
-       "                        <td> 9.77 kiB </td>\n",
+       "                        <td> 32.35 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (1, 82, 101) </td>\n",
-       "                        <td> (1, 50, 50) </td>\n",
+       "                        <td> (1, 82, 101) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 210 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 112 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -2181,7 +2169,6 @@
        "\n",
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        "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"10\" y1=\"59\" x2=\"24\" y2=\"74\" />\n",
        "  <line x1=\"10\" y1=\"97\" x2=\"24\" y2=\"112\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -2197,8 +2184,6 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"69\" y1=\"0\" x2=\"84\" y2=\"14\" />\n",
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        "  <line x1=\"130\" y1=\"0\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -2206,26 +2191,23 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"24\" y1=\"112\" x2=\"144\" y2=\"112\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"112\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"84\" y1=\"14\" x2=\"84\" y2=\"112\" />\n",
-       "  <line x1=\"143\" y1=\"14\" x2=\"143\" y2=\"112\" />\n",
        "  <line x1=\"144\" y1=\"14\" x2=\"144\" y2=\"112\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
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        "\n",
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-       "  <text x=\"84.948598\" y=\"132.374341\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >101</text>\n",
-       "  <text x=\"164.948598\" y=\"63.661469\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,164.948598,63.661469)\">82</text>\n",
-       "  <text x=\"7.474299\" y=\"124.900042\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,124.900042)\">1</text>\n",
+       "  <text x=\"84.9485979497544\" y=\"132.3743405240118\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >101</text>\n",
+       "  <text x=\"164.9485979497544\" y=\"63.661469236883114\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,164.9485979497544,63.661469236883114)\">82</text>\n",
+       "  <text x=\"7.4742989748772\" y=\"124.90004154913463\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.4742989748772,124.90004154913463)\">1</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vbar</span></div><div class='xr-var-dims'>(ocean_time, eta_v, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 50, 50), meta=np.ndarray&gt;</div><input id='attrs-79307b14-0733-4460-99ce-d90e98844b33' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-79307b14-0733-4460-99ce-d90e98844b33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-efeb72e4-4295-487f-9e36-eff439cfcf1b' class='xr-var-data-in' type='checkbox'><label for='data-efeb72e4-4295-487f-9e36-eff439cfcf1b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>vertically integrated v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vbar</span></div><div class='xr-var-dims'>(ocean_time, eta_v, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81, 102), meta=np.ndarray&gt;</div><input id='attrs-1c2df44e-5e77-4d17-a811-c9e2e14e3f28' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c2df44e-5e77-4d17-a811-c9e2e14e3f28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9202f67f-7ec7-45db-99dd-4e349dcbf1c4' class='xr-var-data-in' type='checkbox'><label for='data-9202f67f-7ec7-45db-99dd-4e349dcbf1c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>vertically integrated v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2241,17 +2223,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 32.27 kiB </td>\n",
-       "                        <td> 9.77 kiB </td>\n",
+       "                        <td> 32.27 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (1, 81, 102) </td>\n",
-       "                        <td> (1, 50, 50) </td>\n",
+       "                        <td> (1, 81, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 210 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 112 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -2265,7 +2247,6 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
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        "\n",
        "  <!-- Vertical lines -->\n",
@@ -2281,8 +2262,6 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"68\" y1=\"0\" x2=\"83\" y2=\"14\" />\n",
-       "  <line x1=\"127\" y1=\"0\" x2=\"142\" y2=\"14\" />\n",
        "  <line x1=\"130\" y1=\"0\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -2290,26 +2269,23 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"24\" y1=\"73\" x2=\"144\" y2=\"73\" />\n",
        "  <line x1=\"24\" y1=\"110\" x2=\"144\" y2=\"110\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"110\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"83\" y1=\"14\" x2=\"83\" y2=\"110\" />\n",
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        "\n",
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"84.948598\" y=\"130.242716\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
-       "  <text x=\"164.948598\" y=\"62.595657\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,164.948598,62.595657)\">81</text>\n",
-       "  <text x=\"7.474299\" y=\"122.768417\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,122.768417)\">1</text>\n",
+       "  <text x=\"84.9485979497544\" y=\"130.24271559681324\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
+       "  <text x=\"164.9485979497544\" y=\"62.595656773283814\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,164.9485979497544,62.595656773283814)\">81</text>\n",
+       "  <text x=\"7.4742989748772\" y=\"122.76841662193603\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.4742989748772,122.76841662193603)\">1</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PO4</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;</div><input id='attrs-5eaee808-721f-45e8-a100-c26ee8604118' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5eaee808-721f-45e8-a100-c26ee8604118' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dea9cf39-7db2-4644-99e2-89001c0ab961' class='xr-var-data-in' type='checkbox'><label for='data-dea9cf39-7db2-4644-99e2-89001c0ab961' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved inorganic phosphate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PO4</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-4f975545-bb94-428b-9c27-785c2b00e958' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f975545-bb94-428b-9c27-785c2b00e958' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-536a486f-eb32-44bb-9ef1-6d4b883a61d0' class='xr-var-data-in' type='checkbox'><label for='data-536a486f-eb32-44bb-9ef1-6d4b883a61d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved inorganic phosphate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2325,17 +2301,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 653.44 kiB </td>\n",
-       "                        <td> 195.31 kiB </td>\n",
+       "                        <td> 653.44 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (1, 20, 82, 102) </td>\n",
-       "                        <td> (1, 20, 50, 50) </td>\n",
+       "                        <td> (1, 20, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 126 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 49 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -2359,13 +2335,12 @@
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@@ -2381,8 +2356,6 @@
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@@ -2390,26 +2363,23 @@
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        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NO3</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;</div><input id='attrs-d577f790-07a7-4f6d-825d-1988d39addec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d577f790-07a7-4f6d-825d-1988d39addec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c265d81-c37e-4e38-b56b-b4233243b0f1' class='xr-var-data-in' type='checkbox'><label for='data-4c265d81-c37e-4e38-b56b-b4233243b0f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved inorganic nitrate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NO3</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-54bccdc3-d546-441b-b1f5-6d02352984e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-54bccdc3-d546-441b-b1f5-6d02352984e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0166309a-22a1-4496-931a-ba3ee25cb3f9' class='xr-var-data-in' type='checkbox'><label for='data-0166309a-22a1-4496-931a-ba3ee25cb3f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved inorganic nitrate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2425,17 +2395,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 653.44 kiB </td>\n",
-       "                        <td> 195.31 kiB </td>\n",
+       "                        <td> 653.44 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (1, 20, 82, 102) </td>\n",
-       "                        <td> (1, 20, 50, 50) </td>\n",
+       "                        <td> (1, 20, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 126 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 49 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -2459,13 +2429,12 @@
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"12.706308\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"45.412617\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,45.412617,12.706308)\">1</text>\n",
+       "  <text x=\"12.706308257291242\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+       "  <text x=\"45.41261651458248\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,45.41261651458248,12.706308257291242)\">1</text>\n",
        "\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"95\" y1=\"0\" x2=\"119\" y2=\"24\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"95\" y1=\"58\" x2=\"119\" y2=\"83\" />\n",
        "  <line x1=\"95\" y1=\"96\" x2=\"119\" y2=\"121\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -2481,8 +2450,6 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"95\" y1=\"0\" x2=\"119\" y2=\"24\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"153\" y1=\"0\" x2=\"178\" y2=\"24\" />\n",
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        "  <line x1=\"215\" y1=\"0\" x2=\"239\" y2=\"24\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -2490,26 +2457,23 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"119\" y1=\"24\" x2=\"239\" y2=\"24\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"119\" y1=\"83\" x2=\"239\" y2=\"83\" />\n",
        "  <line x1=\"119\" y1=\"121\" x2=\"239\" y2=\"121\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"119\" y1=\"24\" x2=\"119\" y2=\"121\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"178\" y1=\"24\" x2=\"178\" y2=\"121\" />\n",
-       "  <line x1=\"237\" y1=\"24\" x2=\"237\" y2=\"121\" />\n",
        "  <line x1=\"239\" y1=\"24\" x2=\"239\" y2=\"121\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"179.662223\" y=\"141.132811\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
-       "  <text x=\"259.662223\" y=\"72.897517\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.662223,72.897517)\">82</text>\n",
-       "  <text x=\"97.331111\" y=\"128.801700\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.331111,128.801700)\">20</text>\n",
+       "  <text x=\"179.6622226149252\" y=\"141.13281085021936\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
+       "  <text x=\"259.6622226149252\" y=\"72.89751673257229\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.6622226149252,72.89751673257229)\">82</text>\n",
+       "  <text x=\"97.3311113074626\" y=\"128.80169954275672\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.3311113074626,128.80169954275672)\">20</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SiO3</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;</div><input id='attrs-31ddda2b-ff4a-459d-827a-c7ae704fcedb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-31ddda2b-ff4a-459d-827a-c7ae704fcedb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d038af3-1091-4a00-8f87-f35a5df9bafb' class='xr-var-data-in' type='checkbox'><label for='data-5d038af3-1091-4a00-8f87-f35a5df9bafb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved inorganic silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SiO3</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-57ca12f3-3908-4453-a193-e972c7f7a2a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-57ca12f3-3908-4453-a193-e972c7f7a2a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e149dd7-ca27-4d8e-9c7c-ae3306099a24' class='xr-var-data-in' type='checkbox'><label for='data-0e149dd7-ca27-4d8e-9c7c-ae3306099a24' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved inorganic silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2525,17 +2489,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 653.44 kiB </td>\n",
-       "                        <td> 195.31 kiB </td>\n",
+       "                        <td> 653.44 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (1, 20, 82, 102) </td>\n",
-       "                        <td> (1, 20, 50, 50) </td>\n",
+       "                        <td> (1, 20, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 126 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 49 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -2559,13 +2523,12 @@
        "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"12.706308\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"45.412617\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,45.412617,12.706308)\">1</text>\n",
+       "  <text x=\"12.706308257291242\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+       "  <text x=\"45.41261651458248\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,45.41261651458248,12.706308257291242)\">1</text>\n",
        "\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"95\" y1=\"0\" x2=\"119\" y2=\"24\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"95\" y1=\"58\" x2=\"119\" y2=\"83\" />\n",
        "  <line x1=\"95\" y1=\"96\" x2=\"119\" y2=\"121\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -2581,8 +2544,6 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"95\" y1=\"0\" x2=\"119\" y2=\"24\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"153\" y1=\"0\" x2=\"178\" y2=\"24\" />\n",
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        "  <line x1=\"215\" y1=\"0\" x2=\"239\" y2=\"24\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -2590,26 +2551,23 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"119\" y1=\"24\" x2=\"239\" y2=\"24\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"119\" y1=\"83\" x2=\"239\" y2=\"83\" />\n",
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        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"119\" y1=\"24\" x2=\"119\" y2=\"121\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"178\" y1=\"24\" x2=\"178\" y2=\"121\" />\n",
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        "  <line x1=\"239\" y1=\"24\" x2=\"239\" y2=\"121\" style=\"stroke-width:2\" />\n",
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"179.662223\" y=\"141.132811\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
-       "  <text x=\"259.662223\" y=\"72.897517\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.662223,72.897517)\">82</text>\n",
-       "  <text x=\"97.331111\" y=\"128.801700\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.331111,128.801700)\">20</text>\n",
+       "  <text x=\"179.6622226149252\" y=\"141.13281085021936\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
+       "  <text x=\"259.6622226149252\" y=\"72.89751673257229\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.6622226149252,72.89751673257229)\">82</text>\n",
+       "  <text x=\"97.3311113074626\" y=\"128.80169954275672\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.3311113074626,128.80169954275672)\">20</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Fe</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;</div><input id='attrs-3e84337a-1ae0-4821-85fd-c8ae04e976d4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3e84337a-1ae0-4821-85fd-c8ae04e976d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ca746fa-012d-4e7a-a61b-27f376b18a38' class='xr-var-data-in' type='checkbox'><label for='data-9ca746fa-012d-4e7a-a61b-27f376b18a38' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved inorganic iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Fe</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-385632e0-6071-4e9b-aca4-3cfffebf5fcd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-385632e0-6071-4e9b-aca4-3cfffebf5fcd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f4ea3c4-484e-4fe7-87c3-623c361ade91' class='xr-var-data-in' type='checkbox'><label for='data-7f4ea3c4-484e-4fe7-87c3-623c361ade91' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved inorganic iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2625,17 +2583,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 653.44 kiB </td>\n",
-       "                        <td> 195.31 kiB </td>\n",
+       "                        <td> 653.44 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (1, 20, 82, 102) </td>\n",
-       "                        <td> (1, 20, 50, 50) </td>\n",
+       "                        <td> (1, 20, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 126 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 49 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -2659,13 +2617,12 @@
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        "  <line x1=\"95\" y1=\"96\" x2=\"119\" y2=\"121\" style=\"stroke-width:2\" />\n",
        "\n",
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@@ -2681,8 +2638,6 @@
        "\n",
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@@ -2690,26 +2645,23 @@
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-       "  <text x=\"259.662223\" y=\"72.897517\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.662223,72.897517)\">82</text>\n",
-       "  <text x=\"97.331111\" y=\"128.801700\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.331111,128.801700)\">20</text>\n",
+       "  <text x=\"179.6622226149252\" y=\"141.13281085021936\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
+       "  <text x=\"259.6622226149252\" y=\"72.89751673257229\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.6622226149252,72.89751673257229)\">82</text>\n",
+       "  <text x=\"97.3311113074626\" y=\"128.80169954275672\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.3311113074626,128.80169954275672)\">20</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>O2</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;</div><input id='attrs-eb0db4bf-4a8e-4e2b-bba6-0d22b7cb5a48' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eb0db4bf-4a8e-4e2b-bba6-0d22b7cb5a48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22f9c363-a112-4053-b304-795c7e6b22f0' class='xr-var-data-in' type='checkbox'><label for='data-22f9c363-a112-4053-b304-795c7e6b22f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved oxygen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>O2</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-3aca6d6a-4b06-473b-8c53-0002348d61a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3aca6d6a-4b06-473b-8c53-0002348d61a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-436d3e4a-69c2-43df-9229-1060801a2bc8' class='xr-var-data-in' type='checkbox'><label for='data-436d3e4a-69c2-43df-9229-1060801a2bc8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved oxygen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2725,17 +2677,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 653.44 kiB </td>\n",
-       "                        <td> 195.31 kiB </td>\n",
+       "                        <td> 653.44 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (1, 20, 82, 102) </td>\n",
-       "                        <td> (1, 20, 50, 50) </td>\n",
+       "                        <td> (1, 20, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 126 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 49 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -2759,13 +2711,12 @@
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+       "  <text x=\"12.706308257291242\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
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        "\n",
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        "  <line x1=\"95\" y1=\"96\" x2=\"119\" y2=\"121\" style=\"stroke-width:2\" />\n",
        "\n",
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@@ -2781,8 +2732,6 @@
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@@ -2790,26 +2739,23 @@
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+       "  <text x=\"179.6622226149252\" y=\"141.13281085021936\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
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+       "  <text x=\"97.3311113074626\" y=\"128.80169954275672\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.3311113074626,128.80169954275672)\">20</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIC</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;</div><input id='attrs-93713a0d-31f3-4a41-b86d-2b306a0df0e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-93713a0d-31f3-4a41-b86d-2b306a0df0e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d813064c-4944-4761-88fd-ed9e7b3003ca' class='xr-var-data-in' type='checkbox'><label for='data-d813064c-4944-4761-88fd-ed9e7b3003ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved inorganic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIC</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-27951d3b-6607-4d11-a16d-996b106356a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-27951d3b-6607-4d11-a16d-996b106356a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-46c83fc5-e624-42a6-a5c3-664a21910486' class='xr-var-data-in' type='checkbox'><label for='data-46c83fc5-e624-42a6-a5c3-664a21910486' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved inorganic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2825,17 +2771,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 653.44 kiB </td>\n",
-       "                        <td> 195.31 kiB </td>\n",
+       "                        <td> 653.44 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (1, 20, 82, 102) </td>\n",
-       "                        <td> (1, 20, 50, 50) </td>\n",
+       "                        <td> (1, 20, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 126 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 49 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -2859,13 +2805,12 @@
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        "\n",
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        "\n",
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@@ -2881,8 +2826,6 @@
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@@ -2890,66 +2833,63 @@
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+       "  <text x=\"97.3311113074626\" y=\"128.80169954275672\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.3311113074626,128.80169954275672)\">20</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALK</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>2.263e+03 2.263e+03 ... 2.389e+03</div><input id='attrs-8b7b2195-25d7-4dbb-9244-81ca22d205cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b7b2195-25d7-4dbb-9244-81ca22d205cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba9af1cd-fbd8-42fc-ba2f-3fc538f0f4f0' class='xr-var-data-in' type='checkbox'><label for='data-ba9af1cd-fbd8-42fc-ba2f-3fc538f0f4f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>alkalinity</dd><dt><span>units :</span></dt><dd>meq/m^3</dd></dl></div><div class='xr-var-data'><pre>array([[[[2263.126 , 2263.1196, 2263.106 , ..., 2315.605 , 2317.7295,\n",
-       "          2317.846 ],\n",
-       "         [2264.3733, 2264.3079, 2264.2393, ..., 2316.2578, 2318.328 ,\n",
-       "          2318.4294],\n",
-       "         [2265.4104, 2265.331 , 2265.2488, ..., 2317.8547, 2319.7322,\n",
-       "          2319.8357],\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALK</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>2.263e+03 2.263e+03 ... 2.389e+03</div><input id='attrs-be83b32b-aff9-493a-a189-1d5c88f25bfc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be83b32b-aff9-493a-a189-1d5c88f25bfc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62a044f2-cd94-4424-921f-1c3fa07c19b7' class='xr-var-data-in' type='checkbox'><label for='data-62a044f2-cd94-4424-921f-1c3fa07c19b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>alkalinity</dd><dt><span>units :</span></dt><dd>meq/m^3</dd></dl></div><div class='xr-var-data'><pre>array([[[[2263.126 , 2263.1196, 2263.106 , ..., 2315.5466, 2317.9675,\n",
+       "          2318.0876],\n",
+       "         [2264.3733, 2264.3079, 2264.2393, ..., 2316.2007, 2318.561 ,\n",
+       "          2318.666 ],\n",
+       "         [2265.4104, 2265.331 , 2265.2488, ..., 2317.6655, 2319.7656,\n",
+       "          2319.8691],\n",
        "         ...,\n",
-       "         [2285.269 , 2285.38  , 2285.4922, ..., 2338.0522, 2335.0667,\n",
-       "          2333.1497],\n",
-       "         [2284.9375, 2285.055 , 2285.174 , ..., 2345.4753, 2342.0378,\n",
-       "          2339.7158],\n",
-       "         [2284.6077, 2284.729 , 2284.855 , ..., 2352.26  , 2348.6855,\n",
-       "          2345.9211]],\n",
-       "\n",
-       "        [[2263.101 , 2263.0908, 2263.0737, ..., 2314.9875, 2316.3613,\n",
-       "          2316.459 ],\n",
-       "         [2264.348 , 2264.2793, 2264.2075, ..., 2315.663 , 2316.9844,\n",
-       "          2317.0674],\n",
-       "         [2265.3843, 2265.3025, 2265.2173, ..., 2316.8167, 2318.3083,\n",
-       "          2318.3918],\n",
+       "         [2285.269 , 2285.38  , 2285.4922, ..., 2337.568 , 2334.7546,\n",
+       "          2332.8508],\n",
+       "         [2284.9375, 2285.055 , 2285.174 , ..., 2345.1543, 2341.797 ,\n",
+       "          2339.4841],\n",
+       "         [2284.6077, 2284.729 , 2284.855 , ..., 2352.0186, 2348.5034,\n",
+       "          2345.7432]],\n",
+       "\n",
+       "        [[2263.101 , 2263.0908, 2263.0737, ..., 2314.969 , 2316.5461,\n",
+       "          2316.6465],\n",
+       "         [2264.348 , 2264.2793, 2264.2075, ..., 2315.647 , 2317.1655,\n",
+       "          2317.2512],\n",
+       "         [2265.3843, 2265.3025, 2265.2173, ..., 2316.6697, 2318.3967,\n",
+       "          2318.4814],\n",
        "...\n",
-       "         [2284.8467, 2284.951 , 2285.057 , ..., 2389.1074, 2390.051 ,\n",
+       "         [2284.8467, 2284.951 , 2285.057 , ..., 2389.108 , 2390.0513,\n",
        "          2391.0046],\n",
-       "         [2284.5105, 2284.6213, 2284.7341, ..., 2388.1716, 2389.052 ,\n",
-       "          2389.947 ],\n",
-       "         [2284.1758, 2284.2908, 2284.4106, ..., 2387.2278, 2388.0442,\n",
-       "          2388.9204]],\n",
-       "\n",
-       "        [[2262.6594, 2262.5842, 2262.5042, ..., 2292.9485, 2294.1428,\n",
-       "          2295.1614],\n",
-       "         [2263.8953, 2263.7717, 2263.6458, ..., 2295.0056, 2296.1902,\n",
-       "          2297.202 ],\n",
-       "         [2264.9238, 2264.7932, 2264.6604, ..., 2297.1248, 2298.2573,\n",
-       "          2299.2542],\n",
+       "         [2284.5105, 2284.6213, 2284.7341, ..., 2388.1724, 2389.0525,\n",
+       "          2389.9473],\n",
+       "         [2284.1758, 2284.2908, 2284.4106, ..., 2387.2285, 2388.0447,\n",
+       "          2388.921 ]],\n",
+       "\n",
+       "        [[2262.6594, 2262.5842, 2262.5042, ..., 2292.9424, 2294.161 ,\n",
+       "          2295.1797],\n",
+       "         [2263.8953, 2263.7717, 2263.6458, ..., 2294.9998, 2296.208 ,\n",
+       "          2297.2197],\n",
+       "         [2264.9238, 2264.7932, 2264.6604, ..., 2297.106 , 2298.2637,\n",
+       "          2299.2603],\n",
        "         ...,\n",
        "         [2284.8235, 2284.9275, 2285.033 , ..., 2389.0315, 2390.0083,\n",
        "          2390.9985],\n",
-       "         [2284.487 , 2284.5977, 2284.7102, ..., 2388.0623, 2388.9597,\n",
+       "         [2284.487 , 2284.5977, 2284.7102, ..., 2388.0625, 2388.96  ,\n",
        "          2389.8757],\n",
-       "         [2284.152 , 2284.2668, 2284.3865, ..., 2387.0823, 2387.9   ,\n",
-       "          2388.7844]]]], shape=(1, 20, 82, 102), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NH4</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;</div><input id='attrs-4e14369a-d860-4709-8577-f19806ba402b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e14369a-d860-4709-8577-f19806ba402b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66f00f2d-352a-4ba2-97d0-f8d2ab2218bd' class='xr-var-data-in' type='checkbox'><label for='data-66f00f2d-352a-4ba2-97d0-f8d2ab2218bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved ammonia</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "         [2284.152 , 2284.2668, 2284.3865, ..., 2387.0823, 2387.9001,\n",
+       "          2388.7847]]]], shape=(1, 20, 82, 102), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NH4</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-bea09a29-1118-495e-a64b-4df5cb5ae174' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bea09a29-1118-495e-a64b-4df5cb5ae174' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d4914c7-b113-4c1f-a36c-1d61fd54fef5' class='xr-var-data-in' type='checkbox'><label for='data-2d4914c7-b113-4c1f-a36c-1d61fd54fef5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved ammonia</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2965,17 +2905,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 653.44 kiB </td>\n",
-       "                        <td> 195.31 kiB </td>\n",
+       "                        <td> 653.44 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (1, 20, 82, 102) </td>\n",
-       "                        <td> (1, 20, 50, 50) </td>\n",
+       "                        <td> (1, 20, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 126 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 49 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -2999,13 +2939,12 @@
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        "\n",
        "  <!-- Text -->\n",
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-       "  <text x=\"45.412617\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,45.412617,12.706308)\">1</text>\n",
+       "  <text x=\"12.706308257291242\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+       "  <text x=\"45.41261651458248\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,45.41261651458248,12.706308257291242)\">1</text>\n",
        "\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"95\" y1=\"0\" x2=\"119\" y2=\"24\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"95\" y1=\"58\" x2=\"119\" y2=\"83\" />\n",
        "  <line x1=\"95\" y1=\"96\" x2=\"119\" y2=\"121\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -3021,8 +2960,6 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"95\" y1=\"0\" x2=\"119\" y2=\"24\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"153\" y1=\"0\" x2=\"178\" y2=\"24\" />\n",
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        "  <line x1=\"215\" y1=\"0\" x2=\"239\" y2=\"24\" style=\"stroke-width:2\" />\n",
        "\n",
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@@ -3030,26 +2967,23 @@
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        "  <line x1=\"119\" y1=\"24\" x2=\"239\" y2=\"24\" style=\"stroke-width:2\" />\n",
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        "\n",
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"179.662223\" y=\"141.132811\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
-       "  <text x=\"259.662223\" y=\"72.897517\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.662223,72.897517)\">82</text>\n",
-       "  <text x=\"97.331111\" y=\"128.801700\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.331111,128.801700)\">20</text>\n",
+       "  <text x=\"179.6622226149252\" y=\"141.13281085021936\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
+       "  <text x=\"259.6622226149252\" y=\"72.89751673257229\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.6622226149252,72.89751673257229)\">82</text>\n",
+       "  <text x=\"97.3311113074626\" y=\"128.80169954275672\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.3311113074626,128.80169954275672)\">20</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOC</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;</div><input id='attrs-af28d554-1c05-439e-a65f-c8be3ab9b326' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-af28d554-1c05-439e-a65f-c8be3ab9b326' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5bfd973-4277-4ecf-81fa-589892d3d573' class='xr-var-data-in' type='checkbox'><label for='data-f5bfd973-4277-4ecf-81fa-589892d3d573' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved organic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOC</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-34390b41-03fc-454d-90b0-69342b4f5863' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34390b41-03fc-454d-90b0-69342b4f5863' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14893d28-1266-4742-bd23-a80342c9c28c' class='xr-var-data-in' type='checkbox'><label for='data-14893d28-1266-4742-bd23-a80342c9c28c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved organic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3065,17 +2999,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 653.44 kiB </td>\n",
-       "                        <td> 195.31 kiB </td>\n",
+       "                        <td> 653.44 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (1, 20, 82, 102) </td>\n",
-       "                        <td> (1, 20, 50, 50) </td>\n",
+       "                        <td> (1, 20, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 126 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 49 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -3099,13 +3033,12 @@
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"12.706308\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"45.412617\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,45.412617,12.706308)\">1</text>\n",
+       "  <text x=\"12.706308257291242\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+       "  <text x=\"45.41261651458248\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,45.41261651458248,12.706308257291242)\">1</text>\n",
        "\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"95\" y1=\"0\" x2=\"119\" y2=\"24\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"95\" y1=\"58\" x2=\"119\" y2=\"83\" />\n",
        "  <line x1=\"95\" y1=\"96\" x2=\"119\" y2=\"121\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -3121,8 +3054,6 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"95\" y1=\"0\" x2=\"119\" y2=\"24\" style=\"stroke-width:2\" />\n",
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        "  <line x1=\"215\" y1=\"0\" x2=\"239\" y2=\"24\" style=\"stroke-width:2\" />\n",
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@@ -3130,26 +3061,23 @@
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        "\n",
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-       "  <text x=\"179.662223\" y=\"141.132811\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
-       "  <text x=\"259.662223\" y=\"72.897517\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.662223,72.897517)\">82</text>\n",
-       "  <text x=\"97.331111\" y=\"128.801700\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.331111,128.801700)\">20</text>\n",
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+       "  <text x=\"259.6622226149252\" y=\"72.89751673257229\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.6622226149252,72.89751673257229)\">82</text>\n",
+       "  <text x=\"97.3311113074626\" y=\"128.80169954275672\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.3311113074626,128.80169954275672)\">20</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DON</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;</div><input id='attrs-792088c5-902f-414c-aeeb-435ba1c1cc20' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-792088c5-902f-414c-aeeb-435ba1c1cc20' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e32a527-e820-43a9-8d9a-9309cd22eebb' class='xr-var-data-in' type='checkbox'><label for='data-4e32a527-e820-43a9-8d9a-9309cd22eebb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DON</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-bdc8caa1-7d80-46b9-95f1-2c8649d77163' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bdc8caa1-7d80-46b9-95f1-2c8649d77163' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e48d92cb-faa4-4a6d-8700-6ae889663ae6' class='xr-var-data-in' type='checkbox'><label for='data-e48d92cb-faa4-4a6d-8700-6ae889663ae6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3165,17 +3093,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 653.44 kiB </td>\n",
-       "                        <td> 195.31 kiB </td>\n",
+       "                        <td> 653.44 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (1, 20, 82, 102) </td>\n",
-       "                        <td> (1, 20, 50, 50) </td>\n",
+       "                        <td> (1, 20, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 126 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 49 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -3199,13 +3127,12 @@
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        "\n",
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@@ -3221,8 +3148,6 @@
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        "\n",
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-       "  <text x=\"97.331111\" y=\"128.801700\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.331111,128.801700)\">20</text>\n",
+       "  <text x=\"179.6622226149252\" y=\"141.13281085021936\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
+       "  <text x=\"259.6622226149252\" y=\"72.89751673257229\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.6622226149252,72.89751673257229)\">82</text>\n",
+       "  <text x=\"97.3311113074626\" y=\"128.80169954275672\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.3311113074626,128.80169954275672)\">20</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOP</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;</div><input id='attrs-e9ec6f9b-31d8-49be-bd73-0f68f0ebfe83' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e9ec6f9b-31d8-49be-bd73-0f68f0ebfe83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d24e664-4544-4ed2-be8f-ab9669c85dbf' class='xr-var-data-in' type='checkbox'><label for='data-0d24e664-4544-4ed2-be8f-ab9669c85dbf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOP</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-fb6f390b-1d22-4eb4-b803-e919124256d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fb6f390b-1d22-4eb4-b803-e919124256d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34b6034e-e595-42fa-b3ca-cce9b5bf8281' class='xr-var-data-in' type='checkbox'><label for='data-34b6034e-e595-42fa-b3ca-cce9b5bf8281' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3265,17 +3187,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 653.44 kiB </td>\n",
-       "                        <td> 195.31 kiB </td>\n",
+       "                        <td> 653.44 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (1, 20, 82, 102) </td>\n",
-       "                        <td> (1, 20, 50, 50) </td>\n",
+       "                        <td> (1, 20, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 126 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 49 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -3299,13 +3221,12 @@
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        "\n",
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        "  <line x1=\"95\" y1=\"96\" x2=\"119\" y2=\"121\" style=\"stroke-width:2\" />\n",
        "\n",
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@@ -3321,8 +3242,6 @@
        "\n",
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-       "  <text x=\"97.331111\" y=\"128.801700\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.331111,128.801700)\">20</text>\n",
+       "  <text x=\"179.6622226149252\" y=\"141.13281085021936\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
+       "  <text x=\"259.6622226149252\" y=\"72.89751673257229\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.6622226149252,72.89751673257229)\">82</text>\n",
+       "  <text x=\"97.3311113074626\" y=\"128.80169954275672\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.3311113074626,128.80169954275672)\">20</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOPr</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;</div><input id='attrs-ea23023c-7989-4fc0-a331-94a9cd2f3a95' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea23023c-7989-4fc0-a331-94a9cd2f3a95' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-613318e3-8a2c-4ee8-8e58-5bba98669b2f' class='xr-var-data-in' type='checkbox'><label for='data-613318e3-8a2c-4ee8-8e58-5bba98669b2f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>refractory dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOPr</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-238688bf-a655-4364-be80-d86131024f67' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-238688bf-a655-4364-be80-d86131024f67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a198f146-769e-4b0d-971a-1102b8562f97' class='xr-var-data-in' type='checkbox'><label for='data-a198f146-769e-4b0d-971a-1102b8562f97' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>refractory dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3365,17 +3281,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 653.44 kiB </td>\n",
-       "                        <td> 195.31 kiB </td>\n",
+       "                        <td> 653.44 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (1, 20, 82, 102) </td>\n",
-       "                        <td> (1, 20, 50, 50) </td>\n",
+       "                        <td> (1, 20, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 126 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 49 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -3399,13 +3315,12 @@
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"12.706308\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"45.412617\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,45.412617,12.706308)\">1</text>\n",
+       "  <text x=\"12.706308257291242\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
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        "\n",
        "\n",
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        "  <line x1=\"95\" y1=\"96\" x2=\"119\" y2=\"121\" style=\"stroke-width:2\" />\n",
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@@ -3421,8 +3336,6 @@
        "\n",
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@@ -3430,26 +3343,23 @@
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        "\n",
        "  <!-- Text -->\n",
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-       "  <text x=\"97.331111\" y=\"128.801700\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.331111,128.801700)\">20</text>\n",
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+       "  <text x=\"259.6622226149252\" y=\"72.89751673257229\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.6622226149252,72.89751673257229)\">82</text>\n",
+       "  <text x=\"97.3311113074626\" y=\"128.80169954275672\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.3311113074626,128.80169954275672)\">20</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DONr</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;</div><input id='attrs-ab2fb880-0d56-43ae-babc-f03de2a492e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab2fb880-0d56-43ae-babc-f03de2a492e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4c8d0cd-ff18-48f2-9310-14b9e29930f2' class='xr-var-data-in' type='checkbox'><label for='data-a4c8d0cd-ff18-48f2-9310-14b9e29930f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>refractory dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DONr</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-2cf27776-f73b-4225-806f-657085104353' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2cf27776-f73b-4225-806f-657085104353' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a208e76-cfd0-4619-9203-0c2aeaf7a9d3' class='xr-var-data-in' type='checkbox'><label for='data-9a208e76-cfd0-4619-9203-0c2aeaf7a9d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>refractory dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3465,17 +3375,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 653.44 kiB </td>\n",
-       "                        <td> 195.31 kiB </td>\n",
+       "                        <td> 653.44 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (1, 20, 82, 102) </td>\n",
-       "                        <td> (1, 20, 50, 50) </td>\n",
+       "                        <td> (1, 20, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 126 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 49 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -3499,13 +3409,12 @@
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@@ -3521,8 +3430,6 @@
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-       "  <text x=\"97.331111\" y=\"128.801700\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.331111,128.801700)\">20</text>\n",
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+       "  <text x=\"259.6622226149252\" y=\"72.89751673257229\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.6622226149252,72.89751673257229)\">82</text>\n",
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        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOCr</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;</div><input id='attrs-bcd6e484-4a92-450f-b008-7dc1de704c2d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bcd6e484-4a92-450f-b008-7dc1de704c2d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2bfd00d3-197d-4eec-a720-b7b33dfb7527' class='xr-var-data-in' type='checkbox'><label for='data-2bfd00d3-197d-4eec-a720-b7b33dfb7527' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>refractory dissolved organic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOCr</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-ebabde54-8d3d-4d55-8fbc-553c5950d5dd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ebabde54-8d3d-4d55-8fbc-553c5950d5dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b66659d5-f41e-428d-ac99-b22d0991f98a' class='xr-var-data-in' type='checkbox'><label for='data-b66659d5-f41e-428d-ac99-b22d0991f98a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>refractory dissolved organic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3565,17 +3469,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 653.44 kiB </td>\n",
-       "                        <td> 195.31 kiB </td>\n",
+       "                        <td> 653.44 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (1, 20, 82, 102) </td>\n",
-       "                        <td> (1, 20, 50, 50) </td>\n",
+       "                        <td> (1, 20, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 126 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 49 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -3599,13 +3503,12 @@
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        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spChl</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;</div><input id='attrs-83bc0e22-0e6d-4d88-97c2-b1fd2d3e7e5d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-83bc0e22-0e6d-4d88-97c2-b1fd2d3e7e5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac067b74-efa8-4cfe-b274-5773e8188c1f' class='xr-var-data-in' type='checkbox'><label for='data-ac067b74-efa8-4cfe-b274-5773e8188c1f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>small phytoplankton chlorophyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spChl</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-72f08c81-4651-4d2e-8b8a-d85f4e2765e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-72f08c81-4651-4d2e-8b8a-d85f4e2765e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ebe240d1-9275-4761-a5e6-46aa7d11203b' class='xr-var-data-in' type='checkbox'><label for='data-ebe240d1-9275-4761-a5e6-46aa7d11203b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>small phytoplankton chlorophyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3665,17 +3563,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 653.44 kiB </td>\n",
-       "                        <td> 195.31 kiB </td>\n",
+       "                        <td> 653.44 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (1, 20, 82, 102) </td>\n",
-       "                        <td> (1, 20, 50, 50) </td>\n",
+       "                        <td> (1, 20, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 126 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 49 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -3699,13 +3597,12 @@
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        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spC</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;</div><input id='attrs-dfa2e156-b237-4a66-828c-4736a20986b8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dfa2e156-b237-4a66-828c-4736a20986b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b9891a1-8496-4953-bdd2-5f1e5da43387' class='xr-var-data-in' type='checkbox'><label for='data-8b9891a1-8496-4953-bdd2-5f1e5da43387' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>small phytoplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spC</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-78896511-8dff-4c73-8e87-53b2968bf19e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-78896511-8dff-4c73-8e87-53b2968bf19e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c97d33a-c78d-4bd4-affa-91219abf3424' class='xr-var-data-in' type='checkbox'><label for='data-2c97d33a-c78d-4bd4-affa-91219abf3424' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>small phytoplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3765,17 +3657,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 653.44 kiB </td>\n",
-       "                        <td> 195.31 kiB </td>\n",
+       "                        <td> 653.44 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (1, 20, 82, 102) </td>\n",
-       "                        <td> (1, 20, 50, 50) </td>\n",
+       "                        <td> (1, 20, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 126 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 49 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -3799,13 +3691,12 @@
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        "\n",
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        "  <line x1=\"95\" y1=\"96\" x2=\"119\" y2=\"121\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -3821,8 +3712,6 @@
        "\n",
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        "  <line x1=\"215\" y1=\"0\" x2=\"239\" y2=\"24\" style=\"stroke-width:2\" />\n",
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@@ -3830,26 +3719,23 @@
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        "\n",
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        "  <line x1=\"119\" y1=\"24\" x2=\"119\" y2=\"121\" style=\"stroke-width:2\" />\n",
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        "\n",
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-       "  <text x=\"259.662223\" y=\"72.897517\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.662223,72.897517)\">82</text>\n",
-       "  <text x=\"97.331111\" y=\"128.801700\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.331111,128.801700)\">20</text>\n",
+       "  <text x=\"179.6622226149252\" y=\"141.13281085021936\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
+       "  <text x=\"259.6622226149252\" y=\"72.89751673257229\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.6622226149252,72.89751673257229)\">82</text>\n",
+       "  <text x=\"97.3311113074626\" y=\"128.80169954275672\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.3311113074626,128.80169954275672)\">20</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spP</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;</div><input id='attrs-ce92ef91-aac5-40ca-bb56-9d98a7fb5242' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce92ef91-aac5-40ca-bb56-9d98a7fb5242' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb69615c-e599-438c-a2b4-7e8fbe19bed6' class='xr-var-data-in' type='checkbox'><label for='data-bb69615c-e599-438c-a2b4-7e8fbe19bed6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>small phytoplankton phosphorous</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spP</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-16316ee0-a63d-40f5-9f32-126313eac242' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-16316ee0-a63d-40f5-9f32-126313eac242' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4427293a-ee53-41fa-9ae4-e20d70574086' class='xr-var-data-in' type='checkbox'><label for='data-4427293a-ee53-41fa-9ae4-e20d70574086' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>small phytoplankton phosphorous</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3865,17 +3751,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 653.44 kiB </td>\n",
-       "                        <td> 195.31 kiB </td>\n",
+       "                        <td> 653.44 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (1, 20, 82, 102) </td>\n",
-       "                        <td> (1, 20, 50, 50) </td>\n",
+       "                        <td> (1, 20, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 126 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 49 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -3899,13 +3785,12 @@
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"12.706308\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"45.412617\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,45.412617,12.706308)\">1</text>\n",
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        "\n",
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        "  <line x1=\"95\" y1=\"96\" x2=\"119\" y2=\"121\" style=\"stroke-width:2\" />\n",
        "\n",
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@@ -3921,8 +3806,6 @@
        "\n",
        "  <!-- Vertical lines -->\n",
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@@ -3930,26 +3813,23 @@
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-       "  <text x=\"259.662223\" y=\"72.897517\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.662223,72.897517)\">82</text>\n",
-       "  <text x=\"97.331111\" y=\"128.801700\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.331111,128.801700)\">20</text>\n",
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+       "  <text x=\"259.6622226149252\" y=\"72.89751673257229\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.6622226149252,72.89751673257229)\">82</text>\n",
+       "  <text x=\"97.3311113074626\" y=\"128.80169954275672\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.3311113074626,128.80169954275672)\">20</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spFe</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;</div><input id='attrs-134b0edc-6d8b-4485-9730-26fb15b4be75' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-134b0edc-6d8b-4485-9730-26fb15b4be75' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b0dea3b-2b35-4895-bd4e-1a72221b5b17' class='xr-var-data-in' type='checkbox'><label for='data-8b0dea3b-2b35-4895-bd4e-1a72221b5b17' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>small phytoplankton iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spFe</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-d6bb4995-7177-47c0-a6ab-907cf4353a5e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6bb4995-7177-47c0-a6ab-907cf4353a5e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94896178-fe55-4d03-81a9-127978d7afe9' class='xr-var-data-in' type='checkbox'><label for='data-94896178-fe55-4d03-81a9-127978d7afe9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>small phytoplankton iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3965,17 +3845,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 653.44 kiB </td>\n",
-       "                        <td> 195.31 kiB </td>\n",
+       "                        <td> 653.44 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (1, 20, 82, 102) </td>\n",
-       "                        <td> (1, 20, 50, 50) </td>\n",
+       "                        <td> (1, 20, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 126 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 49 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -3999,13 +3879,12 @@
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@@ -4021,8 +3900,6 @@
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-       "  <text x=\"97.331111\" y=\"128.801700\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.331111,128.801700)\">20</text>\n",
+       "  <text x=\"179.6622226149252\" y=\"141.13281085021936\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
+       "  <text x=\"259.6622226149252\" y=\"72.89751673257229\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.6622226149252,72.89751673257229)\">82</text>\n",
+       "  <text x=\"97.3311113074626\" y=\"128.80169954275672\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.3311113074626,128.80169954275672)\">20</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatChl</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;</div><input id='attrs-b351d920-8401-4b3a-a4a7-f70d45224dce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b351d920-8401-4b3a-a4a7-f70d45224dce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-128ad158-7f18-497e-a33b-30e3435b7447' class='xr-var-data-in' type='checkbox'><label for='data-128ad158-7f18-497e-a33b-30e3435b7447' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>diatom chloropyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatChl</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-036c406e-6625-42a1-9cf3-d4f0cba03ea7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-036c406e-6625-42a1-9cf3-d4f0cba03ea7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b701be34-3f32-4c9e-9ae4-2662a4ed5dca' class='xr-var-data-in' type='checkbox'><label for='data-b701be34-3f32-4c9e-9ae4-2662a4ed5dca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>diatom chloropyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4065,17 +3939,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 653.44 kiB </td>\n",
-       "                        <td> 195.31 kiB </td>\n",
+       "                        <td> 653.44 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (1, 20, 82, 102) </td>\n",
-       "                        <td> (1, 20, 50, 50) </td>\n",
+       "                        <td> (1, 20, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 126 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 49 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -4099,13 +3973,12 @@
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@@ -4121,8 +3994,6 @@
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-       "  <text x=\"97.331111\" y=\"128.801700\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.331111,128.801700)\">20</text>\n",
+       "  <text x=\"179.6622226149252\" y=\"141.13281085021936\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
+       "  <text x=\"259.6622226149252\" y=\"72.89751673257229\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.6622226149252,72.89751673257229)\">82</text>\n",
+       "  <text x=\"97.3311113074626\" y=\"128.80169954275672\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.3311113074626,128.80169954275672)\">20</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatC</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;</div><input id='attrs-e265a5a1-50c2-4e7d-9862-3c659497059d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e265a5a1-50c2-4e7d-9862-3c659497059d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c626eab5-4601-45ad-8dbe-127d898a26af' class='xr-var-data-in' type='checkbox'><label for='data-c626eab5-4601-45ad-8dbe-127d898a26af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>diatom carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatC</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-f1c353ae-74ae-4cdf-be9b-36a498fb62df' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f1c353ae-74ae-4cdf-be9b-36a498fb62df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00ac729d-2e2b-4a05-ab2c-324357efc2bc' class='xr-var-data-in' type='checkbox'><label for='data-00ac729d-2e2b-4a05-ab2c-324357efc2bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>diatom carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4165,17 +4033,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 653.44 kiB </td>\n",
-       "                        <td> 195.31 kiB </td>\n",
+       "                        <td> 653.44 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (1, 20, 82, 102) </td>\n",
-       "                        <td> (1, 20, 50, 50) </td>\n",
+       "                        <td> (1, 20, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 126 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 49 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -4199,13 +4067,12 @@
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        "\n",
        "  <!-- Text -->\n",
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        "\n",
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        "\n",
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@@ -4221,8 +4088,6 @@
        "\n",
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@@ -4230,26 +4095,23 @@
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-       "  <text x=\"97.331111\" y=\"128.801700\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.331111,128.801700)\">20</text>\n",
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+       "  <text x=\"259.6622226149252\" y=\"72.89751673257229\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.6622226149252,72.89751673257229)\">82</text>\n",
+       "  <text x=\"97.3311113074626\" y=\"128.80169954275672\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.3311113074626,128.80169954275672)\">20</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatP</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;</div><input id='attrs-137bea1f-db32-4146-8814-25aeb1f0081a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-137bea1f-db32-4146-8814-25aeb1f0081a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71190385-f7d6-4908-9d7b-6b2739ba1b18' class='xr-var-data-in' type='checkbox'><label for='data-71190385-f7d6-4908-9d7b-6b2739ba1b18' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>diatom phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatP</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-83513da4-08af-4dda-abac-be4fc5a59789' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-83513da4-08af-4dda-abac-be4fc5a59789' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-587f93de-ba13-411d-8cd1-949cb70af682' class='xr-var-data-in' type='checkbox'><label for='data-587f93de-ba13-411d-8cd1-949cb70af682' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>diatom phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4265,17 +4127,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 653.44 kiB </td>\n",
-       "                        <td> 195.31 kiB </td>\n",
+       "                        <td> 653.44 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (1, 20, 82, 102) </td>\n",
-       "                        <td> (1, 20, 50, 50) </td>\n",
+       "                        <td> (1, 20, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 126 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 49 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -4299,13 +4161,12 @@
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        "\n",
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        "  <line x1=\"95\" y1=\"96\" x2=\"119\" y2=\"121\" style=\"stroke-width:2\" />\n",
        "\n",
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@@ -4321,8 +4182,6 @@
        "\n",
        "  <!-- Vertical lines -->\n",
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@@ -4330,26 +4189,23 @@
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-       "  <text x=\"97.331111\" y=\"128.801700\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.331111,128.801700)\">20</text>\n",
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+       "  <text x=\"97.3311113074626\" y=\"128.80169954275672\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.3311113074626,128.80169954275672)\">20</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatFe</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;</div><input id='attrs-1c4c8dd6-116a-408f-b419-67090f39aab7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c4c8dd6-116a-408f-b419-67090f39aab7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed6aba96-4e15-48fa-be3b-05343b404401' class='xr-var-data-in' type='checkbox'><label for='data-ed6aba96-4e15-48fa-be3b-05343b404401' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>diatom iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatFe</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-3afdae37-1013-40c0-9504-0b11dbeb05df' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3afdae37-1013-40c0-9504-0b11dbeb05df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e3d1107-1f91-4ee8-933f-1727fb63b57c' class='xr-var-data-in' type='checkbox'><label for='data-6e3d1107-1f91-4ee8-933f-1727fb63b57c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>diatom iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4365,17 +4221,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 653.44 kiB </td>\n",
-       "                        <td> 195.31 kiB </td>\n",
+       "                        <td> 653.44 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (1, 20, 82, 102) </td>\n",
-       "                        <td> (1, 20, 50, 50) </td>\n",
+       "                        <td> (1, 20, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 126 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 49 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -4399,13 +4255,12 @@
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        "  <line x1=\"95\" y1=\"96\" x2=\"119\" y2=\"121\" style=\"stroke-width:2\" />\n",
        "\n",
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@@ -4421,8 +4276,6 @@
        "\n",
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-       "  <text x=\"97.331111\" y=\"128.801700\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.331111,128.801700)\">20</text>\n",
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+       "  <text x=\"259.6622226149252\" y=\"72.89751673257229\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.6622226149252,72.89751673257229)\">82</text>\n",
+       "  <text x=\"97.3311113074626\" y=\"128.80169954275672\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.3311113074626,128.80169954275672)\">20</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatSi</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;</div><input id='attrs-fbb83bf8-156b-4259-9603-099360a32518' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fbb83bf8-156b-4259-9603-099360a32518' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6393d5f3-52da-41dc-b1a6-967220f5624d' class='xr-var-data-in' type='checkbox'><label for='data-6393d5f3-52da-41dc-b1a6-967220f5624d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>diatom silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatSi</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-03608938-32c3-4137-858a-5f43a0d599a5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03608938-32c3-4137-858a-5f43a0d599a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d34995bf-8651-45a4-950e-f581e1c1b0d0' class='xr-var-data-in' type='checkbox'><label for='data-d34995bf-8651-45a4-950e-f581e1c1b0d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>diatom silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4465,17 +4315,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 653.44 kiB </td>\n",
-       "                        <td> 195.31 kiB </td>\n",
+       "                        <td> 653.44 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (1, 20, 82, 102) </td>\n",
-       "                        <td> (1, 20, 50, 50) </td>\n",
+       "                        <td> (1, 20, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 126 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 49 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -4499,13 +4349,12 @@
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"12.706308\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"45.412617\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,45.412617,12.706308)\">1</text>\n",
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        "\n",
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        "\n",
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@@ -4521,8 +4370,6 @@
        "\n",
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@@ -4530,26 +4377,23 @@
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+       "  <text x=\"97.3311113074626\" y=\"128.80169954275672\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.3311113074626,128.80169954275672)\">20</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazChl</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;</div><input id='attrs-5c0f27a1-d23a-4b4a-921f-5082ffc3940a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5c0f27a1-d23a-4b4a-921f-5082ffc3940a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec337555-976f-44c3-8b9a-3373fb4e16c7' class='xr-var-data-in' type='checkbox'><label for='data-ec337555-976f-44c3-8b9a-3373fb4e16c7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>diazotroph chloropyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazChl</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-305dd2c1-a6ca-4c8c-806b-eb624b3c4596' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-305dd2c1-a6ca-4c8c-806b-eb624b3c4596' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f49149e7-ff6e-4aba-ad35-ea31ab238b47' class='xr-var-data-in' type='checkbox'><label for='data-f49149e7-ff6e-4aba-ad35-ea31ab238b47' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>diazotroph chloropyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4565,17 +4409,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 653.44 kiB </td>\n",
-       "                        <td> 195.31 kiB </td>\n",
+       "                        <td> 653.44 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (1, 20, 82, 102) </td>\n",
-       "                        <td> (1, 20, 50, 50) </td>\n",
+       "                        <td> (1, 20, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 126 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 49 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -4599,13 +4443,12 @@
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        "\n",
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@@ -4621,8 +4464,6 @@
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@@ -4630,26 +4471,23 @@
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-       "  <text x=\"97.331111\" y=\"128.801700\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.331111,128.801700)\">20</text>\n",
+       "  <text x=\"179.6622226149252\" y=\"141.13281085021936\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
+       "  <text x=\"259.6622226149252\" y=\"72.89751673257229\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.6622226149252,72.89751673257229)\">82</text>\n",
+       "  <text x=\"97.3311113074626\" y=\"128.80169954275672\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.3311113074626,128.80169954275672)\">20</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazC</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;</div><input id='attrs-187a811b-1d2f-4920-a24c-6ba4e084e878' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-187a811b-1d2f-4920-a24c-6ba4e084e878' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85872c4d-5a4d-4806-a69f-4c192a374139' class='xr-var-data-in' type='checkbox'><label for='data-85872c4d-5a4d-4806-a69f-4c192a374139' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>diazotroph carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazC</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-1e79a15b-10fe-4a78-922e-2f4ab54052e2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1e79a15b-10fe-4a78-922e-2f4ab54052e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee6f313d-fffa-48ea-82e7-3165585c145e' class='xr-var-data-in' type='checkbox'><label for='data-ee6f313d-fffa-48ea-82e7-3165585c145e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>diazotroph carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4665,17 +4503,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 653.44 kiB </td>\n",
-       "                        <td> 195.31 kiB </td>\n",
+       "                        <td> 653.44 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (1, 20, 82, 102) </td>\n",
-       "                        <td> (1, 20, 50, 50) </td>\n",
+       "                        <td> (1, 20, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 126 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 49 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -4699,13 +4537,12 @@
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        "\n",
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@@ -4721,8 +4558,6 @@
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@@ -4730,26 +4565,23 @@
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-       "  <text x=\"97.331111\" y=\"128.801700\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.331111,128.801700)\">20</text>\n",
+       "  <text x=\"179.6622226149252\" y=\"141.13281085021936\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
+       "  <text x=\"259.6622226149252\" y=\"72.89751673257229\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.6622226149252,72.89751673257229)\">82</text>\n",
+       "  <text x=\"97.3311113074626\" y=\"128.80169954275672\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.3311113074626,128.80169954275672)\">20</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazP</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;</div><input id='attrs-c0895dcd-d3f1-4739-880b-ad64bf7dad47' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c0895dcd-d3f1-4739-880b-ad64bf7dad47' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32487db0-197d-4c20-955c-30b562fce438' class='xr-var-data-in' type='checkbox'><label for='data-32487db0-197d-4c20-955c-30b562fce438' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>diazotroph phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazP</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-ab955af8-5750-43ff-8c29-f83710befb33' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab955af8-5750-43ff-8c29-f83710befb33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3d65812-0aa7-482a-85de-1da9f00074af' class='xr-var-data-in' type='checkbox'><label for='data-e3d65812-0aa7-482a-85de-1da9f00074af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>diazotroph phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4765,17 +4597,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 653.44 kiB </td>\n",
-       "                        <td> 195.31 kiB </td>\n",
+       "                        <td> 653.44 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (1, 20, 82, 102) </td>\n",
-       "                        <td> (1, 20, 50, 50) </td>\n",
+       "                        <td> (1, 20, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 126 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 49 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -4799,13 +4631,12 @@
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        "\n",
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        "\n",
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        "\n",
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@@ -4821,8 +4652,6 @@
        "\n",
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@@ -4830,26 +4659,23 @@
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        "\n",
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-       "  <text x=\"179.662223\" y=\"141.132811\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
-       "  <text x=\"259.662223\" y=\"72.897517\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.662223,72.897517)\">82</text>\n",
-       "  <text x=\"97.331111\" y=\"128.801700\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.331111,128.801700)\">20</text>\n",
+       "  <text x=\"179.6622226149252\" y=\"141.13281085021936\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
+       "  <text x=\"259.6622226149252\" y=\"72.89751673257229\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.6622226149252,72.89751673257229)\">82</text>\n",
+       "  <text x=\"97.3311113074626\" y=\"128.80169954275672\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.3311113074626,128.80169954275672)\">20</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazFe</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;</div><input id='attrs-edeb2e8c-81f7-4a0f-9d0f-8f8a00269554' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-edeb2e8c-81f7-4a0f-9d0f-8f8a00269554' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10c0d9a7-4a84-4d08-b618-644df89d2884' class='xr-var-data-in' type='checkbox'><label for='data-10c0d9a7-4a84-4d08-b618-644df89d2884' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>diazotroph iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazFe</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-381c74ea-210d-41e9-ad0f-fc9b02f0eb88' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-381c74ea-210d-41e9-ad0f-fc9b02f0eb88' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-925fb84a-1a05-473b-9a4c-4853de7f78b6' class='xr-var-data-in' type='checkbox'><label for='data-925fb84a-1a05-473b-9a4c-4853de7f78b6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>diazotroph iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4865,17 +4691,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 653.44 kiB </td>\n",
-       "                        <td> 195.31 kiB </td>\n",
+       "                        <td> 653.44 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (1, 20, 82, 102) </td>\n",
-       "                        <td> (1, 20, 50, 50) </td>\n",
+       "                        <td> (1, 20, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 126 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 49 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -4899,13 +4725,12 @@
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-       "  <text x=\"97.331111\" y=\"128.801700\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.331111,128.801700)\">20</text>\n",
+       "  <text x=\"179.6622226149252\" y=\"141.13281085021936\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
+       "  <text x=\"259.6622226149252\" y=\"72.89751673257229\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.6622226149252,72.89751673257229)\">82</text>\n",
+       "  <text x=\"97.3311113074626\" y=\"128.80169954275672\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.3311113074626,128.80169954275672)\">20</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spCaCO3</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;</div><input id='attrs-4334a119-3af8-4f4d-ae4f-d9a75f15132a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4334a119-3af8-4f4d-ae4f-d9a75f15132a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a27d82a-eaad-4735-b4fa-0dbb7a560ea4' class='xr-var-data-in' type='checkbox'><label for='data-5a27d82a-eaad-4735-b4fa-0dbb7a560ea4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>small phytoplankton CaCO3</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spCaCO3</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-76a4ad49-a0a6-4245-a061-8ce786772d9d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-76a4ad49-a0a6-4245-a061-8ce786772d9d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4bb9aaba-f968-4b8e-b733-9e331cd42c29' class='xr-var-data-in' type='checkbox'><label for='data-4bb9aaba-f968-4b8e-b733-9e331cd42c29' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>small phytoplankton CaCO3</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4965,17 +4785,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 653.44 kiB </td>\n",
-       "                        <td> 195.31 kiB </td>\n",
+       "                        <td> 653.44 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (1, 20, 82, 102) </td>\n",
-       "                        <td> (1, 20, 50, 50) </td>\n",
+       "                        <td> (1, 20, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 126 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 49 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -4999,13 +4819,12 @@
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        "\n",
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@@ -5021,8 +4840,6 @@
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+       "  <text x=\"97.3311113074626\" y=\"128.80169954275672\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.3311113074626,128.80169954275672)\">20</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zooC</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;</div><input id='attrs-d4559ef7-e207-4d86-9b00-2e129d41bfd2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d4559ef7-e207-4d86-9b00-2e129d41bfd2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a3f2095-4556-478a-b831-33818c993569' class='xr-var-data-in' type='checkbox'><label for='data-6a3f2095-4556-478a-b831-33818c993569' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>zooplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zooC</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-5cf40bdc-1dde-45c1-841d-5b211f42014c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5cf40bdc-1dde-45c1-841d-5b211f42014c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5510a9c5-76b8-4282-a706-bf4fcd6e9e2c' class='xr-var-data-in' type='checkbox'><label for='data-5510a9c5-76b8-4282-a706-bf4fcd6e9e2c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>zooplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5065,17 +4879,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 653.44 kiB </td>\n",
-       "                        <td> 195.31 kiB </td>\n",
+       "                        <td> 653.44 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (1, 20, 82, 102) </td>\n",
-       "                        <td> (1, 20, 50, 50) </td>\n",
+       "                        <td> (1, 20, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 126 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 49 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -5099,13 +4913,12 @@
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@@ -5121,8 +4934,6 @@
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+       "  <text x=\"97.3311113074626\" y=\"128.80169954275672\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.3311113074626,128.80169954275672)\">20</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Lig</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;</div><input id='attrs-da2f923d-9556-4a9a-9d8c-61eaf79432cb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-da2f923d-9556-4a9a-9d8c-61eaf79432cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e1b4692-61d8-4457-bd67-eac1b3a70701' class='xr-var-data-in' type='checkbox'><label for='data-4e1b4692-61d8-4457-bd67-eac1b3a70701' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>iron binding ligand</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Lig</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-9311fd73-8f1a-467e-8d7d-926d5951aed9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9311fd73-8f1a-467e-8d7d-926d5951aed9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78460bb7-f85e-4da7-8f0d-6643889c8587' class='xr-var-data-in' type='checkbox'><label for='data-78460bb7-f85e-4da7-8f0d-6643889c8587' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>iron binding ligand</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5165,17 +4973,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 653.44 kiB </td>\n",
-       "                        <td> 195.31 kiB </td>\n",
+       "                        <td> 653.44 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (1, 20, 82, 102) </td>\n",
-       "                        <td> (1, 20, 50, 50) </td>\n",
+       "                        <td> (1, 20, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 127 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 50 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -5199,13 +5007,12 @@
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        "\n",
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-       "  <text x=\"45.412617\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,45.412617,12.706308)\">1</text>\n",
+       "  <text x=\"12.706308257291242\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+       "  <text x=\"45.41261651458248\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,45.41261651458248,12.706308257291242)\">1</text>\n",
        "\n",
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        "  <line x1=\"95\" y1=\"96\" x2=\"119\" y2=\"121\" style=\"stroke-width:2\" />\n",
        "\n",
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@@ -5221,8 +5028,6 @@
        "\n",
        "  <!-- Vertical lines -->\n",
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        "  <line x1=\"215\" y1=\"0\" x2=\"239\" y2=\"24\" style=\"stroke-width:2\" />\n",
        "\n",
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@@ -5230,26 +5035,23 @@
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        "\n",
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"179.662223\" y=\"141.132811\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
-       "  <text x=\"259.662223\" y=\"72.897517\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.662223,72.897517)\">82</text>\n",
-       "  <text x=\"97.331111\" y=\"128.801700\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.331111,128.801700)\">20</text>\n",
+       "  <text x=\"179.6622226149252\" y=\"141.13281085021936\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
+       "  <text x=\"259.6622226149252\" y=\"72.89751673257229\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.6622226149252,72.89751673257229)\">82</text>\n",
+       "  <text x=\"97.3311113074626\" y=\"128.80169954275672\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.3311113074626,128.80169954275672)\">20</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIC_ALT_CO2</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;</div><input id='attrs-9d593e7e-09de-42ae-b90f-a7c81390079e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d593e7e-09de-42ae-b90f-a7c81390079e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd2d6f85-8319-46f7-be45-9906bdf9bc99' class='xr-var-data-in' type='checkbox'><label for='data-bd2d6f85-8319-46f7-be45-9906bdf9bc99' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved inorganic carbon, alternative CO2</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIC_ALT_CO2</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-86644b0b-6129-4479-9f9c-769902fc4e4e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-86644b0b-6129-4479-9f9c-769902fc4e4e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45d50be0-9901-4945-be2a-0601176a2705' class='xr-var-data-in' type='checkbox'><label for='data-45d50be0-9901-4945-be2a-0601176a2705' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved inorganic carbon, alternative CO2</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5265,17 +5067,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 653.44 kiB </td>\n",
-       "                        <td> 195.31 kiB </td>\n",
+       "                        <td> 653.44 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (1, 20, 82, 102) </td>\n",
-       "                        <td> (1, 20, 50, 50) </td>\n",
+       "                        <td> (1, 20, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 127 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 50 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -5299,13 +5101,12 @@
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"12.706308\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"45.412617\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,45.412617,12.706308)\">1</text>\n",
+       "  <text x=\"12.706308257291242\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
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        "\n",
        "\n",
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        "  <line x1=\"95\" y1=\"0\" x2=\"119\" y2=\"24\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"95\" y1=\"58\" x2=\"119\" y2=\"83\" />\n",
        "  <line x1=\"95\" y1=\"96\" x2=\"119\" y2=\"121\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -5321,8 +5122,6 @@
        "\n",
        "  <!-- Vertical lines -->\n",
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        "  <line x1=\"215\" y1=\"0\" x2=\"239\" y2=\"24\" style=\"stroke-width:2\" />\n",
        "\n",
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@@ -5330,26 +5129,23 @@
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"179.662223\" y=\"141.132811\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
-       "  <text x=\"259.662223\" y=\"72.897517\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.662223,72.897517)\">82</text>\n",
-       "  <text x=\"97.331111\" y=\"128.801700\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.331111,128.801700)\">20</text>\n",
+       "  <text x=\"179.6622226149252\" y=\"141.13281085021936\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
+       "  <text x=\"259.6622226149252\" y=\"72.89751673257229\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.6622226149252,72.89751673257229)\">82</text>\n",
+       "  <text x=\"97.3311113074626\" y=\"128.80169954275672\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.3311113074626,128.80169954275672)\">20</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALK_ALT_CO2</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 50, 50), meta=np.ndarray&gt;</div><input id='attrs-f3761f8a-a5f2-4948-a161-740cb10c8d1a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f3761f8a-a5f2-4948-a161-740cb10c8d1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21db9941-93fc-43ad-a872-c6f1b6f2e107' class='xr-var-data-in' type='checkbox'><label for='data-21db9941-93fc-43ad-a872-c6f1b6f2e107' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>alkalinity, alternative CO2</dd><dt><span>units :</span></dt><dd>meq/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALK_ALT_CO2</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-1d0462f7-e074-486d-8c54-3ed81c402bbc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1d0462f7-e074-486d-8c54-3ed81c402bbc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32851cc3-7c87-4ed7-b874-abddd5356c2a' class='xr-var-data-in' type='checkbox'><label for='data-32851cc3-7c87-4ed7-b874-abddd5356c2a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>alkalinity, alternative CO2</dd><dt><span>units :</span></dt><dd>meq/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5365,17 +5161,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 653.44 kiB </td>\n",
-       "                        <td> 195.31 kiB </td>\n",
+       "                        <td> 653.44 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (1, 20, 82, 102) </td>\n",
-       "                        <td> (1, 20, 50, 50) </td>\n",
+       "                        <td> (1, 20, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6 chunks in 127 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 50 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -5399,13 +5195,12 @@
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        "\n",
        "  <!-- Text -->\n",
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-       "  <text x=\"45.412617\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,45.412617,12.706308)\">1</text>\n",
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        "\n",
        "\n",
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        "  <line x1=\"95\" y1=\"96\" x2=\"119\" y2=\"121\" style=\"stroke-width:2\" />\n",
        "\n",
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@@ -5421,8 +5216,6 @@
        "\n",
        "  <!-- Vertical lines -->\n",
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@@ -5430,26 +5223,23 @@
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        "\n",
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+       "  <text x=\"179.6622226149252\" y=\"141.13281085021936\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
+       "  <text x=\"259.6622226149252\" y=\"72.89751673257229\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,259.6622226149252,72.89751673257229)\">82</text>\n",
+       "  <text x=\"97.3311113074626\" y=\"128.80169954275672\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,97.3311113074626,128.80169954275672)\">20</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>w</span></div><div class='xr-var-dims'>(ocean_time, s_w, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-faa3838e-a1ab-4a18-8e50-1bd615bf5204' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-faa3838e-a1ab-4a18-8e50-1bd615bf5204' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90d60239-36ce-41a6-936a-7c7abbda45f9' class='xr-var-data-in' type='checkbox'><label for='data-90d60239-36ce-41a6-936a-7c7abbda45f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>w-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><pre>array([[[[0., 0., 0., ..., 0., 0., 0.],\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>w</span></div><div class='xr-var-dims'>(ocean_time, s_w, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-ea5ea806-712f-4e24-ac64-435390cd1efb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea5ea806-712f-4e24-ac64-435390cd1efb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1300ae9a-4a03-4033-ae13-540df34dadbc' class='xr-var-data-in' type='checkbox'><label for='data-1300ae9a-4a03-4033-ae13-540df34dadbc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>w-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><pre>array([[[[0., 0., 0., ..., 0., 0., 0.],\n",
        "         [0., 0., 0., ..., 0., 0., 0.],\n",
        "         [0., 0., 0., ..., 0., 0., 0.],\n",
        "         ...,\n",
@@ -5489,17 +5279,17 @@
        "         [0., 0., 0., ..., 0., 0., 0.],\n",
        "         [0., 0., 0., ..., 0., 0., 0.],\n",
        "         [0., 0., 0., ..., 0., 0., 0.]]]],\n",
-       "      shape=(1, 21, 82, 102), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Cs_r</span></div><div class='xr-var-dims'>(s_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.9579 -0.8615 ... -0.0002471</div><input id='attrs-8d768f36-5236-4aee-b028-96883c107413' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d768f36-5236-4aee-b028-96883c107413' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85a260f4-ba2a-4a2b-8220-f051d3a40a50' class='xr-var-data-in' type='checkbox'><label for='data-85a260f4-ba2a-4a2b-8220-f051d3a40a50' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Vertical stretching function at rho-points</dd><dt><span>units :</span></dt><dd>nondimensional</dd></dl></div><div class='xr-var-data'><pre>array([-9.5790392e-01, -8.6149198e-01, -7.5501293e-01, -6.4611858e-01,\n",
+       "      shape=(1, 21, 82, 102), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Cs_r</span></div><div class='xr-var-dims'>(s_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.9579 -0.8615 ... -0.0002471</div><input id='attrs-583a87ed-6b7a-411a-a7b8-ebca84a1f1d8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-583a87ed-6b7a-411a-a7b8-ebca84a1f1d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21a8d123-ea84-4f18-8737-4ff7e387a4e4' class='xr-var-data-in' type='checkbox'><label for='data-21a8d123-ea84-4f18-8737-4ff7e387a4e4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Vertical stretching function at rho-points</dd><dt><span>units :</span></dt><dd>nondimensional</dd></dl></div><div class='xr-var-data'><pre>array([-9.5790392e-01, -8.6149198e-01, -7.5501293e-01, -6.4611858e-01,\n",
        "       -5.4124850e-01, -4.4488612e-01, -3.5953298e-01, -2.8607488e-01,\n",
        "       -2.2426941e-01, -1.7319578e-01, -1.3160250e-01, -9.8145016e-02,\n",
        "       -7.1531408e-02, -5.0600942e-02, -3.4358352e-02, -2.1981522e-02,\n",
        "       -1.2814899e-02, -6.3567827e-03, -2.2454644e-03, -2.4712758e-04],\n",
-       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Cs_w</span></div><div class='xr-var-dims'>(s_w)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.0 -0.9114 ... -0.0009921 0.0</div><input id='attrs-81a38d55-3fc5-4ec8-99f2-8c2368d491ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-81a38d55-3fc5-4ec8-99f2-8c2368d491ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5240c141-969e-46a3-8241-60e981743063' class='xr-var-data-in' type='checkbox'><label for='data-5240c141-969e-46a3-8241-60e981743063' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Vertical stretching function at w-points</dd><dt><span>units :</span></dt><dd>nondimensional</dd></dl></div><div class='xr-var-data'><pre>array([-1.0000000e+00, -9.1144282e-01, -8.0901909e-01, -7.0042384e-01,\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Cs_w</span></div><div class='xr-var-dims'>(s_w)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.0 -0.9114 ... -0.0009921 0.0</div><input id='attrs-312303ab-2c6a-4bdc-af47-3240d91c7fff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-312303ab-2c6a-4bdc-af47-3240d91c7fff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab79c584-379c-4d11-add8-38418cbdfdbf' class='xr-var-data-in' type='checkbox'><label for='data-ab79c584-379c-4d11-add8-38418cbdfdbf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Vertical stretching function at w-points</dd><dt><span>units :</span></dt><dd>nondimensional</dd></dl></div><div class='xr-var-data'><pre>array([-1.0000000e+00, -9.1144282e-01, -8.0901909e-01, -7.0042384e-01,\n",
        "       -5.9285146e-01, -4.9180421e-01, -4.0074578e-01, -3.2131144e-01,\n",
        "       -2.5376186e-01, -1.9746487e-01, -1.5129805e-01, -1.1393995e-01,\n",
        "       -8.4058918e-02, -6.0422052e-02, -4.1949075e-02, -2.7731461e-02,\n",
        "       -1.7031731e-02, -9.2730094e-03, -4.0252637e-03, -9.9205703e-04,\n",
-       "        0.0000000e+00], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-16147181-af29-4d58-bff3-829a4af7b962' class='xr-section-summary-in' type='checkbox'  ><label for='section-16147181-af29-4d58-bff3-829a4af7b962' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>ocean_time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-1ab327d3-b50d-48d2-af7d-d8d89025d400' class='xr-index-data-in' type='checkbox'/><label for='index-1ab327d3-b50d-48d2-af7d-d8d89025d400' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([378691200.0], dtype=&#x27;float64&#x27;, name=&#x27;ocean_time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-99fae3ff-874a-45fc-b77d-11e89a41f24d' class='xr-section-summary-in' type='checkbox'  ><label for='section-99fae3ff-874a-45fc-b77d-11e89a41f24d' class='xr-section-summary' >Attributes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>ROMS initial conditions file created by ROMS-Tools</dd><dt><span>roms_tools_version :</span></dt><dd>3.1.3.dev32+g91d580e67.d20250929</dd><dt><span>ini_time :</span></dt><dd>2012-01-01 00:00:00</dd><dt><span>model_reference_date :</span></dt><dd>2000-01-01 00:00:00</dd><dt><span>adjust_depth_for_sea_surface_height :</span></dt><dd>False</dd><dt><span>source :</span></dt><dd>GLORYS</dd><dt><span>bgc_source :</span></dt><dd>UNIFIED</dd><dt><span>theta_s :</span></dt><dd>5.0</dd><dt><span>theta_b :</span></dt><dd>2.0</dd><dt><span>hc :</span></dt><dd>300.0</dd></dl></div></li></ul></div></div>"
+       "        0.0000000e+00], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a0b3f1d1-f37b-49aa-99af-34d72ff0ab50' class='xr-section-summary-in' type='checkbox'  ><label for='section-a0b3f1d1-f37b-49aa-99af-34d72ff0ab50' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>ocean_time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-23c9b725-2fc2-4283-b2d6-ce19c50ee0b2' class='xr-index-data-in' type='checkbox'/><label for='index-23c9b725-2fc2-4283-b2d6-ce19c50ee0b2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([378691200.0], dtype=&#x27;float64&#x27;, name=&#x27;ocean_time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1ec0bdad-7f8d-4909-948a-7ed5236ec613' class='xr-section-summary-in' type='checkbox'  ><label for='section-1ec0bdad-7f8d-4909-948a-7ed5236ec613' class='xr-section-summary' >Attributes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>ROMS initial conditions file created by ROMS-Tools</dd><dt><span>roms_tools_version :</span></dt><dd>10000.dev364+g785fa77b7.d20260612</dd><dt><span>ini_time :</span></dt><dd>2012-01-01 00:00:00</dd><dt><span>model_reference_date :</span></dt><dd>2000-01-01 00:00:00</dd><dt><span>adjust_depth_for_sea_surface_height :</span></dt><dd>False</dd><dt><span>source :</span></dt><dd>GLORYS</dd><dt><span>bgc_source :</span></dt><dd>UNIFIED</dd><dt><span>theta_s :</span></dt><dd>5.0</dd><dt><span>theta_b :</span></dt><dd>2.0</dd><dt><span>hc :</span></dt><dd>300.0</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset> Size: 25MB\n",
@@ -5510,22 +5300,22 @@
        "  * ocean_time   (ocean_time) float64 8B 3.787e+08\n",
        "Dimensions without coordinates: s_rho, eta_rho, xi_rho, xi_u, eta_v, s_w\n",
        "Data variables: (12/42)\n",
-       "    temp         (ocean_time, s_rho, eta_rho, xi_rho) float32 669kB dask.array<chunksize=(1, 20, 50, 50), meta=np.ndarray>\n",
-       "    salt         (ocean_time, s_rho, eta_rho, xi_rho) float32 669kB dask.array<chunksize=(1, 20, 50, 50), meta=np.ndarray>\n",
-       "    u            (ocean_time, s_rho, eta_rho, xi_u) float32 663kB dask.array<chunksize=(1, 20, 50, 50), meta=np.ndarray>\n",
-       "    v            (ocean_time, s_rho, eta_v, xi_rho) float32 661kB dask.array<chunksize=(1, 20, 50, 50), meta=np.ndarray>\n",
+       "    temp         (ocean_time, s_rho, eta_rho, xi_rho) float32 669kB dask.array<chunksize=(1, 20, 82, 102), meta=np.ndarray>\n",
+       "    salt         (ocean_time, s_rho, eta_rho, xi_rho) float32 669kB dask.array<chunksize=(1, 20, 82, 102), meta=np.ndarray>\n",
+       "    u            (ocean_time, s_rho, eta_rho, xi_u) float32 663kB dask.array<chunksize=(1, 20, 82, 101), meta=np.ndarray>\n",
+       "    v            (ocean_time, s_rho, eta_v, xi_rho) float32 661kB dask.array<chunksize=(1, 20, 81, 102), meta=np.ndarray>\n",
        "    zeta         (ocean_time, eta_rho, xi_rho) float32 33kB 0.2544 ... 0.2846\n",
-       "    ubar         (ocean_time, eta_rho, xi_u) float32 33kB dask.array<chunksize=(1, 50, 50), meta=np.ndarray>\n",
+       "    ubar         (ocean_time, eta_rho, xi_u) float32 33kB dask.array<chunksize=(1, 82, 101), meta=np.ndarray>\n",
        "    ...           ...\n",
-       "    Lig          (ocean_time, s_rho, eta_rho, xi_rho) float32 669kB dask.array<chunksize=(1, 20, 50, 50), meta=np.ndarray>\n",
-       "    DIC_ALT_CO2  (ocean_time, s_rho, eta_rho, xi_rho) float32 669kB dask.array<chunksize=(1, 20, 50, 50), meta=np.ndarray>\n",
-       "    ALK_ALT_CO2  (ocean_time, s_rho, eta_rho, xi_rho) float32 669kB dask.array<chunksize=(1, 20, 50, 50), meta=np.ndarray>\n",
+       "    Lig          (ocean_time, s_rho, eta_rho, xi_rho) float32 669kB dask.array<chunksize=(1, 20, 82, 102), meta=np.ndarray>\n",
+       "    DIC_ALT_CO2  (ocean_time, s_rho, eta_rho, xi_rho) float32 669kB dask.array<chunksize=(1, 20, 82, 102), meta=np.ndarray>\n",
+       "    ALK_ALT_CO2  (ocean_time, s_rho, eta_rho, xi_rho) float32 669kB dask.array<chunksize=(1, 20, 82, 102), meta=np.ndarray>\n",
        "    w            (ocean_time, s_w, eta_rho, xi_rho) float32 703kB 0.0 ... 0.0\n",
        "    Cs_r         (s_rho) float32 80B -0.9579 -0.8615 ... -0.002245 -0.0002471\n",
        "    Cs_w         (s_w) float32 84B -1.0 -0.9114 -0.809 ... -0.0009921 0.0\n",
        "Attributes:\n",
        "    title:                                ROMS initial conditions file create...\n",
-       "    roms_tools_version:                   3.1.3.dev32+g91d580e67.d20250929\n",
+       "    roms_tools_version:                   10000.dev364+g785fa77b7.d20260612\n",
        "    ini_time:                             2012-01-01 00:00:00\n",
        "    model_reference_date:                 2000-01-01 00:00:00\n",
        "    adjust_depth_for_sea_surface_height:  False\n",
@@ -5536,7 +5326,7 @@
        "    hc:                                   300.0"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5555,7 +5345,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "id": "7bc1a3da-5a6b-4804-9ed8-420d233942f9",
    "metadata": {
     "tags": []
@@ -5565,12 +5355,19 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[########################################] | 100% Completed | 631.83 ms\n"
+      "[                                        ] | 0% Completed | 72.21 us"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[########################################] | 100% Completed | 419.43 ms\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1300x700 with 2 Axes>"
       ]
@@ -5593,7 +5390,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "c3d63169-4844-4b3f-a723-246a543873a0",
    "metadata": {
     "tags": []
@@ -5601,7 +5398,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 900x500 with 2 Axes>"
       ]
@@ -5624,7 +5421,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "id": "1b5ab4d3-ed89-4aa3-9708-21c761e7ca1b",
    "metadata": {
     "tags": []
@@ -5634,7 +5431,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-09-29 13:35:46 - INFO - Writing the following NetCDF files:\n",
+      "2026-06-23 09:14:11 - INFO - Writing the following NetCDF files:\n",
       "ROMS_TOOLS_INPUT_DATA/roms_ini.nc\n"
      ]
     },
@@ -5642,7 +5439,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[########################################] | 100% Completed | 5.55 sms\n"
+      "[########################################] | 100% Completed | 2.26 sms\n"
      ]
     },
     {
@@ -5651,7 +5448,7 @@
        "[PosixPath('ROMS_TOOLS_INPUT_DATA/roms_ini.nc')]"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5662,7 +5459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "id": "21ca2a07-2758-4913-8768-30ddd6150a24",
    "metadata": {
     "tags": []
@@ -5704,7 +5501,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "id": "52d9c622-6986-4d00-9cf7-06130fdd1cbf",
    "metadata": {},
    "outputs": [],
@@ -5732,7 +5529,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 22,
    "id": "0d2920eb-d8e2-4b5e-be10-2562a819dffe",
    "metadata": {
     "tags": []
@@ -5742,16 +5539,17 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-09-29 13:46:40 - INFO - No path specified for ERA5 source; defaulting to ARCO ERA5 dataset on Google Cloud.\n",
-      "2025-09-29 13:47:58 - INFO - Data will be interpolated onto the fine grid. Remember to set `interp_frc = 0` in your `bulk_frc.opt` ROMS option file.\n"
+      "2026-06-23 09:14:14 - INFO - No path specified for ERA5 source; defaulting to ARCO ERA5 dataset on Google Cloud.\n",
+      "2026-06-23 09:14:15 - INFO - gcsfs experimental features enabled via GCSFS_EXPERIMENTAL_ZB_HNS_SUPPORT.\n",
+      "2026-06-23 09:15:09 - INFO - Data will be interpolated onto the fine grid. Remember to set `interp_frc = 0` in your `bulk_frc.opt` ROMS option file.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 1min 20s, sys: 6.95 s, total: 1min 27s\n",
-      "Wall time: 1min 32s\n"
+      "CPU times: user 47 s, sys: 7.28 s, total: 54.2 s\n",
+      "Wall time: 1min 2s\n"
      ]
     }
    ],
@@ -5792,7 +5590,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 23,
    "id": "23b5084e-181c-439e-ab9c-73c0aa9fc8fd",
    "metadata": {},
    "outputs": [
@@ -6242,11 +6040,12 @@
        "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
        "  stroke-width: 0.8px;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 169MB\n",
-       "Dimensions:   (time: 721, eta_rho: 82, xi_rho: 102)\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 170MB\n",
+       "Dimensions:   (time: 724, eta_rho: 82, xi_rho: 102)\n",
        "Coordinates:\n",
-       "    abs_time  (time) datetime64[ns] 6kB 2012-01-01 ... 2012-01-31\n",
+       "    abs_time  (time) datetime64[ns] 6kB 2011-12-31T23:00:00 ... 2012-01-31T02...\n",
        "  * time      (time) float64 6kB 4.383e+03 4.383e+03 ... 4.413e+03 4.413e+03\n",
+       "    rad_time  (time) float64 6kB 4.383e+03 4.383e+03 ... 4.413e+03 4.413e+03\n",
        "Dimensions without coordinates: eta_rho, xi_rho\n",
        "Data variables:\n",
        "    uwnd      (time, eta_rho, xi_rho) float32 24MB dask.array&lt;chunksize=(1, 82, 102), meta=np.ndarray&gt;\n",
@@ -6258,7 +6057,7 @@
        "    qair      (time, eta_rho, xi_rho) float32 24MB dask.array&lt;chunksize=(1, 82, 102), meta=np.ndarray&gt;\n",
        "Attributes:\n",
        "    title:                 ROMS surface forcing file created by ROMS-Tools\n",
-       "    roms_tools_version:    3.1.3.dev32+g91d580e67.d20250929\n",
+       "    roms_tools_version:    10000.dev364+g785fa77b7.d20260612\n",
        "    start_time:            2012-01-01 00:00:00\n",
        "    end_time:              2012-01-31 00:00:00\n",
        "    source:                ERA5\n",
@@ -6266,11 +6065,51 @@
        "    wind_dropoff:          False\n",
        "    use_coarse_grid:       False\n",
        "    model_reference_date:  2000-01-01 00:00:00\n",
-       "    type:                  physics</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-ff18f04c-f83e-4889-bb11-ef76beb930f8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ff18f04c-f83e-4889-bb11-ef76beb930f8' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 721</li><li><span>eta_rho</span>: 82</li><li><span>xi_rho</span>: 102</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-667f2eba-d924-4ce8-91dd-2e6ba20a61f0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-667f2eba-d924-4ce8-91dd-2e6ba20a61f0' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-01-01 ... 2012-01-31</div><input id='attrs-6997885a-857c-4edf-b6e9-976a73563526' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6997885a-857c-4edf-b6e9-976a73563526' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d56e7be3-847d-4014-b34c-f17e0a68ce04' class='xr-var-data-in' type='checkbox'><label for='data-d56e7be3-847d-4014-b34c-f17e0a68ce04' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>absolute time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-01-01T00:00:00.000000000&#x27;, &#x27;2012-01-01T01:00:00.000000000&#x27;,\n",
-       "       &#x27;2012-01-01T02:00:00.000000000&#x27;, ..., &#x27;2012-01-30T22:00:00.000000000&#x27;,\n",
-       "       &#x27;2012-01-30T23:00:00.000000000&#x27;, &#x27;2012-01-31T00:00:00.000000000&#x27;],\n",
-       "      shape=(721,), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.383e+03 4.383e+03 ... 4.413e+03</div><input id='attrs-95bc86cf-ddbb-4155-9221-5f9e022b1f4f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95bc86cf-ddbb-4155-9221-5f9e022b1f4f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09248e33-9ddf-4bb8-a8a6-46aca109b489' class='xr-var-data-in' type='checkbox'><label for='data-09248e33-9ddf-4bb8-a8a6-46aca109b489' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>relative time: days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([4383.      , 4383.041667, 4383.083333, ..., 4412.916667, 4412.958333,\n",
-       "       4413.      ], shape=(721,))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a3bef1f0-931b-43ae-862b-bd3ae38e61ed' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a3bef1f0-931b-43ae-862b-bd3ae38e61ed' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>uwnd</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-83cf64bc-d1f0-40e6-8c1f-a8498893c38d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-83cf64bc-d1f0-40e6-8c1f-a8498893c38d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ebf59024-2b58-4a09-92ee-d16a057da4b1' class='xr-var-data-in' type='checkbox'><label for='data-ebf59024-2b58-4a09-92ee-d16a057da4b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>10 meter wind in x-direction</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    type:                  physics</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-0958bafe-96b1-4b45-bbdf-e4b8944bafdf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0958bafe-96b1-4b45-bbdf-e4b8944bafdf' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 724</li><li><span>eta_rho</span>: 82</li><li><span>xi_rho</span>: 102</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-dd0ff855-cd93-4727-965f-fc9cd1af4c9e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dd0ff855-cd93-4727-965f-fc9cd1af4c9e' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2011-12-31T23:00:00 ... 2012-01-...</div><input id='attrs-b148e670-7068-49d2-835b-c20be2591e26' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b148e670-7068-49d2-835b-c20be2591e26' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d74f713a-1e6e-4efb-8fcb-251c56ec46a1' class='xr-var-data-in' type='checkbox'><label for='data-d74f713a-1e6e-4efb-8fcb-251c56ec46a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>absolute time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2011-12-31T23:00:00.000000000&#x27;, &#x27;2012-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-01-01T01:00:00.000000000&#x27;, ..., &#x27;2012-01-31T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-01-31T01:00:00.000000000&#x27;, &#x27;2012-01-31T02:00:00.000000000&#x27;],\n",
+       "      shape=(724,), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.383e+03 4.383e+03 ... 4.413e+03</div><input id='attrs-8d6d0fca-9899-4adf-84a3-afd71bedff8a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d6d0fca-9899-4adf-84a3-afd71bedff8a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c6eaa5d-3416-43a6-9684-ffd3a141a857' class='xr-var-data-in' type='checkbox'><label for='data-1c6eaa5d-3416-43a6-9684-ffd3a141a857' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>relative time: days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([4382.958333, 4383.      , 4383.041667, ..., 4413.      , 4413.041667,\n",
+       "       4413.083333], shape=(724,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rad_time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.383e+03 4.383e+03 ... 4.413e+03</div><input id='attrs-ad58d3d4-895f-4963-a173-fe0139d5cc3e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad58d3d4-895f-4963-a173-fe0139d5cc3e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-173006eb-ff72-45a2-98ad-7ea351c15da1' class='xr-var-data-in' type='checkbox'><label for='data-173006eb-ff72-45a2-98ad-7ea351c15da1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>relative time: days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([4382.9375    , 4382.97916667, 4383.02083333, 4383.0625    ,\n",
+       "       4383.10416667, 4383.14583333, 4383.1875    , 4383.22916667,\n",
+       "       4383.27083333, 4383.3125    , 4383.35416667, 4383.39583333,\n",
+       "       4383.4375    , 4383.47916667, 4383.52083333, 4383.5625    ,\n",
+       "       4383.60416667, 4383.64583333, 4383.6875    , 4383.72916667,\n",
+       "       4383.77083333, 4383.8125    , 4383.85416667, 4383.89583333,\n",
+       "       4383.9375    , 4383.97916667, 4384.02083333, 4384.0625    ,\n",
+       "       4384.10416667, 4384.14583333, 4384.1875    , 4384.22916667,\n",
+       "       4384.27083333, 4384.3125    , 4384.35416667, 4384.39583333,\n",
+       "       4384.4375    , 4384.47916667, 4384.52083333, 4384.5625    ,\n",
+       "       4384.60416667, 4384.64583333, 4384.6875    , 4384.72916667,\n",
+       "       4384.77083333, 4384.8125    , 4384.85416667, 4384.89583333,\n",
+       "       4384.9375    , 4384.97916667, 4385.02083333, 4385.0625    ,\n",
+       "       4385.10416667, 4385.14583333, 4385.1875    , 4385.22916667,\n",
+       "       4385.27083333, 4385.3125    , 4385.35416667, 4385.39583333,\n",
+       "       4385.4375    , 4385.47916667, 4385.52083333, 4385.5625    ,\n",
+       "       4385.60416667, 4385.64583333, 4385.6875    , 4385.72916667,\n",
+       "       4385.77083333, 4385.8125    , 4385.85416667, 4385.89583333,\n",
+       "       4385.9375    , 4385.97916667, 4386.02083333, 4386.0625    ,\n",
+       "       4386.10416667, 4386.14583333, 4386.1875    , 4386.22916667,\n",
+       "...\n",
+       "       4409.77083333, 4409.8125    , 4409.85416667, 4409.89583333,\n",
+       "       4409.9375    , 4409.97916667, 4410.02083333, 4410.0625    ,\n",
+       "       4410.10416667, 4410.14583333, 4410.1875    , 4410.22916667,\n",
+       "       4410.27083333, 4410.3125    , 4410.35416667, 4410.39583333,\n",
+       "       4410.4375    , 4410.47916667, 4410.52083333, 4410.5625    ,\n",
+       "       4410.60416667, 4410.64583333, 4410.6875    , 4410.72916667,\n",
+       "       4410.77083333, 4410.8125    , 4410.85416667, 4410.89583333,\n",
+       "       4410.9375    , 4410.97916667, 4411.02083333, 4411.0625    ,\n",
+       "       4411.10416667, 4411.14583333, 4411.1875    , 4411.22916667,\n",
+       "       4411.27083333, 4411.3125    , 4411.35416667, 4411.39583333,\n",
+       "       4411.4375    , 4411.47916667, 4411.52083333, 4411.5625    ,\n",
+       "       4411.60416667, 4411.64583333, 4411.6875    , 4411.72916667,\n",
+       "       4411.77083333, 4411.8125    , 4411.85416667, 4411.89583333,\n",
+       "       4411.9375    , 4411.97916667, 4412.02083333, 4412.0625    ,\n",
+       "       4412.10416667, 4412.14583333, 4412.1875    , 4412.22916667,\n",
+       "       4412.27083333, 4412.3125    , 4412.35416667, 4412.39583333,\n",
+       "       4412.4375    , 4412.47916667, 4412.52083333, 4412.5625    ,\n",
+       "       4412.60416667, 4412.64583333, 4412.6875    , 4412.72916667,\n",
+       "       4412.77083333, 4412.8125    , 4412.85416667, 4412.89583333,\n",
+       "       4412.9375    , 4412.97916667, 4413.02083333, 4413.0625    ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-80ba9c09-d484-4d0d-a638-a411f5d45f60' class='xr-section-summary-in' type='checkbox'  checked><label for='section-80ba9c09-d484-4d0d-a638-a411f5d45f60' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>uwnd</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-20ddaad1-213c-4cc4-95b9-a5ff98bbca26' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20ddaad1-213c-4cc4-95b9-a5ff98bbca26' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce8442fa-451f-4630-854e-7aa8d4019676' class='xr-var-data-in' type='checkbox'><label for='data-ce8442fa-451f-4630-854e-7aa8d4019676' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>10 meter wind in x-direction</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6285,18 +6124,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 23.00 MiB </td>\n",
+       "                        <td> 23.10 MiB </td>\n",
        "                        <td> 32.67 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (721, 82, 102) </td>\n",
+       "                        <td> (724, 82, 102) </td>\n",
        "                        <td> (1, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 721 chunks in 82 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 724 chunks in 82 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -6306,7 +6145,7 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"171\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "        <svg width=\"170\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
@@ -6328,14 +6167,14 @@
        "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"83\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"87\" />\n",
        "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"91\" />\n",
-       "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"95\" />\n",
+       "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"94\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"98\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"102\" />\n",
        "  <line x1=\"76\" y1=\"66\" x2=\"76\" y2=\"106\" />\n",
        "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,109.88539283193447 10.0,39.29715753781681\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,109.86320868889663 10.0,39.27497339477898\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"50\" y2=\"0\" style=\"stroke-width:2\" />\n",
@@ -6357,34 +6196,34 @@
        "  <line x1=\"69\" y1=\"59\" x2=\"109\" y2=\"59\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"113\" y2=\"63\" />\n",
        "  <line x1=\"76\" y1=\"66\" x2=\"117\" y2=\"66\" />\n",
-       "  <line x1=\"80\" y1=\"70\" x2=\"121\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"120\" y2=\"70\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"50\" y1=\"0\" x2=\"121\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"50\" y1=\"0\" x2=\"120\" y2=\"70\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"10.0,0.0 50.42031303256736,0.0 121.008548326685,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"10.0,0.0 50.39977044113951,0.0 120.98800573525716,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"80\" y1=\"70\" x2=\"121\" y2=\"70\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"80\" y1=\"109\" x2=\"121\" y2=\"109\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"120\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"109\" x2=\"120\" y2=\"109\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"121\" y1=\"70\" x2=\"121\" y2=\"109\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"70\" x2=\"120\" y2=\"109\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"80.58823529411765,70.58823529411765 121.008548326685,70.58823529411765 121.008548326685,109.88539283193447 80.58823529411765,109.88539283193447\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 120.98800573525716,70.58823529411765 120.98800573525716,109.86320868889663 80.58823529411765,109.86320868889663\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"100.798392\" y=\"129.885393\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
-       "  <text x=\"141.008548\" y=\"90.236814\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,141.008548,90.236814)\">82</text>\n",
-       "  <text x=\"35.294118\" y=\"94.591275\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,94.591275)\">721</text>\n",
+       "  <text x=\"100.7881205146874\" y=\"129.86320868889663\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
+       "  <text x=\"140.98800573525716\" y=\"90.22572199150714\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.98800573525716,90.22572199150714)\">82</text>\n",
+       "  <text x=\"35.294117647058826\" y=\"94.56909104183781\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294117647058826,94.56909104183781)\">724</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vwnd</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-75fd67cd-c419-48f9-ac3e-f8ae8a683b1a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75fd67cd-c419-48f9-ac3e-f8ae8a683b1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-583bc42b-63fd-4609-a76b-4a143e865ce5' class='xr-var-data-in' type='checkbox'><label for='data-583bc42b-63fd-4609-a76b-4a143e865ce5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>10 meter wind in y-direction</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vwnd</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-396061ce-2003-4b76-96a6-74b1238021b8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-396061ce-2003-4b76-96a6-74b1238021b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5bb60ac-8229-4c48-9318-70d225015656' class='xr-var-data-in' type='checkbox'><label for='data-f5bb60ac-8229-4c48-9318-70d225015656' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>10 meter wind in y-direction</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6399,18 +6238,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 23.00 MiB </td>\n",
+       "                        <td> 23.10 MiB </td>\n",
        "                        <td> 32.67 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (721, 82, 102) </td>\n",
+       "                        <td> (724, 82, 102) </td>\n",
        "                        <td> (1, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 721 chunks in 82 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 724 chunks in 82 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -6420,7 +6259,7 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"171\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "        <svg width=\"170\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
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-       "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"95\" />\n",
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        "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"98\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"102\" />\n",
        "  <line x1=\"76\" y1=\"66\" x2=\"76\" y2=\"106\" />\n",
        "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
        "\n",
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        "\n",
        "  <!-- Horizontal lines -->\n",
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@@ -6471,34 +6310,34 @@
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-       "  <line x1=\"80\" y1=\"70\" x2=\"121\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"120\" y2=\"70\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"50\" y1=\"0\" x2=\"121\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"50\" y1=\"0\" x2=\"120\" y2=\"70\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"80\" y1=\"70\" x2=\"120\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"109\" x2=\"120\" y2=\"109\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"121\" y1=\"70\" x2=\"121\" y2=\"109\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"70\" x2=\"120\" y2=\"109\" style=\"stroke-width:2\" />\n",
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-       "  <polygon points=\"80.58823529411765,70.58823529411765 121.008548326685,70.58823529411765 121.008548326685,109.88539283193447 80.58823529411765,109.88539283193447\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"100.798392\" y=\"129.885393\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
-       "  <text x=\"141.008548\" y=\"90.236814\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,141.008548,90.236814)\">82</text>\n",
-       "  <text x=\"35.294118\" y=\"94.591275\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,94.591275)\">721</text>\n",
+       "  <text x=\"100.7881205146874\" y=\"129.86320868889663\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
+       "  <text x=\"140.98800573525716\" y=\"90.22572199150714\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.98800573525716,90.22572199150714)\">82</text>\n",
+       "  <text x=\"35.294117647058826\" y=\"94.56909104183781\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294117647058826,94.56909104183781)\">724</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swrad</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-8c386aaa-ba20-479c-b0d1-37550353c19d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8c386aaa-ba20-479c-b0d1-37550353c19d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43a29aa1-72ad-494b-89a0-933451b0a7e3' class='xr-var-data-in' type='checkbox'><label for='data-43a29aa1-72ad-494b-89a0-933451b0a7e3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>downward short-wave (solar) radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swrad</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-0fb42ee8-dd44-4249-8ee1-6557cae0fa3d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0fb42ee8-dd44-4249-8ee1-6557cae0fa3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a5c6067-32bf-4eb1-b7b0-4a2135763c07' class='xr-var-data-in' type='checkbox'><label for='data-7a5c6067-32bf-4eb1-b7b0-4a2135763c07' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>downward short-wave (solar) radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6513,18 +6352,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 23.00 MiB </td>\n",
+       "                        <td> 23.10 MiB </td>\n",
        "                        <td> 32.67 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (721, 82, 102) </td>\n",
+       "                        <td> (724, 82, 102) </td>\n",
        "                        <td> (1, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 721 chunks in 284 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 724 chunks in 98 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -6534,7 +6373,7 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"171\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "        <svg width=\"170\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
@@ -6556,14 +6395,14 @@
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        "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"87\" />\n",
        "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"91\" />\n",
-       "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"95\" />\n",
+       "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"94\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"98\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"102\" />\n",
        "  <line x1=\"76\" y1=\"66\" x2=\"76\" y2=\"106\" />\n",
        "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
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        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"50\" y2=\"0\" style=\"stroke-width:2\" />\n",
@@ -6585,34 +6424,34 @@
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-       "  <line x1=\"80\" y1=\"70\" x2=\"121\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"120\" y2=\"70\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"50\" y1=\"0\" x2=\"121\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"50\" y1=\"0\" x2=\"120\" y2=\"70\" style=\"stroke-width:2\" />\n",
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        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"121\" y1=\"70\" x2=\"121\" y2=\"109\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"70\" x2=\"120\" y2=\"109\" style=\"stroke-width:2\" />\n",
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        "  <!-- Text -->\n",
-       "  <text x=\"100.798392\" y=\"129.885393\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
-       "  <text x=\"141.008548\" y=\"90.236814\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,141.008548,90.236814)\">82</text>\n",
-       "  <text x=\"35.294118\" y=\"94.591275\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,94.591275)\">721</text>\n",
+       "  <text x=\"100.7881205146874\" y=\"129.86320868889663\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
+       "  <text x=\"140.98800573525716\" y=\"90.22572199150714\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.98800573525716,90.22572199150714)\">82</text>\n",
+       "  <text x=\"35.294117647058826\" y=\"94.56909104183781\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294117647058826,94.56909104183781)\">724</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lwrad</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-0afd2a92-b9b0-4a1d-9cc0-fa9633db2d0a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0afd2a92-b9b0-4a1d-9cc0-fa9633db2d0a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41bb4f67-f548-4e3a-8fe5-31834b8daf45' class='xr-var-data-in' type='checkbox'><label for='data-41bb4f67-f548-4e3a-8fe5-31834b8daf45' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>downward long-wave (thermal) radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lwrad</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-bb1289ad-7f2e-4193-8891-c65ca3c31cbf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb1289ad-7f2e-4193-8891-c65ca3c31cbf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a67aea6-5961-41a4-99d3-3eeb244d7dde' class='xr-var-data-in' type='checkbox'><label for='data-2a67aea6-5961-41a4-99d3-3eeb244d7dde' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>downward long-wave (thermal) radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6627,18 +6466,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 23.00 MiB </td>\n",
+       "                        <td> 23.10 MiB </td>\n",
        "                        <td> 32.67 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (721, 82, 102) </td>\n",
+       "                        <td> (724, 82, 102) </td>\n",
        "                        <td> (1, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 721 chunks in 51 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 724 chunks in 98 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -6648,7 +6487,7 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
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        "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"121\" y1=\"70\" x2=\"121\" y2=\"109\" style=\"stroke-width:2\" />\n",
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-       "  <text x=\"35.294118\" y=\"94.591275\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,94.591275)\">721</text>\n",
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        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Tair</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-5d614447-49b4-4878-be4c-c1f5f857d4f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5d614447-49b4-4878-be4c-c1f5f857d4f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7404996-2086-439d-8498-e920c77d4c96' class='xr-var-data-in' type='checkbox'><label for='data-d7404996-2086-439d-8498-e920c77d4c96' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>air temperature at 2m</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Tair</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-e067a168-1fa1-4165-9bf5-5c6f03e6b9da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e067a168-1fa1-4165-9bf5-5c6f03e6b9da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-645c608c-54bb-4619-9be3-66a4e6e4df81' class='xr-var-data-in' type='checkbox'><label for='data-645c608c-54bb-4619-9be3-66a4e6e4df81' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>air temperature at 2m</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6741,18 +6580,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 23.00 MiB </td>\n",
+       "                        <td> 23.10 MiB </td>\n",
        "                        <td> 32.67 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (721, 82, 102) </td>\n",
+       "                        <td> (724, 82, 102) </td>\n",
        "                        <td> (1, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 721 chunks in 51 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 724 chunks in 51 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -6762,7 +6601,7 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"171\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "        <svg width=\"170\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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@@ -6784,14 +6623,14 @@
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        "\n",
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        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"50\" y2=\"0\" style=\"stroke-width:2\" />\n",
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        "\n",
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        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rain</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-b2b27c39-3353-4963-ab74-8b6b967b1ad0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b2b27c39-3353-4963-ab74-8b6b967b1ad0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4361834d-0ba7-4832-ac5f-5f406832deaf' class='xr-var-data-in' type='checkbox'><label for='data-4361834d-0ba7-4832-ac5f-5f406832deaf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>total precipitation</dd><dt><span>units :</span></dt><dd>cm/day</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rain</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-c760e8b9-3291-4946-8254-78a9221c4e13' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c760e8b9-3291-4946-8254-78a9221c4e13' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fdec8ef3-2cb9-4189-a89a-620529b2d137' class='xr-var-data-in' type='checkbox'><label for='data-fdec8ef3-2cb9-4189-a89a-620529b2d137' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>total precipitation</dd><dt><span>units :</span></dt><dd>cm/day</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6855,18 +6694,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 23.00 MiB </td>\n",
+       "                        <td> 23.10 MiB </td>\n",
        "                        <td> 32.67 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (721, 82, 102) </td>\n",
+       "                        <td> (724, 82, 102) </td>\n",
        "                        <td> (1, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 721 chunks in 51 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 724 chunks in 51 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -6876,7 +6715,7 @@
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        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"171\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "        <svg width=\"170\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qair</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-026473f4-f120-4954-b865-385f2405ca25' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-026473f4-f120-4954-b865-385f2405ca25' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4322a6bc-7350-488f-814e-95055d7a2aa5' class='xr-var-data-in' type='checkbox'><label for='data-4322a6bc-7350-488f-814e-95055d7a2aa5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>absolute humidity at 2m</dd><dt><span>units :</span></dt><dd>kg/kg</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qair</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 82, 102), meta=np.ndarray&gt;</div><input id='attrs-a4b2b40c-f790-4f0b-8f46-af22f019dd35' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a4b2b40c-f790-4f0b-8f46-af22f019dd35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27f01611-a37c-4b1f-82a0-ffdc9fa65974' class='xr-var-data-in' type='checkbox'><label for='data-27f01611-a37c-4b1f-82a0-ffdc9fa65974' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>absolute humidity at 2m</dd><dt><span>units :</span></dt><dd>kg/kg</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6969,18 +6808,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 23.00 MiB </td>\n",
+       "                        <td> 23.10 MiB </td>\n",
        "                        <td> 32.67 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (721, 82, 102) </td>\n",
+       "                        <td> (724, 82, 102) </td>\n",
        "                        <td> (1, 82, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 721 chunks in 75 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 724 chunks in 75 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -6990,7 +6829,7 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"171\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "        <svg width=\"170\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
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@@ -7012,14 +6851,14 @@
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        "\n",
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        "\n",
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@@ -7041,50 +6880,51 @@
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        "\n",
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        "\n",
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-       "  <text x=\"35.294118\" y=\"94.591275\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,94.591275)\">721</text>\n",
+       "  <text x=\"100.7881205146874\" y=\"129.86320868889663\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
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+       "  <text x=\"35.294117647058826\" y=\"94.56909104183781\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294117647058826,94.56909104183781)\">724</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-8e08310a-6a3b-46ce-858d-00e97614a947' class='xr-section-summary-in' type='checkbox'  ><label for='section-8e08310a-6a3b-46ce-858d-00e97614a947' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-799ff6d6-02c4-46e7-bfef-b6ff5ac6cb9d' class='xr-index-data-in' type='checkbox'/><label for='index-799ff6d6-02c4-46e7-bfef-b6ff5ac6cb9d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([           4383.0, 4383.041666666667, 4383.083333333333,\n",
-       "                4383.125, 4383.166666666667, 4383.208333333333,\n",
-       "                 4383.25, 4383.291666666667, 4383.333333333333,\n",
-       "                4383.375,\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-81ab29ac-3059-4582-a3fb-ac9b69a99927' class='xr-section-summary-in' type='checkbox'  ><label for='section-81ab29ac-3059-4582-a3fb-ac9b69a99927' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-03060aad-5a97-4dc9-a359-7441c1bec59e' class='xr-index-data-in' type='checkbox'/><label for='index-03060aad-5a97-4dc9-a359-7441c1bec59e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([4382.958333333333,            4383.0, 4383.041666666667,\n",
+       "       4383.083333333333,          4383.125, 4383.166666666667,\n",
+       "       4383.208333333333,           4383.25, 4383.291666666667,\n",
+       "       4383.333333333333,\n",
        "       ...\n",
-       "                4412.625, 4412.666666666667, 4412.708333333333,\n",
-       "                 4412.75, 4412.791666666667, 4412.833333333333,\n",
-       "                4412.875, 4412.916666666667, 4412.958333333333,\n",
-       "                  4413.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;, length=721))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-40658dca-5e47-47ce-a6e2-afc3a99ab285' class='xr-section-summary-in' type='checkbox'  ><label for='section-40658dca-5e47-47ce-a6e2-afc3a99ab285' class='xr-section-summary' >Attributes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>ROMS surface forcing file created by ROMS-Tools</dd><dt><span>roms_tools_version :</span></dt><dd>3.1.3.dev32+g91d580e67.d20250929</dd><dt><span>start_time :</span></dt><dd>2012-01-01 00:00:00</dd><dt><span>end_time :</span></dt><dd>2012-01-31 00:00:00</dd><dt><span>source :</span></dt><dd>ERA5</dd><dt><span>correct_radiation :</span></dt><dd>True</dd><dt><span>wind_dropoff :</span></dt><dd>False</dd><dt><span>use_coarse_grid :</span></dt><dd>False</dd><dt><span>model_reference_date :</span></dt><dd>2000-01-01 00:00:00</dd><dt><span>type :</span></dt><dd>physics</dd></dl></div></li></ul></div></div>"
+       "       4412.708333333333,           4412.75, 4412.791666666667,\n",
+       "       4412.833333333333,          4412.875, 4412.916666666667,\n",
+       "       4412.958333333333,            4413.0, 4413.041666666667,\n",
+       "       4413.083333333333],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;, length=724))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-87ed50d9-2c71-44bc-b51d-b4293d9b44b8' class='xr-section-summary-in' type='checkbox'  ><label for='section-87ed50d9-2c71-44bc-b51d-b4293d9b44b8' class='xr-section-summary' >Attributes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>ROMS surface forcing file created by ROMS-Tools</dd><dt><span>roms_tools_version :</span></dt><dd>10000.dev364+g785fa77b7.d20260612</dd><dt><span>start_time :</span></dt><dd>2012-01-01 00:00:00</dd><dt><span>end_time :</span></dt><dd>2012-01-31 00:00:00</dd><dt><span>source :</span></dt><dd>ERA5</dd><dt><span>correct_radiation :</span></dt><dd>True</dd><dt><span>wind_dropoff :</span></dt><dd>False</dd><dt><span>use_coarse_grid :</span></dt><dd>False</dd><dt><span>model_reference_date :</span></dt><dd>2000-01-01 00:00:00</dd><dt><span>type :</span></dt><dd>physics</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.Dataset> Size: 169MB\n",
-       "Dimensions:   (time: 721, eta_rho: 82, xi_rho: 102)\n",
+       "<xarray.Dataset> Size: 170MB\n",
+       "Dimensions:   (time: 724, eta_rho: 82, xi_rho: 102)\n",
        "Coordinates:\n",
-       "    abs_time  (time) datetime64[ns] 6kB 2012-01-01 ... 2012-01-31\n",
+       "    abs_time  (time) datetime64[ns] 6kB 2011-12-31T23:00:00 ... 2012-01-31T02...\n",
        "  * time      (time) float64 6kB 4.383e+03 4.383e+03 ... 4.413e+03 4.413e+03\n",
+       "    rad_time  (time) float64 6kB 4.383e+03 4.383e+03 ... 4.413e+03 4.413e+03\n",
        "Dimensions without coordinates: eta_rho, xi_rho\n",
        "Data variables:\n",
        "    uwnd      (time, eta_rho, xi_rho) float32 24MB dask.array<chunksize=(1, 82, 102), meta=np.ndarray>\n",
@@ -7096,7 +6936,7 @@
        "    qair      (time, eta_rho, xi_rho) float32 24MB dask.array<chunksize=(1, 82, 102), meta=np.ndarray>\n",
        "Attributes:\n",
        "    title:                 ROMS surface forcing file created by ROMS-Tools\n",
-       "    roms_tools_version:    3.1.3.dev32+g91d580e67.d20250929\n",
+       "    roms_tools_version:    10000.dev364+g785fa77b7.d20260612\n",
        "    start_time:            2012-01-01 00:00:00\n",
        "    end_time:              2012-01-31 00:00:00\n",
        "    source:                ERA5\n",
@@ -7107,7 +6947,7 @@
        "    type:                  physics"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7126,7 +6966,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 24,
    "id": "4523d0f1-fd13-410d-ac70-2e278f9e97e9",
    "metadata": {
     "tags": []
@@ -7136,12 +6976,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[########################################] | 100% Completed | 1.25 sms\n"
+      "[########################################] | 100% Completed | 519.24 ms\n"
      ]
     },
     {
      "data": {
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",
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yG9ny3t5exGIx8QCpZnuvbLPUn6v1NqFQCJqmYWZmpuJ91SuKoiz5NvR5CmdZTOjMYvct+5zT9rXcppL9VXLcxSxbzP5pmaqqIhLlwIEDFe9X9ve5bOvGNuX+Xuw+//ObfvFevDC3raLp8/ZVul/bH0HWNMXfhq7NbbPwhXl/p+Ml6wrbaMlpsf+F+6CF1vCx/H7mrwMyQ8fF+uLy/L+zp8ZybV2wnP6NT+buh3PfMfd3PJ4ovs+tzy2n98lMKv8+9ypsnTEz+ff5zxb7FUhZ1oJ95f6K55enbEu8mlUdrWrOoKVxZj0ShAqfsrTGllb5I3NFU5fG1kK+8pWvYHh4+Jz2ccUVV+C3v/1tzdrEnB16EGezWeGpZJYPMgLIQzw2NsbdvozQ5AINMMjQZZYPMrQKg35meaF7++nTp7nbq+CHP/xhVf1Gk9DBYFBMQjOVs1sP4/roAHcdUxENYWwVZpDLzc6Um01auIz+pRsUzTyXWy/bZjH/VvvZavddzX6XehvZ34aRmz1qb2+vaj+13qaWny33vto2VrLPxeyPrvXBwUFYVvnwicW2pdJ2nss2tf7cYvdxrtvMh+4vYpZ3dvac97VSqMYjWOv9UV+3tbVh27ZtVe9vMZ9bTo9uNd7wc922mmXkGZiamlr09pW8r9Vnlyoq4myRIGf7/JZ1q/OROXPLxCvvk6G/SiJ3bBuWqovJhaamKEZGhue2KR5/3n4K25mZBZ/Jr8/mPMG0dP424pVKnNk22DBTydwGxWW5f7OpzNx+8g3JvQesbDbXrnnHEs3KWoVdze2r8LLmtbXwmZJj5vqnuLz4yh2o8H5uvQ0r/7c8qLB+0MhzucTPII2u0wbybjWEsbVq1SqMjIzgKU95Cq688sqq9/P4xz++pu1iGIZhvPOcYJiVwptf8beV52yFWkVKxaOP7cGF55+3qOMoianyh5mRRwtlj/yp7PLUofK5XJN75dEr00fLH2f6uDzcOj6aKH/86cpztmLZnHn177ND+HlqEuuMoHQfDNPQxpZp5n5ElA/BMAzDMAzTiFDkwtbNm91uxopDeGJoPFljb7wXURXybi3xMTDPfdgAsLHFMAzDMAyzkpB4sGxDruxm5oUdDhzZh23btxeXO0WMaZpkmKjKJ68Vf/k26KHyXqGO8zZhct/RsuuMsL/s8vbNfsROlfdumRIvlS0JjRdIUmULRkdYywlG0D/NxtKKRzD1BxtbDMMwDMMwDcLk1KTbTVhx6HmLNNsAnq1ly9lqIBrCPOcwQoZhGIZhGCAYzIl9MYtHzxsH2UaKfWNqBhtbDMMwDMMwDcLOnTtrrgTaKJ6tDPcbUwVsbDEMwzAMwzQI9917bzHih6nMs2U2gGeL8tSW49VIsLHFMAzDMAzTIFCtrVRKLoPOnAl7tphzgQUyGIZhGIZhVhC27iu/XJUP6zL5Yr+dvb2wVa343iCtbxmKZE5elc/VK7q81tdKpdCrjZCzxQIZtYeNLYZhGIZhmAYhFAzBzGYBf3lZ9SXDwQjTA+WNRz1YfnlunV7R8kgwgtR0GhWRl4T36zmpe0sBfAbXbGUqg40thmEYhmGYBmFycoL8TwiFw243ZcVgFNQIG0AgYzlyqjQ0Fg2Vs6U6uL0ZhmEYhmEaIWcrneacrUpopDpbXh3H33zzzVi7di2CwSDWr1+PW2+9dcWoajaUZ0vXG+LrMgzDMAzDlKWtvQPNLa3cO9UYW5yz5UpR49tvvx2f+MQncMcdd2D79u245557cO2116K5uRmvfe1r4XUawvpgzxbDMAzDMExuTHTy+HGsXb+eu6PSosYrxJNSb/zud7/DX/3VX+FZz3qWeD84OIivfOUruOuuu7ASUBvJ2NK0RosSZRiGYRiGmYNSKsbHx7hLKqCRPFtK3jhYypdSYZsuv/xy/OxnP8PevXvF+wcffBC/+c1v8IxnPAMrgYbwbFmWJf5lY4thGIZhmBWPTOJdPfukMqVU6Ocqz54fV1W8ToKilZ/7Vw15O42AXtFy0bR0+bbZZvnlekBHejYDv60XixrL1A6Zypmeni557/f7xWshN910k/jsli1bxFienCjvete7cPXVV6+IbmfPFsMwDMMwTIOgKAp27NrldjNWFI3k2SrU2VrqFzEwMCDyrgqv2267DeX4+te/jv/8z//El7/8Zdx3330id+v973+/+HcloDeCV6ugVsKeLYZhGIZhGp177voDLr3screbsWLQ88WdM5yzVVOOHTuGaDRafF/Oq0XccMMNwrv1ohe9SLzfuXMnjhw5Ioyza665Bl5Hb5R8LYKl3xmGYRiGYZhKaCTp9+WssxWNRkuMLRnxePyMMTw5UAppQl6noYwt9mwxDMMwDNPo9K1aJaJ+KKSQOTuNZGx5kWc/+9kiR2v16tVC+v3+++/HBz/4Qfz93/89VgJsbDEMwzAMwzQQ4UhETEZz/dHF0UjS7znP1tIa4VqFn//IRz4iihq/6lWvwvDwMPr6+vCKV7wCb3vb27ASqHtjq+BipNkbDiNkGIZxnxveeGPZ5e977+3L3haGWYnc9uGPSdfd9Ibryy5X542fT586Cb9hwBeNwrRtGLKxtZUtu9hs6oQ2NVR2nZ1Nl99XNoOV7tkiNUJm+WlqasKHP/xh8VqJ1L2xxTW2GIZhGIZh5jAMH9JpiVHkAjLpdScUSWKR5pP7TVRfeRFu3dQdZenDGaUokOFr8tV1+OVy5mw1CmxsMQzDMAzDNBBrBgc52qcKNULCakBjgTk31EbxbHEIIcMwDMMwDBCfncX42Bh3RYVhhETGXhkKeIx3aJicLVYiZBiGYRiGyU1ET09Po7unh7ujQmOr3kUy5hcdXrJjoH7DMBvas8XGFsMwDMMwDOAPBLgbKmC+8ZG16tvYYlw2tj7xiU9g165dxSJkl112GX74wx8W1yeTSVx33XVob29HJBLBVVddhdOnT5fs47/+67+wadMmbN68Gd///veLyw8fPiwSDru6ujAzM1OyzXnnnYe3v/3tVX1BDiNkGIZhGIaZg8Zo69av5y6pwNhSG8SzRaqV2hK/1MZybFUWRtjf34/3vOc92LhxoyiGd8cdd+Cv/uqvRHExKjL2ute9Dj/4wQ/wjW98A83NzXj1q1+N5z3vefjtb38rtk+lUsIY+8IXviC2p2JkT3va0+Dz+YrHIEPr/e9/P97xjnec0xcrbM9hhAzDNBpX/M2rpOt+++WPl13+59e8WrrNT+74aMVtePlr/1m6jqSmK5GsJt7zgfKSv295+Quk26ipWem6W//jB2WX33z106XbQC3/yLQ1Q7rJv3zxu/L9McxSIPl9zR/f0tjo3rvvxqWPe9yynQO9fz0yR/dWtE24pw2zQ+NSlcByqA5qhEag/G9YleyLMNO5CCldVZG2LKhBDb6A/DfPMOdkbFEF5/lQNWfydv3+978XhtjnPvc5fPnLX8aTn/xksZ6Mqq1bt4r1j3vc44SxReF85KkSB9d1sWy+sfWa17xGVIUmo4y8XOcKhxEyDMMwDMPMQWOxwmQ0s/i8rXRe/r2e4ZwtD+VskRHz1a9+FbOzsyKc8N5770Umk8FTn/rU4me2bNmC1atX48477xTvKfTw2muvRW9vr6j+/MpXvlIUKpvPi1/8YmzYsAHvfOc7z+V7lbSTYDVChmEYhmGYHH2rVnFXVCGSkWUjlVlqY+uhhx4Ssb5+vx//9E//hO985zvYtm0bhoaGhIeqpaWl5PPd3d1iXYFbbrkFo6OjGBsbwxvf+MYz9k95WxSq+OlPfxoHDhxANVCIYgH2bDEMwzAMw5QSCga5SyrAyCcazfds0XiTIrTqiaXO19KWoWjyipd+J2GLBx54AFNTU/jmN7+Ja665Br/85S8r2gflcznx9Kc/HVdeeSVuvvlmEZZYKb/73e+Kf5NYxxOe8ARMTExUvB+GYRiGYZh6ZP/+fejo7BST3I0OmU+mpkG1LKQNAynDB0tV0TozjaGubmR1DWsG1wJjY5jYuAkH/T5snZjA8PAwjh075nbzmXoztsh7RWF+xIUXXoi7774b//qv/4oXvvCFSKfTmJycLPFukRphTxV1HMi7ReGJN9xwQ8XbXnHFFbjrrrvE3+RV+9WvfoV169ZVvB+GYRiGYZh6RNN0Ef1D+fN1YzApCrKKClsB4kYAWVVFNJXAjD+ImC8AM2Bi1ehp7O9bLRRDmmMzCKWSONnTAc000TM6jKTPj4xhQM9mxX6jM9Ni3eiJExibnUXoscewrjmK1tZW8aLUmHpiOTxPGhqLc/6FUYIluVDJ8DIMAz/72c+E5DuxZ88eHD16VBhNlXLJJZcIJcObbrrpnNtHcM4WwzD1xoUv+Keyy2dSuUFCOXZd9Y+SbXL5reV4+ktfI10X9Zd/jDjVokmb5dcZmvyR9KbrXlZ2uZKQRy3Y6YR03VuvulKyTVK6jeKT1CZS5BH5b/3755U/jjYnDFUJ7/rMV6vajmEW/uq27dgBRVXFcktSZFZ1uk718op8ii7fxmkdQbeNDBTQ3SgIG6OWhhktgKBlglp6Wgsio6hYnY3hZLAF00YAumViU2wEj0a7odk2oplJBNMpzPr8Yh0RzKTgz2aA6YT4phtOHi35xuuPHSn+HUiTDMYcoUTuPqLkx5NZm4VFmCU0tt70pjfhGc94hhC9IIl2CvH7xS9+gR//+MciNPBlL3sZXv/616OtrU2IYZCyIBlapERYDaR2SJLy5zLrwjlbDMMwjBOHhicR9OnoaYlwRzENw8T4OOzWVpGHXwsof4nSmTKmhXg6K/5tCfowHk9hJpkR4Yp9to0/TpjIWDZ6gioCmoL9MybS2SC2GWkMWTpmLQVB1cYGPYO4rUHRdRiWDT8s9CENHTZ8uoLB7AyQjeUObujYlRjL/R0EMpaF5ky+ZqtPKfi9kAnSxMiZBh+ZjVZe4n0hVl4u3tDz/pigDn/Uj3uf/ee48L9/gnqD1QhrT0VWDMWmvuQlL8GpU6eEcUUFjsnQ+vM//3Ox/kMf+pDwIJFni7xdlHv18Y+Xr+myGKj4MdXiIrGMamE1QoZhGOZsg8SsybPVTGORTCaRiMeLxhalgpCqNP0egsGgGPNZ8WlEoxHYlo3jp4aQzWSxdfMG7D90BDMjJxAK+LF7wxrc+TDVz1KwpqcDgUQaJ6fiMDQVEX/O+xXy6fDrGtRZYHNUg6GSul9OFK0roGI2LzKxXs2UtHHQyCJhzXmbDeHzclcgw8lrzzDnbGxRHS0nAoEAPvaxj4lXpQwODpaoCBb41Kc+JV7VwkWNGYZhGCciAR80tepKKAzjKY6fPIlsNov+vj4cO3ECM7FZ+H0+9K3dgPvvu1c4eVavWYP47CwOHzqIcCSCiy68CEcOHxYGWKSpCWvWrEEykUBAVaBrGgy/gdWr+uAzDLGvHVs2QV8VLR7z8edtLf5tWVPoiMyF3AaMOdXDzJiC0ApNEdPz4cL1HkZI/rslz9my0VCs0Et+8bBni2EYhnEiGvLnoowYpg4gQ4vSL8hr1Nbaiva2NmEkKT4fLn3cZUX1wd6+PjHJTRFJFDq2ecuWkv2sXbcOejo2b0Ldj0ZGZ88WUyV1b2yxZ4thGIZxYnhqFqZlY21XaZ1IhlmJDK5eXfy7OTrnfUovkHgfHx/H6MjIGUYWc5aixmWisOoJVVGE8b3Ux2gk6j5ugj1bDMMwzNke/OXC2BmmnqFSPplMqfIeI0dXGyOMkKk97NliGIZZodz79U+WXb7qGS+VbqPmQ2EWQupgMjTJNoQhyXUqJJNXIv3u1+Tzfz6JZLSThLpqyMOe7LwkNNHeHBGeLaha7lVDbAdZeCkO27zlH1604iThX/DEC2BaFtb1duDE6CSmZhNC2W3bml7c+ehhHBoaxZU7NiAU8GHPsdNim/M3DODo6DRGp2bgN3Rcsm09fv/Ifuiaih//4SG3v5Knec8H/7Xs8lde/89n/HZC0VYkMpa0sHHEL1cqVPxNZZerTXIPsRouv40RnpRukwmXL8dgZuQlLmQoDslIdl51cCFWXjzHZ+TuDYpfgz9aXemGlcCy1NlS0FDUvbHFni2GYRjGiUzWQjKTFUIZbkLeNRr0ZrOmME7obzJ046k0bMtCwO8XnohEMgXLttHZ2ozh8Ulksu4ptC2GlkhQ5AWRodTb1ozu1qj4mzyKl21fh/7OFgz2dIjP0roC29aWDswv27FR/MvGVm2g2qjk3WIqy9kiWXmGqYSGMbY0rdHqVTMM06jYIsxFoT9yC+bNWtMipUzYnCyMTny2MLgo7GfefkVebP49FUi1yGNkA6ZOifcazGwWNmxoqib+tUwTaVuFQSFM6bTYXgzEDQPx2YRoBw0A6bMpMjJsW0hTx2ZnhVFBg/RwKISJqSlxnDByktEztC1sdLREMTUzi1QmAyOTQGdzGMdHJ0UTm8MBoTo4MhWDlU6ivz2K0Zk4ToxOC2OrIxrCnmPDwpBpbwoJj8rREdrWxsa+DgzPTGF8Ji7acN76fvz+T4dhWTa6OlrREg7h0SMnRVt2rO3HyOQ0Tk9MQ9UNXL57C+784x6YpoWutmbxevjAcdHebetWY2I6hlOj46IfrjhvGx7ce1A8u9pbouhub8X+oyegKirW9HYhmU5hYmpGfJaMrVQ6IwwzLzPfgCJlu/loioqAr3xxXGbpObR/H9o7OrmrKwkjrHPp92Wps6U0lmurYQQy6MHEMAzTCKTGhqAFwkiePiLeB3vXIjMzgezMhCgQGl23AzMHHxahdL5oG4ymVowdOiQ+G+1fj3RsCsnJURHO1rH5PJza+xBMMytCjiLtXRg5tEd8tq1/LSwzjcnRXOjX+h0X4Pi+PyGbSaOjrRWdPatwaN+fxLre/jXCKzM2PISArmLrjl04euggUqkkwpEm9PUP4MjBfcJI7Fu1Shh4Q6eHhLG3efMWnB4ZFR6dUDAgPDzDI7kCpj0RQ9zfJ2diOfW1aBOS6QySwlAjQw8iRLDwaKfPUAibYufU2oI+A01BP2yq+QMI4yy3XBeGwWB3KxQoCBg6+tqb0dMWLSZ3X7CxXxhBiu4T2zx+16biOWhtCmHTQA/sfCjjZbs2l5yjKy/YXvybjKoNq/uK7y/clvPgFDh/y4a5N3YIPe2txbere7uw0tl7fBg9bc1uN4NhFllny9uTG4z3qHtjiz1bDMM0GoGO3MDdiOwqLtNDTUD36mLOVnT9zpJtWrZcUPzbF2lGpGdO0axvy+6Sz/Zvv7D4d2vAQEffQPH9mi25/Ub9OQ/G1t1znyU6e/oQ8eUmvzZsmavNQ+zafV7pZ7vmDIn1g2tK1m3dlDNA1PhErh3RudySVV3tuXWJKfHv2p7c+wIRMq5ScfF3WySI1nCuJhAZTB3RcMlnffqcJ0bXSz0wPj33CLV5Mo9ZoWzZUXofYHDW3FWn/FaGKUfdu3vYs8UwDMM4MRVPYe/JnKeMWX4u2Dhn2DPLy+mTJ1mJs8KixmadK5cWBDKW+tVIsGeLYRimzjAcVP1k9U2cHn5CqU8C5TeV3cZhQCKbGZapFBKGESx/fJ+DdLUlVytT1LnHn53VkVbTQDAKxWEbSCSfFScpaMk6y0FF0VERcZ6K4mJUCqtVKnzrVVeWXW7n86DLoUhyo+3EDCBRk1R0A4dPDWPHYO+ZK+edI+bcyZS5FCenpkS+oqyvnfKTWiPlc72UTMKhFbmw4pVd1JjDCJnKqPs7GXu2GIZhGCdI0rkplAslZJafiZl4UYmRWV6CoTCy2Sx03/IMB7XOVWWX+9Pl5d0JOy+9XtFxjPLfR0vmBHXKYUmk5AvHD+TVSm2DpN/LT/7UAyyQUXvq3tjinC2GYRjGiYBhoLOlfP0fZumhHDpSVFyoVMgsPRu2bBP/OjiVmTzs2WKqhXO2GIZhmIYmkc5gz9Eht5vRsOxe1yck+Znl5+ihAxgfHeGuXwSNIv1OoebL8Wok6v7uxp4thmEYxglKxZDVGWOWngcPnkQs6ZB7xywZhuETJRmYxRtbXNSYqZS6DyPknC2GYRjGCZ+hY7CngzvJJTRNRTYrF95glo6m5ubipDTjTKPU2VI0BUr+uy7ZMRT2bNWlsaVxLDjDMAwjYdYhcZ5ZWrpbmoTByyw/gWCQx0eLRGuQMEKm9tT93a0wY6NyPDizSG7+u2dJ1936Hz+ouB/f8tzLyi634jPSbezEbPnl2UzFx1ccrn3FP6eodHwyjvawH0FDg0oFcCW86/v3oJa86bqXlV8hkYombN1fdvl7PvDhWjVrRdMV9lUsu+4UQ2846MJXE3svk5J3GsRktPKPK8MXkm5jm4v7vZjI4uREDAP9fYDDJlJZeCeJecn+tEwKZqi17DpbK3995zaUPLYdzoNMFl5Jlb/PiHWS7+R4tvN1iMpha6UFoQtYug8BywdbU2EFFnxviRz5m//pJdLjvPuT/+7Uwobms//2flz7mjeULEskkzhx9AgGNm8vu03SwemlKBJBk8hqtKG8/LvWWX5Sw9e5GuapA2XXyXRCA73dSI+Vr4+XHJsqf/yEr2I1QjO/3B/MXZ+mpsJYUPy8nlA1BeoSe7bUBvNs1b2xxZ4thlkcTX4DPof6TAxTr9CDX1YvjFl6RienYegaQguNLWbJ8fn8nLO1SPT88zFbhRQ909jUvbHFni2GWRx7Rmewu7cF2hLPaDGMF3OGLtmx0e1mNPQglgewLvW9YWBwwyaXjr6yMBoljFBTHSNiaoJS5324gLqfxmaBDIZZfGgXO7aYRuWBPYfcbkLD0t3egp6OFreb0ZCQUMHI0Cm3m7EiKExEshohUykNY2yxQAbDOLOls0lUjmeYRhxwxlkgwzWSqQxGJ6bda0CDMzlePu+JKe/ZogLc9QwpEQpFwqV8qY011mgYY4sFMhjGmdF4uuHkWBmmQGtThDvDJbKmialYnPvfJQyfXDSCKVfUuL6NLab2cM4WwywkW14tybYsvPWFT65IPZCwYpPll8+UX+6oOmg5SEOpEmUon0zLqVRZ7PRMEhs7cgNOxS/f5q0vfa50nS1RhpMpkeVWln9w2Q4Kb7DK37puvOGfpZvc/r73o974u+teX3Z5MiuPh59Nl7+Gkln5ACKgqxWvc7LbZUpUTkMYU/KVTC2IAMpfK7bhX7Ta3trV/YDmA0yn605ynHSy8t8lrZK0z/TNqYSeAbWxXBskyn1inUTZU5HsS6xz+v3JjuOgRgjJPYDapts+YCYDa6E6o2x/Dm2TqZve9rHPydvWQHzhIx8ou3xw23nSyTaZgmnDCmTUeX8INUIHBdqaHAONNbFb98YWhxEyzOJoNClWhpnPfY/uw+Mv3Mmd4gKRcBhbNq7jvncRyzSh6ZUNCcfi8sk/X6T8xFsk2iXdRq/CwPc5TGbIyMyWl6U3k+nyy9O5dgVCuUlIUwF8TfKSEwyzEA4jZBhGcPlgO/cE07jwXINrpNMZ3H3/Q+41gGH59wo8W7I6gfUCKREux6uRqPtvy9LvDLM4/nB0nLuKaVg2DKxyuwkNi65rIm+LcQ/FKQSUERh5YyvDdbaYCqn7XxeHETLM4uAHCNPIZLKVhzAxtYEErHq6Org7mRUhkFHvaoRM7WkYY4vVCBnGmc6IXEyAYeqdI6dOu92EhibKapCuMj3B8u+LNbbqfWKyIJCx1K9Gou6NLQ4jZJjF0R2RKxAyTN1T32kYnuexfQfdbkJDk81IFHCZMmqE9W1sMbWnrtUIbdsWL4I9W8x83nrVldIOsSQy7k7yztbsTPltkg6S8LPyIp6KJlFYkkg4i210o6LlhBppybXFtvHoyZO4srMztyK8QIJ5EfLuYp3ur1wSWpYo67SNUvk2b7jhjdJ1H3jfe1EpN7zxxrLL3/fe21ENL3nhVVjV27PoPiXe8/4PVnycl776DRXLOzvVn5QVwtYdNsqPWc7A57CN4dSIrCTfx2lQtKDkwKU7N0vLEJwVh5IMdqa8wpmT9LtiJOT7C0qk341g5eUVfGGHbRysT5lyqcPvTyZNb+WVSSw9gGwgdz8qoEosYMWU96kiKd3x5lddK98mI+/vd33mq2gEmju6IXPYyMpFZGT1GAAEjfLXiG+hvP88lEi6cpVCSZkUp8phqlE+Pzk9Xf55rYcAK5OdUyO0bRjh+p2cLBQeXtJjgD1bdRdCSLCxxTBySF1Ja7CK7gwzn0cOHOEOcZEdO1l2302mxkddPf5KoFHqbDG1p2GMLU3mKWAYRngnNve0cU+4yKGjx7j/XSQWl3s3mKVnaGiIu9lF2Ng6O42Ss5XzbKlL/FLQSKiNkK9FsGeLYeRkLAuzKY7ZZxqX5ohDSB2z5IyOjhTD/hkX4L5fvGeLyxQwFVLXOVscRsgwiyOVyWJsNoGB9ibuMpe46Lxd3PcusmkN19lyE7/fL57ZHIXiDht2nO/SkVeg9HudG6bLoRaocs5W/cDGFsMsPmer8CBh3GHvflZjc5O7H9nr6vEbnQsvvIgNLRd57IG73Tz8ikDPGyDZOg8jZGpPQ3i2KIRQkaknMQ2JTD3QSXVQplLopDpop+QKhoohVwlUfIGKlot1wfJhUGooKt1Gbc4VEm1vBtp67eLvxPLLQ6osJ9UzB7VEKRIFM2cFw/I5mLbD79wpROn1DkqFMmSHuumfXy/fyJSEapoZ/O7u+8qqxsnU1ZyOZWnya+u9t5dXS/y/b7hBuo1TPrhMV8XpnivbxknBUHXoB5k6neKkYlYNkmvS6XfpqIgoO0xWft9A2qj89yK5HpyuE6fJe9nMPk3aSLfJytbllj/yxwexbsNGBENzaqeyyXWfJlfo1GT3EyMIJV3+Pu30G3vrS55ddvm//Pt/o66oc29NLShMSNa79Dvdu5UlFsxSrMYakzeMscUwjJyRyRnMxJNY15eXfmeWFTIEI2G5pD6z9Kzr7+VudhFV05A1a2wcM4vmkXt+J17lePzVryq7PCaRhCeaA+WHl0FdbqgYEll4J2l+NVpe2MlOybfRJJOghoPHykymEQhliwIZmt/Hk/jLzIkTJ3DjjTfihz/8IeLxODZs2IAvfOELuOiii+B12NhiGAaZrClqbTHuzSRu3bSBu98DRm9jzbd6h7a2duhaXQ9JmBXO/FB7el7KagyudFRNFa8lPYZd2f4nJiZwxRVX4M/+7M+EsdXZ2Yl9+/ahtVVet81L6I2gRsieLYZxxmfo0Jb45so4e+Hve/BhPO4iTlJ3iyMnT6Ono7W+JXo9TFM0KrxbDON1NcKid4ujppaN22+/HQMDA8KTVWDt2rVYKdT1c4XDCBlmcUSCfrRGOIzNLUQ+WX1Okq4o76LNxUpdY2T4NCYnxt1rAMOcBW1eHlM9i2Tk6mwt/YuYnp4ueaVS5fMn/+u//kuECz7/+c9HV1cXzj//fHzmM5/BSqEhjC2WkmUYZ46PTGB0Ksbd5OJAf6CPc4bc5Pyt69m76yKappfUxmQYr2HM87yadS6SsVwMDAygubm5+LrtttvKfu7gwYP4xCc+gY0bN+LHP/4xXvnKV+K1r30t7rjjDqwE6jqMkD1bDLM46MHB4bbuYuhyZThm6Tl0fAjrB3oh17ljlpKOri5WxGNWTM5WPSsSzvc8Ldkx7Nz+jx07hmg0WlJvTzaeJ8/Wu9/9bvGePFsPP/wwPvnJT+Kaa66B16lLY4ss40AgwMZWg3DT49dWLONujp2qeBs7I5HtdsBR3j0Qrmg5oYblRYfVcHmJdyUQkW5TkHjv7umFz6fD8vmKMslSjPIy17bqcDupJpHYQcpaJnPtJD3tFCEmk4V3ki+X6Yk4SXArEsn6jK3g8IlT6OzuPnObMnLwZ1PpUmUS8/R7ef3/laxxuL4dchPk31fe4dLvlJF7Nhxl3GXrnERfFrR7NpkGRQbZRhWPRadr3+H82ZLrwenal+3PqX9sicQ7XSemWn5dxuEHk5Wsc9rmbPo78WQKyUQCHfNk9OWlAOR96nOQs9f08vcuy5D3nSopK9BI/Po/P152+ebnvly6zVSyfJ9GfPJrO5N//ixE9cmfiZrkuWfFJqXbKMl4+X1lnc81/VrpkUDXMt0ntEAVpU6YEsjQmm9syejt7cW2bdtKlm3duhXf+ta3sBKoS2OrAHu2GGZxzCZTMHROTncNStmqU2WrlUI4GKhuUoCpCZlMBtNTk+joOnPCgWG8FEqYzpp1nbPlRTXCK664Anv27ClZtnfvXqxZswYrgYbI2eLwKIZx5vTYBDKcL+EahqFj+9bN7jWAwYbVffBX49ViaoKu646FxxnGCzRKYWOv8brXvQ6///3vRRjh/v378eUvfxmf/vSncd1112El0BDGFs8YM8zZfis2y9i6SCabxbETJ91sQsPzyP4jiCXKhxAzS09TtBnrN23hrmZWhPx7tp4nJ5dDiVCrLIrg4osvxne+8x185StfwY4dO3Drrbfiwx/+MK6++mqsBOp6Go/VCBlmcZy3eS1PSriIZZqIxVgN0k1UVWHPioukUkkc3LsHW3fudrMZDONIobZWPYcRepW//Mu/FK+VSEN4tjiMkGGceXDvYZj88HAPRRGiPox79HS0we8gaMMsLZqqIZNhMQpmhXi2OIyQqYCG8GyxscXUAttBqUgNSpQFQ9GKlQXVSIvDNvL9yRQELZ+8WLGV32Y2YwPBKKz8rJ3tsA0kal+OaoQ1xpJUAHbK+agmH8RpG1O2StEqFvULBEPYvGkTZSaf2QaHCVSZqp+jcp9E6c5JRRGKg7KgsjwzvI4qj7J1MrW/MlqJmuET17CtO1zHWm2/q/Q346SCqVehgOakaKlUce1LVAed5mtMyf6KZ07V0NTcWrJvWdsogkuT9pG8EQ4aqwxTWc5WHU9OqooiPP1LfYxGgo0tZsm4+cVPLbvcmjlTkvXU5Cx6W8Kw4jPlt0nMSo/jJPFqp8rnYFgSeXcnFAf565VOc7SJJyVcZDYex/4DB3Herp1uNqOhGRoZR3dHKwI+eXkFZumg3OqWtvYl7WJLUrZCc5LNz6bQ6Jz//FdI18VS5fsuJTFGDk4k0R8tX0spLZnBSgc6EMlOl11na+UnHxSHSQlFUtNQmVd2YCGFaRtdz09I6hpLvzOLpn5Hj+zZWjHQDJHFKlSuMtjf524DGh2Sfpd465jlgWZySSiGcY8De/7E3c94mkbwbCmauiyvRqKuvy2HEa4MUlkTQ1Pliwwyy8P9jzzGXe2y9HtXVyefAxdZ29+H1ih7tVylwUKLmJUH52wx1cBhhIzrZEwLPi6oyzQwFKIa8JcPrWGWh9GJSQT9frQ1R7jLXWLdhk3c98yKUCOkcUu9omqKeC3pMazGmlhhzxbjOtGgD1t7W91uRkOzYXDA7SY0NIlEAsdPcp0tN0mm0kilWQ3PTaan5Pm3DOMlz1YhcophFgN7thjXGZ1JIG1a6G/lGWW3SGcyrh2bySm/cfF1d/EZOjRNrl7ILD2jw6cxMLiOu5rxLHpe4bS+c7byhYeX8hhWY3m22Nhizom3/v3zpOus0RPlly9QHJydiYNURi2/DWtmomI1QluiLGhl5ApTZlI+gy1L3DSictl1mcS71irPw1GbJN68gDxvxDL8FUu/2xIFLrFOz607MjSOgbUb5y33Vyy7vpypGDIhAyd9g2q0D5ZY/bZIJBzGusHBirerSm5fIofuuC8H2XVbcnKr0bxxuk4Upx3KvpNEqUzsb4ECXd/qXP/bDm2oSj7Dqe+k0u9qTc+f029WKuPu8GUzknFm1uFHJhVBmv8jUxQhEV+YeHA65absbDgM4hTJlwo43O8USamLRuL+b3xKum7jc15WdrlTmF1Gcp04XT+o8Leidg/CHjtRkRqh1twuVURW8hMxupFrh6n7RMmXxHc/BDy5fB8wTAE2thjX0VUFgbycKuMOnJfuLslUCuMTE1gd6ne5JY3LsZOnxCB/dV+P201pWHZf/Di3m8AwixPIoGJvdcpyqAUqVmON+er627Ia4cqgI+xHS7CKQp1Mzbjkwgu5N10kk84gNhPjc+AiVGSzmsLXTO3Y8/AfkeG8OWYFSL9zmQimEhrC2OJcCG+zZ3gGs2l5yB+z9L+TRx9j6Xc3UVQFhsHhSm4SbWoSL8Y9bNuCafKzgPEuej6cMFPHni2KVC4oEi7dCw1FQ4QRctKzt8lYFowGK3DnJSgcglXY3KU5GhUvxt1aZ2YdD6BWAs2tbTw5yngaLZ9jaNaxQAZTexrC2FLzbl/Gm6xqDsLHxpZ72DYP9F1mbHwCo2Nj2LJpTqSEWV6mpmOYjcfRFA5x17tEa3sHNK2uhyXMCkcr5GzVsfQ7RVrQa6mP0UjU9V2NwwhXBqRAVJgtYpYfXdcxuJrrbJXDSRxLkaxzVMyVqswpUGhdufXq8ijgZZ2U3xzGFWYNBx1OtwGncHBNLZ/zqekOynQL87P8cVhpC5ZfHkqopsorlTG14fTJE4i2tAqji2G8HEZYz9LvjMvG1m233YZvf/vbeOyxxxAMBnH55Zfj9ttvx+bNm4ufGRoawg033ICf/OQnmJmZEeve8pa34Kqrrip+5s4778QrX/lKTE5O4uabb8bLXvaykgeq3+/Hnj17sGbNmuLy5z73uWhpacEXv/jFRbe3kOzMnq0lxGGgJZVkn50ueX/o9Az69CZHiXfZvpwk3m2Hm6Gal28thy4J59Jau6TbyNap0TbpNpY/7CjHXn6dRKLYUd5dLmtsqgamp6dx9OhR7Nix86zSvGJ/tlXxYNhpEK2xFCICAT9aW1vkncQsOW1tbWhubnb8jJMhJjNwZdL4Yp3kZ0by59I2yLZxsJad9idbl3bQfpdJdDvdN1SJ/Lw9X8JdUUXNP0cJ8LPdN1SH7yp5JFgO+ZKKw72VAYJG5ck36Wzl15ztK3+ObIdyKEqw/DNWSZZfvpi5Ld2Xm9ixjADUSH3es2nMrC5xtJFqNlbEWUXf9pe//CWuu+46/P73vxfGVCaTwdOe9jTMzs4NkF/ykpcIQ+m//uu/8NBDD+F5z3seXvCCF+D+++8vfoaMKzKyvvzlLwsD7tixY2cM2t72tred85fjMMKVAfu03IXyVNRGy1b1GLphiAksxj0SiYQI5WTco62rG5Gos8HLMF4oasz5ncySGVs/+tGP8NKXvhTbt2/H7t27hZeJZsTvvffe4md+97vf4TWveQ0uueQSrFu3Dm9961uFR2r+Z8g4u+CCC8Q+WltbhQdsPq9+9avxpS99CQ8//DDOBTa2VgYX9shnmZilhwb5vT1cW8hNpqamMTR02tU2NDo0ebjwWcQsL2bWRDKR4G5nvJ+zVcdhhIqmLMurkTgnP97U1FQx/KIAhRZ+7Wtfw/j4uDB2vvrVryKZTOJJT3pS8TPktdq6dasI2Xjc4x6Hbdu2lez3iiuuwF/+5V/ipptuOpfmsbG1QtgzJg8RZJYeMUPHoXwuY7OL1wO5i4V8DMYdUskEYlOT3P3MCjC2WLmUWQaBDDKkrr/+emEY7dixo7j861//Ol74wheivb1dPLxCoRC+853vYMOGDSVhhC960YuQTqeFZ6scFF64a9cu/PrXv8bjH//4qttIcM6Wd7FsG7OZ+p0hWglMT08hmUhKf4vM0tPa0sI1nlwmGo2KlzB8GVdQNY3Ds5iVEUa4iLxChjlnY4tytyjM7ze/+U3JcsrFIuGLn/70p+jo6MB3v/tdkbNFRtPOnXMJ+OFwWLxkkLeL8r/Iu/Xb3/62qjZSkVAKZezqkgsbMO5C+bDNAZ5NdvUcmBbXonOZeDyOVDrDeVsuQiGER44cwc4d291sRkPT0tYuXgzjJiSuRmIvJNihq4oQbBlPZMR7qsdHwm+ZUCseHIpha2fojHHwSkfRVPFa6mM0ElUZW5RT9f3vfx+/+tWv0N/fX1x+4MABfPSjHxVGGOV1EZSXRYbWxz72MXzyk5+s6DjveMc7sGnTJmGwVZvwTOqINJBh5vibV76+bHe0B+VGT8SMlV8xcbzirlX0OVUh3baxsUMv/vAUo7yEsxOamim/wkH0QZGoT6ktToqDndJ1alNbRYqDhG2UF0SwffI6P7bmq1hxMOMwAZfJWmjt7BayaMnsnIfRQRSqqPK5EKd7p1N4tiUTFXPaRqbI5qSiWOG+ztYGWT9Us794MoVEMomuMop2TpHttkwBz+HalyrJVaE+R8g2q2bi11n63a5YmU512GYhCRNImjbiZnW5BCWqegvWyJBdr06+fqdUEdk1mani/Dmdc6f9yZDeHkyS0879mYjPYuTkcazeMKdwLMOWniYH9cdqxncS9VUmxx+/9emyXXHF37xK2kUpSQheIiu/b2UC5Z99quQ5SlhGEOmMKSJogj4Dp8ankM6aaFYCIufq6Ni0eH/+YDf2nBzHdCKFiAZs623Bg8fG4dNVrGoJwa9rUJU0opoKzR8QkzJ2KobzNvQLQbcrt17JlwNTO2OLbuQkfkFhgb/4xS+wdu3akvUFo2Zh2J6macWQvkoYGBgQht2b3/xmrF+/vuLt6ZhixjiVqnhbZnmYSmZxciaN7V0skuEWw6eH4PcH0NbOM8puQQ9sjRUhXcXn86F1Xv4xs/yoiopUknN4mTnMbBbpdAqTGQvR5maMjoyIcZ1u6CJq6eGHH0Mmk8Xqvm5hVB0+fkpMb1yyaysOHBlCKpNBcziItb0dSGeyMHQNhqIi5DOwpa9dGFS6qmL3mtxEq53MjWMvXVs6udoczBl75OkiHYJsNutY7mQlo6iqeC31MRoJvdLQQZJr/973voempibhNSJI6IIUzbZs2SJys17xilfg/e9/v8jbIq8UycSTJ6wa3vSmN+Ezn/kMDh06JHLBKoHrbHkfmiE1GkyVxmuQB5ikxxn36GE1SNfR8jnGjMvnIBLhU1AnWJYJ27JEwfbU7AxMMwtfMAQ7kcXsxJgwpLoHN2Do0F4kZ2OItbWib81a7H34QbF93+pB8e/E6DBSkQAiTU1iEp1qsdKYU9cNbF0/CJ9hCOEKMn76e+aiU3auW1XSnnV9OQPKmhkX//pReQpDQUSHwu8ZZkmMrU984hPi3/nKgsQXvvAFIQlPOVL/8z//I/Ksnv3sZyMWiwnj64477sAzn/lMVAMpHd54443Cu1UpBW9avc4+nAvpVEp4HCkh2c3+CRsafA0Wu+s1fIZPvBj3OHHypJgpXbN6NZ8Gl8ik0zhy+BBazmOhGDeNrTYKa2ZWPKf2Pgwzk0Iw2orm7lWIjY9A0w0YPj80n45gtFm8p/FH99qNotZjVzgXDr/zoseV7Ku9qwe9kdxwtae3t2SdP7i8Rac1tf7VCKmg8ZIXNdYaa9xXcRjh2di4cSO+9a1vVd2gcscg7xa9KoXVCOUcO7hXhGs0NbegrasHBx97BCFdwYZNmxGbmcbI8LDwduw673wcOrAfASuJaFMEbS0tGJuYFK74pqYI9ExWnDNd14o3oUo9W+SWZ9yju7cHmla1Vg5TA2j2l3EXCmuxWWHMdQ786WHsWDDYZlYevZvmVKqJzsGNxb8DhopAaM6DqZ1bFaJlhSapiWpSY5jGpa5HWGxsyVm/dU4ZsjCTVBDIoLyFVQOrkc1kxKxTe2cnjMSUMKgs28JsPC5m4SlmOjE5gxMjE2KW57Lt63Hf3iNIpDNobQpjtZ7B/cfGRPL6hq6oSAYfjSWhpRPY2BbE6dk0jk4l0RYwsL4tiJm0CTVjIaApIlGeTDD2Si49+/bswcCaNWhqItlrxg1C4fCiJrOYpYMiM9Zv3MRd7CJ8v2e8TiMUNSa1qyVXC9RWjoFdC+ra2OKcrXNI1te04gxOc3MLInkXPrFh7Zri3+1mK/o758JuLtoyJ5piDR/F4zf0CCOLDKeMZSHk05GepsRSSoZWkDJtWMjJrB6fTiGTzKAvrAuJ1SOxnMrgRZ0B/Gk8g1jWRtRQsCGq44HxDHRFweqwCtWyMZKyxPvBsILxtI2sDQR0Ba0+BdMZW6zzazlVPGHEVdk39TopUTjXjDsEA8sbCsOUf16MDJ8W+ciMe/SvrVwMa8llwLNZUf9LiH/ZwExs1u1mMS5RyNnKZus3jJCpPXVtbHHO1pn8n398nTBkyuEk9y1FIj09X+K9cJHRLYrGlCZyRlRPix9tTSHh+dJUBTv6AkB2TpVwYJ4K+3nhSMnD79IokLVtsS0l2epZS0gV6wEdaiKLbNZCytCBsA+nxhLC4OsO5fKSDk4kYKcMXNDfigNjs5hKpIURuKu3GXcOp4QhuLqzRSw7Ojotwhw39rXjdFpDMp0RybhkYA6NT4nQyXDAD10PC9UjUjXy+4yi0eok4w4jULGMu6WVF7KQndOzraM+I+l3WzOE4VugmoKNmsMmMg2U3Ey2XTvpdwfvkGyVU8qio7NJ0sAK1MaLnB4dF5MP/QNlxAEUuSFM6lvlMKuQcXeS886a1Ui/Vy7DXy21mDyhwfTJ0yNo7y9V2V1KqilH4HSNyybbHWXcJSeQJsdkyM4tXcMyDIcw88C8MLJ4PIlIS+GdUx0FwDJNcd5Idp/EEmZjM1BtC/5AAJZlY2ZqQqzv6unD6PBpZJOzMHQD/WsG8ehDfxSCDd09fWgJ+bB//35xMzhv924cOXoUExMTYhLkJz/9qbQJr7/2RYjFE0hn6Lmgo6O1BdOxWXz0S9WnUtQLhsMNXHbNzWbkBkwsU/76MYLFi+UMFLN8WRhHn0rQWRnZyI9FLMPnWA5mxasRLnWdLZU9W3UDhxF6n4PjcaxrC0GrYLgkPG8Kitvougq/PvfD7Q4bJcbejgWy8j0RH5T8DXVnb3PJusdvW5MbSNi5AYXf0IXh4dM1hDS/OLYwomxgNpGCaVnC+DNUHw6fHBIKRVvWDuD46VGMUG5bIISLd23H7+59QGzT29WJlmgEf9p/CFB17Ny2GaPjE5iYnBLS07u2bcGfHtsjjtHa2oKmSBOGR0eELHhnRztSZkKUMqBE8tbWViFCQ7OttqKJHDtqS6GNi4W2oeRkxkXo4miwh4/XoN8Re3irg+4hlpkVhiDlf5IRk02lRNh5qKkZs1MT5AoQ96hgpAnjQyeFUl20rQPZTBpDE6PCcFqzaRv2P/wAxk+fQiAcxsDa9dj38APiGL0Da8Rxho4fFe+3n3cBjh86gGQigXCkCavWrMXIqRMwSGSjo1PcI4XXXteFERVuaoIv2iTCRek8b966Tayjv6M+Fd3dc8Ic27ZuLf7tZGwF/D7xmk80wmVMGsKzVccCGUztqWvPFocRep/JREZakNQtxOwshTlCQVNwzsvUGgqLXLQCG/rnHs62P4zW6IbiezK46EVFFYkrLjq/5BhXXnx+0YMVDoXQ19NdTM7v7ekWN3IyvlRVETd3mp2lmdp4IoGpyUlhCJKxdeTwYTGrGm5qFqUW/vSnR4XYwsZNmzE9PY3TQ6eEsXjhpZfhj/ffh0w6hZbWNvSsWoXHHn5IFMWlQqKUnzc+NirUKTdv3Y6jhw+JZSTD3NbRhdMnT4jBaEu+DtFs3sijQc3szIwYVPl9PgRDISQTcbFON3yiL6mtoiJ9hUZgI9HZJS+mzSwPdG1uO+9CTz/PxIsGepkMbNvKTZJQOHYyAdsy4QtExN/pVK5WVUt7J0aHToowOH8wLDw+48OnxL2kvbsXszNTmBofF/tcvWk7jjz2kDB+ws2tCLW04eSBx8R+utdsQHJ2BpMjp4UneHDnRTj2pz8ik0mJz7Z29eHUwT3C0OpYlcv3nZ0aF7PXwUgUabonwC6q3/oCAdF23eeH4QsgEgqJdfSKtrZj0+6LxOcMXTlDLKOjp6/oOVm3eVvJOno/X3ApMi8PtSnajOC8STnqC4apFK0BpN+5zlbtqWtji8MIvU/QUB3DxeqZguGh67p4FWhpKQ2L6F81Vysk0BQtmYHdvmNHSajgJZfODUyoSPHg2rXFdbsvKB1IkgGWMS3cf9fv0dHdg9b2DvGbUfJGFImg0OCJDL5AMJhbp6jIZjOidADNTNM201MToshjKBiE4evDkUMHhcHX3dsntjlJM9GWje27z8Oh/XsRm5kRRtzGTVvwx/vvFW0hQRY61tHDB8Xgcfuu3Th++BCmp6fEsbdt34n777tHGG89favg8wdx/OgR0bb1mzZjdHhYGIC6z4fB9RtwcF/OO9jc0ia2H6VBIlR09fSKUCOaDadZbWrj6VOnRBuoxhINAmPTU6INNDgzM2khCU7GY7S5RSh1inNmGAj4/UgmE+I4fp9fGJUirElTRR2YDAnM5B9ctH2ub5WyRmc8Piu+P9WOYeZYKBoi7ulkvJM3N9/fZH5Q6QKaSCAjQniodD1ngNg2fP6A8LrQNUvvSYGVzrFZ8LSEwpicGBOTHUcP7cfmHbsRm6bzbKOjuxfjI8Ni3wYVPW7vxMljR8S6lrYOsb+x4dOiDavXbsDpk8cRn42JY/YPrsXeRx4Sv4XOnj7RphN0fZNq7/ZdOHX8CGampsSgf+O2nXj4vrvFOvqsLxgUXhti/ZbtGBk6henJcfj8fqzfvhsH//SwuJZaO7oQDEcwfPK4+N6dq9YgGZ8VEyhCYbQ9p3RJv9ucnLOGUCQq3tN3jzS3wghGcrlI9Dtcv1mso/6lefv1uy8p9n2kpQ0dq3L5unStr962u+TcDO64oCSMsLljbgKhrbe/JIywpbOnZNuAPnfdb7vg0mJ7GMZrsGeLqYaGMLb4xu1dtnY1sbfDJWjAT7+N7eedL0Jr6FVuRpjo7C4dHJHhUWBVvvAk5d0RW3fsOmPbQs7Wlu2lKpgXPe7ykvdd84r7bt62vfg3DWovuOiSnES68JYVFPyowGUAHV1dwutWyCVp7+gSA2BaRzORoXAElpkzJMUMen6wTmQyabFfw9CFwubM9LRYFwyGEJ+ZwfQUGV+573zq5EkRIhWNNgvVzsMHD4jB/8CaQcRiM8Loo244/8KL8NijjyCdTqOltRXdPT149JFHoNi2qD1Iip4nT5wQx7/k0ktx3333ifYNDAxgzeAgHnwwFzq1bt16YbQdO3pUtOGiiy4W3svZ2VlEo1GsW78B999HBquCwcFB4aEgbyf9e94FF2L/vpxxGw6HsXnLVtx37z2i71YNDAgDnwxjYseu83CIjNupKQQCAWzZsQsP3HOXWEfGLXkfjhzcL95v2rYTp0+dwNTEuKiZs2XX+fjjPb8X6zq6ekW41pEDe0Vk5LrNWzE+OozJsTHR7zsuuBgP3XuX8ISSxzTa0orD+/aIbQc3bsb05IQwbug87bzwEvzpgXuRNbPCsGnv7MahfX+CAkWIKJBBMTE6krvmdp2PY4cOiokAunapLs+JI4dEv3T3rRKelsnxMXHN0/rpqUnhwSFDyx8IIjE7K9ZZ2VwRVppIKDw3ChMhZGxRuyLR5px3xk/eGQWdPb250GZdF8ZZweNExs7GrTtyv7O8R4cmMQqG9rpNW0syknZdPDdRQueIDLsCazbMiXZQqN7m3ReU/G6oTWKdBQSCpXminX39JTlbzfP2S5HW6rzcUfJGF4/jkrT1oT0PY93WXfxcYDytRljXni2hRri0qQWK1lhhmHVtbHEYofe5+/gkrliTC01j3OHooYPYtHXOsPEiRW9QfgBMIhTz82vIMJovHlAIdyzQGQgUxS58vjaqb1Bc1796Tl2TIM9YgUg4LLxhBTZu3lL8W4i67Jqb3W9uacGq/oFiGNPO3eeV7Je8jgWjs4MG0GvWlNQnDPgDxYKdl19+Rcm2ZIQV2D1vv2RcXrbgs319fUXRiu07So3bSy+7vGTg3TXPiN64ZdsZns/5AhlkKBRYt3Fz8W861u6L5z5LtLRdVjR8yRBYvW6uxg4ZUTIjg4yv/sF1xffbzr+oZL87LpjztDSLcNhcv1C3rl/QfvIWzaets+uMCYIChWPSM4PaWzBeiNZ531vsZ8F7MroKkBd1PmSgMZVBhjF5LEWuFcN4jEI9UdNqLGOBOTfq+m7WyJ6t85//ioqVrGQ0WzH5SoccHGXeIGQ+6jzFQUWPQS1R/5EkFzucQ0Um7pAXyCi7O4nikB2U15qyDLmyoO0rH/5l6/K8AJnqYNZBK4lUFsuRcZhkc1YqtDAxOYnkgv1WoxbnpEYou0xkKoVnQ1aTqtZzjU75hFSyoOzyKlQZ+wbWFM9HJchOrZOaZKYKxbpqFPBk/UPIdud2TWF/UysSVDdimZD2g0PfOZ3buETNbeHvezHKcE5qkjLVwYJ3uxwhQ76/+cJnwWgz0qYplF2ddONka6q9p8h48yuvka5TsqnaHqyO+MWXPiZd96S/va7i69QraA0g/Z7zbC2xGqHWWOPyhjC2OCnfu6xpZ+Umt6FQKsZdKPSPcsZYKMNdjh7YI3KGGPdo71kl8ssYZj5xySxROCQvleIPlqoNFyBhKBlKNicuI0MN5qTfTUWDFZLLzjNMwxhbHEbobWjmNDd7ybgJ5dUw7iJqBVXhdWZqDJ8D1xk6eggdPasQWpA3yjBeoBHUCCkabKkjwtQGG/vV9bdt5DDClQAp4R2bmHW7GQ3Pow/kFAEZ94hEIkKYgnGX1RvmcvIY9wazNPnAMF7O2SLhHoZZLHXt2eIwQu8bWwaHizAMmpqahBIi4y7TE2OIzFPaZJafls5uGIY8NIxh3KQRPFucs1V76trlw2GE3iZk6NjawwMbt+nu63e7CQ0PycBPkcQ84yqTYzkpecZFbBrIZvgUMN6Wfmc1QqYC6trYYs+Wt5lKpjESc05GZZZ+QsKYV1uHcQeqCUb1oxh3oTpajLvEY9OYFUWlGca7RY3r2bPF1J66jltp5JwtmTywg5J1VZK5tlb+ElKTM0CgvMpd4WwkU0BW16CG5wp2Qi8/8FccZNyhlm+D7RCKYkkk2WVy7Ll1vsrl3TV5u2US79kqZLvPJu9e/jg5YYbjRw8j2j5Xg6jaEgEKFb+SoEoMCadtlgsHtWrYTuskTbfVyvfX1tUDn+FzPI+1k34vv87p0E7Xg2yV01hEtj8nufFqxjZOUcrl5Ms37r7Y8ffndP+sJU4/P5lUu9O6lIOkdoWXXB7J+TNt+PXKn7fzJb9NqKLWFi1TDaXidldVPsC2qjsZFuftVIMhGWw4/f6TkpIMx6YzaAuWL/9i+OeNLebjb4Kamim/LmM4XiOqv1DTkUq+1KeSL4cR1p6GCCNk6XdvQgOXkN/BiGKWHMu0oEqMVWb5SMYTnHDtAfY+eI/bTWh4oh1daOte1fD9wHiTRsjZYmpPXY+yGtmztRLobg6zIewymq5jcBNLv7vN5OS4OBchrnnmKhar4LlOcjaG+MwUOvpWu90UhpGqEdazYqaiqEseUq041DqrR+r627Jny9vsOTmG8ZmE281oaDLpFMZHTrvdjIaHnPDsgXefaGu7201gKPQ5wc8Fxuuerfo1tpjaw54txjUyWQuGXj7WmlkeRG5EIs7d7TIbNm12uwkMgOaOTu4Hl9F0A4afpd8Zjxtb+cipeoRztmoPe7YY1+hqDiPoq2t7f0Xg44GN6xw+sB+xGUnCNrNsHNv3GPe2y/gCQbR09brdDIZxln5nzxZTAXU90uWcLe8LZNSiqLEZaq1YWVCmemhrDjLoankvnO0gMCERUMqtk8hmyRQHCZlanUxxUKyTHCdrAnowgs5V4TP2W40aoRMy1UGZSmH1x6lcddBpG6uKdU7znbLAk0QqLc5hufPrtD+pEqBdhRphlYJsMhzbILmOMw6zxU5KZeWUBfNr5NtoVVxDDterbDsntUsZjop6Dmp/sj6v9e/ZcMjnkCnNLRYzm8GJfY9icPv5jsqLmqTDbe3sqQULmc4A0Sq0mt71uW9WvhGzotHU+hfIYM9W7alrY6tecrYufME/lV2eotGyBE3yhHcaLFQzKJDLrgelmyj5bfbtH0ZPb1/J+ZFJpVsS44g5N6bGRhCfjaFvzTruShcJRSLQncobMMtC39qN3NMuo2oaC5XUORGHiJZYOlvxhIFsEjLpYKwHJLLtisOEq2JloQYixfbY/tzfDNPQxhZ7trzPSjeEVzqmZYrBDeMube0dMHycp+I2ydkZRFtb+b7kIqRS1tbb72YTGKahwwhVTRWvpT5GI1HX37ZePFv1yoUbWdrXbQLBMCJNzW43o+E5euggC5V4gMnRYdh1nPi+EqDntSzcj2G8IpCRdYgsYpiGNLa4zpY3OTg06nYTGh6aXWLlLy/Ag0tPwAN9TzB28pjbTWAYR2OrEDlVjyiqIupsLe1LOac2vuc97xETM9dffz1WAnVrbM3/IbBny5tMzLDkuNtMjo5gdnrK7WY0PN19/fD7Aw3fD26zcdeForg04y4cjMJ439hiz5Zb3H333fjUpz6FXbt2YaVQt8bW/DAE9mx58/yE/CwI4DYUMsU5W+6TTqUc1faY5eHI3kdFoW/GXdZsPY9PAeNJNLX+c7YKaoRL/aqGWCyGq6++Gp/5zGfQ2lpeidqL1O0UXj15tu79+ifLLt/xvH+oWArZ6fqupp+kUumK/EC2bQlja/fO7bDmSQg7SbXbTmqEkjY47k+iomg5qDXKBsNOY2Qn6WKZ9LvDJtJtsg4RDTKFWpLZbu7uFcbWQsntpMMOqzEK5NLc1SGTfXaSnZb+JhzaZqvy72rLpKerCAk8PXwavnAEqFCRUCYR7qTcJbsenE6r5YEwR6drSKvqfnfmMsvMgk55NfuTbuNw6RuSUBqne7HTuY34yjcwY8kf9bW28WVNTzjUtFh4vxk6sh/9G7c53mtk16RlO9zDpWucn1kybr766WWX3/qfP654X43Edz7zYem6v/6H6ytWUjarKKNSMJoWohvyCAPFMqH4w0Xpd6ugusz5W1UzPT1d8t7v94uXjOuuuw7Petaz8NSnPhX/8i//gpUCe7YYV6C6Qvc/doB732Wmx8eQTafdbgZj2yt+UqgeCEebzzmXgDl3UvEYdyPjSdT8/YGUfOuV5fRsDQwMoLm5ufi67bbbpO366le/ivvuu8/xM16FPVuMK2SzWfg4N8J14jNTCDdF3W5Gw7N+6w4Od/YArR1d0LS6fSyuGPyhSC4VgCcgGI/mbNVzUePl5NixY4hG58YgMq8Wfe7//t//i5/85CcIBFZefnPdPlU4Z8vb6LqO7vaVE29br5AqEOdsuc/RA3uxas06+BzCJ5il59SRg+hbvRZBCulkXKNncAP3PuNxY6uOPVtKTjFwqY9BkKE139iSce+992J4eBgXXHBBcRmdg1/96lf46Ec/ilQqVTw3XqQhjC0Oz/EmwYBDHhazLPSv38y/Dw+QSiZZ/t0D0ACDazy5z4l9j2LVhm3QefKB8RiNIP3uRZ7ylKfgoYceKll27bXXYsuWLbjxxhs9bWjVtbFVTwIZ9cjY5DRMy0I0HHK7KQ3NgYfvx+CWHdANNnzdJBSJQFG9/bBoBDp6VsHHEvyuo2o6TDNLQUVuN4VhSiioW9ezZ8uLNDU1YceOHSXLwuEw2tvbz1juRerW2CrMTpKhxcaW90hnsgj4eYDvCel3HuR7os4WhdYy7pLJpGFm/dANLkvhJi2dPVzvjPEkagN4thRNW/L0AsXjnqhaU7dP98IPoZ4NLScpZJmksJPMtUyESyaTnjtQ+ZlH23IYrKgqelf7haqPNW9QY+sOSY+afH+mWn5dRqaLTdtkZesq10F22kImSeskS5t13EYmcVu5xDzJLQdbOpCyAMW2Fi1Zv1AmvoBDExz7QYbmoAonW2U4xJkHdLViuXjHUgCSVdU8gvf96VGs27YDRoUeRpkytlN3S7dxuJKd5MFreYt1uj85IZNDV63KrqGZiQkh3OMPBitun+wykt2LCZ9kI93x2ncqT1H5PUW6L4d1TtoA0pIWFZSn8BkGtCpLRjhdq1mH+5rst65VMRn15r/YLV337h89WPH+GgnZb8Lx+SJZl3Y437KfmNOzivKMzPx4iDxbKTv3TFHAXi43+MUvfoGVQkN4thjvcXpkFB1tLeKhyrhHIBzh34gHoNpcioNhxywP9LzgnC33mZkYQ7i5hUM6Gc/REAIZ51B0uJJjNBJ1b2wV4msZbzExOY321ha3m9HwDB3ejw3nXdLw/eA23atWsyqkB+gdXA+jwQYBXoRCmKw6HswyKxc1r6JHY0x68YR+fdDW1lbR5+m8U82vNWvWNLax1QhhhCsZVVNhGHV7+TFMRZjZDPeYBxg7dQLhSBjR1na3m9LQtPf087Ob8STzVe9onOl1FbxqaETP1uTkJD784Q+Lwspng4zsV73qVRV5N+t2tMthhN5mx+YN7HX0AKs2bHG7CQyAkVMn0NrZzX3hMqSAZ2ZJBY9xk3hsSni2Wjr4N8F4C7UBjK1G5UUvehG6uroW9dnXvOY1Fe2bjS3GFX53zwO48pK54nSMO0yPjSAYOXtBQWZpEeEo3MmuQ8IYrEToPlY2i3SKas8xjLeYn5pCng2jDvPOqd7gkhc1Vr3l2apUXXJmZqaiz9e9sVXPOVt//Nanpeue9LfXlV1elefWQY1JqiCYj2uW7c/WfLD9TSWLLQfFQZmqkFC+kvxInBT6nBSrZMgiUp32JVM2chBJqkpZ0EltLJWV9I9pY2p8DG39687cxkE6LCNZ56j+KFlnVXMihIKgRFlQlbfbsstfx/68olT5/Tmsk/wsbLtys2n99vNhQcUCUcizUk33yVQHqzwVDoqIVe6wQsVBp2PJlDNznHluA01t4ncuO5TjV5KcdqdQdtk6uj/IVNmc1A1lOJ0LuxolVaUKZcgKmq37/MhWGVqbNC0EqnjQpSX3NS0gn4xSUuUHXIo+Jt3mxsv6yy6//c7jZ21jI/C1T36o7PLnv+J1FV9zTs/FtCQCzEGcVtwbzHn3jWTGhGrYXA2OOSt1a4lwzpa3WdXX53YTGHqwcG0nT3DswB63m8BQ3P7IKUyNjXBfuEy4qRmtXb1uN4NhzmD+BH69irgUcraW+uVV7rjjDvzgBz8ovn/jG9+IlpYWXH755Thy5EhV+/Tutz1HOGfL20TCYbebwABYve187gcPkJiNud0EhlBUln73AIl4DCcP7nW7GQxzBvNztOpZ/r2Refe7341gvtbinXfeiY997GN473vfi46ODrzudXIPa0OHEbIaoTfZs28fOjs73G5GQ0O/kSMP34vBnRe53ZSGJxhu4nuVB4g0t1ZV2Jyp/YC2Xr0GTB0ZW1Z9XqOKqiy9GqHq3SzlY8eOYcOGDeLv7373u7jqqqvwj//4j7jiiivwpCc9qap91r1nq55ztlZyiCefF/fh4q3eoX9t7sbOuP+bMLP1OYBaSeiGH83tnW43g2GcwwgrFFVgVgaRSARjY7m8y//3//4f/vzP/1z8HQgEkEgkqtpn3Xq2OGfLu5C3ceeO7W43g7FtNPGAxhPse/h+bLvwcW43o+FJzsaQzaTQ3kYeLsbNAa3iIMzEMG6OX+hFEzP1amw1ohrhfMi4evnLX47zzz8fe/fuxTOf+Uyx/JFHHsHg4CCqwbvf9hzhMELvkkqlirMGjLuEojyoZJj5oS22g4IZs0woCoaPHeLuZjzt3bLr1NhqdD72sY/hsssuw8jICL71rW+hvT1X5P7ee+/Fi1/84qr2WbeerUYwti554T9J1/n18na0U2/Ixhgk0y5FMvloORxpJpbE5GwSaaX08ktnrIql36uRG68WtYprSSZJ69xu+f5kUrYyeXciKVkXTyYxevwwejfuWPQ2TtLvTnLxllV5Pzghk3jXHeLMZXL7Tm3wa/J1AYlkvNN1Ykh0hdv7BqTn1gu3sGpk3J2u42qk2qlUgfRYVbTPUM/cRm9qhR5pdbj+K7++bMi/k1OZANk620GbWq9KFr6y5Wfrb2m5iwqvB8u2znLdVfFdHdbJnjF+f0DegkBz2eV6c7t8m4nhsstveqI8lPg9v9yPRsfpUpBF/qaryL88m/R7wdgicYxE2hTXjb/OnLDkVV5qz7LiQc/15z//eTznOc8RQhgf/ehHz1j/jne8o+p9171ni3ODvIdlmSL2lXH/PKgevOE1IvU8KbSSSMZmMDVyyu1mMKSUunkn9wPjSdS8SIZdaWFExtN86UtfQn9/v5B4v/322/HYY4/VbN91b2zxIMZ7tLS0Yt369W43o+Ex/AG09A40fD94geHj1dXuYGo/AZFJJblbPcDYqWNuN4FhylKYxK9b6XeahF2Ol8f43//9X5w6dQqvetWrRMjgJZdcgo0bN+INb3gDfvWrX51Tjh4bW8yyc+L4cRw/xg9St8mmU0gnZt1uBiNgz5YX0HRDTEIw7jM7PeV2EximLFreUKhbY6uBaW1txd/+7d/i61//OkZHR/GRj3xEKBBeffXV6Orqwkte8hJ885vfxOxsZWMnNraYZSeTyUDX6zZdcMVAM/jpOBtbXmDd9t1uN4Gh/JxQGE3tXdwXHsDw+91uAsM4C2RUkSu6IqDvtxwvj+Pz+fAXf/EX+PjHPy5qb/3oRz8SaoS33norPvjBD1a0r7od8bL0u7drGATy1bkZ96AHRSH2nHGXU0cOYs2mbXwaXCY1G8PUyBCim/lcuM3qTWcK9zCMFyjIlnPh7cbioosuEq93vvOdwmlQCXVrbLFABipWSpPN0mQdFLNkClymQ2yrpRqA7kNigfqgTA1KrKtCzc5J/UqGc9H08sdymtyStaFa1TXZds7bSFS2om3iVU5dUKY46KQ66KSIKNufQ7Mdlchk17GhyduQlbVbotxJhAy5MZqxym9nOMzYGVb5dsdmpqXn0OmarEYhU9av1agHiu0k65y2kX1XRxVMh9+67FiOypBl1Ajp+KmsiXjGrNk1mdHlbUg7qAfqZvl1Pge5NE3SBieRQifVQZlCptO5SGXLr5P1aW5/Z573g3+8B5vPvxS1xOleLbvynO5rwUBT+X2F5CU11Cb5unf/6MGyy2+6YrV0m/f89qh0HbM0aPmbcr3W2Wp0bNsWoYI///nPMTw8XHKeSQeC5OANw6hon3VvbNWzQIZM3p0IOKxzm+NHD2Nw/QboemUXK1NbZkaHxOijqbOXu9Zl/MGw201gROhaAOG2Tu4LDz3H6/kZzpzJ81/xurLdknaYDZKtcipBI1snm7DIrcuPK/MTajQxI9pVZwEiiqaJ11Ifw6tcf/31+NSnPoU/+7M/Q3d3d03uQWxsMctOJpNlQ8sDWGYWqla3t4AVRf/6zW43gSEUpX7zMFYYLd19bGwxnkRV2LNVz/zHf/wHvv3tb+OZz3xmzfbpXffHOdIInq2VyvqNGyt2wTK1x/AHYQQ4d84LHHj4PrebwNAMeTaD2Ohp7gsP4A+GuI4R42mBjLrN2WpQ6fcCzc3NWLduHWoJG1vMsjN8+jQbwR7ACITEi2EYFCfnuFCpN5g4fQLZdNrtZjDMGRSEpSwualyXvP3tb8c73vEOIfleK+o2hog9W95lfHQE6zZsdLsZDc/MyEkEm9sQjMoTtpnlob2nn7vaA+iGH52Dm9xuBiO8B1r9eg6Y+vBs1atAhpBmX2LPk+pdX88LXvACfOUrXxF1tUjqfWEk1n33VR6JwsYWs+zoHELoCehBUUj0ZdyFxWK8gWWZmDx1FE2cQ+c6Hf2D8HGtLcbLdbbq1dhqcK655hrce++9orgxC2SchUbwbP36Pz9ekZoP4aAaLJXhthz0ueXSwPLjbN59ERJl5IGdpN+TErmhtERmuHrp8MqvFyfZ9ZRkZtZJ4tpJalh6jpykuSXnSG3uQkr1I5M2Fy2T7iT77CzbLTt/TgpTlQsV+BxUOFMSDXW/Kd/GSeLan1UrVgI1JG04eeQgjOZ2yTaVS31Xg0zCfSlk3GXlA5xKDjidCxlO0u/lzpNpWojPxhBLZyVtUCuXXc9U1oaznXen0gKyMgFOl4mTHoisz51+mzKJd6froVzfzUxNwg5HEGyKVl5yw678uaSYlf/GfEb5wstKsFm6jRYuLxdP3HhpeWVYc2oMjYLs3DmWDpDcg/wOv1f4yntuDIdN7Py1UBhXZrIWHC7rFQtNwi71RKzi4YneH/zgB/jxj3+MK6+8smb7rOjb3nbbbbj44ovR1NQk3GvPfe5zsWfPnjM+d+edd+LJT34ywuEwotEonvCEJ5TEPtL68847T7jnPve5z5VsSxdxIBDAkSNHSpbTsV760pcuuq2NYGytVP54zx/cbgJDD4pEjJXXGGYe9LzQ/QHuEw+QSSWRTiXdbgbDyMMIWbm0LhkYGBC2Sy2pyNj65S9/ieuuuw6///3v8ZOf/ERUUH7a056G2dnZEkPqL/7iL8Tyu+66C3fffTde/epXFy9O4mUvexluvvlmfPnLXxYG3LFjx8544L3tbW87py9WiKVlY8tbkBFM/zHuk6TZUk7w9QSrNm53uwmMmG3V0LxqLfeFB9AMn9tNYBhHr0zd5hQqy6BEqHhXjfADH/gA3vjGN+Lw4cM122dFOVs/+tGPSt5/8YtfFB4uim0k7xXxute9Dq997Wtx0003FT+3eXNpDRkyzi644AKxbWtrK2ZmZkrWk3H2wQ9+EDfccAN27NhRzfdiz5ZHISO4s6vH7WYwBE1I5OuFMO4yPnQMPWu51pb72BjZ9xAGtl/odkManubOXvgdwiwZxi0KzgOzXo2tBudv//ZvEY/HsX79eoRCoTMEMsbHx5dXIGNqakr829bWJv4dHh7GH/7wB1x99dW4/PLLceDAAWzZsgXvete7SmIfyWu1detWZLNZvPKVr8S2bdtK9nvFFVdg7969wmD7/ve/X1XbOIzQm9B5ibbmrhfGXVrXVzeRwdSexMw0d6snUJyTgZhlY2ZsGDPZNDr713CvM56iEDFVtwXQl6MOlupdz9aHP/zhmu9TPxcPxfXXXy8Mo4L36eDBg0WN+ve///0iL+vf//3f8ZSnPAUPP/wwNm7cWAwjfNGLXoR0Oi08W+Wg8MJdu3bh17/+NR7/+MdX3D42trxJIj6LoRPHsGELh025zcSBR9C6ns+DFzBYdc0zg6hIV5/bzWDytYzMMuI9DOM2qlLn0u8NzjXXXFPzfVbto6fcLTKgvvrVrxaXFS68V7ziFbj22mtx/vnn40Mf+pAII/z85z9fsj2JZ8gMLYK8XS95yUtKwhErwe/3IxKJlOSKMe5D3kxdr9uKAysKM5vmnEaP0Mc5W55B1fj+5AV8gRACDsp9DONmyRQaw2ZSCUyOjdad0VVQI1zql5eYnq4sumRh+tPZqOqpQjlVFN73q1/9Cv39c8U4e3tzsqULwwIpZPDo0aMVH4cqOG/atAnf/e53K942GAyKbevtR7AYDLVyBUafpkjlfB2UzaVyvrJttEAYzd0+JMps5yQNLJN9dZJdrwYnqWiZ8lA17ZZJX5/tO9VS/cgORuUS15I2OB0/4SDNK5N4r7X0ezWy8EGJBPDZZPhl+SQBmf62wzbjex5AryRPyEkeXPZTr6Uk/Nlk12Vy7U5SzXGJBresRABhObRBVSuXSZddy6PHj8CItlfcD7J7h1M1iZRD2QHZfdypFIDT/auWsv7VXA9OlCuJYGk6FN0nb4Mqb0M1l7/st+RUksRQy39X1S83ErVI+WtLbNckkXi3nDx8cwJl9UBScm+YSTlIv0uux5m0iaYy93eKfNIVBZaZRSqRQDabRripBTOTYzATs8IQ6O5fjf0P3S/ysjp6V8Hn82Po2EEoUBAM+EX9JYrUSSQT2DfCEzQrndbWVpw6dUpoSSyGVatW4YEHHsC6desW9fmKrhC6QF/zmtfgO9/5Dn7xi19g7dpS1SaScu/r6ztDDp7yr57xjGegGvlFMuze/OY3i0S1SqAENqryTIYe4x2ohg3V0QgEQ243peHxtXY2fB8wDONNzEwaM6eOIcheX0ambExeJkVBanYGFkXN+AKiMHlichRmNoOWvkEMnzyO+MwUfIEgutduwrFHHxDb9/WvhhEIYmrkNDTDQCgSFcXlg80tMHx+qKqGDTvPF/8WcrTa8wPxdDYr0mYCkWZ09fUjVm85tw2Ys2XbNj772c+KiLjFQGrslaBXGjpIcu3f+973RK2toaEhsby5uVl4kuiCJAXBW265Bbt37xY5W3fccQcee+wxfPOb30Q1vOlNb8JnPvMZHDp0CC984QsXvR3nbHmT2ZlplvT1CLPH9qF543luN4OhsOqObu4Hj9CylifovCLDTwNnprGYHh9BONqC4WOHRNpBc1unuA6OHz4k1vds2onx44eQjsfgD0fQPrABsZFTUIWh1C7GF75wEzTdEHl/HQPrcmFreYNpcNfF4t+ILzf8bWqZE+yKtLSVRBJokpBiztmqP1avXi1sjcXS09NzhkphzYytT3ziE+LfJz3pSSXLv/CFLxQLDpNoRjKZFBLw5F0io4tqclXqmSpASoc33nij8G5VQiMbW/HYjJiZ0Q3Dc9+fZqJoFolhmDmMAHt6vUJ8+DiCa3JiTox76IYPnQOLC9Fh6gfbsoVx1NTWCU3X4fMHRB5l/7zwy651W0q2aR8sLZtBHqsCZHAtWVHjep0MoO+35J4tFV6iljW1ahJGuBhI1KJaYYtyxyDvFr2q2Y/XjI3l4NSxwwiGI2hu68ChPY8IJeOBdRuQmJ3F1MgpEdB+3kWX4sDex4Tx09TcjJ6uLowMnxZu9ObWFrEfM2sKMQvN569ZP64aXIdsjXNxmOoI9rCksleYPH4IwWYuieAF0rOVJT4zS4SiYHp0GJ2rFxfWw9QHzR25UL1Is1xAzW0K4g51K/3O1Jy6zeprZGNr/dadxb93XPi44t8tbR1Yt3ZQ9A31S//qQWSzGfE3vWi2Jp1OIZvJIpGIY3R4WKzfsmM3Hn34j0glk4g2t2DV6tX400N/hA0bPavXwbZMjI+OiFmowQ2bMXp6CFnTFHlZTS2tmJ2eEi59fyCAw/seE3VTwhFWmXIbK5UAmrz7QGMYN9BYht8T0DMpNjmGztWN7d0iYRgSVPJrKqZTphCD0FUFAU3BqXh5gSNmaZnzbDWeABtTHWxs1SGkLCijYFgRwVBp6FJH75yypD/chJZ8HknatLB+++6Sz2678NKiqphlmugMNsEkZR9LgS0UpJKwMlmoqQxODw+Lda0d3RgbG8X45BQURcW6Hefj5MG9SMZnhScu1LsGJ/c+LNz+Ld39UDQVsxNj4n2ocxWSM1PCsNMMPwKRKNKJWdiqJmK0Kb6/8P2WilTWrEhZLbeu/DZJyb6qVeiTKe05kTx9Ckawo6LjOCkEOrVbts5pf7X2gNIApRwJh1o+TkqFsnVBQ76NX6I+5+9bj+lk+YTbuFa5Yp1WhSKpE07nKVWFGqGTyiMJ6FSqsCgXpnMaDJXv13D/Jqm6WcYhbKgaJUBNMStS6DvbcZyUD2U4KQvKzns1qqiy70OoiuR+o6jSY6VMed9pVYRAybpVcbiGtHmhUDSJSc9CmnQcn56FDktEhtCLnntmNouOzi6kZ03xDCQjaufm9bj7oT8hlU6jq60V3b4mPHxkWExkbh/owkQsgdOTs1ATwMX9UZyM5byuLT4DgaABw06TX7xs2954vnwi7b33T8CrHJ9OStfFJPdqmcqrk5qrTPk0k7YQkt3DrdxFQuMXwjQt0O2vCgFOT6Nomngt9TEaCTa2mHOCjBt6uNCrAIUvRuY9IPvXbyr+3bVqNVp6BoTCD7F6c64gNjGVymBg2/kiDpr2S7NGlIhK8qzFm1smLUIf7VAEM8MnhQct1NohPj916ihgA50btmFq6BjSsRno/gDa127G6T0PCoMs0tEjls2OnRY/9mj3gAgbMjMpYbQFWzqQnJ4QBp7my8WKk4FH2xY8ggxTaxITw2gKlqq7Mu4wuf+P6Np6AXe/B+jbUlsBHzKGaOKP8oKokDhFXdAy+tvSNUxNjIn37d09mBofQ2xqEoamYe2mLXjk/nvEs6m9sxuRaDOO7N8LXVOwaes2EQUyMTYKn9+PHeddgKOHDyKg62hta0O0uRmZdEYYXaJodiiIgM+Anh9snrd1o/hbRJeMHsbjt82Fd7c3hbChtx3Z4RPi/dau0oiQ/ua53CRm+SiMA9izxSyWuje2GG9Byj8FQ0sWC12YLaSPUZJ0YSY83FrqhelYu7mknldonpR5+5o5A4/o2XqBMJpontK2LUQ6e2GbljCqyKAimVh6yFKCGxlbZND5m5qFAUZGHG0bWbUe8fHTSE1PQjUMtK/fgZE994s6MP6WTuihCOJDR3MFD3vXIhOfRiI2A0UzEOhejdToSTF9SrXGbM2AlYiJL6kGQuLhDztn1NHPko5Pn11K405fvX3J9s1URnqm/Ow0w9T1M9oml4ACy7ZErSO6J/uCIaTiMTGxNnLwMWy66ApMDZ8UA9tgU7PYdnr0NFRY6FqzHuMnjwlpbzKYetdvwZGH7xe1yNp7VgmhqKGjB8U2azbvwOTwKSRIxS4QRP+6TZgYGRL3f3ou+fWACA8jYSl6RpFBFYo0IejLCU1t3XW+eF4U7sm7Ln4cgkbuWdUUbcbaDXOiKlu270RwnkdlzeBg8e+QWRpRYsybqGRWBnWfsyUEMtSGEshYaur2V97IOVte5uj+x7B+1yXLflwRPlmUcdVEDlmBQLQFAeREQYjWgVLlzEBTSzGMsHnVOmDV3LquLRcUwwjpmouu3UbTXeJY9MroQWHAEarhyxl8oj6ICYvCIG0Lip4zvMwYDbhtKD0bkT2xl9x4UMItUJtaYQ7lZG/RMQAkZ2HN5Ipfaqt3wDq1D2lYUENRqNEOZEePC0+g1tIFm2ZxkzF6OkBr7YEVm8gNcDQd2fHTYpkw6nz5GVJqn6LAVo3c58TvZ2mNPgZCtpjxBoG27gYwcuzcvUdRRXiblc2I5b5gGKnYtIgmoGtSMfxITo2JySCafCIDKDUzKTw80d5BzAwdRTadhOEPIdjWhckje3OTWV39sMwMZkmQiW5bG3dh9Mh+ZJMJGKEImvsGMbr/EVFJONrdD1PTEBsbFoPY9tXrRd0kM5uGaeZCa8kg0gwyhHzC4Gnu6oVPz93HO/oHc/f3vLz3ut0Xl4SBNbfPTcKtWleqMjmwYUtJ+H0gFC6+J6NLLM+H5M6P3mAam0IRdave4geZIpOTk7jrrrswPDx8hgfzJS95CSqlbu8ebGx5lHqdCZpv0OWf86TiqCtzPzGjec4zl82aULsGiu9Vfwh6S6FgogVjoFTaVh3cOZdDEWqG2tY3t65nA4pjC0WB3t6XM/gMf849KAZXOSNKDLLMbO5f8ubNTgmjSm1qI/lJmGTE0SCsez2s4SOw0wlhiKnt/TBP0kDKhkIGGm0/eTp3yL5NsEePAck44AsA7auBoX3CwEO0U7RBmxwRxzdbB6DOjgPZFKD7YEY6oU0NiXWWPyKMQCU5k2urvwVqNiHaRfuw/E1QU2Q4ArbmE/l6SjaTMw41X36WnK4vReyH+qCYiOFxY7F1/Vw4LeMuKl3DizBWitcacrk6lGej6j5Y2XTOS06/RX8QmdlpsU1B1ZXqA9H2/qYWZBIxZFMJYfSE2rsRO31MeLWpTpAdCCI2ekoYOtGefuFxT85MitDqzvXbRGg0GT1UW4gmhMaP7iPbBe2rNyAxM4X4xIjYb9+2C3DqsQdEm0jxkiIExo7sE8ZJ66pBIYZE+6bPtg1uRnJ6XLSBjCJDfJ+s8LgroJBxA75QE2z6PSkKgiKaQIGq68I4a1u3LTfrn/fKhzt6i/3WsW5bST/2bJsL1STxh/mRC83duRktalPBuJqP7vML71UjzpAz7lOos1W/nq3GK2o8n//+7//G1VdfjVgshmg0WjLZTH+zsTUPNra8ycCG0noYzLkjZnTnJZ8r/rkwFfKawT8X16/lDT4yuMx0CnrHnCgKIYyuvFGn9ZSqgOlrdpQkzCtkSBX+7llPiha5N/QA6ttSYlibrf68R02DFYxCyXvQRFinj9prk3uH/Hq5G1thMEsz7JkkbBpQkbGVnIJi2zCFYeaDHhsWn80290FLTEJNkaGmId2xDr7RfVAsG2aoWRhyxuRx0ZZs22qoiWmoicmcQdi/Deqpx4RxZoeaYYdaRO6E+Crdg7DJwJsZz/UPeRJPPCbalYm2QGvuRObkgdzAuqMfdiaFOBmTNBBdvRXJUweFUasFwzBauhA7cVTsx9/WA9vKIj1FnwVmk7Pwt3TASqehBYLCSxAbos8qCLfl+jk1lfNmRnpWIzExAjOdhN8fEIP0maFjuf1GW0UoVGo6lwDf1NWHxNS4+CwNhiMdvZgW+wX8kWaxLDGZ329njxChIe8DDXAjXaswfepI7twHm4SxQAN4Itzeg3R8Bpn4LLJQEOkZwAx91rbFIF0PhBAfJq+GDX9rN7LJWZjxnBEd6l2L5NBh0d9qMAw9FEVqNJeT4mvrQSYZL3p5/as2In3qEGwzAz0YgRFtz21LXpiOPliZFLKT+TYNbkPi+H5YmSS0YAT+jlWYOr5HrAt29cM2TSTHcp6WprXbMX3qMMxUHHowjHDPIKYOPZK7PhKz0OzNmM3nybSu24bY0BHxXfVAENH+9Zg48Ejuu7T3QNF1JEZPid9hc/96JMZHkE3Gofp8aOpZg8TkqLimfU0tIiyZ1tH7gtEmcnXy4WlknIlQal9QfDbc1pXzShs+kZtKxlJB/KFzw47cbyVv2PRtv6gokEHGV2vfXO5P74LcJzLAiveEcLO4LgqQ16n4u7VsNHWX3iOMYLgoWkF/z4f6opYE8mGDDOMpCjlbYoKPqTfe8IY34O///u/x7ne/G6EFQnLVUreerQIc/uQtKOG4LTwXsse4BA3gmjuXaN85I0pM+hcgL1sBX1D4BASWDStUej1YRmDeutYSLbBsS+nAL+OfE5XIGj00X158n+4qNewzvXMz62SsmS057yBlBVq9pZ5Ea1Uun03xaVBCUaB1nidxVe6zhi8nmuIb3JEzDvNGYrC9q/je372m+Dd5B4K9g7kBNhnB5O3wh4RHJHV8HwItnUUxFkXVEWztEv1EA3BheDbninrSeiPUJJZTvgfN/pPhROjUz3TccC6RngbuVDCZjCohV6xAGEOEUPHUtOKAmfaj+wLi30JOQuGzquEXoVzk1RCf1bSiYVAQkvOFo6KdWv6zRiR3Xum76oGw8PoUBilkNOX2o0PRfcLIEsfxBaBphjBOi5dOR67vNfqsqiHQtz53eak6NNgwoh3Co0PXdHBgY0nYa/PGUiPDPy9EMLqm9Ppo3XS++Hdi7/3C2KVXgebVpTmg7ZtL9xtobi+qBJLhOR8ywOZjBOfqRvl8pbXVcp6iHJqWM7LmFuQe14XjLEWxVq8xdmQ/Wlou5uc440np93r1bOWeQUusRqh69/514sQJvPa1r62ZoVXXxlYje7acpN+rkf9NzVehWEBCInteTpaXzsnpoSEobfOSnhYpFZ2USEVXI0FMkrsyHL6qtH2zaXmtk1gyW7HcuJMsdqWy5jKUdBzazDCy7XOz2IvBUbLbsV8rl5J3WifDSfJcts6XVauSs5edw2RREn6+lDt9l+ycXHy68N3yuVrhNkzT35QrJ9abgJbzSKYKcvF6zgCJp0xADQjlcvFTp2vMyA/g881VfTmjaDZl5T6bD42LJbKALzq/SeQOy+2X2kSf9ec+O5vIFD8rfjP02fx+xWfJTDV84t4Rp88W2kBkgUS+vbluMMi6y3dcBmb+u+U+mz9ufjsRh6vk+0ncX/LbUXeLHIl8f8zLlxClD4rv564by5afW0uiF+/v2+B4L5LhJE0vw1HGXXLtFfJFKt1fNcjur073UHn7rIrbbVHYpWmWFVWS3VOcMBxCDs28rPcZyx0k6x2/k2R5IJybaCiHkkmUXa6l5HLoN15Wfp2Z9+KW4w0byn/XD+x333iYTsmfpzOSZ22bYtS0fIFsXTEqPX8dmZR7TWHE8+45zMrn6U9/Ou655x6sW1e7Gn91b2wx3oHyADjJ2CPY9HhovIkIr96rKESO8QapidMwQo1dSNcrtK3eIHLFGMZLFCfx63WcSeH1S50LqXg31/JZz3oWbrjhBjz66KPYuXMnjLxYToHnPOc5Fe+z7o2tRvRseRUKe1mz/XzHIsDM8mDrflgR+Qwrs7ykTh9BaN0u7nYPkI1Nud0EJo+ogRgKQVfnhVMyjEfCCLnOVn3yD//wD+Lfd77znWesI5vCdCioLoONLWbZSMZjmBg6gWaRV8G4ipCgZ6PXE4iJIZ4U8gpUooHxBkKGvrVNhKsyjPeKGtenZ6vRc7asBVLvtcC7frxzhD1b3oPqufCg0huomQTU1KzbzWAIErTonRP6YNwlvGYrh6F7BJKUFwXeGcaDxhaJGzFMQxtbBTiM0DuQmlgoylK+nsnZ8nDMdENBUvZC6pzxArGDD9VvLsYKo231xqKyJsN4LYywbu8T9P0KtbaW7KXCy/zyl7/Es5/9bGzYsEG8KE/r17/+ddX7q/swwkbESZkuK3F7px1k+GRKgFMSpT3ZNlkrVytpRqI2lHBQAJOpJTopUslc/E6Kg8msWbGyoGw5MZOs/LvK1JNMhxlerdIbl52XNJ1JVbSZUxuqUQ902kZ2rVaqvFiiWFd2uTycIZGRH8snUSmLOyhN+su1wTLhGxsD/J01U1isNU7nyUmxsRpk38npu0p/M7pDuyX7S5O6YiZXyNdVNULJKic1Qlkbaq1S6IQpucFWE3I1M3QUwUhU1Ber5DtlJG0wNKtipcKAwzVkOqhdyp4zSkFpswzRaGnx5gK6JX/GqMnyEQp2fFrettkZ6TqmkjBC9rouF7fddhu+/e1v47HHHkMwGMTll1+O22+/HZs3175265e+9CVce+21eN7znick4Inf/va3eMpTnoIvfvGL+Ju/+ZuK91n3xhZ7trzD7MSokEzVW+bq1zDu4E9NCc9W2s+eRk/AXkbPoIkaYJxD5w0UWKbc0GBWLk/+u1eXXb5nNFZxuY2MTz6UraY8zLyqEguwS+7XNPlIEz3VTDR637O1xJ4nVa3Y03Tdddfh4osvRjabxZvf/GY87WlPE4qB4XBpcfVz5V3vehfe+9734nWve11xGRldH/zgB3HrrbeysTUfNra8Bz00DaN2ReKY6lFNqnXESeeegDwovaVFcxn3UAO1fXAz1WOEwtCoADjDeDFniz1by8aPfvSjkvfkYerq6sK9996LJzzhCTU91sGDB0UI4UIolJCMvGqoW88W4z3CrR1QNR1ptxvCwFJ18WI8AM3cjx4Getjg8gLZ0RPQV22AwnLjrmMEI9DZych4Vvq9zjxaeRRNE6+lPgYxPV0a7ur3+8XrbExN5Up0tLWdGWJ8rgwMDOBnP/uZyNWaz09/+lOxrhrqdrTFni3vkYrHEGxqcbsZDOWl+KMskOEZ7PpNtF6JiBAhPh9eIB2bQiqTQnPvarebwjBzFGsa833iXBlYYLzccsstePvb3+64DeXKXX/99bjiiiuwY8eOml+Zb3jDG0TY4AMPPCBywwo5W+RN+9d//deq9ln3xpa2xNY5s3jiE6MIkLKUVreX3YohNHsaKX8Lsj4OmfLE4D7IuXNewegagKJx6JoXIJESi2oCMoyHKGoB1KuxVVAMXOpjADh27Bii0Whx8WK8WpS79fDDD+M3v/nNkjTtla98JXp6evCBD3wAX//618WyrVu34mtf+xr+6q/+qqp91v2o1+fjvBQv5WxRGCHjPgoVNF5GhTLGCQUIsry1VzBnp0SeEIcRuo8RiiAQDLrdDIYpQc0LZHCdrXMnGo2WGFtn49WvfjW+//3v41e/+hX6+/uX7Mr867/+a/GqFXU78m0Ez9Y//t9/LrvcybUtk1CXyWwTcYlMuWy5bF1kcBviFpDIVr6/jCQR1UkFSLbOSapapnZETMYz5bdxaHcina1YHrzWUtrl+mEmG0Rq1oKlJpf0OEuxP63G0u+amq1Mqv0s7XBqXzmZecVMo2nmBGbb1lW8v1qynIpajnL2kkmAkE+rQtZffv5k68yZGZj+Jqj20j87qpH1l5UccJJDl8nIV4uT/LyMjNM6mXR/OgszPo2A4q/oO+mSPjKq6G+/Q38HMvJ1zYHKh1dqoPzkcJNEEp7QuyXlO7IOPa7LJqFHsVz87398tOzyrqdfsyz3KFl5gPyRHBcXNk1lsmLcksiwBPxSY9s2XvOa1+A73/kOfvGLX2Dt2rVYSdS9sZVIJNxuCpNn7MDD6Niwk/MhPOLZstmz5QnYv+gx6niCbqVhWybS0xMItHK5EMZ7Ahl1m7O1jGGEi4VCB7/85S/je9/7HpqamjA0NCSWNzc3i7pb5woJbezduxcdHR1obW11LBs1Pj5e8f7r3thqaWFBBq9gZZzmNpnlpCkziZQWgskjfdchVchkpMftZjB5tK7Bqjw3zNLkbNk252wxHoOLGi87n/jEJ8S/T3rSk0qWf+ELX8BLX/rSc97/hz70IWHEFf6udY3eujW2ChgGJzp7BX+UDV+voNoWLPZsecbLqGUTMH1cg84LmMOHoTR3QOU8OtfRfH40D25zuxkM01ACGYqqitdSH6MSltqLeM01c+GrtTDeFrLEJaLdo3BiqNI04w1CHAriGU4H+2HX789/RaFYJvTUjNvNYOZTp4OolQYVjZ06+IjbzWCY8kWNuV/qEk3TMDw8fMbysbGxqnUg6n60lUzWTgCAOTcmjuzlLvQI7anTrEboGWw+Fx5CCTax9LtXUBRY2bTbrWCYxsrZUvI5W0v5UrybGys7r6lUqmqF87oNIyx0Vnd3t9tNYQrng9MgPINhShSsmGXH0gOIt6zhnvcIij/EtQA95EEwIhx+zngTKq7L1A//9m//VrzvfPazn0UkEimuM01TyM1v2bKlqn3XvbFVmIFYqfyff3yddF01Kqgyife0gwxqUiJFLltOxBdIodL58HUNiuUxiRy6k4S6TA7dSQpWto2TtPpMUh52GktJ2i35Po79XYX8vO3wXS2HdeVmaWKmjniicsESpzbUGtl3qla8ICXZzikRVqux9Hu5dWT4NqXHMR7sdVX6vdY4tbsaSf20KX9cyeTQnaTfZVLyysQw1GAECLV4U/p9Ga7Js1KFZoXTvdqpDWa4FVOpzKJLBJxN4l26TRVy8U7Xsez5mAlWnkeuBOT5nJGWvrLLdcshhUIv34Y3PblduokabcOvHzuGx28ZOGOdtap8Xt0pRf4bevB0rOzy/WOzNb1WZUMbWbkBp3WFw1v5meOsaYnz7DQWWpHQbytfS2xJj+ExSBijMGb65Cc/WRIySB6twcFBsbwa6tbYKjA6Oio6iHEX28zCyrA3xSuMGJ1uN4EpYueKTDMeQeGcLQ+ROnEAofW73G4GI/DeANnVnK16DSNsUA4dOiT+/bM/+zN8+9vfFhLwtaJuja3Cj8DvP7MYIrP8WJk00rEpBNo4rNML9KeP44ifJyG8gKWoSGnnXieEqQ12pI3DCBmmDBeu5ed3yb2iXo0t8motuWdLhVf5+c9/XvN9evfb1ohoNOp2E5i8Z0vlYqEMcwaWoiGph7lnvAJ54LPshfcKvu7VbjeByXN0bBrpLNc9mxPI4IiEeuSqq67C7bfffsby9773vXj+859f1T7Vep5x0HWdExg9gh6OIty31u1mMHkmtNq5x5lzwzDTiKbGuBs9gpJJABlWsfUKVirudhOYefnJKTa26j6M0FbUZXl5FRLCeOYzn3nG8mc84xliXTV499ueI/QjoBpbJ0+edLspDCW3T40hNTHCfeERbA8mpzYu9fnAXrF4eBDQiGQnR91uApMnEvAtq0iSZ6lzY6vRicViZSXeDcPA9PR0Vfus25wtorm5GW1tbVjJOI2JZT90B2FBqcpO3EEJcKGyYIGU6aRGWLq/VDIpKoZbGVOqOihT4XNa56RwlZIoBDmpHjq3IVvRcZza59TuahQHLYdzYWbP3K4lO4ZxLVzVsZaLWj/UFYmSlZO6oZNSYTVj8nLH0mwgpkSQmq28npBT+2RUM0Co5jjVKDk6qe3FHX6bMmVBny6v5SK9D2gtOXXDeGVqndUopelVqAQul4JhtdTynkfYaROJmdQZ12A1/VCtKqOs/wISBcNqn5um5LfpdIq0YPkohVCzXI1Q0xxqBaUkaoCBJmxo6hB/0rN8PlaofBtiU2bF6r5OOF3HMjIO/S3Dkp4HpWSMlTYtcZ4TdadG2Ng5Wzt37sTXvvY1vO1tbytZ/tWvfhXbtpVX3mxYY4sGFIFAwO1mMHlU3YCiV1cMjmHqGRUWDDuDFPh+5QWM+DhoTJcN5waWjMv0b3W7BUyew0OjMHQdA10rexL7XFEKqozuz0kyS8DNN9+M5z3veThw4ACe/OQni2U/+9nP8JWvfAXf+MY3qtpn3RpbBIURUsVnxn20YBMUva4vtxXFMa18bRZm+dHtDEJ2AjGtibvfE9jVFTFkloaRI0DbKoAn61yHDK1MtnKPVN1R72GEos7WEqcaKN5NZXj2s5+N7373u3j3u9+Nb37zmwgGg9i1axd++tOf4olPfGJV+6zb0S/9CGZnZ9nY8gipkeMwWrugh3hA6TZU06ndmsCQyrW2vAA9cur0kb0isTQ/bIXPiGewTIprdrsVDIWfR0JIZ9jYqneBDAZ41rOeJV61om6NLWL9+vVlk9wYd6TfFa2uL7cVgwobfrvy/CBmaYgrQSS0EHevR7B0P6wa5zEx50Ag4ulZ8EbCZ2js2Zqfw1qvxhbl5C3Iy1uSY3iYyclJ4dU6ePAg/vmf/1noP9x3333o7u7GqlWrKt5f3Y5+acaBChpbPCPmCXxt3VANNny9gY20wufCKwTtBELZJMaNdrebwlDIc3oWupVGJtrD/eEFqMi0Khc6YZYP8modGRpDWzTS2N3Onq265o9//COe+tSnCpG9w4cP4+Uvf7kwtr797W/j6NGj+Pd///eK9+lt0/IcOXLkiMjbYtzHTCU8rT7TSJjQcFrl5H+voNq2EMlgvESdzlivRKZOA8mY261gCjlbJo+pimGEfJ+oS17/+tfjpS99Kfbt21citEe1t6qts1W3ni0iGo1ipVNNNEvaUQ7drli+XCadmnHQmE8uKHyYHBuC3dzlKK/uJLsuk312khOWS8zLHxZpST9kHY4j28aJamWQq8FccP78dgod9iSOWZ0VS98up7x7NdLvTpL1Mol3mST82ahmu4J0cAm2BUtVkUxnlqUNy4XmIIut6fJ2pyXbOUnJy+4dTlLRfsk61dLhVzWYs2eKK2kOoS/VyI07Id2fQ0idbBvZd3Xaplpk9zane6jT/dBIWbCsFEwrveh2y75vrc9FUFJywGldpopoG8dzLlsXkt/fA365Z0rJli/obRshaLaNLS39yAaDJetGk+XP3/FpeXFwWbmZoFG5F1Mmme8kte8kwX+2+3fhWs6atvgeTqVzViLLUXTY9vDk+913341PfepTZyyn8MGhoaGq9undb1uDMMKBgQG3m8EwnszZsgrStYzrpGEgBs7Z8pKAjGayiq1XyAZbYBmlg3vGPY/O4SNHufvzsEBGfeL3+8sWL967dy86O6sTFqtbY4vxFv7B7W43gcmThYYZRV7QmFlewkiiDVPc7R5BszLwZSSFXRlXjF+FQ9c8w8TkJBqdug8jLBQ1XuqXR3nOc56Dd77znchkMsXzTblaN954I6666qqq9undb1uDGYff/OY3Z1SdZ9whfXI/d73HDC7GS/B9yjso9asytgJRMimoac7Z8grzc1galbpXI2xwPvCBDyAWi6GrqwuJRELU1tqwYQOamprwrne9q6p91nXO1oUXXuh2E5g8dprDcrxCECkh/R4HKxJ6gSR8yMJwuxlMnrQeQozkxhlvoCjCu8V4g/N27nC7Ce5T72qEy+F5Urzr6yEVwp/85Cf47W9/iwcffFAYXhdccIFQKKyWujW2NE2Druvs2fIIapAHL15BsTlny0uQEiHl0THeQDeTCKemMRvpdbspDIkfBFtg8u/DM/z+7ntwxeMuRSOjoM6NrQYmk8kgGAzigQcewBVXXCFetUCvZzfvyMiI0MZnzq46mHRSI5QoRTmpKs1X6BM3pOae4jKZep+Tqp90GwdFoWqOI+ufWqsHVqOKZTtsY1UghhRTSIzBdlQclCkBOikEOikBSo/j2IbaHadaZAqGtSSgJGBoGSSt8qGdXgiFrqYfVAcFvLKqjGfZzkndMCNRN0w4KiKWX+czM9CsFMaQrkjdUPab1h36rpr7gNM2MrXEatpdLbJ7ZTWqrYSWicOXmkEi0l26vIp+rfa7+iTX0UxSrmwbkqgRpkPyfqjlvS3toBYcMvzSdZpWfl0rcvkrqqrCNE0xod2oeOGevKQ0sGfLMAysXr1aXOO1pG6NLSpmfOzYMWzevBkrgb/+h+sr3kZuBHlstiWbQXb4MIz+LW63hKEHLRLCl5KC/IHLLB8KpVnbdf7wXkFYioa0yr8NL6EtkH1nlp/TmdxwMdw9gNMZDZo1N3w8OVP+/Ewk5eUsZLag0wSqzFh2KkMTs2pXF8yva6XS75YlJmirnUhgvMlb3vIWvPnNb8Z//Md/1MxhU7fGFiWykXVa9zMQKwHLhKLW7aW24vDbGWTq96e/4pi0wyK0k/EGpqIhrje53Qwmj63qMDXOL/UKwXBEeOAa2LFV9wIZtqIsQ50tBV7lox/9KPbv34++vj6sWbMG4XCpevN9991X8T7rdsRFrsBUikUZPIGmQ23JFTRm3IdqbJkedeE3IlElAV0xMW7zAN8L6HYWbckRDIe4TqMXsFQDqUCr281g8oyePoWOrh4YLY17Turc1mp4nvvc59a8D+rW2JqZmcHo6Ch7trxADd34zLkzoTTn08059MELaIoJjQUAPINdTH9nvIBim4hMH8N063q3m8KIuVMD2Xz9oYbFw16ZmtDAOVvELbfcglqj13MFaMrbYtzHSsaBTAoIRd1uCgOg2x4VRY2nOGfLE2RtDZbCU6ReCiOc8rGwklegcCaWfvcOvasGoEiEWBoOdm0xjW5stbe3N7Rajuc8WyqfCy8JMpBABuMNYnYAKj+0PYUuBBlK4/QZl1BUJEKd3P0eIRGfRTabQXtnqTpkI1H3WgD0/Zb6Oyre6kMSwti7dy86OjrQ2trqeI7Hx8cr3n/dGltUY6vec7ZkAjxZB8VKmVy7k4KhTCLcSaZ2vvyvHekQwTm0LOYglSuTXXdSKHKWiy/fEQUloeWQeK8lioNssdO6hbLdcYRgKoaj/Lasro3TOXeScTezlUvJm5Jz7rSNE059VCnVtqEcnXoMNFE8nC2fs+V0nryMo/S70/WqSSTUJVLaTn3k1AbdKL9OtU34rQkMm2fWBkw5SMnLrq9ay65XI3nud2hDNTjdJ2X3V6dtnNbRdwqnUphdoEjo1A9OcvtOx6l0nS+vTleORMasWYkTB7E9admWjpC8UHrIkLdbk/yW4vnvMzM+i9RsDNN6c3HdqVj5sdaUw/N+Np2t2TM4rdY2ikn2my2oERbgOlv1w4c+9CEhrEd8+MMfrvn+69bY2rNnD7LZbP3PQKwA7KlhKIEwEGQBAC9gQs97trxrWDYa7NjyEvTM4N+Gl4ikxjHrb1xBBi+hGz6kGn5YlS9qXK/3iQbM2brmmmvK/l0r6tbYWr9+vTC4GPexM8mcscV4gnZ7AqcUCsvx1s2uUYmZvpp63ZhzV+s87uvnbmSYMgQiUQTCjT1xypP49cf09PSiPxuNVq4/UNcCGYw3UHQfoMlDGpjlhWo62WxoeQZ2vnuP3swpnPStcrsZTJ6RyBruC4+QzaRx+uAe9G/Z5XZT3KdOQxJIlGbp62yp8BItLS2LNqJN0yFXp9GMrVgsJv7lGQj3UaIdbGx5iCGtE6Ywtlit0ws0qSlR9yxhcuFWr2DYDS5t7TFaE0MYC7O30Qtomg7LbOxyLjyurD9+/vOfF/8+fPgwbrrpJrz0pS/FZZddJpbdeeeduOOOO3DbbbdVtf+6NbZOnjzpdhOYPOap/dD6Nonixoz7tFpTOK2SaAnjHTiM0DMoCpJq0O1WMPPQzfoWu1pJkOx7pI3VIQkWyKgfnvjEJxb/fuc734kPfvCDePGLX1xc9pznPAc7d+7Epz/96apyurzlx6sha9eudbsJTAHLZOl3DxG2Exy75iFGsmFMmDy49xLjGosxeImMFnC7Ccw8r44vGGrs/qj32G8K8VOX+KV41/wgL9ZFF110xnJadtddd1W1z7p1NWRWWIVzmby6aTn9qCXy3A4KORI1benxxTqZlO8iw5XV1t5Fud2rkQY2HQpXy7dZmXHWtZIbz2Jpap7ZDlGJlkzSWHZBCrl4ifS7B+Lknc6Fkzx+OcnzDiOOjKliKhuoXPK/ioe+rP9qKWdPqA4y6ZqTHLpkO8vhhiOTi9dM+XFsS/47GMQJHNRXn7Hc1KyKv5PsOj6bBH41oikyifJElQIs1dxDZdeR0+/CCeqjONpgzqZLBrl6FXL21ci7O0mBO8m4y/bntI2s/EkiLc8RmY2UDz+eTsrHQIbDb1NWRiE1r5TK9L7HEN143tz7VLbidjv1w3LgdL6DkjITTT695B5K/9LYyWn8xKw8BgYG8JnPfAbvfe97S5Z/9rOfFeuqoW6NrdOnT4t/ObbWA/CNyFMc1frcbgIzD59qOhqqDNPo9KZO4IS/H/YSTRQxFVLvnp2zUPfjygaUfl9Yc+uqq67CD3/4Q1x66aViGXm09u3bh29961uohoq+LSWGXXzxxaLwV1dXF5773OdK5dXJ4n/GM54hLsrvfve7Jev+67/+C5s2bcLmzZvx/e9/vyQpjT5P+56ZmSnZ5rzzzsPb3/72yr4d4wmsySG3m8DMY515jPvDQ6QtDVnbuw+eRmRE5TBCL0HqqWq91jRagYRXbXC7CQyzZDzzmc8UhhXlaY2Pj4vXs5/9bOzdu1esW3LP1i9/+Utcd911wuCigsFvfvOb8bSnPQ2PPvoowuHSOkpUgbmc9Z9KpcQ+vvCFLwiD7O///u/FPny+OVc4GVrvf//78Y53vAPVEghwjDfDMN5nPMP3Kq+hsVKnp5jUW0T9M8YbpKdGEQgEoXjYO7EceCGkfUlocM8W0d/fj3e96101219F3/ZHP/qRkELcvn07du/ejS9+8Ys4evQo7r333pLPPfDAA/jABz6Az3/+82WNLU3ThKfq/PPPh67rYtl8XvOa1wglkOHh4Wq/lzAGGW+grdridhOYeUypEe4PD9Hrj6FFZ7U1L9FmTbndBGYetqKwZ8tDmIkY7CpqDdULdR9GyBQhBcJjx849GuicTMupqdwDqa2trbgsHo/jb/7mb/Cxj30MPT09ZSsvX3vttejt7UVfXx9e+cpXirDE+ZDc4oYNG4T8YrVMTk5WvS1TO0QC6ehR7lIPkQB7UrwEPbbrdH6UYWpCJBuDYa0s0at6RvUFYJPKMFPfnq2lfq0AKL2pFoJ7VQtkWJaF66+/HldccQV27NhRXP66170Ol19+Of7qr/5Kuu0tt9witlVV9QxDqzBr8J73vEfESNL+1q9fX3H7DMPASiIpU+ZxOEOysAonpaiMRL3PUQnwXEaClgk7k17UcbygQiTDC+12ohIluR5rFAfV1c4qd3blfeSkNiZV23RQpMpmyj/Mrezc9bQQRa08gb6aQUO1A41yW41lbWRgIWtV7t2q5fd1+k7VzGKrhrxIs6rL1+lG+e/kpPgl28YJp+v/AHpglukP1UEBTyZy4jSmqLUaoVlDdcpqlQVl/VBtyBUpTaazNrJWBml17rmezVTed04eCadzIVNzrEbBUKY4KI6TLh+RE0vJBwKT8fIDwZBEUc+pbU7fqeQZ6O8Bpk1AiYu3cYnqoJefm9UoU5r5a7jwteieRMsKyxmm5sYW5V09/PDD+M1vflMifPG///u/uP/++8+6fXNzs+P6pz/96bjyyitx880348tf/nJVbSQRjpaWlqq2ZWqEbUMJlObzMQwz7yciXhyW4iV6MYrj6Ha7GUyeUa2NfyMeQpk4ATvQBIScx3ErmeLkgJkFsimy7mFHe5AaP42WoI5169bB0DRM73sQifgs6i1s115iz5O9QkIxH//4xyMYDLpjbL361a8WKoK/+tWvRBJZATK0Dhw4cIaBQxKK1OBf/OIXFR2HvFuXXXYZbrjhhorbmE6nhfx7MpmseFumhmga1DaWGvcSJ7Uut5vAzKPNl8a0qSDlUPeJWV4CkHtPmeUnYs3CVDTMKjxx5wlUDQpFrWAFQEaTbUPNJiksArYegGKmoKdjUMwsMq0DMMYPQ80kYfkjyEY6YB3dKzZV2vtz0TmJGSqkl9uPz49kxhLjywHTRNOGnTBm68vYYub4n//5nzOM8Gpy9ioytuggJF7xne98RxhOa9euLVl/00034eUvf/kZyWWkWU8hgZVyySWX4HnPe57Yb6VQW2Ox2BniG8zyYidjsGfGoXUNctd7AdtGkzWLpMZ5WwwjIwE/d46H0GBC4VAtz2D7I8tXa0sYSybF8EJNzwoDyVY12JoP+uwoFCuLbLhdrNNnx8Qmya4t8I8dEJ81A01ivR4bga3qyIYNQNFgGSEgkBsCZ1pX5/OIct9JXT2XGkMoTe25f1UVvqZWpEwLs2RgiYH3ysg9qogGVyN86UtfKnQnFqqsU/7W3/3d3+HXv/710hpbFDpIIX3f+973RK7V0NBQMSSQ3GwkiFFOFGP16tVnGGaLhaQXSf2QVAvrmXQygYnTJ5FJJeELhtCxag0mho4j6Pcj1BSFbhjIZjLQdEP8vWLUcCjXo4q8EmZpUGAjbMcxgtzDg3GfE4kgbKW+728rjdOYE31i3CejGOLexXgEwy88PouCDBIrm/MqaT6o2RTUTEJsn23qgjF5HKqZgeULwfRH4JvICWqlm/uhZWahxSeFcZXq2AAtQcJnivispQdg+cLCgBJ/G0Fkw51Fg4k+P59025q5Ji3M61MqHKOslPEXUxUPPvggdu3ahS996Usiuo6444478NrXvhZPfvKTq9pnRU/4T3ziE+LfJz3pSSXLqWYWWYJLAeVdUS2uT3/606hnfIEg2vsGYGYysPIZxqpuIJvNIJNOI5tJY/TUCZjZDPrXb8bIiaOIx6bh8wewdusO7HngHiE40tGzCkYghImRIWi6jo7efiTjcVhmFrrhg60HkU2nxDpKrl9qo03xBaBoK0uspJ6hAQuF4zDeocOfQswEZk3+nXiFQZzCfgy43QwmD4UPsrG1zBQ8SiSqRYI2iemcgUXP80wC6sRJ2KEW2NEu6NMT0BITIq8u3b0ZvtN7oNgWMv4ozGAzjOlTwijKRLqgmJlcaJ6aG36aoVZkFfJU6cJ7lezeWmyCFWhCpmluAj/TvKqkibRvZgmgceFSG5SKdw3Wu+66S9QRJlvnDW94A/bv348f/vCHoiTVP/zDP1S1z4rDCCulkm0GBwfLfv5Tn/qUeNU7ZAzRq0BrVy8C81SDIs2txb/7N2wpUczZesGlsEyKobaRzVpo6eiCmc0KFzcZWvHYjLi2jY5+jBzeJ5aFWzvgC4YxeuwAUlkbrWs2IT4+jPTsNHRfAG1rt2Dy2H6kLAVGpBmaP4RsfBqKpkMPNsHKZsQUkaJpclU0ulGr3nUXNxqWouGYxjl0XiKomUh4WLWLYdwmbM8iYKUwqrNHfjH5SQoZSUJG2IaSjov8KjJc1FQs915VYLeugnp6X87wIaPJH4Y6fiw3CO4YBOITQDKWM7S61kFJTAkPkB3QhMFl0yvSLtZTnlO2ibxKuWc9GVzz1Qjne5kW+sPIO8UwXoLUzN/3vvchFArh1ltvFZF1v/zlL4termrg2BWPIJOD1Sqx/vM3OltV4Y/kREroVhdq6RAvYiqVRc+mnSWbDTS3YSqZk49t6iOD1xJSz6Zlwd/SgWwyLZJDLctEJpkQEtGK4Ud6cgzZmQmxXWhwG2YPPiR0f/WmNmTVEOyRIyJni5JMFX1G/I2sCpNu8jMj4qZuGwHxUjJJ2DThpflyhYfofy7MfCyXLLyj1H4VcsuLxbAz6LLGcELrcZSLr0RK3kuYqURN91drWfhykxKptIVMOgUzW/mxZJMcjjLuVUi/1xonyXrbLj85U8316jTZZzlIcA/rzeWl5h22kaE6lFGQyaQTimpXLFFOk20Vl2VwqO0h286p75wk+mWoDvd6MgzStg0/ssjMK0HgJI0v6yOnSI5qUkg0Bwn1dFo5e9tEX5E/yIZhZ5FSbKRUPwJWQtQVo7UxPYpucxSqbSGhh8WEWWtqRHiOhoP9aDUn4c/GYak6RsKr0RU7BFvRMGY0Q/EHEUxPiW0SfhWamYFqk7GkIzs6C1XphKVrALWVpPR9a3LPwBk6cgtg5MXORmLQ1NwYAqQ5ZpnQfL0wExrp1YuxQqXPuWqetdWgSSZ7gw7S+LJ2Z/K/lcLXpWudlmVW6PNSSoPnbGUyGaEVQXlbb3rTm4TqOulHfO5zn8Mzn/nMqvbJxhZzxsNIoTCz/GDIH2lGxjf3gAt1z4XW+Np7xatAeN2cETc7m4ayegfMkaOAPwwEwlD8YVjxvGAJ3QBpcEeyqmoW6uw4/Ok00uFOaOlZGImcETfbuRmh0f0IUDilvwnpQCvC08eFLGki0g0lnYKRmRUPl9lwN/ypyVyonOZHBj7o2ZT4rEmzcEV5be+6r5caFZUPFpml5WTcz9koDONAGgZiCLnTR+Qpyv+pwBJGEd1Hk0oAIXMWup2FpaiIqyG0ZcfFulk1LJ6lrfTetnDStwrt2VHhnctoPgz7etCfzOUmTRptyCo6otkpWKqGjNEBzTbFsUwl99yK603iGFnFEP8mQmuKg9VJ/9wzmBhuWlf826+rmNHnZKutfJ2ygqFTeF8xigJfagoJnYWWmPrjoosuQjweF0KAj3vc48Sk3Xvf+15hcFFa08c//vGK98nGFrOkCNl3MuDIeNN9sK1cmKQVLg0HMdtWI5HKFXQ0fWGkI3Py5PGODUjPm0mdblsvZvXIiMramnhg0PscigiZUJUsFGgIpibEwy4eaIVuphFKTYiC3CPRdWiLHYdmpZHWQ5gJdKA9Rg8/BdPBTqF8FcxMi2NMhnoQye8no/qQNCIIZmLioZfR/NAoUtLO5tqjGIX0W0/GJJtQEVNcGrQwZVnblMBw0oeZDN+OvUInpjCJJrebweSxoJafkJhnCOnIGUFZ6DBsCz47LbaaViJosaeh2yayqi7yvzqtnKdoUm2GgSxarUlxuz6i96PXHBbbJhU/RrR2DGRPCoNnTGsV/0atGCwoSJEanp2FBivfPgUJNShyYknQg4yhhG9Vzm+lqDjt6y3xbB0NloqGxbVw0VsXU6Ml65K610Lt8sZWmGvR1SN0vS59nS21qu3I20QhfiTQt3v3bnzkIx8RyuW1Nrb+7d/+rahGSBMnN954I572tKcJNcJq4Kc7s6TYU6ehNHXUVpFw3o2Awg5F6GGeVKClJNRlOjKXUJs2gHigDdm8y3+saXXJboebN+TDOpBL7qV6HGTUgYyqAFRSUyKvnw1hpBm2nWuHZSKSmRKfHQ30oi01DJ+ZFIbYmL8bvfEj4oE77WsThfyaU2NipmQoOIDm9DgMmu1U/Rj3d6I9dRqmrWBWi4iHdsiKiwc7hZIYVlLMeNJymlWlAQHtl97nmk1BRM5GHn2XhMKzkQzD1BBh9OTuPyTTTgYIvU/BhzASwgjKQBdrmhAX76cRFvXMxHpbwSmlA6vsYfGpOPyYQRj9GIZiAaNoQa89grSSu9cfUPqxxjwFDVnMKiFMKFF0W2OwFAXjSoswtoJ2SkyI0d2QjmvSZBgMMeE0rrYIA4kMMzIcJrUoFC133zyhl3qKDhlzKnZETI0U/57WSwUa6L5dYMUoBleDKHrLQktcjWB5+drXvobXv/71+OQnP4lLL70UH/7wh/H0pz8de/bsQVdX7eqHUrhgOc4//3zce++9Ve2z7o2tur7hrQSSswAZWyuF/PVCDxKasyyQWjCzOBPoLP6dBsXSzz1kR4OlAhQnIutL3s8azbmYcNvGuL9DzLDSUWmgMmM0w7asvIcs54nKzZnaMKw0/DYdTUVK9+dCVmwLMS2CtK2iMzsmPndS70GzNY2wFRff4bDSh7XZo+IYKehiIEQDGgo/P4EORJBAGEnx2RNKF3rtUTFXO4uAqDfUgtxMLg2OVArOVLLi/ZQdFtuSnUcDKVom1tkKMtCKM865uee8t485g4mUjpTp3fj1RuQQzixh4gmE+MGcYUO/KM0mL76NjOKDYafFb5vW0YRMxIqJdQk7F44WsWfFNpNqVBQKDtgpZC0Fo2o7VllD4rMzShgZGOiyRsXxjqvdaLFnELFz95Mj2iqst4+J/UwhgjgC6MGo2P9JdCKEOEJIiXvXCXQK44qgz5HZReT8QQpyraV7a275MHLeI7rH0fr9yoDw9pAHKY4gjqhz99b5fxPHtDkjKaUqiIn95phRIiXjgRR4wulcmW4tfa41FPU+rvRoztYH82qA1157rXhPRtcPfvADfP7zn6+qHm81+P3V1WCse2OLcRndyFVeZ8rPDooZ13lGnRaCqcwFzFDYYoEZvRkz8zYfyoelEJZp4cg8VacRtRMjxZ1YOKTnvHg0+KIZ3xG1DaZQr8zlQ9CgqSCtPIkIZb0VB0QpGPMGd7YYHhU+G1GSUBULSdsHU/GhR5sSxuFpM4qQkkarFheffSTTiY2+YfgUC9OWHyPZCNb7xmDpFo6lovCrJrqMWbHPR+KdWB+cgBFII5b14UQqgk0hkhUGTqYigJpBpz8pZhX3zUaxKhCHT7WQMDWcympYHU6AbNnxtE98psVHieYKTiX84m+faiNtKRhP+dAZSMEmT2JWEx7FsJ7rk+mMgYBmioEsLY9lNUTy69KWKgxKOia9J0NJU2xQBBAdL02XvULrFNGOgpmZ67Ezjc6snfsc40TOwCj0YG4CIgcN6gvXJF2vOaM/l2dqwyhOLhA0eRACXbP0WQog04RhQNuSN4X8HpTt2YNJ7MWAmGig7ckooG3b7ZzxMoUwfMgKrwy14xTahXFC2yfhE6FrPRSqBhvjaBFHarGmxbZHtH70WafhszNIqgHxWxwwT4r2jalt4hu05cPajuqrhDiC304hpfhxWuvA6swp8R0ntWZhlNDECvXGsN4pJlhIBCej6EhqfmF80TrqoawIu9aFdz1njPmFpyhnqgHDanu+/3K/+0PaQPG6PE3Hnnc2DiilsvgH5snkx+25e5a4Ty2o6TeOuTyhnGdpTiBDhN+Vgc7TSWVugotxn+bxfZhq2+h2M5gGIZ1OC68SCVYUoHJHT33qU3HnnXfC6/AomFlSlHaameRwA69As90iFEeEgeSTpMXgau4cUZihOU9Igwyx4vaU82XPJVwP2a0FKwJZ28Lh7NyAaNYOYMQq5B6Y2JcudfM/kupBNp0XXzGB05m54+xLtCObKQiz2Hg41lE0+OysguksGYCkbqxgOBUQxg4NEE3bxGjKJ+bJyQiiz8SzOhTFFkYVGUkFtTRapyu2WJdSVZD4W0SncglALKOjSc8iqGaEMUTGWE8wJdZNpAxhrPWFaJAOHIoF0RlII2qYwkvw2GQAW1vi4rOjSUNsuzaSFMfZPxNBuz+DNn9GtP2PE024uGMasYwmjMGJtIENTTlPwIGZIJp9WXT4qfYe8OBEFDtbZqCrtvjcSNLApuaZfBtCwlAk45F4YCyCbS0xYVhOZXScivuxpTlnzB6Z8cGnWegJklkI/HE8gk3NcQRUEzMZFUdjfmxvzak6Hov5hGGyKpxTK31oPIh10RTCuoXZrIqD037sbMt99sSsT7RzIJLb72MxHwbCCdGnSVPDvpkIdrZMiXXDQsFLxUAwJt7vibWg1x9H1EgLr+7eRDt2hIfFupFMGAlTx5oATTUoOJRqRZueQJOWQtZWsT/VgUFjTHx20goibvnQo5MBAoygFWElJV5kjp2w/GhRaQKAruWcgRVW6C8FKZsMMxOGkhXhbQVyBkjOvKOJCTsflkZnPxcsl/sdTSlN+ZC1XHjakNqRz9fJ/bamtKbiZ4/nvTAFT8shvTRUbUptLk78kqd6Pkd8q6WhaxP6XHkQYjwvlV5QFSRxhgKpfLsKv4c0So2k3E/PO7MAnfYETiq1CxVizg3ysDLMuTI9PX2G56ic92h0dFRMEHd3l+YJ0vvHHnvM8yeCjS2PkJRI6Rr5OPJyqDWMmXZQ/4UpuammzbMr29knHhOqhCsFmeRrNVLtTuvcklY3kBEPyULuQy2ltJeO3Mx7YaBKA9/0vEsvac3dxsjkIq/UfJJprShtPp6kvwu/G0sYCDnJ89wRppOFfWVxMkOj3cJN38K+qdLQo6nUnNF5NDPXn7S/P47NrSPuTxbeW5hOajhUbEMaR6Z10Q4yymj9vYnCcWxMJjQcmffZB0fnP4Qs3JssvDdB5sbRqdx720rij8Pzb+9p3JPI9Ytt5YRojk8W1ifx8DB910JYRxb3DBX6MHe+T80U3mfxp9H5fZjFvcOl/TIcz72ncf2+qfn9YOH+sZzohJZ/mI6l5oyDg1nyzIZhBHLbPzRb+lB9NDnnuT1B/Z2z/wT7M6WD8IOZzqI0dwI+jM4TuzhhtYl/dSP3fYfs3HuCcoxidghB4e/BGSIZ09rce7pqMvnrQ4ToKqUDBArFO5usuRMyWfiqSk043KurkXF33KbG97aCoIRPSSBj567ds0m/a1r5ECWnbZzWVSObX03/yCTrkw7XkO5TK06jcCofIGvfwm1iGT8mRmcd+64aefdqtvE5SPCTImOlTMbn3VzKyMWn8mU6aAw0ncwgmS+dUy8I7/cSh0ra+f0PDJR6yW+55Ra8/e1vR73BxhbDNBABm2bsaUafFQm9wpEZPzJWnecArDBOop3l+D0GhXIy3iGhhnKez3rPX2KWlGPHjiEajZ41J6qjowOapuH06fkBzRDve3o8mmM7D87KZpYUpZnDPrwF+X74Z+8lOoMZGBqH5HiJQZwWwbWMdzhmc86Wl2jLjImw24ak4PGt01BK+lrL8SLI0Jr/khlbPp8PF154IX72s58Vl1EZH3p/2WWXoZbMzs7i5ptvxuWXX44NGzZg3bp1Ja9q4KkiZmlhcQxPMaHOSeMz3iBiWBhP1edDe6UyJ7/BeIUNyinstedKeTDuYilavhRJAw8j2au3rLz+9a/HNddcI+pgUW0tkn4nw6igTlgrXv7yl+OXv/ylqKnV29tbE1XzBv6VMMuBPX4SSmQuJ4Jxl3ZrHCn4EVO9ViSzcaEQQhLuYLzDhFDk5HPCMDLGjXahesnUH5SrKcvXrOUxKuWFL3whRkZG8La3vU0UNT7vvPPwox/96AzRjHPlhz/8oZCUv+KKK2q2T/6lMEuGO2IKjBMaFWnmMaSnIEU//qV4i/k19hhvMDZPFZVxH6r7qKh2UdWSYZaDV7/61eK1lLS2tqKtrbZOAja2lpFLXvhP0nUZqQqefH+aIfXvS7eRCf04iB5KcVLFSpNaFRlbneuBecpVKQcVq2oV/5aDqtUIJd+31oaok8LV/HVU3DRN2SiW88yVbH8yhbTcusq/k5Mqlmyd7VBKQNHk63Kqg5VRzTaV7m9rWxIHp3xIVlHYuJr2WTX+TmqN+66WSp3V1uXswAxOwYfMgoGkUsX1WosQlMXeO2S/Wyf1QCelQtn9y0mFT37vqO68Fvo8rpIKqbmoe0fGA7lEi1X1m09BIbOScyTD6bojlc6K97eg3T7ExKQEFVmQKW6qDseRKUZqusNvTLJN2uE4Zl5B8IzjOJyHqUR5dcFQfl/JjFUc50zFM0gl5lQy64GcNu/SH8Or3HrrrcJ7dscddyAUqo2YGBtbzNJBEtMzo0Abx9l7BaqhVSgkyjBMeXKlp708HGg8+rRJTFmsouoVqNYcVZ9jmHrjAx/4AA4cOCDCEwcHB2EYpZ6N++67r+J98qiLWTrMLJBJcg97iE57HKNKq8jbYrzBWEITxZAZ73AKHWIwyXgHe54ZzLjPxIIadEz9QE7ZpQ4msjxspz/3uc+t+T7r3tjivCFXex8wSgueMu5CwxRO/PcWFD7IIuPeogUxTCOMdL4wMeM+h/KFqhlvEEYCTYhjCO1oNHhcWd/ccsstNd9n3RbcqXWc/P9v7z3gJLmqe/9fxc49cWd3NgflDALJCBAimGjCww8wUQIZDJaxASEh/lggE0wSYIwQ4RkEGDC2H9HyQ0KACMJEZaGwWm1Ok1PnCvf/ubenZ7pn+9ZO13aorj7fj1o7XdVVdSvfc885v0P4gBtaA6N06ALElNIPK/xjLF3FpmQJuhLgYb4eJIISyAQOFoNalmqfBQgHKvQA5MZ1krB2M7kx2Y5PL0G9LqJ1ZGfLYYQD6+koB4QYK6CgmJ1uBkEEGhqQCB4xhUv7sB7v3gcHLo6Rp3B0IiQMDg5i586dGB4eFmqEXg6b6enphtdPxhbROrhylIdyHNF++lgGs0qa0poDJv3Oa20RwWECA3SPBIwS00m0JGDGVq5nja1we2V6MWfrU5/6FFKpch4iL5bcbMjYaiMearlSGe6ol3SqRLrUQ9EUmg+/t0zC9njS74rG6wcpNdLv7ZR3lx0f20cbvNrmJWksc5X7kUH2kndftSQ7c8vlBBTmq90yKejjyRPLpKe99kkms614Sb/7nNdJ1sRtHFzQPJ8PzZR39yPJ7ue4+j3eXvLqjS7jNTrpJUs9qk1jXkkip8RWJVddbgPaAvPh2vEq8+B1T8sk3v08B05U0n/PkiCD3dbyAX6RXZO6oTUs/e6FVXAafh573WOy9h0rWc+wTR/HI/Z6qJLaMZpHOQtXci8xJj8+qqy0gY/yBX76DrO5siR8wSof85LNxLTS4nSie7n00kvr/t0sQpuzRXQeYWhJi4ERnWC3sjG8geZdSp/pkL5aAJ9dJP0eLDabs0ipxU43g1iC3yNEWGEt/vQaZGwRrbu4stNQbHo5Bokt7Einm0CsgDsPevHlE2SySozytgKGyxSoJCQTKA44g51uAkF0BaEPI+w1xZNA4Tpg7YqtIVZFr6tHBZEHZ+JgfmLDiJbBC3+7NBYZKOZdntsYzFDgXiWlFJBlvVfeJezdyl7M2Wo1oe0Jk/R753H7RwGjNueB6CxZ0PkIGucM5WjEPmCkWAYxUEH2IFFyyQAOGv1qFj0NheQTvW5sEZ1HXZjs0ejc4DKjpDvdBGIFlBsURCgfJWiktCLSGhnAQcKlrC2CWBWhDyMMEl6KUKpkhMSQqPx4KQt6mTey7XghU0njioMyiraLSG4excRaLiG0vIyHalAQkKkQyaavThurOcqCXiGxXuurZiMbw2PKpuP+TtoGn6qMrg8VKdkyftT0OIoWzBCk6VIEimZClXRc/Oyv1yianztQDYCSox+VwmMV1Fb3LJzT+oRIxsrfeEVFy7bl1W4/90wQVPj8PAf87mvluWfBhaG4sBeV4PxEtmgeCpReyNru1e5IpH73KpqQi0ZFYoaPa0GSF+1hl3opccoUEesduz3YBC5e+EL1N3WX+XH8Yul24on69R4vmvixdJlfDDyjafdKriB/c8vUh3Ol8rW3UCgtfT8yl4eVD9cgQDuKDrOAxWK+9KUvXfVvv/Od7zS8/tB7toJ2QnsJxjto5GYnCE/mS7xUKxEkoqyACCNxnyAxa0cxacU73Qyiis0Yg+5ruJEggkVfX9/SJ51O4yc/+Qn+8Ic/LM2/8847xTQ+3w+h9WxRzlbnKQ1vJ2MrYIwrpB4VNHak87h7KtnpZhBVmLDgoPPePGIZU3ERVy1M2mRwBQnNl688HIS1n8nPaKvPqotgcdNNNy39/a53vQsvf/nL8fnPfx7aYkSM4zj467/+a2GI+SH0ni2ic0TGHqHDHzAMUPFFgjge3NASdQKJwKApLhJaOXyLCAZcRqYn7xMKRQg1X/7yl/HOd75zydDi8L/f8Y53iHl+IGOLIHqIQTbf6SYQK9g1zxUie7DDEmDmlBTmFfI2BgmHcSkZuk+CxAJicHq5GxlSzxbPvmnHJ6jYto2HH374mOl8muv688mFNoywAuVsdQ47MdTBrRP1oM5K8BiM2FiwQv8o7ir62TxUMEwr/Z1uCrFIzjWxv1hfVIHoDP3IohT+bmQdAmwpECfM61//elx++eV47LHHcMEFF4hpv/3tb/GRj3xEzPNDaO+SsMbSdhNMj3S6CcQKdisb6JgEjKGIhX2Z3isMGvxBiaBlFfQ2pmJja3QOO/M0iBcUuFeLVz/rNcI+iN/rRY2vv/56rFu3Dp/4xCdw5MgRMW10dBRXXXUVrrzySl/rDK2xFUQsD5nrONdPbVCeWJN4773U1WXL+JGs95J+dxwXxuxhWJHaZMKYoUnl32Vyq2FEJq0u5kmOt+NxvFcj+awwF+sxgYPK2lUv0wie0s6Sc+4tge9P4r2ZKBLJc9/y83XW53LRdw9pda95snZ4tU/xsUzQ8ZJ4l+ElyV5SIlB5FaEVz1+v7fiRpg8CXvskexZ57avCGj8OXqU1KtvSoMAwFBhuudsSjcsl1OOp+gN9RsTrXvIozSJ5cXodO10ir+4lbS5TcfdENqYZgLHOZ+V+IZ+Za3x9F8/8tO703679U+kysn6FV39DNi+zKBdvLb6Lbac8zfaQkSe6D1VVcfXVV4vP/Hw59cKvMEbPGFthH4EItDWh9nAsdwDhYVE6urdDHVbunaFC00HDVjRp3TOicwV05xzyABOdh4U8jLAX62zJOFEjq0Joe8MURtjxE4Bi//GL5xLtJQfqrASNcwYWOt0EYgUxVkDazdBxCRDcA8zztggiMNB4TCgZGxvDa1/7Wqxfvx66rgslwuqPH0Lv2SI6g+JY0IoLsA2utEYEpbMyDfKiBA1N6Y4Rvl6ChGSCyWZjFn8srut0M4hep0u8Mn7pxTpb1Vx22WXYv38/rr32WpGr1QznTeiNrW5xVYbR2FKtQqebQVRhooQ1mMVBlHO2iGAwXZTnnhCdIa/EUFQCkPRCEETwqHQrSYgtlNxxxx345S9/ifPOO69p6wytsUVhhJ2HaRTyEbScLZ73QASLKTK2AocOGxFWgqWQIRwk9loDnW4CQVRbW6Hdu1b7KRiCy6ZNm5ruqAltzhbRWVwzAStN4R5BwoKOWaQ63QxiBSenfchyES1FZw6ijDzzQaNfzXe6CQSxBA3qh5N/+qd/wjXXXIO9e/c2bZ2h9WwFMYzQ8pIabWIzvRSIDYlCYESXJ/3J5Oe95Hr13JQYGrGTa7yauur1tUsW3o9MbLORyaF7Squv6jp3hYrS6n7bHrzkxv1IkbdLvpzLscu25SXVXn8BxZe8u+cqG23DCaDq7fFge17/Pu5Pr/U5Wnlkd+X7w3U7P3Lp1cHTJHLjTcfjlGtu423QDfkyZmzZu7jOmUJWS4i/146U/63HSLq+EBAvPSJj8L7vrLK1RNC4cOy2hpe5b9NzffcDKl0V/q+pq1Dadd+1Cd5HaHU/wQ1QP2Qlr3jFK5DL5bBjxw7E43EYRm2Ew/T0dMPrDL2xRXQI3kFU6fIKEhGUEEcRWZBoSZDYk5F3GonO5WzlNbpPgijyQxAdZ9FQIM9WeD1bzSa0veHKTRAkz1YvwYwYGBlbgcvZIpW14JEybMxZlBsUJHgIYb87j6PaSKebQlSxR9tIx4PoOEu9SiXEOVtt2EZQufTSS5u+ztAaW0Rn4YaWSwIZgWIeiYA/4nqTNZEiDubIixLEwQkiWGxzDpLBRXSepUH8cBpbvcj8/PxSAWP+d7MLHYfW2CL3bmfRsxOwE2vgaqG9xLqOFLLi1TCHZKebQlRBXfrgYUNDTqEC4EFD5dV5eEc3pB4FossI6XXIU9NanabuBuzFNzAwgCNHjmBkZAT9/f11bQgeKcenO07jedSh7wlTGGFnUFwXTKH4+iBhwCHp9wByz0x/p5tArMCBhjwZW4FjXqGBIiIIBMxSIE6Yn/70pxgcHFz6u9kOm9AbW0FiJm9J5xleEoISVIkL21XkD4KIRDXHa/uG1rgaodM3CtWI1B350dz6yzk+HmBeKoHNVBbkikMlO8g1z4+vumZBhbOYuUW08ng3Nup1Vv8CHpj1J8kvUx1slyqj17b8Kiw6kvtM09SGlQW9VAq91LA018YQm8ZBtbZ8hdcLmGt91kP18Wz3XM5zDKv+MqrkGc7RXfl5sq3658lPhqERkW9naEAeRrtlaFlARi0lsc2I8ovLWz3wELqSyMWvqju9+ItvSpeZO+/P607vu+fbTWtXGDnnwC3SeUfPfEnd6dri/X8gWu4698UMcX2WcvRO7Xae9rSnLf19ySWXNH39oXU9kEBGZ9HyMxTPHDAyiCELCo0KGoZKL+qgwYVkFBJXChzG/BEojt3pZhC9TtjVCEXZi9Z+EODX3sUXX4z3vve9+MlPfoJCoTn1FkNrbBGdRcvP0SkIGGtEthYVag0asyUKMAhiztaEOtDpZhAr4aHprDsjDIgQsaSPEVJjq8d59rOfjd/85jd48YtfLPK3nvKUp+Dv//7vcdttt4n6W34I7VuePFudhfHiyfQgChQKSb8HkrFCpNNNIOrcK3FWQIHytgKFlVoLplGZBKKzLIfih9PY4iHRsrDoZm4jqHDDimPbNn7/+9/j5z//OX72s5/hYx/7GFRV9eXtIs8W0RJKI6fSkQ0Y86KkMXVUgsap6Wynm0DUMbZSjM5L0FDsAhRXnvtMEG0h7GGEhGD37t24//77ce+99+K+++5DKpXC8573PPiBPFtES4iMPYzi2tPo6AYILo3hhnQkjiCaC79P6F4JGmopL2o4Mp1yT4kAqFyH1Nhayqtq8TaCyqte9SrhzSoWiyJ/i4tnXHPNNTjnnHN8G9ihNbaIDhPkO6lHGUAGJehC1poIDnszVNA4aDiKhn3a+k43g1iJqpJwCREAFj1bnW4G0RK+9a1vYXh4GH/5l3+JZzzjGSJnKx6Pn9A6Q2tsBTFnK1OUqygNxJoX3iWThOfIFIBl8u5iHs+/ksihS9uQHJDKzEul15usVm277ZGS94NXCTKVSc6Fx/GGhyy9u7hYWfSdXx2teUV4ymxL5vmRKG+2rLmieUiUexQvbJbsekxzMOMR3tnsY9QuWXjPNngcV9lyrodEuUz63ev5L1tG4DjYzI5in1prcDEP5Ug/95XiIQsvG0H1kpKXrc+rTIfXPBlez/64Wf88xUx5dyNmyM+tp8R7h/mLt7xdOq9os4ZLwDw2Uz/5flwiCc8ZkUzve86l0mXGb/2qdB4BrPvj9zwPQ3TyMfFvfOIRrHngu01TrAsKvVjUuJqpqSn88pe/FHla7373u/HQQw/hvPPOE5Lw/MMFNBqFcraIpsM7OCzqr24Q0ToOYg2s8I6vdC1rY6VON4E4BgUaSPWOIAg5lLMVTgYGBvCiF70In/zkJ3HnnXeKfK1TTjkFH//4xylnayV0E3QQ5kKdOQRnlHK2gsRazOAoBilviyCOAx90zSgU3kkQRJ3nQ4AiplpBr+dsTU1NLSkQ8s+DDz4oJOBf+MIX1hQ/boTQDnMHMYywZ+BhQJIQK6JzxFAMsNhq73LPNHmBA4eiYJ4lO90KgiAIos2MjIyInK2nPvWpeOMb3yhCB88+++wTWmdoja0KZGx1AFWD07+hE1smPCjBAKOU3sBxel8WD85Rxz5obGDjeEzZ1OlmEAQR0H5lWCOoer3O1n333YczzzyzqesMbc5WWG+CrsC2oFj5TreCWMFhDJF+UgCJaJQbRBAE0S2E3djqdc5ssqHFIc9WQLCc+la+6xEGKbvPvcSlFEm/LuqhLiVTFZQpUjlFC5rF66E070HkuM3rkDZTVfB4+FEUY5JdlYhCrlqpcAc7gl1VI/UVlcJG1RL9IFOY86tY12zVQT/LyNonUymU7e+8pftWD/RzXGW4PlUKVcn+eh0HP8fc1eWvK9lzUnYvrYajCh+caC1enTVTournpR4YkywjUwj0qxLYH5erZ/ZJ5i389OvoRl72V3LFwZjHczchOaxJU77M+lSk7vSCI7+Qv/PFf6o7fVy6BEF40+s5W62APFtE8+E9HB8dW4LoRQ7l6newiM7nOBIEQayEPFtEo4TWs0UCGZ1DSw6AuSdWAI5oPlPoo8Ma0JytuyYTnW4GsYI+lsGkMkDHhSCIuoQ1jJBHCnhFVTVrG71EQ56tD3/4w3jiE5+IVCol1Dpe8pKX4JFHHlmaPz09jbe+9a049dRTEYvFsHnzZvzt3/4t5ubmatbzgx/8QGjW89/dfPPNS9P37t0rLl6+7oWFhZpleEGx6667ruEdJIGM9mPPjEFZmOzAlgkv3PA6sgmCIAiiLVC/MrxYloVnPvOZePTRR5u63oZ6X1x3/oorrsBvfvMb3HbbbaJRvJJyNpsV8w8fPiw+119/PR544AF85StfwS233ILLL798aR3FYlGs48Ybb8QNN9yAt7zlLSiVaot6ckOLr+NECOuIQzfAHLv5ST/ECbMGM3QUA8j+bLTTTSDq8JiykY4LQRA918/kKYLt+AQRwzCEGmFHwwi54VQNN6a4F4pXWL744otx1lln4dvf/vbS/B07duBDH/oQXvOa18C2bei6LowtTdOEp0o0YHGaaZpLy3HvGK/czI0yvn4/UBhh51DMGBgL50OIIJqNofZWOEW3sIUdwT5lfaebQRBEwKCcrXDzmte8Bl/60pfwkY98JBg5W5XwwMHBQc/fpNNpYVRx+N+vf/3rMTo6KgyiD37wgyIssZpXvvKVwnP2/ve/X3i/TgRy97YfNRLlMWtEwNiHdZ1uAlGH0VgRR7KhTZ/tWgzYnW4CQRABhPqVwWXv3r34wAc+gJ/+9Kc4evQo1q9fL4yn97znPTVOHS+4c+jLX/4yfvzjH+P8889HIlGbU82dQY3i+w3vui7e9ra34clPfrLwaNVjcnJS7PSb3vSmmunve9/7xLKqqh5jaHG4EcYtyhe+8IV4+9vfLjxkYXDvJiPyw60pjUnCcxxpgmHj++7HpSuTILamj0AzhwA93rB0cZBl3GUoHvsjD6b0CrOsfzJ8q98vyhOPuHM4oo4cIwnfyfvHS97dj3x5uyThOaphNqfditKwXHwFx64Nwa7gSqa3QlLflRw/VZe/2Dz3V9I+t833egbx1j8fPObJnpOpqPw9kozWl13vi8ml2oeS8vOUlGxrMCZfZkC2rZe/WbrMnf/xeXSax73srxp8z/L3tvz8eUm8y4j4iL6/+l3vqjvd9rhfJnP1BxK+9ll5J/LJr/pr6bxfffNG9CJB7Gf2ukDGww8/LOyTL3zhCzjppJNEStMb3/hGke602vQkvszjH/948ffOnTubcs59G1s8xI836I477qg7f35+Hi94wQtwxhln1BW26OvzVkZ7znOeg6c85Sm49tpr8c1vfrPh9lEYYQfhnUOfNXaI1hFnBTq8AeT+2TQ3MTrdDGIFMwo/LwRBELVQGGFwee5znys+FbZv3y6E/D73uc+t2ti6/fbbm94uX8bW3/zN3wgVwV/84hfYuPHYJGIucMF3lnutvvvd74qEMz9w79aTnvQkXHXVVQ0vy71hkUhEeM+I9mIMrQdKFBYVNErwdx8SreWkVBYPz1KphKCxkY3hsaoi4ARBEBzer+Sejw0bNoTygHCvk5dHt1nbqDhmquH9dv5pJjydySvdScauXbvw2GOPCU0KrrDOjWy/nq2GLBG+IW5ocQOKx0Nu27btmN/wA8cVCnlsJJd4j0b9K21dcMEFeOlLX4prrrmm4WV3794tjEGumEi0Fyc3L0KjiADBGA6qazvdCqIOMa094ZIEQRDEieM4Du666y6hvk2cGJs2bRKRbpUPLzHVTLjB9JnPfAZ/9Vf1w4TrMTU1JeTfeYmq5z//+Thy5IiYzpXVr7zyytYbWzx08Otf/7oI6+NeK558xj/5fL7G0OKxkVzJg3+v/IZfnH7gaobcsKuu59WtsbT5Q7tQnDgojNbSzBis+Sm4xbzIUXCsIpjvpJxgYc9NkrEVMBQwbHUPdboZRB0yNnmBg8i40vhIKEEQ4SfsYYQ83a+St9W6DwQHDhwQnqfK593vfnfdNnGnCz/eXh+er1XNoUOHRJTdy172MpG3tVq4VgSPyNu/fz/i8eWok1e84hXHqLKvlobe8jzmkXPJJZfUTL/ppptw2WWXCUv/t7/9rZjGE9Oq2bNnD7Zu3dpwA7ll+YY3vAFf/OIX0e2qMebgOjDGDSp+JbtwrRwUXo/KLmHh6FG4jo30ppOQnxpDKTMHzTDBBp+I3fffCU030L9mLSLxBOanJqAbBmIbRmGVSnBdB7puIhqPi8RALq3fUUL6AOp2Y4v5EE4hWs/+bIwOcwAxQFERBEH0nrHVTtLptPgcD+5R4naGFzw/qwL3Oj796U/HRRdd1LD98KMf/Qi33nrrMWlSJ598Mvbt24eWG1vHM1y4EXYixg03xuotz1VF+KfbBTK0WHLpb3NotGZe3+DQ0t+pDcsXDGfbmY+DY1tLRkw0kYRj2zwyDPlsBgtzs2L6lpNPw8P3/EGEjA2NrEUi1Yf9j5WVVLadcjoWZmcwNz8PMxLBxu2nYOzgPqiahngiBYcZKBXz0DQdeiQijEC/D5L49nOQnS/6WpZoDdzQmqWE/0ByRt8C7p46VpWV6CwDbAHTSj+dBiK08MFZnn+Uz+Vg8z6GB4cfuQ/rTz0Hhx66W/Q/Ev1DSAyuwcSexqKOwkCQ+pWtoB1Fh50G179mzRrxWQ3co8UNLS7bzp1BjWo38Oi8ao9Whenpad/5ZKGNXwniiIPqo03ielRVqObyCU4Oli84w9QxODIqPhVOf9wFNXK0Z53/J0sPh0g0jnj/sAjp5M8K3YiIByzPa7OZi9nxw3BtG0MbtiAzPYmFmUlxkZpbz8LUznuFBy3aNyQ+C0f3wVR15FNrwKwS3FIeiqrDGBhBduddUAe3Alz6WdOF8Ycq402XSBp7ScJr0pvFbVgW3q9cvB/JelnwrNetLysI7VfqhR93XjfXYabHcaxFc+r/Tl2Uka87z1Ialvr2kl33I/3utYyX3Hgzl/GSPGeqU/cY+NmO1/66llz6XSYL7xXGrHhcN5oZ6/jbRfZsVWU1NXi7NdVTrl0Rj60Tf48wj+eN4/H8Ktn1t130KNkQa9Mzz0u2udWS0RWyj//f0nky2fORtLyjVJAc15Ltp/yKvJyL4crvTSWfrT+9lF96jzv8PmXlvzP5POxSEf2pBOYzOcxnc+J1u2PTKB7cdQAly8JQfx/6Ukn8cdceEdtw5inbMZNhmJgYF/VPz3/iBTi8a6941z/nz1+FZLoPc9NT0HQdqb4BEY3DjTILGgzTxMgTLqxp2+aRi3DGWedgYXYaIxs2r/panc7XN+4yJXl9uwe+838QJILYz+x1Dh06JBw/W7ZsEeqDExMTS/PWrVtdrdGnPvWp+NrXviZKV1XOM78HPvaxjwkjzg+hNbZ6ZQRitfCLhYceRtXlUz44snzhOXkLo9tPW/oeTaQwvKksgHIkU8TQKecuH0/GkN6wA8yxUbQV0V1QHLss+c5ftoUssDAJ8E5YNAVM7OPFc8CGNgHFHIzZ8sVvrTsd+sQu3hsBi6ah6GlEMuNgqgYrNgCFuVDtIhgPtVST0Jyyt8xVdTCFpOUbQYeNAXcOOZVU74LG4XxtwUQiGOxWj1XaJYjVwt+V/MM7aYVCoewNSiaRmZtFNsdTCBSMrluLnbseEwOggwP9iCgOHt2zD7bt4NwzTsWho2OYnJ4V9beeePZp+NXdD4gBs/UjQ8KAOjQ2CUMFkvGYiHyJmCZMvfyO37ZpPXRNg2Ho4t+nPqH8DueY60awvSrV48yzzhb/TuTK7/CR9XWufQ/DKRqLi/5FrxD2MMJurrN12223CVEM/lkZBrhae4AbVVwg4w9/+ANKpRKuvvpq/PGPfxSerV/96le+2hVaYyuIYYShOa7ccIuUVSaVbFGEHmrR5Q6juXYzrERVmOTG05f/jvfBii27gu01JwljixtwrOTAig9CcR1hTHFjS3FtqMwBzCQihRloTgm2FkExOoD03D4kHBcL8RForoV4YVqsc6pvO/oyh8BsC0U9joXIIPoKE8JoyxlpPhyJiFOAy7/rKRhuUYz4OYoGWzGgwoXLp3AjLyRQzlZwUbgLhQgcG90xHNBqw72J8FDuG/D8aUc86x2rBKdUEIJVZjKNYmYOTqmIGSuB9OAwxvbvFtEd6YFh/kDFxKH9OKQxnHbWOTi0by8W5ucQjcVw8hln4f47f4+4oWLjpk2IxeI4dPCg8CJt37EDhWIBtm0jEjHF+3R4aBC6piMai8J0S3jCOWeKvGs+77STttd4tp7y+LJRVKE/lRTvSE48Wuu1SyXaN7DGI2T27XwQJ531OPQC1K8MLpdddtlxc7uOx1lnnSWKGd9www1CDDCTyQhldC4SODrq751AxhbR9IeQ3jcMyCMBahHG26KXSgUcc9loc2Aufy85yCVrL/LZoVOQrwo5yEWX895mk5tQsmxhZHCjKmv2C+PNUfglz5dh0N1yGEPUzsF0i7BVA/PGAEbyh4TBxf9mioK+4pT43eHoRgyWJoShVlIjmIyuw9r8QWHELeh9Yt0Je158nzUGEXXzwuNnKxpyWhIxJyfWZykGHMZD+1zxW5fveItHyHiNrQlt+fgQQYFhNJrD0QzlbAUNkwQyAo06PwY3tQbqwgQUuwSmm3DjA9Bn9kN1HNjJEWGI6JlyJEVxzclwjhwA4yHvkTjU4U1wDj9aDtPvXws7YqMwNy1CZo14Uhhd/KnMw+n4tPTQGqiqBjMaFf8m0gNYm+b5zQp2nHZGTdvOu+BJGIwuR18MDVXlZK8IZRqumqeI91n3DfLxY8Q9d71C2D1bThvqbDkBdoRwFUIuSf+e97yn7rzNm2vDZVdD6I0tos0wF6Uje4A1p3T20CuKCEes3M4lfXmUr8R0lLTl+m/zkVoj5GhiS833rNG3lPcwEV2/PIMxjMU2CqOJG3QKY8jqKWGocaOKD3/yIEud536AIeFkxL85LSGMrQFrWnwfN9ei35oVxpit6Digr8eWUlnxZl5Lo6hEsMaeEIOwR/R1SLoZRN2CMO6OGmsxYo0LGdWcGkNRMZFys8Izl1ET0JkDjXc4mC2OSR589FOBDQ0uN/gWvXhCqZDumc4gydEjOktO8V8jspsReYBCNVcBc1S4VlF8V82oyAVkpQKKhopI3zAKU0fF77VITHwKU0fAdBXJNaMo5RaQn50SuYAjp5yD8Z33wbUtRNIDSAyOYGpvWVihf8Ox9TpXxWKeIzPj4gPNEDnC9sBm2Pye4vMVBU5yeGkRbbRWJVnffObS35F0ApH0stx/bKAcgZEeKL87kn0DxzSB+hmLp0LVMLKh9wqA0/kPJ9u2bRO1tUZGRo6pv8Xn+SllFXpji9y97YWPBlZegqGHJ01yo6XimVMApyonLqcn4ajLnrrJyNqapPm8tjxvwqwqOOy42BepLZOwT0uALcr3WFo/VK1sInEWtJTo0DjgxqUi/i1n0pVztaKsiAgroAQTEVaExhzklRgyiGCdOynWM6YOIe7m0ccyYq2PYD224wg0OMgghhkksQkTcAwXh+wBRBULA1pWbG+ntRab9WkgWkLWMTFlx7HeXBAG4JSdgKa4iEbyYr2Hi0kMGgUxrehqmLNc9BmWEAXJOuXjGFFdYQhmLAaTK3tw7yRTYDEFmlKWsC/nv4fDSHlwgRvzVNg4aEwqx3auG6Iy+s34FVu+boWnBI6Yxj3cPGxMDM5AQUkxEXdz4rf8b83VEHPy4vuCnkbcyYrfmzCQiQygPz8m5hX0pHgGpYpTiJc0ZJLrES1MwyhlxIDTfN82RMd4/RkGOz4I14jBnOM19xSUBjbDnRwHcnPCUFE3nAZ2eGd54CUxAFfvhzV9RKjTGkOjwivkFnJw2aIIDK9voxvlj6bDSA8iEYtANyOidEk0PbAk/rLm5LNrOqejZ5x/QofXXTSiGM8LroYL1PgUBCFO4HyEpE7oagi7Z4ufyVbfQi6CfX7rnVseThiN+huEC62xVSFIxpaHMFZTkwj5q7vReX6TFauVDzkqD7lYsxElj46wTOHK9FAHc7h7qN50V21YkcoLP6pdfpW+ZMge4IqulZOuoS51zfkvCytEL7gRVYF7snIuQ9zNIuIWMaMvj9y6LsNBLI9GlpDALModGL3oYD9qk0v3YAssnh9gAgUwzKFfdPbMiIJJDApVS8bPrakKmXmei+QaBlTFRYmVQ3J0Q4OmqzD4F6ZAd2JIRXggJQOzTPHbIaMg+nq7EcWGaA4xzUbB1XAwn8BpyTkup4cj+ai4kjfGy7kMD82lsCmRR1KzkHdUPDqfwDkDC2LekXwEJVfB1vS8+P7wfArrYwWkDQtFR8Uj2QGc08fz/RSMF6PI2To2xbPCk7g7349Bs4C0bsFyVezK9eHkRLnUwpwTQ9YxMGpyoxM4UkwgoVlIGwXhuTxQTGNjZF7sN/8d/wwZRdEHn7JiiKgOElpJLP+7qT6sjRSgKgx5R0PW1jEU4b9VMGlZMFSGuF5+PY3ldQxFHWF4ZqwiMiVgcFEQkFdc4M8ZU8+J7xNZhoGYAn5rWQ6wUGAYiJSvnoWiyExEIlK+3mbyCtJRBbqmwHYY5gsMQ4ny/VVQYqLdqcVLazpX/tvUAC2RwGxRw3CU574AOUspe26T5R/PlAwkdAcRzRHTp4omRpPlfck5mjiu/UZJnPOpYgTpmIqYagmP61gpifWRBdHOkrgOdAzrZcW2STuBuGohqRWh6nkccgaxUZsWhnzWjSDDItik83PFMIE+xFBCGmVjZg9GsVkdFx5f7sGaU1LY4HLjBWLgIQYbW939mFH6sVffhA3OUVF3K+dEMa32Y6NztHx81UHocIT4DL8p92kbMeqMw0QJBSWCCXUYG6wjwpiaUfvEv/3uvPj3qDaCOBZgMAslxUBB7UeEhxpzI0gtm2Cua4vvNnNQdLlHWkfe0ZEvOLBZojzP5oJBKubUYSR0A1xHSFEGwCLcWFQArvqW3rF8I/OLNb3o4XEANzYCmCPLF1B6ufRIsagDicVnhLjVEoCZwMJv/wMyxp/ySmChfF8uk4d9x79Jlzl0+oul80orhBlUx4KranDmVm5jGZnqqkwJlzOcqK8iygWiZNz85U+jUf767VdJ58Wi9Q18I+IRZuxIJNs9co5l7xieZybDQ4hWsOfoQWzdVPvOiOhKw2FkRQ9N8KFnv67u9P95dLLh/oaXMu+2R/8LvWxs9SrveMc7ls7rtddeWyP/zr1ZvI7weeed52vdoTW2yLPVGRiv/8Xrdei9GYITVHJKHDmtmQnTZb9apVAy7wSWqt6dWbZs8NlMQ9Fe3vZ01d+8E3mwUNuRmLPL147LStiXX65Nx3lgYaBG1nyyuLydXQvJGin0u6drCyXOzi6va3d2+W8u733vHA8lXXyBcuNtvk90/rnHYKwYx0TVzh3IJxcFVXThcTtaipeNAbc8gm9VPVYXHHNxHjeQFeQcXXzny3HDw3ZV6Gq5c+EseutYHfEM3l8od3bK37nxpatMKJHx9cUWByO4ocN/l1js5ExmGeKmAlNTULAYMkWGVLQ8r+goYr19sXKnY67AkIioiOpcslnBQtFZ+i2PQuVjCsnF/uhsHuJ3UYOnXJa3HV00BkvcQ+kymFpZolp4cxQGvUoIpHovy36fsreST+XHxWLlMFdOwSkfT0fVYDMVC25E/I7/nXMNWEyFtuhRnnYTS9ecDRVj6C//ll+fMLCA+NKWDytryuHGi63ZrW5aCqXNayr62AL2aOVpB/X1NZ2s3XptzD43pCrvnEN6bW7pPr02vCqjLl97U+ry4AdnQq8KeVNVFNXl52h+8f7lynLiuKy4n/m1WvGsh7mAeTx7FIXYEJyqUHCC6NVB/FbAB5CbPYi8klav3w9333330vm9//77YZrLAzD873PPPRfvfOc7fa079MYW0V5cqwAnNw+kKeE/SKTdBWjMrvFsEStZNCzEgL9SNhQU7lUo/12Be1fErxfDo3joZIW8q6KsbVlrOFaYtpZrUnGjhHu7UqXyyPRkqfa3Rwvl31pOCQUHmCsth+cezZVllku58uj+vrna591Ebnl0+OBs7UjxY5O1iewzi7/leReHZmvDGXdPlr9Xxk6qa5UfzZT/jfaV23IgU+sZKOaW93W6VDvvaLHWUBgrLv92wYlgoaoZ03Z5nsFr9i16FCtwo6rADBisPC9XZeSLNqx4xVXv3VL4r4RJ9QTDCImWIEqBiJwyIkicfu7j0SuQZyuc3H777eLf17/+9fj0pz+NdLp2wPZE6D7Zm1VCnq0O4TqehWqJzsBzuMI82t2tcBtuX6F5D3SiefDQVrpjgkcx2g9XkxcOJzrD3l1lwZNeIOzGlqgP1+IPC7B38KabbhKGFq/VdeuttyKfXy4q7hcytoimoiUHYK7pPVWioFNSIihUhSQRwYBLmpyaKNeHI4JFvzu3JDRDBAievE6ercBRyOfh+lBp62bCamz1OtPT06Ko8SmnnILnP//5QpmQc/nll+PKK6/0tU4ytoimYs9Nig8RLByoQlaeCBi1EYpEgKioBxLBwrBy0G25OAbRGZLpdM8oEobds8Xzh9vxCSpve9vbYBiGqKlVLZLxile8ArfccouvdYa290VhhJ3BtUtQufQuEShSbkbISS/wWjREYODv7PEV+UtEMDiojZYLjhOBgnK2gsnGrTug9kgKQdiNrV7nRz/6kQgf3LixVl3z5JNPxr595RqojULGVhs59MOvSOed+pK/rDvd8jD/ZQqpXve/n5BTw0Mud+U8ZkahmhHEPB66zZRXd3Rt1ZLBFUwPDVvZMl5t8ILX06oHl133g6+Hu1pWs2OqKpT3jieJy5FF6cj2xy9cBl6GbdWf53gY89zYl1ERtGgEr2Vkbfc6R4qoG7aMWIO4VzyuO499srgYTR1K2TnpMtWKjdVEUnLxlNjAOum81Eit+l6FqERKm6N7SExXX6PVaB6lIWyua9/gMl7zuAbDGnsaU9qAUJ2s4Fjy65/Xuqu/Lo971iPqyrbqr88uuQ0fh0yhVhSlmimu3y8hLplnb3qudBlnT/2wWGf02dJlSpNlOf+V1HtGKaw8OGFwiUwJKS6VWYdcSX7A54v112do8vvPD49Oy71ycUNruCyKoRoNSatzFKW+XLzq4cuVPdYqfYCDu3dhZP0GpNK8bmAZU9LsuaKDqOT5OSe5Vn+2c0Latn2Hy2U+6hGrSKiuYEgynbNrxwvgRT76fb5VjI0+AZEdz0Apz9WCPuK5DNE9ZLPZGo9WdXhhJFIrwrRaQjtsR56tzqDFk9AiNFIfNGa1PuTVZRU3IhjwulprzfodTaKzmMwSIhlEsIg6OSSs+oMMROfQdB2OLTeAwwSrjEaG1LPVanEMd/ETVJ761Kfia1/7Wo09wUNkP/axj+HpT3+6r3WG1rNVIciKJ2GkMH4QkaF1AIkxBIqEm0NejaJ0HLlror2IVzU9ogJJUTFJjzCAKIxB54WgO90QoobB4TUwfY76dxsVLzZldYaTj33sY0Ig4w9/+ANKpRKuvvpq/PGPfxSerV/96le+1hlaY4s8Wx2UfvcRrkW0lqibR1GlXLqgwQvz7i70i4pQRLCY1gYoZyuA2KoObbGuGhEwz1aPqRGG1bPVq0WNK5x11lnYuXMnbrjhBqRSKWQyGbz0pS/FFVdcgdHR+mHzxyO0TyxKXOwM5uC6skCGHdwbqRdh3A0e3qjhrkVTXIyYOWSyNEARNNY645hUB0XZBCI4lNQIbMUIb+elS8kuzMO2bSSSKfSMZ0uld2pY6evrw3ve856mrS+0zyvybHUGt5gH4vxhS8ZWkBg31na6CUQdeE5QXOXJ6mRsBQ8KEgoipltEujSDebNWKYzoLJpuoFgo9FTOlsKVdEJIO3Kq3ICn+BQKBdx3330YHx8/pqTBi170oobXR8ZWQJCp0y14qCfFDYmqmIfCm0zdMGnqmCtaDd8U6gq1qNLsGKLD62B63EcxicKVl1tZporlpbgUM+tf3jETyJfshtsgUyr0UujzqzrY7IGH9cVDGDdGYFcpV3nlM8reIWZMhyu5hrxU11TJNWl7XN+y7Xgt5zhG25QhV177q1nfyja4ClB0VM9yCWZ8Wd3rmG1JlAr1aEK+vkT99fWt3yJdZmBtUjrPkciiqh4hNs9n/4NG+Vlanpg80l+/YPcZ+34oXeaXiWdK5zm2i6zaB6gm9Ko8R9k55xTzdkMKgcfDlTxvvO4zpdC4kiPXUWum4qam15+neilDSrZTTzlWZRo0hUnfCWJ9Eo9DSSbhC2BivtiwkqOMoWe/Tjrv94fkSqFRibquTKWQY2iNh7Lx93397cvPkeyy0xavBVdNgKVi2Du7bHAVJNfw0Yw8bPrho/WVBY/M5j3v13pEYjosicrklGRdXmrFFXXMyjMvW3Ixm7Pwjb94PP7jTdLFiC6D19J63eteh8nJybrPPj/hsuE0y8mzFYhjTwQHFa4IJSSChcVU7C+kO90MQlLUmAhmzlbWkA9AEJ3BtoqYHTvUW54tr7IOXUyvFzV+61vfipe97GU4cuSI8GpVf/zmJZKxRTSV9I6z6YgGkKyWoJytABJVHeyIz3a6GUQdks4CDLe+t5/odHhngHtqPUwh6+UnDQ9LUSEhDSPsdcbGxvCOd7wDa9c2L/1CDbt3ZWWsJdFasgcepUMcQApKlEbqgwr1GwMKdeqDCUOqKAsCIzqZs6Ub8jDuUBFy6fder7P1v//3/8bPfvazpq4z9DlbRHtxSr2RINttjFjj2BeR5+QQncFmCqbt+vlGRGeZMobIGxxAGFSoAe6o9bKxNbxpO3rKs0VqhKHkhhtuEGGEv/zlL3H22WfDWDGI8Ld/+7cNrzP0xhYVNW4vulAiJAIJDUAEDsYUlFxSIgxqGGFRjaKgxDrdFKIaRcF4YhPidFQCx4EH78HWc56I0LOkRhjOQX0u8tRqcS83AOJhMv7t3/4NP/rRjxCNRoWHq/o887/J2KqCwgg7Q3R4Q4e2THgxZpL0exAxVQdrzSxmiySSETQMZsNmPVaktUvoL4yjFCdPPdEZqM5WuHnPe96Df/iHf8A111wDtUneS/JsBYRHf/ClutNPftHl0mUMDyUcL6nYRmXhrVWOQHAvYmb/w+g7+bwlOVgv+dRGJFdlsuumh5ywo8vb7bgSKXKPfZXJwsvaJgjI6E3CyaKgrn6E3kviWoZSJZG9Wmln3eM69ZLUl8mNy+SyvZZZHKSsvz6PNsiOkVfO9EoJ7ihKiCkl9EXkHuFiXh5mqMfqS7IzV24kRBP1fQKvGr5Xugz82BxNvvQvmbtdPlOupi3lqVM/aXiZnyaf1vAyXtexF7Lr1etcyEbaXdVf7jKTeF29VNgYUxt+XzX6zNXtAqY8JNkdibCJ17N6t2S613vJ3PCcutMjj0zIl/FYXzJaP+cp5vGclJV68dqOrPyK7N18vHkVbL0fU4fnjiubPy6R2eccmasv8Z7LWw2/X7zKDciOgVdJgUo5GVUpX5d98QiGkuEreu62QS3QDUbXqC6lUgmveMUrmmZo9YRABoURtg/ewVNUCokKIkmnN1Siuo0idIyxgU43gyC6CkuV16UjOkevvP8rAyhhLWrc61x66aX493//96aukzxbRFMN3OgaCiMMIm5IY8u7HQMO0koOOVCuI0GslqnoupDqwHU39tQhaOkh9EqdLWml5y6nHWqBboBFbngtrY997GO49dZbcc455xwjkPHJT36y4XWSsUU0N445wDdQL3OAlAgDiQYXMZQ63QyC6CrW5fZjLL2j080gepWKQAaZ/KHk/vvvx+Me9zjx9wMPPFAzz68oChlbRNNwSwVYCzMw+8I/stVtbCrux4HI5k43g1gBgwIbvRF6QxBEuDE2nCJSN8Kq0ndMGGFIpd8dxsSn1dsIKrff7pEf7JNwXimUs9URKGcruKheKhBExyjAwBHK2SKIhpg3Kc8xiDhz44CHOE/YwggpZ4tArxtbFRUREshoH3o8jdha8p4EkYyW6HQTiDrEUcQGZYqODUE0ACNhgkDCSgUwR64SGRYq/cqweraI5kNhhF0gCT/ynEvrzivIpIG5JGmk/qmN6PKQJUuyvqKHXG61NLY1PyMetObAiKeL2I+EumwZv2iSh2TEUy6eNdw2Jkmg9VvQz48kOyenp45ZlqfYyTfkY5bHMrKXkt9wE9kgiqdcvC1Zxmc4g6rVb3skVl++mdMXr51n2jnEiwznT/9eviEvxf7B47eT6CyeMulezw4fzmgm0dt3PXScZXYLL1NgW05T96nRZWyr/nbWFMcxXoxKC7XL7nWpnL5HG7z2dWUph6XpunwZr3IXilqovz6PNpg+pN91yfpWI+/u9f5L5WzkM1Ow9XK5ilyp/vWTl0wX65P0A7yuKzNav7+TXPG8rUYm2Z6U9J04qcXtKIs355p0DGv65KU5upVeL2rcCkJrlpP0e/txrdKySg8RKNaWjna6CUQdHEVH0SCvI0E0muuoNLuQG3HCLMTWwNbM3gkjDKkaIdF8Qu/Zcj2H74mmHnNd75k6G11FgBNRex0eDmVTzSCCaIjD5nowlzq6QSOZn4Clx1A00wgzYa+zxf2OrS5q7KC3CL2xRbQPPdFHMcwBZcIc6XQTiDoYTgFRa4GODUE0QL89i2m1H054uzBdCVM0qKwHutGLnq2KNgARHkqlEr73ve/h17/+NY4eLUcErVu3DhdddBFe/OIXwzT9eW5De6WQZ6v9FCYOws5lOrBlwgsebhNxi3SQCIIIBSYrQQVFrQSNkhGHrdXPhQoT7qLiYlgFMipFjVv9CRq7du3C6aefjksvvRR33323iIzjH/736173Opx55pniN34I7bAQ5Wx1ANeBolEYYdBQ4CLhZDBrkJpC0ChpMQojJIgGsRSTJ27xhxsRsBzUXvBsLasRUn8nTLzlLW/B2WefLYyrdLo2FHZ+fl4YXFdccQVuvfXWhtcdWmOrF6TfcxKlKC+iHop/J4qRHoJqlEe1tDaFcTZbpdALmVKTl+qTDE8Fwybvk8K4RpnaoLKhxzzJ+8VTtUsyL2bKX1ZeylgyNS0vZKqaXtuJe7RvTaq+ClXfPd+WN4JU3rsar2tcNk/1yC2yfeQUu216djCP3oFXqopMTc5LCbBUbOx+ziG5mI9dbEzRzqM/INN28tpXWbqC5vFOkF4nPt+ZsvV5XauyZ7+f9IvqY5pws0iwPMa1Yc9lvLYjO95eKo4y1UGZ4iBnJFV/3mBSHiZWeV+xxft2pC+Kgf7wqRH2alHjX/3qV/jd7353jKHF4dM+8IEP4MILL/S17nD6QMmz1cGixqG9pLoWRzVwOLqx080gCIJoCuswjQTqy6QTncNVVGg9EN5ZMbZUiuQJFf39/di7d690Pp/Hf+OH0Hq2KGer/RSnjsJI+rsQidZhuCX029OYMNfRYSYIIhTS7ypJvweOAiKYUEPbrawZWA6zGiH3njs9WGfrL//yL0Wo4LXXXotnPvOZWLt2rZg+NjaGn/zkJ/jgBz+It771rb7WHdq7gnK2OvQAohjmwMETydUAuuwJgiD8sIA4rPAG5nS1GNOAO48JbQhhhYdNLudsUdJgmHj/+9+PRCKBj3/847jyyitr7AiuSPiud70LV199ta91k7FFNI3kltNJcj+AuFBRUMMXV04QRG9iQyO/VkA9jgmWwwRCbGxV5VmqIR1cdtrg2XIC6NnicIOKf/bs2VMj/b5t27YTWm9oja1eEMgIGvmj+5DYdHKnm0GswFE0ZLUkHReCIEJBEnmRGTSN+sIIROeMLf6+CTOsSkklrGGEBIRxdaIGVjWhvVIojLD9OMVcB7ZKHI+om0efPUMHiiCIUOBCESFrRMBQFBxURxFm3CpVTVVTQ+3ZavWn2zhw4ADe8IY3+Fo2tJ6tMAlkjN/61brTR55zqXQZy2Gexfjq4S0Ffny0SHx5XR7SrjKpdC8J9ZJEyjcV1TGbt+rOa9fN7Ef6XbY/nGZXKVH5plT1mPhyL8nzZkvgyyTUk1H5yHTMQ+o3ItmW1z75aXefRFKYk4rUf3weuEe6CNHl+JHG9lO807b8vbdk0u9+2uAl1e71fJflsfjZJ9n+lLhHy2WwUWpoX72k8Zstyd6VZQqakIN0mn4ID9sbyuvT6q9P9yipIZN495J+9/Nsl5UdSUbl3eKUqaMIe+l7fyyCqMc7jAgX09PT+OpXv4ovf/nLDS8bemOLwgjbR2x0axu3RqyWohqFpdALgSCIcBBVbJiahWkn0emmEMfQncbpanEdpwdytlo/WO0E0A/ygx/8wHP+7t27fa87tMYW5Wy1n8yePyJ98nkd2DLhhcZsCrkhCCI0aIqLGOpHNBCdZcoNtwFcHR1EaoTh4iUveYlw1Hg5afxEN3DCGXBKnq22Qx7E4BJxi4i4VACUIIhw4DBe0CLcHpRupcjCHUVRHYqqLAqxhY1ezdkaHR3Fd77zHZF+VO9z1113+V53OK+UkOVsdQuR4fWdbgJRBwUuWHhvdYIgeowsM3HUTne6GUQd1muzQpewFzxbYQ0j7FXOP/983HnnndL5x/N6eUFhhERzcF2oWmgvp65mTh/odBMIgiCaRlSxsM6Ywx5rmI5qwHCgQAULreexts5WOAcxe7XO1lVXXYVsNiudf9JJJ+H222/3te7Q9o57QSAj7qHOk7Pqa9oVPFTwTImMqUz5rVq9yLFtWHMTiPSXixl6P4Lqt7uky9vmqTIniaH1WkZ2ozf7ASBrA1dCkikSNrsNffasEMjI6auvteV17GQKTzLFQdGGuFl3en9MHnLipQolm5fwuCcMyfWd9Gq3h9LUr755o3Qe0b3sP/VF4l/FtWEW5lCMVxVoPbrQ1G25MvVA15+inkyJz/V47vuh0/EiquLANRyUisvKcBWa/c73m6MhQ9Y+r/PqB69rSKY66JWDpEmenyuX2aX0ochsUXNLtoyfc+Q6WlPf57L3r5dSsKW5KFnla07VNFhBVHkgfPPUpz7Vc34ikcDTnvY0X+sOp1lOYYRth3GFnpCO8nQ7JJBBEI3DFBW2HqVDF0BspmHWoXMTRPq0Agyl2QVMgkMlNYUKGhONENreMakRthctEkN8HUm/BxFLMWEroXViE0RL4J6tWHacjm4A4SFqRZfyZYJITLVCbWyxxZytsBY0rnhFWy2O4bYhjLBYLOK8884T3ul77ulsAczQXi3Vrn8SyWg9TiELa2G6DVsiGiWvxVFSI3TgCKIh+DskvGHo3YwCho2R+U43g6hDiWkhzdaq7U+SOEbwufrqq7F+fTCE20I73F2duBjmvK2g4FpFuBbVPQkiA9YUFvQ+FLRYp5tCEF0DUzXkk6OdbgYh8WxxEQYieBy20j1R1DjMxpbD2iCQwVq7/h/+8If40Y9+hG9/+9vi706j94pnS9PCe2MEAkWFapL3JIgojFG3hCAahTEYxXk4Bg1SBA8FuwukshpE1uoZoUg4aa9ekKkbpd+VEIcRtpP5+VoPdSQSEZ8TYWxsDG984xvxve99D/F4HEFA7QVjizxbrcdI9sHsIxneILJg9MFS66sBEgRRH/4GMUrNVR8kmseQkafDGUAcqNBCPLxXUY1UldB2n9ta1HjTpk3o6+tb+nz4wx8+obbz/v5ll12GN7/5zXjCE56AoBBaz1YvhBHuvfnL0nlb/+wNdaeXPKRK/bh1jcXjnJs4BDUSRbR/zXGX0ZT624kmI1LJei851lzJaXh/ZNLmMllzgaQNdpvk4jl+XPtcjRCINLQur+MgKwUQM+WPk5hEkr0vLpdWH5DIxYvlJNLvfRG94VIJgx5tiHqMXv5KOofoBqbPeWnd6fsOzYl/FeZitGDhsLtcd8WWPJ+8pLu9JL39yIA7Hs9wmcS71zLStrWxDo6fZPmUmYNtJaUCBkFF1r5mt1soBNdB0TTItqR4hMbZknmaXvvMnbcU6KoCy7bBJM9cL4l5RSKuUSrKj0/OOLYEACdjWg2/47zev7wfMp0tlL+oKiazJelvidVx4MABpNPLoacyr9Y111yDj370o57reuihh0To4MLCAt797ncH6hSE1tgigYz2wl8UYY5h7mZS9hwKagyuQueHIBqRfj8c20wHLKDYbng9C91MkWlgIS4/tRRGGOL+TjuLGqfT6RpjS8aVV14pPFZebN++HT/96U/x61//+hijjXu5Xv3qV+OrX/0qOkFPGFth9WwFLYxQMym3IbA5W00uzEkQoYcxbCzsx8HYlk63hKjDg1kKWw8iUdXBGjOHPflwhq6zJTVCMvbbyZo1a8TnePzzP/8zPvjBDy59P3z4MJ7znOfg3//933HhhReiU4TW2Kq+EUj6vQ0oighPIILHkegGsPCmZxJEy+ChhEQwOSc5gfsyx+98Ee3FYQp0uKFXIwyzZ4unRmgt9mzZLVr/5s210QjJZDnUeMeOHdi4cSM6RUM9MJ649sQnPhGpVAojIyN4yUtegkceeaTmN4VCAVdccQWGhobETv75n/+5UAap5gc/+AFOOeUUnHrqqbj55puXpu/du1d4pPi6ecxlNbww2XXXXbfqtpJnq70Upo6COfXjponOMlI8SvWCCKJRFAVZPZyKaqFAkvtLdJaCq+NgMdUDdbZoAJNYPQ1dLT//+c+FIfWb3/wGt912GyzLwrOf/Wxks8sJxG9/+9vxX//1X/jP//xP8XvuwnvpS19aU9GZr+PGG2/EDTfcgLe85S0olWqTDLmhdf3115/4zi3eDOTZaj08GTfMIz3djOny+4vCCAmiUXIaGVtBZapEYetBJcxKkRUhkzBH8rRTjbDVbN26VaQScYdNJ2kojPCWW26p+f6Vr3xFeKHuvPNOXHzxxZibm8OXvvQlfPOb38QznvEM8ZubbroJp59+ujDQ/uRP/kQYW7zmVWXHdV0X00xzOb73rW99Kz75yU8Ko4yv3y8V7xblbLWe5IYdUHS5qhvROYpaVIzSEwTRGGuKR3Egvo0OWwBZcMKZE9Tt8C70gFHAwWJzixtbRXnkTFGiGjvpsb6iRLkzU7CkarvjC0UcmS47FxxXwe6JZUcDQbQsZ4sbV5zBwUHxLze6uLfrWc961tJvTjvtNBFDydVBuLHFVUde//rXY3R0VBhDPJGNhyVW88pXvlJ4zt7//vcL79eJeLYcx+lJz5ZMFn7D8+RqLo5koEHz6KgbWnlefvowUhu214RvyvA6HTJZ8aRE6vt4svDNRDYS0+zYYz8jPl4yzdPGcMMSt37mecnFpyTnz+u8yuTdOQPR+ob9YExu8PfL2mDK2714eRMh5OEjtcU0Kzx16ic137fmdy/9/UPtydL1yWThveTiHduHXLzHPJmEur9lmixF7mN9Xstsic/gnlz/MV57meS53201O2KjmdLvXstI2+0R7S9bRjXMhtpnOQyOLd+Qp/S7ZJ7mVZaljVT6k0qIwwjdNnie3DaWlggC6olccG9729vw5Cc/GWeddZaYdvToUeGh6u/nD8Bl1q5dK+ZVeN/73ofJyUlMTU3h6quvPmbdvMP+kY98BF/84hfx2GOP+W0iebbaSCkzvypDi2g/o4WDdNgJgggVvK9Gb5xg8seFIYSWHjC2iObj+2rhIX4PPPAAvvWtb/lanleKXunRqoZLNT7lKU/Btdde67eJIlxxy5Yt4l+itWhm/UJ0BEEQBNFsdmeTImSNCB5np6YQCnjZIMagOiXoxQUY+VnhaODKdqfs2AYzOwHNyuOOO+5AmHAYa8unl/AVRvg3f/M3QkXwF7/4RY2U4rp164TYxezsbI13i6sR8nmNwr1bT3rSk3DVVVfBL/l8nnK22kD/1lPbsRnCBzNmiEcZCYLoSdK6hbyjCalxIlioQVKKZAyKMMsZIm4JGnNQ0KKIWgVE7BxU5mAmvh5rMvuguRZg96MYSSO1cEi4TjOJ9VDdEgyU4Ko6GLPLatnTs7CifWCqISK8CKJpxhYXmuDiFd/97nfxs5/9DNu21SYOn3/++TAMAz/5yU+E5DuHS8Pv379fGE2NcsEFFwglw2uuuQZ+c7bGx8eFAAfRWqZ23ovh0x5PhzmAUK0ggiDCRp9hYaoUkeYaE51jqhRt6vq4saTBgQMNCeShwYUNTXyGMI+oo2BaHUDKzSLNFuBCwV59MzYW9kFlLhb0FLJaCml7Do6ioaSacBUdRT0ORyl3gycTm8DNstRi3u/M4MlVLUhANcu/Y85Dol+J9FowrSzSErYUinaoBTo9lrOlNxo6yJUGv//974sQwEoeFg8JjMVi4t/LL78c73jHO4RoBhfD4MYZN7S4OIYfPvShD+HMM88UqoWNQmqE7SRcD5sw0W/NYMGozaMkCILoZiymLnosiKAxb9cqRfLzZCgOdMUVdbgSahFx1RLTx+0UtprTYt6CE0FJiWGTPi2W22cPoU/NoU8twIWBfViHNHJwoSKLCFzoWEAMeTUK7nuaUgcwiYEl9d2D0S017RiPLEdYlVaoJzNldVk1vSCQQTSfhiyYz33uc+LfSy65pGY6l3e/7LKyyt2nPvUp4VHini3uUeK5V7ymll948eM3vOENQiyjUajO1rEc+uFXpMdr+wsvh19iQ2vRDAzJA6xfoj7nhZeinl6oPy+nyBWXSnb9eXFTkyoieiklNnNkx0vdic9qVEFQpgpZkcCtO92Q50bKVAfjXstItuOlVJiOyteXitTfp4Qh31caQuhufhy/WDqvNJmrO/1mLEdhbMNh7MH6pe92vr4sNMeVuFgcr2eAU3+e67GMl4qXK1mflzJcM9Xx/OJHQXDnDM8T5vtbW6fTtWu/txKZel+zlQX9LONHRfHYZRh0hb+XXRRdBf2mDV1hKDiq8B6NREswDRUHc3GMRAsYMEqwmQqNG1ZxTXiKJkpRZGwTGyJZWEzBwXwSisXgqA4sV4Nl2zhsR2G7KmCacG0VM1gu+TMHntufghHhz3wbe9FX00IrEgOEAru1aiVQI6JhSiIlPyd5J5lmefr4bPm5UXSAfZMk/U60KIzweESjUXz2s58VH7/Fx1byhS98QXz8GltUZ2t1zOy8G+ktp6EwMw6nmIcWiSE+sgn5ycPiIWwkUlB1E06pAEXToZlRMD6CpCgkkBFgDsc2d7oJBNGV8HAlIphsS+YwWTSxYJ1QBZsQwkTJCl11hUGkgiFpuNBVhumCjsGoLb7zXLcDGQNnDhaEQTWW11F0XWxNlY3Vh2ejWBe3kNBdFJiDxxZiGDAt2ExByVVgOSrmSjocx0TJLRtcBxAXBtaZ6Tk8vDAImy0bLvOZZW+XZdees+xizTTDv2Zb+2Dh92xRGGHzCfVTqhJG2It1tvwwcMrjhGEaG14P17bKSjzcaDUjYI4N5rpwrKIwxvhIZGLdFmTGj6K4MIPCzAQ2/MmfYuqRe8QyscER6LEEsmMHRaX11PptsLLzcIoFQDMQHx5FYXZSGHFaJCqMOL4NV9XEtLDFQHeS4eIYxmMbOt0Mgug6Moh1ugmEB9yQ6H64ecJr+jFENBe8Pu9CSUV/xEFU46ptEEbSlnRJeJUmC7r4/WZuFDGGB6ZMbE2XDaicreDRGRNnDReFh2ksZ6DoKEibLkRJN4V7ZFRYbtlg4k7Sh2e4V6ncCv7unS4uR5Hsni8bS/z9zNmTide0vFhSoRrHRp3szSbgrDIsr9uoeBN5v4YgVkuojS0KI2wcbuQougG1Kp452r+m5jdGfFmyP7phGxL2RszueQiKotaIZDDmwogm4LoONF4UMZYUHjEe6sKNOu4940Ybf3hpho3s+AER7pJYvw2luSmUFmbEw7//pHMwu+teUd/C7BuGkepHfvygmBdbswH2QgaslBfr1gfWwlmYEd42ZmsA3w/HBnh4RI8acaZLAjEE4YdZJOnABRTu0eJelvbAhM+F2yvca2RqrjB8FhwVA6YNU2PCgFmwNGyIF4VHdKKgw1SZ8A5xr9L9U3Gc3FdATHeRsVTsXYjgzMG8WPuBjCkGhYejtvA45SwThspE6pHtKMLgmszz/QWKtiq+TxViSwPJj8zU5kjdMxGtCQnk7aowV6o1EqwWjEUndBsuc1B09fAaWyE1Jjnk2Wo+4bsTqiCBjDYdZ1VDetNJdY6/CsUwlwIDjFhCfCo5B4m1m2p+37/tjCVlKW7QJUa3Ls876dzlkFDGhEHGDTXudVNNCy5/DS6+eNxiTnjJABNgcWD6EE8KAAY2gBUyQGYKhs1grTsN+vijUFwbbjQNNzGE2OQ+kShrJddCcS1oxSyYqqKUGkW0MCO27WgmbD0G3c6L3/LvymK7ykm25dDKoFBSqQYaQfhhEybwKJbLmxDBgRsQPEyu4hnimKoLRXeE94Z7ibhniMuQTxYMjMZLwugp2KpYdnOyKAynQzkDSd3F2jgXbADunozjjIE8IhoThsmRnIHT+gvCC7QvExFGFg/Fc1wFmYwptsNTYkuMe4oUEVpnOTyvSUGGKZgp6uK9xlv50Gytp/TuyURNJ36qsNwlG8/XeoxWGklBJqnbKDk8zwuhg0f4+M2JI3qXUBtb5NlqDzwhuTg/Az1aG2LQMgNa5IgtS8uq0YT4VDCGyyFz+dxiwuxolYRrLAUMjMIqlJNj7bWnLM9jDPnB7VCYA6ZoUFxT1NCoxGjzlyWvycE/XE49UpoX/xYiA1CtAmLFWfF9Or0V/QsHoTkWMohgPjqMNdkDYh1zkWHw1OJkaUaMhI7FNqK/NAWN2cIo4qqBfaUpMRsCxVIAAEJ2SURBVI6a0+JwFRURpyCMuJyWgOGWhIKTq2iwFEPUDHG5F4+btPUMPMYwGWmOeAlBEIScstHDx8u4QcLD4vj3gqsirdvC6OG5Pdxz029Y4vtUyRT1spJqSSzHw89OTS+IZWdLBjK2jh3JrJj32EJC5AwNRUpiHTycbksiL2ptTRUN8dmcKMB2HBzMmIhqrgitq+iJ8Oct/3Bji/97KGuK9ZQcBQslDYdz3LgpP0P/OFP7LruryijiTBSMJWGio/nawayZktFWcZEgwo9voGpttcTYCrFni7lwWpx+4yz2q3qFnjC2SCCjtbiWBbtQX9mrq+DGisoVlMojVkzV4CzGqnOK0Vr59ExyWaWspMaQjwwsfZ9JlSVnc6XyS/dIasfygowha/TBsh0hYTtv9ItaIGIWFBTVqDCouIGlMgaD2cKIy2txxJ0sTFYStUFmjEGsLR4Wv53X+8TvB0pT5e1FN2DAmkbUySHlLGBn+hxsyO0V8/j2bNUURp2mqZiOrkPczsBwCnBUA7ORNUjlxkRbSkZcTItYvMOjoGimoFkWVNcW27PNJDSLn3seW8NvOo1LopWPpbI48hcgLx9BNMIRDAb0gPHhFVbOsxGKcWUfTx4KYipXjONGhoK8q6NfL4KpNrK2ITrAab0k/j1ajGPYLCCmlGC5Ko4UYtiRXBDrnSxFRWdrYywnvDYPz6exIZZDyrCF1+iRhTTO6Z8VLRkrRIU63ZZEThg2j2ZSGDaLSOiOMK52ZxMYjhSFYTNvGcI44tvn3xlvo6PDsrEUErgvGxdt5/O5R+juGa4+V563YBvYnysbQusiWezOxGuMnYfmkktqhFmbG2HLR4x7t6rh84nWcCCfWMrzCm0YYYiNLaL5hNrYIoGMxvCS+3YlSpQ8JJDHjSs6jzmv/U0zi02qHh32lJCEbUz6XTbP9pBV1jwfrm4D2ylPU5gizDqGCCrjoHwLBSVd8+t5I7a01II2XLOWsURtHZG8uSyLO62PCi+dXjwIw9Ax3rcY6rl4LmfMOEydj0BqsHQNDivnp5iaCsVMCQPQ1Lh3T4PJyhkLqqEirhShuuVcg5LRh0hmBgpcJKIDgBYH5g+WtzG4EcjPAdlZKFkTie1nI7vnAWGM6ckB6OlB5B57WKwntWEb7NwC8jOT4vvQqedhbvcDIocvnh5AengtJvbvEobb4Ohm2JaLuelJcY9vPvl0HD2wF45twehPY3BoDY4cOghVVTAwNCyuz8z8PJyIivXrN2B6agq2bSMSiSDd14fpsXHx22SyvP/5fF6sl9cNzOXyIueQ1/njSquVebx4OxF8rPkMbOiiIGpFWbAIAzEuF16wYfGpjBso3GMMZF3uEXFgKtzzzZXcLMRRFAZNkRlwHA39Wk58n3XjiCoWEirv3DMcKPZhozELHa5Yz4IbwQZjDlAZjlopxFQLQ3p52YcKIzglMgldsTHvRDBuJXBStFxb6EApDVO1MWKWZaX/mF2Dk2IziKg2Mo6JA7kETo3PiL05XEyKdq8xuKGjYFcxhX49J6S6C46GrBNDVC/A5t54LnDEFFhwhFHkWBYW+MCPqwpDx7It7Js3xHr4d5dpmM6nliQodlnV3h4Xd0/VPqemC8vf99cUtXXx2HysytvjIFesGDo2yvZQ5dnqIC9OE98q8/QQTfIxHe7ld2ql3r2WabbHyU8omZ828GdQ4wvVX0Ztcptd61ip/ZFoEaqiCkO8/voaN8SY5N3sVSaBvyfqYRXl73JdIv1eWpR+L+TK+8srwWTmw5kPzftyrS467FJR4/BAYYTtQY+nxIcIIgrmzRWj8xWVTkWHu2hA8ryz6tepFantSBXiy6PHdiQOxJa9eMWBsrR8Irn4Ah09dXnBaFLkyiX6yi/dxLazatY7cNrjlv6OpPpr8vg2nHrOole6PH6/bsfpIoRD0w2kohpiiaSYLwyjwSG4joNkPAJV05BMpYRAi6aVa7PwaRWzlxtavAYgh69vfmFerIcbVHxQYWxsvJwXmEhgbHwM2WwWsWgMW7duwaO7domX/ujocnFMIrhsUKZwmA2hT8khhiJsaDjMBjGkzMPSXCy4UeRhiKKp/EorMB2m4ggjymUqBrUsppxlkQz+G67yxq9Ih6koQQdzl0vrzjjlgRGLaeJ3B60+kUrK/865Bqbt2FKG0c7iMBwhEVfm/txyuC+/Hses5e3uzA8t/c09TvdlakWLpqzydhlzcLBQK+pxoJCqqT2Vc5YHpzK2AeYsdzy5l6lb4AIS/MO9akSwEKGkajjDCCtGLOVsEY3QPU9WH1AYYXsozk4KSfjECCWSB4+KaEeXKmNWeTR1dXk01DBN8amQSJU9eslIefRxaM1yUUwxPZVGerGo8ej65fBPzskn1Yq7DA0uG6c7tm+vmXfeuWWhFqI72MvKRvEEqy2EepCtQbGq0O9Bu6oGkBh1L3txhrQMJtzlgQceuDftLhszvI5QfqlPyT1atfk7RW6YedanDGmHtA1E1HJOFhegIIJF3q6Il4QPPojHUSph8iGEe7XUFnuenB7zbHVnL2yVUBhhe+AhBjTKE0y4gEayVM6tIAiiMR6yRumQBRTemad00GDC8/LyIfU49oJABtF8Qj0kRJ6t9qBFYj1Zv6ob4JL0jM4NQfjiJH0cu2xS8wwi3KNFXq1gwvMeN8QtPLqQDLFARng9WzwNTmmx58nuLTHCcHu2KGerTcdZ16FWSbETwcFSDcxEqbNIEH4wlN6W8A4yXO1wW6rQ6WYQdeBCLFy+P5RUJMvJs0U0QKg9WxRG2BgyxUExTzLKwRUHc9MTMJJ9MFdIvRq8YmSDyoKSRTyVDSOSMQPNYzslybCKlyKjH3VDP8v4QbYuw7GQcrPImMsqhtXEFxWWVmJ6HIdUtP5jY7AikFGHpFl/GV78cyBWX9mvT7IdTkxXGprOMdlyjk41ilVWVqzHhz/9Oek8Ijj8wL6w7vRCdrHWXh1syzmuGtm8YtaonTmOfDjWkTxT/JQeka2LUy12sRplONEGH2p27awVJduWpwqeZkGHBddWT1y5zwdc1U/Wvma3wc+5aKZSotc1V287BZvhAFf69FiuHvWf0IttkCgYeqnaye5Xu+ShRmjWX0ZffF5YhfI+8UNVyDa2f90C5Ww1n57wbFGdrdZCOVvBRWU2NFfe4SQIQs6ETSqrQYX3fTNWqLswXQs3f/pML9Ope+mFMEKi+YTas0VhhO0hNrweik51h4KIkKhW6dwQhB+2m5N4sEgiGUHEchXMFKjDG1RjayRq4WA2GlqBDHSpyu9qIM9W8wnv1ULGVtsozk8vxzETgaKkRZGNrKizRRBEA4Q096TL4ZHOJ/WHM4yr+1muPRc6WMWzFeruM9FkQu3Zopyt9mAtzCDaX1tkkwgGMTuDuF3CQqy27hRBEMfnkFVbn4sIDjxVp4lpr0STuXsqnCG4S56tEIcRkmer+YTaNKecrTahqBS/HGTp95AWlySIdqgR0t0TTLho0t55CpEOKmcPZBBGWMWzFeKixkTzCbVni3K22kPftjPatCWiUSwtAlWL0IEjCB8Ma1lMOwkasAgkCpKGi9kidXqDiK6GNJCwB4oac4VH7t1q9TZ6CTK2iCUe+d6/SI/G9hdeLpVqn370XgyefO4x8+JVEsrVGB6xH4bW+APMksi7eknZy6TNNwzGcHS28dotzZRxb+52FLg+ihrHJOdOzJPJxXucOy9J/b5I/cdQzJAvk5DM81pGyc1K5xHdTalYX7K6WPCQfi/Vn+c4y94SS3XFOtzFIBDmo4Pg1amQSW07xXzj2/GQ2faSDm+nxHuj2z+ehPqamIX982pH9scrS7ldUvteinhSGffjyNk3C6+C036k/qX748jfb6rsfXkCu0lqhIQfwmuak2er/THMROCI2lmYduMdN4IggIdzQ3ApkJAgGmaiIK+7GI6crVB3n4kmQ54t4oSJ9tcvmEsERROKsk4Iwg9bo3PYX+iDTfdQILlrLJwd+jBwcjqPe6dDGILbAzlbPIRQaXGYn9NjYYShNs1JjbA9GIl0m7ZENErG6EPeSNKBIwgfmFwgQ+mtTkE3cfYwFWwPsoBJGPO2WA/kbBHNJ9RXC6kRtoeFQ7vbtCXCj/S77lItGoLwQ9Y1wlsvKASYGp2doLIvE4Xjhsyr1SM5W4yrGLst/rDeund7wthyKaeI6FEMtwi1wwnwBNGtjJWScFioX5NdzWyRzk1QiWhuOD1bjHK2iMahnC1iVXDVQRnpjTvqTlclKnh9UfllJ1Mw9KJgu9IwBhlzBbthxT+ZgqFX/HFElzeiJGl3M+EqaizEI3AE0Uq2Rmexr9CHIgv1qzKwHE+d7uAc81RhbCXNVj1kTgAGxZr4rkgbDoqOipIbDoO4oirKnErfQQutfDnfr1bvmxvSYycj1G8Q8my1Hu4KLi7MUt6WD2TGm18jrJ7BN2MMwuSJvA0+2Pwkr3pJ+kc9DFWZgW16rE+XzFPtonQZxZLPgw95fCI42Fb9jqpVkJdxkMmrMze2vF7dhl0swHJ131LbXrhWqWky7l7bt0tyRVLZPjVbirwVRsuZww5+fyTU3ZiuxWEKNCXMOVs0iEmsnlA/pUggo/XwF6WV4fWLNrdha0SjDBeOohAbQklf7kASBLE6xopR2BRGSBANszcTCedRqwwOhFggQ+RstTinilHOVnggz1Z7RnkUbbkIKBE86XdGXhuC8Hn/EEFmz2x4O7zdzkjUwpqoFV6BDIWuPWL1hNqzRWqErUczTPRvO70NWyL8kNOTcJRQ3+YE0TKGzYLIObEd6lgFEVNbHFAiszhw8DE+XQmvQIaihTeMsKIY2Opt9BKhfoOQZ6v1WPks5g8+1oYtEX7ghhajETiC8AV14oPN2oQLj9ROooPkbRWlEA5SLOUYhrioMdF8Qj3kTcZW6+HKPPSuCy59pSnMmVEyuAjCB3tySbiMnnBBhQ+Oc2PLS3mW6AxZW0MxhJ6tSs5WmAUySI2w+ZCxRayKR3/wpbrTdzznlWCpPqh1hhdlcvERD/e7qakNK90lJGp2QzEDWZlKmSNX/CvY9ZdZ8JCLl6n3lSTr8kujSoU8p67kyGvNydZX8jg+MupdA6s5f4bkQpFJ8HvO85JPrtRHqcM/fv7r8uWIwPMX/XfXnf4vk1uky8gU/6o7UevjeUwXDeQc/69KL7U9WRu81AhdH2qEMuXFZncamx1adTw59PuOcoOL/0ZZ1XHodAe52XLxfs6D5/UomefHPxUzHKyNWdg1H23KeZAto0r6DOVllIamax6quRVBh6Xjp6o9J/JA+IeMLeKEUHQDZiJFRzGgTEbWiVpbBEE0jqkuFmYNQAkk4li2DzAcnFfgMQ5GdAjubQxlUeMlgYzwerb4uKTH2GTTttFLhLoXRmGErae0MIPC7FQbtkT4IWXP0YEjCJ8UHFXUCyKCiaHyDn2nW0HUgxc0PpIzwiuQEeIwQqL59IRnywlCZfaQwsM86KETXOJ2BnMY6XQzCKIrGStEKGcrwGRKDddrJ9oEPy9x3cWcPCK2O6E6W02B9VgIZqjHhEj6vfWY6UFE0v1t2BLhB4eHOlCdLYLwxaZ4Hn1m+GoFhYWjGQVFCiEMLOviVnjDCNVQ+yqIJhPqq4XCCFuPXchCM6Mgh3owORLfQueGIE5A+p2CCIPL5j6GmYKCabn+B9Ehwlo2gYtOcZTFyKkwQmqEzYeMLeKEsDLzUNL8oZOgIxlANmT34Ghqe6ebQRBdyUzJCGWtoLBJvxMtPs4ylUJVk6ob8vSCeyfjxy6jmw1vXzNjjS/jI5nPWYXaLy93I37rqLBLLh64/s8wPz/f8LaI3oKMLeKEiGhAIhJBxDz2UopLJNllUt9insebM6IrDS8T5xnUDcqKu5JY4oyH5FWjkuxey+S5VrsEWyJlL4PnK3q1zZYkPMik7L3QPMIVDQ95Xl1yLqIe14l0liZPyP7HL5C8e1j5t+nz6k5n7qR0GbtU3x1SnYPqKDZcV4HraE2Xd/fC8VhGti0vmXQ/kuN+llHcJku/H6cN4wsAr67hrjKU0MswaAe+jqlH23ytTyILz7fT7ONz2kAeD84ca3B1M73g2SKaDxlbxAkxuPlkOoIBZkElWX6C8Eu/aSNnaygcx9giOgMfc/EYxyE6TFQLoQiCqOsWbjVC5jLxafU2eolQP6ZIjbD1TO55GHax0IYtEX7Iq42HXxAEsYyi9FanoJtIRYC+Y2vmEgFhvhQ+g2TJ+6eEuvtMNJme8Gz1msRkO3GsErnTA8yIPY5DIO8WQfjhSM4MbaJ/GOCD41RnK7gczDaenxV0ekKNsA2eLZBnKzyQGmHriSTToXanEwTRu/SbDtIGaYsHlbEMcIi0CQLLGQM8LzJkg91Lxhb1e4LMf//3f+PCCy9ELBbDwMAAXvKSl3S0PSE2zcnYagfx/mGokoRbovOMGWs73QSC6FpMzYXDyLMVVNIRIGGSwRVotUj+L0IYRhhigQwuEqa0OCLMbeH6v/3tb+ONb3wj/vEf/xHPeMYzYNs2HnjgAXSSUBpb7373u/GpT32KcrbawP03yxXeXvyXfye9yUxJVrNXsrPaoJodJ6XXv6EThly1zlCTdacXPVT9pjKNK47JiJmaXEFQovDIqef2Tzs5zPkQyTA9YnN4++oxniliy0B95am+iPxRkzTrbyvFpS4lfOyjH5XOI3oPXXJf+FFyq1YPLFqO6DAeT+3OSxXOj7KgazXveeK1nePNawcnsn11UbV0tSFPsuuh08fAC6+2dXx/juPd2Z+JHOPXarYyJmM+JN6dxs2/yjVWaYvrKA2rAxOthxtWf/d3f4ePf/zjuPzyy5emn3HGGegk4TXNybNFEEg4GToKBOGTqbyGmQJ57oNK0QaK1N8NLKrCoIVIYEbk/zM39J4tvp8VRcKWfVj5uuA1yqo/xWLxhNp+11134dChQ8LZ8rjHPQ6jo6N43vOe13HPVnivFu4lWQxvcxfrIhBEr+GSYhJB+GYkbmNdgnK2gkq2BBydD09nPmwMRWxEwiT/XuVhC7VARhvZtGkT+vr6lj4f/vCHT2h9u3fvFv9ed911+Pu//3vcfPPNImfrkksuwfT0NDpFqI0tEsggep2D0S2dbgJBEERL4PlaJ6+hnLqgYjMFmhoeY4st1tgShHggs+VeLXdZ7fDAgQOYm5tb+vA0oHpcc801UBTF8/Pwww8vOVfe85734M///M9x/vnn46abbhLz//M//xOdItSmORlbRE/DGDYW9+NoclunW0IQXcmCpYZ7RLLL4f01jWytwLJnIcJfQ+GhKkpKIWGwppBOp8XneFx55ZW47LLLPH+zfft2HDly5JgcrUgkIubt378fnYKMLYIIKQqvEBSqNx1BtBfHVYAQ5ZyEDZ6vdXSBzk9QGYlaKLoqZorh6GqyKqUcRQlvLie3KZUW18FyG8zuWbNmjfgcD+7J4sbVI488gqc85SlimmVZ2Lt3L7Zs6VykTzjuAAnk2SJ6nYxeX1mRIIjjkzJdRDUX2Ux4O1bdDB9LIsdWcOHqwmaHhN/9FOVVPJSNy/OqPFu6LkLTiGCRTqfx5je/Ge973/tEPhg3sLgyIedlL3tZx9pFxhbRMr7/L59uy9H967dfJZ3HJNK0EY/YoKRRf+ZIMiJdZihpolH64nIZLZn0e8n2kJhe+XJhLnRnCDFT3u5ktL4E/khKvkzuZ9+QzntIMv30N71NusxNn/lE3envuuqd0mVk8z768eulyxDhxZCUCdAiMekymm4eV2ba5X+rbFnuWSJB7SVN7SXjvhr5+Wbg2lbd6c0uzMraLEbF02Y29WkYW6gWLlAbPt5+jkOz5dWb2QY/JQ9aERaXt1UoDXiGmeM03FOVGlUtGB9ZOnaKGmpDS6gRtjgqhrVw/dy40nUdr33ta5HP50Vx45/+9KdCKKNT9ISx5chuYIIIMarrIJkdw4JJOVsE4Qcu+z4X3j5V18PLJYW4z9v1zBY1eDiLuo9FY6vZgxREczEMA9dff734BIWeMLY4XKGk+jtB9ELOFvVECMI/ccNFTGMYz4f6Vdm18LHx+w7TYGpQ6Ys46DMd7F2QR0p0E0uerZAbW7yUWKWcWCu30Uv0RJ0tDtXaInoNV9WQiR0/oZQgiPrwiOK4QQIMQWbHcKi7MV3veQxVUWPybBE+6SnPFkH0EorLc7aKsJDodFMIoms7i5L0SSIgpCJhilMLF3lHxVQhRN3MHjG2XJe1QY2QoZdQe8XYorwtotdQXQumle10Mwiia5krqTiUCVFnMYTkrN7qtHUTLlOgh6mocY8YW0TzCfVbhDxbvcGNnyrLejbClVddLZ2nSTJ6+yLy22Vjf0w6PWfVzyko8WHzBtUI86UG1AgLCpApQBtJSZeJmVrDioN++M4X/0k67/VvvbLu9KwjH7G+4ZONn3MivOiS6zgSk+eKWNn696xVyCz93R9xMBxzsWvW8FYP9FAc9FIWlKsR1lcP9MKPAl23Ur2vD425q1JE9FIpDALtUkpsp6GgKgyjcQuThfqqt6vF+/rVG/acqD6k3zVNhaq4S8eQfw8rXOHRj3R+o9voJcJ7tZCxRfQ60SQw3LkifgTR7TCq4hR4zt9IXoYgFwUPV87WoiGvhtpPQbSAUBtb1SIZlLNF9Bz5eWDmcKdbQRBdS9Hm8tWhf00SREvgpskfZ+S17roNCiMk/BJ685yHEvJ8LcrZInoOPgrX4sKEBBFmHKagGK4IvNBxcI4UTIKLgu3pAh6ZDYfBxVy7J3K2KIyw+YR+yK6St0WeLaLn0HSetNLpVhBE1xLVGUYTZG0FGUlKLBEQEnqIjOElgYzQ+ymIThtbv/jFL/DCF74Q69evh6Io+N73vlczP5PJ4G/+5m+wceNGxGIxnHHGGfj85z9f85tHHnkET37yk8VvPvjBD9bM27p1q1jvb37zm5rpb3vb23DJJZc02lwKIyR6FyMCRJKdbgVBdC+8Lnin20B4sm0w9GPGXU3WDs/5WS5qHJ59qofLWFs+vUTDV0w2m8W5556Lz372s3Xnv+Md78Att9yCr3/963jooYeEkcSNrx/84AdLv+HfX/Oa1+D73/+++PzP//xPzTqi0Sje9a53oZmeLQojJHqO3ByQnel0Kwiia8naCvYvhDtkiCBaye55uSJot8GcShghebaIxmj4inne854nPjK44XTppZcueaHe9KY34Qtf+AJ+97vf4UUvepGYNjMzg/PPPx/nnHOO8JDNzs7WrIMvw71h/+///T88//nPx4lAYYREPfK2fFTFkkiSxg15p2vvzV8O7MiJ1/hRDp3nps98otNNIALCbdGLpfNsSbyY41FCwYzJJadjA8N1p2u55dBbU3UwZJZwpFCeVlqYblj63QuZTHnYpNpbyX1HnK6SeG8UP9dCkHKKzh7M467JxKrapiwKmh0z3WsZiVy7Kpmue7zHVb3+taMb5emV5qm6DiMSnGPcbChnq/k0/al00UUXCS/WoUOHwBjD7bffjp07d+LZz3720m/e//7341nPehbi8bgwhp7znOfUrGPbtm1485vfjHe/+90nnGtFaoQEQRCEr/eHwpDSG695RbSPzf0qhXoSbaFXBDKILjC2PvOZz4g8LZ6PZZomnvvc54qQw4svXh6x5N6qiYkJHD58GN/97neXDKJq/v7v/x579uzBN75xYsVVKYyQIAiC8ANjClzqygeaVFSBRy1aosMcypoIC70i/c4dJRXvVss+jHK2TtjY4uIW3Lt155134hOf+ASuuOIK/PjHP675XSQSwZo1a6Tr4fPe+c534r3vfS9KpcZCNB5++OGlv4eHh9Hf309qhARBEERDFFwNuzIpOmoBJldiUMjYCiyOEJnp9o41g8qHXZgjhN/WDPVjUFkAH4rZtWsX7r333k43kAg4Tc3yy+fz+P/+v/9PeKte8IIXiGk8L+uee+7B9ddfL0IHG4GLbdx4443i0wjcwKowMDAgDLtes6IJgiCIEyOiOtiWyOLhhTQdyoDy8LgLSZotEQDWxS3MlTRYbuctYm70aXCF6ZdQS9AVBznXhKk46NdyMDSG/fYQtuhTiCoWsiyCo3YaJ6uTcKDCMhhyyST6UkmoiwYk729yUbcwwT1PbotvKtZjN21TjS3LssSnErpXgYcJ+sm9SiaTuPbaa3HdddctiWs0amzt379fhCueeeaZDW+fIAiC6F14d6D7R+XDzZnrVOyadJGn1LrAFgbXFMBqmoeJQVcZSq6KtG4hpltwmIqsa2CDuQBddTFeiiNhuBjRs2KZB/Mj2B6dFkZVFnGM2yn0aXnYTEUBBkpMw4wThwtdhA3vsXkfctk4fMTdIP6dzv5BpMBYR6bBWHkAhkdOrezzEsQJG1u8jhZ3m1bgeVXcczU4OIjNmzfjaU97Gq666irhat2yZQt+/vOf42tf+xo++clPwg9cmfBTn/oUvvnNb+LCCy9seHkSyCDqceOnPk4HhiACRrEg75K5dv0BOyMif40l0nLZaZlaGa/zWIGPgmc1DZFEuV6dU8zXXcYu1Z/OYaX6anKubYVOHc9PLou/ZZY7twwKNE2FstissOfTnMg54sdGNo859aerRuM5V5q5rOj52EIcNlOWVAO5YWKqbtlgclRENBdJ3YZpaDhYULEtnkFUdZB1dEyUojg1OSfavS+fRkyzMWSUjaRHsoPoj/EUE4a8w4P8VEw7cdi2iqKrIedomHAWQ4BV4LHSmuXnhQ4cZkOLjS3/w+9GXVfrdop1s/wjRSk/g1TDWJoW2pytFkeDsR6LNmvY2PrDH/6Apz/96TWhfhwu9/6Vr3wF3/rWt4SK4Ktf/WpMT08Lg+tDH/qQUBf0g2EY+MAHPoBXvepVvpYn6XeCIAjCD3yUm4+YE8FlIstgk1J+G+AeJUBXgIKjoD/iImLasBwFRUfBaMIGV0g/ktXQHy9irTCEgLG8gdGYBW6mzJa4AaViSzIP21VwOB8Vip+cvFs2Xo4WY8IbZrsiSwr3zA1B1csG36wNHCmWBz44R0q1+ZQLzvLgitYKYZseEcggAmBs8fpZXhbpunXrcNNNN/lu0N69e4+Z9spXvlJ8/EBqhARBEIQfNMXFJnMeD+XlYk5EZ+HO0N4aI28MbnLoFdtAAbizlxtNs4Xy331R7rEBds8BZ4+44A6b6TwwlVdw6hCDqlnYOaOhP8IwEHWFcfXHKV38DZVhwVWEcTRX5HlZQM5WUciZOJIzhddxa7KA3QtRzJSMpRpVD83VGklzlrHkQcs7wS0Y3CtFjanOVvMJ9xVDYYQEQRDECUi/E8FmbUrBVJYJAyEs8Ig7Hu3Gc514LloyAsQMRRhORxYYThpSlgymgs2/q9BU4MExFyNJBcMJBbwG+F2HXJy/UQWv+z2eYZgrAYNxgEfkzhcBHuHK11GpF/7IFPcoAe7idf/7w4oImePMFYF988senT1zOjR92es7Xaz29izfN9wLxsIm/S4pvkwQPWtskWeLIAiC8AMPY9pf7KODF2C4qFmnpN+5UWRoZU+RMIpMIGqU2zSd4wWXy0bRTJ6JabwAMzeKHhp3salfwUBUQcEG7jvi4oJN5Ty0wwsMmSLDpj4Vtsuwe5ohqivC41RJ/+Pr40YR32bFqKqIuy0UGR6bWjZv/nBwOddRUYFdU8vt59te2hedr7/5B3Isb4amUl2veLaEEmGL1QJdUiMMFySQQRAEQfilXy8gWwpPYdawcXCWiVpOK+FeIW7YcK8O/zfKPUXcG5QHhuJlzxE3WKZywLaB8rzxTPnfjX3l3KR7jwInDZWNqKwF7JwAHr+BezyBA7MKSg7DhnTZKHp0kiEZURDRykYMT7fIWUxsP1uCCLF7eMIV3/l2d07wRi83/LcHagVgZvLL3wuZ2h3kba4myFoDIzELqsJwJCcXq+kWmLtobJFni2iQcJvn5NkiCIIgTtDYOlSiOlsn9B5Wyh9uZMQMbgjxPB8mvDIDibI0+FyBIWooIodIVRTsmXaxfUgVRlKmxDCRYTh9rSZ+u3e27O3ZkFaQMIF7jrjY2KcipgPzJWDvDHDm2rKhdWCuHNTGDSxulM0Xyp4oTjlkDpjMlX+bt/mIOzd0yt+5DfPgeO2+/PZAbZ2gyeyyUXR4vtbq4cZbNXydvQY/5oYaYGuwASqKjWH3bPFwSS/V0WZto5cI9xVDni2CIIhQIAs7ifAetgSdx3hJqEhRr8SpkZhnIqE/wi0ELg+dqB9S6HhIv8vk4ptVecir88K9NNw4UXSgaAOpiCKMHh6O5rgM/Qn+XcF0zhXzuGem7LVxcdKwtugJYsiVGLYO8hA4BY9NlnOCRlKqMFR+v9/GEzfrYjuTOQUTWRenj5SP+84JF30xBWsS3LgC7jroYNuQCnVxvTzXqi+qiPVwg8plDEWbf2dLHhz+Fw9v45/7j5bD8ZhSXv+hhbIXKmpouH+sdt/vPFT7faqweC0owFi1Z0gBZou13wXcIAtwh5D5qF3qvb7W7GvW4sIZy1L9Mq+Ql8KfVrGOV1m+QSyjNb7M6j1boe86E00m9FcM5WwRBEEQx8A79IoCRXTsy517rp6mwxGFjG2oooDqfnsQUaWEAjMQ1yxoYOIXRUdDyrDEb2c0VxgbSZNneQHjOQ1DMQemxpBlZaNhc3+5H8//5jlGawY08e/OcQdbBzUkTJ6/w/DYpIPzNhqi3tfBWUcYRduGyq/qew9Z2DqkoS+qIm8xPHDYwgVbTbErh+YcFCyGHcO62Jv7D1sYTWtIi7wghoeO2tg8oAljZSrrImdxNTpVeJgq/U8uoc5D4jjcAOPr5cty42zXpCO+F52yYcQ9TxW4wVXdYf7t/uWOO88v2ju9fNh5vlI1j03Vfp8vLBvVXMShmoodXJ2jRbLvwYbX2DKCHOfoK2eLBDKIxugZY8tt8igQQRAE0VxOxkFh8hzACPqRQRJ5OFCxB+txsnpYGDZzLIEMi2KjWs70n3E3IOFmkXKzZUELczM2lw6AV+nJsxQyWhJrS0fFbyfMEUTdAlL2PCzXwWPaZmxyj5SLFysxzCkprDUmhXvjqJ2GqTg41TyKI3Yf9tlDGDYK0FUXWccQo/VpvSTstAWVh74xES4l+pVKOTyN1xLihoq7GL7GZ/FQNf7HoWLZmOEeH25Ucfjf3KC466BV9uAs9lHHFso1izjcaKrm13uW54l9zCx/f2yy9rd/XLHsY1PLRlHRrs0hms7VdpCrUogCxdEVoXpEsIiorqi5NW/p4VEjDLmxRWGEzaf7r/5VCmQ4kuroBEEQRDB4FBuX/j6K2oT6R931Nd8fcTeIf9NqDAU1hikML83bb26qCSPcH9u2NC+vJTBjDCHnlI2S/VrtendZa5e/MGDWjWOvXV73/kJtfaD9+fL3gpUV/86XljthU4Xy67USRcjzgqrhoXn1VOF6NbfHL2uTZfGLfbOdbglRD16Di3t9Q+XZojBCokFCb2yRZ4sgCILwy6wTo4MXYLgH8ATScIgWU3BU7FmIhuI4945ny22DQIaLXqJ+BmGIIM8WQRAE4Zd5l4ytIMNl1RdW5HYRwYHbwesTxZB5tpaLORPEagi9Z4vqbBEEQRAricbrd5hKvFJsFduVMexk5ZDFYr5+vS07mpQeYFuiVOjazVUj9KKZo9Reo/rKYo50s9a3mmVsrmDoytUlT4RmezDCJnctOz7V0xkYBiIu9ma1FlwHSluK6dqL1aQdu2xsMaYuTQsjXOK+InPfym30EqE3tkiNkCAIIljcnr6k7nS1SgjimHk+Ola6RDKaUwpxZ6mX4Kr8oyngEa5rQgQQrtwZjiCqivQ7qKgx0SChN7bIs0UQBEH45RAbpIMXYLhiI+VsBZuH5xIIl/R7uLvOjLWhqDHrrcGucAw3eECeLYIgCMIvCSUc+SZhhdcLIyXCYHPu4ALCQK8IZBDNJ9zmOXm2CIIgiBOgH1mMo5+OYUDhXq2hOJCb63RLiNDj9ob0O9XZaj7k2SIIgiAICY2n0xPtNrYGSTAy0IxJhGW6jYqoA3m2iEYJt3lO0u8EQRDECfDoohIhEUwcVq61RQS71lYYcHtE+p08W82nZ4wtt8cKqBEEQQQVRamvLOhytQMJjlP/GV7M2/7aIFFVMLm8XRVbnUPYq5UNrmiiflK3bfVJt+PacoVFGU6xPXLx7Rqh95aLPzHpd35GHpjg09B0vEQCZO0Om7x7M9iSLGB62tu75XXcmFv/XnVseb+OySxwq/HzU3n2OFb5Xi7kbCgzeey68aUNr4voTUJvbJFABkEQBOEXTXTniaCigOHxowx3HgmH9yS8cOOn+bXQ2krFIAy7GqHbBjVCt7eeq2qveLacHiugRhAEQZw4GSVOhzHgXXiSfg82oZF+JzVCwifhNs8pjJAgCII4AWaVFB2/QKNgIkdJW0FmOFrCkVwENlNCUdQ49HW2XLcNni0XvUToPVsURkgQBEH4ZaM7Rgcv4Mzku7sTH3YSugNdZaEpahz2MEKi+YT+iiGBDIIgCIIIL6cOM/zuEBlcQaXoqN2erVWmR8IIXb6fLfZsuT2WsxVaY+t973sf/uEf/oFytgiCIAjfjKlDXX/0VL09UtWyTmjT1QgXc7GXJ7Bjp7UYWZhVkDviqi5XBFSN+vM0j2VWu697MrETOp6OXV9x1HUbP9YydVB3UWmwHnYhU26jU1YDzc9NwbJDYT4SbSK0xlYFCiMkCIIIFqpE0cD1kHK2S/XnZe2idBmrKJeFNyL6qiThI8xCZnGSbtSPvI+skIuvhrmDku3IO4qWXu7crVYSvrwdp6ny893EzungGjgEsDVZwFxJx0zJCIdAhhLu643UCJtP6HO2KIyQIAiC8Es/m6eDF3AGor2VbN+NhadVpbtzthivAch6Q/qdaD49Y2yR9DtBEARBhI818e7uyIed+ZIu8ra6GuZ2RagoEUxCb56TsUUQBEH4Zbe6iQ5ewHHJ1go0BS6Q0e0pTtVhuiE3tiiMsPl0+VDD8aGcLYIgCMIvm9wjdPACzp1HQz9u3NWkTQfDkbK4RLfX2OKQZ4tolNA/oShniyAIgvCLAbnIBhEMHrfWxt1joe/OdC2Oy51BXe5+rORrcUIukAHHAVNbLM3ukPR7qKAwQoIgiGCh6/WDKop5uWFTyNVX1HM8FAw1yXa81Ah1o7YjNaXpyDgF6XrqLVNNNBGVtE1vWKLblqgUchyJ4iBXMNTMWMMKhs3EU/pdItneiPfA0Fzx+2Z7HFyUGm43k3Qim60I6UdmX4vEGpZ3l12LXjLyK5m3dGTt8vmRHR8ZXteon2PtlOoretqS6WJePgO3sCiUo6gozFKhc6Ix1F7ybAk1GYIgCIJYJRNOio5VwJnK84Qger8HFV1lGIjY4RDICLtXSygvOkt5Wy37sNYN9OzcuRMvfvGLMTw8jHQ6jac85Sm4/fbb0Ul6JmeLQ4qEBEEQRCNsNybogAWc8VzouzJdDS9d12d0t7G1ZBxU9SmJYPJnf/ZnsG0bP/3pT3HnnXfi3HPPFdOOHj3asTb1jGeLQ8YWQRAEQYSLkwccaN2udhdiHFfpfr9jjxQ05jAeCdZqz5bbmtp4k5OTePTRR3HNNdfgnHPOwcknn4yPfOQjyOVyeOCBB9ApesrY4qGEBEEQBLFaDjv9dLC6QPqde0+IYFJ0VeycTyAUYYQhl33vdoaGhnDqqafia1/7GrLZrPBwfeELX8DIyAjOP//8jrUr9PI9FEZIEARB+MVAb6lmdSN75jQ43HVCBlcgUcFw1kAG989178DFklCHEnofRXlfWyyewxbXPz+/KDyySCQSER+/KIqCH//4x3jJS16CVColbABuaN1yyy0YGBhApwi9scUPPD/Y3KtFYYQEQRBEIwxrC5h0kz3Xk/ej7OdHWVCmhNfI9lMxC5aqw3Kbe45USRtkyo9eKG57PCKqYTZVwbAZCo/cJ6QpzFtZ0EfnXqY66KUsyNU56y5TyNadbuXLCqBOrmIUKChlaw0Ewj+bNtUWjX/f+96H66677pjf8bDAj370o57reuihh4RX64orrhAG1i9/+UvEYjH8y7/8C174whfi97//PUZHRztyukJvbFVCCcnYIgiCCAa2RK5d8YgFKy7MNyTl7NVR5LLVxYXVdVaL6SLm5+dEJ0smd+0lMc8H/OpuR5Mvo8eSq2rbqjqRHp1lL6PBtZorVd5KBqIOMpYKq00GDdEoCiaLRncftl5SI+QpNy1Ou2GL6z9w4IBQDKwg82pdeeWVuOyyyzzXuX37diGKcfPNN2NmZmZpvTfeeCNuu+02fPWrXxVGWyfoGWPLsizybBEEQRAN8cB894Y+9VLOlsSmJQLCXEkPSc5W+MMI20k6na4xtmSsWbNGfI4HF8JYmUJU+d5J3YaeuGqosDFBEAThh+2Jhd54UXYxO2cjyFjh9zh0MztScg90V9BjOVvt+LSCJz3pSSI369JLL8W9994ram5dddVV2LNnD17wghegU4T/qiFjiyAIgvBJROOjoV0vXB1qNqcspE0SMiFa79nqBen3bmZ4eFiIYWQyGTzjGc/AE57wBNxxxx34/ve/L+ptdYou9+s25tki6XeCIAiiEbJ2T7wmuxouvsA/RHDZtRBHN8OWcrbC76NopxphK+AG1q233oogEf6rhjxbBEEQhE8O5eNwe0yJsNvIWirsJisREs1lwLS6+5Cy3gkjJJpPTwzZUc4WQRBEcLCt+qOapby8Q2YXMvWne6gRykZPmSMfVV2p+HfyGgsPTpdlxXUzVncZTTLdS1nQWw69fodO06PSZWTr0yKxhhUHZZLax5vnZ7S6GXLkc44uAj1VD1XIZrZZJlfvpfDopSTpdT02itdxk0nZeyE7341Kwg8aBexz5cfAD7K2NSrv7vUcqaidulZB/MuOo4AaBlzXgdLFnq0g0hMmOhlbBEEQhB+oVm7wGYlaGIzYnW4G4YEoOt3NuY89JP1ONB8ytgiCIAhCwqGMBruL+4i9AO8Gk/R7sLl3KtHdhcEXPTEKhRESPqAwQoIgCILwGJHs4i5iTzBT7ImuTFdz+kAejy3EYbldOsbfSwIZjgsoTuu30UOE/6qpKm7mNDEumiAIggg/axMOjJ54U3YvmgJEVHI/Bp2uVozsIWOLaD49MRxEOVsEQRCEH0T3kFxbgSamO4hpLuasnujSdCWzRQ0uU7pf+t2HyEi3wbjyYqsFMlhvOT964slExhZBEAThh0emdXRzxIsRlSsYQjLPseU7LFNikykbHk95rFFVu7rLqPw9b0P1UAn00zbZPC9FRj/7I92+RzSOslg/tFM0qiY3k1fgwAVjrKFjJjvWXiqksmW8zptMwXBpP92KAIvSc0p6xIlDxhZBEATRVmyrfmd+YfyIdJlSdq7udC8ZZua6J9xZ3jik4XDGQcECLN1oWPrdKNSXftej9adzmEQuXtPlr2yveWFnumRgplT/3BDBYDRhYaakYLbUpddpD4URCmOy1Tlbbm8ZrF161TfG1NSU+Pc3v/kNHnzwQSiLskUr/5X9fSLTVjP/RNfb7r9jsRhKpRLcxY5Mo8s3+n216/SznSAtI5u22mWrp3NvLj8/lVHEE13v8eY10ja/6z7R7fv9XauW4bmkiUQCCwsLDa+zWW3wQ72R6UaZN/srK6uZXpo4IF3Gys1Xt0LiUaldH3NZ/WUcW7rMSrUxM90HNp+FXbChanrdZVwPj4q7ZIjVLmNXG2grjoMWidadp1Z7M6qms5WS2lVfa3an5mcrzyNbWq1b48qr+h1jHl4D69hlKosyd7mNK9qwfLzrXFeL+7hy/5au58X56ZiOwYSOvZOFqmVWtqXedcvK4WFLs2rbWDfUibHFjmL99S5fWyuvRbf+Mef/VTryK4710qe2xbXHYOVxFdNXHuvFZXj++vEk2Gu2V72PK9qx8ncrj+GKafMb1yCXK2B2eqHOOqvbvOK6XnHcKttdOgfH7A+/Rvk5OLZ9bMk7dex+usKLuPI+EguV/yyWnz+sMHvMISOI49ETxhY3DDizs7PiQ5wY559/Ph5++GFks/ICgURn2b59Ow4fPoxCoVyIkQgefNDirLPOwu9///tON4XwYLZwCnKHDiFPz7vgMjCA9MaNmLn//k63hJCwZyIhRMq6/p1UbbCFFPJsNZ+eMLYuvvhijI+PI51OI5VK1Y6OVI+i1Pn7RKad6DLN+ns1v21knZs3bxYdRdu2G25TI/MaWWczt+Nn/bJlVrsuv22QzTvppJNgGAby+WNDrBppZyuW8fO7ZmwnaMvwkWluEA8NDTW83ka31S5W60HLKvXD7qziSq+J4hF2UvFurAwVrG7DymOywhtQl9p92Ds2D0eJQ03Eaud5etmr5i2q4XpPXzlvRQ7LqrzFK36jyKbLl1nhMDr2d/wHldDMFesve2fqH59jVrg0T1n8c/n7cdu/0vuoKFBiEZSiaUQ3qCvaW6f9Uo718tfcU5VIE56zIzxeSt3tCA9W3baz4xyfOm1eOqZ15tWJfFmaLjnHy57Zqun85zzpreY8VLdR0ualc1dZTqm/34s/6k9GxHmbzZaOOScrz2f1Nsod/2O36wjvVWU1/KwsL+uUisceA2WlF7zS3soyhRX7UNk+344CVlwA7DyU5LraY0QQq6AnjK3zzjuv000IHWeccUanm0Ach4suuoiOERFIfhR5at3pe+5+RLpMcWG6Izlbp6/VcGjOxXyBQfWTsxVrPGdL95GzJUNRGw8h7TaBDAUMtupi+FSt4wIZjmSen+34EcjwOp6aJNy1maIeMoZiFgxdhZ01Wy6QYecz9acXPKJxJMt4neuwQp6t5hNKY2t+vhxb2/XuaoIgiBBis/qdHtfyMJys+s9zZhcbNraYVwdqRcfPsTS4tgtm8wwSyfo8PCauxEByPdTkXK1+Er7itsfY4vsrnSc8APWWaaOxpdQuE9UcbIzm8Oh8OXKlWRLUfoyt2ty146/Lc56HscXcxo0t5RgvcGWZ1os+lFQHusvvcacpxpYreR54PRM8nxVOqaHp1X3Nyr+hwbE8ff/N2kYvobAgxJ40CW5cbdu2DUePHu10UwiCIAiCIIgeYN26ddizZw+iXqUWAk67+9DrQnDMetLYqlwsFUEMgiAIgiAIgmglpmmGwmhoZx/aDMkx60ljiyAIgiAIgiAIIgiEvzobQRAEQRAEQRBEByBjiyAIgiAIgiAIogWQsUUQBEEQBEEQBNECyNgiCIIgCIIgCIJoAWRsEQRBEARBEARBtAAytnqMhYUFvO1tb8OWLVsQi8Vw0UUX4fe//33Nbx566CG86EUvQl9fHxKJBJ74xCdi//79S/MfeeQRPPnJT8bGjRvxwQ9+cGn6X/zFX+C5z31uzbpuueUWKIqC6667rmY6/7558+aW7WeYzxE/dqeddpo4NwMDA3jWs56F3/72tzXr+PWvf43zzjsPW7duxZe+9KWl6X/yJ3+CN7/5zTW//fznPy/O0Ve+8pWa6Zdddhme+tSntmw/w36euNDre9/7XoyOjor5/Dw9+uijNeug89RaHMfBtddeK2rH8HOwY8cOfOADHxDnpgK/9ut9Pv7xj9N5Csg5qoY/v/j5+ad/+qea6XQvdfYcfec738Gzn/1sDA0NifNzzz33HLMe6jsQPQuXfid6h5e//OXsjDPOYD//+c/Zo48+yt73vvexdDrNDh48KObv2rWLDQ4Osquuuorddddd4vv3v/99NjY2trSOZz3rWezGG29kf/jDH9gTnvAE9qtf/UpM//znP8+SySSzLGvpt1dffTXbtGkTe9rTnlbTjosvvpi97nWva9t+h+kcfeMb32C33XYbe+yxx9gDDzzALr/8cjF/fHx8aR2nn346+7//9/+Kc7Njxw62f/9+Mf2aa65hp5566jHb4+fo0ksvrZm+ZcsW9t73vrct+xzG8/SRj3yE9fX1se9973vs3nvvZS960YvYtm3bWD6fX1oHnafW8qEPfYgNDQ2xm2++me3Zs4f953/+p3hGffrTn176zZEjR2o+X/7yl5miKOL+ovMUjHNU4Tvf+Q4799xz2fr169mnPvWpmnl0L3X2HH3ta19j//AP/8D+z//5P9wCY3ffffcx66G+A9GrkLHVQ+RyOaZpmnhgVvP4xz+evec97xF/v+IVr2Cvec1rPNdz/vnns9/+9resVCqJDuR///d/i+mPPPKIeMj++te/XvrtBRdcwD772c+yaDS61Mnk/0YiEXbTTTe1YC/Df45WMjc3J477j3/846VpmzdvZrt372aZTEYYxH/84x/F9FtvvVX8lncqK6xdu1acI25cVeDL8t/dfvvtLdjL8J8n13XZunXr2Mc//vGlebOzs+K6/7d/+7elaXSeWssLXvAC9oY3vKFm2ktf+lL26le/WrrMi1/8YvaMZzyjZhqdp86fIz6IsWHDBjHAxJ9VK40tOkedP0ccbozJjC3qOxC9CoUR9hC2bYtwgJUVu3lYwB133AHXdfHf//3fOOWUU/Cc5zwHIyMjuPDCC/G9732v5vfvf//7RUhUPB6Hqqritxy+3Pr163H77bcvhVndddddeNnLXibC2XiYB+d//ud/UCwW8fSnP71t+x6Wc7QSXun9i1/8ogj5PPfcc5em8/C1008/XUznoYNnnHGGmM7DPw3DWDpHDz74IPL5PC6//HJMTU1hz549Yjqfz9vwpCc9qcV7HM7zxI/j0aNHxX1SgZ8Lfj9V7gMOnafWwkM7f/KTn2Dnzp3i+7333ivOz/Oe97y6vx8bGxPPQH4/VEPnqbPniL+bXvva1+Kqq67CmWeeWXc9dI46e45WA/UdiJ6l09Ye0V6e9KQniZC+Q4cOMdu22b/+678yVVXZKaecIrwd/JKIx+Psk5/8pBiZ+vCHPyxCan72s5/VrKdQKNSErVXgI13Pfvazxd/c48XDrDhvetOblkLSrr32WhFORTR+jir813/9F0skEuLc8JCa3/3ud8esh3u1pqenj5n+5Cc/WZwPDvdoPf/5zxd/8/PGQ6g4r33ta9nTn/50OkU+7yUevsnvpcOHD9cs87KXvUyEH9J5ag+O47B3vetd4j7RdV38+4//+I/S33/0ox9lAwMDNaGeFeh+6tw54t//9E//VHiMOfU8W3SOgnEfeXm2ONR3IHoR8mz1GP/6r/8qklo3bNiASCSCf/7nf8YrX/lK4aHio4ecF7/4xXj7298uBBauueYa/Nmf/ZkQUaiGL7tmzZpj1n/JJZfgV7/6FSzLws9+9jPxnfO0pz1NfOfwf8mr5e8cVeDHjycgcy8hFyV5+ctfjvHx8Zr1VAQ06p2j6nNB56h152k10HlqHf/xH/+Bb3zjG/jmN78pvOxf/epXcf3114t/6/HlL38Zr371q4/xWNJ56tw5uvPOO/HpT39aCPhw4QUv6F7qzDlqBOo7ED1Jp609ojPwUdrKqDsfaefejWKxKEatPvCBD9T8lotcXHTRRataLxfU4JcVH9nnuUL//u//vhRvz/NVpqammGma7Otf/3oL9ir850jGSSed5DliXw3P7eLniJ+TNWvWiPw7zh133CHyHirnkH8n/J0nLq5Qb3SXC8P87d/+LZ2nNrFx40Z2ww031Ezjz7eVIjGcX/ziF+Kc3XPPPQ1tg+6n1p4j7sHinhSeI1n58PPEvcjVeaZ0joJxHx3PsyWD+g5EmCHPVo/CRwC5JPXMzAxuvfVW4c0yTVPIvHN51mp4nDaXt14NXBJ206ZN+MEPfiA8L9yjxeGj//zziU98QuQZkWfL3zmSwb2SPA9utfH3/FzfeOONKBQKOP/888V0fu4nJibE6D7f9gUXXLCq9fU69c4Tl0het26dyHOoMD8/LyT6V5sHR+fpxMnlcsd4GjVNW/LiV8NLJPB7oTr3cTXQeWrtOeK5Wvfdd594n1Q+PDeY52/x+43OUbDuI79Q34EINZ229oj2csstt7Af/vCHQm3uRz/6kZDRvfDCC4WyYEVa1zAM9sUvflHIWX/mM58RI4m//OUvV70NLumeSqXYaaedVjP99a9/vZhenXtENHaOuBfl3e9+t1B83Lt3r5Df58eVew25Stdq4R4Wfi6e+9zn1kzneVp8eiXvjvB/L3Hp9/7+flE64b777hMqdyul3+k8tRZezoAr2FUkq/nzbXh4WHjrVyp68lzVz33uc762Q/dT689RNbKcLTpHnTtHPGqFe7N4rjbvWn7rW98S36uVb48H9R2IsELGVo/Bw/q2b98uQvm4NPUVV1whJKmr+dKXviTC0rhcO+9A8jpBjcAl3fnD9s1vfnPN9K985Sti+l/91V81ZV968Rzxjvr/+l//S4hi8Pmjo6NCfr+eQIYXvCYUPxfcIKjmuuuuE9O5MArh/zxxeDI/F4Ph0vrcGH7mM58pyiPQeWof8/Pz7O/+7u9EeCx/nvHzxaX5ech0NV/4whdYLBY75lm4Wuh+av05OlFji85Ra89R5b2/8sOP+2qhvgMRVhT+v0571wiCIAiCIAiCIMIG5WwRBEEQBEEQBEG0ADK2CIIgCIIgCIIgWgAZWwRBEARBEARBEC2AjC2CIAiCIAiCIIgWQMYWQRAEQRAEQRBECyBjiyAIgiAIgiAIogWQsUUQBEEQBEEQBNECyNgiCIIgCIIgCIJoAWRsEQRBEARBEARBtAAytgiCIAiCIAiCIFoAGVsEQRAEQRAEQRAtgIwtgiAIgiAIgiAINJ//HyaOfr6emO6XAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 1300x700 with 2 Axes>"
       ]
@@ -7164,7 +7004,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 25,
    "id": "5c8d839c-9d90-424a-a16d-1a7e41c2d70c",
    "metadata": {
     "tags": []
@@ -7174,7 +7014,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-09-29 13:48:58 - INFO - Writing the following NetCDF files:\n",
+      "2026-06-23 09:15:18 - INFO - Writing the following NetCDF files:\n",
+      "ROMS_TOOLS_INPUT_DATA/roms_frc_201112.nc\n",
       "ROMS_TOOLS_INPUT_DATA/roms_frc_201201.nc\n"
      ]
     },
@@ -7182,16 +7023,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[########################################] | 100% Completed | 19m 22s\n"
+      "[########################################] | 100% Completed | 842.10 ms\n",
+      "[########################################] | 100% Completed | 307.73 s\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "[PosixPath('ROMS_TOOLS_INPUT_DATA/roms_frc_201201.nc')]"
+       "[PosixPath('ROMS_TOOLS_INPUT_DATA/roms_frc_201112.nc'),\n",
+       " PosixPath('ROMS_TOOLS_INPUT_DATA/roms_frc_201201.nc')]"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7202,7 +7045,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 26,
    "id": "c38c4d4f-7528-4423-ba1f-2d7649210cac",
    "metadata": {
     "tags": []
@@ -7223,7 +7066,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 27,
    "id": "8a729e51-392c-4d1b-82ec-17c9e3c78a6c",
    "metadata": {
     "tags": []
@@ -7233,15 +7076,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-09-29 14:09:56 - INFO - Data will be interpolated onto the grid coarsened by factor 2. Remember to set `interp_frc = 1` in your `bgc.opt` ROMS option file.\n"
+      "2026-06-23 09:20:35 - INFO - Data will be interpolated onto the grid coarsened by factor 2. Remember to set `interp_frc = 1` in your `bgc.opt` ROMS option file.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 79 ms, sys: 4.52 ms, total: 83.5 ms\n",
-      "Wall time: 86.4 ms\n"
+      "CPU times: user 47.1 ms, sys: 17 ms, total: 64.1 ms\n",
+      "Wall time: 73.5 ms\n"
      ]
     }
    ],
@@ -7268,7 +7111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 28,
    "id": "1b62616a-d76d-4145-9758-085ac2a2a174",
    "metadata": {},
    "outputs": [
@@ -7718,27 +7561,24 @@
        "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
        "  stroke-width: 0.8px;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 630kB\n",
-       "Dimensions:       (time: 12, eta_rho: 42, xi_rho: 52)\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 420kB\n",
+       "Dimensions:    (time: 12, eta_rho: 42, xi_rho: 52)\n",
        "Coordinates:\n",
-       "    month         (time) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12\n",
-       "    abs_time      (time) datetime64[ns] 96B 2000-01-16T12:00:00 ... 2000-12-1...\n",
-       "    pco2_time     (time) float64 96B 15.5 45.0 74.5 105.0 ... 288.5 319.0 349.5\n",
-       "    iron_time     (time) float64 96B 15.5 45.0 74.5 105.0 ... 288.5 319.0 349.5\n",
-       "    dust_time     (time) float64 96B 15.5 45.0 74.5 105.0 ... 288.5 319.0 349.5\n",
-       "    nox_time      (time) float64 96B 15.5 45.0 74.5 105.0 ... 288.5 319.0 349.5\n",
-       "    nhy_time      (time) float64 96B 15.5 45.0 74.5 105.0 ... 288.5 319.0 349.5\n",
+       "    month      (time) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12\n",
+       "    abs_time   (time) datetime64[ns] 96B 2000-01-16T12:00:00 ... 2000-12-15T1...\n",
+       "    iron_time  (time) float64 96B 15.5 45.0 74.5 105.0 ... 288.5 319.0 349.5\n",
+       "    dust_time  (time) float64 96B 15.5 45.0 74.5 105.0 ... 288.5 319.0 349.5\n",
+       "    nox_time   (time) float64 96B 15.5 45.0 74.5 105.0 ... 288.5 319.0 349.5\n",
+       "    nhy_time   (time) float64 96B 15.5 45.0 74.5 105.0 ... 288.5 319.0 349.5\n",
        "Dimensions without coordinates: time, eta_rho, xi_rho\n",
        "Data variables:\n",
-       "    pco2_air      (time, eta_rho, xi_rho) float32 105kB dask.array&lt;chunksize=(6, 42, 52), meta=np.ndarray&gt;\n",
-       "    dust          (time, eta_rho, xi_rho) float32 105kB dask.array&lt;chunksize=(12, 42, 52), meta=np.ndarray&gt;\n",
-       "    iron          (time, eta_rho, xi_rho) float32 105kB dask.array&lt;chunksize=(6, 42, 52), meta=np.ndarray&gt;\n",
-       "    nox           (time, eta_rho, xi_rho) float32 105kB dask.array&lt;chunksize=(6, 42, 52), meta=np.ndarray&gt;\n",
-       "    nhy           (time, eta_rho, xi_rho) float32 105kB dask.array&lt;chunksize=(6, 42, 52), meta=np.ndarray&gt;\n",
-       "    pco2_air_alt  (time, eta_rho, xi_rho) float32 105kB dask.array&lt;chunksize=(6, 42, 52), meta=np.ndarray&gt;\n",
+       "    dust       (time, eta_rho, xi_rho) float32 105kB dask.array&lt;chunksize=(1, 42, 52), meta=np.ndarray&gt;\n",
+       "    iron       (time, eta_rho, xi_rho) float32 105kB dask.array&lt;chunksize=(1, 42, 52), meta=np.ndarray&gt;\n",
+       "    nox        (time, eta_rho, xi_rho) float32 105kB dask.array&lt;chunksize=(1, 42, 52), meta=np.ndarray&gt;\n",
+       "    nhy        (time, eta_rho, xi_rho) float32 105kB dask.array&lt;chunksize=(1, 42, 52), meta=np.ndarray&gt;\n",
        "Attributes:\n",
        "    title:                 ROMS surface forcing file created by ROMS-Tools\n",
-       "    roms_tools_version:    3.1.3.dev32+g91d580e67.d20250929\n",
+       "    roms_tools_version:    10000.dev364+g785fa77b7.d20260612\n",
        "    start_time:            2012-01-01 00:00:00\n",
        "    end_time:              2012-01-31 00:00:00\n",
        "    source:                UNIFIED\n",
@@ -7747,18 +7587,17 @@
        "    use_coarse_grid:       True\n",
        "    model_reference_date:  2000-01-01 00:00:00\n",
        "    type:                  bgc\n",
-       "    climatology:           True</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-f151bdd5-ad7c-4a28-9841-ad7b56ca8045' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f151bdd5-ad7c-4a28-9841-ad7b56ca8045' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>time</span>: 12</li><li><span>eta_rho</span>: 42</li><li><span>xi_rho</span>: 52</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-159fa6ed-42b8-4eb4-b10f-580dc4a2d503' class='xr-section-summary-in' type='checkbox'  checked><label for='section-159fa6ed-42b8-4eb4-b10f-580dc4a2d503' class='xr-section-summary' >Coordinates: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>month</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-56c83d0e-d811-49b5-b826-eb4d1ccc3e30' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-56c83d0e-d811-49b5-b826-eb4d1ccc3e30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ebd99f5-b85d-41bc-b7a6-3a5325fed18c' class='xr-var-data-in' type='checkbox'><label for='data-0ebd99f5-b85d-41bc-b7a6-3a5325fed18c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Month index (1-12)</dd></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-16T12:00:00 ... 2000-12-...</div><input id='attrs-dff8de03-d85e-4e4e-95cd-f7fba775ffa4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dff8de03-d85e-4e4e-95cd-f7fba775ffa4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9869ef8f-0a33-455e-9ed4-17b718406fba' class='xr-var-data-in' type='checkbox'><label for='data-9869ef8f-0a33-455e-9ed4-17b718406fba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>absolute time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-16T12:00:00.000000000&#x27;, &#x27;2000-02-15T00:00:00.000000000&#x27;,\n",
+       "    climatology:           True</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-cd2745a4-a0f4-4f69-a489-cab3951ba9cf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cd2745a4-a0f4-4f69-a489-cab3951ba9cf' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>time</span>: 12</li><li><span>eta_rho</span>: 42</li><li><span>xi_rho</span>: 52</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-05074b26-bb44-4eed-bc79-bbd455f5fd9f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-05074b26-bb44-4eed-bc79-bbd455f5fd9f' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>month</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-c872742c-ff7a-4ccd-a63a-e83d0fde2be4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c872742c-ff7a-4ccd-a63a-e83d0fde2be4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1f20083-128b-446a-9528-d69197eb9790' class='xr-var-data-in' type='checkbox'><label for='data-a1f20083-128b-446a-9528-d69197eb9790' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Month index (1-12)</dd></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-16T12:00:00 ... 2000-12-...</div><input id='attrs-42805346-4c1a-498c-b2d9-523606378b07' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42805346-4c1a-498c-b2d9-523606378b07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a769a046-4ea1-4fac-b655-b484739c748e' class='xr-var-data-in' type='checkbox'><label for='data-a769a046-4ea1-4fac-b655-b484739c748e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>absolute time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-16T12:00:00.000000000&#x27;, &#x27;2000-02-15T00:00:00.000000000&#x27;,\n",
        "       &#x27;2000-03-15T12:00:00.000000000&#x27;, &#x27;2000-04-15T00:00:00.000000000&#x27;,\n",
        "       &#x27;2000-05-15T12:00:00.000000000&#x27;, &#x27;2000-06-15T00:00:00.000000000&#x27;,\n",
        "       &#x27;2000-07-15T00:00:00.000000000&#x27;, &#x27;2000-08-15T12:00:00.000000000&#x27;,\n",
        "       &#x27;2000-09-15T00:00:00.000000000&#x27;, &#x27;2000-10-15T12:00:00.000000000&#x27;,\n",
        "       &#x27;2000-11-15T00:00:00.000000000&#x27;, &#x27;2000-12-15T12:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pco2_time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.5 45.0 74.5 ... 319.0 349.5</div><input id='attrs-f2ae2919-5509-4460-9ba3-f81a07da1a9d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f2ae2919-5509-4460-9ba3-f81a07da1a9d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16bde51c-d2bd-45ba-aae3-060aa42a747f' class='xr-var-data-in' type='checkbox'><label for='data-16bde51c-d2bd-45ba-aae3-060aa42a747f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cycle_length :</span></dt><dd>365.25</dd><dt><span>long_name :</span></dt><dd>relative time: days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([ 15.5,  45. ,  74.5, 105. , 135.5, 166. , 196. , 227.5, 258. ,\n",
-       "       288.5, 319. , 349.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>iron_time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.5 45.0 74.5 ... 319.0 349.5</div><input id='attrs-65cae499-cce1-4f67-9e38-ea32223cab0c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-65cae499-cce1-4f67-9e38-ea32223cab0c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1b63983-b67c-4653-a975-755b9549ba99' class='xr-var-data-in' type='checkbox'><label for='data-f1b63983-b67c-4653-a975-755b9549ba99' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cycle_length :</span></dt><dd>365.25</dd><dt><span>long_name :</span></dt><dd>relative time: days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([ 15.5,  45. ,  74.5, 105. , 135.5, 166. , 196. , 227.5, 258. ,\n",
-       "       288.5, 319. , 349.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dust_time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.5 45.0 74.5 ... 319.0 349.5</div><input id='attrs-818223be-ba08-49b3-96f2-9889a1cda18d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-818223be-ba08-49b3-96f2-9889a1cda18d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9bf2a874-d097-42d9-8318-ef613184b036' class='xr-var-data-in' type='checkbox'><label for='data-9bf2a874-d097-42d9-8318-ef613184b036' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cycle_length :</span></dt><dd>365.25</dd><dt><span>long_name :</span></dt><dd>relative time: days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([ 15.5,  45. ,  74.5, 105. , 135.5, 166. , 196. , 227.5, 258. ,\n",
-       "       288.5, 319. , 349.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nox_time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.5 45.0 74.5 ... 319.0 349.5</div><input id='attrs-eba205b9-e570-469e-836e-b799aeaaa29e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eba205b9-e570-469e-836e-b799aeaaa29e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16e891dd-1931-45ad-83cb-784c6a672085' class='xr-var-data-in' type='checkbox'><label for='data-16e891dd-1931-45ad-83cb-784c6a672085' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cycle_length :</span></dt><dd>365.25</dd><dt><span>long_name :</span></dt><dd>relative time: days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([ 15.5,  45. ,  74.5, 105. , 135.5, 166. , 196. , 227.5, 258. ,\n",
-       "       288.5, 319. , 349.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nhy_time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.5 45.0 74.5 ... 319.0 349.5</div><input id='attrs-8089dd8a-c727-4976-b8d3-595de6a22d5e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8089dd8a-c727-4976-b8d3-595de6a22d5e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75786d30-c1ac-4d55-8112-4b0ea8713dd2' class='xr-var-data-in' type='checkbox'><label for='data-75786d30-c1ac-4d55-8112-4b0ea8713dd2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cycle_length :</span></dt><dd>365.25</dd><dt><span>long_name :</span></dt><dd>relative time: days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([ 15.5,  45. ,  74.5, 105. , 135.5, 166. , 196. , 227.5, 258. ,\n",
-       "       288.5, 319. , 349.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-135707ae-2966-4008-80a2-f4c7baf10445' class='xr-section-summary-in' type='checkbox'  checked><label for='section-135707ae-2966-4008-80a2-f4c7baf10445' class='xr-section-summary' >Data variables: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>pco2_air</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6, 42, 52), meta=np.ndarray&gt;</div><input id='attrs-723fd392-3353-493a-a3ce-0c0595233a4a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-723fd392-3353-493a-a3ce-0c0595233a4a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c9931e6-bfda-48e3-a05d-38b32d3eb8c9' class='xr-var-data-in' type='checkbox'><label for='data-3c9931e6-bfda-48e3-a05d-38b32d3eb8c9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>atmospheric pCO2</dd><dt><span>units :</span></dt><dd>ppmv</dd><dt><span>source :</span></dt><dd>Landschutzer et al., 2016</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/pCO2/OCADS/OCADS_ref2002.nc</dd></dl></div><div class='xr-var-data'><table>\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>iron_time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.5 45.0 74.5 ... 319.0 349.5</div><input id='attrs-bd354a20-d7ae-4e63-861b-c5a07e24617a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd354a20-d7ae-4e63-861b-c5a07e24617a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d328c2cd-33f9-4742-8a8f-9b9b84badd7a' class='xr-var-data-in' type='checkbox'><label for='data-d328c2cd-33f9-4742-8a8f-9b9b84badd7a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cycle_length :</span></dt><dd>365.25</dd><dt><span>long_name :</span></dt><dd>relative time: days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([ 15.5,  45. ,  74.5, 105. , 135.5, 166. , 196. , 227.5, 258. ,\n",
+       "       288.5, 319. , 349.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dust_time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.5 45.0 74.5 ... 319.0 349.5</div><input id='attrs-fdca86ef-1935-4329-bc43-e2eeb10537c3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fdca86ef-1935-4329-bc43-e2eeb10537c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5acbed92-5eed-4ca9-bf9b-ef552bd3f474' class='xr-var-data-in' type='checkbox'><label for='data-5acbed92-5eed-4ca9-bf9b-ef552bd3f474' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cycle_length :</span></dt><dd>365.25</dd><dt><span>long_name :</span></dt><dd>relative time: days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([ 15.5,  45. ,  74.5, 105. , 135.5, 166. , 196. , 227.5, 258. ,\n",
+       "       288.5, 319. , 349.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nox_time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.5 45.0 74.5 ... 319.0 349.5</div><input id='attrs-7d691d07-658c-4acd-bd9f-e43d16ce4781' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7d691d07-658c-4acd-bd9f-e43d16ce4781' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e323cf3d-2889-4afa-85c6-7784ba3c80fc' class='xr-var-data-in' type='checkbox'><label for='data-e323cf3d-2889-4afa-85c6-7784ba3c80fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cycle_length :</span></dt><dd>365.25</dd><dt><span>long_name :</span></dt><dd>relative time: days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([ 15.5,  45. ,  74.5, 105. , 135.5, 166. , 196. , 227.5, 258. ,\n",
+       "       288.5, 319. , 349.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nhy_time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.5 45.0 74.5 ... 319.0 349.5</div><input id='attrs-ce807ddb-0223-4222-812d-3948098ddc35' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce807ddb-0223-4222-812d-3948098ddc35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e7ae1af-1d70-4d23-9d14-03d4e81c6991' class='xr-var-data-in' type='checkbox'><label for='data-6e7ae1af-1d70-4d23-9d14-03d4e81c6991' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cycle_length :</span></dt><dd>365.25</dd><dt><span>long_name :</span></dt><dd>relative time: days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([ 15.5,  45. ,  74.5, 105. , 135.5, 166. , 196. , 227.5, 258. ,\n",
+       "       288.5, 319. , 349.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e57f4b4c-9a53-48c9-827a-fd40a38fd006' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e57f4b4c-9a53-48c9-827a-fd40a38fd006' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>dust</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 42, 52), meta=np.ndarray&gt;</div><input id='attrs-bb48d5f7-4d20-4ccb-aef5-781df4874952' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb48d5f7-4d20-4ccb-aef5-781df4874952' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-817158d4-5750-4329-b02c-791109b5dba0' class='xr-var-data-in' type='checkbox'><label for='data-817158d4-5750-4329-b02c-791109b5dba0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dust decomposition</dd><dt><span>units :</span></dt><dd>kg/m^2/s</dd><dt><span>source :</span></dt><dd>Kok et al. (2020)</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/Srf_BGC_Flx/KoK2021/DustCOMM_totdep_seas_bin.nc</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7774,17 +7613,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 102.38 kiB </td>\n",
-       "                        <td> 51.19 kiB </td>\n",
+       "                        <td> 8.53 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 42, 52) </td>\n",
-       "                        <td> (6, 42, 52) </td>\n",
+       "                        <td> (1, 42, 52) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 2 chunks in 21 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 41 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -7802,7 +7641,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"99\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"101\" />\n",
+       "  <line x1=\"16\" y1=\"6\" x2=\"16\" y2=\"103\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"105\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"107\" />\n",
        "  <line x1=\"22\" y1=\"12\" x2=\"22\" y2=\"109\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"111\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"113\" />\n",
+       "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"115\" />\n",
+       "  <line x1=\"30\" y1=\"20\" x2=\"30\" y2=\"117\" />\n",
+       "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"119\" />\n",
        "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"121\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -7810,7 +7659,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"16\" y1=\"6\" x2=\"136\" y2=\"6\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
        "  <line x1=\"22\" y1=\"12\" x2=\"142\" y2=\"12\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
+       "  <line x1=\"28\" y1=\"18\" x2=\"148\" y2=\"18\" />\n",
+       "  <line x1=\"30\" y1=\"20\" x2=\"150\" y2=\"20\" />\n",
+       "  <line x1=\"32\" y1=\"22\" x2=\"152\" y2=\"22\" />\n",
        "  <line x1=\"35\" y1=\"25\" x2=\"155\" y2=\"25\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
@@ -7832,13 +7691,13 @@
        "  <polygon points=\"35.054364554030855,25.054364554030858 155.05436455403085,25.054364554030858 155.05436455403085,121.97744147710777 35.054364554030855,121.97744147710777\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"95.054365\" y=\"141.977441\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >52</text>\n",
-       "  <text x=\"175.054365\" y=\"73.515903\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,175.054365,73.515903)\">42</text>\n",
-       "  <text x=\"12.527182\" y=\"129.450259\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,12.527182,129.450259)\">12</text>\n",
+       "  <text x=\"95.05436455403085\" y=\"141.9774414771078\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >52</text>\n",
+       "  <text x=\"175.05436455403085\" y=\"73.51590301556932\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,175.05436455403085,73.51590301556932)\">42</text>\n",
+       "  <text x=\"12.527182277015427\" y=\"129.45025920009235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,12.527182277015427,129.45025920009235)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dust</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 42, 52), meta=np.ndarray&gt;</div><input id='attrs-8b095eaf-4484-448e-a96d-1313efe56950' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b095eaf-4484-448e-a96d-1313efe56950' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2dbe2454-1a88-4daf-bb2a-241a352ee879' class='xr-var-data-in' type='checkbox'><label for='data-2dbe2454-1a88-4daf-bb2a-241a352ee879' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dust decomposition</dd><dt><span>units :</span></dt><dd>kg/m^2/s</dd><dt><span>source :</span></dt><dd>Kok et al. (2020)</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/Srf_BGC_Flx/KoK2021/DustCOMM_totdep_seas_bin.nc</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>iron</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 42, 52), meta=np.ndarray&gt;</div><input id='attrs-1d1dcd4c-2bfe-404a-b1db-58b1971b1675' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1d1dcd4c-2bfe-404a-b1db-58b1971b1675' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eac8eafe-2411-4353-9e43-0a7e254f31e5' class='xr-var-data-in' type='checkbox'><label for='data-eac8eafe-2411-4353-9e43-0a7e254f31e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>iron decomposition</dd><dt><span>units :</span></dt><dd>nmol/cm^2/s</dd><dt><span>source :</span></dt><dd> Hamilton et al. (2022)</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/Srf_BGC_Flx/IronDeposition_Hamilton/Hamilton2022/CAM6-MIMI_2002-2020CLIMOMEAN_IRONDEP.nc</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7854,17 +7713,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 102.38 kiB </td>\n",
-       "                        <td> 102.38 kiB </td>\n",
+       "                        <td> 8.53 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 42, 52) </td>\n",
-       "                        <td> (12, 42, 52) </td>\n",
+       "                        <td> (1, 42, 52) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 40 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 22 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -7882,6 +7741,17 @@
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"99\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"101\" />\n",
+       "  <line x1=\"16\" y1=\"6\" x2=\"16\" y2=\"103\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"105\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"107\" />\n",
+       "  <line x1=\"22\" y1=\"12\" x2=\"22\" y2=\"109\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"111\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"113\" />\n",
+       "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"115\" />\n",
+       "  <line x1=\"30\" y1=\"20\" x2=\"30\" y2=\"117\" />\n",
+       "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"119\" />\n",
        "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"121\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -7889,6 +7759,17 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"12\" y1=\"2\" x2=\"132\" y2=\"2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"16\" y1=\"6\" x2=\"136\" y2=\"6\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"20\" y1=\"10\" x2=\"140\" y2=\"10\" />\n",
+       "  <line x1=\"22\" y1=\"12\" x2=\"142\" y2=\"12\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
+       "  <line x1=\"28\" y1=\"18\" x2=\"148\" y2=\"18\" />\n",
+       "  <line x1=\"30\" y1=\"20\" x2=\"150\" y2=\"20\" />\n",
+       "  <line x1=\"32\" y1=\"22\" x2=\"152\" y2=\"22\" />\n",
        "  <line x1=\"35\" y1=\"25\" x2=\"155\" y2=\"25\" style=\"stroke-width:2\" />\n",
        "\n",
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@@ -7910,13 +7791,13 @@
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+       "  <text x=\"12.527182277015427\" y=\"129.45025920009235\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,12.527182277015427,129.45025920009235)\">12</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>iron</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6, 42, 52), meta=np.ndarray&gt;</div><input id='attrs-6f5a2db8-3247-4c06-97bb-47dca586e7d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f5a2db8-3247-4c06-97bb-47dca586e7d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65e11ad2-6489-4ac5-b914-08762092bd73' class='xr-var-data-in' type='checkbox'><label for='data-65e11ad2-6489-4ac5-b914-08762092bd73' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>iron decomposition</dd><dt><span>units :</span></dt><dd>nmol/cm^2/s</dd><dt><span>source :</span></dt><dd> Hamilton et al. (2022)</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/Srf_BGC_Flx/IronDeposition_Hamilton/Hamilton2022/CAM6-MIMI_2002-2020CLIMOMEAN_IRONDEP.nc</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nox</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 42, 52), meta=np.ndarray&gt;</div><input id='attrs-41c32c7c-1910-44db-993f-70ca97fcaeb3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41c32c7c-1910-44db-993f-70ca97fcaeb3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbb4c4ea-6ed6-4d6a-9870-d4e626e71d23' class='xr-var-data-in' type='checkbox'><label for='data-dbb4c4ea-6ed6-4d6a-9870-d4e626e71d23' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>NOx decomposition</dd><dt><span>units :</span></dt><dd>kg/m^2/s</dd><dt><span>source :</span></dt><dd> CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/monthly_NOX_NHY_CESM.nc</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7932,17 +7813,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 102.38 kiB </td>\n",
-       "                        <td> 51.19 kiB </td>\n",
+       "                        <td> 8.53 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 42, 52) </td>\n",
-       "                        <td> (6, 42, 52) </td>\n",
+       "                        <td> (1, 42, 52) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 2 chunks in 21 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 22 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
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        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nox</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6, 42, 52), meta=np.ndarray&gt;</div><input id='attrs-95cb4a79-5988-4168-9e2f-f9dfcb41555c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95cb4a79-5988-4168-9e2f-f9dfcb41555c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b0effd4-a211-49c9-af1c-8f7c7ae49dc8' class='xr-var-data-in' type='checkbox'><label for='data-2b0effd4-a211-49c9-af1c-8f7c7ae49dc8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>NOx decomposition</dd><dt><span>units :</span></dt><dd>kg/m^2/s</dd><dt><span>source :</span></dt><dd> CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/monthly_NOX_NHY_CESM.nc</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nhy</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 42, 52), meta=np.ndarray&gt;</div><input id='attrs-d2263f87-e7d5-4aeb-865d-c049c75c64a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2263f87-e7d5-4aeb-865d-c049c75c64a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-722d8fbe-161d-4798-a4a1-1c80cd0b7b21' class='xr-var-data-in' type='checkbox'><label for='data-722d8fbe-161d-4798-a4a1-1c80cd0b7b21' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>NHy decomposition</dd><dt><span>units :</span></dt><dd>kg/m^2/s</dd><dt><span>source :</span></dt><dd> CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/monthly_NOX_NHY_CESM.nc</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -8012,17 +7913,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 102.38 kiB </td>\n",
-       "                        <td> 51.19 kiB </td>\n",
+       "                        <td> 8.53 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (12, 42, 52) </td>\n",
-       "                        <td> (6, 42, 52) </td>\n",
+       "                        <td> (1, 42, 52) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 2 chunks in 21 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 22 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
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        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nhy</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6, 42, 52), meta=np.ndarray&gt;</div><input id='attrs-0b766faa-6535-4543-8af2-72d54f9a8b7a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0b766faa-6535-4543-8af2-72d54f9a8b7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-172d39fe-c472-4806-8cd0-2b95151f1479' class='xr-var-data-in' type='checkbox'><label for='data-172d39fe-c472-4806-8cd0-2b95151f1479' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>NHy decomposition</dd><dt><span>units :</span></dt><dd>kg/m^2/s</dd><dt><span>source :</span></dt><dd> CESM</dd><dt><span>source file :</span></dt><dd>/data/project7/pdamien/DATA/BGC/CESM/monthly_NOX_NHY_CESM.nc</dd></dl></div><div class='xr-var-data'><table>\n",
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-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
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-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 102.38 kiB </td>\n",
-       "                        <td> 51.19 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
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-       "                        <td> (6, 42, 52) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 2 chunks in 21 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pco2_air_alt</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6, 42, 52), meta=np.ndarray&gt;</div><input id='attrs-127eb515-680a-4513-ad24-4eb52035613e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-127eb515-680a-4513-ad24-4eb52035613e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef8f27d3-ba89-45c4-b05f-4afe2dc208a6' class='xr-var-data-in' type='checkbox'><label for='data-ef8f27d3-ba89-45c4-b05f-4afe2dc208a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>atmospheric pCO2, alternative CO2</dd><dt><span>units :</span></dt><dd>ppmv</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
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-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 102.38 kiB </td>\n",
-       "                        <td> 51.19 kiB </td>\n",
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-       "                    \n",
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-       "                        <th> Shape </th>\n",
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-       "                        <td> (6, 42, 52) </td>\n",
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+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-d25f4634-833c-4862-81a0-384e31407c4f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d25f4634-833c-4862-81a0-384e31407c4f' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-f67b856d-995e-4fe7-9893-31a9571811f5' class='xr-section-summary-in' type='checkbox'  ><label for='section-f67b856d-995e-4fe7-9893-31a9571811f5' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>ROMS surface forcing file created by ROMS-Tools</dd><dt><span>roms_tools_version :</span></dt><dd>10000.dev364+g785fa77b7.d20260612</dd><dt><span>start_time :</span></dt><dd>2012-01-01 00:00:00</dd><dt><span>end_time :</span></dt><dd>2012-01-31 00:00:00</dd><dt><span>source :</span></dt><dd>UNIFIED</dd><dt><span>correct_radiation :</span></dt><dd>True</dd><dt><span>wind_dropoff :</span></dt><dd>False</dd><dt><span>use_coarse_grid :</span></dt><dd>True</dd><dt><span>model_reference_date :</span></dt><dd>2000-01-01 00:00:00</dd><dt><span>type :</span></dt><dd>bgc</dd><dt><span>climatology :</span></dt><dd>True</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.Dataset> Size: 630kB\n",
-       "Dimensions:       (time: 12, eta_rho: 42, xi_rho: 52)\n",
+       "<xarray.Dataset> Size: 420kB\n",
+       "Dimensions:    (time: 12, eta_rho: 42, xi_rho: 52)\n",
        "Coordinates:\n",
-       "    month         (time) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12\n",
-       "    abs_time      (time) datetime64[ns] 96B 2000-01-16T12:00:00 ... 2000-12-1...\n",
-       "    pco2_time     (time) float64 96B 15.5 45.0 74.5 105.0 ... 288.5 319.0 349.5\n",
-       "    iron_time     (time) float64 96B 15.5 45.0 74.5 105.0 ... 288.5 319.0 349.5\n",
-       "    dust_time     (time) float64 96B 15.5 45.0 74.5 105.0 ... 288.5 319.0 349.5\n",
-       "    nox_time      (time) float64 96B 15.5 45.0 74.5 105.0 ... 288.5 319.0 349.5\n",
-       "    nhy_time      (time) float64 96B 15.5 45.0 74.5 105.0 ... 288.5 319.0 349.5\n",
+       "    month      (time) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12\n",
+       "    abs_time   (time) datetime64[ns] 96B 2000-01-16T12:00:00 ... 2000-12-15T1...\n",
+       "    iron_time  (time) float64 96B 15.5 45.0 74.5 105.0 ... 288.5 319.0 349.5\n",
+       "    dust_time  (time) float64 96B 15.5 45.0 74.5 105.0 ... 288.5 319.0 349.5\n",
+       "    nox_time   (time) float64 96B 15.5 45.0 74.5 105.0 ... 288.5 319.0 349.5\n",
+       "    nhy_time   (time) float64 96B 15.5 45.0 74.5 105.0 ... 288.5 319.0 349.5\n",
        "Dimensions without coordinates: time, eta_rho, xi_rho\n",
        "Data variables:\n",
-       "    pco2_air      (time, eta_rho, xi_rho) float32 105kB dask.array<chunksize=(6, 42, 52), meta=np.ndarray>\n",
-       "    dust          (time, eta_rho, xi_rho) float32 105kB dask.array<chunksize=(12, 42, 52), meta=np.ndarray>\n",
-       "    iron          (time, eta_rho, xi_rho) float32 105kB dask.array<chunksize=(6, 42, 52), meta=np.ndarray>\n",
-       "    nox           (time, eta_rho, xi_rho) float32 105kB dask.array<chunksize=(6, 42, 52), meta=np.ndarray>\n",
-       "    nhy           (time, eta_rho, xi_rho) float32 105kB dask.array<chunksize=(6, 42, 52), meta=np.ndarray>\n",
-       "    pco2_air_alt  (time, eta_rho, xi_rho) float32 105kB dask.array<chunksize=(6, 42, 52), meta=np.ndarray>\n",
+       "    dust       (time, eta_rho, xi_rho) float32 105kB dask.array<chunksize=(1, 42, 52), meta=np.ndarray>\n",
+       "    iron       (time, eta_rho, xi_rho) float32 105kB dask.array<chunksize=(1, 42, 52), meta=np.ndarray>\n",
+       "    nox        (time, eta_rho, xi_rho) float32 105kB dask.array<chunksize=(1, 42, 52), meta=np.ndarray>\n",
+       "    nhy        (time, eta_rho, xi_rho) float32 105kB dask.array<chunksize=(1, 42, 52), meta=np.ndarray>\n",
        "Attributes:\n",
        "    title:                 ROMS surface forcing file created by ROMS-Tools\n",
-       "    roms_tools_version:    3.1.3.dev32+g91d580e67.d20250929\n",
+       "    roms_tools_version:    10000.dev364+g785fa77b7.d20260612\n",
        "    start_time:            2012-01-01 00:00:00\n",
        "    end_time:              2012-01-31 00:00:00\n",
        "    source:                UNIFIED\n",
@@ -8271,7 +8029,7 @@
        "    climatology:           True"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8285,12 +8043,12 @@
    "id": "000c337a-98c1-42d6-8a7b-a14fe7f45242",
    "metadata": {},
    "source": [
-    "Let's plot atmospheric pCO2, which is one of the BGC surface forcings."
+    "Let's plot iron, which is one of the BGC surface forcings."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 29,
    "id": "3636e22c-fbfb-4ff7-b8ae-f97b24a9f021",
    "metadata": {
     "tags": []
@@ -8300,12 +8058,89 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[########################################] | 100% Completed | 103.16 ms\n"
+      "[########################################] | 100% Completed | 111.06 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1300x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bgc_surface_forcing.plot(\"iron\", time=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42ac9c54",
+   "metadata": {},
+   "source": [
+    "##### Time-varying CO2 \n",
+    "\n",
+    "By default, ROMS assumes a constant value for the molar mixing ratio for CO2, xco2. If we would like a time-varying xco2 applied in the model, we can create a BGC surface forcing object containing xco2, similar to the BGC surface forcing generated above.  \n",
+    "\n",
+    "The xco2 data is interpolated from the GML's (NOAA) Marine Boundary Layer dataset (1979-2025). For more information, see [details on surface forcing](https://roms-tools.readthedocs.io/en/latest/surface_forcing.html). Here we let the code assume defaults."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "76ad014e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-06-23 09:20:36 - INFO - No path specified for MBL_co2 source; defaulting to the MBL dataset from GML, NOAA.\n",
+      "2026-06-23 09:20:36 - INFO - Data will be interpolated onto the grid coarsened by factor 2. Remember to set `interp_frc = 1` in your `bgc.opt` ROMS option file.\n",
+      "2026-06-23 09:20:36 - INFO - Time-varying CO2 values being used.Remember to define the following flags in your `cppdefs.opt` file: {'PCO2AIR_FORCING'}`.\n"
      ]
     },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 26.7 ms, sys: 13.7 ms, total: 40.4 ms\n",
+      "Wall time: 59.5 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "mbl_bgc_surface_forcing = SurfaceForcing(\n",
+    "    grid=grid,\n",
+    "    start_time=start_time,\n",
+    "    end_time=end_time,\n",
+    "    source={\"name\": \"MBL_co2\"},\n",
+    "    type=\"bgc\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7eb1f4bd",
+   "metadata": {},
+   "source": [
+    "Here we plot a time step of xco2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "959d035c",
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1300x700 with 2 Axes>"
       ]
@@ -8315,7 +8150,7 @@
     }
    ],
    "source": [
-    "bgc_surface_forcing.plot(\"pco2_air\", time=0)"
+    "mbl_bgc_surface_forcing.plot(\"xco2_air\", time=0)"
    ]
   },
   {
@@ -8328,7 +8163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 32,
    "id": "507ce151-7469-4e3c-97ce-2e34bdf00c1b",
    "metadata": {
     "tags": []
@@ -8338,7 +8173,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-09-29 14:10:00 - INFO - Writing the following NetCDF files:\n",
+      "2026-06-23 09:20:36 - INFO - Writing the following NetCDF files:\n",
       "ROMS_TOOLS_INPUT_DATA/roms_frc_bgc_clim.nc\n"
      ]
     },
@@ -8346,7 +8181,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[########################################] | 100% Completed | 106.55 ms\n"
+      "[########################################] | 100% Completed | 216.65 ms\n"
      ]
     },
     {
@@ -8355,7 +8190,7 @@
        "[PosixPath('ROMS_TOOLS_INPUT_DATA/roms_frc_bgc_clim.nc')]"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8366,7 +8201,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 33,
    "id": "89bfa09f-875e-4cbb-956f-95e4bd238db8",
    "metadata": {
     "tags": []
@@ -8408,7 +8243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 34,
    "id": "1c35951a-392a-47f9-8138-d37fc60d88b3",
    "metadata": {},
    "outputs": [],
@@ -8428,7 +8263,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 35,
    "id": "23b04a5c-cb48-4b8b-a374-da88bdeeba36",
    "metadata": {
     "tags": []
@@ -8438,17 +8273,18 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-09-29 14:10:17 - INFO - Sea surface height will NOT be used to adjust depth coordinates.\n",
-      "2025-09-29 14:10:17 - WARNING - The southern boundary is divided by land. It would be safer (but slower and more memory-intensive) to use `apply_2d_horizontal_fill = True`.\n",
-      "2025-09-29 14:10:17 - WARNING - The eastern boundary is divided by land. It would be safer (but slower and more memory-intensive) to use `apply_2d_horizontal_fill = True`.\n"
+      "2026-06-23 09:20:36 - INFO - Using boundary configuration: {'south': True, 'east': True, 'north': True, 'west': False}\n",
+      "2026-06-23 09:20:36 - WARNING - No records found before the start_time: 2012-01-01 00:00:00.\n",
+      "2026-06-23 09:20:36 - WARNING - The southern boundary is divided by land. It would be safer (but slower and more memory-intensive) to use `apply_2d_horizontal_fill = True`.\n",
+      "2026-06-23 09:20:37 - WARNING - The eastern boundary is divided by land. It would be safer (but slower and more memory-intensive) to use `apply_2d_horizontal_fill = True`.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 1.02 s, sys: 238 ms, total: 1.26 s\n",
-      "Wall time: 1.12 s\n"
+      "CPU times: user 770 ms, sys: 143 ms, total: 913 ms\n",
+      "Wall time: 899 ms\n"
      ]
     }
    ],
@@ -8480,7 +8316,7 @@
     "\n",
     "Note\n",
     "\n",
-    "For an explanation of the warning message about the division of boundaries by land, see this [page](https://roms-tools.readthedocs.io/en/latest/boundary_forcing.html#1D-versus-2D-horizontal-fill).\n",
+    "Boundary data is regridded near land with a NaN-aware approach (masked bilinear interpolation plus nearest-neighbor extrapolation). See this [page](https://roms-tools.readthedocs.io/en/latest/boundary_forcing.html#Horizontal-regridding-near-land-and-coarse-source-data) for details.\n",
     "\n",
     "</div>"
    ]
@@ -8495,7 +8331,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 36,
    "id": "c2a1428b-af05-4052-875f-6bf76c3cffb8",
    "metadata": {},
    "outputs": [
@@ -8946,29 +8782,29 @@
        "  stroke-width: 0.8px;\n",
        "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 3MB\n",
-       "Dimensions:     (bry_time: 31, s_rho: 20, xi_u: 101, xi_rho: 102, eta_rho: 82,\n",
+       "Dimensions:     (bry_time: 32, s_rho: 20, xi_u: 101, xi_rho: 102, eta_rho: 82,\n",
        "                 eta_v: 81)\n",
        "Coordinates:\n",
-       "    abs_time    (bry_time) datetime64[ns] 248B 2012-01-01 ... 2012-01-31\n",
-       "  * bry_time    (bry_time) float64 248B 4.383e+03 4.384e+03 ... 4.413e+03\n",
+       "    abs_time    (bry_time) datetime64[ns] 256B 2012-01-01 ... 2012-02-01\n",
+       "  * bry_time    (bry_time) float64 256B 4.383e+03 4.384e+03 ... 4.414e+03\n",
        "Dimensions without coordinates: s_rho, xi_u, xi_rho, eta_rho, eta_v\n",
        "Data variables: (12/21)\n",
-       "    u_south     (bry_time, s_rho, xi_u) float32 250kB dask.array&lt;chunksize=(1, 20, 101), meta=np.ndarray&gt;\n",
-       "    v_south     (bry_time, s_rho, xi_rho) float32 253kB dask.array&lt;chunksize=(1, 20, 102), meta=np.ndarray&gt;\n",
-       "    temp_south  (bry_time, s_rho, xi_rho) float32 253kB dask.array&lt;chunksize=(1, 20, 102), meta=np.ndarray&gt;\n",
-       "    salt_south  (bry_time, s_rho, xi_rho) float32 253kB dask.array&lt;chunksize=(1, 20, 102), meta=np.ndarray&gt;\n",
+       "    u_south     (bry_time, s_rho, xi_u) float32 259kB dask.array&lt;chunksize=(1, 20, 101), meta=np.ndarray&gt;\n",
+       "    v_south     (bry_time, s_rho, xi_rho) float32 261kB dask.array&lt;chunksize=(1, 20, 102), meta=np.ndarray&gt;\n",
+       "    temp_south  (bry_time, s_rho, xi_rho) float32 261kB dask.array&lt;chunksize=(1, 20, 102), meta=np.ndarray&gt;\n",
+       "    salt_south  (bry_time, s_rho, xi_rho) float32 261kB dask.array&lt;chunksize=(1, 20, 102), meta=np.ndarray&gt;\n",
        "    zeta_south  (bry_time, xi_rho) float32 13kB dask.array&lt;chunksize=(1, 102), meta=np.ndarray&gt;\n",
        "    ubar_south  (bry_time, xi_u) float32 13kB dask.array&lt;chunksize=(1, 101), meta=np.ndarray&gt;\n",
        "    ...          ...\n",
-       "    v_north     (bry_time, s_rho, xi_rho) float32 253kB dask.array&lt;chunksize=(1, 20, 102), meta=np.ndarray&gt;\n",
-       "    temp_north  (bry_time, s_rho, xi_rho) float32 253kB dask.array&lt;chunksize=(1, 20, 102), meta=np.ndarray&gt;\n",
-       "    salt_north  (bry_time, s_rho, xi_rho) float32 253kB dask.array&lt;chunksize=(1, 20, 102), meta=np.ndarray&gt;\n",
+       "    v_north     (bry_time, s_rho, xi_rho) float32 261kB dask.array&lt;chunksize=(1, 20, 102), meta=np.ndarray&gt;\n",
+       "    temp_north  (bry_time, s_rho, xi_rho) float32 261kB dask.array&lt;chunksize=(1, 20, 102), meta=np.ndarray&gt;\n",
+       "    salt_north  (bry_time, s_rho, xi_rho) float32 261kB dask.array&lt;chunksize=(1, 20, 102), meta=np.ndarray&gt;\n",
        "    zeta_north  (bry_time, xi_rho) float32 13kB dask.array&lt;chunksize=(1, 102), meta=np.ndarray&gt;\n",
        "    ubar_north  (bry_time, xi_u) float32 13kB dask.array&lt;chunksize=(1, 101), meta=np.ndarray&gt;\n",
        "    vbar_north  (bry_time, xi_rho) float32 13kB dask.array&lt;chunksize=(1, 102), meta=np.ndarray&gt;\n",
        "Attributes:\n",
        "    title:                                ROMS boundary forcing file created ...\n",
-       "    roms_tools_version:                   3.1.3.dev32+g91d580e67.d20250929\n",
+       "    roms_tools_version:                   10000.dev364+g785fa77b7.d20260612\n",
        "    start_time:                           2012-01-01 00:00:00\n",
        "    end_time:                             2012-01-31 00:00:00\n",
        "    source:                               GLORYS\n",
@@ -8977,7 +8813,7 @@
        "    adjust_depth_for_sea_surface_height:  False\n",
        "    theta_s:                              5.0\n",
        "    theta_b:                              2.0\n",
-       "    hc:                                   300.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-fa779f8e-57ea-4555-bbe3-7005d8b5bd48' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fa779f8e-57ea-4555-bbe3-7005d8b5bd48' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>bry_time</span>: 31</li><li><span>s_rho</span>: 20</li><li><span>xi_u</span>: 101</li><li><span>xi_rho</span>: 102</li><li><span>eta_rho</span>: 82</li><li><span>eta_v</span>: 81</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ad72d35b-aff5-4561-bd4b-4222e57bf029' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ad72d35b-aff5-4561-bd4b-4222e57bf029' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-01-01 ... 2012-01-31</div><input id='attrs-0a7abc9b-8a71-4833-a75f-38cba1f23c7c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a7abc9b-8a71-4833-a75f-38cba1f23c7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6d8908c-8cf0-4154-8c1f-cb4ea84f8041' class='xr-var-data-in' type='checkbox'><label for='data-e6d8908c-8cf0-4154-8c1f-cb4ea84f8041' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>absolute time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-01-01T00:00:00.000000000&#x27;, &#x27;2012-01-02T00:00:00.000000000&#x27;,\n",
+       "    hc:                                   300.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-f8cc0715-6bee-40fb-85ec-a501e550875d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f8cc0715-6bee-40fb-85ec-a501e550875d' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>bry_time</span>: 32</li><li><span>s_rho</span>: 20</li><li><span>xi_u</span>: 101</li><li><span>xi_rho</span>: 102</li><li><span>eta_rho</span>: 82</li><li><span>eta_v</span>: 81</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8818dc58-84b3-47b3-855c-a403a10f40ef' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8818dc58-84b3-47b3-855c-a403a10f40ef' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-01-01 ... 2012-02-01</div><input id='attrs-7ec80393-706d-41da-a75e-41027e7a28f3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7ec80393-706d-41da-a75e-41027e7a28f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d83ff10b-1040-498c-8aca-06c139dbc8d7' class='xr-var-data-in' type='checkbox'><label for='data-d83ff10b-1040-498c-8aca-06c139dbc8d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>absolute time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-01-01T00:00:00.000000000&#x27;, &#x27;2012-01-02T00:00:00.000000000&#x27;,\n",
        "       &#x27;2012-01-03T00:00:00.000000000&#x27;, &#x27;2012-01-04T00:00:00.000000000&#x27;,\n",
        "       &#x27;2012-01-05T00:00:00.000000000&#x27;, &#x27;2012-01-06T00:00:00.000000000&#x27;,\n",
        "       &#x27;2012-01-07T00:00:00.000000000&#x27;, &#x27;2012-01-08T00:00:00.000000000&#x27;,\n",
@@ -8992,10 +8828,11 @@
        "       &#x27;2012-01-25T00:00:00.000000000&#x27;, &#x27;2012-01-26T00:00:00.000000000&#x27;,\n",
        "       &#x27;2012-01-27T00:00:00.000000000&#x27;, &#x27;2012-01-28T00:00:00.000000000&#x27;,\n",
        "       &#x27;2012-01-29T00:00:00.000000000&#x27;, &#x27;2012-01-30T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2012-01-31T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>bry_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.383e+03 4.384e+03 ... 4.413e+03</div><input id='attrs-b20d2405-8853-48ac-b226-fd653f6c78b9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b20d2405-8853-48ac-b226-fd653f6c78b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61e602d1-6d72-4a0c-a1fa-540f9e926cbe' class='xr-var-data-in' type='checkbox'><label for='data-61e602d1-6d72-4a0c-a1fa-540f9e926cbe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>relative time: days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([4383., 4384., 4385., 4386., 4387., 4388., 4389., 4390., 4391., 4392.,\n",
+       "       &#x27;2012-01-31T00:00:00.000000000&#x27;, &#x27;2012-02-01T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>bry_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.383e+03 4.384e+03 ... 4.414e+03</div><input id='attrs-529d7ac9-ff6b-4d04-a9dd-1bbfca35ae88' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-529d7ac9-ff6b-4d04-a9dd-1bbfca35ae88' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86a050b8-ef09-42a5-96d8-698933b09c42' class='xr-var-data-in' type='checkbox'><label for='data-86a050b8-ef09-42a5-96d8-698933b09c42' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>relative time: days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([4383., 4384., 4385., 4386., 4387., 4388., 4389., 4390., 4391., 4392.,\n",
        "       4393., 4394., 4395., 4396., 4397., 4398., 4399., 4400., 4401., 4402.,\n",
        "       4403., 4404., 4405., 4406., 4407., 4408., 4409., 4410., 4411., 4412.,\n",
-       "       4413.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6fb9eb7e-991a-447a-88db-ebd21a4d15aa' class='xr-section-summary-in' type='checkbox'  ><label for='section-6fb9eb7e-991a-447a-88db-ebd21a4d15aa' class='xr-section-summary' >Data variables: <span>(21)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>u_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 101), meta=np.ndarray&gt;</div><input id='attrs-87f113b5-d2aa-42ad-8387-2c08a1ee3b58' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-87f113b5-d2aa-42ad-8387-2c08a1ee3b58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27279b25-3fb0-4bc8-ad67-2afb9a746fbc' class='xr-var-data-in' type='checkbox'><label for='data-27279b25-3fb0-4bc8-ad67-2afb9a746fbc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "       4413., 4414.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-aad230c5-fe23-4f02-8cd8-ce5f0fb4a0c7' class='xr-section-summary-in' type='checkbox'  ><label for='section-aad230c5-fe23-4f02-8cd8-ce5f0fb4a0c7' class='xr-section-summary' >Data variables: <span>(21)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>u_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 101), meta=np.ndarray&gt;</div><input id='attrs-1eeabea5-b9f2-496f-93d8-476b4cc3c933' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1eeabea5-b9f2-496f-93d8-476b4cc3c933' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6588458d-a245-4504-a18f-e59303eb88c4' class='xr-var-data-in' type='checkbox'><label for='data-6588458d-a245-4504-a18f-e59303eb88c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -9010,18 +8847,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 244.61 kiB </td>\n",
+       "                        <td> 252.50 kiB </td>\n",
        "                        <td> 7.89 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (31, 20, 101) </td>\n",
+       "                        <td> (32, 20, 101) </td>\n",
        "                        <td> (1, 20, 101) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 31 chunks in 91 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 32 chunks in 73 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -9041,45 +8878,45 @@
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"41\" style=\"stroke-width:2\" />\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"42\" />\n",
        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
-       "  <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"45\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"45\" />\n",
        "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"16\" y1=\"6\" x2=\"16\" y2=\"48\" />\n",
-       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
-       "  <line x1=\"19\" y1=\"9\" x2=\"19\" y2=\"51\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
        "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
-       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
-       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"22\" y1=\"12\" x2=\"22\" y2=\"54\" />\n",
+       "  <line x1=\"22\" y1=\"12\" x2=\"22\" y2=\"54\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
-       "  <line x1=\"25\" y1=\"15\" x2=\"25\" y2=\"57\" />\n",
-       "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"60\" />\n",
-       "  <line x1=\"29\" y1=\"19\" x2=\"29\" y2=\"61\" />\n",
-       "  <line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"63\" />\n",
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-       "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" />\n",
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@@ -9089,7 +8926,7 @@
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@@ -9100,16 +8937,16 @@
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+       "  <text x=\"95.72024052980876\" y=\"87.68797250481987\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >101</text>\n",
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        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 102), meta=np.ndarray&gt;</div><input id='attrs-230380be-1bbd-4f76-ae8b-f90700e673ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-230380be-1bbd-4f76-ae8b-f90700e673ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c5279fd-79c3-49c5-bb20-b27dec99b5e9' class='xr-var-data-in' type='checkbox'><label for='data-9c5279fd-79c3-49c5-bb20-b27dec99b5e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 102), meta=np.ndarray&gt;</div><input id='attrs-2efa8b28-0dda-4245-a254-69eb1766f62e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2efa8b28-0dda-4245-a254-69eb1766f62e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d94a823-ec51-427e-b67a-d8376f1ea4ad' class='xr-var-data-in' type='checkbox'><label for='data-3d94a823-ec51-427e-b67a-d8376f1ea4ad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -9124,18 +8961,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 247.03 kiB </td>\n",
+       "                        <td> 255.00 kiB </td>\n",
        "                        <td> 7.97 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (31, 20, 102) </td>\n",
+       "                        <td> (32, 20, 102) </td>\n",
        "                        <td> (1, 20, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 31 chunks in 91 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 32 chunks in 73 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
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-       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
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        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
-       "  <line x1=\"25\" y1=\"15\" x2=\"145\" y2=\"15\" />\n",
-       "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" />\n",
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+       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"148\" y2=\"18\" />\n",
-       "  <line x1=\"29\" y1=\"19\" x2=\"149\" y2=\"19\" />\n",
-       "  <line x1=\"31\" y1=\"21\" x2=\"151\" y2=\"21\" />\n",
+       "  <line x1=\"30\" y1=\"20\" x2=\"150\" y2=\"20\" />\n",
+       "  <line x1=\"30\" y1=\"20\" x2=\"150\" y2=\"20\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"152\" y2=\"22\" />\n",
-       "  <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" />\n",
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        "  <line x1=\"35\" y1=\"25\" x2=\"155\" y2=\"25\" style=\"stroke-width:2\" />\n",
        "\n",
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@@ -9203,7 +9040,7 @@
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        "\n",
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        "\n",
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        "  <line x1=\"35\" y1=\"25\" x2=\"155\" y2=\"25\" style=\"stroke-width:2\" />\n",
@@ -9214,16 +9051,16 @@
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        "\n",
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        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"95.640067\" y=\"87.565845\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
-       "  <text x=\"175.640067\" y=\"46.602956\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,175.640067,46.602956)\">20</text>\n",
-       "  <text x=\"12.820033\" y=\"74.745812\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,12.820033,74.745812)\">31</text>\n",
+       "  <text x=\"95.70144694908606\" y=\"87.62722539445895\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
+       "  <text x=\"175.70144694908606\" y=\"46.664336171772504\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,175.70144694908606,46.664336171772504)\">20</text>\n",
+       "  <text x=\"12.850723474543031\" y=\"74.77650191991592\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,12.850723474543031,74.77650191991592)\">32</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 102), meta=np.ndarray&gt;</div><input id='attrs-4253ae58-a2b3-466b-be41-630cb4e8e50a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4253ae58-a2b3-466b-be41-630cb4e8e50a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-686c6065-d5bc-4075-b646-c7902634f052' class='xr-var-data-in' type='checkbox'><label for='data-686c6065-d5bc-4075-b646-c7902634f052' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary potential temperature</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 102), meta=np.ndarray&gt;</div><input id='attrs-796565e1-f3d9-4f9d-91c7-a7cc791143d8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-796565e1-f3d9-4f9d-91c7-a7cc791143d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9966f421-34fe-4090-a677-45d4d72a3ca6' class='xr-var-data-in' type='checkbox'><label for='data-9966f421-34fe-4090-a677-45d4d72a3ca6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary potential temperature</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -9238,18 +9075,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 247.03 kiB </td>\n",
+       "                        <td> 255.00 kiB </td>\n",
        "                        <td> 7.97 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (31, 20, 102) </td>\n",
+       "                        <td> (32, 20, 102) </td>\n",
        "                        <td> (1, 20, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 31 chunks in 61 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 32 chunks in 42 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
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        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salt_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 102), meta=np.ndarray&gt;</div><input id='attrs-b6185b40-8d03-4a82-bc16-0f2521127502' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b6185b40-8d03-4a82-bc16-0f2521127502' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2406bf1c-6667-46f9-8a3c-68d49dd0f350' class='xr-var-data-in' type='checkbox'><label for='data-2406bf1c-6667-46f9-8a3c-68d49dd0f350' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary salinity</dd><dt><span>units :</span></dt><dd>PSU</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salt_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 102), meta=np.ndarray&gt;</div><input id='attrs-4bb06950-71ad-42c2-82ce-06076deff3ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4bb06950-71ad-42c2-82ce-06076deff3ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f3a3111-da9c-4849-96ec-8338b4480c11' class='xr-var-data-in' type='checkbox'><label for='data-5f3a3111-da9c-4849-96ec-8338b4480c11' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary salinity</dd><dt><span>units :</span></dt><dd>PSU</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -9352,18 +9189,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 247.03 kiB </td>\n",
+       "                        <td> 255.00 kiB </td>\n",
        "                        <td> 7.97 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (31, 20, 102) </td>\n",
+       "                        <td> (32, 20, 102) </td>\n",
        "                        <td> (1, 20, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 31 chunks in 61 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 32 chunks in 42 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
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        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zeta_south</span></div><div class='xr-var-dims'>(bry_time, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 102), meta=np.ndarray&gt;</div><input id='attrs-9bd1dc84-f2f9-4c8a-bd09-3ea142acd0c0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9bd1dc84-f2f9-4c8a-bd09-3ea142acd0c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6ff068f-89b9-4715-81d5-6273e5d5c309' class='xr-var-data-in' type='checkbox'><label for='data-b6ff068f-89b9-4715-81d5-6273e5d5c309' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>valid_min :</span></dt><dd>-6228</dd><dt><span>long_name :</span></dt><dd>southern boundary sea surface height</dd><dt><span>unit_long :</span></dt><dd>Meters</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_geoid</dd><dt><span>valid_max :</span></dt><dd>5684</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zeta_south</span></div><div class='xr-var-dims'>(bry_time, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 102), meta=np.ndarray&gt;</div><input id='attrs-6ad70c60-a362-4e1f-aef0-f7c96696f203' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6ad70c60-a362-4e1f-aef0-f7c96696f203' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f201788a-faee-48a3-aea2-6287de8e4e12' class='xr-var-data-in' type='checkbox'><label for='data-f201788a-faee-48a3-aea2-6287de8e4e12' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>long_name :</span></dt><dd>southern boundary sea surface height</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_geoid</dd><dt><span>unit_long :</span></dt><dd>Meters</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_max :</span></dt><dd>5684</dd><dt><span>valid_min :</span></dt><dd>-6228</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -9466,18 +9303,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 12.35 kiB </td>\n",
+       "                        <td> 12.75 kiB </td>\n",
        "                        <td> 408 B </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (31, 102) </td>\n",
+       "                        <td> (32, 102) </td>\n",
        "                        <td> (1, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 31 chunks in 31 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 32 chunks in 32 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -9493,21 +9330,21 @@
        "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
        "  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n",
        "  <line x1=\"0\" y1=\"4\" x2=\"120\" y2=\"4\" />\n",
-       "  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n",
+       "  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n",
        "  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n",
-       "  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n",
-       "  <line x1=\"0\" y1=\"12\" x2=\"120\" y2=\"12\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n",
        "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
-       "  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n",
-       "  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n",
-       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
-       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
-       "  <line x1=\"0\" y1=\"26\" x2=\"120\" y2=\"26\" />\n",
-       "  <line x1=\"0\" y1=\"29\" x2=\"120\" y2=\"29\" />\n",
-       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
-       "  <line x1=\"0\" y1=\"33\" x2=\"120\" y2=\"33\" />\n",
-       "  <line x1=\"0\" y1=\"36\" x2=\"120\" y2=\"36\" />\n",
-       "  <line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" />\n",
+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
+       "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" />\n",
+       "  <line x1=\"0\" y1=\"28\" x2=\"120\" y2=\"28\" />\n",
+       "  <line x1=\"0\" y1=\"31\" x2=\"120\" y2=\"31\" />\n",
+       "  <line x1=\"0\" y1=\"34\" x2=\"120\" y2=\"34\" />\n",
+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" />\n",
+       "  <line x1=\"0\" y1=\"38\" x2=\"120\" y2=\"38\" />\n",
        "  <line x1=\"0\" y1=\"40\" x2=\"120\" y2=\"40\" />\n",
        "  <line x1=\"0\" y1=\"43\" x2=\"120\" y2=\"43\" style=\"stroke-width:2\" />\n",
        "\n",
@@ -9516,15 +9353,15 @@
        "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"43\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,43.58811355743922 0.0,43.58811355743922\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,43.69245981344631 0.0,43.69245981344631\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"63.588114\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
-       "  <text x=\"140.000000\" y=\"21.794057\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,21.794057)\">31</text>\n",
+       "  <text x=\"60.0\" y=\"63.69245981344631\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
+       "  <text x=\"140.0\" y=\"21.846229906723156\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,21.846229906723156)\">32</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ubar_south</span></div><div class='xr-var-dims'>(bry_time, xi_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 101), meta=np.ndarray&gt;</div><input id='attrs-1c40d2eb-73b0-48a7-b8e0-4bdc7c26df2c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c40d2eb-73b0-48a7-b8e0-4bdc7c26df2c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15ba4267-69a5-426e-a582-7b82330243cb' class='xr-var-data-in' type='checkbox'><label for='data-15ba4267-69a5-426e-a582-7b82330243cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary vertically integrated u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ubar_south</span></div><div class='xr-var-dims'>(bry_time, xi_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 101), meta=np.ndarray&gt;</div><input id='attrs-287bcdff-ee0c-43d0-9b70-3e52b8ff0c12' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-287bcdff-ee0c-43d0-9b70-3e52b8ff0c12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ac56c2d-54b0-4b7d-a343-2506617135e6' class='xr-var-data-in' type='checkbox'><label for='data-9ac56c2d-54b0-4b7d-a343-2506617135e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary vertically integrated u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -9539,18 +9376,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 12.23 kiB </td>\n",
+       "                        <td> 12.62 kiB </td>\n",
        "                        <td> 404 B </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (31, 101) </td>\n",
+       "                        <td> (32, 101) </td>\n",
        "                        <td> (1, 101) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 31 chunks in 101 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 32 chunks in 83 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -9566,21 +9403,21 @@
        "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
        "  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n",
        "  <line x1=\"0\" y1=\"4\" x2=\"120\" y2=\"4\" />\n",
-       "  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n",
+       "  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n",
        "  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n",
-       "  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n",
-       "  <line x1=\"0\" y1=\"12\" x2=\"120\" y2=\"12\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n",
        "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
-       "  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n",
-       "  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n",
-       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
-       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
-       "  <line x1=\"0\" y1=\"26\" x2=\"120\" y2=\"26\" />\n",
-       "  <line x1=\"0\" y1=\"29\" x2=\"120\" y2=\"29\" />\n",
-       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
-       "  <line x1=\"0\" y1=\"33\" x2=\"120\" y2=\"33\" />\n",
-       "  <line x1=\"0\" y1=\"36\" x2=\"120\" y2=\"36\" />\n",
-       "  <line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" />\n",
+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
+       "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" />\n",
+       "  <line x1=\"0\" y1=\"28\" x2=\"120\" y2=\"28\" />\n",
+       "  <line x1=\"0\" y1=\"31\" x2=\"120\" y2=\"31\" />\n",
+       "  <line x1=\"0\" y1=\"34\" x2=\"120\" y2=\"34\" />\n",
+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" />\n",
+       "  <line x1=\"0\" y1=\"38\" x2=\"120\" y2=\"38\" />\n",
        "  <line x1=\"0\" y1=\"40\" x2=\"120\" y2=\"40\" />\n",
        "  <line x1=\"0\" y1=\"43\" x2=\"120\" y2=\"43\" style=\"stroke-width:2\" />\n",
        "\n",
@@ -9589,15 +9426,15 @@
        "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"43\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,43.62072221601965 0.0,43.62072221601965\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,43.72440890067491 0.0,43.72440890067491\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"63.620722\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >101</text>\n",
-       "  <text x=\"140.000000\" y=\"21.810361\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,21.810361)\">31</text>\n",
+       "  <text x=\"60.0\" y=\"63.72440890067491\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >101</text>\n",
+       "  <text x=\"140.0\" y=\"21.862204450337455\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,21.862204450337455)\">32</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vbar_south</span></div><div class='xr-var-dims'>(bry_time, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 102), meta=np.ndarray&gt;</div><input id='attrs-ce3dad9a-ccee-4668-9e99-40d3aa771b51' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce3dad9a-ccee-4668-9e99-40d3aa771b51' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4e70227-a4e7-41cb-a464-1bde755937dd' class='xr-var-data-in' type='checkbox'><label for='data-a4e70227-a4e7-41cb-a464-1bde755937dd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary vertically integrated v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vbar_south</span></div><div class='xr-var-dims'>(bry_time, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 102), meta=np.ndarray&gt;</div><input id='attrs-81bb956d-2ab9-472c-80f4-19f8628591d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-81bb956d-2ab9-472c-80f4-19f8628591d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd318981-4992-4b66-bcc9-bb992a394d57' class='xr-var-data-in' type='checkbox'><label for='data-bd318981-4992-4b66-bcc9-bb992a394d57' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary vertically integrated v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -9612,18 +9449,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 12.35 kiB </td>\n",
+       "                        <td> 12.75 kiB </td>\n",
        "                        <td> 408 B </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (31, 102) </td>\n",
+       "                        <td> (32, 102) </td>\n",
        "                        <td> (1, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 31 chunks in 101 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 32 chunks in 83 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -9639,21 +9476,21 @@
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        "  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n",
        "  <line x1=\"0\" y1=\"4\" x2=\"120\" y2=\"4\" />\n",
-       "  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n",
+       "  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n",
        "  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n",
-       "  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n",
-       "  <line x1=\"0\" y1=\"12\" x2=\"120\" y2=\"12\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n",
        "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
-       "  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n",
-       "  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n",
-       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
-       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
-       "  <line x1=\"0\" y1=\"26\" x2=\"120\" y2=\"26\" />\n",
-       "  <line x1=\"0\" y1=\"29\" x2=\"120\" y2=\"29\" />\n",
-       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
-       "  <line x1=\"0\" y1=\"33\" x2=\"120\" y2=\"33\" />\n",
-       "  <line x1=\"0\" y1=\"36\" x2=\"120\" y2=\"36\" />\n",
-       "  <line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" />\n",
+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
+       "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" />\n",
+       "  <line x1=\"0\" y1=\"28\" x2=\"120\" y2=\"28\" />\n",
+       "  <line x1=\"0\" y1=\"31\" x2=\"120\" y2=\"31\" />\n",
+       "  <line x1=\"0\" y1=\"34\" x2=\"120\" y2=\"34\" />\n",
+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" />\n",
+       "  <line x1=\"0\" y1=\"38\" x2=\"120\" y2=\"38\" />\n",
        "  <line x1=\"0\" y1=\"40\" x2=\"120\" y2=\"40\" />\n",
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        "\n",
@@ -9662,15 +9499,15 @@
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+       "  <text x=\"140.0\" y=\"21.846229906723156\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,21.846229906723156)\">32</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82), meta=np.ndarray&gt;</div><input id='attrs-ee3f83e9-dab6-44b3-81f7-6d3b32f8aa69' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ee3f83e9-dab6-44b3-81f7-6d3b32f8aa69' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35e35eef-aa21-46a4-9989-741e97b46da7' class='xr-var-data-in' type='checkbox'><label for='data-35e35eef-aa21-46a4-9989-741e97b46da7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82), meta=np.ndarray&gt;</div><input id='attrs-fd1b8b66-8e5f-4795-b701-1e331e727c40' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fd1b8b66-8e5f-4795-b701-1e331e727c40' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fc332e3-4ac8-4efc-80eb-49978905862b' class='xr-var-data-in' type='checkbox'><label for='data-0fc332e3-4ac8-4efc-80eb-49978905862b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -9685,18 +9522,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 198.59 kiB </td>\n",
+       "                        <td> 205.00 kiB </td>\n",
        "                        <td> 6.41 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (31, 20, 82) </td>\n",
+       "                        <td> (32, 20, 82) </td>\n",
        "                        <td> (1, 20, 82) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 31 chunks in 90 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 32 chunks in 73 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -9706,85 +9543,85 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
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        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_v)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 81), meta=np.ndarray&gt;</div><input id='attrs-5f6a9ad7-8325-4d40-b91b-c13e1205807e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f6a9ad7-8325-4d40-b91b-c13e1205807e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6b50403-baec-4c2f-b99e-3280c26e098d' class='xr-var-data-in' type='checkbox'><label for='data-d6b50403-baec-4c2f-b99e-3280c26e098d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_v)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 81), meta=np.ndarray&gt;</div><input id='attrs-1b9b4d26-2526-4b0b-80d4-d8a751da78d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b9b4d26-2526-4b0b-80d4-d8a751da78d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d53f9b02-7aa0-4c09-b401-4d99709b8ad5' class='xr-var-data-in' type='checkbox'><label for='data-d53f9b02-7aa0-4c09-b401-4d99709b8ad5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -9799,18 +9636,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 196.17 kiB </td>\n",
+       "                        <td> 202.50 kiB </td>\n",
        "                        <td> 6.33 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (31, 20, 81) </td>\n",
+       "                        <td> (32, 20, 81) </td>\n",
        "                        <td> (1, 20, 81) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 31 chunks in 90 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 32 chunks in 73 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -9820,57 +9657,57 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
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+       "        <svg width=\"207\" height=\"120\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82), meta=np.ndarray&gt;</div><input id='attrs-718fb127-5e05-4584-b700-0a2852d8487a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-718fb127-5e05-4584-b700-0a2852d8487a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5795029b-687a-4061-a90a-24c531e12a14' class='xr-var-data-in' type='checkbox'><label for='data-5795029b-687a-4061-a90a-24c531e12a14' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary potential temperature</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82), meta=np.ndarray&gt;</div><input id='attrs-be157f49-d3e3-4d60-b91d-c6de1bdacbba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be157f49-d3e3-4d60-b91d-c6de1bdacbba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09951457-3237-4f7e-9a8a-fa97be18bf9f' class='xr-var-data-in' type='checkbox'><label for='data-09951457-3237-4f7e-9a8a-fa97be18bf9f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary potential temperature</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -9913,18 +9750,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 198.59 kiB </td>\n",
+       "                        <td> 205.00 kiB </td>\n",
        "                        <td> 6.41 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (31, 20, 82) </td>\n",
+       "                        <td> (32, 20, 82) </td>\n",
        "                        <td> (1, 20, 82) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 31 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 32 chunks in 42 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -9934,85 +9771,85 @@
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        "        <td>\n",
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        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salt_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82), meta=np.ndarray&gt;</div><input id='attrs-5b8e34e1-e548-40a4-a7a4-b8f718af6bad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b8e34e1-e548-40a4-a7a4-b8f718af6bad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a057c978-8665-4b81-a62b-5e286fcdda10' class='xr-var-data-in' type='checkbox'><label for='data-a057c978-8665-4b81-a62b-5e286fcdda10' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary salinity</dd><dt><span>units :</span></dt><dd>PSU</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salt_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 82), meta=np.ndarray&gt;</div><input id='attrs-c63f20c4-1865-4402-a154-4c18d37f9c17' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c63f20c4-1865-4402-a154-4c18d37f9c17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35155d54-e505-4d96-8ace-895fe65ec5af' class='xr-var-data-in' type='checkbox'><label for='data-35155d54-e505-4d96-8ace-895fe65ec5af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary salinity</dd><dt><span>units :</span></dt><dd>PSU</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -10027,18 +9864,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 198.59 kiB </td>\n",
+       "                        <td> 205.00 kiB </td>\n",
        "                        <td> 6.41 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (31, 20, 82) </td>\n",
+       "                        <td> (32, 20, 82) </td>\n",
        "                        <td> (1, 20, 82) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 31 chunks in 60 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 32 chunks in 42 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -10048,85 +9885,85 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
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        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zeta_east</span></div><div class='xr-var-dims'>(bry_time, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 82), meta=np.ndarray&gt;</div><input id='attrs-4bc15fea-f909-4e2f-9698-1b0d46080057' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4bc15fea-f909-4e2f-9698-1b0d46080057' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3088ab06-547a-4e4f-a17d-f3c628cc1e5d' class='xr-var-data-in' type='checkbox'><label for='data-3088ab06-547a-4e4f-a17d-f3c628cc1e5d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>valid_min :</span></dt><dd>-6228</dd><dt><span>long_name :</span></dt><dd>eastern boundary sea surface height</dd><dt><span>unit_long :</span></dt><dd>Meters</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_geoid</dd><dt><span>valid_max :</span></dt><dd>5684</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zeta_east</span></div><div class='xr-var-dims'>(bry_time, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 82), meta=np.ndarray&gt;</div><input id='attrs-38742ae7-a3b4-4847-842d-bb0f2f053adf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-38742ae7-a3b4-4847-842d-bb0f2f053adf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-832d1c66-95e5-45c9-86a3-8049ab397483' class='xr-var-data-in' type='checkbox'><label for='data-832d1c66-95e5-45c9-86a3-8049ab397483' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>long_name :</span></dt><dd>eastern boundary sea surface height</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_geoid</dd><dt><span>unit_long :</span></dt><dd>Meters</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_max :</span></dt><dd>5684</dd><dt><span>valid_min :</span></dt><dd>-6228</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -10141,18 +9978,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 9.93 kiB </td>\n",
+       "                        <td> 10.25 kiB </td>\n",
        "                        <td> 328 B </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (31, 82) </td>\n",
+       "                        <td> (32, 82) </td>\n",
        "                        <td> (1, 82) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 31 chunks in 31 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 32 chunks in 32 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -10162,44 +9999,44 @@
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        "        </td>\n",
        "        <td>\n",
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+       "  <text x=\"60.0\" y=\"66.82926829268293\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >82</text>\n",
+       "  <text x=\"140.0\" y=\"23.414634146341463\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,23.414634146341463)\">32</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ubar_east</span></div><div class='xr-var-dims'>(bry_time, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 82), meta=np.ndarray&gt;</div><input id='attrs-3ade35b0-4561-4201-adab-e50a34403785' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ade35b0-4561-4201-adab-e50a34403785' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf7d4694-a65c-426b-bb8f-4c9fbb4d6cd6' class='xr-var-data-in' type='checkbox'><label for='data-bf7d4694-a65c-426b-bb8f-4c9fbb4d6cd6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary vertically integrated u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ubar_east</span></div><div class='xr-var-dims'>(bry_time, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 82), meta=np.ndarray&gt;</div><input id='attrs-c7d5f1aa-7308-41a8-bf99-40efab6f29b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c7d5f1aa-7308-41a8-bf99-40efab6f29b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0be338c3-ab15-43e9-a23d-2ee14d31ce62' class='xr-var-data-in' type='checkbox'><label for='data-0be338c3-ab15-43e9-a23d-2ee14d31ce62' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary vertically integrated u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -10214,18 +10051,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 9.93 kiB </td>\n",
+       "                        <td> 10.25 kiB </td>\n",
        "                        <td> 328 B </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (31, 82) </td>\n",
+       "                        <td> (32, 82) </td>\n",
        "                        <td> (1, 82) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 31 chunks in 100 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 32 chunks in 83 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -10235,44 +10072,44 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"170\" height=\"95\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "        <svg width=\"170\" height=\"96\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
        "  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n",
        "  <line x1=\"0\" y1=\"4\" x2=\"120\" y2=\"4\" />\n",
-       "  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n",
+       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
        "  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n",
        "  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n",
-       "  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n",
+       "  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n",
        "  <line x1=\"0\" y1=\"16\" x2=\"120\" y2=\"16\" />\n",
        "  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n",
-       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n",
        "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
-       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
-       "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" />\n",
+       "  <line x1=\"0\" y1=\"26\" x2=\"120\" y2=\"26\" />\n",
+       "  <line x1=\"0\" y1=\"29\" x2=\"120\" y2=\"29\" />\n",
        "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
-       "  <line x1=\"0\" y1=\"32\" x2=\"120\" y2=\"32\" />\n",
-       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" />\n",
+       "  <line x1=\"0\" y1=\"33\" x2=\"120\" y2=\"33\" />\n",
+       "  <line x1=\"0\" y1=\"36\" x2=\"120\" y2=\"36\" />\n",
        "  <line x1=\"0\" y1=\"38\" x2=\"120\" y2=\"38\" />\n",
-       "  <line x1=\"0\" y1=\"39\" x2=\"120\" y2=\"39\" />\n",
-       "  <line x1=\"0\" y1=\"42\" x2=\"120\" y2=\"42\" />\n",
-       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"40\" x2=\"120\" y2=\"40\" />\n",
+       "  <line x1=\"0\" y1=\"43\" x2=\"120\" y2=\"43\" />\n",
+       "  <line x1=\"0\" y1=\"46\" x2=\"120\" y2=\"46\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"45\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"45\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"46\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"46\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,45.36585365853659 0.0,45.36585365853659\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,46.829268292682926 0.0,46.829268292682926\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"65.365854\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >82</text>\n",
-       "  <text x=\"140.000000\" y=\"22.682927\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,22.682927)\">31</text>\n",
+       "  <text x=\"60.0\" y=\"66.82926829268293\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >82</text>\n",
+       "  <text x=\"140.0\" y=\"23.414634146341463\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,23.414634146341463)\">32</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vbar_east</span></div><div class='xr-var-dims'>(bry_time, eta_v)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81), meta=np.ndarray&gt;</div><input id='attrs-6259a636-c638-4755-9add-b592250032f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6259a636-c638-4755-9add-b592250032f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bfe81f72-527e-437b-b880-85064c9803b3' class='xr-var-data-in' type='checkbox'><label for='data-bfe81f72-527e-437b-b880-85064c9803b3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary vertically integrated v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vbar_east</span></div><div class='xr-var-dims'>(bry_time, eta_v)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81), meta=np.ndarray&gt;</div><input id='attrs-2639d658-f390-46dd-9a0d-3e53bd62a109' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2639d658-f390-46dd-9a0d-3e53bd62a109' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c82ddcd-7691-47c5-8159-a977c6d55953' class='xr-var-data-in' type='checkbox'><label for='data-8c82ddcd-7691-47c5-8159-a977c6d55953' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary vertically integrated v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -10287,18 +10124,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 9.81 kiB </td>\n",
+       "                        <td> 10.12 kiB </td>\n",
        "                        <td> 324 B </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (31, 81) </td>\n",
+       "                        <td> (32, 81) </td>\n",
        "                        <td> (1, 81) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 31 chunks in 100 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 32 chunks in 83 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -10308,44 +10145,44 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"170\" height=\"95\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "        <svg width=\"170\" height=\"97\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
        "  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n",
        "  <line x1=\"0\" y1=\"4\" x2=\"120\" y2=\"4\" />\n",
-       "  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n",
+       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
        "  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n",
        "  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n",
-       "  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n",
+       "  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n",
        "  <line x1=\"0\" y1=\"16\" x2=\"120\" y2=\"16\" />\n",
        "  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n",
-       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
        "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" />\n",
-       "  <line x1=\"0\" y1=\"28\" x2=\"120\" y2=\"28\" />\n",
+       "  <line x1=\"0\" y1=\"26\" x2=\"120\" y2=\"26\" />\n",
+       "  <line x1=\"0\" y1=\"29\" x2=\"120\" y2=\"29\" />\n",
        "  <line x1=\"0\" y1=\"31\" x2=\"120\" y2=\"31\" />\n",
-       "  <line x1=\"0\" y1=\"32\" x2=\"120\" y2=\"32\" />\n",
-       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" />\n",
+       "  <line x1=\"0\" y1=\"34\" x2=\"120\" y2=\"34\" />\n",
+       "  <line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" />\n",
        "  <line x1=\"0\" y1=\"38\" x2=\"120\" y2=\"38\" />\n",
-       "  <line x1=\"0\" y1=\"40\" x2=\"120\" y2=\"40\" />\n",
-       "  <line x1=\"0\" y1=\"42\" x2=\"120\" y2=\"42\" />\n",
-       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"41\" x2=\"120\" y2=\"41\" />\n",
+       "  <line x1=\"0\" y1=\"44\" x2=\"120\" y2=\"44\" />\n",
+       "  <line x1=\"0\" y1=\"47\" x2=\"120\" y2=\"47\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"45\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"45\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"47\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"47\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,45.92592592592593 0.0,45.92592592592593\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,47.407407407407405 0.0,47.407407407407405\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"65.925926\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81</text>\n",
-       "  <text x=\"140.000000\" y=\"22.962963\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,22.962963)\">31</text>\n",
+       "  <text x=\"60.0\" y=\"67.4074074074074\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81</text>\n",
+       "  <text x=\"140.0\" y=\"23.703703703703702\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,23.703703703703702)\">32</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u_north</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 101), meta=np.ndarray&gt;</div><input id='attrs-3cc918bb-ef72-4439-9486-3f9c61468920' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3cc918bb-ef72-4439-9486-3f9c61468920' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73c03ba2-8a0f-4a44-86db-09131d009c40' class='xr-var-data-in' type='checkbox'><label for='data-73c03ba2-8a0f-4a44-86db-09131d009c40' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u_north</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 101), meta=np.ndarray&gt;</div><input id='attrs-315b4a84-839b-4af7-97a7-edf2230244d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-315b4a84-839b-4af7-97a7-edf2230244d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a672eda-80c2-4b94-9942-0adfabadf811' class='xr-var-data-in' type='checkbox'><label for='data-8a672eda-80c2-4b94-9942-0adfabadf811' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -10360,18 +10197,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 244.61 kiB </td>\n",
+       "                        <td> 252.50 kiB </td>\n",
        "                        <td> 7.89 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (31, 20, 101) </td>\n",
+       "                        <td> (32, 20, 101) </td>\n",
        "                        <td> (1, 20, 101) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 31 chunks in 91 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 32 chunks in 73 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -10391,45 +10228,45 @@
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        "  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"44\" />\n",
-       "  <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"45\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"45\" />\n",
        "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"46\" />\n",
        "  <line x1=\"16\" y1=\"6\" x2=\"16\" y2=\"48\" />\n",
-       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n",
-       "  <line x1=\"19\" y1=\"9\" x2=\"19\" y2=\"51\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"50\" />\n",
        "  <line x1=\"20\" y1=\"10\" x2=\"20\" y2=\"52\" />\n",
-       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n",
-       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"55\" />\n",
+       "  <line x1=\"22\" y1=\"12\" x2=\"22\" y2=\"54\" />\n",
+       "  <line x1=\"22\" y1=\"12\" x2=\"22\" y2=\"54\" />\n",
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n",
-       "  <line x1=\"25\" y1=\"15\" x2=\"25\" y2=\"57\" />\n",
-       "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"59\" />\n",
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+       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"58\" />\n",
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+       "  <line x1=\"22\" y1=\"12\" x2=\"142\" y2=\"12\" />\n",
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@@ -10439,7 +10276,7 @@
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        "  <line x1=\"35\" y1=\"25\" x2=\"155\" y2=\"25\" style=\"stroke-width:2\" />\n",
@@ -10450,16 +10287,16 @@
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+       "  <text x=\"175.72024052980876\" y=\"46.704106517314315\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,175.72024052980876,46.704106517314315)\">20</text>\n",
+       "  <text x=\"12.860120264904385\" y=\"74.82785223991549\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,12.860120264904385,74.82785223991549)\">32</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_north</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 102), meta=np.ndarray&gt;</div><input id='attrs-2a295400-e2ee-4287-b94d-a2e571b5cf9f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2a295400-e2ee-4287-b94d-a2e571b5cf9f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac8f781b-f38f-4a1f-9baa-3c59b4c58438' class='xr-var-data-in' type='checkbox'><label for='data-ac8f781b-f38f-4a1f-9baa-3c59b4c58438' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_north</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 102), meta=np.ndarray&gt;</div><input id='attrs-e8aaf719-bb56-4289-8bf0-9b305e9d99b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e8aaf719-bb56-4289-8bf0-9b305e9d99b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3fe0f1f3-857a-4a35-8d45-a6e8e46a3a76' class='xr-var-data-in' type='checkbox'><label for='data-3fe0f1f3-857a-4a35-8d45-a6e8e46a3a76' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -10474,18 +10311,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 247.03 kiB </td>\n",
+       "                        <td> 255.00 kiB </td>\n",
        "                        <td> 7.97 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (31, 20, 102) </td>\n",
+       "                        <td> (32, 20, 102) </td>\n",
        "                        <td> (1, 20, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 31 chunks in 91 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 32 chunks in 73 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
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        "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" />\n",
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        "  <line x1=\"28\" y1=\"18\" x2=\"148\" y2=\"18\" />\n",
-       "  <line x1=\"29\" y1=\"19\" x2=\"149\" y2=\"19\" />\n",
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@@ -10553,7 +10390,7 @@
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        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"35\" y1=\"25\" x2=\"155\" y2=\"25\" style=\"stroke-width:2\" />\n",
@@ -10564,16 +10401,16 @@
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-       "  <text x=\"95.640067\" y=\"87.565845\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
-       "  <text x=\"175.640067\" y=\"46.602956\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,175.640067,46.602956)\">20</text>\n",
-       "  <text x=\"12.820033\" y=\"74.745812\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,12.820033,74.745812)\">31</text>\n",
+       "  <text x=\"95.70144694908606\" y=\"87.62722539445895\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
+       "  <text x=\"175.70144694908606\" y=\"46.664336171772504\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,175.70144694908606,46.664336171772504)\">20</text>\n",
+       "  <text x=\"12.850723474543031\" y=\"74.77650191991592\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,12.850723474543031,74.77650191991592)\">32</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_north</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 102), meta=np.ndarray&gt;</div><input id='attrs-3719cbed-119f-4c5a-9a98-04a77a4e447d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3719cbed-119f-4c5a-9a98-04a77a4e447d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f22a29b4-7d68-4b91-938b-6b39a658305c' class='xr-var-data-in' type='checkbox'><label for='data-f22a29b4-7d68-4b91-938b-6b39a658305c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary potential temperature</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_north</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 102), meta=np.ndarray&gt;</div><input id='attrs-59a6a2e2-2eec-4eef-84f0-c2601aab6f7c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-59a6a2e2-2eec-4eef-84f0-c2601aab6f7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54da50ed-bccc-4faf-b812-b1c00fa33145' class='xr-var-data-in' type='checkbox'><label for='data-54da50ed-bccc-4faf-b812-b1c00fa33145' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary potential temperature</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -10588,18 +10425,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 247.03 kiB </td>\n",
+       "                        <td> 255.00 kiB </td>\n",
        "                        <td> 7.97 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (31, 20, 102) </td>\n",
+       "                        <td> (32, 20, 102) </td>\n",
        "                        <td> (1, 20, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 31 chunks in 61 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 32 chunks in 42 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
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@@ -10667,7 +10504,7 @@
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@@ -10678,16 +10515,16 @@
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        "</svg>\n",
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        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salt_north</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 102), meta=np.ndarray&gt;</div><input id='attrs-352841cf-2ce5-4c76-92b0-f2c64bc3a1b0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-352841cf-2ce5-4c76-92b0-f2c64bc3a1b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb200922-65b5-49cf-a38f-13f5f7851a4a' class='xr-var-data-in' type='checkbox'><label for='data-eb200922-65b5-49cf-a38f-13f5f7851a4a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary salinity</dd><dt><span>units :</span></dt><dd>PSU</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salt_north</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 20, 102), meta=np.ndarray&gt;</div><input id='attrs-8bf416da-d063-4683-be5d-d6220106aa22' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8bf416da-d063-4683-be5d-d6220106aa22' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3e8ebae-8334-44ba-9113-7a55717dea19' class='xr-var-data-in' type='checkbox'><label for='data-b3e8ebae-8334-44ba-9113-7a55717dea19' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary salinity</dd><dt><span>units :</span></dt><dd>PSU</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -10702,18 +10539,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 247.03 kiB </td>\n",
+       "                        <td> 255.00 kiB </td>\n",
        "                        <td> 7.97 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (31, 20, 102) </td>\n",
+       "                        <td> (32, 20, 102) </td>\n",
        "                        <td> (1, 20, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 31 chunks in 61 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 32 chunks in 42 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -10733,47 +10570,47 @@
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        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zeta_north</span></div><div class='xr-var-dims'>(bry_time, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 102), meta=np.ndarray&gt;</div><input id='attrs-66381cfd-3d61-43db-a580-23da734c7787' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66381cfd-3d61-43db-a580-23da734c7787' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11fbd787-275e-4a70-878a-84845e2b27c5' class='xr-var-data-in' type='checkbox'><label for='data-11fbd787-275e-4a70-878a-84845e2b27c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>valid_min :</span></dt><dd>-6228</dd><dt><span>long_name :</span></dt><dd>northern boundary sea surface height</dd><dt><span>unit_long :</span></dt><dd>Meters</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_geoid</dd><dt><span>valid_max :</span></dt><dd>5684</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zeta_north</span></div><div class='xr-var-dims'>(bry_time, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 102), meta=np.ndarray&gt;</div><input id='attrs-0255fe98-cd6f-4174-b5b2-31660a20562e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0255fe98-cd6f-4174-b5b2-31660a20562e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f879ed1d-794d-42c7-9f41-cd66e90e152a' class='xr-var-data-in' type='checkbox'><label for='data-f879ed1d-794d-42c7-9f41-cd66e90e152a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>long_name :</span></dt><dd>northern boundary sea surface height</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_geoid</dd><dt><span>unit_long :</span></dt><dd>Meters</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_max :</span></dt><dd>5684</dd><dt><span>valid_min :</span></dt><dd>-6228</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -10816,18 +10653,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 12.35 kiB </td>\n",
+       "                        <td> 12.75 kiB </td>\n",
        "                        <td> 408 B </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (31, 102) </td>\n",
+       "                        <td> (32, 102) </td>\n",
        "                        <td> (1, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 31 chunks in 31 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 32 chunks in 32 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -10843,21 +10680,21 @@
        "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
        "  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n",
        "  <line x1=\"0\" y1=\"4\" x2=\"120\" y2=\"4\" />\n",
-       "  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n",
+       "  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n",
        "  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n",
-       "  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n",
-       "  <line x1=\"0\" y1=\"12\" x2=\"120\" y2=\"12\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n",
        "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
-       "  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n",
-       "  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n",
-       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
-       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
-       "  <line x1=\"0\" y1=\"26\" x2=\"120\" y2=\"26\" />\n",
-       "  <line x1=\"0\" y1=\"29\" x2=\"120\" y2=\"29\" />\n",
-       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
-       "  <line x1=\"0\" y1=\"33\" x2=\"120\" y2=\"33\" />\n",
-       "  <line x1=\"0\" y1=\"36\" x2=\"120\" y2=\"36\" />\n",
-       "  <line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" />\n",
+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
+       "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" />\n",
+       "  <line x1=\"0\" y1=\"28\" x2=\"120\" y2=\"28\" />\n",
+       "  <line x1=\"0\" y1=\"31\" x2=\"120\" y2=\"31\" />\n",
+       "  <line x1=\"0\" y1=\"34\" x2=\"120\" y2=\"34\" />\n",
+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" />\n",
+       "  <line x1=\"0\" y1=\"38\" x2=\"120\" y2=\"38\" />\n",
        "  <line x1=\"0\" y1=\"40\" x2=\"120\" y2=\"40\" />\n",
        "  <line x1=\"0\" y1=\"43\" x2=\"120\" y2=\"43\" style=\"stroke-width:2\" />\n",
        "\n",
@@ -10866,15 +10703,15 @@
        "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"43\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,43.58811355743922 0.0,43.58811355743922\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,43.69245981344631 0.0,43.69245981344631\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"63.588114\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
-       "  <text x=\"140.000000\" y=\"21.794057\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,21.794057)\">31</text>\n",
+       "  <text x=\"60.0\" y=\"63.69245981344631\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
+       "  <text x=\"140.0\" y=\"21.846229906723156\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,21.846229906723156)\">32</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ubar_north</span></div><div class='xr-var-dims'>(bry_time, xi_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 101), meta=np.ndarray&gt;</div><input id='attrs-ca79036e-b939-4e7f-b9bd-7594a124f9eb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca79036e-b939-4e7f-b9bd-7594a124f9eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da45f2ef-a914-42b4-987a-fa1b7b28ba77' class='xr-var-data-in' type='checkbox'><label for='data-da45f2ef-a914-42b4-987a-fa1b7b28ba77' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary vertically integrated u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ubar_north</span></div><div class='xr-var-dims'>(bry_time, xi_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 101), meta=np.ndarray&gt;</div><input id='attrs-ea76cb84-235e-4c45-8d6d-905da00e4bcc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea76cb84-235e-4c45-8d6d-905da00e4bcc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-290bd42b-45bf-4ffa-bcd1-a03233c54c7d' class='xr-var-data-in' type='checkbox'><label for='data-290bd42b-45bf-4ffa-bcd1-a03233c54c7d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary vertically integrated u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -10889,18 +10726,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 12.23 kiB </td>\n",
+       "                        <td> 12.62 kiB </td>\n",
        "                        <td> 404 B </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (31, 101) </td>\n",
+       "                        <td> (32, 101) </td>\n",
        "                        <td> (1, 101) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 31 chunks in 101 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 32 chunks in 83 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -10916,21 +10753,21 @@
        "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
        "  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n",
        "  <line x1=\"0\" y1=\"4\" x2=\"120\" y2=\"4\" />\n",
-       "  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n",
+       "  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n",
        "  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n",
-       "  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n",
-       "  <line x1=\"0\" y1=\"12\" x2=\"120\" y2=\"12\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n",
        "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
-       "  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n",
-       "  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n",
-       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
-       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
-       "  <line x1=\"0\" y1=\"26\" x2=\"120\" y2=\"26\" />\n",
-       "  <line x1=\"0\" y1=\"29\" x2=\"120\" y2=\"29\" />\n",
-       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
-       "  <line x1=\"0\" y1=\"33\" x2=\"120\" y2=\"33\" />\n",
-       "  <line x1=\"0\" y1=\"36\" x2=\"120\" y2=\"36\" />\n",
-       "  <line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" />\n",
+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
+       "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" />\n",
+       "  <line x1=\"0\" y1=\"28\" x2=\"120\" y2=\"28\" />\n",
+       "  <line x1=\"0\" y1=\"31\" x2=\"120\" y2=\"31\" />\n",
+       "  <line x1=\"0\" y1=\"34\" x2=\"120\" y2=\"34\" />\n",
+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" />\n",
+       "  <line x1=\"0\" y1=\"38\" x2=\"120\" y2=\"38\" />\n",
        "  <line x1=\"0\" y1=\"40\" x2=\"120\" y2=\"40\" />\n",
        "  <line x1=\"0\" y1=\"43\" x2=\"120\" y2=\"43\" style=\"stroke-width:2\" />\n",
        "\n",
@@ -10939,15 +10776,15 @@
        "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"43\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,43.62072221601965 0.0,43.62072221601965\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,43.72440890067491 0.0,43.72440890067491\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"63.620722\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >101</text>\n",
-       "  <text x=\"140.000000\" y=\"21.810361\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,21.810361)\">31</text>\n",
+       "  <text x=\"60.0\" y=\"63.72440890067491\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >101</text>\n",
+       "  <text x=\"140.0\" y=\"21.862204450337455\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,21.862204450337455)\">32</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vbar_north</span></div><div class='xr-var-dims'>(bry_time, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 102), meta=np.ndarray&gt;</div><input id='attrs-ab8ecd90-3cf6-43b1-be41-e1c6ffde8240' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab8ecd90-3cf6-43b1-be41-e1c6ffde8240' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc3f3fef-182b-4fd0-aa27-78d2a327f811' class='xr-var-data-in' type='checkbox'><label for='data-dc3f3fef-182b-4fd0-aa27-78d2a327f811' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary vertically integrated v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vbar_north</span></div><div class='xr-var-dims'>(bry_time, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 102), meta=np.ndarray&gt;</div><input id='attrs-18c1f67b-3340-4178-b2fa-5004fa2b1aad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-18c1f67b-3340-4178-b2fa-5004fa2b1aad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d01fe41-6795-43b1-a06a-357531fbae82' class='xr-var-data-in' type='checkbox'><label for='data-5d01fe41-6795-43b1-a06a-357531fbae82' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary vertically integrated v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -10962,18 +10799,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 12.35 kiB </td>\n",
+       "                        <td> 12.75 kiB </td>\n",
        "                        <td> 408 B </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (31, 102) </td>\n",
+       "                        <td> (32, 102) </td>\n",
        "                        <td> (1, 102) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 31 chunks in 101 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 32 chunks in 83 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -10989,21 +10826,21 @@
        "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
        "  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n",
        "  <line x1=\"0\" y1=\"4\" x2=\"120\" y2=\"4\" />\n",
-       "  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n",
+       "  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n",
        "  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n",
-       "  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n",
-       "  <line x1=\"0\" y1=\"12\" x2=\"120\" y2=\"12\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n",
        "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
-       "  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n",
-       "  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n",
-       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
-       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
-       "  <line x1=\"0\" y1=\"26\" x2=\"120\" y2=\"26\" />\n",
-       "  <line x1=\"0\" y1=\"29\" x2=\"120\" y2=\"29\" />\n",
-       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
-       "  <line x1=\"0\" y1=\"33\" x2=\"120\" y2=\"33\" />\n",
-       "  <line x1=\"0\" y1=\"36\" x2=\"120\" y2=\"36\" />\n",
-       "  <line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" />\n",
+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
+       "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" />\n",
+       "  <line x1=\"0\" y1=\"28\" x2=\"120\" y2=\"28\" />\n",
+       "  <line x1=\"0\" y1=\"31\" x2=\"120\" y2=\"31\" />\n",
+       "  <line x1=\"0\" y1=\"34\" x2=\"120\" y2=\"34\" />\n",
+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" />\n",
+       "  <line x1=\"0\" y1=\"38\" x2=\"120\" y2=\"38\" />\n",
        "  <line x1=\"0\" y1=\"40\" x2=\"120\" y2=\"40\" />\n",
        "  <line x1=\"0\" y1=\"43\" x2=\"120\" y2=\"43\" style=\"stroke-width:2\" />\n",
        "\n",
@@ -11012,45 +10849,45 @@
        "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"43\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,43.58811355743922 0.0,43.58811355743922\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,43.69245981344631 0.0,43.69245981344631\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"63.588114\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
-       "  <text x=\"140.000000\" y=\"21.794057\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,21.794057)\">31</text>\n",
+       "  <text x=\"60.0\" y=\"63.69245981344631\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n",
+       "  <text x=\"140.0\" y=\"21.846229906723156\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,21.846229906723156)\">32</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-f58bd7eb-f850-4ec0-aab2-aa1e0b750865' class='xr-section-summary-in' type='checkbox'  ><label for='section-f58bd7eb-f850-4ec0-aab2-aa1e0b750865' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>bry_time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-5032c085-37dc-4437-a037-3cf26f237f65' class='xr-index-data-in' type='checkbox'/><label for='index-5032c085-37dc-4437-a037-3cf26f237f65' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([4383.0, 4384.0, 4385.0, 4386.0, 4387.0, 4388.0, 4389.0, 4390.0, 4391.0,\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-36c8d490-fb05-4a4e-8678-17e7f7cd16e4' class='xr-section-summary-in' type='checkbox'  ><label for='section-36c8d490-fb05-4a4e-8678-17e7f7cd16e4' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>bry_time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-9afd0be3-786c-4a6d-870d-cde91ddd06f6' class='xr-index-data-in' type='checkbox'/><label for='index-9afd0be3-786c-4a6d-870d-cde91ddd06f6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([4383.0, 4384.0, 4385.0, 4386.0, 4387.0, 4388.0, 4389.0, 4390.0, 4391.0,\n",
        "       4392.0, 4393.0, 4394.0, 4395.0, 4396.0, 4397.0, 4398.0, 4399.0, 4400.0,\n",
        "       4401.0, 4402.0, 4403.0, 4404.0, 4405.0, 4406.0, 4407.0, 4408.0, 4409.0,\n",
-       "       4410.0, 4411.0, 4412.0, 4413.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;bry_time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ca7343ee-7a56-4c50-93b3-528dfc6e220c' class='xr-section-summary-in' type='checkbox'  ><label for='section-ca7343ee-7a56-4c50-93b3-528dfc6e220c' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>ROMS boundary forcing file created by ROMS-Tools</dd><dt><span>roms_tools_version :</span></dt><dd>3.1.3.dev32+g91d580e67.d20250929</dd><dt><span>start_time :</span></dt><dd>2012-01-01 00:00:00</dd><dt><span>end_time :</span></dt><dd>2012-01-31 00:00:00</dd><dt><span>source :</span></dt><dd>GLORYS</dd><dt><span>model_reference_date :</span></dt><dd>2000-01-01 00:00:00</dd><dt><span>apply_2d_horizontal_fill :</span></dt><dd>False</dd><dt><span>adjust_depth_for_sea_surface_height :</span></dt><dd>False</dd><dt><span>theta_s :</span></dt><dd>5.0</dd><dt><span>theta_b :</span></dt><dd>2.0</dd><dt><span>hc :</span></dt><dd>300.0</dd></dl></div></li></ul></div></div>"
+       "       4410.0, 4411.0, 4412.0, 4413.0, 4414.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;bry_time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a570443b-da35-4d0b-89b0-a2712c50650f' class='xr-section-summary-in' type='checkbox'  ><label for='section-a570443b-da35-4d0b-89b0-a2712c50650f' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>ROMS boundary forcing file created by ROMS-Tools</dd><dt><span>roms_tools_version :</span></dt><dd>10000.dev364+g785fa77b7.d20260612</dd><dt><span>start_time :</span></dt><dd>2012-01-01 00:00:00</dd><dt><span>end_time :</span></dt><dd>2012-01-31 00:00:00</dd><dt><span>source :</span></dt><dd>GLORYS</dd><dt><span>model_reference_date :</span></dt><dd>2000-01-01 00:00:00</dd><dt><span>apply_2d_horizontal_fill :</span></dt><dd>False</dd><dt><span>adjust_depth_for_sea_surface_height :</span></dt><dd>False</dd><dt><span>theta_s :</span></dt><dd>5.0</dd><dt><span>theta_b :</span></dt><dd>2.0</dd><dt><span>hc :</span></dt><dd>300.0</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset> Size: 3MB\n",
-       "Dimensions:     (bry_time: 31, s_rho: 20, xi_u: 101, xi_rho: 102, eta_rho: 82,\n",
+       "Dimensions:     (bry_time: 32, s_rho: 20, xi_u: 101, xi_rho: 102, eta_rho: 82,\n",
        "                 eta_v: 81)\n",
        "Coordinates:\n",
-       "    abs_time    (bry_time) datetime64[ns] 248B 2012-01-01 ... 2012-01-31\n",
-       "  * bry_time    (bry_time) float64 248B 4.383e+03 4.384e+03 ... 4.413e+03\n",
+       "    abs_time    (bry_time) datetime64[ns] 256B 2012-01-01 ... 2012-02-01\n",
+       "  * bry_time    (bry_time) float64 256B 4.383e+03 4.384e+03 ... 4.414e+03\n",
        "Dimensions without coordinates: s_rho, xi_u, xi_rho, eta_rho, eta_v\n",
        "Data variables: (12/21)\n",
-       "    u_south     (bry_time, s_rho, xi_u) float32 250kB dask.array<chunksize=(1, 20, 101), meta=np.ndarray>\n",
-       "    v_south     (bry_time, s_rho, xi_rho) float32 253kB dask.array<chunksize=(1, 20, 102), meta=np.ndarray>\n",
-       "    temp_south  (bry_time, s_rho, xi_rho) float32 253kB dask.array<chunksize=(1, 20, 102), meta=np.ndarray>\n",
-       "    salt_south  (bry_time, s_rho, xi_rho) float32 253kB dask.array<chunksize=(1, 20, 102), meta=np.ndarray>\n",
+       "    u_south     (bry_time, s_rho, xi_u) float32 259kB dask.array<chunksize=(1, 20, 101), meta=np.ndarray>\n",
+       "    v_south     (bry_time, s_rho, xi_rho) float32 261kB dask.array<chunksize=(1, 20, 102), meta=np.ndarray>\n",
+       "    temp_south  (bry_time, s_rho, xi_rho) float32 261kB dask.array<chunksize=(1, 20, 102), meta=np.ndarray>\n",
+       "    salt_south  (bry_time, s_rho, xi_rho) float32 261kB dask.array<chunksize=(1, 20, 102), meta=np.ndarray>\n",
        "    zeta_south  (bry_time, xi_rho) float32 13kB dask.array<chunksize=(1, 102), meta=np.ndarray>\n",
        "    ubar_south  (bry_time, xi_u) float32 13kB dask.array<chunksize=(1, 101), meta=np.ndarray>\n",
        "    ...          ...\n",
-       "    v_north     (bry_time, s_rho, xi_rho) float32 253kB dask.array<chunksize=(1, 20, 102), meta=np.ndarray>\n",
-       "    temp_north  (bry_time, s_rho, xi_rho) float32 253kB dask.array<chunksize=(1, 20, 102), meta=np.ndarray>\n",
-       "    salt_north  (bry_time, s_rho, xi_rho) float32 253kB dask.array<chunksize=(1, 20, 102), meta=np.ndarray>\n",
+       "    v_north     (bry_time, s_rho, xi_rho) float32 261kB dask.array<chunksize=(1, 20, 102), meta=np.ndarray>\n",
+       "    temp_north  (bry_time, s_rho, xi_rho) float32 261kB dask.array<chunksize=(1, 20, 102), meta=np.ndarray>\n",
+       "    salt_north  (bry_time, s_rho, xi_rho) float32 261kB dask.array<chunksize=(1, 20, 102), meta=np.ndarray>\n",
        "    zeta_north  (bry_time, xi_rho) float32 13kB dask.array<chunksize=(1, 102), meta=np.ndarray>\n",
        "    ubar_north  (bry_time, xi_u) float32 13kB dask.array<chunksize=(1, 101), meta=np.ndarray>\n",
        "    vbar_north  (bry_time, xi_rho) float32 13kB dask.array<chunksize=(1, 102), meta=np.ndarray>\n",
        "Attributes:\n",
        "    title:                                ROMS boundary forcing file created ...\n",
-       "    roms_tools_version:                   3.1.3.dev32+g91d580e67.d20250929\n",
+       "    roms_tools_version:                   10000.dev364+g785fa77b7.d20260612\n",
        "    start_time:                           2012-01-01 00:00:00\n",
        "    end_time:                             2012-01-31 00:00:00\n",
        "    source:                               GLORYS\n",
@@ -11062,7 +10899,7 @@
        "    hc:                                   300.0"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -11081,7 +10918,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 37,
    "id": "4e220538-0cec-4ddd-98f1-4e12bf9796d6",
    "metadata": {
     "tags": []
@@ -11091,12 +10928,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[########################################] | 100% Completed | 107.41 ms\n"
+      "[########################################] | 100% Completed | 111.33 ms\n"
      ]
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 900x500 with 2 Axes>"
       ]
@@ -11119,7 +10956,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 38,
    "id": "1b0f66e1-9468-4fdc-b416-f329b66d2393",
    "metadata": {
     "tags": []
@@ -11129,24 +10966,27 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-09-29 14:10:24 - INFO - Writing the following NetCDF files:\n",
-      "ROMS_TOOLS_INPUT_DATA/roms_bry_201201.nc\n"
+      "2026-06-23 09:20:37 - INFO - Writing the following NetCDF files:\n",
+      "ROMS_TOOLS_INPUT_DATA/roms_bry_201201.nc\n",
+      "ROMS_TOOLS_INPUT_DATA/roms_bry_201202.nc\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[########################################] | 100% Completed | 4.92 sms\n"
+      "[########################################] | 100% Completed | 4.55 sms\n",
+      "[########################################] | 100% Completed | 226.27 ms\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "[PosixPath('ROMS_TOOLS_INPUT_DATA/roms_bry_201201.nc')]"
+       "[PosixPath('ROMS_TOOLS_INPUT_DATA/roms_bry_201201.nc'),\n",
+       " PosixPath('ROMS_TOOLS_INPUT_DATA/roms_bry_201202.nc')]"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -11157,7 +10997,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 39,
    "id": "27cf1a06-6cbe-440a-a6df-5f0d2445622a",
    "metadata": {
     "tags": []
@@ -11178,7 +11018,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 40,
    "id": "e5989c47-fd88-40f4-a409-d61fbebd2827",
    "metadata": {
     "tags": []
@@ -11188,15 +11028,20 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-09-29 14:10:33 - INFO - Sea surface height will NOT be used to adjust depth coordinates.\n"
+      "2026-06-23 09:20:42 - INFO - Using boundary configuration: {'south': True, 'east': True, 'north': True, 'west': False}\n",
+      "2026-06-23 09:20:42 - INFO - use_density_interpolation=True but no physics_forcing provided. BGC tracers will be interpolated in depth space instead.\n",
+      "2026-06-23 09:20:43 - WARNING - Optional variables missing (but not critical): ['Lig', 'DIC_ALT_CO2', 'Alk_ALT_CO2', 'temp_WOA', 'salt_WOA']\n",
+      "2026-06-23 09:20:43 - INFO - Density-space interpolation requested but no physics_forcing provided; falling back to depth-space interpolation (south boundary).\n",
+      "2026-06-23 09:20:43 - INFO - Density-space interpolation requested but no physics_forcing provided; falling back to depth-space interpolation (east boundary).\n",
+      "2026-06-23 09:20:43 - INFO - Density-space interpolation requested but no physics_forcing provided; falling back to depth-space interpolation (north boundary).\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 1.53 s, sys: 33.3 ms, total: 1.57 s\n",
-      "Wall time: 1.59 s\n"
+      "CPU times: user 698 ms, sys: 24.3 ms, total: 723 ms\n",
+      "Wall time: 767 ms\n"
      ]
     }
    ],
@@ -11221,7 +11066,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 41,
    "id": "f9cde529-9e79-48c5-b321-5d65d2bc42da",
    "metadata": {
     "tags": []
@@ -11231,12 +11076,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[########################################] | 100% Completed | 106.90 ms\n"
+      "[########################################] | 100% Completed | 111.05 ms\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 900x500 with 2 Axes>"
       ]
@@ -11251,7 +11096,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 42,
    "id": "6b940bcc-c841-4033-8af5-f9c078e03d5c",
    "metadata": {
     "tags": []
@@ -11261,7 +11106,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-09-29 14:10:37 - INFO - Writing the following NetCDF files:\n",
+      "2026-06-23 09:20:43 - INFO - Writing the following NetCDF files:\n",
       "ROMS_TOOLS_INPUT_DATA/roms_bry_bgc_clim.nc\n"
      ]
     },
@@ -11269,7 +11114,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[########################################] | 100% Completed | 8.49 ss\n"
+      "[########################################] | 100% Completed | 7.96 ss\n"
      ]
     },
     {
@@ -11278,7 +11123,7 @@
        "[PosixPath('ROMS_TOOLS_INPUT_DATA/roms_bry_bgc_clim.nc')]"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -11289,7 +11134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 43,
    "id": "4c4831f0-de39-45b5-a7c8-5940e89a5d84",
    "metadata": {
     "tags": []
@@ -11319,7 +11164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 44,
    "id": "918df45a-5233-4b05-bed7-9808aee47b2e",
    "metadata": {},
    "outputs": [],
@@ -11329,31 +11174,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 45,
    "id": "fdd4f2bf-1318-49eb-aa6f-a48888bff669",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-09-29 14:10:50 - INFO - No river indices provided. Identify all rivers within the ROMS domain and assign each of them to the nearest coastal point.\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 3 μs, sys: 9 μs, total: 12 μs\n",
-      "Wall time: 25 μs\n"
+      "CPU times: user 1 μs, sys: 1e+03 ns, total: 2 μs\n",
+      "Wall time: 4.77 μs\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-09-29 14:10:50 - INFO - Compute climatology for river forcing.\n",
-      "2025-09-29 14:10:50 - WARNING - NaNs detected in 'river_volume' and set to zero. This may indicate missing river data and affect model accuracy. \n"
+      "2026-06-23 09:20:52 - INFO - No river indices provided. Identify all rivers within the ROMS domain and assign each of them to the nearest coastal point.\n",
+      "2026-06-23 09:20:52 - INFO - Compute climatology for river forcing.\n",
+      "2026-06-23 09:20:52 - WARNING - NaNs detected in 'river_volume' and set to zero. This may indicate missing river data and affect model accuracy. \n"
      ]
     }
    ],
@@ -11377,7 +11216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 46,
    "id": "e8bbda3a-5b9d-484c-a908-48fc7e7a8f21",
    "metadata": {},
    "outputs": [
@@ -11846,17 +11685,17 @@
        "    river_index       (eta_rho, xi_rho) float32 33kB 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
        "    river_fraction    (eta_rho, xi_rho) float32 33kB 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
        "Attributes:\n",
-       "    climatology:  True</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b63db096-7604-4e87-9886-47ac46bcdc3a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b63db096-7604-4e87-9886-47ac46bcdc3a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>ntracers</span>: 34</li><li><span class='xr-has-index'>river_time</span>: 12</li><li><span class='xr-has-index'>nriver</span>: 34</li><li><span>eta_rho</span>: 82</li><li><span>xi_rho</span>: 102</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ebacc07c-1311-4d77-9d06-d517cddf0c0b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ebacc07c-1311-4d77-9d06-d517cddf0c0b' class='xr-section-summary' >Coordinates: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>tracer_name</span></div><div class='xr-var-dims'>(ntracers)</div><div class='xr-var-dtype'>&lt;U11</div><div class='xr-var-preview xr-preview'>&#x27;temp&#x27; &#x27;salt&#x27; ... &#x27;diazP&#x27; &#x27;diazFe&#x27;</div><input id='attrs-e753cf86-56fc-4fa4-8db4-0b54a662d9cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e753cf86-56fc-4fa4-8db4-0b54a662d9cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d26efadf-a523-4ecc-96f4-f41ef66787e8' class='xr-var-data-in' type='checkbox'><label for='data-d26efadf-a523-4ecc-96f4-f41ef66787e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Tracer name</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;temp&#x27;, &#x27;salt&#x27;, &#x27;PO4&#x27;, &#x27;NO3&#x27;, &#x27;SiO3&#x27;, &#x27;NH4&#x27;, &#x27;Fe&#x27;, &#x27;Lig&#x27;, &#x27;O2&#x27;,\n",
+       "    climatology:  True</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b96f1d63-bcaf-4fc2-9e3b-d501db2ab0d2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b96f1d63-bcaf-4fc2-9e3b-d501db2ab0d2' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>ntracers</span>: 34</li><li><span class='xr-has-index'>river_time</span>: 12</li><li><span class='xr-has-index'>nriver</span>: 34</li><li><span>eta_rho</span>: 82</li><li><span>xi_rho</span>: 102</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6914f57c-a025-4231-8630-7ca93c6d7509' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6914f57c-a025-4231-8630-7ca93c6d7509' class='xr-section-summary' >Coordinates: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>tracer_name</span></div><div class='xr-var-dims'>(ntracers)</div><div class='xr-var-dtype'>&lt;U11</div><div class='xr-var-preview xr-preview'>&#x27;temp&#x27; &#x27;salt&#x27; ... &#x27;diazP&#x27; &#x27;diazFe&#x27;</div><input id='attrs-bd71ede2-d728-40ba-98ac-49164a339e8d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd71ede2-d728-40ba-98ac-49164a339e8d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99f9c366-ee15-439b-8aed-7b8b977b15df' class='xr-var-data-in' type='checkbox'><label for='data-99f9c366-ee15-439b-8aed-7b8b977b15df' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Tracer name</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;temp&#x27;, &#x27;salt&#x27;, &#x27;PO4&#x27;, &#x27;NO3&#x27;, &#x27;SiO3&#x27;, &#x27;NH4&#x27;, &#x27;Fe&#x27;, &#x27;Lig&#x27;, &#x27;O2&#x27;,\n",
        "       &#x27;DIC&#x27;, &#x27;DIC_ALT_CO2&#x27;, &#x27;ALK&#x27;, &#x27;ALK_ALT_CO2&#x27;, &#x27;DOC&#x27;, &#x27;DON&#x27;, &#x27;DOP&#x27;,\n",
        "       &#x27;DOPr&#x27;, &#x27;DONr&#x27;, &#x27;DOCr&#x27;, &#x27;zooC&#x27;, &#x27;spChl&#x27;, &#x27;spC&#x27;, &#x27;spP&#x27;, &#x27;spFe&#x27;,\n",
        "       &#x27;spCaCO3&#x27;, &#x27;diatChl&#x27;, &#x27;diatC&#x27;, &#x27;diatP&#x27;, &#x27;diatFe&#x27;, &#x27;diatSi&#x27;,\n",
-       "       &#x27;diazChl&#x27;, &#x27;diazC&#x27;, &#x27;diazP&#x27;, &#x27;diazFe&#x27;], dtype=&#x27;&lt;U11&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tracer_unit</span></div><div class='xr-var-dims'>(ntracers)</div><div class='xr-var-dtype'>&lt;U15</div><div class='xr-var-preview xr-preview'>&#x27;degrees Celsius&#x27; ... &#x27;mmol/m^3&#x27;</div><input id='attrs-d6200dc2-0a3f-4cc3-bbd1-69b320edb0c9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6200dc2-0a3f-4cc3-bbd1-69b320edb0c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d63d166-02b0-4c75-87ff-de82db191870' class='xr-var-data-in' type='checkbox'><label for='data-9d63d166-02b0-4c75-87ff-de82db191870' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Tracer concentration unit</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;degrees Celsius&#x27;, &#x27;PSU&#x27;, &#x27;mmol/m^3&#x27;, &#x27;mmol/m^3&#x27;, &#x27;mmol/m^3&#x27;,\n",
+       "       &#x27;diazChl&#x27;, &#x27;diazC&#x27;, &#x27;diazP&#x27;, &#x27;diazFe&#x27;], dtype=&#x27;&lt;U11&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tracer_unit</span></div><div class='xr-var-dims'>(ntracers)</div><div class='xr-var-dtype'>&lt;U15</div><div class='xr-var-preview xr-preview'>&#x27;degrees Celsius&#x27; ... &#x27;mmol/m^3&#x27;</div><input id='attrs-584803e7-3f2c-43c3-a0ba-89b96db63b4d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-584803e7-3f2c-43c3-a0ba-89b96db63b4d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-666dfe41-e771-4cf9-b052-11951e437f0e' class='xr-var-data-in' type='checkbox'><label for='data-666dfe41-e771-4cf9-b052-11951e437f0e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Tracer concentration unit</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;degrees Celsius&#x27;, &#x27;PSU&#x27;, &#x27;mmol/m^3&#x27;, &#x27;mmol/m^3&#x27;, &#x27;mmol/m^3&#x27;,\n",
        "       &#x27;mmol/m^3&#x27;, &#x27;mmol/m^3&#x27;, &#x27;mmol/m^3&#x27;, &#x27;mmol/m^3&#x27;, &#x27;mmol/m^3&#x27;,\n",
        "       &#x27;mmol/m^3&#x27;, &#x27;meq/m^3&#x27;, &#x27;meq/m^3&#x27;, &#x27;mmol/m^3&#x27;, &#x27;mmol/m^3&#x27;,\n",
        "       &#x27;mmol/m^3&#x27;, &#x27;mmol/m^3&#x27;, &#x27;mmol/m^3&#x27;, &#x27;mmol/m^3&#x27;, &#x27;mmol/m^3&#x27;,\n",
        "       &#x27;mg/m^3&#x27;, &#x27;mmol/m^3&#x27;, &#x27;mmol/m^3&#x27;, &#x27;mmol/m^3&#x27;, &#x27;mmol/m^3&#x27;, &#x27;mg/m^3&#x27;,\n",
        "       &#x27;mmol/m^3&#x27;, &#x27;mmol/m^3&#x27;, &#x27;mmol/m^3&#x27;, &#x27;mmol/m^3&#x27;, &#x27;mg/m^3&#x27;,\n",
-       "       &#x27;mmol/m^3&#x27;, &#x27;mmol/m^3&#x27;, &#x27;mmol/m^3&#x27;], dtype=&#x27;&lt;U15&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tracer_long_name</span></div><div class='xr-var-dims'>(ntracers)</div><div class='xr-var-dtype'>&lt;U43</div><div class='xr-var-preview xr-preview'>&#x27;potential temperature&#x27; ... &#x27;dia...</div><input id='attrs-257b3ff4-50da-4583-b1b5-0a85dc021da3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-257b3ff4-50da-4583-b1b5-0a85dc021da3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f49b2586-722d-4391-bb64-8bf0332554a5' class='xr-var-data-in' type='checkbox'><label for='data-f49b2586-722d-4391-bb64-8bf0332554a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Tracer long name</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;potential temperature&#x27;, &#x27;salinity&#x27;,\n",
+       "       &#x27;mmol/m^3&#x27;, &#x27;mmol/m^3&#x27;, &#x27;mmol/m^3&#x27;], dtype=&#x27;&lt;U15&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tracer_long_name</span></div><div class='xr-var-dims'>(ntracers)</div><div class='xr-var-dtype'>&lt;U43</div><div class='xr-var-preview xr-preview'>&#x27;potential temperature&#x27; ... &#x27;dia...</div><input id='attrs-a8259736-7cd3-4669-a9a4-d7a62b56149e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a8259736-7cd3-4669-a9a4-d7a62b56149e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ea7dcbf-2a7a-41e4-886e-c0bc6f636527' class='xr-var-data-in' type='checkbox'><label for='data-1ea7dcbf-2a7a-41e4-886e-c0bc6f636527' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Tracer long name</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;potential temperature&#x27;, &#x27;salinity&#x27;,\n",
        "       &#x27;dissolved inorganic phosphate&#x27;, &#x27;dissolved inorganic nitrate&#x27;,\n",
        "       &#x27;dissolved inorganic silicate&#x27;, &#x27;dissolved ammonia&#x27;,\n",
        "       &#x27;dissolved inorganic iron&#x27;, &#x27;iron binding ligand&#x27;,\n",
@@ -11872,21 +11711,21 @@
        "       &#x27;small phytoplankton CaCO3&#x27;, &#x27;diatom chloropyll&#x27;, &#x27;diatom carbon&#x27;,\n",
        "       &#x27;diatom phosphorus&#x27;, &#x27;diatom iron&#x27;, &#x27;diatom silicate&#x27;,\n",
        "       &#x27;diazotroph chloropyll&#x27;, &#x27;diazotroph carbon&#x27;,\n",
-       "       &#x27;diazotroph phosphorus&#x27;, &#x27;diazotroph iron&#x27;], dtype=&#x27;&lt;U43&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>month</span></div><div class='xr-var-dims'>(river_time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-e80009e0-d5d8-4ee8-bcef-c9d5288acf2b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e80009e0-d5d8-4ee8-bcef-c9d5288acf2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97794134-ead4-41d2-9169-6d0f2e23f897' class='xr-var-data-in' type='checkbox'><label for='data-97794134-ead4-41d2-9169-6d0f2e23f897' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Month index (1-12)</dd></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>river_name</span></div><div class='xr-var-dims'>(nriver)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;Mississippi&#x27; ... &#x27;FisheatingCree&#x27;</div><input id='attrs-8031e3eb-1299-48d9-917a-c69a0cb81740' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8031e3eb-1299-48d9-917a-c69a0cb81740' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4939d3d2-9ec8-42a4-a69f-9818d0fc115e' class='xr-var-data-in' type='checkbox'><label for='data-4939d3d2-9ec8-42a4-a69f-9818d0fc115e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>River name</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;Mississippi&#x27;, &#x27;Usumacinta&#x27;, &#x27;Red&#x27;, &#x27;Alabama&#x27;, &#x27;Papaloapan&#x27;,\n",
+       "       &#x27;diazotroph phosphorus&#x27;, &#x27;diazotroph iron&#x27;], dtype=&#x27;&lt;U43&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>month</span></div><div class='xr-var-dims'>(river_time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-b8002d43-c3a8-4b0e-9e93-35c1b0ce1a7e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b8002d43-c3a8-4b0e-9e93-35c1b0ce1a7e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3a8c554-d438-4ea6-99f9-cf2cb84ee16b' class='xr-var-data-in' type='checkbox'><label for='data-a3a8c554-d438-4ea6-99f9-cf2cb84ee16b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Month index (1-12)</dd></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>river_name</span></div><div class='xr-var-dims'>(nriver)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;Mississippi&#x27; ... &#x27;FisheatingCree&#x27;</div><input id='attrs-aefbec78-64b7-4de3-9d16-dbc1025596ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aefbec78-64b7-4de3-9d16-dbc1025596ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-beab53ab-75a0-465a-a2f2-31b5195f6f8c' class='xr-var-data-in' type='checkbox'><label for='data-beab53ab-75a0-465a-a2f2-31b5195f6f8c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>River name</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;Mississippi&#x27;, &#x27;Usumacinta&#x27;, &#x27;Red&#x27;, &#x27;Alabama&#x27;, &#x27;Papaloapan&#x27;,\n",
        "       &#x27;Apalachicola&#x27;, &#x27;Grijalva&#x27;, &#x27;Pnuco&#x27;, &#x27;Coatzacoalcos&#x27;, &#x27;Pearl&#x27;,\n",
        "       &#x27;Pascagoula&#x27;, &#x27;Sabine&#x27;, &#x27;Trinity&#x27;, &#x27;Brazos&#x27;, &#x27;Suwannee&#x27;,\n",
        "       &#x27;Tecolutla&#x27;, &#x27;Escambia&#x27;, &#x27;Neches&#x27;, &#x27;RompidodeSama&#x27;, &#x27;StJohns&#x27;,\n",
        "       &#x27;Colorado-TX&#x27;, &#x27;SantaCatalina&#x27;, &#x27;Antigua&#x27;, &#x27;Nautla(Bobos)&#x27;,\n",
        "       &#x27;Tuxpan&#x27;, &#x27;RioGrande(Bra&#x27;, &#x27;Cazones&#x27;, &#x27;Candelaria&#x27;,\n",
        "       &#x27;CalcasieuRiver&#x27;, &#x27;PeaceRiverAt&#x27;, &#x27;Nueces&#x27;, &#x27;GuadalupeRiver&#x27;,\n",
-       "       &#x27;Damuji&#x27;, &#x27;FisheatingCree&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>nriver</span></div><div class='xr-var-dims'>(nriver)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 ... 29 30 31 32 33 34</div><input id='attrs-4305cdf2-944a-4edb-885e-db31bdeb3a6f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4305cdf2-944a-4edb-885e-db31bdeb3a6f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-716fdda3-0449-4f28-a463-be899d76a75d' class='xr-var-data-in' type='checkbox'><label for='data-716fdda3-0449-4f28-a463-be899d76a75d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>River ID (1-based Fortran indexing)</dd></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
-       "       19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(river_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-16 ... 2000-12-15</div><input id='attrs-199db774-9889-4cb8-9f26-261f261a4e7b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-199db774-9889-4cb8-9f26-261f261a4e7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a2df47d-bc33-4211-b9a3-881076931895' class='xr-var-data-in' type='checkbox'><label for='data-9a2df47d-bc33-4211-b9a3-881076931895' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>absolute time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-16T00:00:00.000000000&#x27;, &#x27;2000-02-15T00:00:00.000000000&#x27;,\n",
+       "       &#x27;Damuji&#x27;, &#x27;FisheatingCree&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>nriver</span></div><div class='xr-var-dims'>(nriver)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 ... 29 30 31 32 33 34</div><input id='attrs-4c7045d3-69fd-4a39-940e-66cc73c6ed61' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c7045d3-69fd-4a39-940e-66cc73c6ed61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d5ffa91-4c00-4e70-8d34-07503d304da4' class='xr-var-data-in' type='checkbox'><label for='data-7d5ffa91-4c00-4e70-8d34-07503d304da4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>River ID (1-based Fortran indexing)</dd></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
+       "       19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(river_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-16 ... 2000-12-15</div><input id='attrs-62cbebea-4fee-4072-b5c8-0e22fae8e489' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-62cbebea-4fee-4072-b5c8-0e22fae8e489' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7efe1bb-f235-446c-a127-31dead128cc9' class='xr-var-data-in' type='checkbox'><label for='data-a7efe1bb-f235-446c-a127-31dead128cc9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>absolute time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-16T00:00:00.000000000&#x27;, &#x27;2000-02-15T00:00:00.000000000&#x27;,\n",
        "       &#x27;2000-03-15T00:00:00.000000000&#x27;, &#x27;2000-04-15T00:00:00.000000000&#x27;,\n",
        "       &#x27;2000-05-15T00:00:00.000000000&#x27;, &#x27;2000-06-15T00:00:00.000000000&#x27;,\n",
        "       &#x27;2000-07-15T00:00:00.000000000&#x27;, &#x27;2000-08-15T00:00:00.000000000&#x27;,\n",
        "       &#x27;2000-09-15T00:00:00.000000000&#x27;, &#x27;2000-10-15T00:00:00.000000000&#x27;,\n",
        "       &#x27;2000-11-15T00:00:00.000000000&#x27;, &#x27;2000-12-15T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>river_time</span></div><div class='xr-var-dims'>(river_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.0 45.0 74.0 ... 319.0 349.0</div><input id='attrs-500c5bdc-7424-4319-8062-d9ee9ee6b430' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-500c5bdc-7424-4319-8062-d9ee9ee6b430' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-121512f3-3acd-4d15-8236-a43c359a1fd5' class='xr-var-data-in' type='checkbox'><label for='data-121512f3-3acd-4d15-8236-a43c359a1fd5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cycle_length :</span></dt><dd>365.25</dd><dt><span>long_name :</span></dt><dd>relative time: days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([ 15.,  45.,  74., 105., 135., 166., 196., 227., 258., 288., 319., 349.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3a4fe731-3208-44b6-9867-8639db9e98db' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3a4fe731-3208-44b6-9867-8639db9e98db' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>river_volume</span></div><div class='xr-var-dims'>(river_time, nriver)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>2.12e+04 1.93e+03 ... 2.322 1.927</div><input id='attrs-78dc8cfe-cbe2-4236-9e47-0c6a2fff8945' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-78dc8cfe-cbe2-4236-9e47-0c6a2fff8945' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-beffe25f-d6e0-4c10-8afc-98b383ea2a20' class='xr-var-data-in' type='checkbox'><label for='data-beffe25f-d6e0-4c10-8afc-98b383ea2a20' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>River volume flux</dd><dt><span>units :</span></dt><dd>m^3/s</dd></dl></div><div class='xr-var-data'><pre>array([[2.12004805e+04, 1.92959888e+03, 1.63644055e+03, 2.33782471e+03,\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>river_time</span></div><div class='xr-var-dims'>(river_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.0 45.0 74.0 ... 319.0 349.0</div><input id='attrs-7f917ddc-b568-4de6-a263-6a4d3dc70d64' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f917ddc-b568-4de6-a263-6a4d3dc70d64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-becfad00-4bc3-4ebb-819b-7adc450406ff' class='xr-var-data-in' type='checkbox'><label for='data-becfad00-4bc3-4ebb-819b-7adc450406ff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cycle_length :</span></dt><dd>365.25</dd><dt><span>long_name :</span></dt><dd>relative time: days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([ 15.,  45.,  74., 105., 135., 166., 196., 227., 258., 288., 319., 349.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a84fb5d5-b584-4812-b556-4a787a52a9e4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a84fb5d5-b584-4812-b556-4a787a52a9e4' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>river_volume</span></div><div class='xr-var-dims'>(river_time, nriver)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>2.12e+04 1.93e+03 ... 2.322 1.927</div><input id='attrs-8efa540a-e6b8-4f92-8809-c98668ee675b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8efa540a-e6b8-4f92-8809-c98668ee675b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f752af7-8c88-4fb6-8b4b-fbc9305a8f71' class='xr-var-data-in' type='checkbox'><label for='data-0f752af7-8c88-4fb6-8b4b-fbc9305a8f71' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>River volume flux</dd><dt><span>units :</span></dt><dd>m^3/s</dd></dl></div><div class='xr-var-data'><pre>array([[2.12004805e+04, 1.92959888e+03, 1.63644055e+03, 2.33782471e+03,\n",
        "        3.61386566e+02, 7.67759277e+02, 3.71978790e+02, 1.69769562e+02,\n",
        "        2.34250000e+02, 4.28094299e+02, 4.18948151e+02, 3.51523865e+02,\n",
        "        2.67241333e+02, 2.24770584e+02, 1.72997269e+02, 9.88750000e+01,\n",
@@ -11926,7 +11765,7 @@
        "        6.63669662e+01, 4.50714302e+01, 3.01709671e+01, 4.76250000e+01,\n",
        "        5.48750000e+01, 3.25065880e+01, 1.97500000e+01, 6.45000000e+01,\n",
        "        4.08401070e+01, 1.33040304e+01, 5.88058424e+00, 1.04727592e+01,\n",
-       "        2.32222247e+00, 1.92739880e+00]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>river_tracer</span></div><div class='xr-var-dims'>(river_time, ntracers, nriver)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>17.0 17.0 17.0 ... 1.5e-06 1.5e-06</div><input id='attrs-bb40b0c9-462e-450b-b6c0-cd731192d4d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb40b0c9-462e-450b-b6c0-cd731192d4d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40015552-acb5-489b-8755-e3f84fef7c0d' class='xr-var-data-in' type='checkbox'><label for='data-40015552-acb5-489b-8755-e3f84fef7c0d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>River tracer data</dd></dl></div><div class='xr-var-data'><pre>array([[[1.7e+01, 1.7e+01, 1.7e+01, ..., 1.7e+01, 1.7e+01, 1.7e+01],\n",
+       "        2.32222247e+00, 1.92739880e+00]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>river_tracer</span></div><div class='xr-var-dims'>(river_time, ntracers, nriver)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>17.0 17.0 17.0 ... 1.5e-06 1.5e-06</div><input id='attrs-5570b668-8d15-483f-953a-6af21661da1d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5570b668-8d15-483f-953a-6af21661da1d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b75e9ea0-47b0-415f-9011-d3c1d9dd7d00' class='xr-var-data-in' type='checkbox'><label for='data-b75e9ea0-47b0-415f-9011-d3c1d9dd7d00' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>River tracer data</dd></dl></div><div class='xr-var-data'><pre>array([[[1.7e+01, 1.7e+01, 1.7e+01, ..., 1.7e+01, 1.7e+01, 1.7e+01],\n",
        "        [1.0e+00, 1.0e+00, 1.0e+00, ..., 1.0e+00, 1.0e+00, 1.0e+00],\n",
        "        [2.7e+00, 2.7e+00, 2.7e+00, ..., 2.7e+00, 2.7e+00, 2.7e+00],\n",
        "        ...,\n",
@@ -11966,25 +11805,25 @@
        "        [7.5e-02, 7.5e-02, 7.5e-02, ..., 7.5e-02, 7.5e-02, 7.5e-02],\n",
        "        [1.5e-02, 1.5e-02, 1.5e-02, ..., 1.5e-02, 1.5e-02, 1.5e-02],\n",
        "        [1.5e-06, 1.5e-06, 1.5e-06, ..., 1.5e-06, 1.5e-06, 1.5e-06]]],\n",
-       "      shape=(12, 34, 34), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>river_index</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-13c0aa6d-8a09-458a-ad00-7bce2b683178' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-13c0aa6d-8a09-458a-ad00-7bce2b683178' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d798dd28-5495-4483-b98f-462109ca0e81' class='xr-var-data-in' type='checkbox'><label for='data-d798dd28-5495-4483-b98f-462109ca0e81' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>River ID</dd><dt><span>units :</span></dt><dd>none</dd></dl></div><div class='xr-var-data'><pre>array([[0., 0., 0., ..., 0., 0., 0.],\n",
+       "      shape=(12, 34, 34), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>river_index</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-170319d5-0b66-44fd-a0ab-28fbec3edd11' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-170319d5-0b66-44fd-a0ab-28fbec3edd11' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c3d4a6f-6275-4aab-a505-73588cb3c9de' class='xr-var-data-in' type='checkbox'><label for='data-2c3d4a6f-6275-4aab-a505-73588cb3c9de' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>River ID</dd><dt><span>units :</span></dt><dd>none</dd></dl></div><div class='xr-var-data'><pre>array([[0., 0., 0., ..., 0., 0., 0.],\n",
        "       [0., 0., 0., ..., 0., 0., 0.],\n",
        "       [0., 0., 0., ..., 0., 0., 0.],\n",
        "       ...,\n",
        "       [0., 0., 0., ..., 0., 0., 0.],\n",
        "       [0., 0., 0., ..., 0., 0., 0.],\n",
-       "       [0., 0., 0., ..., 0., 0., 0.]], shape=(82, 102), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>river_fraction</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-02518ab0-885d-48a8-a1d1-eb949c8c7b9b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-02518ab0-885d-48a8-a1d1-eb949c8c7b9b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2fb360fa-8912-44b4-b321-7fe300553f20' class='xr-var-data-in' type='checkbox'><label for='data-2fb360fa-8912-44b4-b321-7fe300553f20' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>River volume fraction</dd><dt><span>units :</span></dt><dd>none</dd></dl></div><div class='xr-var-data'><pre>array([[0., 0., 0., ..., 0., 0., 0.],\n",
+       "       [0., 0., 0., ..., 0., 0., 0.]], shape=(82, 102), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>river_fraction</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-b616c168-26eb-4181-ab42-778fa8d1c540' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b616c168-26eb-4181-ab42-778fa8d1c540' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18a145bb-ebea-4ede-bd78-01c700e10168' class='xr-var-data-in' type='checkbox'><label for='data-18a145bb-ebea-4ede-bd78-01c700e10168' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>River volume fraction</dd><dt><span>units :</span></dt><dd>none</dd></dl></div><div class='xr-var-data'><pre>array([[0., 0., 0., ..., 0., 0., 0.],\n",
        "       [0., 0., 0., ..., 0., 0., 0.],\n",
        "       [0., 0., 0., ..., 0., 0., 0.],\n",
        "       ...,\n",
        "       [0., 0., 0., ..., 0., 0., 0.],\n",
        "       [0., 0., 0., ..., 0., 0., 0.],\n",
-       "       [0., 0., 0., ..., 0., 0., 0.]], shape=(82, 102), dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fe091ed9-e992-4b1f-abee-6f384cdabbac' class='xr-section-summary-in' type='checkbox'  ><label for='section-fe091ed9-e992-4b1f-abee-6f384cdabbac' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>nriver</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-1fa34104-1659-457a-a2cb-b05e26fefb2c' class='xr-index-data-in' type='checkbox'/><label for='index-1fa34104-1659-457a-a2cb-b05e26fefb2c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
+       "       [0., 0., 0., ..., 0., 0., 0.]], shape=(82, 102), dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-50a4290a-f113-4845-a8e8-9990d164e462' class='xr-section-summary-in' type='checkbox'  ><label for='section-50a4290a-f113-4845-a8e8-9990d164e462' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>nriver</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-4b3b975f-9c68-4032-afb2-b809fbfe6416' class='xr-index-data-in' type='checkbox'/><label for='index-4b3b975f-9c68-4032-afb2-b809fbfe6416' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
        "       19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34],\n",
-       "      dtype=&#x27;int64&#x27;, name=&#x27;nriver&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>river_time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-4629eadc-7379-4b90-af04-071c4c14a74c' class='xr-index-data-in' type='checkbox'/><label for='index-4629eadc-7379-4b90-af04-071c4c14a74c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([15.000000000000002,               45.0,               74.0,\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;nriver&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>river_time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-babae0b0-734d-4aba-80ea-1bfbe3638d5a' class='xr-index-data-in' type='checkbox'/><label for='index-babae0b0-734d-4aba-80ea-1bfbe3638d5a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([15.000000000000002,               45.0,               74.0,\n",
        "                    105.0,              135.0,              166.0,\n",
        "                    196.0,              227.0,              258.0,\n",
        "                    288.0,              319.0,              349.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;river_time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e93ebfaa-9dd6-4160-b580-f7f6a870a4a1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e93ebfaa-9dd6-4160-b580-f7f6a870a4a1' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>climatology :</span></dt><dd>True</dd></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;float64&#x27;, name=&#x27;river_time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-465655d4-8c13-4e3d-a6e7-ae0c80e9d721' class='xr-section-summary-in' type='checkbox'  checked><label for='section-465655d4-8c13-4e3d-a6e7-ae0c80e9d721' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>climatology :</span></dt><dd>True</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset> Size: 134kB\n",
@@ -12009,7 +11848,7 @@
        "    climatology:  True"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -12028,7 +11867,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 47,
    "id": "a80dd5ee-d5a0-475a-9049-099fdf457705",
    "metadata": {},
    "outputs": [
@@ -12036,12 +11875,12 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-09-29 14:10:54 - WARNING - Only the first 20 rivers will be plotted (received 34).\n"
+      "2026-06-23 09:20:52 - WARNING - Only the first 20 rivers will be plotted (received 34).\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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33nqLw60kEolEEtzAm19pw8CDHuOIGAPhDQFPQ9grSm86eK6p80BhrBdtMFbDAwQeG/Ba88SOQHt42WGMx7gqxnjYI/A2O3TokCNsFccH+wAhY0rbRnireIrwVpsMhOTC409434P29nYe/xERIYBHH7wIS0tLnWwUvB6obRREbMA+mw74/LB94Cmj9H6EB05PTw9HFQB4YWLcRySBGnc2SmdnJx83jg92Ih57i7huYLsqbSNEw+D8uIqyUdvEsFGUdipsFNhPOO8SiURboH/G7x+exvAsxN/oV93xhS98gYteYo6DiDqkaUBkFkCaBXgWI5wYkXAoQIU+2RWTtYVXPMYyjC9f+cpX2OMZHoiTgf0pvc+RIgt9rjJlB8YmZf+KNhi/RlWpFPSIDD2W6AZhXAvB0FtBEaEiUwERBwKeui3y33iKMlGsACGoyk5V5LBBPojKykqnzgwu1dPFk88IYGwjLGfjxo0soCFvEPILKnM4oJOFsAZR0BXqBOQIXVJObLxBnHf1eYZhDmNViGs4ToB8iJNRXV3N4dfIs6QezKZjhCPkGgMmhFw1mKjg+8SgJULLXV0HuAaAOB5MCr7+9a9z7g2ESiGsCUIkhEeIrhKJRCLRLq7CqdTjKdpg3BM5hMVYp26H8dZVbmGEXCFkFHkGlQtOntgCCKPFGI/0Iri5AuITxnYclzq9BnA1JroDIbYid/JU7TA2Iu8wQo0xHmLhEp9PKRTCRkEorLtE9Tj26dhHrsDnx3ciFniV4794XtgoSB0zVZEAhO9CTIQNJibjShvF2+IA4v1dfR8QCrGYqUTkTpzMToXoiAVlLCzjGkCqFIjKyLcokegNhP/uvftMr17zvx8fcYQfK0HY8fdOLPDqvacDwo+RKgGInOzuwJwZC0wI8YVjBOZQyBmINFWYh2HBYMOGDfwcUjkhHQJSRKn7VSxgTdYWoiUWWOCYg8Wg2267jdtD7JP4HikUSnQDDCesvKOi8WTgeRg18JxT4k0unZngzqNP6b2HPIkwzNGJo4AHjEoYoMhH5GlycFd4+hmxkoMVGhiBEApxj/fH6o8Ax4FJDAqHuPpM6spUvji/3ubgcAVEV6yMwVsCeaBgJCOfEjwjsJI1k/Pry+sAnxUJf5G3CPmJ3nnnHb4eMOnDttmo/CWRSCSSmSO8tpQFspRA/JntqrQoNCaSvf/kJz/hwhgYd5CfTiyqTYYYC1HEAh4ervBmkXQqlJEUU4E8hMhRCPsDnw82CsZyeJcojx9eKw8//LDLfSjzOR5PG3Aq8N3A4wafB8eO48QiK7wWsWh4PGyUqSJPAK4n5M6EbYLvATdM8uEV66qIi0QSzMBmjzR6LrugcIlSJIyLDKeOPttiDraHh4XNSiETJRD1kQcQx+6uj1cv0iBnLm5YyIBACK9z5LbFPpDPFjeIiPA+RP54dSQYmKotCqrghjzz8P7DopA7oRBFPjHOiv5bzI+U/TtESCVog4WdMA/6uWBHCoUSXYGKvXBjxuqoqFysBAlTsTKPRODTAZ0ZjDR4+SlX9LHy7ksgECExOBKPK0FS7Mlcw30FhDOcS4TuwFDFqg7CTpRFVJDUGqIWVoBKSkpm9XjEeYeHgFihB0jGjXOC58Uxgd27d7udwCCZORKZw5CFQStwFT7jqTCJlXcIqwcOHJjwHDw5MPlRT0o8BYMjbkjMi8ESoVyoNIgk6BKJRCIJPMSYhDFB3fdDJISHOTyw1GCMU4IxFvaFKC4i9ot2IoQWwJsOdolSKIMdAS9DeNspxzJ12Ku7cU54KMJbERPDqT6v+tjF558NUDANC8OwTWDrYbIqCnoIYA9gEgnRzReLjFN9fixCw05Rip0Y/8Xz4pggrGGh0p1XIRYG4SmJiAdl5IE6VBp4+rmU16PyuhHbxPPeAgETC8u44bPDyxACLha6fSkiSyTBhLvqxsrt4n42xUIIZfC6Fn9PBtIgIfUA+l54F2PhAr95eClDiENREYxpWEDAY0RaKedrgsnaYgxDESwUccJ8E30TxhXlXE0NhE4UKoHnIeb3GN/gJanshxFFBhESc394RcKLEU4XEiKZo1CiK7BqjlUFdAbIo6MEhhlyrkDQQbvpIFZcEBKsBJ2OL0GHra4WBdFO5AM6HiCEB1V/UTURxrYypAdgBQnHidBY9bHisfr8z4QvfelLfK8uXy88BUTVaQw8cI+Hx8TAwMCEY1IOhspjxt8I9XYlmAKIkZOBfeK9//a3vzlCxISQCXEPExm1B+tUIMRHfV5RpRJgEiGRSCSSwATiFEQUVK9Ve4BhIoRcgQjZVPPSSy85pU+B2IccTaIt8gtiYeq3v/0tT5AEyDOlHqdcjXWYlCGcVYnI76R+PSZxSHkB4cdVnihM7pRjNLw0UFVS+TzyWM0GmAQifyNENVThxPlU2ygIg4XNhMVjNfBAQX49X4HPj8rPyryJOCbYhvBuQSoRgJBpfB+wm9RMZqMg3Bjeeq5slKnsE3Hd4PvEdaO0H+AFCKFA2FDeoLbx8J0IQVvaKBKJdyIhwD0eC9AO7WcTzE08mZ/AexCLTlhogKiHvgSViZFSCa//6KOPuB+E4whEOwhxrsa4ydpiLMLiCvpJPIdc7qjyPplzD8ZaOO5AwMQYAJERIdFKIDSiz0cOXewPVdmxb4n0KJToDHQW8BSD1xVCTrDKAI83iDfwzkNCaHRswvPMW5YuXcodGAQrGEnw9EKSanioAV+tWsOb7+677+biHwj9hRccDG5XOYhmC3TiEN0QdgTDFZ9bCc7hvffey67hOL8I/0F7rAjBhRydMF7rC+AlcfHFF/MEC0YxjG5MSPBd433hfSkGIITmwNtu+fLlnM8PK18QOuHFgfYI58Gx49gwicBrkGDcVeJdfN8A+TIgEuM8IOTJFTgX8EqEKIhVdbjoY4IFg/lXv/qV158ZxwpB+qtf/SofLyaPmPDgeIVwKpFIJJLAA6IMQqowCcIEBpMXTIKQlwk2CBaW4IWlBl5mGEMw9mOhCbYGPLNQiEx492GswcQJEzZMjDDmQkRS2wewIzCxwxgCIQjtMLlDwQ4kexdgcRXbIHJhcoZjQJ5f3JC3CscDewrHgPfAcUFsrK2t5bEV/PSnP2XBDqFsmIRBwMJ4LTztZgN8dghx8CDB8am9V5BwHyHJWCCGNx7C3JB6BBNRbIdnHwQ0XwB7B+M9Qr2RzzkvL49FXuQaxHcocmLDVsFx/eY3v2FPGZwvCMmIdsFz8ITBtSE89fA947vC2I9rSi3YwkaBGI1rAtcJ2qg9BsV188ADD/B1BfsJRVfwPWKBFMd6ww03eP2ZYWdhAR7vh9ySyIOI7wMLmq48iSQSvbO5qs2tSCgQj5Wehcvy4ml53uR5TT0Fi0qTgfz0AuQjFGAcUD5Wgt+7KPY51XtO1jY1NZXnj+6AAKgWAQEWXlwtvij7P/TD6CtdUaVw8NAdx6MMs0QSaOzcudN6wQUXWNPT063h4eHWtLQ0frxr1y6X5dbxU0FZeHfPKUEZ9quvvppLsqME+3nnnWc9cOAAt7v//vsd7Z5//nneVllZ6VSC/ctf/vKE90H5dtwEAwMD1htvvJGPPyIiwrp27Vrrxo0bJ7TDvvEeeK/JEMeyefNmt88pj1Pwne98h58744wz3O77tddes55wwgnWqKgovpWWlvL5wTlRfr7y8nKrp7g678PDw9af//zn1vz8fP5Os7OzrbfccgufKzVvvvmmdc2aNXzuLBaLdcWKFdZXXnnF8TzKxuMz4ftLSkqyXnHFFdYdO3ZMOJcjIyPWa6+91pqcnGwNCQlxOib8jeNUsm3bNuuZZ57J+42MjLSeeuqp1g0bNnj0XXzwwQe8HfdiX7hmc3JyrCaTyZqSkmI955xzrFu2bPH4PEokEonEf/zhD3+wrlq1isdG9OMYHzGOqcct0f9jnMK4hv4e4xfshaNHj07Y71NPPcVjIfa5bNky60cffTTBPhgbG7P+8pe/ZLsD7RYvXmx96623rBdffDFvU4JxaunSpVaj0ThhbKuoqLBedNFFbEdh7M3MzOSx6K9//esEuwvvbzabuc0999xjffbZZ93aF0pwTDhH7p5TH6/4fLADsP97773X5WuHhoasDzzwANsfOAfx8fH8OfEddHZ2OtphH7BbPEV9rkFjY6P10ksvZZsC53H+/PkubTPYFb/+9a/5WkA72Bdnn322devWrU42zIIFC/hc5uXl8Wd47rnnJpzLhoYGvkZiYmL4OXFMantC8Oc//5mvA5wL2LCw8Wpraz36LtR2Gb7/L37xi3yt4nPAVrnyyiut9fX1Hp9HiUSL9Pf38zwC997y8L8PWHNvfsv61AeHJ22H59EO7SWuQV917rnnTnp60Cded911ujqF/V5cnyH4z99ipUQS7CChMxKvInk4vBklEolEIpFIPAGeGvAoQ4oRhNRKJBKJJDBBaiN4aCNibTpFqeBZ6ImHoKft9Ao8uBFlpvSCVIP0GfByVqeuCmYGvLg+ZTETicTHKKsrCdABIT8LQowkEolEIpFIJBKJRCJR4qn4J0XCmYVRA3fh0hIbUiiUSHwM8s0hBw1W/5GHDsmgcUOOmulWtpVIJBKJRCKRSCQSiUQimW2kUCiR+BgUF0HRinvuuYeTTOfk5HBy1f/+7/+W51oikUgkEolEIpFIJBJJwCJzFEokEolEIpFIJBKJRCKR+DFHoUQSKNdn6KweiUQikUgkEolEIpFIJBKJRCLRBFIolEgkEolEIpFIJBKJRCKRSCTBl6MQ7pRDQ0P+PgyJRCKRSAISo9Eow2GOI9IukUgkEonEPdIuGaela4iSLEaftZPYCAkJoddff53OO+88eUr0KBTCGEe8dUNDg78PRSKRSCSSgCQtLY3zk8jcObOPtEskEolEIpkcaZfY2FfTQ/uP9VJ5djSVZEa5PV8Hj/XSnpoeKs2MorLsaJ9dXqeccgotWrSIHn30UaftL7zwAl1//fXU0dFBWqW+vp7i4+M9bv9CEHzmmRJUQiE8CSES1tTUkDHS4vXrO9rbKC4+Ycp2VqvV7U39PLnYxv8mbFe9TvGcq/dU79f+gPc9sT1Nuk/x99jY2ITPqNgw8TUuPoeiqdNzrt5P7MPt/lX35G67Yj8TX+v8vU12XG0tzZSYnDJpW/V14EmbOa3/poWtbzq2DYZEksna53i8PeErtD/+jAn7nQzHeXNxPN7sR7mb0dFRCgsLI00S4kGTkJApt4V4sKMJr1E+tv89aRvVNvW9r9oo24o/J7RRfF4IGgP9fZSQmOT2s+BvcXNqY9+Pcv/uj8t5X46/FftQ799l+0n+dn5MdKxjwHk73kvxN7b3dnfQ4EAfJaVm8VPKY1A+FsfKj9Wf0+n9nd/P+bjEfl3fZos5aVHU1dVF2dnZPF5KoTCw7ZLBgQEaHRujyMjISdtNZiu4slHIVXv7eD6Z3TDZe7jar2PMVu3b3tRze8fF53RlD6jtn/G/nc+Vu/cTx6poPKn94u441PtxZ5e4tGUU23q6u8gQHk5mc4TbNurrwJs2E7Z5YO94Y5dMaz+q78pqHaPQUP3YJS7H+JnYJR7YDq4eu7M5ZtrGlR0woa3CFqivO0bpGZluP48rO8CVXeKynep4XNoUynF8kmPw9u9jHf3O4774jlW2UG3FPsoqLKPQ0NDxY7TbJG6PVWlLqGwSx2tU50Ztkzg/N3t2CWwSIO2ScQ9BiIQAIiBwJRYKkRCgfXKsUXoWeihGS7zEGkR0dnbCxOD7/mGrdcHCRdaQkBBrZGSkNT4+3hoXF2e1WCzWmJgYvkVHR1ujoqL4+YiICOvpp59uNZlMVqPRaA0PD7caDAZrWFiYNTQ0lPdjN2GC8oZzsGDBAr8fh79u+I5POOGEWdv/T9cardY7LRNu2O7vzy5uev7+cSsqKrKmpaX5/Tj8dUP/OHfuXL8fh79uGCMC6TeAMQf9EsYgjEWG8HBruNFoNZpMVpPZbDWbI6wREZHWiMgoa2RUNN+iomOs0TEWa4wl1mqJi7dGRkbxfjKysq1763omjJOS42uXHKqs5u8Dtzi7TRIbG+uwS9Q2SXFxsbWkpMTJLtGLTYIbfo84D/4+Dn/dysvLrYmJiX4/Dn/d8JvIy8vz+3H48zabdqkWbsuXL+fxz9/H4a/bmjVruN/393FM1y5xskli49guCQsz8H7uuP9Rl+NkMNDf32/du3cv33vLgdoe6/9tbHDc8Nib52fKySefbL3uuusmbH/++efZXgEffPAB/zZhq2AbrtOqqip+7uKLL7aee+65Tq/F/rBf5Xtcc801vB12UEpKivWZZ56x9vT0WC+55BK2hQoLC61vv/224zUjIyPWyy67jMcEs9nMttGjj45fQ4Jnn32W5zKwmTCnu/rqqx3P4Rp7/fXX+e/Kykp+/Nprr1lPOeUUtjVgc2zYsMHxGUl1/d9555383EsvvWRdunQpH2dqaqr1ggsusDY2NlqD8foMKo9CNb29vbwi2dfXx7epGB4epsHBQQokPPGcmc5qlvpvi8VC0dHRlJyc7HaFbqp9icfevPdk9y6fc7NC6On+FDt2eg6rdRZLLC1bvsKr/bhbSVV/hg1E1Dm8i2LDRx3bO4fDaGPYajrxJA9XlafRxpvns7OzKDkldVY9mnyBR54N02gTHxdHwyPD1N3d47aNV94ZPvLymMrrxOk5L/bjzlOnvHzeBA9dT/YzpaeOC++gqdq78zSeygtJ/dzomBuvJ4Wn08DgILW2tpLBYJj0uI8Xvnzfgf5+n+xHMjMGB4cc32lHe/uU7TEuIeQlUO2S2bZNUlNTqampiW2TCW1mYF94aptMtR9HG5r+fhQNJrRNSIin2Lh4tk2n2p96u6tt07FlZqPNVK8RRJjNFBUdTYVFxQFtl8yWTQJycrLJEG6cts0x1Wvc2SRT7cdTu8L+x6TvPdl++vsHaM6cUts2lV3i6X48sjW8eO1knsae2iawSRzvpfKKVrarrqnhSB8R8aXe1/HGl+89OCDtElcID0LhMaj0LFR6EoKpwpNng5GREc7xd8UVV9Arr7zCUROfffaZ1330iy++SD/96U/5tX/+85/pqquu4vyBX/3qV+nWW2+lRx55hC688EKqrq7mqAr8BrKysugvf/kLJSYm0oYNG+j73/8+paen0ze/+U3e59NPP00//vGP6f7776ezzz6bOjs7af369ZMex3//93/Tgw8+SMXFxfz3BRdcQIcPH6Y1a9Zw+PUdd9xBBw4c4LawRQDG5HvuuYfmzJnDNgre85JLLqG3336bgo2gFgrLy8up4vAh+vFNN9NFF19id912b7z29vVSTIxlSkN3qtt026pfdzzp6uqk7u5uyszMIj2CsNu+3l6KsXgfsu4JYRseofD3tzttg2j43j3n0uiaGygQ+HzrZlq8dDnplZbmZhaJ4rzIXxFMIMxxz+5dtGRZ8F0Dlc22UI7JgPHb3tJICcnuQxOmnBSoQzKF0T9FGgllW9vN8YYT2ou2yufU+xXPvf/vt+mhe2+j3PwiH5xFyUwxmUx8D1tk2449U9oH/QP9ZAw3cvipr+2Pqdopn1f/fbzYv28vzSktC2iRaDbp7uqiyKgo7aYEmSEdHe3U1tpKBYX67b8qDh2kwuIS0iu7dm6nvLyCWbPNA9kmAT1dHRRuNJHJHOG2jbtFUrVN4nhOZZdMsElsDV3bJS7aurNL3B3Hjy7/Nh05fICycvJJT9gEYs/aFqRF0pjVSvtqx8OQD9b10vDouEhblhXF7UYU29wRFurZoo0nIEwcAtw555xDhYWFtmMpK/N6PwsXLqTbbruN/77llltY3EtKSmIBEkCgg/C3c+dOWrVqFYWHh9PPf/5zx+tRk2Ljxo306quvOoTCe++9l2688Ua67rrrHO2WL598TnPTTTfRl7/8Zf4b+4d2BKGwtLSUYmNj+bypQ5Yvu+wyx98FBQX0m9/8ht+np6fHISYGC0EtFIYZbB8P+ZjmlNpWpCbzPgxvb6PMrGzSIyYMRElm0iv9fX10pOIwLVy8ZJZEwrscj60R8RTSb/MmEdsDQSycv3DxrO6/7sAeyphT7rN23tDVuYUsscsmbRMVFUU9PZ8T0VrSI+gvh4f1WzEexkB9TeWkQqE/xJKZkLV/D9+HwkqUBMSClBAMp7JJAMak3Lw83QpFWLjU0u/N1xw6uJ/KyudTRIR7kSCYQZQHbnomPjGR9ExYaBiNjI6QXunqaCVzRNSkQqHW7JJou+iLBbN99b1Uln58PeL8BUTCv29umvbrlSIhgIgohMSp+MryFDL4yIxISEhg77kzzzyTvvCFL9AZZ5zBQh08+7xhwYIFjr9h48BLcP78+Y5tiCgA8NgTPPnkk/Tcc8+xl2F/fz97M6LwimhXV1dHp59++rSPQ3wG7AtCoTu2bt1Kd911F+3YsYPa29sdHr84rrlz51IwEdSzh+GhIUfJ9ano7elmjzK9Uld3jFpamkmvcCEPX/Wik4iEw6fdRYM3VvG9AM+jnb/ZtHFy9+yZ8Nlrf6TX776Ztv39r5O2w/Noh/a+orrqMdq143yqrfmfSdsdqfgNHdx/EbfXIxiok+zFfCTBQbg9ZE2ELkq0Y5MAJPLHZEqv7NyBhRv9MqLlAmM+oLW1hQ4ftIV86ZV9e3aTnkFxtXBDOOl5DB8JsgVceIYBPS9MBzJIRQaPQTVIgwIPO/D888+zNx/CcxE2XFJSQp9++ik/B5tFHZ7uygYV14EAYrdymxC/hQj3pz/9ib3/Lr/8cvr3v/9N27dvp0svvZTFQjDdBbXJ3tOdYxlEUpynP/7xj7R582YOmQbiWIKJoPYoFF+Y+mJ02XZ4mCKmqCwY7BOY6OgY0iv47nNy82ZdJBSeg+JePB9InoW+Bh6Cm//vFf57459e4PslX/mGS5FQPI/2WfMWzdizEJ6ENdW/4b+PVv6K77Oyr5zQDiJiR/vv+G+0j4tfO6UHYrCBATI+Yeqq78FM0VzbymSwEG4XpIRAJdGOTQKQCkFLniIS31JUXMLXgG7xUx42SeBgsYf+6ZX4SSIctL6AOTSoL7sEgR3w7POGw/WuvQYRdlzkhSemN0ElyLsHIU7Ntm3bWBAULF68mG8IG169ejW9/PLLHCKMWge7dzsvcEDU89TucQdyDUKY/OEPf+jYVlFR4fg7JiaG8vLy6L333qNTTz2VfIHRaHREggj279/P+cwRKo2IVbBlyxYKVkL1YJQb7XmBJiMrK5uysnNIr6SlZ/CArFcGBgY4R5uvCKne6FYkFOCx2rMQr/MXs3X9Q+xb/a1LHI8hBqo9C5UiIUB7X4QfQ+zLzf+p4zHEQrVnIR4LERGgvd5EQoHevRfqq49QMCEMs5ER6VGoNZsErFytzzQIgpI53uc9CiZQ8EbPHqURkVGUkJREembuvPFQPD3S0txErTqOdsIiX0vDMQrGBUy92SUQvA1hnt+ONPQ5iYThhnHBHNvxvKf78kZsR1GRgwcP0o9+9CPOD4hCHg8//DAXLkH+v8rKShYH4VF49OhRFhUPHTrkyFN42mmnsXD20ksv8fY777xzgnA4HVBsBPt95513+Phuv/129uZTgnDghx56iHMG4r0hbj7++OPTfs+8vDzOOwjxsaWlhQvj5uTksICI/R45coTefPNNLmwSrITqwij3IMwHCXNRzEOvoJiJng1ShJ4jQauvsOasppETf+ZWJHQlFqI9Xucv4Lbd0zZ1dXDgaTsBPAjdiYWuREJXHodKQjqqPH5veBC6EwvVImFi0lUuPQ4l+qC/r2fC6mFQhB4P6csgDwabBKE7n274hPQKfof9/d6NM8EGQs/1DMKuPQ3TP554an94Y6e4o7VZvyIZgEetnlNnQOBBQZPgTImiL49Cb3BV3ficZSl8L+ACJ8d8nzINxTk++ugj9pxD/sGVK1dywRBUGz7rrLO4AjGe+/rXv84ehqg8fPXVV9OVV9rmTgjLhYiHisYo8AFt5aKLLprxcWH/X/va1+j888/nY4JXn9K7EFx88cVcqfipp57ioiQouALBcLqsWbOGfvCDH/B7wlPyV7/6Fd+/8MILfD6QjxCehaiaHKwEdUyDN0Y5kmKKioR65FhtDaWmeZeINJgYHRvzeS6gkZNvodH8U6YU/yAWjmWt8qtICCp31lD4UBRllKZQWqH7VfyGihaq299EacVJlFHiuRu9EP+EKMhi4Vuv0WDPuEB/4vI0WrwwmSaTasKqP6TwI/+ikdzTaST/DI/eW4h/QhTE/bGaZ2hkpMNJJDQYvkJ6pnzeeFJfPRIVHUujoyNBkxfMEXosDfKAYGjYc5tkcHCQqx3rFdhv8CbydUoQiXbo7Oignp7ugCpoYqj8DxmOvkfDBWfRaM7JPrVTXNHY2EBFJXNIryQmJrF9rlcwhgdb+oFwoz1HoUyJ4rFIWJJpCzMW9+J5cS+2+woIfK7Cj0WREZGTzx2oHqysUKxm3bp1E7ZVVU1cWFHmOoRGg9yIuCm57777JgiKQrScbH/wFlTnUoyLi5uw7emnn+abkgsuuIBv7vYdTAS1C9mIfRXKk7j4xITEGcfPa5mRkZGgG4y8AaHnuXn5Pt+vp+Kfv0VCeAhCJAQQASEGTiYS8t+HWmbsWagWCVcsSmbjGkb2ZMY3gLE+E89CpUiI7YnJM1/x0jqYmOuZzPxiMgRR4nRHLqChQX8fikQxMfIkOT+MzjQdL97hXAmhW6+sOeEk0jfWgMpPB3sDdgeYLTtlwr6CZNFqJgV9OtrbSK+EhRkoqyC4hOJxu0R6FKpp6RpyKxIK8FjtWYjXSSSzQVALhd6s3scnJgaUQXK8Wb32RF1//qNVldTc1Eh6JTohkpLyx1ftXYmFSpEQwPMQr/MWiIUmVeEcPF78tQsdj10Z4UrjG2BF3xrnnbcJxEKDIc5pGx5je3Jyiq5X7oX3QrCuinlCQ00ldbW3UrBgCDc4LZpJtBPlICp+6pUYi0X3Hs5bNm8iPYPrH4u4gQLsDdgds22nKFm15gTSM6MjI1z9Ws8c2OGch03riMVYaZdMJMlipFK7MOhKJHQlFqI9XieRzAah+qgwOPUPSO9J/LdtDa6ByFuGBuFxo1+hFPRSB4t/rsRCVyLhZOHJk4GchEpPQoDHm3c0uzXCXRnfk4X9uAM5CZWehACPsb29rY0O7t9HegYePGM6D/MZHh4MwlxAUigMBMT34ImnXFNjA7XpeILc1NjIKVH0jN5D83q6u6mjw3f52eoO7JlxO9gdvrJTujqnrpS5+bNPqb1dv4JxmMFA1jH9Ll4GIyL0WG/FTDylLDuaTpwbP2U4MZ5HO7SXSGYLXQiFU63eY2KsZ2864MuKv8NVnT5tdzyIjIriYh56BpNSiH9qsXDHvw/4VCRUFi5RehZiuyux0PTJ3T4TCZWFS5Sehdje0vIih+DrmRUrV+s61CnaEk8RkcFjdMkchYHDwIh3HoUDA8ibbCa9gs+vd7tMzx6loH+gn/r6fJOs/7PX/kiv332zo4iaO/A82qG9N2Kht3ZKddVjtGvH+Y7Cau4YGfob7d31bW6vR6KiomjBosWkZ1IyAser1rdF1vS9EDIZnnoISk9CyWwTqovVew9yD5bP13cS/4TERJ/sp/e9o9T5vzup76PJPQHwPNqhfSAQH5/AYqGeMZltk1K1WDg6PDorIiFyFX7vf16ZUA1ZLRaGjPT7XCRETsKVa7Y65SxsaniCrNa3Sc/s3P459fb6voqaVgg3msgaRB6VIheeDPEJtCiHqW2SzKwciouPJ72CxOVRUcEj2k+HzMws0jNhoaE+yR0OD8HN//fKeBE1N2Kh0kZBe288C72xU+BJWFP9G/4bdok7sRDbx8Ze5b/R3hMPxGCsfr5500bSM7BLginSQ/ymZaSDRBL4BLdQ6KFR3tfXR70948lD9QYGoJycmVcWhIdg/wfV/Hffv6vcioXYjucB2geCZ+Ghgwf4OtAzS5etcPwNMTAs3NmzDI99KRKKKsjqAidCLLQanD088dhXIqGogqwucNLR/syUK/zBTEhoCOcE0isjI0PU3FBLwYIY+zDZwk2inQJrjQ31FBoa1CbapMTGxpElNnCq3R5vkCt2187tpGdS09Ip0wc5CjPmlE+wMdRioSsbBa+bDNgj07FTLLHLnOwOV2KhK7sFr9Mb6AP1XvSiueEYDQ8FY0oUfX+vEokWCGor1NMwn57uLurXsUg0ODhIB/bvnfF+wvNiKfKL44KjK7FQKRICtMfr/A0m0QYdh1yCjes/dvyNnIRKT0KAx+6qIU8GVuXdiYQCV2LhsVrn98KKvbsqg+7ACrw7kVCgFgvRXo8r9yLUTc+hxwaDMajCYQwKQUp6Ffofb0KPG+rrdC0UHjywX9d2GWwSPffFoLammmqqfRN14srGEGLhZAuZkwF7ROlJ6I2d4sruEGKhWiTMyLx+gt2iF5B+ICUllfQMhLWRIBLVRJE16VEokQQ+tl9rkCI6oamMcrQz6zg/HSbGniRX94TIk2yrv0IMFPfY7kokFO3VDA4cI5M5c9L36mmso/BYq0ftolMzJm2Tm59PRpOJ9IyodqsuXAJPQiEaiu3eeBZiVX751y7gUJ7JDHCxHQb76iUplJUW5VihF8Y4cgC19wxRa/Iayk+eOlQcK/DZOT/isB1XIqEA2zGJrz/2KLfX48q9CMEPxsmp+lqpbO51K6zllUzuRaIllIW8ZOLwAAo99mC8NRgMus7RNzTsO7tEi0AkLiqZQ3q3SVz9Bnra+ig6IXLK16vbKW0Mcb/trdeciqt5IxIqcxKOhEWQYXTcTgFTeRYKe0SIgrg/VvOMU8G18PBvU0raZaRnkoNQKHRlv7qzSzLyiigsLHim69KjUCLRDsHT88zAKM/OySW956bz5TlwJRb2f1xL1v4Rj0TCzvYN1N35KVniTyBL7Hg4rJK67Z9RZ9tGiikYpZjYVRQbv8Zlu4Zd2+jYto2UvnA5ZSxyvS9RXQ8iiZ7JyMxyW91YuX06YuGKr3+HsuYtmjKUZ/nCZMrpLXCIhCLXj9IoT2l639Y4+QsevXdO3nUUF792SvEvN++HZDLNo8ysU0mvINwRucFwLegRTEobaiopt3guBQPKsS+YPCX1kDd55eq1pGfy8gp8kp9Oy9eKL4vMaZGYGMsEobDuYBM1HGqZMl+ysFnSipMooyTFrVjoC5GwKeU0XrxMbN7gsE+mKxYqRUIsbsbEnM9jsp7Zs3snrT3R+7QzwUJfdyeME4pLSKZgEgpHhvWb5kYi0QpexbU8/fTTtGDBArJYLHxbvXo1/fOf/3Q8PzAwQFdffTUlJiZSdHQ0ff3rX6fGxkanfbz55ptUUlJCc+bMobfeesuxvaqqyu5inkLd3eMDN1i0aBHdddddXq9EipxMUxmbSODfo+MchTBGfZ0oFyKgMgzZU5EQnoQQCUFX+yfU1fmZSw9BIRICtMfr3ImEoH7HZn7dZCEuevbeANYeg9vqxq6qIXsbhjyVSCiMb7VICHAPY1wAY9ybMGRPPARtfUY+6Rl4Mem98nNP1/hETevAO1T0a8Ea5qMluwRecsoiM+7AeLxp43rSM6h2q+fQa6SEaWlpJj2DKA+lSAYPQYiEU9kgyoVNtMfrlEAMNEXHOG3D45mIhAD36mrInoYhGwxxTtvwGNt7e3uCtu+WeAbmsgO9PUGXEmUoiPIu+pyjG33bzs+sW7eObamOjuCxr/WCV1ZYVlYW3X///bR161basmULnXbaaXTuuefSnj22ymA33HAD/f3vf6e//OUv9OGHH1JdXR197WtfczJ8YLA/9dRT9MQTT9BVV101IUktjPEHH3xwxh9Mud+pQo/7+/t0vWLX3t5GfbNQ6RRiYEiEs9MqHrsTCQHCiOFJKHAlFo6Zax0iIeg+EkbthxrdioQgc8nqKcOP9SwUwpDuqBm/Blyt1rsSC9UG+HQJ6ahyMr5dVQ2EEa4UC9Eer/MVmJw3NNSTnklKTuGbnjFFTB3SpkWvwmCdbGrJLhFenVPZJDgmPXvTicU7PTM6OhKUaSC8obmpkdraWh2PEUY81YKlq6gIdZgychIqPQkBHrurhjyZnSJEQnfVkD2xU5CTUOlJCPAY2/H5hwb1LaiUzZ1HesZoMrNHYbAgxjaZDsUNH9xH9PxZRJ88MvmJxPNoh/Y+5JJLLuH5MG74rvLz8+mnP/0pL7pK9IdXQuFXvvIV+tKXvkTFxcW8+v6LX/yCV+g//fRT6uzspGeffZYefvhhNtSXLl1Kzz//PG3YsIGfF8YvDB+sxC9evJi9V7BNybXXXsv7aGoaH+ing3JSNJXBnZSUzMei6xyFszApQU5CpSchwGN31ZAFCDd2JxbiHo+VImFfjYFFQYiD7kTCtPlLJn3P1WtPJD0DQ3rYaBMKJwvpUYqFCOnxJE+QJ1jj8mgk93S3IqErsRDt8TpfAe8Vq489a7UGKh53d/m/Crk/yS+Z58jXGQwYDMFtlGvRLvFkvE1JS5/Re0m0TVxcvO4FElc5CieLbnCXOkWJunCJ0rPQVTXk6dgpSrFwKjtFXbhE6VmI7UMDb/g84keLzgzBNCZ7S7QljpLTs4LOo1AWWHPjIfjh/ba//3OXe7EQ2/E8QHsfexaeddZZVF9fT0eOHKFHHnmE/ud//ofuvPNOn76HRBsYZuIKjRX63t5eDvXBaj6M4DPOOMPRprS0lHJycmjjxo20atUqDgu69NJLKT09nQf/e++9l2JinN3/L7jgAnr33Xfp7rvv5tV9XwiFU63exyck6tqbrKCo2Of7VBcugSehEA2VBU7cIXITClEQ9z2dW2hsbHxFA2KiIdlAfTW2DpLFwj3baFQxyfNEJASfb91CS5e7z2GoB8pWlFDoaPiU4h8M7+j4SG7nKvmyJ4UjXCVyHsk/g0bjixxGtbvEzhAL+yKzqD8qh0jVxtOiFa5eA7FA5AXz5HXu9qNlEBrZ2tpKqToWKQ7v3U4FpQuCppACf45efRjlWrFLPLm2EhISSc+sWjO+WKhHWltaOOw0N0+/6TDS0zMoxEX4uRD/lHmTGytaHUXXQERWHPVbIngsF2OzWiScc863qfCMc6niP3+jA2+9zNvE867CkNkuiF5NEXmZ1B8x0f5wtOEDWEYReSlsp+S7adPd8hx1Nz/q2B6TfD3FJF3mtL2//yXq7U2jjtHv0nQIBrsEnqV5+QW69bAdHh6iqgO7qWT+UgqqHIU6T3PjktzVRGfcNS4CivsTbnAtEgK0x+t8CKIs09LS+O/s7Gy2oWADPfDAA7xwgftnnnmGGhoaeIH29ttvp298Y7zPfPvtt+n666+nmpoatrMuvvhinx6f5PjhdQKYXbt28Wo9LqIf/OAH9Prrr9PcuXP5YoEgFxfnnGcjNTWVnxNAkW5paeHJKFxZ1cBQRxgRLsCKiorpfi6n0KGpBpd9e3eTnjmwfx/199uqtfkCV9WNE/97tVPOQjzvrWehWiTE8xABIQYKpiMSgoEB331+rdJQX+exh6CvPAnVeOohyCLhLLDlM5uXkV6BN9WYPberXoGIM2LPJRcMCG/5YA091qJdMpVHIYoKKUMu9Qa8h3Zut0UI6BXk79K7Jxk8gvv7XKc3UXsWqkVCc0asU3t3IiHAPQqZeOpZ6Kn9MVk7dyIh/510GRcyUXoWor1eQX+ph4WuyaICgunzC49CWWDNDRAFIf4JlJ6FrkRCpYg4C+zevZujMITT1X333UcvvfQS/fa3v+UUL0jv8t3vfpdTuwCIg0jvgmiP7du30/e+9z362c9+NqvHKAkgj0Ik+8YXjwH8r3/9K6vE4uLwlNhY5wFczZlnnkknnHACK9Qvv2xb5fMGTADaOnsoISGBcwshjAgXuKvE2DDE9OxNCHp7usngo5U6VyKh8Bx0VQ1Zud0VEAPVnoShoWanasgQA9WehGFYDfFQJBRepXqnubmJSkrLSK+gH1CHHOoNiyWW5i1YSHomITmdQoPAcwFjGwSXqMhICklKou6OVl7Br64OvtxvWrBLjtVUc2oDiJmY+EI0xL0r+wO5gJJUxRb0BM7NiM4XLIxGkyNtgF7p6e4iq9X97wBiodqTMCw8bIJIWHdgj5NICFEwftXZTm3U1ZBxn1ZSNmURtukw2LfNrUjorhoy2hsjF5Ep0nO7NlhYunwl6RnMXZPSMknrwB7BQnS4wcDjbYTZRF0dbdQfF0r79u2joAfh88Me5nVf8X2i0SGiD35pewxxcP1jRP3t421OvdXWbsiDCKjwSK/yXKKoGxZfYTNiXoRrENEU+PuXv/wl/ec//+GoDVBQUECffPIJhyeffPLJXGCusLCQHnroIYd9hsVceCFKdCAUQnArKiriv5HvZ/PmzfTYY4/R+eefz8YdKtooV+9RXVC4r3oDVu9xEf7kJz/x+rW4oLEKiY4IF/nuXTt45SI7J5e9KyoOH+J2i5cup5rqo2yMVR6p4BCPumO1/BmjYyx8j8mWO2M+WIiKjvFJmN1wVadbkdCxzYVYGJ5jofA815M05CRUioQAj7FdiIXISagUCQEeY7unYmFuru9y3Wl9lU/PJCa5zs2oF9CH7929kxYtmbpKdLBiNJsDJlclG9ZjoxQSEkp9PV3sVYBiK6Mjw9TR0kQjI0OUmV9CxyoPUW93J0VERVN2QSkd3LWFX5OWlUsxlliKjoqkoYF+3p86rDYY0IJdgnNvtdqEQpPRSNu3bWEvT+TH/XzrZhYHsWAFO6Szo8MmJBrDiazEodT4jOifsB12TFCH4Vmtug+9jrFYdF31GWDBZrJzgJyESpEQ4PFAXaeTWAixb/nXLqDN//cKi4QQBV2lF1GKhWg/GyIhgNgXnfQD6mn5rUuRUCkWImdwW+v/cHs9ioRg/749lJGZxQuZeiWQUqEIT+fhoUEaHOhjuyQhOY0aaqtoaGCAzJGRFB0bz+HSGPey8kuov6+HWhvrKAxjl8HAqT5MxnAaGx3h8SwlRQdF9CAS/nLyopqTohQJAUREISROxa11REbP0xCceuqpLPjB9kCOQnxHX//619mDsK+vj77whS84tYddghzPAKLvypXO4r4QFSXaw+CLDgMKM4xzCGrvvfceX0zgwIED7L0wnQtkxYoV7Lo6HXdVdDh9g6NUWVnJk4NlqtWotHTbDxUdGAqZhIaGUHxCAj+GsAiREXlRUAgYAiI6wYWLl1JVZQV1tLdTZGQkLVi0hA195FrA/iIiIjjhLs4BqoZiAhBi79wDvVBKTm6eTwxSiH0Rp+ZQ/wfVLkVCV2Ih2k8mEioLl8CTUIiGYntfta2QidKTUIiGYrsnYuH2z7fS2hNdJ6bWCytXOVfvc4Unuft81WY6TGe/yteMRiRO+9im+7pAyiGEvsqXaQiwEIMFGl+18xU45+6+LwhuEArNkVE+E/owhqByIfY9NDhAoaFhFGWJpYbqI1xkJD45jds0HjvKryuZt5Rqjuyn/r5eio6Jpcy8IjayIeaLW0xcPBnCjRQWZqCcojKnPrx86fhvGUZdZcUhMkZG8/gUHx9PwU4g2iVZObnU3d1DXV1d/P2tUPS3wmPGJiZaKSo6mtIzMshsMvPvEakxuro6KSExkQ4d3E893d08ac7NL2A7BBQVz+FrqamxkQVGVApFOgnsLzIqimJj49i2gU2CcxLIIhQmkllZ7iMN9FL1OSoqymGv6pH8gkK3z6kLl4SEhZJ11CZg9NfaKggLsRB9ffJJ59GqrFKKLyh12feLbfA0XJWSTwlu2k0HV/uxJP+QTFGr3Ip/4jWhcd+jOPNyirRML4d2MORbZkcsF6G3gWpfzAZ1RysoPinVJ/uyzXWHeREKPjA9XZ38OCY2nvp6uqmrvYXz/RaUzqcDO7fwc7HxSRSflEJVB/fw/Di7YA7bMn3dnWyHYJ8RkVEUFW0hozmC7Z2yxascTjaxCUmUlmVzyGho+ift37+fws1RFJeYwuNRYqKM6gokMPaIxdfnnnuOFi5cyIXh5s2zVSD/xz/+QZmZzl6uWASVBB9eKVi33HILnX322ZwIHCG9CL9Zt24dvfPOO+y9d/nll9OPf/xjDvlFgnBUCoQxjkSW0wHVC8vLy6cltHlSXRAdGJLEhoaEcoU5gIS5SpKTx1c5yuctcPyNTrF8/kL2VITRL0KYMSmDazVyDLW3tfGq//yFi2jj+o/5dZlZ2fwDPHTwABvsc+fO41xEeJ3ZbGbRDq/FPqMio8gI4Quu2rPo1YiJhq9EsqjTc8lYGOdW/FOKhVN5EipFQpGTULkd993N8KowOOUkVFY99kQs1HM1NSW4RvVe/bly/y4nkUVvQDxISPSNV+XHH35A6z/+kE457YxJCxN8uuETWvf+f7gPOvHkU8nfYPGnl0PerPZ+nWigr5cXkUzmCM5f2N3ZzsJeRm4Be/NB0DNHRFJqVi4d3ruDZzbpOQU0OjpCrQ11FBYeTvlz5rFQiPEBIiTEwtiEZO7rYVhD8EtKHRcFUFBFSW7xXKfH5ogo7/IBBVGOI83bJW68QzDG49bZ0U7l8+bz3yazmeIU4u68+c6pAZRjN67R+PgELkqE3zKiJfr7+1hshD2BaArYPNk5eXx/tKqSX7d85Wrau3sXC5JYNM3LL+S2xvBwSk5NpbDQMLZRDOEGtpXwPrBt8B6zYZe0tbZwGHlxyRzScwV69Al6BnYyrrdklbeRWiQUOQnhSShEQrVYCCD+eYKn7WaKJx6CbU31FG7MpkgL6RY4fISrwvC1al/MBLGQBGCHQKjDQqQlLpGa62u4T4+IjGY7BcIiBD5483W1t1J7SyN76JYtWskRB0QhLNLFJiRSb3cHhfH5tbIdE27MsD8mLqCi7OPV9nFiynjhO9gznhDsNsmk4b/w7POGjU+49hpE2PHqa7x772mCcf7WW29lO+rgwYMsCGKxFWHGrigrK6M333zTadunn+o7/7uW8coKaWpqoosuuohLZsMAX7BgARvjwgUV7qm4oLByj9V85PR56qmnpn1wqKRz2WWXcQLx6SYNnyqcEh0V3KS9BR0nhD3cBBAAlSuhytVQtRCXmJTM780eHlYrmc0RHJYE4OqLDn84Np5MZhMbSzAakTusvq6OWpqbeKKxYuVqHgSx+p6Wls4G1bFjtZz/AW76+A5wg3cBKijCmwBtMcGZTW+CqUTCqdq5EwldVUOOKbCFncQnn+gQA8W9N2KhnickQIgiuieIUwx4CjyZZgpW8GHEAxjpwJUxL4x4gPZYqJnJyj+uYSFiYAxAn4fHmGiibxwcGGDv77j4BKrYt4P71dTMXBro76WWhmMOw7ix9ijt/fxTssQlUEpGDjXVVfOkPcFuFIcbTXaPwxBKyczl/hSGNe7LFcWVQHJaluNvvJcSS/zsr6KLPGfBWmFQS3bJMLw4cP1MkXsOY/R0BDi8DrdIstkiqarw6oWLncfArOwcp+ewKInfEH4/eO3IsE2sgigO8RLHHxNjocOHDlJXZwdFRkbRnLK5jiJQ8HDEwmtNdRVfdzab5Rj/FuHViCq2TY0N/Fx0TAwfK36DsNPwnuIzi/yNeiYlLY3tQj3jKgWEO5EQiPvJxEKtgd9EoKTC8BfqCvD+si9mYltj/EX6iM7ODu5XIbZAuEN/iOfgQQ07A6Ie7NCiuYto77aN/HqE9cJ2wGMsMOYUl1FvVyf193azLQKhEKlGsHiIG7z5sgvnkMFg5PeItsRRRu74fHTOguVOx5iZV+z2+GdjIUgssrFXo57AufQi/JcLlyhFwoj48fBjbA8zznohE8H/+3//j1OuIA/hTTfdxAVMcF0jbzMW9davX88LscgPjYJyyE+I9ihksnXrVnrhhfEcsZIgFgrhdjoZEM2efPJJvnlLXl6eS88uXJS4zYZHIfCXOzomLsJNFwYzboLComK3IaEIN5qjKDiB8CUOc7Z35knJyWzUY/8wthGuBCMDA+2BA/t4W2JiEocwwYMApKSkseHPnowGA61cvZZ2bv+cn0M7eBjB2MdzySmpbNhjwoXHsXFxjkSnvhAgBweOuRUJHeduIIu6j4Q5RELcx6c5u+S7EgujU9IoWuGxI8DkSHcrWy6A2Kx3EOKpd3bt2D5jD2P0q1jpF0a62phHX7/hk4/YK0CwfNVqfl3dsWO8UAIRAuICvJ7Q5+Tk5bGXNvoivH7NCSfRZ5s2slc3+r30jEzavXMHiw5FxSUsCra3t/PCCYTCsNBQXszBftH3ZuYW2fPlhHNYjBDxsL+YuAQnwzqvxBZuIYiKGZ94YvU+kBGLZcFUNTHY7RKMwccb2BBKL0nYCUqUXo1zy51/D8r+AucrJTXVsRAKG2Kgf8AmeFit7JnIorV9/il+3/MXLqbKisPU0dHOHoxl5fMcYgAiLSIiI6m2upqPsbC4hDra2zivo8lk5sWN5qYmCjOE8e8b78seefZcjlrMMc2LGgHet8w2ltg4MkeMn4Oetj4nkRBVj/stzufIlVhoiDGRIWZ8UV9LIORT/Fb0SlNTI/cd6Ac8sS/UIiFA+8nme0LQExFc3d1d3E8h/QNGgPY2WzEw2BkQ97o6O7l/gZMBFkrwOkSMRUREcnoIgBRVSCGARRb0XwsWLubHWChB/4oUE4gag12CfcEjDzf8DeYqFh3rayqpqHyxw95AiK8SdbETsY/AtkmCc/HSJ7irbqzcLu6Pg1iIcfeaa66hX/3qV5zWLTk5masfHzlyhD1+lyxZwl6HANEdr732GouJjz/+OKdsQQEULLBKtEfg9iQzxFODfOeOz6mgoMhJqNMSSsERIOeiIMVsZoNdsGjx0gnGPQZHDFwYQDG5Fl5lGPyG7Z45uMH4RhgdQHhSa2srP4ZQePDAPjbY42LjeBDFOQUlc8qot7eHC8SIQRwiBLwSEB4Fr0dMDGDcp6dn8nt3dUHknEs0tpfCzUtpZLSQenp6OC+kECQjk9MoNmE1dR7ZyCJhTOwqMpknVgRTioXpC5e7FAmFMACRFIN8MLG1qo2W5iV41G5Jbjwl6LyQBxjs97Ai2XGgcbCWUk1ZPms3FeOG8ggbxLZJfS8bwFiggOGMST7mLPidIzQRz8FIQHhkFfK5jo7SnDllLObhOSx6iOJRMNpbW1s4vP3df73N+V8FpXPLKTsrx7HKzAsP4Qbuv9PS09mwRh8QHR3jlFMVXtVKIB4qwXG6+hu4y0GIfXd1tDoJhVpGeK/pbvVeo3YJfoOffbph0nC6QEYIjkJ0RN+hLEKgXgh1Su8y3xZuD9EPURPCRhF9E4Qz9E04f/C2G2MR1/Y8BEY8hxBzTL6PVBxmsXBu+Xxq/PwINYZ28nbkrEaaDbwGfQIiMQ4fPkiGsDAqjMyi7pgRDmGH9w+OtfpoFfcJ6HsMhg4aGIwmQ5iBJ/lsL1mt7LUjvtOexjoKj7W6tEmUoJ07mwQ01NdTVBTCCH0rcIVUbyRrzmqftZtNUHUT35njcUIkpRUnUcOhFhYJUfXYVQ4+pViIv7UqEgKMx7jOPE01EYx2CWwO2B5sk3DE1TDNnTefRsfG6ON17zvsC6RYgGfevn17HI4QAIsOKF4pFh5QMArzGvQRYMmyFVRx+CDnfsVcsLSsnKoqj9iLbKQ5UkBB4EJfEBsXz8WGkKYEj5FjVrkgoZx3lc11LoijTh0Bu8aTRUeEGeMW6AuTniBtkmmKhEDcz6JY6M77D7mZRX7m6667jm/uOOecc/im5NJLL/XZMUqOH0ErFIpJ0VRC4UB/v5Mhojcw4NbX1zlW2sQEHKtfEW7CETGwwbNQgFUyd94FyZTilPcREwEY7wCDbnpmps3wNxrZi4jzRo4UU1paOTU0DFF/az2HCsKg37dnFw/WEArGEtOovbmYehutlJy+lHZ8vo16erp58IZQsWXzJn4PvHfS8hPpWE8fNWz5jPNKHqut4ZBEfEYYDPCmbGtr40I1OCabR0MoF6WBUCImJgi56u3p4US+mETAMMDx4JzNVq6m6fL4ewfpyQ8O0Y1fLKUrTnIvePzuowp66N/76YenFNLyiOYpcxSqk1zPVlESf9HSWDchPHS2EFVQDzd08bWDnDOYEBSlx9GOrg20p/8zKjUspeUpp3ChpKFBW7VT5BCDuI+2rdG1tHd4M2UMFlFGfyEXNKg5WsWexJhgQqzHajfa4ncDwxYGMUBl480791FPVweHqhSWLeDE1UODg/T5viNcXbe18RiFhho4954oxIHE1civj+q5ofZFBPweiueU8WQbv+XiOaVUYvd8Vq7sY6EAqRTQ7wrUOYaE14C7sCNf4e5axmfCZwwWILiKBZF99b2UGRjzTV2C72Aqu4TTheg87LaxsZ6yjbkwNhx5G3nxIDra0Qb9YDyNL4SVzCl12W/0vneU4j7opowvFlDkiuwJCwroG7GI2v9xLQ2/UUHRJ6RRxPJUso7ZPEkxztuK3G2h0eFdNDhcQgODOZSTk8f9svB2Wr5iFW1+6zWKjqzlBUyjeREdOmSrTI3wbHggYQEFFMRFU922T8malE4JpQu4YAn6ZbwXFixhlzU01LE3HRZt4cFkE4siWHSFjYLzASERoD0ew1bCsYrzpY7wMHx4Hxk+vp+GT7uLRte4n1iGbXiEwt+/i0ZO/BmNnHwL+YuamqOUkprm5N2aUZJClqRoFg3dFd5AX24TCLXrSShA/tvRYVuhidlGiPKHGzo5lBX572Cvw04py0mhTU3v0f6hbTTPvJLmW1axl93o6Bj/FpELFdXacZ32pbTStq6PKWOgiOaEL+ZUSwf376PRsVG2yWFHww4X3tOw3/F6OBAgfcEHn9gigTJyCmwFmo5Vs1dlWFwmNdRWsnd8RFQ0pRTMp/y6Jqo8uJvbb9q4nrabTNyHCtaecDKtXLOW+1T87pV2unLxUC3gqec1CKsUqPtnXxWsdHctA+QODJZ86uM5Cm3joUTB0Y3uRUKBK7EwexVRrqwsLPE9hmBfuTdOMeHDAKZnoxznaapz5EuUngYAhoEARq7S80BVV4ZX/SbLr+RKrGTDJyOT0oaH2YDBd52als4TtpBQ2wQEYYnxiYksECBMG0bIoD1XU3dXF3sXIHwRHgUVFYe4DYRSCDEVhw46Ctscq62m1pYW9vDECiNCK4UxYomNpQP79joMEngwNjc3OUK9IaZgPzg2GMXw0oQgWVw8h0Uf7FcUxtm3d7fdkyueJ0PV1VX8OTIystizc8OBOnryA9uEBCIgiuVcujqHV0FRBRNt8R7PbaiiJz+yVVl9al0F3bAwhPLqjrFwjgqZLc3NnFIjxhLLx9bb08052RDmCYMQE5Ke7mGKjI7h4g5ArHoLz7woSxwN9PWwscmiUrSFujvb+DmT2TYJxPMg2hLPueLw3YSGhVJMbAJ1tDazxwhEK+TKglgFD46Y+EQa6O2hQa4gG8o5XJrr4blqJTOquxpN1NnWzMeNSnH9vT3U1wtBLpTSs/M50TOKSuDYI6It1FxXzW2HhwY5V11XB47RygUljuzfZasIZ4mj2MRkqj60j9tm5BXyflub6nnFu3TRSqrYu52GhgYpxhJPyRnZdGTfDv5sGblFNDw0QM32PHilC5Zz9TgIb6h+i2pwRw/t5e881GShPcOfcbv9I1vJ3BVBCb0ZnDYAYS3id3Q05AAdHt7Oj+tMh6kgtpS3Z2bnUKbV6shHiutLOWGEh54gt2g8jQFAouuG2ipHGItycqJcycb3hmtVibt+VIiAQiycTCQMBPDZ8ufMp2AB4nAw5yjUEh4VWUNuQR1XugUYA3xhlw1XdVL/B9X8d9+/qxyF1JSgbxz5tJGG7WPmyCcNFFua4sihDOEOKVGaG2weSqbwg2wzYFESN7GwwR6CdpEQDA1sp2XLz3d4FiLao6CwiOp3bWWREIS01FOcaSnbDLwAOjrGnpLIQyaEC/RHGO/R//PfkVE8NsM+Qc5HbK+vP8Z2CcK3D+zfy0IMPKMKC4tp29bNPF6VRnVS5sf38z5ZBBweoV2xZ7CnOGwbeHVjUSm3+jUqqnyR20FU3NWbQHmnXEhHj1ZxWywUw+NqFyJHQkI4ZQnCv48dq+HjKygsptaWZo4EwQIScnVjMRbPIXwdoZwQV/EYthH2iYVb2AfwBqu1C0jRUdG8OAbvUixewdYZGByg/r5+m4cnRfJz+GzmCOQLj2C7hL/3YQOP2UMjA0TtPRy+CxsF50gUj+rr7uK25qhoGhsdocEB27iEfG9YPMM2LBjBphH2DcQp7APiHewDhInCnoGQh7ZISdHRajsm/G0lK/V2dXDVXhR96GxvoWGk7QkPp7ikFBbA0BapLnA+xGthD6D4BOyL/r4ePibYIeI9Yd801lbZwnELS3mBE8cBz3uEpyKXHb4bvGdkVAzVVh7k8Nm84rnU3tLEthFSbpQuXE77Pt9EY9Yxik9M4eOoqTjAtjGq2sLe6u5oo1Dk500dYpEQ7B7YxLZUbHcK2yy4FpGbFN/30ZD9dKjLbpeYD1NJzHxO/4CFQ9iwnI4gIZGvdyHaLVnmnDNPnecXnx/HAptGnU9vxalnU0x8Au3cZLO3lSJhINoX0wW5koNFKAwP8nQoMwJi38k/I/rwftcioSuxEO2lSCiZJYJeKJyqmAmEqkDyBDvewOiD+BSsCG8EZXg2F51RFJ7BKn1ba6tjxRDGvysxEixc5CxIKkO9YTwrUed5S1I8Rsi28LgCEHOURsDCxUsdRi2OD4aVeB4TF5voaWTvreTkVH4ORhpYVZxG3++y0jMbbdW1nt/SzMb5D06LZ0MNbZ//tNohEoIbziihk1KGOHQDYP8QBtkIN0fwSrAwwpFzCuIlcij1DdpCOGFMAhiLMFBhEAMIcaggOzjQR0ajmSKiYqi73dY2JD6EDb/uTtt+IdjBMIbBzsnuLfGcrBlAJAwxhtgEyJAQiraHyMJAtxpsXhtY9Ra/Zdzza/D9cxgrzqOt0qwjxxzyCpvMtoJCyWm2iUtuAWxxfl7sKyu/mN8TebMwmSmev4SNZDyPCQiMekHxPOfrQ10hDsaeoHCu8wq2eC1WlYe6+mhbl83w3d7zCS2JO4nmW1Y62rbH1tNhuzEOllhOokJLmUNwV+Jt/wbhE16VvuwXYax/unG9k0iI6zpQjfj9Oz6bMFnRKrqtMKhRu8Q6i160WgG5vXxRcA1iX+QX8xwioSuxsO+jGsd2fu6LeRMKrUHsQ75kkT9Z3CvzJ4+Zx0VCgDzKhu5GSps/7rXUsGubQyQEmUtWU0KWzYMdi3OO90Po9OFDbJuhH1anRSktc66Arixao/aMUnpPDg/cxSIhiPj4Xlp8moFGTxyfiJ4Uto3C7SIhtz/1LipZ9T3+LpCaBoIlwGN4kMM+wPiJY8zOznU8xmdB/872gD383GajmHjMh3DEIaXwfLSPpQKbB5uVRkZHKCMjk9o72tguQZjp4MAgR46gPcI7YYdgwW9szMI2AzzcwIg5gW0OCH74QWGc7kGl+hF4osWwSCZsFFSkHxkapO4OmxiIBUqIUhAx4MUFGwFVYWEwwN7C+wm7xBKfxIt9aGsPgndExkB8w6eC2SY+HuwHzptpP2dYAOVQfXsIKxbmYFtwAYrYeF5Y5fRCEVFs+0DAE21zi+fa7BxDOKXnFFBGSIHDLlHbHXPjx8cy2DZs09gpW7zSue2S8WrwKIIhIixSTVFsZwi7ZFf/RlqSqrBLoqKoJuygQyQEaJ9rKZpQ7NFbRkeGqbOthReEXcGVfHdu1ox9MR0gHOMawHetdUSBNaS3krjg1FuICk6ZWvyDWCg9CSWzTNALhVOtSsM7a6aJ+7UMvM2QlwMhuHr2XvD3ypYQNAVC9AMQ95RCp9LrEiQqcgvCyxFC54+/nMah0vAoBE+vr+HCEAhDRrjxE+sqHa9BePJla/M4F5OyUjcSxitRTl7F9SLCIrLyndsqPdFSMpwnOVkFJW4LQ6gTMqvzxGFFXwAD3p0Ix+dCsS9MUJSgeIXz8drO96E9n1Nx+WIiRXP1+0CAnG2E8S2McnGP7bu6NjkeC2NcKSLOFExCROVTXwGPWaURD/AY24PJmA9EdFthUKN2CcJMbXmBnfszPbFz+zZOjeALhCjoSix0JRKqPQ4FQhR0JRZ2dX7mVIQNImFfjYH6ajY68iVDJBTF1YRIKPIou0IIYr5EhBsLsVDcY7sINxaI8OQwN9EgauFHabOwt6XiOXX17YjM8dx1sG0SaXw8VtqiqFSflZXDtotY2Fbmf1PnuxSLr7BLYCvEJY7nv1QLLPCYUwJPPYFy8Q+ovdjgeeeqqr2r10LsE8Qp3gOohS+l1z48AQHy5XY31E5IiXI87JBAsUvg0ShypLti3/ZNQW9fQCDusS+qa51gL7DmEzz1EJSehJJZJiiFwoERzwxysaKpZxCqKgqY6BWs8EZptJjNZIjchEIsxP3/flxBnf3jg7PIYYjwJYRCK4VCPTJkDz/yJ0J8jaZ5lB82RJWjNg8UGOF7ej6jwbEBl8a4r/JFli5c4dN+UV19ECv9wqh3Va0wEEhOywya8UEa5YGDJ3YJPLXjde5RyEWTfPjbcyUWIiehtX/EI5FwMrGwp3MLjSn6ZHgeGpLHRUKIgw17ttGoIiRyKpFQRB3MRv/jSiw0bHyMQvptHnVgqhyGxwt4CCIEWc8gskTkNtWaXeIrmwQCamGZs6esUiQUYcdasS+mAyJ3YuJmP0/l8fQolIuXEkngM/PYjgDFU6FQVNnTK6jeh/w0egaedymK4izBBERAiIECVyKh+C0gtFbvINwnkMg2LKH8sPFQIHcioS+prthPAz6q/qwWCZEz6Pobb+Z7AZ5Hu0AzyhFiFlxGuQzz8TcjHtglCDNFLjc9g/y7vgYiIMRAgbcioVIshBgoUIuEeB4iIMRAgbciIUB+wdkCIiDEQEEgioSuIi30CPIRmlTpRALBLoH9cbzsEi6wsufzKUVCrdgX0x3HkUYnGJA5CiUS7RAcvc4kBvlk1aiQ9wSFKvQMJiTIOadn6o7VcpGPYAViYGyE88QUj5XVkG0FWJyLxeiR1MzAC/eDUW4KdQ5/xuPZEAmFBwNyAs2GSChW9nEfyMZ8S0MdDQ7637vUp6HHMszH7wyPTG2XNDTU+yQ/n5bJL7TlNfM1EANDIpzPPR57KhIKIAaGqvpkPFbmLIQYGKZIGQLw2BOREHy+bQvNJhADrRHOgiweB4pICMrnLeBCcHoG+QmVYdGBAuyP42WXoD9EkbipREKt2BfTAYJ5XdVhCgbC7HnFEc2m94g2iSTQ0bVHIXLzodqankHVXlT21TNI0O3LfGyBBnISKj0JAR5juzLcbfvnW0nvVOzbSYFGzcg2pxV7gMfICTQboAqzKPoyXWqqj7oVCQWujHm8LhBA0vqRAAj38m3icJkPyN8Ir87J7BIsWuldKPx86+ZZ2S9yEio9CQEeY7s3ICeh0pMQ4DG2C5CTUOlJCPAY2z1htiucIieh0pMQ4DG2Bwp7du9kO13PoHBK/dFxWy1QgP1xPO0SiyLao7m+1kkkXLDyJE3ZF9MVCoOj5vG4TQJkpINEEtgEZY5CT6sLYuKkrhCqN4aGh6Ys+BLsJCYmTVkd21umk5sF1W59DcRAkaNQeBIK0VBsh2fhmNUaNKGWwQREQpELSKzYC+NcmUjcl8B7IXSG4V7ZOblcJGr9xx+6FAkFYjuMeLTH6wKB1Ky8oAnFN4RLj8KAs0sUEyU1GIv0HG4JD5PZEErVhUvgSShEQ1fVkN2hLlwCT0IhGortfdUGp8Il8CQUoqHYPpVnYYGPvSqVNkni9icoZdMvHY9HTPFkGGx35Cxs7x2i1kXXzNgu2VrVRkvzEqbdbmRYpktggcg6Fnh2yeDxtUuSFEVjktOzuLLznq0bWCRE1WMt2RfTpaA0OFJlKedbMk+hGzqqiOLyfNdOIpkmBj2v3GdleRduEoxkZmZzpdzZQC2WeWJw+ir5sTd0tXeRKSKSTH3HR7w7XqhFQpGTULld3F+8Klv3CfRBhqoyYiCJhMhViDBk5XYY5ZjUYbuvaGuq57xIiSnpM/ptn3jyqZSXXzClcQ5jHnnZAsmIH+jrodGREYpP0n7uUiFKTVY1UhI4kQ4rV63R9dcBT7rCopJZFQkH12bQ0LI0Mm5pINP6uinFQtHfhQ5tp7DhcY+p0fCVNGxc5LQdYmF3c5jDvI6Ys5QiCudTf8Uu6j+w1SEWtvUO8XZ3tLT20kC4a3toJnaJWiRsWnkri4LK7eJeiIXT4fH3DtKTHxxyyoXsCmGPXH1qMV17esmE1Di+XsTVGkZzBCWkZJDe7ZIjB3ZRuSLv57xla7kSNERDd3MH/E4C0b6YLs11NZRTNJ5zPCg8CmVKlIlU/Ieo8j2iorOI8k52fyKrPiQ6/C+i/NOJCse9Z2ebdevW0amnnkrt7e0UFxfn0WtOOeUUWrRoET366KOzfnwS3xIa9Ab5JCv3u3ftoC6dh90ODPQHdditJ7S3NNKwKv+J1nEnEroqcIJ2z284SknJKaR3sLo52yFfMzHGXRU4QTu09xXoDyCS+QJPjfNAM+LHRseoryc4Qt7Gqx5LoTDQFzDR92xc/zHpGdvE0TrrIiHAvbLACdq5C0N2JRKOGRfx37jHY0FMwShFZo84REKAezwWQDSEeOiOprpq8jXuREKAezwWoB3aTwd4CEIkFPaFMs2JOzsF7fE6JQmJSbqPeAEjw4GRBsOfdokrhEioNftiunR3tQeEfTpTlDl6fWVrBg3wEIRICCACQgycTCQEaI/X+ZiNGzfyfODLX/6yz/ct0Q6hek4ajmImKOKgZ6qPVuk6zAmMjY0GVU4oGNvuREKBWix85D8H6R8bd5PeaWk45vfkyp1j9W6NcYEroxyv8wUxcYkUMwtVR7VEeBCNC6hsD2SIT+BXPR4aGtK9B1VvXy+1tLT45HwPV3U6iYQQBYVI6K4aMtrjdUpCRuvdioSCwZ506j4S5iwW5iU5tXElFg63NU44bogBvpYDIuo3uRUJBa7EwpDq8RBqT0EYsXoxUi0WulrMVIcfHz54gPMn6xkIKW3NDf4+DL/bJZm5s1PgSEtEREYFRYogzLfEnEvmKFSBMGJ4EgpciYVKkRCg/SyEHz/77LN07bXX0kcffUR1dcFb8FMyOcGjjqgQqxSThfgkJCSS0Wg8jkclCUSyC0vJHBlNwQKMbYTxgMnCfpRi4RVrc6g0Wf4WAiEfUGxoOuWELXNrjLsyytEer/MJVisNDQRHxd/pEhkVQ+k5+RRUOQrlyn3ghx5brZSeHjhhhv5geGiYjD4KNw3Pi6WIU22V7CEGustBqBQL0R6vU2INS6fR8KVuRUJ+r4RUGjMtdYiFaI/XqVGKheaihfw6V5TMGxcUfUF/+kpqXvpjtyKhK7EQ7a054+Ge3uAqckGIhZNFPChh7ymdL2TbbBL/e5H52y4ZHOgLiPPgT/JK5lFokESAiUgHMSZKFCDc2J1Y6EoknCw8eZr09PTQn//8Z7rqqqvYo/CFF15w27a1tZUuuOACyszMpMjISJo/fz698sorE9rBBr3mmmsoNjaWkpKS6Pbbb3f6Tf/+97+nZcuWUUxMDKWlpdG3v/1tampqcgp5Rn/4zjvv0OLFi7nGxGmnncZt/vnPf1JZWRlZLBZ+XZ+iUO2//vUvOuGEEzhMOjExkc455xyqqAi8AlGBikHPuYDi4xN07023fKUXRmB7L1F81LTb+SP/oCfUVVVQem4BhYYa/XrM7nKsTAfk+llTmDRlInEY50ty4mlhloV6e3qmdYzBRFH5Ygqze2D5kzzDCooPzZ7SyIZRbglN951IyBP1Qepsb+WiJv64dv2F8nhh0GzdvIlSiiYKAlrNByQ9CgOpmInrPgYmc3xC4nE+qsAiOSWFkpKTfWaXRJ2eS8bCOAqPNbjspxzbyhIozGKk7sxoIhftxozLyBqW6VL8E0SWLKbhtgwaMY9N2g5ioSE+xa1IiHyiddUVlFtU5tNxuGXZTdSbeSKLhpMBsbAvdTm3a5lGrmmBEP+UOZH/9+MKR0G1qRYzUdBlqqifYLdJjCYzFcxxn8tSL3YJvCpR0MSbVEnTyZMeCDQPDFKy2TThmPfu2cWFh2LsFaC7RkfIEgD26nTtkqHBQX3ZJRDFxjz8vNmricZGiI78x1ksHFEs4hecYWs36kFqgtBwrxZdXn31VSotLaU5c+bQd7/7Xbr++uvplltucamZDAwM0NKlS+nmm29moe4f//gHXXjhhVRYWEgrVqxwtHvxxRfp8ssvp88++4y2bNlC3//+9yknJ4euuOIKh310zz338HtC/Pvxj39Ml1xyCb399ttO73fXXXfRE088waLkN7/5Tb5hnHj55ZdZ4PzqV79Kjz/+OB8P6O3t5X0tWLCAn7/jjju4zfbt24MqmnC20GYP4wHCeyJsktDjfXt3cyUsPbN39y5atMSDleuKRqIjTURFaRi53LerbCY63EBUkEJk0YaXXl9vV1AKxp5UGxTt0NH39vZQrIeJaYOVuqrDlJ5TSOEB4GnsqZHtS2MchBoMNKbzwheYjASLB54YA0dHtB+ypHXENeWuQENzUyOnPoiKCg5v1ulQd6yWTCYzC4a+skvCrUNEW6opLjWOOtLdj4ujEAknYTLxz/FeCakehQ27Ewn5OEZGaGhwdkJupxIJvW3nrVjoqUgIuru7KCo6Wtd5tCEa1x45SHkl5RQI+MsuwQIuwm6D/VrY1d5Fuzt6aGF8DM2Ni3F6zhhupAH7YtOxoUGqGRqgzHATZZtmpyDlbCIWy3SVoxAi4Qd3Tv/1SpEQQEQUQuJUnPpzojCjV2HHEAjBWWedRZ2dnfThhx9yURI18CS86aabHI8RrgyvP4iNSqEwOzubHnnkEZ5vQwzctWsXPxZC4WWXXeZoW1BQQL/5zW9o+fLlLO5FR4+Pzffeey+tXbuW/4bwCAETHoJ4DfjGN75BH3zwgUMo/PrXv+50vM899xwlJyfT3r17ad68eR6fE70SqlePQr27sItz0NfX69mKPYxxABEQYuBkIiE40kSmHm3kljFHRFFoaHAbH1MxNDRIrT7KC6VlBgcHOGel3sNuc4rmkp6BIYNKicGAKOilq5V7jXoUIh+b2ay9SZ8v6ev10ENsGnZJfGOHZuwS9MPBAsTA2AhnWxyPJxMJQWNDvd9zBvubEAqhgf7g9pr0hKLyRUGfvxWehBAJwY72btrb4VxQLSEpiYxms0MkBMeGB9mzUGuIMVDaJYHHgQMH2OsP4cTiuzr//PNZPHTF6OgoewIi5DghIYFFPQiF1dXOBblWrVrl5JSzevVqOnToEL8ebN26lb7yla+wlyHCj08+2ebIpd4PPAMFqamp7FkoREKxTRmyjPfAZ0EbeDzm5eW53K9Epx6Fk4Uez5u/kPQMfpzxCR54nSGsByv2QgQU98oVfKVICIrSaDBaGxOejLwi6X4M3Tz4nCqnNTkLRu9SbxgdGabaSngw6HulLTIyinqDqeqxnlbuAxAIHmKB0p1dkp6Zpfsqr0aTicwREbNil7SlJ2jCLgk3mig105ZbMRhATkKlJyHAY2yfTCyUy/lEIaEhFBFEObSnS93RI5SYkk4RUcF7LhBuDE9CiIRA3AvPwsiISGruHKQGu0gIso1mTYYfj9slOlqYR/gvPPu84ejHrr0GEXace6J37+0hEARhL2ZkjOdLhu2C8F6E/Kr59a9/TY899hg9+uijLBZGRUVxqDKKs3kKwoPPPPNMvv3xj39kjz8IeXis3o/SfsJ8TW1PYZtygQniY25uLv3ud7/jz4Tn4EnozfHpGe31Lj5auUfF466uTl2HWuLHVFBoK3oxJcL4dmWUuxAJebtGcscc2rWVypeuoUBkOjlWppMvzhIbS1FpRVPm+5nu+2uFlIycSdMV6IGQ0DDq98TTWIP5gZS5f6Zqd+jgfsootSVv1zIiVEtU3JX4B2XSdnd2CTyosnNySc+kpWdMmZduunZJZ3QkaYHe7k5qb26knKLxYiCBgrf2hbpwSYzZQN0DtkULsd2dWJiYN4+OdQ5TSMiIbm2SkJBQSs8Z95bRKwg7nqn3mRZyKQtR0JVYuLu9i5T1ryESZho97CsDDJELXFcehXBC8CL8l3MSKkXC8AiiYXv4MbaHGnxeyAQC4UsvvUQPPfQQffGLX3R67rzzzuMiJchdqGT9+vV07rnnOkKVIcQdPHiQ5s51jkzatGmT0+NPP/2UiouL2Ubdv38/F0W5//77OUQZII/hTME+4SEJkfDEE23C6ieffDLj/eoJ3VY9RngLFGw9093VRYcPHvD8BTC+IQIKYISv2zthxb4yOlJbhpnOPchAV2cnHas6THqntvIADfhAJNMySO4bFW3x+X7RJ6hvxzv3z3/qWyeE86jB82jXaUkMihQVYuUeua4k/kOZi8mdXdJQXxf0ObimYuf2bd55v3phl2gF5IgNDdO+ea4WCS9dW0CvXnkC37uqhqzmyL6dLBDpnUN7Pie9ExkdMyuRP/60SdwBURCehQKIha8dradDg6NBIRI62SUy0sE1rqobn3yH+2rIPuKtt96i9vZ2zv0HrzvlDbn+XIUfQ+x79913acOGDbRv3z668sorqbGxcUI7eAiiqAiEOwiOKDhy3XXX8XMINzYajbztyJEj9Oabb3I480yJj4/nSsfPPPMMHT58mN5//30+BonnaN8SmaZHIZ5HaW09Mzw85H2Yk9ooHx51MsY7U7XnoZmaqW8PDjA6Nqr7AhZi9d5q1XlOpJAQSgmisDdPcv8IsF2s4HdaEqg7CMQ1g1i5H9b+Z9EywwrPCXd2CbyZ9V6FDyKhu/OjF7vEFBHpVdV5rYiE31xmG1dwj0ImU4mFw8ODpHf0ngpFEJuYTCazfuZsarFwaMwaNCIhMIhIBz15FM5EJBSeg7ifRbEQQuAZZ5xBsbGxE56DUAgvv507dzptv+2222jJkiUcJoxiJ2lpaex9qOaiiy7iaE4UOLn66qtZJETlY4BQ4xdeeIH+8pe/sCciPAsffPDBGX8e2FN/+tOfOP8hxM4bbriBQ6UlnmPQa3XBdEXsvV6JjIom43QGGxjlR5udjPHRsFDNGeMCaYjZCh5ERAdP8vTpkpCcRuEarCDnayr27gjYcPzZyP2jFglBDg2TCUasvRiIVnHkAgoC0VPLKHMxuVugW7kqeH5z0yUvv3B6Y3IQ2SXWMaumPUu3VrU5iYQQBc8oS5u0GjLul+TE09K88bzZ0ZZ4ztGnd9KybMn39Ux7cwMv5CanZ5FegH2yr7PHSSQMGRvTvEgIZO5kN3RUuRcJBeKxaIf7uFyiuJn3E3//+9/dPgeBT0TZ/OhHP3JsRwGTN954Y9L9rlu3zvH3008/7bINCo6IAioCZVQPREh1lM8ll1zCNyV33XUX3wQQPlHh2N1+JZMT/B6FbpK87tu7mzra20nvHoWh0zFGK52NcRA2OkaxjR2kRRpqq0jvIHm8JS7R34fhf0JCyKrzKotMEHoxuArnEZ6FapEQ7RL6e2hoQBsVUidD5NyUOQoDY/ESIpg7r8GN6z8mvTPtqvNBZJf0dLVTT1cnaRWIfVefWuwQCd3lIMR24VmI9kqRECSmprM4pHfkpNaW005v6TNglyhFQmANDeWqx1pH5Cgc1VMxE0+A2Jd/unuRUKD0LER7H4iEEom+PArtA4q7lfuB/n6urqdnWpqaKMYSy2XIPUadIDw8zGGcJ9S38b0WV/C1wnRzqEz1uq6OVupsa6Hsgjkzfn91cuhAyfviCZ1tzRQbn6SrEBdXZByn5OnHu+CJq0Th6hV7iIRod7QjkiKjTJSsOCYtXcvqEB+9TbICDSHUurNJICRK73aio1WVlJXtZeqDILNLRkdHyRDuRdJ7P+OqXzxnQSblJUbRvMw4l8+LbfA0TLOYXbarOrCH5ixcPunvYjo2iaevCxQajx2lpFR9R0FFx8Ydl3x2gVLwRL14aQwNcdgpNUMDlBAV7rBntHQtC0R6CYQe76vvpczAqinjXwrPIEosmlr8g1joI09CiUR3QuFUOQrj4hM8r6wXpAwND1O40YuwOnfVjRXbtWiUF5cvJr0jV6xtyByFNkaGhyhYUYuFrkRCkJObFxS/i3GPQikU+hMh1LqzSVApMCNTP2F1PiMI7ZKktEwKIe17dUP8m0k75AuW4rnERggNB4EnnSe4inCAXaLc7ip9iibtElnMxDWein9SJJTMMkHr0y9WntwZ5bGxcZrOAeMLikvmUFxc/MyMcZCfzAnDBTDKtRLuAyGg7qis9hsRFUMJKemkd9Ky8yg61jn8SY801ddSMAPjGiv0SvBYaXQ3NTbQkcOHSOsY7DkWlcU0JP7LUejOJsFYlJAg0z8sW7HK85MapHYJ8rH199mKL+mZjLwifx9CQFA8bwnpHYiE7S1NpFeREOA+rrPF8dxkhdm05FEokUgCl6AVCsUqhbswH+Qo1DuHDu73bDWnvde9MW4HK/Vqo9zUE/j5veDF0d+nPbf92RDWg9mLzFM6Wpqot1sbk8nZBN4s+G3oKfcPHiuN7nCjkb2utY5YuR8blbmAAtkmaWttocZGxTirQ4aGhqji8EHPGgexXdLf2ytz5RLRYH+/9CgkomOV2l+w8sU4hpD8YKZ5YNCtSCiI7+ui+bHjcbpo36XBtCJCKJR2iUQS2Og29FhC1NnR4dH5qRwhikuNo/jGDja6O6MjiVzkxBBhPTDG21PjaDA68KvHIrQlMkqbrvu+ZLC/j3q7Ozk/30zRYr4UpwI/bgoN6Ik5C5YF7QRtstw/ynCeqMgoio/30OM6gBEFvWSIT2DbJAMDA2Q2B/6YOZsMDw3R0KBnC1bBbJcYjOEU5kZQ1hMtjccoNdPLfJVBZpOAvl5teo35EnNEFOWVzKVgJtlsonlx0bS7o8elSAiQvzU1JpLtVNgraB89EqbZYibDMvRYIgloglZFE5Mi4U2hpnz+AtI7UVHRHosiHekJ1B8TOaWRDaN8IMqsCWNcDFZZ+SWkdxD2FqzCkDcYTWYKlYsLdOzoYUrLyuPzodfcP6WWKApXFRTQYlL8MIMsZhJI6VDc2SSpaem6T4eCccgSG+vxOQ1WuyQlI8cxkdYz0iKxEREpKz2gGnr14f2UP2ceBTPz4y2UFmFi0dAVMRYLR3vAbkk2G122C3SbRDkOHo8CNRKJZPqEBr1Q6CIP4eDgIHW0t5PemVNa5lV7T41srRjjYKC/l44e3kt6Jy4xmdKy80nvxCWmUIzMUchh6MFmwE2V+wePBWi3v6uXDh7YR1rHYPdMksVM/MvIFMVMmpsaSe+YIyK4iJDe7ZLKA7tpaDDwQ6RnmzkLV/j7EAKC7MJS0juhoWHUrxPPSnciIWisr6euzs4p22kld7LMUSiRBDaheswH1N/XRz09+hhwJmPL5k2kd5AfAwaI3ulqb6HWxmOkd9qa6qi1sY70TmS0JajcOTzJ/eNKLOwP164hrg49Hh0L7vxOWilm4i5HYWNDve69upGjsab6KOkdaZfYqNy/08/fRGBwcNdW0juIeGG7ROdw7uQgqP5scEQ6SLvEFdsat/m0nUQyXYJeKHS1eo9cQRERkX44KkmgEW40UXxSCukdLmYSZB5k0yEkJJTD3/ROUmpGUIUdi9w/wF3uH7VYiPYryrzzug7kEJ+RICjMomVGp/AoRL/jTkTUU45CTIT1TmJKutsQdT0xKL0qmSBas5sR6TkFpHcyMrMoKVn7cxaRWiHYIld8wVPbn6KL/3UxPbvr2Unb4Xm0Q/tgIy8vjx599FF/H4YkmHMUis7HlVGenJLCNz0wWa4KU3yGJnJZzCaYnBlUecj0iMFogkpGeicyxiKrsBFRU10NRURFU3xSKukl949Amftn/749VGopdzynxf7SYZBrsDJiUC5eusk9t3L1WtIDk/2GesZMZBgN1+TvzJeEhoXJolpEFB3jeb7KYCY5I9vfhxAQVOzdTnOXrCY9MzQ4SJ1dnZSVNX5NaLG/dOQolB6FEzwEn97xNP/96DabUHb5/MtdioTiebRflb6KlqQu8cl3c8kll9CLL77If2PxMicnhy666CK69dZbZYFYHaJLj8JDBw9Qc1MT6T0xcIis7kp9PV3U2d5CeicyOoYscQmkd1DcRxZ1sYlLwbjS62lOH9GutaVF8x6mjhCfIPw+g8mjcOP6j0nvcMit9KSj+ppKf38VAUFqlnf5KoMVWdhGIkAhk462Ns2fEDEOykgmZyD2Xb/kesdjiIFqz0KlSAjQ3lcioeCss86i+vp6OnToEN14441011130a9//WufvodEGwSvUDjqvpjJwEA/GU369iJDiE9rUz3pHZkLyEZHS5O8HpC/tLeHOtukcBwTn8DelXonMjKSDXMtI1buh6VQGBA5Cl3ZJBCjtX6d+YKWxmM0KkPkJXYq9u2Q54KIGqRwzKRJ4ZiMJkT/BI/4LYuZTAQehO7EQlcioSuPw5liMpkoLS2NcnNz6aqrrqIzzjiD3nzzTXr44Ydp/vz5FBUVRdnZ2fTDH/6Qenp6HK974YUXKC4ujt544w0qLi4ms9lMZ555JtXU1DjaVFRU0LnnnkupqakUHR1Ny5cvp//85z+THk91dTW/Bu0tFgt985vfpMbGRq/2iXDme+65hy644AI+/szMTHryySed2nj6+d555x0qKyvj9xKiarCiA4/CiTl/YmPjdJ+jcHRkWLoQ41pITKbE1Izjf4EGJEFgfcwQmaPQRmhIqPRAI6JFS5ZpPgRQGORYFJEEgE3iIg8hRELkntI7OEdhOs/TCArLFvr7ECQBhLZ92n2HXEwhioiIoPJ5C0jrhNkjHfRkl2BBsG+4z6PbBaUX0NWLrna8FuLg2lfWOomEeB7tPNnfTCNjcN0NDQ2xPfyb3/yG9uzZw+HJ77//Pv30pz91atvX10e/+MUv6KWXXqL169dTR0cHfetb33I8D+HtS1/6Er333nv0+eefs9D2la98hcVAd797iIBtbW304Ycf0rvvvktHjhyh888/3+t9wity4cKF3OZnP/sZXXfddbw/gaef78EHH6Tf//739NFHH/F73HTTTRSsGPQYehwdHUPGIEyY7U2eCnNkNGUVzCG909XeyhO38LhE0jNRlljNh1j6gpjYeIqSeZFoYKCPejo7+Hzomb27d1FmVjbFxdvOQ35y1IQ2gZ4fSIyBMvQ4cG0S5GlKSkqmYMSb30d+SbnMGYwK2LVVlFs8l/ROena+vw8hICiaK4Vj0FhXzQv7ek8Ps+GTj2jtiSc7HqvtkkC3SZS5evUUetw/0k8rX1457dd3DXU5PX5y+5N884RN395EkeHeF3HFvBDiGzzorr32Wrr++uudPPTuvfde+sEPfkBPPfWUU8HYJ554glautH1WCG7wvvvss89oxYoVLNThJoCX3+uvv84ei9dcc82EY8D779q1iyorK9nLD0CELC8vp82bN7P3oKf7XLt2LQuEoKSkhIXMRx55hL7whS/wNk8/329/+1sqLCzkx9j/3XffTcGKtl0lpmmU79u7m/ROT1e7DLFEVb3+PlkN1L6qZ5WhbzQ40EftLePu7HoFXmh6Wul1BxI5YxU1KEJ8ZDETvyKEWlehx52dHXTsWC3pndrKg7oXAUBPd6e/v4qAQC5u2GisPSq96ezePtKrMHgKNgFZZC0weeuttzisFqHDZ599NnvvIU8hwnlPP/10DtuNiYmhCy+8kFpbW9nLTgDdBeKdoLS0lMN19+3b5/D+gwcexENsx/vgOXcehXgOAqEQCcHcuXOntc/Vq1dPeCz2ATz5fJGRkQ6REKSnp1NTENe9CFqPQlFJyZVRLoFA1k9Wq8yJhMEqXFY9pt7uLp6g6d2bDv3GQF/gr8bONrgOUPVY7yQkJZFR4/2D9CgMDEbH3Nskg4ODbJDrnb6ebn8fQkBgMslrATTV11ByugzJ7+3pkhEf8KwsX6z5VCC+IBjSVDiKmQzrx6MwwhDBnn3e8NLel1x6DSLs+KK5F3n13t5w6qmn0tNPP83RlxkZGfx9VVVV0TnnnMM5CxFanJCQQJ988gldfvnlvKAOAc0TIOgh3Bfhu0VFRRzW/I1vfGNGi/K+2Kenny9clR4Fc+dgjsjzqse97777WCWGypqSkkLnnXceHThwwKlNQ0MDK7BIgolkkEuWLKHXXnvNqc3GjRtp0aJF7Nb57LPPTjjhMJiPHj3qtB3vhZLd3q5Eqr9QUD5f+/kdfCGQmczedRzBSHJ6NkXrPLzShlWmKLRP4l3lENMbI8ND1FAtK29GR0Vrvjq8IxfQ2FhQGjNasUtG7cVMXNkkSYlJlJYuc+XKxQkb+aXSRpWMYzJHIgZQ96cEnpVDgwO6Pw8xQVBoTkQ6CKcePQA7AuG/nt5e2f+Kk0gYaxp35MB2PO/pvrwN14edBMEtJyfHIepu3bqV7ciHHnqIVq1axaG7dXV1LiM6t2zZ4ngMewx5CuHtBxDuC7vpq1/9KhcOgV0Gkc4deB2KoSgLouzdu5f3Cc9Cb/b56aefTngsjsvTz6c3vJoBIYnk1VdfzScWyi3itL/4xS9Sb++4B85FF13EFwXiwhFT/rWvfY2r0yBxpADq7O23304vv/wyG/nKLx/ggr7jjjtmJfQYnVJri6xqGpuQRDE6z8sHqg/vo74e57wPeiQxNZMSktNJ70RExVCqrKzHfXBfr/Tu6e7uprpjzuOT1ggNHfdgC0ajXCt2yWTpUFpammWoPxFlF5ZO+/wGE/t3fObvQwgI5sxf6u9DCAiQr1KEauqZkeFBLsSodw4e2Kf5Rb8wkTtZpkRxiavqxp986xO31ZCPBxAOYV89/vjjXEwExTyQq08NFkORz3DTpk0svkHAg/CG/IQA1ZD/7//+j7Zv3047duygb3/725OmFEDFZYh/3/nOd2jbtm2c6xA23cknn0zLli3zap8QFH/1q1/RwYMHueLxX/7yFy5o4s3n0xteCYX/+te/+AtHAkkkjUSZaMR/40IQbNiwgS8QXBAFBQV02223cby4sg0MeKzoYx/x8fE8GVOCxJB/+MMfaPfu3T43ygcGBqirU+Z/qT1ykPqlEEBjY6NOE2m90tHaRN0dbaR3+nt76OihvaR3QsMM0uOYiMMutJ6jUDkGBmPOL63YJcImERMkJc1NTbqvbIqJ78Gd414Iej4P+i7VME51xX5/H0JAULFvBw0Pa3sc8gUowihDX4Ijd7LBkaMw+BYvZ0MkvHz+5fw37v0lFsI2evjhh+mBBx6gefPm0R//+EdeVFWDEN2bb76ZxToUD0G+wD//+c+O57EP2Fhr1qzhysRnnnkm217uwCLt3/72N37NSSedxMIh7Ljp7PPGG29kb8fFixdzoRK8Dm29+Xx6Y0Y5CjvtghviuAX4kvDlffnLX2ZD/NVXX2Vx7pRTTnG0wao8XD1hOCMWXLiOCnBhQe1FZRok1JyRUa5ahcNgG+FhHH0wMzIy7HLCojdiE5IpPAgrYHvL8NAgkTwNFBKKKZq2V2p9AfrNjNzxZL16JTomhgoKikjLKD1RbAVNgju0PlDtksmKmSB/oclkIj2D1X/pNWUTCpPStJ+DzBf0y3zB4zmwZLE5SsbvQucVj0HZ3HkuU1hoCVnMxDXbGre5FQkF4rFoh/vFKYtpSap7sc0bsNjqjhtuuIFvSpDWRQ2iNnBzBdK7vP/++07bEBWiRB02jBBoiIXu8GSfwGKxsP033c93ySWXTEg3gxQ0WvfwnYzQmRh1KCMN4xnKqwBfAFw3ExMT2fC98soruUQ1XDqVIT6oItPc3Mwunq6AigtPgY8//nhaxydWKdSGZ1xcPJWWOU8A9EhCUiqFG/U9MQHIRycnJ8TXgizqguvBSHEJyf6+LAOCg7vGva30PEHTejVagz0XULB6FGrFLpmsmMmy5StdhiTrjdSMHNI7mHCYI+RiNoiMivH31xEQJCSnycKM9uI2na3BW13UUzo7O6i3p4eCIvTYnrtXYgNi31ULr3IrEgqUnoVo7yuRUCLxmVAIpRYhOH/605+ctiPHDxJMosQ03Dt//OMfcy4g5AVSJ8qEm6g7sJqPGHSs3nvL0aojPCEAg4MD1N/f74hVP1pVSXUan/j5Opmsnqk/eoSsY8G7EuBNzsooi74rHgOEoUMslEiEUNjc1KhZwQGe44MDfZScnMxCVHtz44RCH8FEINslqGyM/ZuMJurp7ubQMbEKvXH99BZEg4mx0VEySO9+zsMGQURClJlfLE8DUmCgCrb0pGOxVCy46Jn+vj7q06C3LcY73Ab6+yg8LIy9+63WMaqvPsLFKSQ2frjoh/TiWS+6FQkFeB7t0F4imS2mpRQhVw9Cbz766CPKyhoPkaioqKAnnniCDXXkCxIx31h9R9JIb5NC/vznP+eqM2+88YZXr4NnVGhoKA8qzY2N1N3ZQSmpabx9++dbKTIikmLj4qn6aBX19nRTVHQMzSkto8ojFZyTCs/B62BocJAfG00mrysGzZTK5tkdBOprKikhRRav4ByFMkk0NdfVkCkikleu9QwSKzceO0qWeFnoJzUzl/QO+n2MAZORnxw1a323EJIG+ntpZHiY0yRg0auztZlFwLSsfGo8VkXdHe0UbjJRfsk82r9zM78mJT2Lq2W2NtVTUlKSLSQ3NMQpJDeYCHS7BFEOsEtgW1QcPkRDQ4O0eOly2rXjc6qqPEKW2FjKLyjix7jmcvLyKYRC2HsENkh6egb19fbyPvD4eHsgzrZNMtDXQx0tzWTReZG1sdEx/o4lRId2baXypWt0fyqa62spLTuPDAZth5vOlOjYeFn9GbkaIyJodHTMY5vEl/23sElgf6AC9cjwEPfZrY11NDjQz6J2TFwCHT20h9N4ZOYWcrvmhmP8utKFK6i5rprCDaEUExPDUQ6Ye6TGIP+kROCph2CgeRK6Cs0NFCarrCxxj8HbDgIJwRGys27dOsrPz3d6vq+vj+/VRg4Eu8kq2rgjOzubjf9bb72VCgs9z5eVmpZOtbU2r8HSueVOE6PlK1ZRXHwCJ9uEODg8NEQj9jDlyMgozmGICRlWt+F9ODQ8RHPnzqPq6ipqa20ls9nMxv2nGz7hSVtGRibFWCzUWF/Pj9MzMtmLEcYeHsODgJNTB9pqYKAdj5/InzM/8L4bPxCQ16gfwMQ8mHNNeEOInKwyy1asmvY5xLWEKo1hhnDq7+uh4cEB7nujY+JYkMZYE5uQyONjfXUlr64XlS+m+uoKXsSKiIyinKIyXnE3GIwUl5TC44rJHEFR4bG8yJGWlcf5JEVRpvIlq52OAaL3oUOH2GiPiIxh78JgQit2CXoVFEjBey5cPG7cw56wWGKpsLiEP8uSZSvYLkFaDCxW4vvGPZ6rrztGPT3dHJqal5dP27baROGCwiIWImtrqllknL9wMR2rreFtUVHRlJySQh3t7RRuDKeIiEiHyBhIfT6uT3xmvWM0R1BmnrbzovqMALo+/UloqMxRKPps9I16Jys7Z0Z2KsYF2Bqwd/t6ulj0i4y28IIkihrCLkGlbRTRGezvI0tcAiWmZVLlgd0s1GblF3Ob7s42tktiYhPYFomKsXD/BbGwsGwh2z1ijElRpJXg6vbvv0c1NTW0YNkIOygkRklPUYlE80IhwnpefvllTiiJlYCGhgbeHhsbSxEREVRaWso5f5D/58EHH+TwX6y6v/vuu9MuSnLLLbfQ7373O6qsrKTzzz/fq0ImrvIBoZAJjhXw6r7ZTCJTX3pGhlPbOEUIEpLHKlm15gTOeSSSDMfGxdHQ8DA/h8lAR7utguzc8vkcVoROHSIivAYO7LO5WM9fuIjFx66uTjKbI6hkTil7OSJ3Q99QGHe4KDKBzhYrib5cZS6au8hn+9IyLQ21tkFL50BMkJM0W87KnCJ5PYCGmkpK1LHXsQiT+eiD92jRkmW8SIQ+vr2t1WZI5xewF3p3Vxev8EOs2bp5E7X3wdMvjw1qiIHovwtKF1BXWwuPTebIKJ4AR0TF8PVmjojiPh+FlUQfn1fiPN7g9UoiuPqjHQ88otlremTEaWwMFrRil7grZoJJW1JyCv8NewJJ6kWieoh+KKgjKJ7j3DetPfFkx9/CxkBIM4RA5GNG2hWIDHiP5uZGfg6eibBdcO0KIRw2SXd3Fy96lpaV097du/gY0jIy+L6zo4PaekY4RQUXvqIQvnZ96eFkiU+gmNg40jtIFdDb3UnJaTJPYZr0amfScwplShT2rO+jrvZWirbos58Q4mBDQz011B1jGyQhIZHzKGMxCQ4wWBiCxzqcXgqKinmBCAtMHX3DVLZ4FeeeRiQVcnHHJ6dSe2sT9+OoKB0ebmLRT8wF8ufM4wVIIfapFyETU8fnzOpopNDQySMxhK0T7HmTJRJdCYVPP/003ysrBYLnn3+eXU1hUL799tucvwflqXt6ethAf/HFF+lLX/rStA4Q3oAos43Ve09RlltXh+fs27ObVq89kWaKCCESpJjHO8nMzCy+CdaccJLTa5NOPNnR4ZtMZkpITBwvvhIayh3+0JBtMtFQW2Vz384roo6WJupobeLcgqWLVtC+7ZvYOwGFSWDAoy2eS8nMoeHBQTY4MUlFBw7DEx5CGAgwCNQdreBBQO90dbb7+xAChkDyLvEX+F0izCe7YI6/D8X/aOx6YO+90VG+jiHk9fX30cjwCCUlJ1NDfR2LJlgYSkxIpIMH9rNYkpOby9VvsUADVqxaQ3t27aDe3l5eKCosKqHWtlaqra3hPl2ktEDfj/eBR3lIVjYLOhhrINwoQ3yUhnRatrOnW3ySTRw6fvloB3mCEGxoxS4R515tk/T29tDRo5W0IG4xzQRcj7g2xUIorl/lYicEQCUZChtlwaLFTuJ4Uckc/g3h9wL7A3YGvE0sYwmc57K3p4tTucAuObBzi8NjBII30prAU6Vw7kIORxsaxO8uklKz8qi5voavxcgYC78eIWl4jEXR9pZGLqoVG59EegZex4P9/f4+jMBAY2PQbAFxDDmk9V7cBX0For20BPpO2CUYh7o6O2l4ZJiM4TZ7oampkftZLPC0tbVSS3MT978iag2vS01No8TkZF68QR9fVj6PF27q6+o44gBCIc4J0lFgwRK3wqJi24KT0cjPYxFT2CVzFixzOr6cCY4SMcclj70YB4PRJpFIdB16PBXFxcX02muvTfuAXL0HVu9x8xTlCoV69T5QQizFMWDCqRQc4VIORKeu9CSJirY4haSULVrpMOxxw0QUnx3FGHCPUwlDHLQ1N7DLPlzDsRp39NBe6uvtpuyCEurt7qK2pno2yuYuXkWH93zO+0OeiYTkVGqsraYwQxjFJ6fxgARX9NAwA8UlJlN/bw+FhIaw+zkESLxOS/l1ZOVnGx2tzezNgQmd3unp7PD3IQQExeUzEy4mF/RGeKUaIS+YGMMgRngLBD14PcHDGp5UKEwFoSI7O5c9r1GICm2x+LJ1y2c0ODBACYlJlJ2dQzt3fM79PUI4YXy3trbwSnlikk14gEcg0kvAeM4vgIdGOAt8eE12zng+RngPKlm6bAU/L8YSeI8LoqK1kVdHHHswehRqxS4ZsVd3VOfExTVsRrGCAAB2CW7RqusanppjUTabJD2nwOk5dQ45LExicoz9JKVl2j0Qxyee+O3DPoEt0dJwjB8jlO1YpS08Ht7tCFvb9/mn/Jqk1EwyR0ayAInXZxfO4fA4TohvNLH3bnNDLYWFhpE5KpqMRuSXHuDzzEUgIFvaP5dWQB8lsS2U69mrXQCR3mRfANAzkdExZDL7Piwf/Tv6LOvYqC1NSG8P90v4HaLfqK1p5b4Jgl5zcxN1drSz/VJaNpc2b9roeC4mxkIH9o9HjMGTz5ayKoIWL13GKazQh0G8g/e2WHTEAktiYhLFJyQ6BDRErbnzHofXINJaCbtFab8ApRd6oCLGQTEuSiSSwCQoy94qq2KphcLy+c4hXFpHaQArVxuxWq90z1d6SGGijfwTwqMQiWjTFd4u8AQQJesh+sE9HYOmWM2DS/vYQD8LhRAg4TGAUDi0O7J/F4diZ+QW0fCQIoHtguVUeXA3DQ30U7QlnhMzV1fs58EWkwkA4x/fV3JGDv+NlSZMBKJiYjmPBhv+RhOFIBcXBN/Q0BmLkrMlhmgPTIS1M5GaLfBb0mMINhvKo6P8m+P+JCSEqg7upZSMLE4gPtDXyzeA32tddQVP9rF4YYqM4hx6MLSRuBqic0dbM/82sZixf/tn3H/EJaaw53P14f38W4Y4EG8ao47eNjIZwik/OZs27ahhJxLzsIEixoZoODyW8jItnDIChnVmVjbvF8e4bPlKp8+g9txOSx/35oMRrwT78pT4hAT+bOqxREsIo1xr3hjBhBBp1deRJTaOC6oFE2JchlBnE+tsiLFeoLRR4JGI/gG5stQCJPon2A3oR9A/R8cm8KIW8mzxQunYGA0OIxeoweYVXlfNNkx6bgFHYrSLSIyFy2nf55tozDpG8YkpvBhafXgf2yEQIGFnoPgdJuuwkdDPwTsSAkVEtIXtErSNtsRyX4nvFI+RMxLHFhJis0lmIkpa4pPI4r7wtkSHYPEf16HewLg7NmrzaMZcoLerkxqOHaWMnAIOlUWEFX6HsEO4yFdbM49xadkF7L0sPJ+zCkrowA57ka+MbD6fddVH+HFh2QJqPFbNv32kATllzXLaXX+IDGEGSrKksIfenopm7j+GWvtoeNhAmZnYhy3/Hrz/0F+I33xS8righ9yzSubNX+j0ODJyPG+fN8BbEIueWkZ4K6LflEgkgUtwCoWKalBqoxxVkJG7R89gQJvobj4ODN9Q4/h5i0GlMTtIoq807jHZVwKPRCXKBLbIi2jzfsT3E0JZ+SU2MTDcxIMFJgg2ocI2eEBUhBgB93p4HnCYdXIavx65v1Awpqh8EdUdPcyJ/zHI5xaV0oFdW+3vnc2DETwRQkNCqaBsPu+nv6+XPwe8Mzf+5y3KKZzDxjmqhiKPGIRIvA+KD4zYc0TCy6G7E6uIoWywwFNpeHiQJwZ4LDxOcG615FEpgFcGC7A6B98fiknMNrjOuZACPHBhgA72k3XMygYsrm94y2B1GxPi3q4OrpCKaxnXIQxgvD4Gs8mQEBbr0RbiPK7vnu5ONiRziubS/h2f8fslp2WS0RRBx44e5scFpfOppb6WujraKNxkZsH80J5t/NuH4I9Fh7bmejKg+EFUNHsj47cYZhdR8TzaGs1m/v2iH8DnwO8iUuX5jDQJSuYuGe8jspKjKCsr2/EYv1klmMhbYv1rEKNAREZGFguGWsVgHweVi2iS44tIL6IOPUY+Ywjhege2gtrb0ims2mDgG4BNgJvytUqiFDk+0Veh2I+gbLHzAgMESVHUBtU3Y+ISuT/l/cTEcr+Hvg1iJLwjIUTAw7G/p5u6OlrZ3oTYgCgNeDLCPkLKgcr9u7h/zsor4v68pbHO9v6LVlLF3u28L/Rv6PN48SQUi6TZ1Np4jHq6uiguMYn7dPTDXBnUHMle11iEwflAdAjsKLwnHqPftXlvIqrDwEIGjhURH7BTtORRKZALuTY45+3xWiy02hbF0F/BMx/2CH5DPV0d/DyuK9gMCIfGbyI6No5THeF5EdnUUFvJ22CTowAXnALQ1pYWaYDTu1jtYbD43SBKCdcvvJUR0YTfTXpWHreBTQObGo4NuPaRzgC/A+zblrrA1mfguKJi4rgPwQ2/QdzwPGynuUtWO/0G4pNSHX/nFpU5/sZ7LVjobAOmKnJlQniMix/ve0Q+2eMJPsf+fXucvAy1hhgHlanCJOPUD9RSunk8PchM20kk08UQzCv3atEG25EHQu9AAMOKuzph/vHzgLQN7FiFF4ST0emxOtwkr8Q5v5JykFd/DnXCXbQVIecY8MUKFoxneArEJiaz1wMnkjeZ2aABMM4HB5AMfogFmk6sXiIk2xLHOZVYrBwbo6y8Ys6v1NnewgZJyfyltPfzT3k/EF2QHBiGkjBIYGDBw8HhcbUDHldILpzEXldVB/fwsWEVtL+3mz20cGzF85bQ0UN7+DrGZARtkWsSyeqT0rJYzIHHA2yhzPwSaqqrZkMPxROQ9wkCE/YDrw0cNww7rFInpWdR1aG9LHjCuxRGGMQnLGCjOitAjkvsGHnV8DrsFwM9Jjrw2AAR0TG8/76ebn6M98QqLTxQYaxhAoYVYP7uI6N4G1aIAc4vwj0giOG84LPBIBRtEQLSxQWCrBSbmEIDvT0cjoP3S07P5u8C5xuilikiiidXsDITUtJ4v/w+IRC2S6i28hBXokWeLHipNNQc4esDk014zSHkDcYuBEN4yMIzVkz+8BimJiZwEPdg8OK8lC5cQZUHdvH1Eh0Tyx6zh/fu4OOHty6uuaa6Gn4tro/ayoPcFgY4Vr+PVR7mCR2ue3jKduI7Dw3liaqtEvsQhfJvxxYuz9eq0cQGMURtnDMYyvguccNj3CDcK41jeAEL1EV88BmUQOzDOYGorg5Jx/ejREzggxEOZdR4pUUhwAhPcUngeBQiRD7N6FxITY+gwibEAH+kAxF2IhLwQwgQqPMlKgUbRFEkKOwUhEsrUXpExqrEBozlImUMgPciFn+wUNTZZuJK6BgT0Xej/xmG0IfKt1a7WDmGnGRmvu9oaebtGCdRnE0shKZl5VPFvu28AAUBEuNHfW2VY9EWXuDwuDKbI9m+ciwqpWfzd4AFWAw4haULeOzq6WpnuwfpcFAQAceG8RWCTWNtFdssGDMgniKdDcYjRLJABAUQlDA+Y1zHaxNTM3ls7u/rZpEU55bHcauVzy0+z4Edn/FYDJsFubt5zIfNkpbJNgrsGHxWHAPel9/HEsfju/CAR8oc2GYQufC5cAywwQDEViw4wyYW41o3xF+k8DEaKSo6lsUpACEL57mvu8u+31S2SUaGB/l7w/eFz4avFB6n+IxYiMPnwUJdZ3srC2I8TsMr/yjsjjG2t7AY3Yr0P3ahDXYSn0NDOHu6bvvkP3zs8JDFWAy7D21zisr4PODzoS2EN7Y9rVbOXY5oALY9rVa+xlzZnjgvKKQF26Cm4gDbHfgecU7w+WBf5s+ZT51tLXy94Tzg80LwCxG/G4OBvwcsNrONGZ/Ex4B2OG44JuA7hp0gbDZPfjfquUBmXjHbueK3pIyEArgOBGFhMlRbGzaJ9ChUs6V9PW3t3EAr40+iRbHOC1tKtnduok3tH9HS2DW0LH4taQ3klV60aBE9+uijPtkfclJ3dHRwsTp35OXl0fXXX883X5Dn4/0FIkE5uxMrFGqDHJO9CIUYpVdGOURHP+GVyvBsGC64CbCCCXFMoBREbMUGUtwKK0rPShifSq8FtWelUrzE65Rt1QINVj6FsAmDE4YSjE8unpBbxMYxBlmIkjCurWTlojjs6WjPE2nzOIjlsA181zAMcYycQxIu/yE2o9nWlthzksNb7JoSe31abfecUw45L3kDsUgII9E6Bu83Kw3a82BiEgHDEcYwGIsdY+EPnqEinFc8J74D8VoIsJhIc5VvYUDYBV0IgPj8whPV/qXaQ8/DnPaHe07oz1VhkdzfwF4iXLktNMRJLOOV8XATG52Y5GDihbaWxGQ2qkFucRlPTPBeOKdq4U3tMevuOwfJaVluxW2E+ytRXpNq0Vz9WBnaZ1R4As+UjJxCx0RAzyBvrBa9hJWI35QMPfYfImn7hKrHI6MBk6PQn7DHsk7tEmUfHhufyGOTCMGGiDOZd5lyrICQokRtWyiFTfXiq1qgURZbyikqdQibNq97W3QIIjUwFkPMwuNwo5mP12Yb2T4bxESM4bA38P0iQgXyKBex4fzc4/k/IdLabBSb4IRCU7ZzFMJiF9sC9rYQA2HrQyATAiraiJBwYVuAoYEBbo/nISYJO8QWMm7Luw3w3rBZ2JOOj8zqyLHJv197fl1b43H7yP6N2kPPhb1gizaxfdc2+8ImluH3H2Kr8B0SwjZnmH3BT6Q+gY2CRVgx7iSnZ7FtgkVv7APnX4S5Z0YVOUX2zMT2VHr/o63SZlFGCbgqzoXwX8ffMc4htxARfQE+LyKAJETzFjjbjFojzH5ty2ImEz0EIRICiIDAlVgoREKA9pkRuT73LNy4cSOdcMIJdNZZZ9E//vEPCgY2b97M+T0lOhcKx9yE+KB0vKjuF0goq2QeD2CwqI1PPQIDLxCryCknEPC+FGKYekIB4BEpwBxLaZApQ8bVXpj8WsVnx0p6THyiw9ibEMqlMPzUQlX6JAbjhLaqRPjKYzCqqsAqPTCAOWL8mOBlQZTg9n2Un5WNdcX7KAVePgbF8eJMp2bkOM4zJiF65VjVYQ4bgsdDsPR90wETzsGBQUpNG69sPxn5yVEB9zkd+YBk6LHfU6Ko7RIkvQ9Ejvc1m5SWoXlB3hfwYmYAFjNxWnBVjYthYZEqT64It+Ot0vZEpIDStlAKmWJcF95nsG0gngmUXmkTvT0hoCa4TWmhtkOUtpJSHFPvFzgdb7LzmABBT4kyPF5tj6ltcKUnrbraLMZhiKzivOv5d4LUQupFWF/i77HaUxrr6ykWYrOHqO0Sf39Oh0ehDD12AmIfPAmFCOhKLFSKhADtZyP8+Nlnn6Vrr72W7+vq6igjQ/uRD8nJUvvwlqAcbcSKn3rlHhU1K49UkN5BDhJZ7de2GgwxRGIzum1eeJJAFI/9ATwfpAcaPGhHqJ3D3rWLw0tXhvn4DXHu1XbJxvUf++mIAgs9eRNOBnIUirBZvYOc0xLbArF6gUGib1pamh25VbVtk8h0KGogCkL8E0AUhDjoTiScLDx5uvT09NCf//xnuuqqq+jLX/4yvfDCC47n1q1bx4tG8DJcsGABmc1mWrVqFe3evdvRprW1lS644ALKzMxkJ6358+fTK6+8Mul7/v73v6dly5ZRTEwMpaWl0be//W1qarKlqxLs2bOHzjnnHLJYLNzuxBNPpIoKZ13nwQcfpPT0dEpMTKSrr76ahoeHnUKFlaHOCFW+8sorKTU1lT/HvHnz6K233nI8/9prr1F5eTlXJ8drH3rooUk/w8MPP8yflauSZ2fTD3/4Qz6XWiZIhcIxlwY5whj8kXg20ECukf4eW54VPQMRxF3ydL2BnHnIJyghqjlyQJ4Ge/VNU4TM82M02QrMaBm5eh+4KVG0PNnzJSg6JrFdJ3r2GFMiCtPpHeQWtOVolqSovDb1CsQXkfdWi4hIKT1FOSCKbXhsyKNbecxiWhY3nncQ4uAL1Y87iYR4Hu082d94igTPePXVV6m0tJTmzJlD3/3ud+m5556bsI+f/OQnLJwhnBeeel/5ylccohz0lqVLl7KYCAHx+9//Pl144YX02We2XLiuwGvvuece2rFjB+cZrKqq4ryDgmPHjtFJJ53Eot37779PW7dupcsuu8zpd/DBBx+wcIj7F198kQVOpciptr3OPvtsWr9+Pf3hD3+gvXv30v333++w0bD/b37zm/Stb32Ldu3aRXfddRfdfvvtbvcHMHb/5je/YUET74/j/OlPf0paxqAngxzl5M0RMhcQcrWESa8pztWH3DoS2wCmxaqIktmD80p6Z1sEJRZLLFnmOudc0hoyH1Dg2iWpac7hlhJ9g0JYyJ0rcWQk1j3IR6j1xSpfIT2PbSxasoy0jPCQ1VPUyoh1mJ6rfmzarx8cG8+7CrZ0rOebJ1yWcx2Fh3ie0gLhxhAIAXIUdnZ20ocffsgFSAR33nknfeELX+C/IYplZWXR66+/zuIaPAlvuukmR1uEML/zzjssQK5YscL1MV52mePvgoICFtyWL1/OHnnR0dH05JNPUmxsLP3pT39yOH2VlJQ47SM+Pp6eeOIJtrMgdMIb8r333qMrrrhiwvv95z//YeFy3759jv3gfZXegaeffjqLg+K9ICb++te/dhIwlSiLmsAD8d5776Uf/OAH9NRTT5FWCcplS6Euq1314VYKsVDvIPcbKtDpnZGRYa4SLLHlKETFZwlR3pzjXw08EEHVRVEdUu8Cz6aNnhljgYqoSq1lD4SgCT1W2CVYoElMdK6sq1fU1U/1SkertkMKfYk6D6FeiU1MpjhVjmm9gmrdEqL9+/ZQW1ur5qMcpE0SeBw4cIAFNIQOCy3l/PPPZ/FQyerV47lCExIS2PsQopuwm+EdiDBcPAehD0JhdXW12/eFBx+8EnNycjis+OSTT+bt4jXbt2/nUOPJIkMRJqxcjEUIsjp8WYD9QdxUi40CfJa1a52rSePxoUOH3ObWhPgIcRFCKT4DvCgRht3XZyuYpUV05VF48MA+mr9gke5zfTTX11JSetaEhMl6A5V+ZbitDVFlUELU3tzoVJBFr6D/HBrsJ72DUAKtG7MizGfMnpZDcvwZtYs/SrtkaGiIDh8+SMtXOFcq1RuofNlcV03ZhTInXVdH24SiHnpFKarrmaGBfq7EbEyUifhRKVpCZAgz0NDg0LRPBYqb+LOgSZiwSXQUemwICWfPPm/Y2bXFpdcgwo4XWJZ59d6eAkEQNq+yeAkWNRHyC289T4DX3WOPPcb5AEXOPnjbweZxRW9vL5155pl8++Mf/8ihzBAI8Vi8JsKDVEhqERHzWncLb57szxsQKo38icjr+Itf/IIF0k8++YQuv/xy/gxIF6BFgtKjULgyq/PP4YsymmRIR3dnuxSF7B0fKgxKbAnUhwed3dr1Smdbs78PISCwxCVScpr06IChkZau7WpvjsThOjLKAw1x7sUECQwODpJJUe1Ur2BS0t+v3RV3XxIaEuJ0jegZ6T1mY2hwgPp7u/38bQQG0vPYRnxCIkfJablYnt6qHsOWDA81enzb0/25k0hoCh3/vrEdz3u6L08dQTAWv/TSS5x7EB534oa8gRAOlQVJPv30U8ff7e3tdPDgQSorK+PHyPt37rnncvjywoULOaQXz7tj//797HmHHIHwGkTYsNoTEIVTPv74Y6fiJDMB+6utrXV7XPgs69c7i7R4DA9EtSOa8IiEKIlzh+IuaIdq0VonNKgNctUXObd8vhTI2I04nG96JyomlrILZI5CwDlq5UrteG4+CQ0NDVBbc708E5z3JEHT50GMhXrKB6SFSAesaBcUFpHesY6NUoRMC8MUlS+WnnQyb/LEiA9Z4IZprq+h0VFte/j7guiYGE1Hx0mbZHJcVTe+JOdat9WQfQUq/kL0gxccKgArb1//+tedwo/vvvtuzv+HYiXI2ZeUlETnnXceP1dcXEzvvvsubdiwgUN4UVm4sbHR7fsi3NhoNNLjjz9OR44coTfffJNDl5Vcc8011NXVxcVFtmzZwiHAqJSMUOnpgNBmFEfB58KxVlZW0j//+U/617/+xc/feOON/PlwHBATkYcRHpXK3ItKioqKWMQUnwHH9tvf/pa0jq6qHjc3u45T1xt5c8qlYGrPBVR31Lmsul7Jyi8mc4Q23aJ9zZwFy/19CAHjcYs8hRKifXt3a/o0yKrHgSkUdnV1sleh3gk3mikzr9jfhxEQ7P183EtD7xTIvJVMXEIypWXl+fvrCAgG+nod+V71THd3F9XWus/3pp2qxzIdiici4aLYlfw37mdTLIQQeMYZZ3DREDUQ1CDQ7dy5kx/D+++6667j6sYNDQ3097//ncU+cNttt9GSJUs4dBgFUNLS0hwioisQaoxqwn/5y19o7ty5vO8HH3zQqU1iYiJXEUZxE4h8eN/f/e53k+YsnIrXXnuNC6YgHyPeFxWKha2G43/11Ve5eAqE0jvuuIPFUXeFTOA5iQIoDzzwALdHCPV9991HWke7yxHTyFHY1NhAxSXH14PMnzkg3LF/+2dUvnQN6Z2x0RG5SmuntbGO8yKZzL7N2aBF9n2+icoW2wZlPYMcpsfb8xh5cwIVLVcGV+YDOtAQeGOSHnBll3R2dPg8T44W7ZKu9hZelMjMk96VVjlxdvS3TceqKadI5q1EUTHkrsSCrt7BgjauDb3bJRBk3OV70wLSo9A19QO1bkVCgXgs2uE+1ZRJ6easGX8vEPvcgWrF+O2tW7eOH59wwgnsTegK5Od74403Jn0vsR8BxDpRQEWg/q0jXBhFUVwBoVENciSq8wiqj/O5555ze4xf//rX+eYO9f5uuOEGvilBQRMtE5QehSI5qtIgR9x4uF3plkiA0Rwhi1bY6evpJqtVruxxXyHPg+33YTJTppyYMPPma7sia2hYqO7yAQUaIuxbaZcgH5BJVpvn8yDTodiIT5bVbW1Yqben0w+/1AAkBBNmaZ+BzPwStk30TlRUNOXnF5JWkVEOroHYtzR2jVuRUKD0LER7X4iEEonuPApRrVKAv1esHC/lrWeS02WHAmzCsTY9hHxNuNFEISFBuW7gNQlJcqImFlcO7tpCZYukd2VnZwcnDjdpNHm4GAtljkL/e4ophcKSOdJbSngJhUTKsRjExGo7H6qvgCOJ0SQjHIDRaJaL2nZqKw9SfGIKxcTp+3eCMb2xoZ5iLBbStE0iC6xNYFn8WsqMyJ1S/INY6CtPQonEHaFBvXKvqBzX0d6u+TxTvgBuvHI1zkZ7SxP1dHX4+RsJDLIKiuV1YSc6Nt6/X0YAGXIQCyW2fED9/f2aPRUyH5D/GXUhFG7+7FOfVfDTfDVIWf2ZqT68z99fR8CMP/lzyv19GAGBwWgkc1S0vw8jYFKiSM94W5/Z2NhAWrdJpI3pGk/FP3+IhMg5CC0hLi7uuL+35Pijm6rHSBgeLiv9cqdcX1Ppx28nsARlMVjpnYp9O2locMDfhxEQyInauCGanJbp528jMEB4KMIjtYrMBxRAkQ4Ku2RocFDTlSt9RWtjPQ0OaFeI9ykazYPqa1DZ9uCubf4+jIBgsL+Pmo4d9fdhBATwJDRq1LPf12h57Aizp0ORUQ4SSWCj3V7Gy6rHSPwal3D8XdXVSXD9nUR8dGSYwqRgyiSkpFFY2PEt1hCwoFCDIlRf18iJmoPjHfrlqn8MhETiRcUlM3q9q89wPMcCIU7J1fvAKmaSmprmlwI5gWaXjLBdEpTmqNfkFpX5+xACAw0Xj/I1SAtzvAt4BCpGo4mO95kIVLtk5eq1mh0HZI5CiUQbhOpl5R7lsy2WieW+9YYh3Ei5soocgyqLo6My7AvEJ6U6herrfaImjXIbx6oO+fnbCAzq645RVeUR0iqOqseymElACYUJSUn+O6AAIjOvkPMUSlABulWeBohjoWG8mCshMpkjKDUrT54KIk4X1NXWIs8FEX264RPt2yQyvY1EEtCE6sUgP1pVSX19/l01DwQG+nqpq73N34cREHR3tNGohsMJfQnyVoaEytV7cV1IoVCiDvFBmKhWcazey8ThAWOXoI/Zt0fmTQZNdTWysJid9pZGf12iASmQSWxiSk9nuzwV6D8NBhodk3a7uC60aqtKj0KJRBsEpVAoViiccxQOsMu63hka7Je56BQovU71DPJWygTRNjrbWxwVSvVOQdkCfx9CQBAZFU3RMTGkVWQ+oMCzS9DfajnHlC/p6miTYaZ28RjFGiS2HIUNtTIvHxgbG5GepoochUlpssorSEvP0KxHniNvskaPXyLRC8EdeqzIuVZYVEJmmQAXaV9kImA7eSXlFBEpK8kJZD4gG6FhBrJapfECWupryd8gb47y5g8wdkRFa7evEPlH5WKA/xATImGXoL8tmzvPj0cUOGARV44/tmuidNEKf38dgZOj0N/HEEA5CmXuZBvDQ0PUEQBet4FglyQnp2jWo1DYJGMyysElfVu3+rSdRDJdglIoFHmYlB6FzU2NTsKhXolNSKKkVFnJFBzeu52NDglRTlGZ9GSwUzJvCefylBB1d7Zr1hD1tchzYN9e0n4+INvYKPG/XdLX10e9vT3yq8BC7txF8jzYRRDYJRJbPu3swjnyVNhTwxTJ3wiDaI+erk55XRBRdXUV9XR3a9omkYuXE2l+/Ak6+p3vUsvvfjfpOcTzaIf2gcS6det40aujo8PlY0845ZRT6Prrr5/Fo5R4SlAqZ2N2byClMNjY2ODHIwoc6quPUGdbs78PIyAYGR6S4rGd1sY66UVnp2LfThmeb8dojpDXhT1H4YiG85mO5wOSnrKB4lGICR7EQgnRvu2b5GkQQr5cmGFGR4apvaVJXhf2quCH9nwuz4U9R6HSCUTPhIcbaUijzg6O0GNZYG2Ch2DLk0/y380PPexWLMR2PM9/P/nkrHgWNjQ00HXXXUdFRUUcVZOamkpr166lp59+elLbZc2aNVRfX0+xsbEuH0u0RVAmQxmzT4aUg4nMBWQDxTtkDhwblrhEmaPQTncnCngUHN8faoBiHRuV4pidwrKF/v0yAgSth4kKcUrmAwqcYiaY/JtkOhSJKhzPEp8oz4n999LbLT3H+LqgEBodHpbXhd27EhEwEqLMrGwyaFQ0FcUTpU3iTOTSpZR8448dIqC4T7riCpciIUB7vM6XHDlyhEXBuLg4+uUvf0nz588nk8lEu3btomeeeYYyMzPpv/7rvya8bnh4mIxGI6WljVesVz+WaAvdFDNZuXqtH48ocIiOjSNTRKS/DyMggEEu8yKNh/nIc2EjBgKyPSxC71Ts20GDA/3+PoyAoLVFu57Yjkq7MnF4wNgl2Tm5lJWV7b8DCiCSUjL8fQgBI+hj/JHYUvLBY0oiBWR1P7p/x2fysmBRZoi6uro0HXoMpFjoDERBiH8CpWehK5FQKSL6ih/+8IfsYLVlyxb65je/SWVlZVRQUEDnnnsu/eMf/6CvfOUr3A7zRngYQjSMioqiX/ziF1OGHre2ttIFF1zAYmNkZCSLkK+88orbY7n11ltp5cqVE7YvXLiQ7r77bv578+bN9IUvfIGSkpLYc/Hkk0+mbdu2+fy86BGDHlbuBwcHac+uHbRkmf+TROcnR03YdjwT4RrCw/kmITp6eB+VL1ktTwURFZcvlufBjiU+QXqa2kEfOjYaWCG3rvpLV/2qr0H6iqIS3+XMUh/zbI4DQvgelTkK/YY498K7c8/unSwWWiz+D8c5nteiK3stQsOFgnxJf28PtTc3Uk5RKekdkzmScovn+vswAgJMspFfXGLrPwNxwcsfdsnQ4BB1dLRTckqK5uanopjJuFAY/KWLkO/b2u/ZwnvCd75D1uFhavnN4/wY4mDr/z5LY53jXtZJP7qW2415kMIkJCLCY2cQCHn//ve/2ZMQ4p/L/Sn2ddddd9H9999Pjz76KIuL8EacjIGBAVq6dCndfPPNZLFYWHi88MILqbCwkFasmKjTfOc736H77ruPKioquA3Ys2cP7dy5k1577TV+3N3dTRdffDE9/vjjfJ4feugh+tKXvkSHDh2imJgYjz63REdCoToX0NDgIBkMUhwDx6oOU8l837ooaxaZC8jB3m0bqWzxKulViLwcNZWUmplHkdFycIlNSKYw2XcyWs6LJEOPA8gusV9H/f39XO1X74wMD1JTXQ2nAtE7yFEoi+7Z6O/r4ZzaBaULSO9gUn700F6aKxe2+VwkpKT7+ysJCIwmI40G2ELudOwpW57CoJQjnIBIeGDJ9OffSpEQQEQUQuJUzNm2lUIiPYsmPHz4MIttc+Y4L4zDWw8iH7j66qvpgQce4L+//e1v06WXXupoN5VQCE/Cm266yfH42muvpXfeeYdeffVVl0JheXk5ew++/PLLdPvtt/O2P/7xj+xliPyJ4LTTTnN6DcKjETb94Ycf0jnnnOPR55boKPRYeBQKgxyGV1Kyb1ZctA46ZBlWaSO7UK7aC2Rd23FCQkJljkI75sgoKR7bWbXmBNIqoWH2HIUycbj/hcIQ23cRH5/AuXv0DooEyRzSNiKjYigxTYZhA0xU9eBl5CnSRhsnIlJ6IIO4uHjN5k5WLojIysfa4LPPPqPt27ezcIdITcGyZcu82g++73vuuYdDjhMSEig6OpqFwurqarevgVchhEIxNiBUGdsEjY2NdMUVV1BxcTGHHsNTsaenZ9J9SjwjKCV84ZYuViwgGMYnJPj5qAKD/NL5cuJvp7ergyxx8roACclp8rqwk5KRzQmzJUQdrU1slMvzQbR921YqnVvO1d+0mg9I5gIKnByFCQnIhRqUa7VeYTZHUnqOLKQFkA8WYr4UQlCAMFyG2yrILijx10804KitOiSvDXvhiJ3bP6ely/2fVstblA4rY9bACyWfDRD+C88+b2h94QWXXoMIO0685BKv3ttT4KUHz90DBw44bUeOQhCh2pe78GR3/PrXv6bHHnuMQ5UhFuL1119//aQVvJHTEKHKyDuIaIyamho6//zzHc8j7Bgh09hvbm4uF15ZvXq1ZquCBxJBKRSKTkcY4Q31dXzRRGRm+fnIjm/uH1e0NtZRZIHv8mxpmbbmBjlBsRMVY/HvlxFgkzUsLqDAi94JCzVwpfRAR92vzkZuIIwnw0NDmhQKRT6gQMztpBfUKVH27tlFa088mQIBf9olfb3dNDI8xDnp9A7CbfEblZWPbQv88GiX2Ojt7pLh+Rpjtu0SeGIPDtpCQbWGcpFML3YJxDdPw39F4RKlSBgWG0uj9vBjbA8JD5+VQiaJiYlcGOSJJ57gsGBvhcCpWL9+PRdF+e53v+uwjQ4ePEhz57rPSZuVlcUFShByDKEQx5eiyM2JfT711FOclxBASGxpafHpceuVoFzOHh11NsjhIms0yVxAoKu91a/fTSAhq/yOU3PkoB+/icCip6uDhjRqfPma+JQ0uXJvJzE5WbMeYOK4ZTGTAEiJotFraLYY6O+l4aHxMCY9gwmzMsm/nhns76Pmuhp/H0bA0NZU7+9DCBjyS8r9fQgBM4dJ1mharYnFTCRKXFU3Ltn0qdtqyL4GohvSgiCs+M9//jPt27ePPQz/8Ic/0P79+2eUsxvhwe+++y5t2LCB93vllVdy6PBUINT4T3/6E/3lL39xCjsW+/z973/P+9u0aRM/r/Z8lEyPUD2s3GdmZVNsbJyfjyowCJfJ0x3IxNAKZGEXByEUQtYxmREIDA30U0+XcwJlvYJQUa3mUhNG3Zh9EU3if7tkbvl8+TUAK0nvbTspGTmUnOb/yJfAyVEokUykralBnhY7ySmpQVLMRDKZSCg8B3F/PMRCVBf+/PPP6YwzzqBbbrmFi4lANERVYRQiQY7B6XLbbbfRkiVL6Mwzz6RTTjmF0tLS6Lzzzpvydd/4xjc4vLivr29C+2effZba29t5v6ig/KMf/cjJ41AyfbQ565kC4TUh8jI1NzVSbl4+6R0YXsXzlvj7MAKGAzu30JwF3iVhDVby5Aqtg8z8YultagchgX09XRSfJAdcjCNYwc/OySUtFugBcuXef4jJECZIyJvT1dUpcydj4pOWKftbOw21VZyfMDYhifQOzkNaVp6/DyNgkAvb43R2tJKU023s2b0zYFJYeIPSs14vOQo9oW/rVrcioUA8Fu1wH7lkCUUunX5FZVekp6ezMIibNws6EP+U2xHVCds50h52jQImb7zxxqTvvW7dugnbUMVYVF1Ws3jxYtq8efMEYVEyc3ThUYgchTLcxzZRObDT+YekV9CJjY4M+/swAqpohcRG07Fq6mxrlqcDooYhXE7i7YSHGzWbGFmMf1IoDAy7ZGCgnzo7O/x4NIFD9eF9nO5BQjSi0f5lNkA4urwuxjm0e5sfv43Ai4ySY1nwpH6S3+U4EPuSrr7arUgoUHoWor2vRUJfgZDiv/3tbxwabDTKvO9axKCH6oIwzGU+OqKRkWGe+EtsQqFctR+no7WZMvOK5aXB/ceoI5+Y3omJjedboOMqSbivE4knJCZSjMWiyQISKAwArHLlPiDsElSrjDDL/DlgdHSEwjQa0u9rULzDqMFiSbPBMHuzd/v7MAIGmcdznBKNREapbQ5XY/5M7ZKyufN4PuOLOe7xLmqFsRC2tkyJ4kzytddQ1JrVU4p/EAtnw5PQl6C4SHd3N+c8lGiToLTORAUl4UWxas0Jfj6iwEkeG5+kzXwWs0FCSrq/DyFgkBO1ccyR0WQIl4K6qMJZX11JBaUynxryeHZ0tFN0dDRpjdBQ2yRCrtz7DxGKA7skMTGJbxKimLgEmTvZTnRsHIUbpVCoXuCQkKyEreDw3u2UXTCHTHKxhXp6uik6JkaT3lpc0ARCoSxmMgFPxb9AFgnB1q1b/X0IkhkS1KHHYoXl0w2f+PmIAofI6Bh/H0LArM7WVh7y92EEDKULV/j7EAKGKEssmSNmtsobLISGYsV3xN+HERCMWa3U1FCv7arH0lM2IOySqsojnBJFQmQyRVBYWFCuWXtN3dEKGhrs9/dhBATwZM/Kl1EOgsTUDL9+H4E2nskCGDba29toaHBQ23aJva6ARCLRuFB433330fLlyykmJoaryaDqDMplq9m4cSOddtppFBUVRRaLhU466STq7+93en7RokWUl5fHlWqUwIg2m8109OhRp+14r0suucQrj0K4NcM4l9XTbPR2dVJHi8xFB3BdyLyV4+zbvsnjfiDY6WhplDkK7RgM4WSJS/TvFxIgYMV+ZESboqkoZhKM1c21YpcIkRZ2CXIUIuelhKim8qBMDWNH2iXjdHW00rEquZgrqDywW3YXdmITkqW3qR2j0cSpLLRIaBDbJRKJLoXCDz/8kK6++mr69NNP6d133+XO6Ytf/CL19vY6GdtnnXUWb//ss8+4Cs0111zjJMpcfvnldPvtt9PLL7/MRn5NTc0Eo/yOO+6Y9ocSFZTwnjDO09IDZyUO+SjUt+MFinfIEFMb4UYjpWZqr3rpbCFXZ537H5nLbTz0KzIq8ENtkVtHffM1EHhWrFpDmi5mEoQ5CjVjlygWMKOjYxwVAAMBf9kkEmdSM3LIEG6Sp4UX/K18k9iRYopTZBSiHQKd2bZJQNnccopPSCAtpxaQoccSSeDiVbzHv/71L6fHL7zwAq/gIwYdq/PghhtuoB/96Ef0s5/9zNFuzpw5Tq+DAb9kyRJ+bXx8PCe6VAID/uGHH6af/OQnNG/ePK8/1JjduED+A3gTJqfIvHzAkiBzIilF02FZYdCBzF3pnDOL5PzEQXXFfipfqk2BzNd8tmkjrVi5mrRGMFc91o5dYg89Dg3lnFLmCFnMBOSVlHt9LoM5J2yUJc7fhxEQhJtMnAZEYiNThmE7aGtqoKiYWIpLTNb95VFbU81jSmZmlubORTDbJRJJsDCjHIWdnZ18n2BfzWhqaqJNmzaxob1mzRpKTU2lk08+mT75xDlHIFbly8rKKDY2llatWkVz5851en7t2rV0zjnnOBn1080F1NnRTnXHaqf5CYOLno42GhnSZi4LXzPY3089Xe3+PoyAwRIvw0uV3pWouCgZz/MqsaHVxQVOGs7XdvAb5FqwS/bu3jXNTxd8tDc3+PsQAoaWBpm3UhAebqIIDXizHy8G+/v8fQgB5Ykmcyfbz0VoGA0ODJAWcQiFMneyRBJ8QiGM3uuvv56NZ7G6fuTIEb6/66676IorruCVfqzQn3766XTo0CGnEJ/W1lZqbm6mxx9/3OX+EfqD13/88cfTOjYR4jM4OEgmkwzlAD1dnTKZveIa0ULowvHi6OF9/j6EgKG/r5d6uzr8fRgBQ26xs2CiZ5KTU0jLBnmw5+vVil2C70GK8DY621un8U0HJ1ayytzJdnq7O6i1UZvFo2aDlkYpIgsSktNskR8Sio6OpoiIwEljMZ0FzGC3S6ZFfb1v20kkx1soRE6g3bt305/+9KcJhvCVV15Jl156KS1evJgeeeQRDvF57rnnnF6PhOII73EHVvMvuuiiaa3eo9OBMY7JUUJCIqWkpnm9j2AuTiBBMuQkysgtlKdCIAdqVY5CabgIOtta5O/ETmp6uuauDRxvaEgIhYeHk8EQRu0tjRSsBLJdgn2LvqV0rgy3BTgX0iYZp2zRSq+vq2AF3awU0yWuGB4apP6eLnlykK8xKooLeWmNsbFRtktw7L3dndTSIu1MB1s2E735BtH2zyc/iXge7dBeIgmEHIXKXD1vvfUWffTRR5SVNZ4XIT09ne/VITsI56murvb6fX7+859TSUkJvfHGG169DtFyqFBpMhrpow/fp1hLHK1cs5Y2rrd5AWRl55AlNpaO1dZwxaicnFyufoiKliaziSyWWP4bYmMwGSo5RaX+PoSAAZNluLsnpWX6+1ACgpyiMn8fQkDla9SaGDSbdLQ1U2Zekb8PIyDYt2c3LV+5mseG4wmuR1ERta+ni0aGhyjcZObn2psb+XFadj411VVTV3srGU1mKpy7iPbbq5kbkBcPngemcBoYCM4QtkC3SyBAIsJh/97d1N/XR8tWrGQP/6NVlWxnLFuxio5WHqGR0RGKibFwEbaW5iYuvIXiJwaDgdspC7BoHXyeOQuW+fswAoaKvdupeN4Sfx9GQID8hBFRsrCOoGzRCr9+HwEnFPb1cPVjvTM0OEiHDu6nxUuXH/f3hl2C28jIMIfGww7Bd9LaeIwG+vso3Ghie7pi3w6yjo1Rek4BjY6MUFN9DY9jWLxMTkqkgb5uGtJoWhefAw/BrVtsf2/61Ha/aLFrkVA8j/bIUWm3dWbKJZdcQi+++CL/je8oJyeHF0hvvfVWtkMk+sKrbxwdwrXXXkuvv/46rVu3jvLz852ez8vLo4yMDDpw4IDT9oMHD9LZZ5/t9cFlZ2ez8Y+Ls7DQc++voaFhFv5Gx8bY2C4qKuFOae2JJzs6Nhjo2dm5NDQ0yPkucN/Z0cE1DGCkb928icXCjMwsirFY6MC+vbzv+QsXUWtLC/X0dFOEOYKKSuZwMln8mKKiY8hsNvPr8Ph4Tyan4sDOzVQ0d7GsfExEI8PDQSUCz5TuzjaKiXXvSaMnujvbqb+nm1KzZFVsECZD9B2EhxvZoI2YRiEKjDnodyD8YPxob2lz5Aetr67g5/B3SEgo1R09zM8Vzl1IjbVHqaerg8yR0ZQ/p5ya62vIYDBycSqT2cxJ3Q3wFgw3sqCblV/i6NvmLrEVXtn02WZqb2+nto4uSs9yHre1jlbskqqqKr6ft2ARVVYcovSMTMfCJURgfGcZWdm2fFMhts+FAitD7W38N/JjVlUe4bYQFWGTdHd3sX1SWlZOe/fs4gVS2DzhhnB+DtdrQmIiV4IWE7NAGvcGB/qp9shBvs4lsF1lDmnB8OAACw/mCCkWgop9O6m43IVgoEPCwgyy4Jwd2BPo36eDTeQb4TEF89XO9hYaHR6myBgLDQ0M8CIx7JK8krl0ZP8uFgOjLXFsG1fs3WEbs3KLyGodo+6ONrZBLPFJZDRFkMkcSUazmRctSxeucFrgSk63LeSNWa08DlsSUnmMlvDKJtHKVeMioCuxUCkSArT3kUgoOOuss+j555/nxc23336bozVgP9xyyy3ya9IZXgmFuFBefvll+tvf/sbuwg0NtiTUSP6NiRM6DVQEvPPOO2nhwoW0aNEiVqX3799Pf/3rX6d1gLgof/e731FlZSWdf/75XicNRydoVOQoxDaxKh8bN15dLjUtnW+ClavXOu0zyS4yApPRRAODAzzxE6LjgD2ZLFZWDh86yJ1r2dx51NhQT83NTTyRW7lqDW36dAN3yKmpaRQfn0D1dceopW+UV12w0oJqvGHh4RQZFcP7xXH6yrDHuRDl6PUOVroCTcj1J+0tTZSRI0OxAVY+h4flhE1QqnNPBrG4BNLS06mnu9vW74+MUGtrCxvpuXn5VF1VSZ2dHTzezCmdS5s3beTX4DlMbKoqK9ionzd/IXW0t1N/bzeZzLZxMyY2gccIGNi4RzVH0e+rc0TmlThX3IVBPhXCSMeYEmxoxS4RIAk/FhRdfT8IT8ZNUFhU7NROiItggX3iIK5PLIjiWoR4jLEeBj6uVdg5uDY7Ojs4XzPsEvW1WVNdxZOAueXzqampkeqbu/jajEtKpe6OVgozhPN1Fmb3JvCVVyPsHWmTjBNjkYt1AogSg4MD0mtMIZzK3KY2sKCm9wJ8SqcXpNdqaWmmpKRkOnaslhebzOYI7vsPHdhHQ8PDlF9QSL09PRxJB1avPZE+37qFPdiTEpN4kaq7vY3nnxHRMRyxEJ+Ywo+xeFlQusCp3y9fusbpeDCHVaZ2UuJuDhvMdsmMEKKgK7HQlUjoyuNwhsBWSEuzpW276qqreCH2zTff5AXXjo4OOuGEE+ihhx7ihfNvfetb9Oijj7INIb5vtD/vvPMc+4uLi+M28FYEtbW1bJe98847bKsgwuPJJ5+klStt6TeefvppevDBB6mmpoYXf2+77Ta68MILff45JT4WCvHFgVNOOcVpO1Rn8eUjkThEsxtuuIHa2trYMH/33Xe9WnlXgsqFN998M6/eezPRBxCCli33Xc4X0dnBEMdNAGNbifI90VGXlJY5PQcRkeOj7c93DNlybQwO9FF3RzvnbsgrKecwFFRfjUtI5o6XC05YrZRTWEq9PV28ggMDvqB0PtVXH+F9RkbHUGS0hfq6O/k5c2QUhcA9AR15YkpAeRP4O6wlmEK4Zoq8LsaBmOOJ+KIX9m3fxCvCWrpGbKG6MD5DaGx0hD2Xmq29lJiURE2NDdTX18feV8kpqeyZBYElMyubjWaEfwKl11ZsrK0frzxSQZGRUZSXX2D34gvnROIYa2BowzsM1w8ew4NdSbpixTwnN49GI3vdGta+JjTU/t0FYUi9VuwSAa4XX4aJicXPaFWeKovF4vhbaYMA5bWJ30pScjL/BnDtIvWKqWfEkU8RHtawWXCNsrdrdQVvLyxbQI3Hqtnb1WQyU37pAjq853MWE1FsAEIj8pvaHqfT0GA/i6TwhkU1W5sdFEqxOp/wK0nOyPb3IQQO9utaYiPaEi+FQjt9vd3UXFejuUJrcGKBXYK8rH093exY0mX6/+ydB5gkZbX3z3Sc6ck55zyzOcECEhQFA1fEHFBU5BMFLyqGe68KRgyAoAjGCybECMaLAopK3GVz3tnJOeecvud/aqq7uqd7tydWdfX58TSz3V1dXV3hrfP+T5olh91OnZ0dPCYiYAUOx+6uLrZHMGfc8+LzHFGOZmqIGD965BCvr7yyirPn6mrPcPZbcrJiR0AkhMMJDqnS8koWcDjNNzWNbReV7Tu9ncA5RWXeG+zyOK3W4lq0RFhMa5f4Bb9zZia4Zas3QEH11B+EOAiRcFITxLBjp7JcMBGlcPKt4BjC6Ypmb+Af//gHl3TB3zNnzrCzFA5YNIsLhpGREbrkkksoOzubxUcIkvv373cHeUFk/M///E8WFi+//HIuKYP60igpc9llly37NwjrlHocDCj0vZxi34G+A977pYS7zs0rJxuEoBeff3ZRdKCeYAKpjWSDJ2jUohj4MKy1Xpmyjdu9Plu9kEYGXLHxbIzD8Faex7nTaVF7b3hwgL31qMGHmla9Xe00PjrCNSJqju7nGh9xCUmUnJFN9aeO8r7Kzi/hVI8hCJBWK2UXllFfVzsfE2eUi6IhQI4O83sOjoRRBvnVjHpcL7rbmjiEXmqcKFRtPV/nI2IcnFHRZHNIp3Q38xiXkRppXbPaexg/EKE3PaVEauPahMiAyE673ckOkM7WRl4mOT2Lo016Olppdm6WKjbv5DFMTYvJyC2gM8cP8biUmVvE3zHU30OO6RhKTFI6JcKAhmDTMjBF1oRMclhtNGpxUHFmHOXk5C6K2tJGdMEARzqnrxjjchlXXFbHatUQMxOhYpeoIJq0v7eHikt9JmU6gWtPnUiqzsukaU/TM6Sya0G0q0q+pratKh7CJolAuYL5eRYEcc3CV4maVXBgotNllquE6k8dYfEQqWpwarY01PBnCss3sL0CRyjsjJKqLbysxWpjmwXrRB1Oi8XK4iUm2/gOCJJRrhh+jvdC0RFYd/Kwl50XzsC+FTyk5+SFnJ29VuDaZkfDGsF2CTK6rFaaGB/lf2OMwvdiXMIYl5CcRiOD/TQyNOiuAX/66H5O3Y1PTuEsgSYEdyxkBWDMGujtJpvDQWUbtlF3Rwsfz7EYKzkSEnlehSiutqFpmpuPImdKLrmsdqrvHqVd53mPCRdcdLHXczg3KyoV0TQbteo0aCPUjdr1WJ2zmx7cC//3h8v/vFYkBBARg21k8r7rUTtnWdfCU089xZF/KPHS3d3N9Zbvu+8+PmcrKirota99LS8TrFCIDBCsZ+/evex0BSUlnjroiCSEk/dDH/oQP//Yxz5GL7zwAr8uQuH6Y8qqlHNz816px2bEV3CMT/SOSMkp9KQsoYZVamYuHdv3HO8TXwESIpFnsj7NoiAb+xERbHxDVISAiNdQOH9uboaS0rJoanKC62UhgrN8005qOH2MoyIR0QhB8szxg7z+zFwl4rKvu4MNeNxQezvb+EYfGeWihJQ0Xi9uyhAEsAy+E3+xLfDAKeniq9dcBjd+nswIzKnDL0lR+QWQEorzM9S81Wsh4EElRC28sdERjkvG9YjaNTCesUxyWiYLeLiWo1zRnLLS3lTP12xGTgFHG8HZACq3nk+nj+zjsQRpQ8lpWVR/esFJUVBC05OTNIQ0R6udxyyIhtyx12Lh9EiMcRiP4FDBOJGYmu4eEyAmaPGdbCempFFhqmIwwyOvMtA9ymUeggViynLrAemJOm6GjUFuYJAlgAmh2cA5hhpVeKigPpUKxgo8VGCHsNg/O8OCnzaqFjaEGn2DMSA9p8A9WYfojQkmxEEsg8gcTNgBxmzU0ppZyMTAOMOZGJjIF1fwJL+/t4vHFHQYhjCH9UOAjE1I5u2xWC2UlJKhFOifGOdlIQrA4ar9jYhUhnipRnSuCuESWRMEuG/g3IDtKhDVnzpGJdVbwrpLuDomwB5wulx87cMwQR1LOAPhZIQdgsipwd5uJS03O4/6uzpoZHiQGzHkFlfQyUN7lEjq9Cyeg7Q0KDWB2UnR2caZXaivV1y5mbO1VKcE6gRj/Oa5V0QEP0eKrlqSAZ/HtqlOCm16LpyYWfnFi5wsGQt2CbIRwHi3J9PA7LjtEhM6MEMdRPGhAR7sXRyfd7zjHXT77bdzqZfq6mov/QHRhUeOHAl63QcPHqStW7e6RUJfTpw4QTfccIPXaxdeeCHde++9K/hFwnIxpVCoev8xCKWleSL0BP+wILhw0VscTq7fp6KNcPTXORk3WhXfguSYrKvHAjd3TP5h+OGmi3/DmMe/EbGE1yEOYmIxNzfJRiI+k1tUQR3NdTQ6PMQ3bnTnhagF0jJz+Qbd3lyvfH8lCv83cFo2DAcuwHviMAuQMPQhMChipYVi4hNpamKMutpG2PBKTM2ggd4unoRADIEggEkCJiRIQ51fmLDgOZY3W60YCMSCgppyt96o34lrAeI7ro15mmdBHs9hlE6OjyoNmCKUc7i3q43fi4qOZQOVu3nPzVJaVh4N9nWzUIf1YAKNZkbzc/MsmmEi3lx/miemKHOAGpVYHhNxROjBkFavf6SxdDTX83mfnp3P1wWi91TjGOMFF6+OiiKHI5Ij+jCe2DgSMI4/o14rvh1OfQU9RAuq+E4QjVCTKDfP81tCCXXiopblEPQDImFcfLwcAr7vzHANrEDnrHre+or5qRmeqBnc170ESJ+uwdoxBg27MFlXx9qconKam0EEJJwRVoqNT2BxAc+5Vu3UJAuUGAcxRuI5IhqRiYBoIbyXmV+kRDh3trEzBTVd0XhiamKcvw9CBZ6rEc4QGLEufEd+aSV1tTVzFBLKxKRnF3DzIrzHzcUiImhibJRLB8QmJNHk+DhHd2PsxZiL7VG2XREsQSiOT4FFIRmvvOyShSCI9UK9TpQIuxluPoEGSQgI4Ig7i5Xv/yPDAwt2iBJBPDo8yMcOTSsgvsOGgW2P66i57jSfw3AUYp2dbU38WXT7xnvoZozI4Nyicjp9dB/bKLAhcP10tDbyd0KQwzXX0dLAQh8cCZgjcFRepFLnF3Y8B1RA5EtOpbik5AVHQ8Si5hrazCJXUbnXPkBtPi0sRC7g8Mk8wb7RA98Iw1Ah7OwS2MyI7FsKhw/5jxpE2vGmJTQBW2K3YkTuoawLSvSg0Yy227GafaDCmYyaY+hvDqV1sC+nGaCgH+YUCjU1CtMWinEKi4virweq0QpDljuVabxrWmAIaIGHXwUewLOJC9oUFfxGDFhKqqSFP8seSBgIC7XA5maVCQCM7XlSohIADBTl3EE0VSR1t7fwe5l5hZwGqaQ7WaikeiudPLiHo3NQ9xEGfFPtSeX7Syp5OY5aiLBQ5dbz6OShvWwQ4bshWDbUHOf9gpSuibERTkfAhAAd7TD5gFAEIRUpXh3NDfweBFl4MhE5gc9m5hUr0RgLEaDYX71dHVyCAtuD3wFvK5ZF5BU8pFgvDBmkrSOyAjtEMXoQJeHkaC4cF0xMMFnC9QNxCpEUgNPNKYKmppTGPfjs1CQmUkqqGQw27EOAiAvsKxiIAGKrGpWKbYKQi1R4ZVk7i7kQoEBkpIt/K5bHstgXELxw40ENLa7vgs8itS0mlo+l+j2IIsH+xDGHYYrvhacZn41NSOQ6L/g9uIelZeVST0cLRx3DuMRvxzHHd2NiivWon80tLqe2xlr+HkzoYOSqNUOxHkTU8fkyP8eRMs21J2l8DNFqMZzCj3R/NnhzCvg3wcDF+YhzCR1uR9FF3RXNQviZYwd53+GYYz9iAornmOBCBMcEFMY5hEKcy+qEGucz9hX+jX2E8w3pL9i3HHVXvdUdgYdHdaLnOsLntFHIiLZRQdQNIv5gvPNxj8bfNL/XH3DZgo/QCzWQNtrb083d7kMJNcVHDwFc8MYVHU2xsZ6U9XAG2QR6pAirdglP9DWTfa1ggHsC7qUq2vHRn9NDa8P4dqfVZm3g3oRSMXiO8Rj3btw/ME4jjRGTMMWGUcQZRLizozUugbuQ4v6Me1t6dh411hznZVFiBr+ovUXprI0Ia0RC4V6HeyCcQScP7+V7EMQbuyOSa02C4opNLFaODCGKKoqFEdyvVNsB9+qutia2p7Lyi9h2gG2B+3Z2QSm1wOFEEXz/hA0AZxUMEdwXIOYg+hzjD8RdpJQrTq9o3r+ofwlwL8P9HtHqODQ4DrgP4/ej3I3TGcV/AbYH+wP3ffXexbbb/DzbLPhtcKjx8XVGKh3Dp6f4Oe5hLHItdHnFstg+wA7yeXKvF9uIey3KW+AcxX5020KIlI2I4N8H4BDDd6pp73iOJkAYbmGf4fMQzwCON+wX2BJ4HeIZbAd8ORx+uOfDaY0PJ6Vl8uewv/k+b7WybYHtxz6DzdDR0sj2LjJ3sD9hq+LYQXhTI/i1tifILSpjYY2j/SMi+PzkkkSI9kdJovQsLufB531BCTfHwHHFccS5hfqA+K34nXCy45zA+YHoOhxf5Vgo9j5sSBbv0EDQZqeMnHz+HSgpEmGJoPhkZQzANqPe+llt/QWHANaPfV+leR82ihZtt2zVqaliFjFdZd9Le2jb9p0hV24h7OwSnHdLSf9FTUKtSIhMBDX9GK8jwGcNGpmoKevadOClkJqaSu3tSiYRqKmp4brgKps2baIf/vCHXC/aX1QhGps8++yz9J73vMf9Gp5XVYVvlpeemFIoVIUfeGFra07Tth3h3bUTwLDCzdxXoDMjys1SuQHBQNGiRkvWHj9E2YUlXsaEb9ff6LJq979hYCKVUgUCYCCDxjfFABFaWmCUqRGJMHITUzIWhM0IjsZSJxAwxDEBwLKIHoCxA6FO/Swa6szO2NypXjB8sR6gdEObWeiMphi/anQEhB7FaF0w5pDeabVwqgYEMoiKMKZh9ME4RioG1qNM6qzU19Wm7K/8EjZoMZHBfsVzFjYX6ljhNcUAhhFXzMIbjFP8LniLO1uVZVGjChMMpLFjwgExDZOW0aFB/k3YR4gExXZisoS000EY0qit5XSywaikhVlYKOS0WJ6UWdigh/GrerjUfQcDFUAgxT7AtsJ45ahXdB+fnaGo6Ghln7OwBuEumyP8sDz2eUFpFb9utSEaN4KNWFWE8/VCL4qc00TA+Hax9T1ftJF02kgagMmiFu357mscLzdlCcec64wJfD6OLwjaoYSk+BiHpoYGro/p65UPRyBsxMTFk53Ml4odMGtjwZGjgnuOaqNBdNM6aYDWZlPLuKhADPInpACIg+cqx6DNFNHeH1FrUs2ggM2B+tVwgkFYg3gKQUslLiGZ7Q7YCxaUhohyeT6rPhYa6nFU2uwcOzjn5hx878Y9FeuDSKjaJSwUTk3yfXhsIVIM5WnwHkQsiGxwyOJ7IFb1daERBKI9Y1kQQ5MdZdls/i5EcAJEcPZ0tPHnIbLBvlJtluT0TF5fP2wNimAxDeIYbCEIqIjSh3MW+wf2De6LLIoSHJ8uFnnZbnIoNXVRJxy6AB9vu10RTNFcKz5RsTEQYbNwj2bdCin1XGJHsVvUcju4p8M2UQS4Oe5EznVF2WnqYJsRz2EjctReagZZFoQwpCqr9WnZOag5B3DctOfT2WqiAzhEAznvfeuYas9ZrdMf4HitFLbnVrwW86CI4dMhV9JC7JKzEKi7sfZ1bTdkA/Hyl7+caxju3r2bg07Q+E1r77z97W+nr3zlK9wV+Y477uDU5QMHDnDkIj6DbshvectbOD0ZzUz++Mc/0u9+9zt68skndf1d4YqphULclH1Dw8MVRE0NDw2Qx4wMb5S6h/p437Q1jXxrTfp221UnEcBODi9h07cRC4zeQJ/1fc/X+9pUd8pd5y09J/+skahaw08rngJfjzCnTwWIGi0s3xhwe2HEa0Ux1JfSojVUIeJqU+R9t0n7HPtPu03ayRLABAARG62NZ9gw1h4PRBho0dbfMjNInTPDOIqi4EtdRq1rqAJDPCYm9CIm1UgDtX6voB9T01OcziMQR10tpUaomVEchPpFBGntEm2dSV+nkxU2SFTgkhDaCHPcN7T3W99yEqhlrb3naxvlqCmequ2iLXujOFjTA0Z7IjVVy9mW9bVZ4Gz0t33+xFdtxKlWSPP3PVqBzOFjd/juF63DD4IgUDNItPtzUQS/xpYEXNrHpPjahOFsl6SkpIZkfVO163HYRBSuVCQE6l8Di4V33XUXdyl+2ctexuIfagvu27fP/T7sn7/97W/08Y9/nF7zmtewPoFowe985zv8PgREfAbNS9D9uLCwkB588EG69NJLdfxV4Yu5axSiBkyBtwc2XEEdHpmceCiq3GRqI2rJyI3aDaINxXDxgEgPnB++k8dwxOF0KkZ5iKFG9Mh1rj/FJaVyL16AUxSXWDvJrCBSHeUhBAUlujG0UinNVqPQyKAMj69oGq5kZGRy1GmooWaAq5lQAupHtAcWCVX8iYXpGegqsiq78KGHHlrSe/fcc4/Xc4iD6JKsZWBgwOt5fn4+/eY3vwn4PTfeeCM/BP2xmDqiMCIiJDtUrgWIhvJNcQxnUNtHbk4ecoq8U0fCGaTwFMq14gb1nxBlKSgcPnQg5HaFWgsI9bYE/etcmq1G1nJBiQbf8iDhCtJh2xa6rwpKpGJcov+umOFIQWk1p3ULClyORmBampuov68vZO0SaVqkAWLf9h2BRUIVvI73AZZfJZFQEMJMKETdT6XJQbiDAsdoxiAoQPgQb7UHtbGIgDT9aWppqJFdsQBS48WQUwhVgUfdbomU1Z/uLtQ1E8CJgy/KObkA6vGJY9sDGo+hyYuggAZoEwvNUwTc00w5fV0WSE9HSYtQQ+ySAKCj8X9cfe50YryP5bC8IKwRpsz58EyGUOA39GtrrQZKNza5sWrrxYTqpH8tQDdC39p+4YxE0HlAEfdwBbWBfOsBlVVULms9eqKO/SIU6o+2Jm24o3YCFpR6XWiCIShwExO73at2cTijNKOTpmIqVdsWoqkESktLX1ZzF7FLDEywEYISSSisMaZUjtSUUnQaS05O0XtzDAEaL7hiV95tzCz4FqkOe6RGoZeoggYpggK6VSOaQVAYGR4OXc/9QrS9oB+7L3yZ7P4FEpO9mzqEM3ank5LTsvTeDMOAbsgiIXuXD5K62h5OHX5Jh7PSmMABODoSellBnq7HUntTEIyKSYXCeXcDj6mp0AvHXqvQ9CiXeKtVzhwLvTpja0nVtt16b4JhgDGekSvRlSqw5SQFzENbawuFGlILyBiga/bB/Z7uf+GOb8fccG/O0NkqDhmV9Ox8SkqTulsqyelZ5JB6nl5ZUhIhr4C0457uLgrV9HFxYAqCcTGlUOjxTsyT3W7XeWuMQU97K42NDum9GYJBqRHh1A2Mz9rjh/Q8HIYCkxNEMwieiNNQq9kotYCMQVRUFFmspjS7lt1UTFDAmCIRYx6625u5trag0NFcT6PDg7I7FkhIShWhcAG7PcRrFC4rcVoQhPXAnDUKFwYdi9Um9W80hbKtVlMe7mWRmVuo9yYYrv6N4DFepCO2dxdo1HsVFM7bfWHI1Xu1iEFuGFJSUvXeBMGAREXHcIkYQWF2Zobm7NKlXSWCxC7RkpSaIZfKAtHR0bR5y7aQ2x9SO1kQjI8plSM12sOIRcP1Kh6bmplLTklbcBNqEUFrTWx8ot6bYCihMF0au7iZmpzgjunFlZv0PCyG4dTJ45SXX8jGebD4NkRZ73uB1Cg0BjMzM5RkwLrJetgliNzOK1l6YyCzMjc7q9R5EBhnlEuEUw3xyanSnFFDa0MN5RaXkzPSJVcMEe1/aS9t37lrSfvC1y5Z7/uAp0ahzMcEwaiEVlhEkKh1K2ZnpvXeFMMwPNAnQUEautqa9DsYBiQ1M0fvTTAUUvvGAxwu0m1Rc27MzdNUiEXgSuqxMcjKyqLenm69N8MwWQ5jI1IORWVkaED2h4aY+EQpebEo00FSNFWQMTYLcV3gc2NiYjzk9oS7472c14toOzOwqssJwnIxrVCoDEDinVXp6+6QNGwhIPUnj8je0dDZ2ij7YwGb3UEZkqrvJj4xkawW40WrB9PMRCaa+jczcTicOm+FMZiZmmJxTPBEFEqNQg/dbc3c4EXwOPvHRoZldyyQnp0nY6kGI0aqnwuJKPTPnj/W0aN37qf9fz37PATvYzksL6wv1113HV199dVhsdtNKxQCqffijRihHko3bl/Xc1IQQhUYc1JE3bvGnDMytGqJSUShMejo6KD4hAS9N8MQzM7Nks0mzeZUUrNyKSE5TddjYjzE2e/ZFRESeaVhemqKpqcla0wlNzePQg1xYPqPENz75wb+9/OP1gYUC/E63gdYfi0iC2Gv3HzzzVRUVMROztzcXLrqqqvoqaeeWrXvKCgooHvuuWfV1iesPqatURjJE7n5sK1J6Evl1vP03gRDCcnNtaeoqGKj3ptiGLLyi/XeBENRuiH0CkOvpcjU29VOGWFat9F3DO/paOVC++k5+RQqeGoU6n9PDGeSk5N1b4RjFJskOiaOokur9N4Mw9DT0UbRsXEUEydCMkhISRMh2ac8jDtVU1hwXs5TlCv4WsFmwd8YfmzfC1S9/QIKVQfmqQ5j3Jf0JqskgXa/odgtAqp/t12R71ckBFgen1tNGhoa6MILL6SEhAT6xje+QRs3bmRh/q9//St9+MMfppMnT67q9wnGxbQRhVxoXuoeuDl5aI+eh8RwQvLkxJjem2Go6wUNKwQPrfU1sju8TxLZH5pU7JkQq38r3QWNU6NQb6HQKPT3dFFni5R4UIFNMi9F/b1S0+fnpcmBylB/Lw0PSCq2isVqlRqFWkKwhiU6eYNQ2+61BqIgxD8VbWShP5FQKyKuFh/60IdYyN2zZw+98Y1vpLKyMqqurqaPfexj9MILL/AyTU1N9PrXv55iYmIoLi6O3vKWt1BnZ6d7HbW1tfx+eno6L7Nz50568skn3e9feuml1NjYSB/96Ef5u1ThGK+rz7UPiJfg7rvvZuESOg+iHLGtIyMjXtv/7LPP8npcLhclJibSFVdcQf39yvg5OTlJH/nIRygtLY2Dyi666CLau3ev+7Ooffr+97+fCgsLKSoqisrLy+nee+89p67w9a9/nUpKSjj6Mi8vj7785S+73z9y5Ai9/OUv5/XBYXzDDTd4bfPTTz9Nu3bt4t8EcRYiLfaNETClxYpBx+FwuMOaBeXEF9wnCLli4mR3+ERJCR7GRqUWkJaqbbvl9FjAFRtHCcmpIbY/xCA3AjB2RShUmJmZogiLREipoHal1S6p2CpDA700GYINGtaKmekpmp4Sh64KMhySUjN0PSZGIqeghEINT0RheDgEoE1MT84G9dh4aQ7tuqrQ/VmIgz/82L+8REK8j+WCWd9SxNi+vj56/PHHOXKQg658gJAFYQwiIJb95z//SU888QTV1dXRW9/6VvdyEMJe85rXcKrygQMH6Morr+TUZQiM4He/+x3l5OTQF77wBWpvb+eH+rr6HI9rrrmGxToIjgA21Le+9S06duwY/fjHP6a///3v9MlPftL9vQcPHqRXvOIVVFVVRc8//zw988wz/L2qDoJlf/vb3/Jn9+/fz+IehET8FoDfhu369a9/TcePH6fPfe5z9N///d/0q1/9KuA++6//+i/66le/Sp/97Gf5Mw8//LB7e0dHR3n9ECwhSGK9EExvuukmfn9mZobrHV5yySV0+PBh3mYIiUaJIDdl6jEuiLa2Nil0qyEhKfQK3a4VEJCz8iTV1nunGGNAMgrhmM5yNmqOHaAyScf2dBgcH6Po2HgKOYPcAOU4whl45wUFhyOSo4IEheSMbLJaTWmSrzjiSCCyO5wSeaVhoLeLI/tTM3Lk9FgQG7A/7HZHyOyPcKudPDM1R9//z38u+/OTYzNez/f8sZ4fwXDDvZeQ3Rnc/fbMmTN8TCoqKgIuA/EPUXL19fUc1Qd+8pOfcNQhxDBED27evJkfKl/84hfp0UcfpT/84Q8skiUlJZHVaqXY2FjKyPCI/nhd5Zvf/CYLgS+++CJH44FbbrnFq8bhl770JfrgBz9I999/P7+GyL4dO3a4nwNslyraPfDAA/TQQw/Rq1/9an7tBz/4AQudP/rRj+gTn/gE2e12+vznP+/+bGFhIYt3EAoRNenL8PAwRxzed9999J73vIdfKy4u5khFANFwYmKC948qvGJZiJdf+9rX+PsGBwfpda97HX8OVFZWklEwZ8jd/DyVlpbS3JxE0Sm7Y16KZGtAmm3TmRN6nZ2GpHzTDr03wVDkFQe+QYYj01OTem+CocbT3s42CiXCzSA3Ktu3SxMtFWdUlDhkNDTVnKCpSYmgU8kuKKGY+MT1vUANTFxiCsUnhVok+9qBexnS0wWFof4emg61EkILfgCxS4xFMMfjxIkTLBCqIiFABB+iDfGeGlF46623suiF15F+jPfUiMJz8X//93/06U9/mn75y19y6rMKovEQMZidnc0i47XXXku9vb00NjbmFVHoD6RDo9YiUntVINQh7VfdbvCd73yH7bXU1FTe7u9///sBtxufQzpzoO/E+xBMtdGZ+H5ELp46dYqFUXRRRtQhxEOIjmp0pRGwmfUkR166JcKcOuhSgWDaVHuSKrdIQxN1f0haugeE/TfWHKeiik26naNG49SRfVQt6bZu4mTC5gadWtHMJJQQodAYoCSKoNDV2kyJqekUGy/7RLVLLBaJsFRB/cq4xOSQitxe6wg6OOzCtamYL87IKBGYfOySUKudHG52ic1h4ci+pXDwySa/UYNIO95yed6SvjtYEGiFY7PShiUQCRGpd+edd3J6LyIC3/SmN9FUEAI/0nff9ra3cTrvq171KvfrqFOIyLsbb7yRawBCZENqMWoKYr3QftTIw+XyyCOP8LbfddddtHv3bhYj0dAFUY3+WOn3gQcffJDrJiLlG8LoZz7zGd53559/PumNKZU0DDoIBZW0FgVManETERQQmp+cJrVNtNfL1ESIeSLXGEl48iZF0nvcoD5K+ebQisB11zoJE4PcqMegp6dH780wDJjUWm2m9FUvi+T0LLKKneZmemqKIy4EhYgwElSCIdIVLR3CNWQXllFsvCdlMxQIN6EQvxfpv8E+jjzd4iUSOqM990u8jveDXddS6t1BfEN0G6LqkKrry8DAAEcJNjc380Mr7uE9RBaqDUUQKfeGN7yBm48gvVhtSKJ1nvr2UICdhMg6NFFBoxMt+/bt4/sCRDyIaIg0RKk5LZs2beLUaH8gtRffiW1TQYQh0qW1233BBRdwk5StW7eyyIlIxLMJqxALA30n9tWhQ4e89iW+g+cS5eXu1/BdqHX43HPP0YYNGzhl2QiYVihEGKoUDVeAMZ6ZV6TzUTEOGGQsUgtIQwRFx0pzFy2pWZ5weoGo7uRh2Q0aak+E1v4IN4PcqMdA2xEw3EnLyuWoIGGB+XmxWTU4o1wiJGuIiokVYUzDxNgotTfVyfCxQH9PJ/V1d4TU/hAHZmD8dTe+/q6LA3ZDXm0gEkLAQ0ouGn/U1NRwCi2aiCDK7vLLL2fx753vfCc3BEH95Xe/+93ckAP1AVUBDY1JkAoMoewd73jHIucPagz+61//otbWVrcjFQIhIgNvv/126ujocD+wPRDtIOx9+9vf5uYpP/3pT+m73/2u1zohtkH4g9CH5iCIjERdQqwf6b+IRkQtQkTvQdz8wAc+wGnLiEpUt/ull16iv/71r3T69GluUKLtiuwLOid/6lOf4iYpqEMIURGdoVHzEGAfYRnULzx69Cj94x//oJtvvplTptHwBHUesc2og4hOx3/72994fxulTqHFrAYX8sFFKFRAugLq8gkeA2Owr1t2xwK4TtKy82V/aJCi8t4gVV+iOzyE3HgqQqHuxMXFedXzCXdGhwYpQsrDuOlo8Y60CHeQlu6MdOm9GYay02ROo9kfVqvUoQ9xu0QcmMGLhNuuUOZo+LseYmFRURELgJdddhl9/OMf5wi3V77ylRw1B9ENx+73v/89d/K9+OKLWTjEZ5A2q3L33Xfz+4jOQ4QgohS3bdvm9T3oeIwoQ0T6oR4ggHAIQS0/P58yMzPdD0QvQtvBetEEBNv085//nO644w6vdSLKEGIbxEkInRA2sa22hQwGpDNDjIRQh+1B8xaIgthW8P/+3//jTsvo4Hzeeedx4BlEx7MBMRH7CR2SIfDhs11dXfweRE+sH12V0eQF6deoZ4iGJur7EDOxTdh2dDxGx2lshxGImDdRiMHQ0BDFx8fTKy6/nNMW3vXu99K177lO122q714ctrveQBQbHhygnMJSvTfFEPT3dNH46DBl5UvnYzAzPcURUtLQxMOx/c9LjUINg309FJuQJBOVBRrPnKD8EmN4+4LhX3//G33wXddQ1cYt9NBv/kK7yrO4yxrEK2F97JK0tDQWCp954SUKd5sEHN//PFVuPX9JKVFmRu453jScPkapmTlSo3CBgb5uGh0aoOwCsePVkkqjI0MUlxBa6bZrxejwII2PjlBKRjaFCu9982voxWf/Rd/4zv/SJZdfaSq7BF1uESmGjrmIJguWtjMD9Oid+/2KhGcTE99w6zbKKklYhS0XwoGJJZyfpowohPSJcFIxQD03VKkF5CEuMYnTngTP9SLXijcydfVmehr1oqSLvEp6dvBFpI2AeO6NIRiqHmZBQe47Hkqrt8ppIZz1WpmfM01cx4qJsETQ5LjS5VQgioqOYWduaGY66L0hxgFi387XFpxVJPSNLMTyIhIKa4UpK0kjSBKtuMUIVUhKy5TaVBqGB/tpfm6OElPS9TlBDZjCIcKpN8VVm3U6GsYEUckxsfHSFGmB+lNHqWzjDrJaQ6NLqQiF+hMTExMy58t6gGhCwUNb4xkqKNsgu0RjtzqcUsNSJSYugVzRsXJ+uImgno5WjjoVlLTjlvoaKqnaEjK7I2LBJT9PohRq2XVVEeVUJp1T/INYmFEcLyKhsKaYMqIQkyIUiBShUKGztUlq8mmYmhinmelpfU5OIzI/L/XnfOholnpRWiwWqQekBd1JZ2emQ84gF/QD9XfsdrscggVOH9kn+0LD6Miw7A8Nc9wJUwQElamJCW5YIShgfjc3L12xVWxsk8yE1Okhc/TABBshKJGEwlpjSqHQEhFBk5OTem+GoZqZyIDs3ajC7nDqeESMxezsDPV2ereXD3dGhgf13gRDkV9aRVESzeAmIyefI3FDDROVJA45HA4Hd+sTPLVxBQ+RUdK4Qws6uKLkheCx01CDTlDAnKZi8y7ZHRrnpZREEQRhtTFl6vH0zAy3tl5vccwoRcL9GaCOSEnhUElIkWjTReKBFJT3winXixddbU0UHRMXejVw1gjUikJUMrz4oYDnXihCoV6gAx8yHdYbo9olcYnJem+C4ZwxgjcSCe0Bjimb3SGniIb6k4epRGp7KtdKRARNhViAjNsuEQemIBgWU0YURkVGUklJiYgfC0THJYjwoaGjuY6GBnp1OTeNiMMZSYVl1XpvhqEoLN+o9yYYLg1sJoRSbdeakaGB0IruEEeA7uzYsUMi+zXOqeT0LH0PiME4dWiv3ptgKPJKKsgpUZZuolwxlJFbqOchMRyTkxN6b4Kh6O5o0XsTBEEwGaYUClEwfJbrmwigpe6U1OTzET1Qc03wpKZLTT5vTh3aI2maGiJd0ZzaIijY7KFVo1BFUo/1w+mUchcquHaaa0/peDQEowObBLaJoDAxNkqNNcdld/g0eBE0hFhknjRZEwTjY8rU45mZGRoaGhLv/QIocGu1mfJQLzvlSVJLPczNzdLkxLiOR8R4RERYWFSR2p4KcQlJkrSqQem0GDoNQjwZPqE1kTATPT09EtmpqbcmNok3aVm5639SGpjJ8TGal2YVHiIiZH/4EGo1+daakKvZuGCYiF0iCMbFYtYahYODgzLJXyAzr0gi6DREWKwcdSos7I8IC0W6pJC6lpQMSYvTMtDXIw1vNIwOD1F3e3PIDCEieOtPW1ubHIcFEJ2ckVOg7wExEJgoS/05b5B2LJkfHtAxPSE5bZ3PTGNTe/yQ3ptgKOpPHxXRzSQM9Y6t6nKCsFxMKRQmJiaQS4QPN+NjIzJB0YAJvnSf9IAO0OnZ+et4hRofh1Oa/2iBsI6UfcHDxHjoGWjiudePjRul7qm24zGaAQme61LKf3gDmwS2ieBxcNsd0sxECMzM1FRI2WmSeuyfppNddPSZBmqp6Tnr/sP7WA7LmwGcD4899pjemyGEg1BosVhocnJSxLEFejvb9D0gBmNubo7PEcGT4tMk9aK86GhpoLnZGTlFFnDFxFF8UorsjwUcTic3AQoVxCDXn6mpKbFJFhgfHaXR4UF9D4iBmJ+bowhL6JQyWA8aao7RlDSr8ErX72hp1POQGI6s/GK9N8FQxCYk0lwIpuuLA9M7QrDllCIQNh3vCigW4nW8z/8+1bOqkYXXXXcd2yp4OBwObg77hS98gcu6CeGHKdWS/v5+McqFgOQVV4inWsO8VJ9bBG6QYrx474/p6SkZVTQRp0mpGSGzPyT1WH9qa2vlOCwAJ4zUKPRgsVqpsHyDPiemUZEawV5EoCau1JhdVH8dNbYFhbSsvJBK1xe7ZDFxyS7Kq/KUGPAnFmpFQoDl8bnV5Morr6T29naqqamhj3/843T77bfTN77xDVorJ6pgEqHwjjvuoJ07d1JsbCylpaXR1VdfTadO+e9ch0n2q1/9ar+hpH/4wx+orKyMysvL6U9/+pP79YaGBl4e6x4eHvb6zJYtW/hEDQaHwymTfA2VW89fymE2PV1tTXJ+aLDbnZSUFjqix3qQV1IhXX41QCQc6OnU74AYDBTZrztxmEINM4rfoWKX2KShmJuktEwpd+EzvvZ2tgd1HoULyelZZLVKEz4VCOtwcgse+ro7aHZGhEKVjub6kIzUNqNdshJySlMCioX+REIsv9o4nU7KyMig/Px8uvHGG+nyyy9nGwnZmrfeeitlZ2dTdHQ0nXfeefT000+7P9fb20tvf/vb+X2UgEPJlV/84hde67700kvppptuoltuuYVSUlLoiiuuWPXtF3QSCv/5z3/Shz/8YXrhhRfoiSee4Dpvr3rVq2h0dHTRsvfcc49fbwFOMqzj/vvvp/vuu49PQF81Gcb4nXfeScslLjaW/4q3QqH2+MFl70szMjzYL+eGD5YIUwYXL5v+7k6anZEaWipWi5VmQ6j2zVoDr30oRTKY+V4YKnYJhEwzH4elOuuG+s9efymcwL1mfGzx+RruTdbc7doFzv3o6WyVPeETiTs3J+mQ2iZRoWS3htv9EILo7MxcUI/MwiTKrUh1fxbi4J6/nPQSCfE+lgtmfSsVY6OiotgmgsD3/PPP0yOPPEKHDx+mN7/5zRx9iMhDMDExQdu3b6c///nPdPToUbrhhhvo2muvpT179nit78c//jGnNT/77LP03e9+d0XbJqwtS3LXPf74417PH3roIfay79u3jy6++GL36wcPHqS77rqLXnrpJcrMzFxkkKMwPjzxvAE2G7+GE0bl5ptvprvvvpsNd6xfWFk9vmkJ610UcSo1Cj1MTU2wZzYuMVkutQXGRoa5JpCdpJi62oGyoLRazg+fbvKC/oSKXSINtDyg9lyUK2bJ+9DMOCNDp+bpejWdi4lL4OtS8Di5BQ+FZRtYLBQUEpJTQ2p/cDp9GDE3O08v/vnksj8/M+1df7L5ZDc/guG81yJLaun7GwLjU089RX/96185UvDBBx+kpqYmysrK4vcRXQgbDK9/5Stf4UhCvKa1m/DZX/3qV7Rr1y7366WlpfT1r399ydsjrD8rCiMaHFRCnJOSktyvjY2N0Tve8Q76zne+w2GrvsTFxdF73/teNtRxosFzD0+7FpyMavHM5dDY1LSsz5kRXOQiAHlTWLFJp6NhUCTsfxHSbdEbdNJrrvOfzhmuQEgOOcLgWjeqXdLc3Lysz5kRZ6SL7E5xwqhERkVTVn6JrsfEiLZrmAUcnTPC0maXrse+kcnjYyO6HROjEWGxhGZgSBjYJaEGyq/ExMRQZGQkl2t561vfSm9605s4swglWvCe+kBWB2owA7z/xS9+kVOOYYPhfQiFEBe1IOpQCA1sK4lUQ375hRdeSBs2eIowf/SjH6ULLriAXv/61wf87G233cafRVSXrzGuhiN/9atfpauuuorXV1y8tM5WWVmZYRnWHIjUzGy9N8FQnDy0h6q37dZ7MwxDVHQMZReU6r0ZhiK3uFzvTTCcATo+Kga5lq62ZkrNyKFQIFzuhUa2S+Lj48PmOJwLOC9DqWv4WjM82EeDvd2UKzXo3KC5C1IpBQWMHSVVSsSzoDAzPUUz06GTarvWTI6P0+jwAMXGJ1IoEG73Q4s1giP7lkJbba/fqEGkHWcVJy/pu5fCZZddRg888ABnVcB5iiyLX/7ylxzhjWwN30hvCIIADU/uvfdeLvMCsRB1DGFX+ZZyweuCyYVCpN8g//yZZ55xv4ZCl3//+9/pwIEDQRnNZwPFLS+66CL67Gc/Sw8//PCStk2cEx4mx0eps7VJOuoJZzUuBvt7KCOnQPbSAo01xyg1K4+iY+JknywYdBCUhdCP0oGYZlaMbJdI6rGHpjMnqKB8A5cBERSBO5RSBteDjuYGyi4skYYmGk4ceIGqxMntJtIVI2WENNjs9pAUTs1ul2jt6KWk/6JxiVYktNmtNDOt1MbG6xGWiDVpZKIKecig0LJ161aOGOzq6qKXvexlfj+HmoNwyL7rXe/i5ziup0+fpqqqqjXZTsGgQiGKWSIs9V//+hfl5HgiKmCMI/w0ISHBa/k3vvGNfFJpO+MEA7z3u3fvpk984hNL+lx0tNImvK+3m5799z8pMyubkpKTqamxgQ3TvPwCmpgY5wHV4XRSQkIiG/FQzM3m4ZiZmeFuaYKHlHSltoKgniPTNDUxLrvDiwjxOGj3RkQEZeUvLYLK7JRv1C91Qi1MPT01yfXe0FglNj6JOlsa+L7mio3j9PmWutP8HparrKyksvKKkOzWbAa7JDk5mdJSU9kmATt2nsc2ydT0FMXGxlF2Ti51dnSQw+mg2JhYnvQBM9ZoQ9q+dLT14HRGkSVeGoppGRsZWvfzUggtElPTw67O3dlwxcTpWvsVdgkeE+OjbIeg7ir+Dvb18N+s/CJqbaylkaEB3k6r1UKXXHIJzU+P0UCvp0mHELi7sfZ19e9aiYW+IOX4ne98J7373e/mes8QDru7u7mG4aZNm+i1r30t1x78zW9+Q8899xwlJiZyXefOzk4RCkOYJSlIGABQmPLRRx9l47qwsNDr/U9/+tN0/fXXe72G0NNvfvObnK6zVFD48pprruH1LoX6+gb+m5ScShe+7BKl09DsLBUUFtHU5BQLgjPTMzQ4MEBz83MsFB48sI9rO6RnZFJiUhKdOHaU11G9cRMvNzQ0SE5nJE+0WluaeR3R0TEU5XLxAKga9UYjMspFyWnehdvDHdRHEjwgksEuaWCLUuOMek3rRc3R/VS9/QK9N8MwwOCFeGpfZt0oeFrZsJ6b5Y6nuI/ExifQ0EAfjQ0PsVaNkgCnDu1lYSU5PYuN65b60/z5woqNNNzfx0Y50jjjEpL5Oo6KjqVIVzQLhWWbdrDQ9NILz9CJEydofHKG/qfaXOlroWKXoEYhUvhVmwTk5OZx0xT3BGtinG0NjrCYnaO62ho+T7bt2EVnak7R0OAgRcfEUPWGTXTsyGGyO+yUkZFFTqeTP4dzEfbL7MwMf5fdoGNYZm6hRAJp4GMlNRu9cEZFmc5xv1KSxJb3orezjR0OKRlSXgnMzc5Qa8MZyi9dXvSWct+Z5TkBaj/CwYjamK7oGC61AhsFDVOmp6eos6WR5mmeyjZsp+a6k1yaJhoOr4IyjgbGvScxNYOPD7JRUF/TYrVRTkEprx/X9uzsHNe3e8Xr3kxJqYtrB4crgURCoP7VSyxE05IvfelL9PGPf5xaW1spJSWFzj//fHrd617H73/mM5+huro6zr5wuVzc9fjqq692144WTC4UIq0H6Ta///3vuYZPR0eHO10HrbNRJNxfofC8vLxFxnuwfPnLX6bq6moW5oIldiFXXjUy8Befj4vzpBWlpafzQ2XnrvO91qE15qOiXJSYmETTC23nYbgPDw/T3Nw8C42nT51kkTE6vZCGB/qov6eTB8LKLefR6aP7+fvjE1NYfOjramcBIj4pldeDVvY2m50ndkrx5tU1jDDQr7QtutlobTzDNztBAeIDiqkL3gK7xSKRuEJgJifGaWx4kMU5XD8DPZ0cnQuhDuN4T0cLR6rnFpVTV2sTDQ/1c0R7cdUWOnHwRbJEWHiCAyO6v7uT7wvRcfEs8MUmJPJfrKd0wza3YQ3ikzwGoSvau5ZeQKeQiSfcoWKXaFGPJRyNeKgUFXun+qRrtnvjJkXgVe/nJWXlbHdAJJydm2PBcWR4mOLi46mpqZH6+3q5vlBMZinX5cU5gJqayKJAORKcbzmFZTQy2M92AsRmTOzQWRW/C+c1bBPt9q4WiIIVEci7RuH83Jx0gtaAeo0QKQQKON6HO7gvImJeUMCYijno2Ogw2e1Omp6eZKcj7JK0rDzqaK5fcCxGce36M8cPceZMZl4RO5e62pvZsVhSvZUGerrYQYlzLgKRihD7eK4aQzE2KyWmpLudPUU+DSKLKjZ6PfcqW7PwmYUNlkPnw1DvWECRUMWfWBiX5KK45NUJgnnooYcCvgcB+POf/zw//IEGJo899thZ1x8oi0O0CmOyJCsXhS3BpZdeukhhvu6662itQl3f97730fe///2gPzMzuzo3DtWQhSGOh0puXr7XckghAvXdo1xEVpuiV1y5ib0wWBe8xhgwMWgTzdPE2AgN9ffyYIyBFhE7MNgxEYR3pf6UEtWYU1TGqaFD/X2cRgxvUXe70kERAmN0TDzfGPAeUljwPfx9ERE0OjzIz2PivNOuBEFlZKifUwMgaAgKvV3tLO7bHcEXCzY7GbnLE1WMgmqEQKTAmAsPN7znGCPhsEG0Bhw5YyPDLKLg954+so8n8GlZuVxYv725ng1riH2T42PU3tzAYz4M7cnJCRZY4EG3OxxsmOMzEPzySrwLWPs2U9KOz75126R0hDnsktVCtUvU4uEq2gYspWWesRx2iTYSGNdBdFwCzU5P83nqjHLxhG1+4b3B3i4uWcJR1TYbtTSc4c8VlW9k8RsRrxARS6u30pljB/gch+MNtg3uIzj/E5LTeJKKawfnL4R0rsO3MEns7mjl60NQQPSoxSqimJaaI/upfPNOEZQ1NNefpupEacSnvW+Gem07NYoP4yUy31AXe6Cvm2ampsjmcLDzANF7s7PTlJKRQ+Ojwzx+wg6p3Hoe1Z44zPND2CFp2flszyCiLzUzh5dRbR2AcRl17fAc0ee+dgh/ZgHfUjMQBoW1B2JfTnkKtZzq8SsS+hMLsfxqiYSCsOLU46WylM8UFBT4Xf573/seP4JlfNw49dZgNGtr8WgHW6TAap+X+dS8Uo179UYSm7CQUhQRwR4h3FhgYEJ47O/uYOMeAz1uJD0drfy5xOQ06u5s5YgW5UaSRw2nj/E2pefk8012ZKCfjXk8h3CJzyG6AJExkxNjZLFYeUIB764ZogAw4RE0SMDpInCei3fLG0z61wt1zIORi6gBjHsQ5vAc0diI2oZxDIMXxm9z3Sl+D/VHsWxnWxOvp2zDNmquO81jIsYzdLNGIwWIG8lpGfx3enKCxz/UOoqOjecHhEKMkRWbd3mlSGrTYyq37GInjDq+I51SCwRCYX3OlVCwS4w0tmGSqKbMY7KtFap9O+/ievN9T90fcFrC/lCvAZzzEN2RkoZIFtSiwvfllVTSqcN72d5ISklnu+LYvuf4MwVl1SwwIpIRn0c0Cq5n2B2IsEVEC97Dd+A5xiFEEVmtEOEda5KJsd4kpcIODO3fsBJ863UAAOelSURBVNqY4biuNrI3vMG9eT2bdyhZYDM8/jgjo3h846wwu4MfsEtgt6B24ujQII9rs3OzbIecOvwSj5XxicmeQJCICMotLOPxcLC/lx0zrtJqbjAIw9wREcm2SWJKGtsq+E7YJ6mZue5rAw4bLVsvfIVXR3nMG1VcMRKRGgrkVaRRQmrMOcU/iIWrGUkoCP4wZW6d2Sb4Sqckm1dkiW/qLAxxFRYEF7z1uLFl5BW5J7vYN6XV29zFxHHjwgQZz2GG4KaLiBv8RdRAe1Md3xghQKIuBepUqKJmc61SlwIFdLMLS6n2+EE27vHd+D4Up8UkHxNoRCHghmp3RHKkAtKd8J4jMoqsFivfTPFXm2K3ViAqwncyFM7gGCB6TvCA812K7XsD8Q2psqqIx0REsJGL53B8ICIatW1QKwfe7662JpqenOR6U7juIdph2fTsPE7dVT3jFVt2seGM12Ji4ykjt4Cf4xjAuYFlMIbgOdaDcQaRUDCIMS5yzTPU2nQ4+D1tzSLfNBik8mrRiiQwwrWcbSzCtuP7pQasEG6o1wXOf+2kFBNZFVyD2usQpVhUEPmibdICER9Ru4ioARD/YXdAzJyfn2MxfwJjTJTLHQGv2DJbWYDE5xKSUvhzDTXHeR2IkEdpAETnYNxAOh3qe87PIaIynifQSPnHdiDVH69j/MI4ApsG9o4lIoLrammdBWsB0gVxH8b4JSggNVLwJr+sWnaJBghqg/09fA9X7BI4FlGDfoqvX9gNuJZVAQ8ZWLAX+ro6eNnUrFweSxAgAadfCcqCHHiRS0pBnEODsKbak8q+L6nk65Qjqh0OzgLDehFAEbNQMgTjlSXSRjabg+2UmPgEdmiA8k07vI6dd73nZBb/VGAfadGKfedCakmbg2DFPxEJhbXGlEKheCE9QOiDd0utbeIrOnK6so9xr8W39gRqHPl7Dzfpso07lEK4Fovi8bfZ+HmExcrGOMRHCJKICMDNHe8hWgFzjs62Zr5xF5Zv5DoaI8ODvF2F5Rvo5EG1vlI2T0raGmv5po73YOgj7Rqvo94SJgIQCnCDxueRro3twfdMTSqdrnu62lkohMjJYfg2OxsX2C6sd60nBUZDPScED0g/xTmlpmwYCU+qzTyfz7jWIMBPT01wzTLVWdDXDWNYmVzDsMW1gs8mp2fSyNAgjQz28bIFZRuo5tgBNq7REAOlDxrPnHBPtkeHBqivp5O64STYtpsNUeXaTeGIaKS54PpOyczhdEZM6jHZBkg5hHHucESyIY1rlKOsbTaKt1i80g9hpGtBNF+gyCYY8QFr4KwT8P7D+SEIwtKoP3mYhTsV3HMtFkTlkHv8ClT2IMoV7WWH+I4T2nQ6rAcTcNgduM9B1IcAqUYQI90XIiPGRTgpIBhgbEOkZFvDGX4NUTj4vtqTSrfwrLxiHmu721vYLqnYtJPqTx+lqYkJbkaUnlPA4oLisMjiiP2hgV5+np6dz7YPtgclBmLik7g+IWySpJQ0drbMzEzx/sCYDpEUDlw8Dze71mZXarQKHnAPR2qq0VCaMc2xaIZ7oir4456P7tW4vmCPY5nRYTyfpaSUDBbxkSmAaDlcK821p3hZBELApkBpD9jlhRUbuK4qIvhgl6Gk04mDe/iaU4T+JGpvrOPrOb+kisW/8bFhvoYgFOI7YJNgOzkyOTaen8O+Q11WOBhUGxjpvIHGEwiSKDOi4luuBw3HhGWcP5LWJAiGxZTqgHqTElBwf2xdjC0Wm6yKIAisZPNKvdNO8v3dYLXv+3bs8u20qjaCgfGMqCU11YnfS0zh99RGAPjpLE7Oz7GxAAFSTVXoaKnn1G0YGZgMtCDaaW6O64nB0IARg+9AJATESkQ9JiSlLhZShgeVBjYWC09a6k4e5nMQkxQYPO1N9bwtHJU5NcXRlBAkYXAgRVxJY3CxuIkCwlgW/0aEAwoP43lcYorb4FIKCrtY6MQPhAiDnBSlFiaiPKJ4MoR9gogIGGE8UcJ/CxMONeoW/0YEGMQg32iqUEH9LWqqEgtp+LfFwscdxxj7At5eRIyo5w7SSyfGx5RUe0ckHwdE0+I59vXoyBAfLxiQENpxPsBoxX7CepEOh2UxicTxx6QSglxyWha1NijdStWaMD3treylhle6s7XRbfAi7c6fED6/EAmHyaiakofomeMHXuDjCIMUneg6Whr4d6I7Ls4rpNjC+MW5h+1BwwxsoxqBh784H+ABx+fxHBSWVS+I5EpEr9Y4jktI4nN1eiESz7dEgm/EHjzpKtrGG2qTGLMQE58YMvWRcM4I+iPCh+LomJqcXLf9rXWMQjTQohUcUQsM93cVjM1afOt5+To51C7m+M7cojK+73Ad1LlZikW0EzsiYZNY+D6CUjEsnAwN8Lg9E5/I952ezjbuhJ5XWkUdTfU0OjLI9/T8UqRvv6R8d2YOrxuOYNwrIJywkDLYz5GJeI77CsZ0lGPA2N/R0sj2AGpeD/b10vjoEItx2QUl7GDlyCh2sLo4GwS/A44g2JC4lyDjA3Vcca/DiOKMjOSsk/GRYb5/4X4CO0OxQyxsU8F+AYiqwjGAMwrLwsmC2zaLSyzYKiKOeszwaG+qXeQQClWbRBXScLCwHxDtptyXYY8hfVZpOghhC/Ys76eF8xW2As4X1KuDw061DREVy5G2Y6P8WTj5YU/C9sPxhvMMXYE53T81Y0EIVyJxYWNDlJuammDbJiktg0sS4T1E58MO4mh/CPGbdvJ7ExNjLFLCFjhz/CC/l5lTwOtH5gB+R8mGrSzYTU6O8/kAYRzbgPOQxTirlbcZwQOqTabYYkppI/wGJTPAybZISVyC2yaB7eR7PeK7EWDAJUg01y6ck1q04h7QRkGbjVCqJS33Q0EwPqYUCs2WerwSYPQhDN5MqKIkgDCjTdfRihQgNdMjTMDIhfdQFSkRvagF6Y9azyEMaH/v+YtagEGknndYvypGIVIRaQQsRiHNGpEMC4ajUmsyciENXPk9LGyhmzZqoUxPsxiIZWMTktlghJHliFQixDiigeYpKTWTJyNowIFlCzDBaKmn8bFRiox0UWZ+EdWdOKyIWpk5/L0ojgxDrbhyM7U11LIBieULyzbQycN7lX2Xkc2Ga1tTrTuCFFFlvt1bASI1MNGCQKZOspDeMTiAuit2TruAyIVtQI0qiCzalA5EcvQjVV0VZg/tWUgnS2WxVZtOBmEW0XKQPqoWItwwOYlLTGKRrhHLRiiRHzCOeWITEcGTOUysYKTD4MX5gGg4iwXCWzYfh6G+XhZbYXgiqhCTGHjCYXRDZFSOnRLlAc80DGB8F5ZR6tTY+TWcZ3x8I6N4cuYsjGJDGMtgu/AZPMfv9RXCYVCr5PmkyPtOVrUpKYi00eJbM09bX0+tT6YSTOQkBEzU+FPFxXAH+wFp1YIgBA/GT6T6mg1tJgLsLi3arA1f8QtOnqz8Er9dzc/VBElb49pXSKncer6XYFVQGs37HiJMQnIKO4vUrqNwsPJ7KOVgtZLTiWhGxdkGcYudbXPKuti5tuAgwRiICEkWuXDPHB3m9EzVBsD9FcIV7CnYZpz6vdBlFeIo7s14Xr5pJ9WdOMSOOQhhEIyQZYL3YId5CVebdy4qU8HdWxfuebiPc4mchSwX1LtE40Dcw7MLy9heAOlZefz74GwDqn0AByEcWhDTfIXZtqY6fg4hljvZu4XZzYotBMEuPZsdua1oAhQRwfsBkYCIHMU68D31J4/wemDbQAxkhx9FsK0G4RgR/zgOeSVxXH8chxHHC7bKyOAAn2u436N0EKfIL5x7XDN3QRiH4IaUfs6aQUmOhdIc6nmWnqMI3RaLje0WRPiqNgmW0QrhxVWbz3oeajORfM9ZX8e/1iHta68vbrp4dlsDgipESmQqCB60jaMEQRBWQsS8iVS1oaEhio+Pp/N3X0AvPP8c/fyRX9M1b3zTun0/ugsaDRh1MFLFc6MwNTXJ3lEUFxYU1OLM0nxBOBuYNBVVblokMoYrEKwh1vtGHhmRfS8+R9e+4VVUUFRKv/q/f9Ku8iwaHBykuDjjpbGZDdUuAfkFBXSypn5dv99odgnuNXDsmDmqZ6nUnzrCzkbfiMdwBtGIKF0hCIEQez6AnVaxKSTqnd703rfR3//6J/r8N75Nr/6PN5rKLpmYmKD6+noqLCykyEi51wmhe36Ky8Hk1J5QPK2CAlJ6xNPmDVKPUaBZ8AAvNVLABPLumGcev9KK4VS6EKlRKDWABKOAVEk18lxQUNKSJVJbS1er0jhP8IBGG4IHpMMj+lXwgAye2dkQsUvEnhQEwyNCodlZSHEVyJ1moa1lIhCn1KIOkeB76YRG/bn1AuliqG8oeNILfdOqBEE4O9xMZKETqOBJwUT6p+AB3a0Fb1DnWPB21iFtX/CAWqaoaRpKSA3lxbScPLaqywnCchGh0OT4FvENd6CZRiEySnDDxaRFAPIC9RZR31Hw0FJ3iutKCR5QEF4QhOBBTTc0TxA8IMUWNW8FD6iXKHiTqKlfLCicPrpPdoUG1O9GUyQhdHnu1z+nX972Kdrz+9+cdTm8j+WwvLC6FBQU0D333CO7VYTCldf+0T6MCDqoCd41G3u4CYiggo7K6B4teEDXQIfUFVkUjRsqXX7XA7VjuCAY1SYxol0iNslikIqN2o2CBzQIEShg8zJBQfKlfMfXGZqaGJfTI0RBhODzv/kF//vfDz8UUCzE63gfYPnVjCy87rrr3N3nHQ4HlZSU0Be+8AWamZlZte8QQgeJKDQ53P1NcANjHN3fBG8PZGdro+wSDajZ2N/TJftEQ0JKKjmcTtknPpGngiAEz8jQAI2PisCuBR2FpZu8NycOvCCXlQ9NtSdln/ig7c4sKE5uqXcauuRUVNPL3nGd+7k/sVArEgIsj8+tJldeeSW1t7dTTU0NffzjH6fbb7+dvvGNb6zqdwihgQiFQljhio6ltKxcvTfDcEgdSz/7Qwote4HUOCk+7U1uUTmFBHIuGwK5fhZqFNokzVZLXkmF3IN9kdraQhBIc0JvEpJTKTYhMTTOHbFL/LLr9W8KKBb6Ewmx/GrjdDopIyOD8vPz6cYbb6TLL7+c/vCHP9Ddd99NGzdupOjoaMrNzaUPfehDNDLicfw1NjbSVVddRYmJibxMdXU1/eUvf3G/f+zYMXrd617Hna1jY2PpZS97GdXW1vJ7e/fupVe+8pWUkpJC8fHxdMkll9D+/fu9tuvkyZN00UUXcafeqqoqevLJJ/ne+dhjj7mXOXLkCL385S+nqKgoSk5OphtuuMFrGy+99FK65ZZbvNZ79dVXcyRlIO4+x+82MyIUmpzyzTv13gTDpR6PhcnFHSyRrmiKjonTezMMl+KTmJqu92YYiuHBfhodGtR7MwxFqNVHkrm3oDdp2fmUILXWvED0ujjrvElJy1rP0zIkyC+p1HsTDEd7S4Pem2Aohgf6qaO5nkKJcBn74CicnpgI6rH1itfRBW95p/uzEAe/8763eYmEeB/LBbO+lTopIbpNTU2xMP+tb32LBb8f//jH9Pe//50++clPupf78Ic/TJOTk/Svf/2LBbuvfe1rFBOjZN60trbSxRdfzCIkPrdv3z563/ve505pHh4epve85z30zDPP0AsvvEClpaX0mte8hl8Hs7OzLOi5XC568cUX6fvf/z79z//8j9d2jo6O0hVXXMFCJYTHX//61ywm3nTTTSv6/ZZz/G4zY2q3brh77+G5RwOCgrINem+KYZicGKfpyQm9N8NwkWKS9uTNzNQkTU1OcGdbQQHniNTR8iY8zFtBWD06WxtYKER0v6Aw2NdNOYWlsjs0RLrk3uvLUH8vxcQlyHkiBATR2pj7CcZjZnKSvvWe5Uf/TfiU7HjuVz/nRzB85Me/Ifsy6q5DR3nqqafor3/9K918881ekXho+PGlL32JPvjBD9L999/PrzU1NdEb3/hGjr4DRUWehqrf+c53OFLwkUceIbvdzq+VlZW530cUoBYIgQkJCfTPf/6ToxCfeOIJjj58+umnOdoRfPnLX+YoRJWHH36YJiYm6Cc/+QlH/4H77ruPoxwhWqanLy8A5JZz/G4zIxGFJmZ2doamp6b03gzDYZUOv16MDPbTUH+PXofDkEAkHB2WznFa0Kk0MVW6lfpGR4W7Q0oQlsLE2JjsMB/EJllMS32NnCc+DPRK3WRfSqq2yHmiAc7t+KRU2SfCivjTn/7EkYBI8X31q19Nb33rW7lOIaLzXvGKV1B2djanDl977bXU29tLYwv39Y985CMsol144YV022230eHDh93rPHjwIKcaqyKhL52dnfSBD3yAIwkhKCI9Gem9EB/BqVOnOO1XFQnBrl27vNZx4sQJ2rx5s1skBNgWNGLE55fLk+f43WZGIgpNDCaw0bHxem+GoUhOy9R7EwzHPM2HTeh/sEREiA/FFzQgQERuSrqkhKnYbDaam501fM01ETONgRwHTGQjyWZ36H0oDEWFlIhZjNgki3eJRewSX9qb6qigbHUbOYQyVpuV5ufnKBQIt/uhzenkyL6l8NKfH/UbNYi04x2vfcOSvnspXHbZZfTAAw9w1+OsrCy2dRsaGjiyDzULEcmXlJTEacLvf//7OS0ZKcHXX389p/7++c9/pr/97W90xx130F133cXRiEhfPhtIO4b4du+993JtRKQo7969m9e9miCN2Pfcm56eDrh8QxC/28yY8q4joocCjPFUadzhRWdLI/V1tetxWhqWhKQ0SkiRenxa4pNSKLugRLdjYkTmZmdoYmxU780wFL1d7TQ1FTqlDOTeKOhNamYu2UUo9OLEgRf1OhyGpVBK5iyicst5ehwKQzM6MhR2gtPZiaCO5hCr2xgmTgHYX0j/DfZx4K9/8hIJI2M85TrwOt4Pdl1Ltf0QkVdSUkJ5eXksEgLUFERkHoS/888/n9OG29raFn0WUX9Iy/3d737HHZN/8IMf8OubNm2if//73wFFuWeffZYjElGXEE1QIBT29Hiy3crLy6m5uZkjD1VQh1BLZWUlHTp0iGsVatcLcRCfB6mpqdzRWQW1D48ePRpwX+wL8nebFVMKhYLCyGAfdUqhXy9m52bC5qYULGOjQzQ+qhSLFTyNO1obJPXJ1/Fg9Mi59cZms0s9IEFYAmeOHZD95YPUfl1Mb1f4TMSC5fQRZcIqeIhyxYhQqAGCyFyIRBQKgfHX3fjDP/pFwG7I6wGEQ4h83/72t6muro5++tOf0ne/+91FtfxQz7C+vp47Fv/jH/9g8Q6gocjQ0BC97W1vo5deeolqamp4HWpKMFKO8Rzpw2hW8s53vtMrChG1CIuLiznyECnNEAA/85nP8HuqEIrPIF0ay0D8w/cjmhGpwmp9QtRCRMQjHuiijEjBgYGBFf1uM2NqoTDcvUxQydGoQvAQ5YqlyChzhwkvlcnxMZqaGNd7MwzH7Mys3ptgKFwxcZQmEcpeZOWXkCsmlgpTo70ewbCczyyXcL8XGgU5Dso+kMhWbxIlot9v4w5hcd1xGUMWd4KW8cSb0uqti+yLYGyM5XxmpWWPhOBEwl2vV5qg4K9eYiFq/919993cFGTDhg3085//nFOLfXUHdD6GOHjllVdy9J3a8CM5OZm7BaPu4CWXXELbt2/naEO1ZuGPfvQj6u/vp23btrGwh+jCtLQ097qtVis99thj/PmdO3dymrPa9RjiIEAaMITKvr4+XuZNb3oT1xZEQxMVdFqGkPjud7+btwMNV5BqvZLfbWZMqSLJTUMhKjpGRDE/nfQk7WlxN1upfeON3eFgAUjwgLTj9uY6KqrYJLtlgeGBXrLCyEmPC419ItHUgs5k5BbqvQmGIzYhSe9NMBxSx9J/SRTBm7pTRyivuIKckWevfxZOtDWeofKcZAoVZM7uoeXksYAioYr6XF0Of7PKKymnYnVqdT70kOf7ffnoRz/KDy0Q9VQQdXc2kH4MIc8fW7duXZRKDKFPS0VFBdcHVEFUoRr1p4KOyxAkAwFhEuLl2ToWoy7hUn63mZGIQhMzOzMjApAPXW3NNC511rxIyciWJi8+2O1Oio5LWNsLNMSwWC2S9uQnwmNy3PjRuBKFYgzkOBDNS+rkIppqT+pxOhqa8k079N4EQ9aTFlHFT6qtjClejA4PUSgg98PFQOzb/aa3BxQJVbSRhVh+tURCo/Poo4/SE088wUIeOhHfcMMN3NUYKcnC2iARhSZmsK+H0wVRw0NQmJ+bZcNC8NDZ2sgp6hAMBYXJiTHqbG2iwvINsksWsNkckiLng8MZSdOr3JFtLZFJpr7IxIios61J7jXCOTm+/3mq2rZb9pSGlvrTbJNYHEvrYGpmklIzpHayD5GutU0ZXu37odgl3lzw5ndS3sYt5xT/IBauZiRhKDA8PEyf+tSnqKmpiVJSUujyyy/nJiPC2mFqoTDcjXJ0KZXmA96kZaPluqQoaOHrRFISvYmICPvxwxeL1co1QgQPsQnJIREhJQa5IBgXpE4K3oS39R54bhPu8xpf7CKa+q3bKLVgQ5tgxb9wEgkB6griIawfphQKBYXM/GKuPyd4GBnsJ3uqk2SveHBFx7IIJHiAmJyenSe7xIe2xlpKSPYUF9aLtS6yHSwDA/3U2txMlLlpxdvn7zP13aMr2j7BWMgkn6hsw3a9D4PhOh6PDA1QbHyi3ptiKJKkwcsiMvOKyGpTCv8LCgM9XVzj05Gkb5SlUWwSsOeFQzSdsoMcDseq2yWraZOIA1MQjI8pczAlolCho7meZqZDJy1uPRjo6wmJCKD1xBEZySmUggfUvBkfHZFd4hvNIHvEC7vNTtPT04bfK2KQG4NwFwohirU21Oi9GYZibnZWOvz6QWoE+28ohvNF8AAnt9QoXNysYWZmxvinSZjfDwUhFBCh0MSMjQxTRIQpD/GKkBqF3vT3dHGkpeBhbm6GBnq7ZJf4UFwpHY+1OCMjKT0jI2TscakFJOjdYG1qckIOgoa5+XkpEeOH5rpTcp74MNjfSzMzxndMrSepmTkUmyDRuFqysnNDqkyM2CWCYFxERTIxiBKTGoXelG3YRja7dzh+2CM1Cv0gtYD80dXaHPaXixYY42LkCkET5hEUiKhEgzXBg8PhpJKqLbJLhHPC95owH0N8GR0ZotGhAb03w1A4nc6QiLKUTAdBMD6mrFHonriF+Q01u6BEoud8OHV4L5Vt3CGTew1JaRlksZhyKFiRyF5SvVXvzTAcw4N9pqnjs1r3mrraM5SRmbUm6w9mfwVVM0i6CxqCcE89hpMuI7dA780wFONjI9TV2kT5pVV6b4rhGjII3qDjsTimvJmZmqLZ2RmKX+bJYjabBPT0dFNsbCxFRUUZ1yaR+6EghASmjCiUGoUKNUf363wkjAfqdoih5c3YyAhNTY7rdESMyezMNNWdPKL3ZhgOR6R0DA9F8cezjQtONEHQATTtaG+slX2vATXnQmEMWW8G+3r03gTD0VJ/mksKCd7OBykn5I3NZguJ2slu1OAewc1QR9uqLicIy8WUQqFKuBtf4f3r/ZOQlKL3JhiOsZEhmpqc1HszjEVEBE1PyT7xpbhysy6Hw8hs3b6TjM683A0MQbjbJKhRaLFK9LoWlIeJiUvQ7ZgYlYG+br03wZCiMhoCCR7ik1IoOT1bdomGrOwcSs/INPw+kdRj/zTt30NH/vwotRw6e7AP3sdyWN5oPPTQQ5SQcPb72u23305btkjZDaNjSqFQIgoV0rPzdT4SxiMhOU3vTTAc8MZaLOLR04ImQLHxMnnz5cTBF9fxzAwN6mvPkNERg9wYhLtQGOly8cRe8GCz2SkmXpox+BJKzRjWC9T3lLrji8uhSCd1b8bGRqmzo51Cxi6RTAevCMHmA3v5340vPR9QLMTreB9g+dWOLOzu7qYbb7yR8vLyuOZlRkYGXXHFFfTss8+u2nfceuut9NRTT63a+oS1wZSuXREKvfeD4KGh5jhVb9stu0RDZl6R7A8/4mlSmvE9sutOmAsd/hgYMH7HcBEKjUG4C4Xzc3MidPgwMjRIo8MDlF1Qqs9BMSgVm3fpvQmGIy4xmYVlwYPFYuVIS8HD3OwcDQ0NhUymg0xVPcRlZFH+jt1uEVD9m7N5m1+REGB5fG41eeMb30hTU1P04x//mIqKiqizs5NFvd7e3lX7jpiYGH4IxsakQqHyN9yN8o7mekoWscObMD8n/NHWWEvRcfEUnyiRHioYO+pPHaWqrefremyMRspCio/RCoD7K57tu43BLLMcXC4Xny96OWaC+Z0iFBqDcLdJUHfOGeWiKJdMDlSQShphMWVyz4qj1yu3nKf3ZhiKrrYmttMgGAqexnOxCUkhYZf4275gllkqDoeDbDpG5AbzOxlpsuYXVRT0Jxb6Ewm1IuJqMDAwQP/+97/p6aefpksuuUT5nvx82rXL47y5++676cEHH6S6ujpKSkqiq666ir7+9a8vEv4ee+wx+sQnPkHNzc28rh/+8IeUm5vrTj3G+wcPHuTn1113HX/3RRddRHfddRcLlW9729vonnvuIbtdcZBMTk7S//zP/9AvfvELXnbDhg30ta99jS699NJV3QeCB1NaJ2oYczgb5XpOXI1MbnG53ptgOLjmTRhfK/7AtRPO40cgIqOjaW5uTu/NMBQbN4dAd2wxyA1BuI8ps7OzklLqA+oTJqVk6HNADIxEiQWyS+T+qwWp2HaHc93Oy1AgOiaGikqMH6Ecbg5M/N7Z6emgHplVGyl3m0eYgzj44k9/6CUS4n0sF8z6lmJ7qJF+EPEgzAXKuvrWt75Fx44d46jDv//97/TJT37Sa5mxsTH68pe/TD/5yU84ZRnCHoS/s/GPf/yDamtr+S/Wi1qHeKjcdNNN9Pzzz9MjjzxChw8fpje/+c105ZVXUk1NTdC/T1gapowoVEMKw90oL6kOgQnsOjM6PERxCeKN9a17Y3dG6nZMjAgMl+z8Er03w3B0NDdQdEyc3pthKA4f3E+V1RspKsq4HaHDzSA3KuFuk2Tk5FOERWrPaZmanODzIpKMFQ2lN1JPejFJqRnc5VfwbpCE7KlYqfPpZmZmhva/tIfO231haNwPw8QumZuZoRd+8v1lf37Gp8Fi8/49/AiG8999A1kXovKC6ZoNce4DH/gAffe736Vt27ZxNCBEvk2bNvEyt9xyi3v5goIC+tKXvkQf/OAH6f7773e/js7b9913H513nhIZDuGvsrKS9uzZ4xWdqCUxMZE/gxq1FRUV9NrXvpZTnrEtTU1NHMWIv1lZWe46h48//ji//pWvfCWo3ycsDXNGFIbJoHM2ZqanqautWe/NMBx9XcYv8LveuKJjySEe2UVMTozrcTgMDbyIiAoSPKBmFAyiUEDujfoS7kIhbJKZaekmr2VsZIgmxvyk5YU50vTGv10PYUzwtkmkE/RioQdiodERB6ZxQY3CtrY2+sMf/sARe0hDhmCoRvc9+eST9IpXvIKys7MpNjaWrr32Wq5fiChC7Xm4c+dO93MIf+iEfOLEiYDfW11d7ZV1kJmZSV1dXfzvI0eO8PyjrKzMHfWIxz//+U+OQhTWhrBuZhLR0U7zQbSQD3Y5IzEzMyVChx9koryY7vZmik9Oo7iFOi+CQk9HC6Vn58nu0NScSdu5lSLXOHLOby2bNVrPatQHysjMctdPMQL+fsPxOCU1y2m3Un6KRC6FslAYrD0S1dtJ48npZDRRLDl9dYuuhzoo5WC3m9IUXxGNZ05I4zkfRocHlRqf0VLjU73Pzc+7KDN2O8XERpvSJlmOXYJ5Tm5ePhkJf7/BaVNildLiIsPCLrHYbBzZtxRajx70GzWItOPsDVuW9N1LJTIykl75ylfy47Of/Sxdf/31dNttt3E9wNe97nXcFRmpxahR+Mwzz9D73/9+riuIut3LxdeWxrmsljsaGRlhEXHfvn2LSphIU5S1w9QRhWczym3795Lzz4+R9dCBs64L72M5LB9SzBNFucw/8C6VSmlOsYjwjnE5CxKZvIje3p6ANUvClcioSMNHionn3his9DwJ1m5JrjlKBc/+lVJOKkXCjQJqiaGmmOAhI6eAkqTp3GIMPqbqZpPIfvHZJRHU0iLZU744Q6CcULjZJfidSP8N9tF+/IiXSGhzempx4nW8H+y6VmMfV1VV0ejoKAt1EO/QcOT888/nCD9EH/qCqNaXXnrJ/fzUqVNcpxDpx8th69atHFGICMOSkhKvR0aG1PldKyzhWKMQHnnbAeXktb/0QkCjG6/jfYDl8blQAV7H9JwCvTfDcNQc3a/3JhiOlIxs8VD7oWKz/xoa4czw8BBNTk7ovRmGorenh/r7+8jIhJtBbkahMFi7BSJh2gnlPpd6+jBHFhqF7MJSTtUXPKC+2vCAsccPPcgrrtB7EwxHenY+JabKhNiX3p5uXY6HkTlTc4qMjtglgfHX3fi8d13Pf1XwPpZbbZBC/PKXv5x+9rOfccOQ+vp6+vWvf81djV//+tezMIdyO9/+9re56/FPf/pTrmXoLzrw5ptvphdffJHFRXQ1hrAYqD7huYAg+c53vpPe/e530+9+9zveLtQ7vOOOO+jPf/7zKvxyIWyEwnNFFCJtZ3rH+e7n/oxurUgIsHwopR8P9nVTZ0uD3pthKHA+oMaL4M3E6IjUvfFD/cnDcqr48VIbPXpuvYExZPRxRQxyY7CSaycYu0UrEoKuym2GSj8+ddgTXSAoTE9Pya7ww8jQgOwXH/q6O2igV6nXJXhwaCKthNC7H6LOpHB2kTBn8zb+N/6utViINF40IPnmN79JF198MW3YsIFTj9FQBI1GNm/eTHfffTd97Wtf4/d+/vOfs1jnC1KQP/WpT9E73vEOuvDCC3m9v/zlL1e0bWhaAqHw4x//OJWXl9PVV19Ne/fupbw8KRO1VpgyB0QddNS8dn/MblY6AqtioPoXr/sTCdXlQwUUPLZaTXl4V3RTikuUjse+DA/2kyMyCrnquhwXoyKRc4spKS0TodCHtPQMw+8T9V4oBrm+rPQ8OZfd4isS9pZuWNH3CWtPpCuaU7KFxaJYZl6R7BYNc7MzND8feF4TruzYqXRVFTxs3rrd8LtDHJiLGepoCygSqqjP1eXwNy49g+IyVqf+r9PpZOHPn/in8tGPfpQfWtDQRAXRg3iAa665xu86br/9dn6oqI1StNxzzz2LHPOf//zn+SGsD0uS8XHSoIMNOtykpaWxkoucc5W+vj4OM4XKGxUVxQrvRz7yERocHPRaD7roIIQUy/3pT39yv97Q0MDRgFj38PCw12e2bNnidUKtRjMTGNe+Hnrnz/435EVCgGLHMfEJem+G4ZBC6kKwxMSFx/WDItPneqjU1Z6hjvbFtUiWCwp3+z6M8DuXwuTEBHV1dpCRMbNBHip2ybmcl8ESjN1iVJEwLTNX700wHGgixo46wYuICIky8sXhjAoLUXmp9+rnnvnXqn7/etkka2mXtDQ3rsr9Zi1Rtw/310iJa2Eg9uVu3RlQJFTRRhZi+dUSCQXBlyXdidGC+sMf/jC98MIL9MQTT3CO+qte9SoubglQzBKPO++8k44ePcrq8OOPP86dcFRQCB/ruP/++zmEFV1z0CVHC4xxrGO5qJOhYAZJX6M7QlOoP1RFQhWbgTpxGoGZ6SlqqTN+3Y71Jr+0KmxEsaXWAxK8QUQaigkLHubm52hwwNhpcmYWCkPFLgGrFXl6NrvFqCIhkEYmi2ltqKXJiTEdjoaxqdrmOb8FBTj/Y2LjZXcI52R4aIibSRgZM9slKyFv2y7a+No3BBQJVfA+lsPygrBWLEnDh3GtBQY3vOwoUqnmsf/2t791v19cXMyts9/1rnfxgGWz2dggR1treOJ5AxZeczgc7s/B+4/8dxjuWP9SCTaiUGt0244c8DK2553OkBYJ+7s7KTYxiZyRkk6qMjc3SxFSC2MRzbUnKTk9m1wxset7khqc2hOHqHqbpxaIQBQXL5MUX+x2B1ms1pDx3JuNULFLwGqmqPuzW2bsDsOKhKC9uZ6SpBmDF/Nzc1ISIEA9y/JNO9bpzAwNBnq6aHZ2RpyYPmRmZetzQAxMdHSM4Z26UqMwMMFGCEokobDWrCi2X03dSUpKOusycXFxbHgD/Pu9730vZWZmUlZWFnvukTKk5e1vfzt31fnCF76wrO1aqlCI2j5aY5vXgYlDgK6CoQCMCalR6I3d7hQDK8C5Mk/GrrGmCwavO6cHrigXOcIg9WkpIJ21orKKjEw4ee6NapestlDoz26xTU9xQxPDImPqItKycsMinXQ5GSCCN3B0G70erh7ES5mlRVRUVVNkZCQZmXCySwQhVLGtJELhlltu4U428Nj7o6enh774xS/SDTfc4PX6bbfdxp9FGpuvMa4OGl/96lfpqquu4mKZiABYCmrB9mBuqL6NSxBJqBrf2kLhoUZmfrEIhT7MzEyL8ekHV4xnwixorqEwKaTuW4PnbDVxBgcHOKWlpKx8Vb5rPVnud/vuD9/15CdH0YvPP0u7L3wZGZVw8dwb2S4BqzXJP5vdojY0MWJkYXGVErEpeJgYHyOXpJMuQhrPLSY6No7mDB4ltlb36rPZJSdPHKMLX3bJqn3XerKc7/e3L3zXM9nTQKmp6ZSckkKGJUzsEkEIZZatDiD9BvV+nnnmGb/vDw0N0Wtf+1qqqqryW+w7/hwpbFdccQVddNFF3JL74YcfXtK2JS9EEkxNTtDxY0coKSmZEhOTuKi5w+mg2Ng4HpgcRw+Tfd+Li2oSao3wUBULu9uaKS07j9OpBIXpqUkaGuijxJR02SU+BrnNLhEN/jqHC94gxXZ2zvwTlaWAe4neRcMhQEHIGhke5tp6NruN7DY7tba28POpqUkWtgry82nPC8+RWTGyXZKbm8vH4sihg2R3OKiwsIhGRkY4PQyRH7Fxce5U6LNFWPiKhP7sFiOKhRhPu9ubKbdoeU4Gs9Lb1Sbp2H5ITpPi/P7GeaOnk+oBxkv1Higo2Gx2Do7QExwT3NPGRkf53peYlETdXV00PDTojo6FU6+zvY2bhgmCYBKh8KabbuKugP/6178oJydn0fso+n3llVeyV/7RRx/ldtbLAd773bt30yc+8YklfW5gYNA9qc3MzCarzUpz8/M0NjZKAwP9yvbs30sxtTXuz9SlZVL90BClnjxBWUUlNN7eSrmtzfwejO/h4SEar6xm0ZEnYgaPwBoZ6qf0HGnG4Fuj0CKGxCLaGusoM7eQO2ULHjCpTc1cPL6Fe4qPy7W8LnxmJjtn5d1cVbERXZTHxsdodmaW0tLTudM06uVBSIqNiWXnF5atqt5IvT3d1NnZwfej83ZfSA0Ndewcwn3KmZBIMTGx7BxDB1EY4nn5BbTr/AvIjBjdLmlububjlF9YSFOTU9zYA41XMGkaioig6JgY2v/SHp5Y5eTmUVSUi2pOn+TPbt66nTtrx505RTktTe519ldU03xpOTlnZ2mqspr6Ricp/cQBQ4qFmLQiek7wZn5uXmon++6T+XlqOH2UqqRGsBcTY6M0PjpCsfGJchlpqN64SfaHD0nJyWSz2lZ8HcLWgACLTBLMfVFqBQ3cOtvbaWp6ikpKyqiurpb6+3r5nrVx8xbOsACwN6JcLupoa+N6v6hxDXskEfZJZCSvG03IPvXfn6WCggI5hoJgQGxLHTRQ0BtG9tNPP02FhYV+PfbwujudTvrDH/6wohoJu3btomuuuYY+/elPL+lzbi0oIoI9GCrFJaXKyx3t5NSIhPDIZ23eSlmagXHugotp8Nhhij9+hJdJOnWcaux2Stq1m8Pcx8fGaMrqomQIjKeP8TI5BaU0OTFOg/09PAnJL61msYEogqJc0ZziOTE+SlabnWvSrGW0n83mIItFogm1xMYnUUycGFiLmccpKizeK4KfqKC+3h6KjhaxUAvEU4g+eFgX0mgQwQ5xJDUtnXp7e6i/t5fvLzCkn3/233yfycjMooTERDp5/BhHJlZt2MgGORxTTmckC4VYNwxs/MX9FEKfeu/A/U2bBr5h42av7crIzOS/cJAgEsWMKT6hYpcAiIBxcfFex0c9RgBirxYcfzVdOddmo1iNSNhVXEqdKWkUPzxEE+N2OnPmNHUPj9J5xdWUU3vMLRaOJqbS4fYWFiYTk9MoKiaWBvt6OOIkPimVZmen+Vy02R1rXH90niKjpLmaL+Wbd0gklF/EKFm0RyKkRqE/entgk8QYPoBjPYmKjKKp6Wl2Mk5PTZErOpoDZTB3hf2QnJJKNadP8Xu4B83OzlF93Rn+7LYdu+hMzSkaQi3f+HiqrNpAzc2N5LA7yJqWxrZJfEICR8bb7HYqLSvndaoRnb5p4Ckpqe5/4x6sgvuOWe0SQTALtqWm9SDd5ve//z175Ts6OtzpOvAywBh/1ateRWNjY/Szn/2Mn+MBUlNTlyWMoTthdXX1km4Alghl0AmUDjafkUkzW3eQ7cBL7rQdFQx02E4UN6fdF9G0K5ojCrF87radvMzmLdu8akJoO6Ni0ItPSqHZhZBvhzOKpqcmeIKASSOEQ/w7LTOXxsdGqaejhZer3Hoe1Z44TFMT4xSXkERp2fnU2nCGjfnk9EyeLMCbCGMfxj0ESWwrBmks40tBebUMvj4M9ffS5MQYpWXlBX0uhQPYHw6HsYse60H5xu16b4LhwPjW19tLuXn55knlmpnmhj5w3mCMRUQfRA2Msx3N9TTRbeHSFTCKz5w+Rd1D41RYvoF6O9t4TOlJiuXPwGMOB1F6RibFxsVzBDuew3SGOIRoQDWK7fwLLvISB7SGtW9hdq2QtFzUe6EZDfJQsUtUlpoipy5rzy/wslviNm+lOM1yu5J3s00yDBHR4WSRsLtsE40np1FhXCLbH+rxR6duPJ+fn6PhwX4aHujn13FenzjwIr+O7sTRcQnUXHuStyG/tIpGhwdpbGSYr5XsghLqbm/hzA04Qh2RUTSNJnA2G4uOvuea3RFJ2QWKs1bwALuvtDq0StusBzi/hMVlYmITxNnty0B/H3c+NotQiOwnzBNRww+BJSODfeykjYqJ4zrrp3ub2ClZXllFp04eZ0FvfN7B10zNsQP8udGsNLZBMA5jvK+s3kCjIyO8Xjgdcd+DbQHxLzIqip9nZXu6R2/c5F1P1tcJuRrOYmlmEpjJhkFyFsSv2nKCsFyWNKo+8MAD/PfSSy/1ev3BBx+k6667jvbv308vvqjU/EN3QC319fXLCi0uKyuj973vffT9738/+A8F0fV4ZttOms3KYdHwbEBEnEvPOOdyKhhsrdYo+HP4eUKyx5MCCso2eN3007M9ohWMRWyzut0ZuQV8c4BRjvp6mMyi7iKESExSEZ3ojIzirnlnjh/iCWlGbiEvd/SlZ1mMLN+0g9qb6mh6eoojGlMysrl+IYz5mPhE3l5McvE3KjqWb1DwWuLmYrZ6H5gYhUMh6KUyOT5GzkgXSfypN41nTlJxpaS0aMG4gXFBjwLgytiINBgLj4Nq/R2IFP09XSz4YYwDvV3tPHbmFJZSZ0sDDQ3281hZWL6RTh5U7lFIK4cjp7O1gY3x3KIyFkGwbjuaQ0REkCsmlqZtdhqcdZJtxk7JBRsodcFz7tLUWju27zmqrlQcSWgpMTlBZInPIow2bcP4P0QTC9HUPNGo/313tmLtq7HvgNnG9JCySxZYSS2tYO0WpBuPJaXSeHI62wUQufFQwbWgkpqRww+Vqm3ne62revsF7vPH7oyk6Nh4z300gmh6UrEfYHdBWMdENCuviEX0/t4utiUqt5xHR/b8i0ZHhim/pJLrBLc11fLEHo4q2DdwnGJ8SUnPZvES+8jhdLLAODc7QxarzZRCN8YbwRt2jI+PkUx/vRkbGWKxPj3bHI661QJjG5xhetklSnou7vvjbHdgfMWYNjoyRHMoIZKdx2Mj3ndGuXj8qz+JEiKzlJVfzFF9SvYZUcXmXdR45gQLgjFxCZSamcvjIWyUSFcMO2HGZl1kddmosXecHMl5lJZqdY+NauAK6rFPjM1QbmEFP28dnCaKUhqboAAEPksUSWykTCiNsNbbLkEKs1ntkpUw+EQjDT/VRHFXFlDcpYHL2gw93UxDjzdQ7CvyKP6V+o0JqPf82GOP0cGDB4P+DOwuNI/DQzBZ6vHZgKG+kq5+OHH8ff573/seP4JFHTDPVWA+WPEv2OVWAwyY6qDpivZ0XnQsGOgqmABr0UY1AhjfVVsVgz8d4uH0NBv1WDduVBAT4XWampykwd4ujhTCOtua6tgQwY0ut6iCTh95SVlHdj7v1/aWBo7YLKrcRD0drXzjQzoRRM2O5gaOLkD9FEQ6opYKnmO7Z/D9qpC6EAG53jcHeNUkHXsxA73dHDliJ89EUkA3Sn274RkRRGht2rI1oEHOZRtmZ3g8wZg1PjbC1z7GjihXDPV1d7DIEBOfwGNQf3cnL5tXXE6tjbU0hpTbKBc3PDh9dB+PURh7UPi6A2OPxULFlZt57EGktjMqmiKjotkot7KQYOWxByUhMPbAqM4qKKUcjeMDwocWrTPHmeGdGpmQnOb9AwPoFIUVG8nImFkoDBW7RHuNrETwCtYegUi4WqjnDVKTtenJWoERFFV4HCuY5GISrJJXUsk2AbrJY3/mFJbxWIFxAkBgxCSbm/IM9fPkGU6A2IQkajpzgmt2ImIGYwocpQA17BBBMzM1SXEJyZSSmUOtDTVsZ6Rk5PBYA3uGBciMHBoe7OMxBYInxo2pyXEWIJUGMvo4R7GfBG9wfvR1t3s50gVl7EDWkeCNO/ptzL9dAluDnRsL8ytkDmCsgSMQcxgIsFwKJKeAxw9O1Y2J5WAKjD34LMarsdFhdkICzK/cY09iCqVkZHFQBuwQBGTArsHnMPbgfMY4FsdZYA4e88o37/S6DyDgQ6XIx57Ad2vBuKgS6F4SHRPHdQCNjJntkuWCCEGIhAAiIPAnFqoiIcDykaUJqxJZeK5jcdttty1qBHfrrbdy+ZelsHfvXq+oVHwvysdcffXVS9xiYa0xR5x2oBN9BZODUCc1I9vLuCeNcY+UIi3awsh5xYr3ScV3Up2UlukubpualcteL4UIboahRhrgJjw6PMQTARjkEAhGhgb4JlpQVk0nENUzP883VAiNLQ1KbQw1pW94oI9TmSAK1B4/yJN+pALiO/q6OvjmmJyexdFw8Mhz/aWUdPaiKZEIkXxDnpmeZPGAvXHRMVL1xg/zhAiXlZ1vZiQ6xiPUhyK4BlVPN4Q2XCt4jujR6elJNpZxvXKUcXszRx3jvYzoLKqtreGJeW5+Pnesa21pZsN3+87z6C9/fIwcscksLmMiV3v8EH8fRAFE+fH1abVSUflGGujp4tcgEka6ojkiENcqxgFcn7iGsSwm6xABcK36esZVIP4FSkuDga9FFR/WCwieHJVr0C7zZk49DjVWIlqGMrg+cM2rzlBtvUK8rkYDg6w8j8AIyjcp0boAGRXa671swzavTAyMDeyocETSzMwUOaej3J3aFXthkiMVUbIF5V2wbFpmDosDXW3KBA2ZGIgoh7MI94Gs/BKqObqfx1F8N7YfggHGKzg5YN/g83CwJqZmUF9XG49pEBuwDMQIXHt4rjpNMe5hHNQKBIJKeF4j5wKCNuziUEV1JKo1Ubks08L1gOt/oLeL38PYgOuHny/YKC3NvdTf18fXEZqX7H3xeaWMU3oGl0MZGhqg8TkbRyyjButAXzdZLVaq2LLLbaNgDoHSTsq1a+F9qdhIFnI47O5lcH0jkwvbWFK9la9TvIYsMN+xJ5CjBOC7AjkE1locw++CLQbHjFFR7RKj2k16ALEPkYSqCOhPLNSKhPzelQWrln7c3q4I4eCXv/wlfe5zn6NTp065X4uJiVnUhR2vaV8PBpR9EUIDUwqFwUYUmpm1nCir+9c3ugBCnRat4Y+uulp8RQDcnN2fKyp3h/OD3OLyhSgMiAiIWExwP1e9hRwhuVCHEIaIKzaeXNEx1NZYy8tCxGg6c5JGRwb5ho1UqJOH9vLnEpJSOHKooea48n3wHI4MUW93B0dPwtCoP3WUl42NS6DE1HT2HMK4gAEDQwciaIQlgjJyCheippT0A4gpQ309PMFQ9sc8p3rDSECkJSYQLJ5Ybe4Uc+xfGElYRk1T00aarjYwbsSjt5jMvGL3OYj9g9RWHA/1vMOxQuoEjFocU4jm6nGFoYrjiug4LI9IFnS3TExJo5GhQZoYG+FzAtFyLXWnuCs7zi0sj8g5fC8mvDCUsS6caxWbd7LADsMZ1xqMVqSoQHBHtA6+A8IclkXN07qTh3l7sRwm152tTTw5hRMB245JM7YNy8AgVj3dSOPJzc1Xao9FRXF9PTTdgDGHZVGDL7N8e0BngjbKSBtRBHzrg2pTIENZxEKKEc4Poxq8IhQah3AVCtXJ91qtW72HwTGpgrEMTopA4w8ckVq0Xe59y05gnFOvo/m5ObdjFPdqjPkQCtUxDMeYy8XMRNEszXDWBj7riCzmaGhEOWJfIHL6xb//hZ2uGJeRZo2UbPW+jDIxw0P9bGcVV21ZKJsQwdFLSENUo6xR+3F4oJdLJ2AcR3YIRFDlfhTHkde4j1gsEWxrwbmKB8Z/RFSzg5VTy51ktzuVGtiWCBZbcR9TxRTsT9hByv5euwhMbJdWHBYUcBxtDqe7RJAivM2ysxcZMzimeA/nhM3upPHRYT4XYVfAJmXn4Nwc27s4H+BIx3Koe4h6ozivYS9gGdgSOPawQ2A7IJIXTSwgPJ05dpBtH1wvsF3bm+vZDimp3sKZRbCH8Z2FZRvo1OG9LPvi/IYw19nSyNuXW1zB9g22CXYH7GPOQoKAx47+CP4+RVC3UmJ8MjfWwPepneDxOpZHvb7C4mIanlecD7Dxtc7EMp960xATvaLzziLohapdgksT17WRhUKJKPSPKgr6Ewv9iYRnS09eKhkZnkAi1HnGGK++hmZxl112Gf3lL3+hz3zmM3TkyBH629/+xq9rU49R8mVgYIAuuugiuuuuu7hb9tve9ja655573HW6tanHavmXN7zhDfw3Pz+f11lUVER79uyhHTt2uLcJ6/jmN7/JZWNC9doMNUwpFKrGS7ga5ABpfIvS5kIItamMGokQSFS0O5LZsAmUkg1voApEmoycfI6KBBBeAomXXD8yBylnSvQkDHo2ziwWNlRSs/LYqEKNMzz31E9Ttnt+bpYDWiEOKWLgPBs/iPIa7OtmYQip5X1d7e70bRhdSHNQjSqsv7OtiT+L39FaX8OpDzBsEPl5+sg+XhYRCWysNdWxdVBStVkx1hZSyLku26E9/LvYWHNGUWujEsGJqK/jB17gSQu2oXTDNjpx4AVeb3JqBhtvzfWnedmC0iquBYeu3vBsw5CHcKUKYEitaK495RZ3ERWKtGZMOFB3BduL3499m5CSRo0Lwiy6heN3YV+AyoWUDky04uITOZ2s7qTSfTw7v4TTxbo7WtkwrdhyHtWfOsLpOJisIQUe9TpVcRqTmq62Zndzkqbak1wLC/s+u7CUo0QAp7eixlZLg7te6Ev/+huLwph45pVUcNF5HH9EtsEo7+5oYWMd59xQfw/XVMKEDvsME0E+F2yKADw3C2Eb45LSgAhGtFrvLz45TSk4DQ+2zc4GrjIpc1J6dsFCBItyQ4TAHeicRWSwNhIHx1JbX6YqP90rZVi9jniZNM9kWlnGTjTtv3bNfOTapsoFUxNHj1pEZwPXGcaHlbBav8nf/hOD3DiEqwMTogDGvVDufOyemMAO0DRO0JaF8SdIasUHX+cJ7vvacRz3UhXcd7Qg1doTPTnP2RmwEyDgWZJSeDvUaz0mLp7m5uZZWMJ9xmK18L0a7yPKG44iAKEQIiMiyF2xcSwsom4rO4Wy8zkzBDbL/EIEFe7FM1NTfL9Hdkr96aO8npyCEpoYG6Oerjb3vQrZIFNTkxQbl8h2Su3Jw3zfRoQm7u98D8V6EcF5+jhNTIxxyiS+d98zT3B0Ju5/qi2Ez0L0aa47zcIVRGB3mQrcx7PyWFiC/QOKYQu1NHBJC4hUsGFghwCIrRBm2W5ayGbB9qAeHO69JVVb2BYCsBkhDLfU1yjLllWzQ5i7h9vtbAvBjsI+Y1soHrbQyYVjWLmoXifsMUTiLHJSF5Xx/sa6Ac4LbC+i/RGdBrsFwltiSgafRxAG+3s6F+y+LSzCIYoWy2GbkZ0D2yEpLYPvT1g37DHFET/rcUSTIsrBvlGjfmH7qE5rOMRxvlksNs7OKd24zV3L3DdLCU2PfM9ZLVvLNdH/eale9z712vDcwzy2vrKMzSfFWIlGHKMo6hyeJY1PgMLdJuFgg1UQUtbSLvE44sND8OGxezq4+3/MBVk0PztHw0940pCH/9lC8+NKUAyIfWUeLzc3dW7bM8K+eo6dT3/603TnnXeykJeYmMiini//+Mc/KDMzk/+eOXOG3vrWt9KWLVvoAx/4gN805LS0NK4rfeWVV/IcGhGHl19+Ob+mFQrV2tMiEq4fEfMmUtPQyRAK+Lvf8176yY8fpM/e9nn67898bs2+z2g3BhUcUhg4vjfocAdRPxC4fIXHcAdiGYy7YKNQtaKDOmnxFefV91QwqGsnyNr3/d28jBDhyA0qfKLlQg1f48zfmBXMMloQLbCWdbVC0Sg3khDnb//98Pvfo5s//EG66vVX0w//98eUnhxPg4ODFBen7ZsrrKVdotI3NMaRumuB0a4LLS31p9mZoXX0CcRCj282RriD6PzaE4eWFFXoO5VR7RLt62d77u89LUYY25HpgvR4bVPEUMPf/cl33ApmGS2TE2NK+vAaZVKFok2y0sZZ67EPN1SWUu2ZM/T3fz5L27ds4PukWeySiYkJjnorLCykyIVakRD02j73nC7bk/WFC8jiWFrGy0MPPcQRf4gO1EYUInrw9a9/fcBmJhDysGxtba074Octb3kLzwMfeeQRv81M/NUo/NWvfkUf/OAHOR3a6XRyYzqIhnV1dctqQiec/fwMhCllfFVphscuXCn0qZUhEDd0MY8svnogCmEp3hltmhdHKiw0ilD/rT5X/62uW/vcd1nfhxFAB3FhMYh6FbxBmh+iSo2KNoVe0JdwtUsQZRcT56mHLCh46jwLKhB9omOX5ozytSH82SFne+7vPaPZJIiARDSh4A0yVxC1KXijRsQaFbFLQhNthF8gqqurvUrxILqwq6trSd8D0RDrgICoCpcQKkUkXF8k9diEoGZef3cHRfmkuIQ7KG6MNI9QTn1aC5LT0SHWlEOBqTyyRsFEQeirBowZpNUbFalRaBzC9fpBOiUi57QpuwJRV3sLpWZKQxMtSJlMzUS6q+C1XyLCd/w4V03LlZb+MCNGP1PCzS5B+i8i+5bC8L9b3OnHWpB2HPuynCV992qh7VYcCLUWofv7IyKWXHbF4XDQu9/9bk43vuaaa+jhhx+me++9d8nbK6wMU16dal2GcK0FhPozqMMmeIM6N+FyQ1oKjTUnDC1y6EVXq1IfUvAGNZcEb5CKbeRulOFmkBuZcLVLRgb7Q2DqqgNyj1kEGkPBLhEWBwH0LtSAFDygWRsegjeoM25kws0u4ahlhzXox8hzbV4iocXlcbLhdbwf7LqMHvAAYdFftsX1119PTz75JN1///3c5RyCobC+mNK1qzQNWHvP21Jre60X3G31HDnn4UhOUVnYFM1dCiKGBcAE3vtgxqSljlvoroxaY4IHNGlQO6/rjb/j2TM8wX/Hp+aosccY96lwZS3HlKXW9lpPELWudiwVPJT7NFUTFuRkg09sdcGnjmIoshY2CdL30dhF6o97g8Z6RsHfMZ2eUYTCjqFJkmJZ3gTqbqx9XdsNOdRBOvFTTz1FF154IdcjRJMUUFlZSeeffz596lOfove9731rVt9ZCIwpVRPLghgUrrWA0Akuu8C7+69A1N5YR9NTyoRZ8IBO0Fbb0orchgPFVVvCxtO5FND5WvBmbMTTtduIGKnZSrgTrnYJOvSio6rgfV1KzdfFQFBGB2PBGzTryC/x7iosEHfQHhkalF3hQ3PdKUMLywhqAei6LZxbJOR/X5rLz93LPt7Ay4c6d911Fz3xxBOUm5tLW7du9Xrv/e9/P01NTbFQKKw/pk49NvIAudaFfdub6vTeDMMxMTGm9yYYkukpKabuj46mesNEiRnRESN4QN01I6fvu1N8NMWlBX0IV7vkxMEX9d4Ew4G6alMT4rz0JyCghI7gDeyRjpZ62S1+opVV0UnQ7hcrzRnYMSV2yWImGwYDioTu1/yIhfjcaoPuxWrHY3DppZey/ZKQ4N1oCl2P1Y7HatMRdEHWcs8993AnZJWGhgZ3x2Nw1VVXUU1NDU1PT/N7WlpbW2njxo20c6dE3+uBKWd8ahRQuNYCmpudkQmhHyKjoslikdQnX3o628L2WjkbE+OjND8XnpP6s1G59Ty9N8FwuKJjKS3buBEw4VYLyMjIWCu4mSeKjjVOeqBRgNMFzW8EH+aJxkdHZLf4gFIouUXlsl98yC+tcgfOGBHperwYZ0E8xb4iL6BI6E8sxPL4nNkYGRmho0eP0n333Uc333yz3psTtphSNVEnQ+HquUe9LDwEb9Kycsnm04lJAPMhEfrvr/7WcmrbBFvHC8KysJhTh1+iso3b1y2N1Sg11s4GohkGerooI7eQjIh6LxShUH/CVShMTsvUexMMBybx6QYdM/S3SUKDtbJL/H0mwhJBTgM3zdKL8bERbj4HYWy9CAW7BA2kkMZv1HMGDSa1fQUEhfhX5lNkacI5xT+Ihc6COFOKhOCmm26iX/ziF3T11VdL2rGOGNfVsALCPaIQNwa7w6n3ZhiO2hOHONVH8Ka0eptEoPohq6BYhGU/4BoK17E1IBERNNDXQ0ZFNcilmZN+hLtdgtrJgjeomSw1ChfjcEZRYflGOV382PbZBSWyX3zAfW16Wkro+ILoU9RvNHxEoYGjHvUiWPHPrCKhmsI8OTlJv/zlL8kqZXN0w2Jmgzxci4b3dbdLeoIfkEZqsUiNLl8az5wI2+jbs9Fw+hhNTY7rvRmGIyEpVe1LKWgmcEZuFDKnFg038DaanXC3S1oaavTeBENOlCXKdzHoYNvWVKvDETG+k67m2AG9N8Nw2Gw2ionzrpsmEAeMGNkxNSdN1gTB8EjqsQmZnZkV9d0P6Tn5MlH2w+S4NHnxB9KxRUBdTEJymlxHvudKRASVb9pBRkWdLFitpvQNhgThXhJFWAy6QCenZ8mu8RMBDbFQ8IYdPTJ8LMJqs1NcQpKcLj5k5Rcbep+omQ5WCeAQBMNiyllDuKf4pOfkkStGCmQHuikJ3rhiYmWX+CEpLYMNUMEbRHrIJG4xxw+8YPzUY0nx0V8oDNP7UGFZtd6bYDhwLoRCfeD1BuOUpKr72S8RFkrJzNbhiBgbOF8aa07ovRmGo7u9mbo7WsioSOqxIBgfk0YUKobXereFD7ZJwlrT39PFhcOt5jy8y6azrYlSMsTI8gUNGNYyJTGYYt96f5e/z8VZ4ig2NpocDkfIFK9eD5C+Pze7dmJHqO5nI0eKqemukuYYfg7MYJokrDX4zbBLxIHpzcT4GA30dVNcYvK6HxOjp0ymZOas6XcY3S4J9BnXbCxlLLwXqvfK1Qb2q1peYy0I1f1ssdpCokah2CWCYFzMGVEYEd61gIb6eyU7QQia2uMHZW/5oa2lhSYmpEahL2nZeeRwSrMkX5JS0o0fUSjRS7qhFuMOR7tkdmaGRocH9d4MQ16XMklezMT4KLXUndbhiBifutozem+CIYXC3MIyvTfDcES5oinStX6C+PK7HptSihAEU2DKq1NNrzJyhMdagkFXOgQtpnTDNh2ORmggTQ787xM0wBG8mZqYoOnpadktPhi5mLoabWGRznG6Ec41CtGEQZwLi4lNSOSIfkEIlnAcP4JhbGRI700wHI7IKHI4IsnwdonUKFy8b/pGV3U5QVguphQKw71GYXHVFu7CKXgjHmr/ZOQUyKnih9KycoqJlfqNvoyNDNLkhDTA8aWp7pRhJ3FzC4K3eO71I5ztEoczkgrKNui9GYZjZHCABnq79d4Mw2G3Oyk5PVPvzTAkW7cZt2mWnvR2d+i9CYZjamKcOlsbyahI6rF/pms6aerFOpqpPfu9Ae9jOSwvCGuFzcwRheGY4gNOHHiBqrbt1nszDMf42Ijem2DYm/V61usJFTo62ikpKZli46Qx0OIaheE5tp5LCELklBGdNOrxEqFQ/6jtcLRLRoYGqL+7k/JKKvTeFEOB+mEz01N6b4YBmUfonNglfqivr6XqDZvW/5AYHYM66fTEarPR7OwMGRWxS/zsk75Rmj3Txf+eOa2I37biVL8iofo+lrcmx5AlaXXmcddddx39+Mc/dj9PSkqinTt30te//nXatEnGnnDDlBGFatptOHrugdwuA9frEPw3eREWMzY2SlMyiVsEUuWSUjPklPEhr7iCu1Ia23Ov3BuF9Sec7ZK52RlJew/QbADRloI3U5MT3PxGWMxAf7/sFj9IcMRi7I5IysorMuz54rZLrMa0m/QAYp+tzGNfQwz0jSzUioQAy6+WSKhy5ZVXUnt7Oz+eeuopstls9LrXvS7g8lKOyLyY8uoM92YmRi6qrye5RRLNIASP0xlJVhFWFtHX3UG9Xe1yKvmpkWTU6CC1FpDUItWPcM50wIQ1Nt64NTz1Ii4xieKTFkeLCEIgXC6X7Bw/1BzdL/vFB9zvjVraQFumRewSbxBBGEgs9CcS+os4XClOp5MyMjL4sWXLFvr0pz9Nzc3N1N3dTQ0NDXzMfvnLX9Ill1xCkZGR9POf/5x6e3vp7W9/O2VnZ/M4tXHjRvrFL37hXqf6Od/HpZde6l7mt7/9LVVXV/P3FxQU0F133eW1Xffffz+Vlpbyd6anp9Ob3vSmVf/tgjfGy5FazaLhYRpbF23govp6cvroPqqWlOxFlEmTF78UFBaJAROgU52R01n0YmxkmKKiYw0ZIeTuLijNTHQjnGsUWm1Wstokot+X7vYWsjuclJwm9fi0YBzNKSxdxzM0dNi0RZry+WPaoE46/YXCLsrKLyajoXWYhUumA4ujs8HpEtb8ZMXW1qQhz9R3E0179pu1JE1ZbiYIm8KqiHLLYWRkhH72s59RSUkJJScn0+io0kAF4iGEvK1bt7JwNzExQdu3b6dPfepTFBcXR3/+85/p2muvpeLiYtq1axfl5uZyhKJKR0cHXX755XTxxRfz83379tFb3vIWuv322+mtb30rPffcc/ShD32IvxPp0C+99BJ95CMfoZ/+9Kd0wQUXUF9fH/373/9e1m8SwlwotFrD13MPmutOiSAmLOF8OU1l2efLHvPhzOlTlJiURKlpEqGrxRnlohnperwIm91hWBFIvReq90Zh/Qnn1GNEtaB2pzNDoqG0YCIo0TSLmRgfpaG+HirNSly3czRUeOG5Z+jCl12i92YYjrh4OVf8YVQRTnsfDJvaybPzNPnEseV/XiMS8urOdLmFxHPhfGU1kS14ofBPf/oTxcTE8L8hDGZmZvJr2mN1yy230DXXXOP1uVtvvdX975tvvpn++te/0q9+9SsWCmEDIUIRQFS8+uqraffu3SwMgrvvvpte8YpX0Gc/+1l+XlZWRsePH6dvfOMbLBQ2NTVRdHQ0p0DHxsZSfn4+i5TC2mJKoTCcPfdCYNKz8sJy95yrUUnbycl125ZQSxU0ahdbPYmKjqHZGYko9CW7oISMijui0KA1FMMB1S4JRwcmitYjck7wJiY+kex2R1julrPZJf22SYoYkXuvEDwpGTmyu/xQsWWXIfeLtiFe2AiFIcRll11GDzzwAP+7v7+fU35f/epX0549e9zL7Njh3YEdts1XvvIVFgZbW1tpamqKJicn/ZZLeN/73kfDw8P0xBNPuI//iRMn6PWvf73XchdeeCHdc889vO5XvvKVLA4WFRVxDUU83vCGN0g5hjXGZmYPSjga5KCgtErvTTAcEHwk7c4/MbGx63w0QgN0PEadDMGbseEhGuzvodyictk1PmmEEJdT0rOMW6NQDHLdIwrD0S5JSssgi8WU5uaKkWtyMYg+jZJafH7JyQ1Ph/e5qD91lEUxEZ28OXHwRarcch4Z1SYJqzHQGqFE9i0BpBv7ixpE2rGtcAm1Ca1LSztG5B5SjVV++MMfUnx8PP3gBz+g66+/3r2MFkT+3XvvvSzsoT4h3kfUIQRDLV/60pc40hCiIyIDgwXL7t+/n55++mn629/+Rp/73Oc4GnHv3r2UkCAl19YKU16d4RxRiEnIUH+v3pthOObn56iztVHvzTAkFZVLu3GFCw6HQ8TlQJGWYTi2BrNfZg2aku1JPTZmGlI4YFmoDxSOUcrDA/00O2vMa0NP+ns6aWpiXO/NMBxIeRNBzD9RUZK+7w90zhW7ZDFGnQdrj1W4NA3k5h02S9CP2cZeb5HQbvVOO27sDX59y6xPqN12aCvj44HvV88++yxHBL7rXe+izZs3c+Tf6dOnvZZBs5IvfOELHHWI2oVaKisreR2+60QKsmq7ovsy6hp+/etfp8OHD3ODlL///e8r+m3C2TF3MxODDpBrCZoMDA8NkJTGXnyzFE+jf17a84LUvPFDd3cXRUZGSVi7D5GuaEqS4vuLiHJF07SP59QozM/Nh5fn3oCo+35uLvwiCocG+sgVE6f3ZhizRqFck4vo7++jrs4OqqzaoMdhMTRnak5RWrrUTfYlK69YriU/JBi0q7pXMxNxYC4iUHdj7evq37XoeoyUYTQbUVOP77vvPm5qctVVVwX8DLoR/+Y3v+EmJImJiVxzsLOzk6qqlCzHo0eP0rvf/W5udoLOxur6EZSRlJREH//4x2nnzp30xS9+kZuZPP/88/y9SHsGqJFYV1fHzU+w/r/85S88ty8vl+ymtcSUQuF6p/iMDA9RTGzcOeuwYLnuCeva1wIK05o350plKSgTo1NYmgctXDunn5V5osmJcYqR7upeoMmLUQ1ed0RhmHjujYjezUxUO8Vfbbj6bqWLIZiZGCVbZPSadD4WvEE3UqvNlGb4KrCyCBgh/Loez83OkMUi8x8tCcmpHMVutKZJYdnMZIUiIf974e9ai4WPP/44NzBRU34rKiro17/+NV166aUcxeePz3zmMyzkXXHFFRxgccMNN3DDksHBQX4fXYvHxsY49RgPlUsuuYTTibdt28aRhkgphliI70f0IRqZAKQX/+53v+N0YzRDgTD5i1/8gkVHYe0wpYWynkXD21ubqbO9lTKzcyk9MzvgclgGyzrj0ygyYe28gZFRLiosF0HMl5npKerpaDV0wwG9yC8o1HsTDElWdo7hjCsjMDc3Q/3dHZQsUYVeTI6PcZ3CgjLjGS1qFJsY5PqhNpLRo0ZhsHbK5GA3TQx0rLqdUlSxcdXWZSa6O1ooOS2LnJFRem+KoXBFuSgtTaLm/LFxs3T5DNRZHVHLNgmU8KK14QwVlm8ku8NYAqo2sl5xooVfBqA/5vpGA4qE7tf8iIWWRBdZklbHwffQQw/xIxAFBQV+S6ggKvCxxx4L+DkIfqroF4g3vvGN/PDHRRddxIKisL6YUsZfr4hCeOhhfGsN8bOJhGBysIs99mvF8GA/tdR51wQQlJTsifG12++hjNQt88/Q4CA/hMXNooxa90ZPEBmEccaIqMdL0hzDr5lJsHaKKhKuhZ1y6vDesEy5PhdjI8NKiLbgDephWU05PVkxLc1Nem+CIbFyjUIZY3xBloMR7RKtDSkOeQ8Q+9CoJJBIqILX8T7A8qslEgqCL6a8E0dYlCigtZ7MIo0HHnoVf0a4ViQEkQkZa5LWozI7MyOTwQCI194/dbVn1ux8DGXGx8f4IXgDj31J9RbZLT7YHZGUkVNgyP2CkhRAnALh12QtGDtFKxKuhZ0yA7tkIaJS8GB3ONnxIngzMjJMXZ2dslv80NfbI/vFD3klVRQVHXwH1XAhK6/IkOWo5maV+6DYJIuxl6aT47yic6YT430sh+UFYa0wpeWm1mFaD8890ngCGeG+IiGWc8avbWFZhJdHx8av6XeEIg5nFGXmeXdYEhTEm+cfm80uqZoBqDt5RC4fP0KQUTvOq/dCST3WD7V+pR6px+eyU3xFwtW2UxKT0+Q+44ecwlJJlfTHvFQoDITT6Vy9C9NEdLY20uiwZID4MjE+RlNTE2RYm8SgdZ31JtgIQYkkFNYac9YoXOei4WrNH1UUxN+ujnavcG+1NpC2aPhaRfvASy14AwMCddXySipNv2v8Fas/G1u37VizbQlVcJ3O2xXBfa2v2VAUltHMRFhMX3cHZeYVGW7XzM2L994IqXF8LHRK2w/GTlkLkRDEJSav+jrNQM3R/VS6YRs3WzM7S7FLEpOSKC5eHN6+wBZJKtggNkmAqPmZ6emVnaQmZHx0hB2EUa4YMqRNIhHVgmBoTBlRuJ7NTAJ57P2JhOtBf08nDQ30rct3hRLz83OSkh2A2tqa9T0YIUJfVzt7qYXFxEjUsl8B1ag1ANXUY4ko1A819VaPiMJg7JS1EglBY83xNVlvqDM/h26kxhwz9GRkeJi6u7v03gxDcvzAC3pvgiGJdEVLB/EAmTF63nPOZZMY1WYSBEHBlFeoXkXDYYT7eobxfL1EQnXwlZoPi3E4IyWqIQCDAwNre1KGKhERfjt7CUTpOfmyG/xQtfV8Q+6X2YV6QJLmE352STB2SoTFuuZlUYTFJKdnSkq2HyYnJ2l8TOoDC8ETn5hMUS5p6ODPVktOyzTcqaTeB2W+KgjGxtRC4Xqn+KDWj293KTwP1GVwLUhKzaCYuMR1+75QivYxYkFfI+AS4yqghzo6Jm69D0dIUHvisN6bYEjQ3dWIzC/cCyXNRz+MIhT6s1PQLRQNTdaKcCj5sdwGSFIjeDE2m01q8QUgOVXpdCp409fdyRlVgjf9PV2GzIxxZzlIjUJBMDQ2UwuF62iQ+zYugcdeNcbV1+HJ91enZTVroKEWX0x8AmLoVm2dZmB0aJAmJyfIJcLPIjZulg62/q5BCMuSqikstburEZmdE6Ncb4wgFJ7NTkFDk8Ro+5rUUh4e6KPYeHFg+tLWVEuJKWmruq/NAGoUJiSG9/kS6BoUG9Y/sNV8HSACosUjaHpqyrg2SUSE3psiCMJqRRTecccdtHPnToqNjaW0tDS6+uqr6dSpU17LTExM0Ic//GFKTk6mmJgYeuMb30idnd5enj/84Q9UVlZG5eXl9Kc//cn9ekNDA3tXse7h4WGvz2zZsoVuv/32JRUNXy+D3F93441bdwTsMriWDPb36h6xYEQQXWqRWkB+efH5Z9f7cIQEo8ND1Nfl6QYqeMgyYMMOIxCfmEJGZM7EaT6hY5esvwPTCHYKyjcM9Eq9OSF4ujo7qKGuVnaZH5rrT8t+8QMCJGLjk2Tf+OCMjOLsGKMxt1AOxYw2yWrQ2Ni4qssJwroIhf/85z/Z2H7hhRfoiSeeoOnpaXrVq15Fo6Mez9dHP/pR+uMf/0i//vWvefm2tja65pprvGqPYB33338/3XfffXTjjTfSlI+3A8b4nXfeuewfpaZXrYdg5s/4VmsS+hYOXw+xEEa5DLyLSUhOpSQD1ukQjIuSEiY1Cv0xM208D7URSEpNN2RdS/VeaDFhh8GQsUt0jChcqp2ymmnIc3OzZLXZV219ZqKoYpPem2BcJNLI/25Z7+MQQszMiF3ii8MZRdGxcca1SUQoXMQ//vEPevDBB+mZZ5456z7E+1gOywuCIYTCxx9/nK677jqqrq6mzZs300MPPURNTU20b98+fn9wcJB+9KMf0d13300vf/nLafv27XwSP/fcc2zEqwY5DGZ44rdu3cq1SPCalptvvpnX0dW1PC+0OvCooc1rxcjwUEDjW8WfEY7PrRXFlZvIGelas/WHKiNDAzQ+6h0NIijk5kljCn/ExMVTSsb6NSIKJbraW/TeBEPSVHtSt4ixc4k12mh7MxEydslCd8f1FgqXY6cgDXlmYnXSj5HeXL5px6qsy2x0t3uOi+AhOjqGEhMlOswf+aVVcqr4YXJinIb6emXf+HHqttbXGNcmMaHzciUgQhDOTPDkk08GFAvxOt4HWH61Iwu7u7vZYZqXl8f1YjMyMuiKK66gZ5+VDLRwY0WzBhjgIClJuaHDMIc3//LLL3cvU1FRwSfa888/z8/j4uLove99L2VmZlJWVhafiEgZ0vL2t7+dSkpK6Atf+IKhDfKY2Di3we3P+PZnhOPf+NxacfrIvnVv4hIKTIyP0bREQfklMjJyvQ9HSDA1NUkjQ8oYJwjBoK35ZiTUe2HEwr3RzBjdLlnv+/Ny7BRnfBrZIqNX7d7bcProqqzLbAwP9uu9CYbEarOR3S5RqP4Y6O02ZNS63iBaXuY+i4EDzIjlqNRjJRGF3uTn53vZKv7EQq1ICLA8PreaoDzLgQMH6Mc//jGdPn2aS7Nceuml1NsrYny4YVnJRX7LLbfQhRdeSBs2bODXOjo6yOFwUEICmml4SE9P5/dUbrvtNurp6eET7pOf/KTflL+vfvWr9P3vf59qa5dep0RNr1qPwRGGdUl5VUDjWwXvYzmt114FDU60j5UAMUy66PmfpMH4FBZzpkZq3vi7JjNibRQdMbkq16XZkAgh/2TmFRlynNHWAyrPMO+5bGi7RMfU46XaKZWlRatml8wZUDg3Cg6HU+9NMCQD/X3U07N2XbhDFVyH1qkhyk+OEpvEh+jYeMouLNHnwBgYlH2AXWI0zFw3eaVcdNFFAcVCfyIhll9NBgYG6N///jd97Wtfo8suu4xFyF27dtF//dd/0X/8x3+46zYfPHjQ6zN47emnn+bnO3bs8CrVgtrRcP6MjIzw85aWFl7+zJkz/PynP/0pfwYOWkQvvuMd7/DK3sB6sfxTTz3Fy7lcLrrgggu86lGjXjSyQrCugoICio+Pp7e97W1etaVhI6KudWFhIUVFRXEWym9+8xuv33/06FF69atfzfWs09PT6dprr2XbMFxZtlCIej7YmY888siyPo8D6Oux14IQV5z8n/3sZ5e87q7OdkpMTKRol4uamxqpo72dT46xsTFOJ1ptb1ywEYJrGUmoLaYvQuFiUjNzDdtoQDAmSImwhkEE1nJorDkhUQ1+mJqc0LV+I+5t+P7xsVH+92BfN3W3t1BsbDQbRVNjw7R3714yK0a2S5IWurhOTIxTa0szDQ0NcZfs8bEx/rvWUUJ62SkQSGPiwruDbSCKq7bovQmGROLlAmN3ONbxSITWvbe9qU7vzTAcmA+Ojaxduatg04xxfJClg0dvVzsND/SyTYIo/jPHD9JLL71EZgf3eNQ+DuYBYQ4inQrEQQh3WpEQ72O5YNa3FPsCAhkejz322KISLMFyySWXuEVDfDeERzhrVcET6dLZ2dmcpQGQ9fHFL36RDh06xN8LMRIlZXz5n//5H7rrrrv4fEGJmPe9731e78OJi8+jIR0e+B44eFUgEv7kJz+h7373u3Ts2DGuX/2ud73Lne4NwRMlalCCBt/x+OOPc+O7t7zlLRSuLCv04aabbuID8K9//YtycnLcr0MFxgmJHa313mMn472lgoO7e/du+sQnPrGkz6WmpXMh86npaX4+OjKM8AFqbKijkeFhio6JpfyCQjq4XxmYSkrLaXpmmro62vkmXFW9kdrb23B2kys6muLjE2h8fJzVcEQmGFmIS0xJ03sTDAlqdMQmJolY6IdNW7at/wEJARISE/khLGZqcpydL+IN9mZ0eJBsNvuK6sSy2DczTbPT02Sz29mwHhuGqDRN6dn5PJZNjI9SVHQMN2iqO3GYP5ddUMrHpb+niz9XWL6R6zbx8VowFiOj42jTJnM2UDC6XTKxYHDPz82zUTwxPk7z83NUW3Oat6+iqpq6Ozupq6uTDeDzdl9I+/buIavNyjZNYkIidXd3kd3uoOSUFJqfm+NzBTYLljcqVqudYhOk3pw/Thx4gaq3X7Dux8TopKamiSMqADt2nre+ByOEQJkDYTEQ5jJyClYs9s1MT3NplcioaOrv6eTnkS4Xl1zpbG3k5zmFZdTX3U6DfT1kszuobON2OnUYoo6dElPSKTougaanJt3R9cNDQ5RbVEZ58eaPLMR9/ytf+cqyPw8dQguamATbyOS///u/Wb8IBtgTqPX8gQ98gAW1bdu2sfCH6Lxg7UekKaM2NI4xnLf47re+9a0sHl555ZX8F+tU0Qp+RUVF9K1vfYt27tzJEYgQLVW+/OUvuz/36U9/ml772tfSxMSEu4QW5iXYdtXhi2hARCHicxA9sf8htsKGU78L4uX3vvc9Xi+a2UEk1B6n//3f/6Xc3FxOwS4rK6NwY0nWJYxSFPR+9NFH+SAjdFMLioRDTMNBQX47QFgoCourB2UpQClHZ0KcDEvBFeViwxsee22jhsoqJRVJ5cKXeU5SnMzJSck0NT3Faap2m53Gx8f4wnS5ounM6VOc1puXX8DrbmpsULbx/Avo+NEjPJlLSExiAbKhvo73Q0pKKqehIYLAYXew6KitVbQWNNQcp+ptS9/XZmdufo4sERId5g+cy1XV3teGQDQ4MECtrc3sOBC8iYmNZ0eK4A0MZrUWKqL6EN2H12wOB/V3dbDYl5iazsJfX7fyvHLLeVRzdD8bz3GJyZScnkXNtafYsE7PyecUHRjn9oU0xaT0TBZoYYRj3b5CQ1pW3qJ/Dw4O8T3O7ozkwtRmIlTsErdHPyKCCgo9qWDbduxy/zshIZFKyyvczzdu3sL2BmwGLp9htbI9gQjE/r5e6ursoNm5Odq563x68fln+fW0tHQWFk+eOMaiYklZGYuScJJCVIQN09fby+uCce1YOB/WygE6PNhHk+Njhkx/E4zJ0NAgj3tp6UsX8s3OS3tfpE2btwY96Q8XcC+MjJJGjv6AUwkCCv5OTiCCfZpcMXFKk0cE0kREsBOy7sQhxUZJTmcBsGWhCQqcjv09HRx0Exnpotzich7T4ZBEarPTGUmZuUVktdv5npMTU8aCoQpsHC1RrmiOGMO9bXp2lh2rNpvx6iiGM7CVIMIhEhBN3/7v//6Pvv71r9MPf/hDFgHPxcte9jJO+UWdQzSOgwiHz6nRfYjg0zpbUUsaqcOIKOzv73fXsISdVlXlaeCkFSoRjQqQooya0wApx9qsECyjpjAjzRmZpa985Su9thXnIcRBgO+H+KoVJ7XRiiIUBpHW8/DDD9Pvf/97PhBqfR+k6yCEGH/f//7308c+9jEuJI4C4TDgYYyff/75tBygAqOb4VI85mqNqNmZ4GvjwGiOcrkoipQbTbpPpMGWbdu9nmsFSBjz8BRgIoD1wNh3TxbHx6izvZ3fr6zeQCePH6Ph4SGKjYuj8vJKvumDgqJisllt1N7eypOa0rIK6unu5gliVJSLBuciuWuvUuTZyek8/gx748Y66ktsfCI5pGmHXzDhDCfqu0eDWmZ0eIy6B8YoKojlw4303ELTNcbA+I0IL6TI4N7hjHLRxNgojY+N8KQVhnRz7Uke26Nj4ik6Lp4aa46zkZ1bVE6jQwPsZcfYnJyWSV2tje6xJ8aRxGO3MyqKDen4pFR+4DWM4/C6n60GJAREFVd04NTYQKgNVnCPMRuhYpfYFmoxzSyhZh/EAK0gkJPrEYGjo6O9niMCUXsu7zxvNxvAsCdQt1mJVFW+u7e3hwXHuLh47i579Mghfr+ispoGBweova2Vz8vdF77M/V5SUjKlpKZRR3sbr3N4PorXhWsF57HDqXj0fe0SXDsWE553qwGcAsJiIGyr52q4EKxd0js8SQ3do2R3KBlTggJEK62TzAyoziVE6sHpiOeRLiWab3ZmmqKiY3m87elo4esFdkhHSwMLgBiPiyo20clDexaWaWWBrqezjfcVogLxujMyip2Q+DcEQJvN4Z5fwkZRccV42x2+jh84LpfC7IxaozB87g24byKybymg4Zq/qEGkHS/F0bmc5lBwJEJUwwPlVq6//nqu5QzxEGjTmaFxaEH2Bur/wXmL34B1XHzxxRxViMi8mpoad2QgMkBR1gWPn//855SamsoCIZ7Dhgn0O1RbQ9vEyPd3Yhn1fbU+4p///GdOe9aiOtCxzFVXXcVp3r5kLgiT4caSrtAHHniA//qqyQ8++KA7l/yb3/wme76hRiPMEwf6/vvvX/YGQr1FSCoKiAeLdZ2LhuP3aqM00tLTvd5PTvbUxtu0RVGtfaMaccFhe6NjYvimgN+AEx7enYnJCZqz2rnWFCZ8CN9GNE97ixLVWFK1hWtzIO2N1zMzQ3UnD7PxnpSawTeBof5e93Oko8F4x8COmw6+A4P1WkY66g32ATxfwmKcIqD6BdeH1NbyT/OZk1w4HMamXuDmj0YJECGmpyZoiuvPzlFsfJJ7rER6LhoGdLQ08vPs/GLq6+7k2n0YDys276ITB1/k8RDjanxSCnU0N/B7iOaDMQ4vPMQ9gFRfGNF4jvFEG80Xl5DE4qIqyuWXerygICXj7I0k1hL1Xmixmm+MDxW7ZD2brME4ZudnlCLmQUDVetlLy8q9lr/goovd/0Zac1FxCdsSWA+XZpmeJktEBD9X0uGnaM7qYCF9oLeLJ6mwQ2qOHeDIWIjjqZk5VHfyCF9DGbkFPFEd6OtmcTWvpJKf4xqGDRIbl0gjwwNsh+AaYltkXqlvaORSLyslyrU4akFYEA9MfNxXAu5RERbZN75wGYcThxZFr60nGDORogubAXYJgjsUp2OUkmY70MfP07LzqLutmcZGhznwIzO/iE4fVkph4T2c/+3N9fwc4yqEwEmkV7pclOEq4DEWNgoesFNTs/J4XMU8J6+4wmvMRIbZ6SP7KC1LaaSpLQOhOndUVlIyZanMzoVfMxMcl6VEAiMlVisS4n6uph/jdey71W5kcjYQ2Yf6fxDyQHt7uzsST9vYRAVCILZzz5497FyFo7ayspL/DdFNjc47efIkN5FDtCFSfMFa1KzE9kOrgQipTXvWgjTr3/72txyZaOSSLoZOPQ5Ggf7Od77Dj6WCA+PvO5A7jodRhcLVgA1wm80r3DU1Lc3Lk1hQVu31GUxaVdT3IBjCuMYkVfX0Y5/Ca6ROYMdHR/gGhYlLVn4R17jCe8lpWbxca6PShaiofCPfoIYH+8kRGUXFlZsVAdJqZU8TDPyB3m4lijIlnaZRyH9mmlPm0IEMgiS+A9ugtwjZ1dbEHjBsm+DNtu07ZZf4AecK0jOExSCaUOvFCwSWwdgCIQ5GLNJVuD5NBLEIy2LB7Ay5YuN5jMBzLJ9TWErdbS00NNDLn0PRfwh6ICUti8ee1oYzPLbklVTQ8EA/ixZIrYxLSOY5JtJhYAhDKIRQAeMb68ouiOX1q/hOLIqrNgecyGNcOxczPh5QI2DmDoOhZpeox8LoqJNNTE5UwRHk5OS6bRJcH+y4XKBsg3e9W0xSIcqzUzIqmmLiE/n6joiwsCA4jWtlXql/NdzfpzhCUzN4jOhub+Z9VbZpB0fuYuzAeIz7OIrfY/syc5FmHsERNti/2YWlNNDTxbYPbBZE4mJyDpsIYwbGGCXC0crjgZ4iJM6p1oYaFn4EbzLCNHIjGGLiEvj6EbzBPoFAFwwYHzxClY2bfbBj0YVAjSmOyOMsgpx8jtCbGBvjtGZEAGO+hFJGmTkFbN9gboG7A7ICkHGAeQ9Ks2TkFbHDEjZKgiWNxyd8F2wSbCvGTZQhgcMRtqZvCREEdaig/rAW33qD5+qerma4GQkz2ySrQaDuxtrX1b+rLRZCtHvzm9/MzlCk+sLJCOEOqcevf/3r2R5ANgaEPZR6QWrvZz7zmUXrgfP229/+NguLFRUV7tdQBxDrV0HaMARULPvBD36Qaxqisclqg99x6623cgMTXLvYb4ODg/Tss89ypsl73vMezlD5wQ9+QG9/+9vpk5/8JIubSFlGgzykXYfj+WpKuTQUhcLVMjxhMMOQZk+R5t4BATBQZItvmltCsifcPLe4YsFLptyAs/NL+IaKqAI29p2RbgESN0hENUZQBE+om+tO8Q0ZhnB0TBw11Z5kL3F+SSUXukUkAgx2TNRPHkInznlKSE5j456jeqxWSs3KpamJCXfaNVILUKxf+U2R7AFDYX+sx+GIVHKv5xUhA5MAdSKAAvJiXPnn+Wf/7VWvU1CYmpqgjpZ6Tt8wA3wdz87SPM2zgI9aNbiuYWRirIQDARN2NETCZHtycoKv88TkNBbllPcy2HOPjnWos1e2aTu1NZ5RatdERVN+SQWdOrKPI55xrUIggCGNaxlRREMDfdxww+GMYqEQlyfq5iFCD9+FKCR44u2OSMoqKKbsiFL3Nexbe1U7wfYV9NDlfCVpMcsF4sdad69dDqhjp41qE9afcLVLhgcHKDY+gayx8e6yMCA+0VsgyynyLhKO9H0VOCm1YCxQbRI4GhCxwwKgxcolRmAzwBDAWAWbBDZK/DwawMyzAIljUFSxkdoaa/l9iIhwrqLoPkjLzOExA3YIbAs4YlFTdHJ8lMeujNxC6miuX+jonMAOitHhIf7+mPgEFhvmZhFVZGWBAc8jLFYWKrXpUmYr37BatDQ38TiKepqCN21NdRw1ZrV6bPpQL/eBawHXDqKPIWpB8MMcYrC/RxH9F5x+uAYVOySd7RVkSuGzuJYRvYy6wJh3QNCrP3mE5xs5BSXc5AROSDzHfKPxzHGOgo6NS+Bl+7uVkiGq4KY6FTGhSED6bZISSY3Xyjfv9Ap80M6nfG1F3+AOrePZ6lq/bAxkO6gR4kbBk+UgNkmwIiFQ/66lWIiApfPOO48zMVCXDxkFiPRDcxM1dRoNPlDSBTWgy8vLWUR81atetahOIa5PbfQehMJ7773XKwMEQiIakGDdaGKCqL4777yT/uM//oNWGwiQ+D50P66rq+MUaXyf+ruysrJYOPzUpz7Fv2dycpLy8/O5AYveAU96ETFvxFnNMkHzEtQj2rv/EO3ctplcLhf1Do6GTf0SDLynj7yka+j9SkUMJoJochxdVWfZyEbNMEQLYcDBTRnCA9KzMSmHMQGDHctCmICgiA5fWBZphYg8QHQCBMXMvAJqPH2cvX9ZecWcrgiPH4yHis07qenMCY5wwDohWKCQLwYGTFggriAyAWIjapUhSgKGBowXhPLjOZZFqiOWgWiKmzLqemCdMIZYALE7OOoSjVVUMZN/8irewAtTl2aAPPvvf4aVUBjMtQQgQLfWn/GKMPOHOoSyCLdwnDExhdGLkw2TUExGcU5i8gnv8ejwAD/HJBPnCoxeLIxzGB5sGMf4nOf8nuPUEVwHirE8xx7s2uMHuZESUvcgqtefPOx1fsM4xvOKTTupoeYYXwtYJ87hlvrTfM4mpSl1skYG+/k51oNzXY0CRJc6eNzxHnvDLVaO9IM4J8XDF4/BM9PKeGMkXnX+BmppaqBf/PHv9JqLt/N9Ep5UeFGF9bFL3vu+6+nB//0h/c9nb6PPfO72sBlHm86c5MgZpCOHnogxrylvMMn3boCxD/d8Tp3mVGkrOz7xHLYDMi0w5mPMhIMEqX+qwIEao831p9mZkp5TwGM6BBGIE7BDThx4kSOW4LBBdDScNLAVsgtK2DmDaCcsi0hqCJ3YRggQ0bFxvA2wP+BsxbZOsR1iYacKtgcogkck21UWS4TbiQJxBt+jOhNWW1RYil2CBmtcM01TC9zsBHs9waZFRPzZyn6oNokqwuFcQ+QanHQQy9UyPHAW4ri7YuPYBsE5jnMA511vZxt/HnXwIPDDeaie30P9PQvnt43r2p05doDfg6MfdfD4/IZQVlrF5+RAn5/zOzmNbWfVDkHzC9gZcCyo5QmQIQWiYmL5HIcwiGURmcxNvmBLW5RoYfy7r6eTUjNyDCWIGQEIqAgWMZLQ8cRffk//ef07advO3fSz3z9B2dGzprJL0Im3vr6eI+7UjrzB0NjYyGVT/ImEZxMT3/ve97KgJQirfX6aMqJwPWsBGS1iAeJYKMI1lTTRBtrCuTBktZ4434LFWq8dhBdtNJGaCtXWVEuRkdFU5ROVhHWpRlVecSUb81AqsS1IV+T6ZM5Imp+bpbm4hAVjOkKpnYSUStQjmZ/jtCgYSmyALUR24jlSBmBwwQhDd8nM/GJOXcBnUtKz+Xtg7AMIUp2tTe5CxMWVm+jEwT38Xkp6FgujrWoXsoqNvF5EaEF8hGh0fP/zLArNlxRQfHwCd73Efq3euIm6u7qot0epD7V953m0b+8eNtZSU9MoPiGR9r+kfE9xSRl3HMSyMBg3b9lGx44eppnpGUpITORO3rW1yjbk5xfS2Ngo9fR08/MNGzfT6VMnWRhFsx50LKw9oxiM2dm5XNequ6uTn6OxT92ZGq6/GRMTS1lZ2fxZCLJZ2TkcjYGC+qBqw0aqr0W3qlHuQI7GP0cPK/UwsrJz+Tc2NzXwb9+4aQvV152hocFBbgKEz+554TleFoX/EQF77NBR5bdWbKKutmYaHupnTzKntx54gYVjpLfiuCPt/tj+56mwbAP1din7G0IfonDV/Y0UEZx3SCPDpCy3qIw/hwf2dwE+29nOQiBEN0zolOgTC5/niqcXX6uMWzB6ASJkcWxhmONY4JzAe3FJKe7IlJLqrV5Gsb/zO1BUjq/3G15nFRj8WvD7tIwODrDYLUKhN5iYtzfXGS4KVbz3+qNGTpilSQOEn2DEDQgJoYiakWCxKEIaJtpO8kRxaVOuAZyEKko9sNyAWRvVibtZyEOdMog+XqUQtp7nJVTCDlBtC9wPcP9QbRZEL6oOHdxEcJ+AjcKNa6an2O7Av5ElgiwOvAbhJy4xaUFkhAMqjwWjvq52FpGQ6o3up4gox7ifnp1HtScO8/dl5RWxkxZRmfMLvwtp4RA7sV1Z+SVcp9KdHjk/Tx0LTZ2yLrmI7/Fo5od7fkVVNdsd2Mc5eflkt9mpsbGek0Kyc/Kos6ON7YXIyCgqr6yiwwcP8LJoNIhskubGRnYqo/Efun/DbkH6WnlFFTfgAbBXXNHRyrI4Z4uKqb+/jwYG+tlerqzawPYNfndCUhLFx8VTQ4MiThUUFNHg0CD19fbwvW7Dps107Mhhvn5hC6H2+Jka2DfzVFBYTCMjw7wdODTbduykI4cO0uTUJDc3zMzKpmMLTYFgY8GWaW1p5ufnX3ARHT64n9p6BtmJB7vzzPFDvB8QuQqnY1drEx/f8o3b2b46c+wgL4tU+5qj+5X9nZ3Py8DRiM/CNkCdO5wDEO+wbHPdad6HsFlgx8DpiHPG6YrmYwjxGNGxyvk3x/aM0m3dxucNhGXYrKgDjHIhVotSQxTRuFgP9ik+o43+5+0sWHx+u59rAhsUJ31uwIYZsIMDgXlCT3sLpflkEwjEYiyiULWZZXqj3gfNWDd5JUDsQwQeugIHEgl9IwuxvIiEwlphyojCA0dO0NaNlTxJHx43R2ewYAxyRCbBA+cb7i4Qd/6C4RQONQpVz7320tamj+O6gGiH12AE9vX1UByEoPl5FjNxA+eb+Pw8N9cZHRmhufl5pViy3c7t5WEcQ4hD5JvalQri4MjwMAuQ2M/wUuA5iIyK4u/D5AOg2ya6S2FSA8MTy6LzFS8bGcnLYr0weGHoo5AztsEKozYykrsiwihWO1xBCIlYeK6KIsokz2OEqIJasJ57TKpgSJutk95qgEkmJg6IehG8x2CUWCit9m5apTeXbS+jzvY2+s3jz9Ard280lec+VOySG/7fjfT97z1At37y0/TFL99BZiCYsRRCEsQPo0XZ6o1Rx4q1tEs8kW5KGiRsEm1KpCoe4N6P+zgKz3NnVqdTsTtgh3AXb2RtTLBYicwhNLKankHtywhOmxseGuL1QPSCeDU+PrbQKTxGWXZ6mh2+sEPgVISD0rFQ/mKc7RvF7sByvKzGvnFHZdrtbjsE26fU45112zCqXaTUprO63/OtcbVUu6S7o4XT9n0bUQjEzltfZ6lAXFs+M7fIy5mhN39+9Ff0iQ+/j8678GJ68Nd/kYhCP5GFwYh/wS4nCFrCPqJQ7VQTbhGFEG3gBRQWY/auzv7QRpr5imbazltnTp/2Sj2GIavt4g2xUItWXICxDBFQBca0lvgE70g0bYiztgun73qBdhuiXN6TTN/n2u5Uq9WpCoY/ojBEKFwMOvNJzc/FYGKKCByjoU7AtVHbwvpiDVO7BM4hlCsQfJl3d1MPF3xLrfiKZurzjvY2pUlegmcsjY72TrVVnYSqPaCNlYqL9246pbV3fO0D32XtmudYVtvIJ8bHZvG1jQLZL/6+d7mgcRci70QoXEyJTzMlQQE2rBoparS6ydzhXFhEsOKfiITCWmNK5UQ1NrRRVOEA1+7wSREUFEo3bJNC/sKSYCFMat34BbWCUMdQWHzOKPUmjYVa/1XSfPS3S8ySehwsaGQWjp0CzwUiLCX7wz8ceSj3Xr+ww9c8iWCrSutCbUTBG7VJjNECW4A4LwXB2JhSyrdqah7Aex8ukWRIh4hHhy5hEcf3v0BV28435Z5ZavMSXzZu3kJmJtiUHl/ggQ2XtLDlGJ4z88YyPI0AImV6Otu4WYyRcKe9SWSXboRr12M0MpGokcWMjgxxjUIzioUrtUkyMrNM3ZBiuTYJMOP5slqMGdBJZ5SxBvcdNGEymvNSbBJBMDamVNBsmlDmcDLK0TwBXU6FxaAGjeCf9rY22TV+QLF5tTC74A26ZqqdEwXj4xYKJfVYN8K1JEr9ySN6b4Ih4cZoJhbDVgLqBo6K6OOX5rpT7g7WgjdGEsKMBBw1qjBnFGbnVJtEos0FwcjYzNxdMNzSfHAj0P52wUOST4dCwUNPdxeVV1TKLvEhgiJodtoczZBWG3TNRKdIYTHaLo6GS/OR+4PumQ7qsQgbRAwLmAESq+k0L3iASOhb409QQDmlcCqptBRCtcP6WoNO0kZzSohNIgihgTkjCjVRE+HkvY+OS6AEST32i9RuDIzRihwbhQiLheISk/XeDEMy1N9HHS0Nem+GIak9cYiMhtt7L4XDdUPd9+Fkk4CcghK9N8Gw54ORupAaCZQLEqdG4JqfEJmFxZw+sk92ix+GB/uou73ZUPvG0wXclPFKgmAaTCkUWsM0onBqcoLmFiaEgjdNtSdllwRg13m7Zd/4AR7Y+GSp+RloIidjTYBxeGLccNeTeO/1R432DyebBEyMj+m9CYZkdHiI+ro69N4MQ5JfUEjpGZl6b4YhiYlPDLtu2cESoTbCEbyZh10yYUihULLg/DM0NLSqywnCcjGlUKhtXmIWoxzFoX0fvowM9tOkwW4GhsFgYfdG4vln/633JhiWxtPH9N4EQ+KMclF8Yorem2HYiZyRwMRJTVWTekD6ZzqYxSYB57JJAJr7CIuBoyVcGu0tlbraM9TRLueNPxAZNjYyvO7HJBRIy84XodAPDmckOSIjyUio90FtBqCg0NzcTMePH6fW1rP3HMD7WA7LmylA47HHHlvxegoKCuiee+5ZlW0Kd0xppeBEM6NRfi5mZ2ckXSMA+SVSgy8QUu8mMOKb9o8F3XNFe/dLenaeoSYr2lRX6TCof5O1mdnwsUmEwMTGJ1JSWobsIj8Yafw0GhERFpqflxqF/rDZ7HLuBHLsGqwsldpchW1JwStCUBUIIQAGEgvxuioQ4t+rFVkI/eRsj9tvv91QR+uhhx6ihIQEvTfD1JhSKNSmH4dTPaDsglIpjh2Awb6e9T0YIURGZpbem2BIcFPMLSzTezMMyfT0JPV0SMSHPxpOH2OnjVHQNs+QrscGaGYSRjaJUZv7GIGJsVGaHDdemQIjEBcXRy6X/wjVcAficlRMnN6bYdhoy9kZaUDny/TUpOHKL6k2kk26Hi8a+3Jzc93P/YmFWpEQYHl8bjVob293PxCRh/VqX7v11ltX5XuE0MG0QqEaURhOHQZbG2pockLqAfljoK973Y9HqJCYJJ0XAzE2Kik+/pAahYFBcW7VW26kRiZAhEL9UIu2h1OWAyLDjNjcxwiMQygUe80vEAml67F/picnaGZ6ai1PzZAF0WlSO9l/4MycgZyX2vug2CSLyc7ODigW+hMJsfxqkZGR4X7Ex8dzwIT2tUceeYQqKyspMjKSKioq6P777/f6fEtLC7397W+npKQkio6Oph07dtCLL77ofv+BBx6g4uJicjgcVF5eTj/96U8DbsvTTz/N3z8wMOB+7eDBg/xaQ0MDv//e976XBgcHzxnxePfdd9PGjRt5m7DPPvShD9HIyMiq7DOzYzO9UGigCdtaMzkxzheKsBipBRSYE8eO0oUvu0ROGz/0drVTRk6B7BsfHM4oyiuukP3ih/ScfEN18tM6y9T0V0G/LAcjichrDX7r7LRE+ATCItejX9rbWik2Lp7SMyQ125fRkSGKmpsjV3Tsal6qpiC3pILTjwVvrDY7pWfnG2q3qIJuOKUea+tFnwuIclgewhuAONjW1ualaeTk5PBywegcmAOvVB/4+c9/Tp/73Ofovvvuo61bt9KBAwfoAx/4AItv73nPe1h4u+SSS1i4/MMf/sDbtn//fvdvfvTRR+k///M/OVLx8ssvpz/96U8s9OF3XHbZZUvengsuuIDXhW06deoUvxYTExPw93/rW9+iwsJCqqurY6Hwk5/85CKhU1iMaWcN4VijMMoVY6gJqpGQ9CdBWD3m5+aos6WBckUsXMTc7BzNzEwbxlPuVaPQINsUjoSjTTJP8xSbYKzmPkYhLcsTMSJ4gwmy+Lz9E0ERUocvAP1dHRSXlMxzIUFzzkRE0NSksRpdzszMhl0zEwhme/fuXfbnfQVBiIiqkHgudu7cueIeBrfddhvddddddM011/BziG5opvK9732PhcKHH36Yuru7+TciohCUlJS4P3/nnXfSddddxyId+NjHPkYvvPACv74coRBRidqox7Nxyy23eDU6+dKXvkQf/OAHRSgMAtOnHk+HkTc7OT2TPUfCYk4e3CO7JQBVGzbKvgm0b7aeL/vGHxERNDI8KPvGD8ODfTQ5bpwSENp6iRJZrR/haJPgfEvJyNF7MwxJR3M9DfR26b0ZhiQrJ5fiRWD2S0ZuISWmpK/3IQmZrKqZKUnL9kd3e3CC0npnOoRTRGEoMzo6SrW1tfT+97+fo/bUBwQ3vK6mBSPSUBUJfTlx4gRdeOGFXq/hOV5fa5588kl6xStewdGOsbGxdO2111Jvby+NjRnHVjcqppXyLWHYzKT2xGERNoKo0yV409vdTYmJUqfQHzXHDlDZhm1yyvgRAByOSNkvfkBUt5HuO7MLnnt4k6U0hX5YF4q2G+ncWGsmxsaos7WBCsvFGeWLch5IqRh/DA8NcTqb1ClcTE9HK9nsdhEL/eCIjKQIi2njX1aGwUJ03c1M7PawspsR2bcU0EDEX9Qg0nUzMzOX9N0rQa3n94Mf/IDOO8+7QZkaqRgVFUWribrNiDBXWY6jFfUMX/e619GNN95IX/7yl1nIfOaZZ1j0nJqaIpfLtarbbTZMO6KGY5oPkIngYjDIJCSl6nA0QoPOzg69N8HQ3eIE/+NMQVm17Bo/pOcUUHxSimH2jRQNNwbh2MwEdaikHIp/oqJjWNgQFjM0OEATE8ZKlTTSNRVOTRqXms7vko7QfqnYtDSBaq1R74Ph1PUYdjNEtWAfHR0dXiKhNk0br+P9YNe1Um0gPT2dsrKyuL4f0om1D6Qgg02bNnFUYV9fn991oAnKs88+6/UanldVVfldPjU11S2WqmD9vunH53K+7tu3j9O+kTZ9/vnnU1lZGdd7FMI8ojAcmpkUpkZ7Pe/PK9JtW4xOQnKqKY/5aoioK61bEWr7rL57NOjPxsVLfa1AnDy0h6q3X7CiY2NGhvp72ChJTgve27sennsRbPQlHG2SyVgrndZta4xNVHQs2R0OMgOrbZdwB0uLsSKg1np/BWuXOCNdUkIiAJ0tjeSMjKIkg9x7jUTD6WNUXLWZjNbMROwS/wTqbqx9Xf27ml2Pz8bnP/95+shHPsJ1Aa+88kqanJykl156ifr7+7neILodf+UrX6Grr76a7rjjDo54RMMTCIy7d++mT3ziE/SWt7yF05PRzOSPf/wj/e53v+O0YH9AhMTvRidjRAKePn2axT4tqDeIaMennnqKNm/ezNGBvhGCWA8iEb/97W/TVVddxeLkd7/73TXdV2bC9BGF0zPhUw9oWmpzBJwoN9fJdCWQQX7+BRet7YkZwqRkSn2tUEllMVKjFyMVDlejT8zuEDA64VijcGp6WhovBKCrtZEmxoJ3WoUTpeUVlJJiDufuahMdG88is7AY1Lub06QpCh4mJoxVi03skqWLhAB/8VwFy2H59eD666+nH/7wh/Tggw/Sxo0bucPxQw895I4oRHTf3/72N0pLS6PXvOY1vMxXv/pVt+0JAfHee+/l5iXV1dXcBAXruvTSS/1+n91up1/84hd08uRJjlb82te+xjURfTsfoynJW9/6Vo5A/PrXv75oPRAQ7777bv78hg0buHszhEwhOEwfUThnYu+9L90dLdJJzw/zc/PigQ0AIp9e2vsi7Tpv95qem6FK3ckjVL1N9o0/UjPWx4sZajgio/i6Mgozai2gMOouaETC0SYZGhqk0eFBipXI7EXMz8+JXRKAmlMnKSklhZKTjVPCwSgM9Hay+J6Wlaf3phiO6DilA6rgZ98YLCXb0/U4fGoUBsPQ0FBAkVBFfa6NLESDjri41T3G6FCMh5Z3vOMd/AhEfn4+/eY3vwn4PuoE4hEIbT1CtdnJ4cOHz7rMAw88wA/fuoRaPvrRj/JDCxqaCOEcURiG9YCEwEXks/KLZfcEQOrdBEZMzsDYpZmJX1wxsYaqUehuZiJCoa7YFrzq4WST4N4ikaz+gdCDNFJhMTNhJKYvmYgIkqA5/6AxhjQz8U9mXpGhorvdJVHELvECYp8qBPoTCVW0kYX492qLhIKgYtoQg3BsZqIWqz3Zf5AqErecc/lglwt1MFEeHxuhmLgECvfaP/5ITU0jM7OUmoS+ZOWXrOq2mIm2plpKTDH3ubMcxsdGqau1iQrLN5CRHAESUagv6oRIjfA0Mwe79tOWtG2Uk5tH05GBx99wsUH8MTo8xM6WUCsIsB42CWpgOR1OMisrsUkQnWskwcdIjA0P0eTkBLkkNXsRjTXH2SaxG+S68tgloTYCrj0QADEGnkv8g0C4FpGEghAWEYXWMBQKUaz2V2d+QJ/b+0F6rP4nZ10W72M5LG92ZmamaHjAfxemcAdpGmnpGXpvhmGZmhzXexOEEAMRVEZKL52VouGGIFxsku8fvp8+8MR76MfHfkQtzU00MjRA4W6D+GOgr9td0F/wJj4hkZzSEdovuLfMhFGd06UQYbFyjWBhMRar1R3FZwRUh5k0M/FPsOKfiITCWmNaoTDcCofDw3h6+Cj9pu5H/PzhmvsDioV4He8DLA+vvpmB4WCxmPZUXxHowHn82BG9N8OwdLe36L0JhqWoYpPem2BI4LFPTs8ioyBFw41VDsXMNgkiCX9wRKkVdN/Be+hX9b/gWnzhboP4ZX5e0iQD0NhQT8PDQ+t7PEIENMAJJL6HO7EJidKALgBpmbmGqgeo2iUQMAVBMC6mVU/CLfUYxfOrk7fTO0o/5H7Nn1ioNdABljd76o8rJo4KyoyRBmhEpPjzWXfO+h2IEKO7zVNwWfDuvDgzPWWYXTIzowhTknqsL2qKlZltEqQb37TlFvfzX7Y+TH/repTC3QbxR+mGbeQwSBqg8ZinCKkQHNgmkdRjv0xNTtBgb9danpghHxhgtNrJdgOJl4IgLEaEQhOJPedtraaP7rrRy1DXioX+DPSrC99NZgfe19aGGr03w5Ag0rK0vELvzTAsFZuVup/CYoYHJZ0/EF1tTYY5ZVRhSoqG60u4NFh7T/X7vWyQXzf+iP7V9QjXtsPfcLRB/FFz7IChJu5GIic3n6JjYvTeDEOSkJxG6TkFem+GYdOyx0aG9d4MQzI00Me12g2XeizNTATB0Ji2mYndbg+rjq7j4+NUc+oEbdm2gw11NfUHwDD/Q8PPaGR6KCwNdBgPiLgMx6LgwaSsDw0OUlJSst6bYkjqTx2lkqrwi3YJBoczUu9NMKzTxkjF5lXPvZHSjsIRdUIUDjaJrw2Cvz89/iAN/v/2zgM8jup6+0er3nu3muUiuXdTAoZ8EAgEMD2E0EMJECAFCKFD6KGHEgg1hBo6JLT8McFgDO5dlmXJsq1q9V73e96zmtXsama1klba2dnz87PPemfuzNy9O5o5895Tupv80gbRoqery3De/EawSUBbawt7QIeEhHi7K4ajtbmBOtraKDUz29tdMeQ1VkJZXeRONtC9x54SRYRCQTA0pvcoNHM+IDVIUqu+QTrP6vurSAiCgoM5/FgYCjwaaqqrZGh06Opol7HRIX/GPEMJYkZi2uxFZLyk4ZILyJv4m00CG2R50qn2zyISOhITl2A4odAo1NUdoO7uLm93w5D09/UbKrWFkQgLj6TM3Kne7oYhScnMptjEFDIKSmGVQMkfLwiGxrRCob9UGFQIDQ2jzElZQwz1qGBHgQyf/UkkBMGhYRQdG+/tbhgXeVjRJSombiJ/CZ9i5+Y1hqruayT27t5BRkGpkIkJE8F7+FveZPCruZdRTEgs+bsNokVCSroIhS6QHIXaBIeEUIhUhNbNUViyfaMH/jrNR0tTAzUaKH+jMmEmdok2jY1rPNpOEEaLxeyhx/5ilMON29mwemnrcw6ehACf9aohm5WWhjqqq67wdjcMCUJ7liw92NvdMCxpWZILyFXRDiOG9BuBjrZWw83cSzET7xLsh0LhG7v/Sc2qcGN/tUG0KC3a7O0uGJZZs+dSbJxM0mkRFhFJcQnJE/6b+ErObavYJJpgXLo6jRMho+RnlZQoQ9m9+1Fau+5MKtvzN5djiPVoh/b+TFlZGU+6bdiwQbfNihUruE1jo1SMHymmzVHob7P3LS3N1NLcTAmJiXaRUMkPBGJDYu2hP8hZmBAZYs8jNByltW3ky/RbrRQwSvd2d3L2aI2P83ZabZyXeSM/UHd3N23fuoXmzl8w4cf2BXZt20gzF4iQqkV8cioFWCR0TouIqGgyWi4gEQq9i8XPbBLYIP8ofdEjNogZ7ZLhaO/so4gw7XQBaluhvqWbEqJDRmVfOLdxx5aZCHZs30qZmVkUHSMpY5xpa2miprpaysqXInTOWAKDKD7JOOG1RiI4JJR6ursNF+mA6L/C9EhqbnZ0avFX4CFYWvYY/7+k5H5+z825VFMkVNajfULCoRQX55mUN+effz699NJLQ5Yfc8wx9Mknn5Avcsghh1BlZSXFxjpGOAh+7FHoLxUG1bMzSri1s0iIXIVfnL7SIWch1qOdPxAdGyczsDogx1yP5LsRRkF4RJSEh+mQmTfNMOdU78DMfeDAPVHwDv4U5SA2yPBMytPPpVbb2EXl1R1U1+T6wb6kso1WFzVQ8X7jeDB7gp7uHrKS5L/VApFDMjL6HoUhYeHjem76KsjTjsldo6DcB5VndcEGxL78/OvswwEx0NmzUC0SArT3lEiocOyxx7Kwpn699tprPh09l5aWJuk+RoHF9InDe/0jcXhqWjplZedoGujKrL1zgRN/EQu7u7qo10/Og5ECV+x4qXisS3pW3kT+HD5FTcVe6urq8HY3DMmuLesMU+hFPXMveN8mwXmhhF2ZEbFB3KOzvV3Xk7CuyfY3W9vYrSsWQiTcOSAQ7qpsY89CsxATGyshiTqERkRSfJJxBB+jsa+02NtdMCSdHW20t6SIjILyTBYYJEXWnIEHoZ5YqCUSankcjpXQ0FAW1tSv+Ph4tl9uu+02ys7O5jYZGRl01VVX2bfr6uqi66+/nrKysnj9lClT6LnnbDoD7J6LLrqI8vLyKDw8nKZPn06PPvroEG/G5cuX0913302pqakUFxdHd9xxBwvL1157LSUkJNCkSZPohRdeGNLnHTt2sOdgWFgYzZo1i7766ivd0OO6ujo666yzKDMzkyIiImj27Nk+LYSOJ0Fmn71Xwq7MTlVVJRW3FumKhArKZ6Ud3ucmz6d5KeYNPW1vbWZvGilMof3wmpaW7oVfxTdQ8rsJQ5F8QMPkb1R5eRsj9FiKmRghRyGA0WvGKtQbatY52CCnppwhNogOB6r3U+qknCHLEW6cHBfCIiFQ3hNjQzRFQjAtM0oz/NhXSU5O4YdMQQOrlfrFLtFEqojrg/uNkc6bvt4+vyuyBpGtv9+9yfWsSeeQtb+bdpfa7qcQB/fs+Rv19g7m/J2cdw236+sbPvekxRLukb+Pt99+mx5++GF6/fXXaebMmVRVVUUbNw4WEDr33HNp1apV9Nhjj9HcuXOptLSUDhw4wOuQ0xwi31tvvUWJiYn07bff0iWXXELp6el0xhln2Pfxf//3f9zuf//7H33zzTcsLqLt4YcfTqtXr6Y33niDLr30Ujr66KO5nQKExEceeYRmzJhBDz30EJ1wwgl8fBzLmc7OTlq4cCGLmjExMfTxxx/TOeecQ/n5+bRkyZIxj5OZ8P5TzHh7FA54U5idjvZ2KoiZQRfP/jU9u/kpTZFQSyxE++FEwtHm6TMKuDgFB6ucZzdvIJo9T/e7Kjl/OtetJUpeqLtfvdxAvgRmf3Zs30YLF8uFUc9rLjlt8EYkDJKWlUfBwb59/o8XKZnZhqkmrszcB8nMvVdRi8awS8wohMCWUNsgU+qneMQG8SW7ZHN7B82OGD78sSQwmGbq2CSKKKgWC+MjQyizcgftTy8YIhLmp098HsHxpLi4iCbnT6XoaOPkejUKPd1d1HCghmKloIkmedNmTfRP4hMEBYdQYmoGGQW7XeJHoccQCVd8NXvU26tFQgARURESh+OIZZspMDDC7WN99NFHFBUV5bDsT3/6E3vrwbvwqKOOYocseBYqwtrOnTvpzTffpM8//5zXg8mTJ9u3R/vbb7/d/hmehRAVsY1aKITXIIRGOCPA6/D++++n9vZ2Pj644YYb6N5776WVK1fSz3/+c/t2V155JZ166qn8/6eeeorzKcKb8brrBr0zFeBJ+Ic//MH++Te/+Q19+umn3BcRCh0x7V9okB/lA1Li70PDQumSOZfTkrSDhjW8Yaib3ZNQIW2SqnLti38jeulZokuuJDrr/CFtkesHYTzTvv4XWZ77K8X9+gqKv+LKIe2UGf0p6ZE0NdPxYuruA4o3koQPAYVejKFnGBIZGtdJ1cMjIjn3jTB09t7a34f/GCcXkHgUGiLKwex2idoG2bRxvV/ZIC/W1tFLdfV0SXIinZWYoNvutbp6eiYsjupr6+j8j/+laZM4i4UHnnmGLG89Q/UnnUd08gXDioTuFFQbbhuvYZV7ry5ssBkjrYURqaupoIio6d7uhjGjHAxUEdpul/iRR6EvceSRR7LYpgYCXltbG3vtQQBEHsPjjjuOPffgnIWqw7B9ly1bprvfJ554gp5//nkqLy+njo4OLqg5b56j4w48FSESKiAEGaHECjgGvARramoctjv44MHCk+jPokWLaPv27Zr9QBg0wpshDO7fv5/7AccZhCELY8xRCFdQnBSIS4cb63vvveewvrW1lVVduIMiBh0uoE8//bRDm6KiIjr00EO5zZ///GeHdbm5ubzf7777zmH5NddcQ0cccYTb/fQ3j8K09AyKjY3j/7treJvJQHdF9f491NLUYJu1h0EOnvkr0WuDFRmV3EAQCRM+fpVFQtD41BM2z0KT5gYKDgmh3Lx8b3fDsEybre9R6u90trdRd1ent7thSBoOVFOXQcZGMciNEAY9HviaTeIPdoliW0yfXuhWOzMAT0KIhOCZ2joWA3VFwto6/j/ab175P12bBGIhhxV//CqlvPUML0t6/yUK37nJlJ6ECtk5uRQaFubtbhi2iFjWZKl4rEdzo/bfnUBUtbfUeDkK/cijEOG/8OwbyQvhxVpg+Uj2g2OPhMjISM4vqH5BKETuQdhLTz75JNtTl19+OYcEw6bBZ1cgXBlefAgl/uyzz1hYvOCCC1ik05tUBbC/tJaNRfh+4IEHOD8iQo+//PJL7guqOjv3RRiFUAg1GXHnUIW1+N3vfsfunq+88goruTCmYaR/8MEH9jb4/Mtf/pLef/99fiH2XA1cW/HjjQXlpDK7Qa5QtGMbtba0eLsbhqS3u5us1n5baA9m7RWcDHPkBoInoWKQg/6LrqSwBQtNmxsIF9qOTilIoUd5SZFhilIYDdtMsPhcamGxBHGOQiMVMzGrR6Gv2CQwbJW8hGb2KFTAdXPND6vJX0C4MTwJFbTEQrVICNB+9v87erCBllj470GRENScfgn1Fs41rUgIOjraDeX9ZCQwOVe1r8zb3TAsZr3PjRXOT2eg8CF77uRg/xEKbTZAhNuvvfv+4RBaHBRkcwYCWI717u7Lk/k7IQhichbhwSgSgvDhzZs3c0EQXLfVRUTUIN8gio1AXJw/fz6LjyUlJR7rl3oyFzbW2rVrqbCwULcvJ510Ett9sB/hIYnQacEDQuFPf/pTnnE/+eSTNdfDwD7vvPN4ph0z8UhUiR/h+++/t7dpaGjgJJJz5sxhLwClCo0CtsEP/u9//5tGizJ77w8GuZIY1mKAMDcjEhoRQcHBA/mgENqjJxa+9qLdk1AxyHcedhqLg3oioa8b6xARaqoqvd0NQ3vNiVCoTWpmDsUlJk/wL+IbZORMpshoY4Rk9w4kMFd7tJkJX7FJ/C3SAddNdfiQP4BwYz2x0FkkPMPSbwtPHsYmaXj4IQebpP74X1BPn9Vul5iRmupqv7HdRwomvbs6hy9e4K9Mn7PI210wLFNnzifDhR77kUfhSNCqbrzs8LW61ZA9DcJwUahE/UJRkhdffJHz/m3ZsoV2797NE7AQDnNycti+gq114YUXcmQHColASER4L5g6dSqtWbOGcwFClLv55pvphx9+8FifMVn87rvvcvXjK664gu069EUL9AW5FGEfYgIZxVGqq6s91hcz4fG/UKjFmKnHjwODGycJTghUyVFAqWskukR8+s9+9jN291SDBJeXXXYZJ6xEDPxojE3Fo1DxpjA76RmZXk2Ojtw2o0kcPhE5cdKisjmHo73K5FVXUGNkyKAB/sxfyfLGP6i/qcnBk7D+sNP4/xAHS6va2Dh3VyQ0TK6fYbD6eKW48U5WHxElydT1qKkopwCLRYq9aNDUUMeFXmLih1Zb85pHoR/N3BvRJlHsEhjgijeF2cnNG0xkPlFUlTRSWv6g54UejeUtFJcd7fF795+SI7nwyH1ltgk4iINvNjRS40CVT3B9bjqdGx9JkVGRbtskEAlbTjybaMAOUSYt3Z2s9BWbBPjy5Nx42yTINRcaJnm09Ni2bhXNWDCYq0wYZH/ZLsqbboxiL5KjcGQiYW7Opfx/5V1Zr7wryz0FojBQjVgNCougiAheiNRAnj94EX744Yf2ysLIa4iiI/AarKur42InShESiHHr16+nM888k587zzrrLG73n//8xyN9VvqGMGJ4K8LuS0pK0mx70003sdAJWw95CTEZvHz5cmpS3XMFGx5/cnj88cd5wJHrB7PnMKifffZZjmFXQPLL2tpaam5upuTkZN0f8YUXXqB//vOfXLJ6pPhb6DFmGe1CmODAruIiysnJo5jYWPuyuIsu5nfFMFcb5PG//Z1tvcqDcCQioS+BC+SceebJE+VpMnOn+LSQOp5AJLRKeJgmfb091I9iJoaqeuyfIVlGsUn8zaMQIUgTnX9q3ce7af1/ymjRSfk09+gc3XZ7VlZQyX/3Uu6yTJp8hOer2v86K5XfFbHQWSTE+m++/ooOPWyZWzYJRMKkSy6hpemRDpENIxULfYW58xfIfVeH4JBQSsnIntgfxIfwZZF5vGlvbSaj4O92iR6NjWt0RUIFLbEwLnYhxcV5xpsWXoN46QFBTQ+kaXnooYf45QycmWBD4aXmnnvucTi2M5jcdaasbDD9AjwZlb97iI9aIKJEfW1AvkXnfNaCNpbxMMoRogMlF/HhDz74ILuAfvHFF0NOGD2DHGAdkl7ecsstI04uuWHdGrIEBLAXmcUSQPv27aW6ugN8kqDENmYyzHYzKd3tuTh/s2Htt2qKPTDMLSrxEOCzYrDD+A4OdNwOn81klMODpmiHdlUogahk+0bD5JozYlL1sAjz/C14kuCQMAq0TPzEDe5ryGGFitStzY084xsUQJynJTwsjBrraofk3zM7RrBJwOYN69igxXEqK/ZTZUUFdXZ0sIdhV2en6XKydXZ2UsX+fRPqSQiREKx5v4Q2fr7HpUgIyr7az56F4wHEwLggx2sAPmO5nv2pZZP0RcawSKjYHXjHZKUCxEKzhSHv2LaV/yaEofR0d1FZ8VYZGh0SktNkbHQIixi8bkwUuNZxLvT2Vi4q2d3dxe95ublcyRZF1tatW8c54wRisS8v9ypdkVABy5UwZLT3lEgoCM4EeVp0gIspYsSPP/54XoacP3AD/ctf/sKhPSMBrq2orIPXSCiYMYvWrVvPgmBPTy+HXrX19XFV4KLtW9nIz8icxF6HJbuKeZv5CxfT3vI91NHeTlFRUTR5ylQq31PGYmNMTCyFhIby/pQQVqN5GeFC7G/5gNwlMyuLwjSqMTU+96zDrD3AZyyHwQ7jW+1JCJTcQGYRCyGCdXd1ebsbhgV/51wIRxhCSFi434RQjpTYhKRReVsqAgI8Ers6O3jWPSomnprqaqijvY2N6qTUDCrZvonbwLME1/3KgeT2U2bMowNV+/lhMjwyikPncd9CCAgSmUfHJVBB5lLyF4xik4D8qdOpsanJ/uDU3t7GoaetLa1UUbGPr8NLDz6UNqxbS52dHRSfkEg5uXm0Y9sWrk6fnW2bNW9tbeGw9pTUVBYaYY9gvdHu/7i3BDoJZeMJwo3hSQiREOA9v7Wbcn6UoSkSgvz/l6UZfuwJntpb7eBJCPAZyy+blEJTp013yyYJbGum+H+/SjQwgcn9HrA/1J6F8VHBPl1YTQ3Of7NN5nsKfvaQsdElMmb4tAP+Ss6U0VXLxt8izjvk7O7p6eZ7TlBIKNVXV/DzdXxKGrW3NNOB6v18b5sx/yAq3rKO7ZCYuARKTs+iyvLdFBQUQklptutxS0sLT5IFBQbS7JmzKT8/38Pf1neZPPlqSkg4dFjxD2KhJz0JBWHchUKE0+DlbLDiojKa2XIIdkh2edttt9GJJ57o9nZwfcWsPY7Z1t7mkCcHgqCatPRBI3Jy/hQ21vsG+oqqnjzb39XF4uKest38PmvOPP5/Q309J/GcO38h/bB6FQuPECAjI6OopqaaP2P/XV02b4GQkFAO9VQuup5k4aIl5G0mOgfOD02ttDh2+Bmy7xtb6aiY2CEGuTpJOGbxFQMdy+uau7mQidqTUBENzRTug7/N6BhjFF0wIjBwArzgGeYLwDCE5xrCswUbuLZDwGusr6X21haKjU/iz+EQ7Hp6qPFANd8js/On077SndTW0sxiXmbeNDasQdqkXA7ZbKyrZsM6MjqWLEFBFBEVQyFhYRQYFEyTC+dwyI5yH0lIGcwl4/x7tLZ3UE1NDYuM+Hv3pwwVRrFJQERkJHt4woaIio6h/ClTeTkmIjMyM+3tFi5e4nA+zZw9l7eBTQNbQhELIRSWl5dRa2srRUREUm5uHq1ft4a3y58yjR/o4LkIUXH23Hm0f99ePn5UVDQlJiVRY2MDr4MNo4REe9IuiYqOpmnTtSsOjhdKuLEiFkIUjI8K4eXwMFSLhMOFJ48FiIFK2LHiSaiIhliO3/DEgG6XNgk8CSESAmW5Eu3gLBZOSY80jUio/E1IcT5teLIobfB6ITiyt2QHzVx4iAyLCqRBwYThzk1rKL9wLnVyVfE+SkxJp4ryEurpsk0sRsXEUdnOrZy7PGvyNLZP6mur+L5QOG8pVe/fw/+HGBsTEkbBoWFs24SEhFFocjglpKTZ001Mm73Q4TeYXDDH/n88Z1dW2q6PmOTCs7LRnG+8jbvin4iEguGEQhilu3btsn9GVRvMziPeG0krly1bRtdee629Cg7KZL/88sua8erugNxCSDr+6quv0tKlS8c1RyG2UbYDkyZlOaxPGEjWCWbMnO2wbvHSg/lYNg8kK0VHR9uPjbxHTQ0NFGAJoILCmfTdtyu5DURFGO07i2yhn3Pmzqfa2hpqbWlhD7hp0wuorBSzMEFsOGEZZlrxgAgxFMdSLq7FxUU0e8488hce3lNJj5ZX2/P9uDTYKxrpCgqka/OzNA1yJSehejmqHyc0d3OFQSUnoRlzA8FTNiNDjE49AgIgMIhngxYQX4ySh88jAl9fL//e7M3XYUtRAc/A+ppKDueFQRsTn0Tlu7azJ2VKZja/11TutVdb3FO8nXp7uthYtgQGUUtTPQt7CPnB9rGJKXz9xrrsKTMcBKyZTgnY1RWl4xIcQ2Ih8LiL2XMB+YpNMhq7BPd33OvxAhALYQsowJ5Qo855h3M6NS2dBWplQqizA+GcVj63UVkWYmJ6eiZ1dXfRntLddltm+9YtLEYiry/Evm1bNnPf0zMzuUplU1Mjn0+paWkchcHfLSSEIy6Ufjc3NVFLSzNlZY+PGOeuWIj3zZ/voa72Xq+IhIqNol5+/54q2hfYQ3dnZevaJP1nnEeWN1+yL9cTC83kSaiQOSnLwRYX1Iig4gpce8wSYYXvAbsE93tMyuJejv9DLK6vrWZbJTE1k1oa6zitCNoXzF1MOzZ8T339fWw3wIbZW1JEgcHBbK9hP4hWCBqwIaKi48gSF0ghoWGc/7Jw/kH258qYuERKz8qz9ydn6gyH/kFoHA09AzaJme0SQfBboRClrY888kiHUByAkthIQvn6669zZcCzzz6b6uvr2TC/6667uGLgaICxcOedd9IvfvGLEW2nzJBPVNVjm6fGoKtGWFq6g+CoFh0P+dFgEnWQnJJiD7OAcIPZFlzwsQzfHw8VnBMpgGhPaSnfLKZMnc7eAhAW4fmI77nqm695u7S0dEpMTqbdu4r5IpyXP4Xa29qora2VDflJWdl04EAt59CC+IiHDzw4KInejQ48CSESAsXw1hIL1Yb5ExV1dERSPE3912uaIiHAOzwJIRKClLeeoaSYEMr/7RW64T7q5b5IW1sble0uoTnz5nu7K4aktmofRUbHTHhSfl8AnnAwLr0FDGhc9xDiiNnw5sY66u3upqCQEAoLj6Tayn0s5CWmplNHWyvV1VTw54J5S6lk2wbOlRMdE0/JGVlUumMTi3cZ2ZPZ6woCH66dMLIh9IUHBVNomM2Qzp5SOCD22VJQJKcPFkPIL7TNmmPf8LhUi30A/ZpoentsAgkeLsyIr9gkDnaJ6kFpvMC5GaIS7xISHCtwF85wFBnVNgqKSQDYIHhNLyjkh7vQEJutwGFoA+GhiJ5oaW5m2wVRGWu+/463ZWGys5NTuIAFi5ZQ+Z5S6mjvYM9M2CXIrYwxiU9I4IgL2CjBQcHsjQhbBscZTaoXZ7HQ2yKhVoGTV/uCKWtvNZ312Qe6NokSbuxKLDSbSAi2bt1Ms2fP5Wuu4Ii1v49qKvZSfJL+BLk/AzHLW95puB7CJunr62F7ArQ1N1F/Xy9PMsKugF2ANCDw/C/eup7FvrjEFLYzIehhXe7UGdRwoIZaGuspODSUU4rUVVewzYFUJGyPhIZRYGQU34/ik9L4pdzjC+Y5RpgVzrdNaNXVVPKx1M+qMfGO94WJQP1cbla7RBDMwoj/Qp0rxziTlpY2pKLNSFBXslFAFRu9SjZ6KLORSgl2o6Pc2CDa4aWeWVWDECIFGOLTCmyhPchrNG/BQgfjvnDGLOrp7WVvBOTLwoO1Em7VWF9P3T3d7J0Ar4Ed27fxxRseCg0N9VRVWcF9gqj5/epVvC45JZXS0zNo166dbNwjZxIEzObmJv4MzwEUjQFhcEmPiGDREzcCT4qQCDeGAa4Y3FpioZbBPrukiCr1DHIiqm+xhRvDkxAiIYBo2LnsIAobGFvz5QayygS1CxQPYWEouJ60NDfoFjRRHvRx7ejt7ebPkVExPPONbZUZbHjs4XNSaiYX4Gisq+ExR+hK0aYfbJ598UkUn5TCYTEga/J06uxoo6aGOhZx86bP4jAZ5G6yDFxrELILgxi5FEPDI1i0g+iHPk2d5Vjpe4aTNx+OpfV/EBo2NN+pM5iEMUokDcbWzDP3vmKTAIhgvmaX4IWwaTUQ8hQgDmp5NdbW1LCnoiJA4jfKmzyFerq77elXEFHBoqnVFo52oLaGx2bKlGksMMIWgQcNhEtEYgBMdIaHR1Dxzh38dz577nyqqa6yR2Ig99/ukl0UOyWQQsIDqbtj0Os5NCKICg5P40IZil3iyQlMPZFQwVksxHvmBx/TbB2bRKsaMt7D5i+w2ySCnyE2iUsgrmFCTkuAsj0bwS6xUFdnO08ywrMOy+Fph89xSSnUVH+AJxdhP6Rk5lBp0WZeF5+cytej/XtsExCTC2ZTbcU+FgBhz+TPmMdtIeglJKWybdTR1sKfrWTlNrbrjs0OgSCIzxa2FwIcQqZhv6jBBOVI7RBn0HcIzd7OP6LYJLY+mdMuEQSzYFopH152Iw099mVmzp4zxLiHUKfcSoJjbYKgwhSnRNpLlh7sEGKt5E9S1ikJ2HnbqdPZM4dnewdCo+CFA+BZgDxKyNMII54FyL5eysvL5+Tt+/aWc7uDDvkRbVy/jjo62ikuPp7Xb1i/lm/MePDo6+tn4x/J0KcXzODqifgtIyMjKSk5hU6y9FJrYhQ9UddqN7iRS/KqyZPoyfIq+sveGnv/zw3pp0szk8mCKoS/voIan3pC0yCH2IdcP7uO/wV7EkIkRHtng9xMuYHCwsKHiNGC4+y0GQQWRUjp7YFg12eb7bYSi234jPCTttZmNp4tARbOeVexp8S2Liaewwqr9pXxNSArbxp759VVV/I2yWmTaNu6VRygDeMY+Wv27i7i/WRPKWDxD0Y4DHIIhTCccX2C0QoDOjwy2l6QAWIeZrgVI3/6HMecsmpDGu1QzENBHSIDnL35JhKMW/X+cop1Chn2BsrsvRjk3mc0KVF8FXWqFi0PR5CW7hi6Fhs3WIhAmQTVCqsGyNEIuwN2RmbmJJ607B8oPAU7Yec3tQ4ioeJZuOrdrRSV30OWgAD2cvx25f/4+oh80vHxCbR92xZuO3PWHI7YqDtQy31G282bNgx8tySKj4unior9fO1CiPdUaw9dFB9BzzW0s0j4i5hQnkSFpyS2hwCKtihkgt//of0H6OrsVDrsJ0dR41NFmjaJllioZZOYjZycPLsNLziCiTHkqDWLTYJ7JZ4n8B4aFsECHSYP8LeCCb4mDqntY7sC7SDiQbSDR17lXngot7Idm5qVR7u2rKMD1RX8XIJJyKq9pfz/qTPn0/6yXWx7wN7I4hzBxTyWCcmpbJsg3QjnxbQOCnpso1gsNClvKgUGBg+Iehb2ylOAjaPGOTcfcgsr4FqgZqIjMuprKzkEWQk79hb2KAcDFgYVBMFPhEJ/MsjB9999O8SQ9iRKCBBA2JCCLWfS4I0wb7Jj5aoFixwf9FHFUW8dxEPF8Mc7vBhgELB3UFSUzTsxMIgNDIiRJwf1UWBqHD1W3cjbP1JZTy/WNjlUGvxDVgolffMFrepu4WMHnnwyHYgIp4opU2hBezuVlBRTU2MjJ4OHt2Z9+UaKtYRT2BknUdC8QiqPj6N9mzawOIoHBoRwc4hVbh51NtdQSE87NTb08DggOTxmKvGABI8FnHsYMzz8QEBlcSQkhL0YlPAtdRJ5b9ww2dPUqTKjr1Ba2zbux6it2MuhpVoGneK5izEMsFj43RZSCEMz3JYwuq+XRTmcMxyC0t/PYbKYUUWxC2yD0Fjkm+nhPHihFJuYTFXlu6nfaqX4xBTeBmEnMJZzp82iij27WKALC4+grPwCKt68lkW6lIws/vuA8QymzJhLleWlLNJhZjt32kwqLdrCf08JyWnsaddQW82fYTwjBIa/S7DtnEQoDGa6Q8PD2RMPxjI+w8jMzJ3KCdVRyU7LI0+db895Zjw9e7C4FAhx8tgzA3jgUM+aexMlH5AnvaeE0eFPE5jwCIRANp4TUYpNgklLdZhq9ZYeKl5R5+BJqIQfl6xspEWJg+HHzqlg1HYU7t22iUvbPRIekfi7xuQRRAPkosY69pzu7aVfhAXQrPgQOjkrlSdJ4T2JyI/EpGSuXo22mKQ9qr2Buur20NKAZor99eW0Jy6WKrKzKWHbFsrKyqGNG2yFjeAdiQlQpJmxzJ5NhS+8THujImnvxvUUHR3Dea4Rvh1osVBKahrfj2CHsHg6KYvq6+q4X7BP8F0aB3JlR8ILPCCAxVW+/oeHcztsr6TRUbw+vWGXIHemePJrg3Gp3FMyJF+cej1P6uO3DAriNBi4r+Pejd8ak4FoAxEOVWm7Ozt4O0xqoXAFbG72wmf7oIrbIswZglxLUwN/zplSSHuKt/HvhPs7UnTgM44D2wBtESkAu6RwINUHjhU1kOpj945NnGkxI2cKT142HKhmGypv+myOKIAtEhYZxX3ARJcl0GKfXIyOjbN54FkslJyO/Nq2ZxPYKMivBxsHUQAQHdU59BB1oAbhvGrUdkp0bIjXU4aMBxZLEPUbwJvdbpPIZIAgGB7TPjn4m1BoBmDEKOHJHDKo8jxITExyaKsIkshGFR4+GGasFgmV0J8tLXNp1uy59uXpF1xk/796OQyghYuX2hMhW1NSKaLDlocJfYFHJvKCwNAGcVHBbBgBPAB0dHTwtjDIm1uaqbmpkQ0afEZRGiQXxveAgb+ruIiFIOR+QogWvCdxTIilq1d9w/tDqDfyV27dvImPUTBjJhv66rDwH77/jh9G4OGAh4bNG9dzWySgR74nVLoEBx96GG1cv5ZzRsXFJ1B2Ti5t2bSBujq7KCg4iG/c8NrEN5u3YBHtLNrBQheqc+bk5NK2rZvZIJuUlcXjUbF/P4cso6jPnrJSFlBR0Tt3cv5AW+IwdQgmFfv28Wd813379nKIGB7q8BC0ZdNGDslITU2nkNAQ2juQz2pawQx+OMIYQlyFd8e6NT8MtE1jj9WiTTbPD8wONx6ooeaGOg47nTZrAW3fsJp/CxiM0bEJXIACD0YwcNEOxigMTRixOzZ+z+Mdl5DEM8Vlxdts+508jau8YYYa1xMYoahMC+M4Ji6BQ2URCovzISM7nwteKAYvKrwdqEKOvB4W4eKT06ip4QD/xvg+tlw2toc0TKxzzjteF2o7ZxJT+DMMdi5oEBHBIjQMq0mTp9sf4PByFukgAipAHHQ1240qdwohTjmP1LPmwNmzEsYzDHthKPgNEZZkBJTZezHKvY8/2SUQHNS5sCYKVDdWchOqcxKqlyvvw+UqVCZItSZJATwJFSC2JSUlkzJVWlDoKOSgSMxg2wg6NTzcHrY97+xzdMVKzjudnsHvsEOy29s5SoMnbVABNzmZ7RDcV+Bxhb9zxS6BSIR7PsRNhIvX1lZTf7+VvTFt9/F9ZO23UuHMWSw4ovgMvgeiOJBrEseErQCBpmTXTltfFyziwjcIC0fbufMX2sPCIU7i3oywcOwfBfr2799LdQcO8GTZ4iUH0Tdff8Vt0zMy2TbaucNWzG/GrNl0oLaW82dX7NtLx52wnDZtWMd9QBQJcmwW7yzilA6o6g1vTYSq4347b/5C2rJ5I1/rEKGC9iXFtv5ighjRLPAOBeiTWsTFb1hctIPXIX0Oxqy6qoptDRQIhC2E4g8Ik5+UnUPbt27me3ZWdjbbDbCxrAP73VW8k8qr6ig8IpLSs/OpZPsGHgd4vsO2gle+dUCkwiQbT+KF2ybxdmz6gfsAD33YAZXlJdw2v2AO5yREmg94pOUVzKHSnVuotaWJhTAca1+ZrZhT3rRZLPYhFBb7wHH2IF1HQAAXtoiIjqGa/eV8XqdOyqGO1hZqb2vhCUYIhRDzbFh5XFHVFm1hV6EgFyYJFeEY29v+PoJYpJs2exHbLFgG4TBtUq79HHZO9eFcuEtdxTkjx9HZAMdRo051AjHQGaPcd41IenaePXeiEaIczBCtM150dOyn8PBMj7UThNESYDXRtB2qC8fGxlJ1XRN9+81KOvnE42nBgoX0zeo1ZHb2lu+Z8OqCRmLeqs0OImFcUCBtONhmMEDAg0Hr7ygecADGHIxcCI8VFftYiOO8WRx2MVBQB/neYCwGB/PDBtYpIheMbIDwdqxjz8/AQA5DR4EUZT84DrwrAbxC8X/2wLBYWFhsa221hYkE24QyLtoz8NCFY/RBtA0IYGERx8H/MUuO/ZZUN7PxDcNn8HvZRBolP95YvSEQnoJZay2D1N/B77OneCtNmSmFcLSAh4We18dEctk5p9L//vsp/fmhp+iUn59DmZF9fJ9sampy8AYXxt8uOf2Uk+h/X62gl155jc448+emHnLkK0auYlz3vS0Surt+osA9UPH082fsXvkDtoZSxA8ToAsXLbHf15VJZMU+gGCqjCFsDUSfYLIS7SGeQixVqnEjbyUE1G6IE1YrC6awUSCmoi3sFKUt7Az0ge2bgAAOYe/EBPCApyWOCzsEVgVsIcDRIQOFCDEBUHagjdNuwB5CFABmVJ1tkbHYJfiOyIOHyUhhKHt2bafktEyHkF/BBrw8g0JCeaLbm5QU76ATli2i2PgEWrW1nArTI+33SbPYJbhOlJaWUl5eHj8XjYS6um+poeE7Skz8EcXHOxamUdPQ8D3V1a2k+PiDKDFxMC2PL7BixQouQtfQ0EBxqpQjgvHOT+9PLYwTfjVz39/v1wYnCpeoRUKAz1gOj8J1a74f17BsX8HZWIWRjDBpzMA7V+12rjYIUU+N2tvTeR3CsfTaQljUS4rvnEfNuQ/OYi+8HPTwVOEc5L2DqCkMBZ6UE1G91VeBx4cRUER98Sj0PvYia35gl3BqjQkMLasqaRxWBHSuhoz31MmxlJY/sQ8qTU2NVFVZSTNmOoZD+rtNotggixYvZfvEWVBT2wDOUSeuCu5gX2p7wtkz1NkOUdvTzjaLs72jTumA/gQHqyq6joP3FsbEubCFoEKVT11wBM/DRhibnm5Jh+LKQxAiIYAICLTEQkUktP3/O4qIyPGYZ+H5559PL730Et1zzz30xz/+0b78vffeo5NPPlnSQvgZpnwCDgtSCYV+8CCLGU2Ef46H4e3JduOBc3VjeBIqYDnWC/q0traOy7kzXjkJ1a+JoLxkB3W22wrmCI5wiHS8Y0oAYWxVCccDRcwNlhyFXkcRNvxhAhMpJ+CNNVFA7Jv/09xhPQWxHOsB2k+0SAiU9CaCNkXbt9nzQhodb9glRZvMHyU1WqJi4yWkVQd4wRph4ttuk3i5qIoRgdgHT0IFiIEQBfVEQoD2ng4/hpfZfffdxx5/gn/j/SvGOKHkPvAHg7xvHHIBrft4N3388DoO1XEF1qMd2ntbJEROQoQb410B679JyJjwvvkKyF3UOZDMWhgKHBpMlJ3Bo+BBN9rLISxGxjl5ureQHIXGwZ+KmYyHXTIcC46fTMf/dsGw4cRYj3Zo7w2Qxy81bTCXrOAI8ivLfVcY1d9WdAznTBSGgrzTyNvtbRSPenUEkTAIPAj1xEItkdBVePJoOeqooygtLY29CvVYuXIlHXbYYezpnZWVRVdddZUt9dQAKMR1/fXX8zp4aU+ZMoWee+45h32sXbuWFi1axJ7ahxxyCBUVFTmsf//992nBggUsXE6ePJluv/12W8qJgTQMt912G2VnZ/P+MzIyuA+CZzGtUOhPocf4Aymc6bkEvvAQXP+fMntojp5YqM73g/YT6VmoJRIizBjgXS0WPlnfJp6FOgQFBvFDi6BNXGIqJ/QW9PPwCdoUbbQlpzfK7L0kDvc+/mSXTCsoHBIOOhG46yHoDU9CBeT05UoVgiYoMiIel/qkpE+SM0eH+poqam32XpSTkWlurOMiOsaxSUyb/cxFRfIet16xsfMpIWEw7yDEwd27n3QQCbEe7dzZ30gnXjDJd/fdd9Pjjz9O+wYKUqopKSmhY489lk499VTatGkTvfHGGywcXnnllfY25557Lr322mv02GOP0fbt2+lvf/vbkLQPN954Iz344IO0Zs0aPh8uvPBC+7qvv/6a93H11VfTtm3bePsXX3yR7rrrLl7/9ttv08MPP8zLi4uLOTR69mwpZuRpTPtX6k8GORIxo6Jbbp5nZsdhQCM0x1WFQK2k4BNleP/Q1KorEioon5V2eF8UE0mLY0UUU4MHOedcgMIgXDhFBkQYBUbRAZR7oJJ8X/AewX4U6VC+p5Sr04rgMxQk7kcl3vgE8cjWIj9/qpw3LkD1YUE/h6OVi8gIzgRaAqnPKZ+7N1Duf/6WN9lq7aXdux8f9fb9/bbCkAr19d/yyx0mT/4NBQSMbLyRj3DevHl06623DvEEhKfh2WefTddccw1/njp1KguCy5Yto6eeeorKy8vpzTffpM8//5y9E219GKpRQPTDNgD5EI8//ngutAEPQngPYtl5551n3/7OO++k6667jvuEY8DrEfuH5gPPwiVLPO9d6e+Y36NwIJG7menq7qL2gaptnkKdx8fZs9DblQMh9l2dnaorEiqoPQvRXkTCoTQ1NlLJrp3j+nv5Mg21VdTZ4dm/LTORnV/g7S4YFlReNAL+OntvRPwpd3JDff2Yq86bFatVchS6Yv26NYYoumBUKgzgFWZU4pPTOE+hMJSQsHBDpItRQkclysH4IE8hCpvAI1DNxo0b2bsPHoLK65hjjuHrNqrpbtiwgb0SFRFQjzlzBqu3p6fbntdramrsx7jjjjscjnHxxRdTZWUlax6nn346dXR0sICI5e+++6793BI8h2mfHPwpaXgABQypCOsJtCoEbv58D3W193pNJFT4bU46/SgueljxD2LhnPAQOjRJDActrOz3JA9zumB2WnIU6oIQHyMYnkY1yo1QtEA9e1+YHsneTIKX7RI/mMBEShRvn/tGJTUtXe4rLpB7rjBaerq7+D1EUsYMAcIcomS8jXL/87coh4CAIPbsGwmNjWs1vQYRdhwXt3BExx4Nhx9+OAuAN9xwA1dDVhfCvPTSSzVzAsKzb9euXW7tX52nUplYVCaJcAx4FZ5yyilDtoPHIXIfIqfhF198wZ6Ll19+OT3wwAP01VdfSf5LD+L9K8Y4oZx83X5gkMfGxfFrPHAWC40gEiq46yEYva+MSIRCTSIiIik1VZKq65GWlTfhCfl9ifoD1ZSe7Z2CAEanet8eioqOJYslxBiJwyXBu9fxp5Qoc+YtEI9CHWprarhQFgRDYShIoyMisz75hYNeOIIjnR1t1N/XT9HiVahZvLB6XxnFeHly11+jHCCEjST8F4VL1CKhxRJmDz/G8oAAy7gUMnHm3nvv5RDk6dOn25ehwAjyBqJAiRbIFQjBD6KdEno8UnAMCIF6xwBwkjrhhBP4dcUVV1BBQQFt3ryZtxU8g2mne/2pumBlZQXtKSsdt/1DDAyNcLyg47M3RcKR0NIiHjTC6Giqr5XQ42G8mQVtLIGBXPnV2yhGebDktfI6/mSXfPftYNJ1wZGurk6/OAdGi3gUuqZ6f/kE/RK+h8USyKH9gp5N4v3QTKl6PDxa1Y0nT75ctxryeALRD/kIkYNQAdWMv/32Wy5egjBjFBNBhWKlmElubi7nFkRxEhQZQTjyihUrOG+hu9xyyy308ssvs1fh1q1bOfz59ddfp5tuuonXI/QZuRO3bNlCu3fvpldeeYWFw5wc39AmfAWL2UN8EK9udqMDFfTG0+sJOQnVnoQAn/WqIRuN6OgYb3fBsLS1tVJNTbW3u2FYujraqdcPvJJHy4wFB3m7C4YlO386BYeEGsYoDwr2r9l7I+JPKVEEfSDah0popC5lpZKDzxVS1VefxJR0SpuUK5cfDQIDgyh36kyvj42SR04mL90XCRXPQbx7QyxErkB13ljkFoS34M6dO+mwww6j+fPns7CXkZFhb4OiJqeddhqHBMPTD3kE29ra3D4mQp4/+ugj+uyzz2jx4sV00EEHcZVjRQiMi4ujZ599lg499FDuD0KQP/zwQ0pMTPTwt/dvTPvkoI57h1GuGOhmJCo6etzCNJwLl8CTUBENtaohG5HphTO83QXjYuU0fIIOwcGhFCB5tnQp2rSGps9ZJOePBk31BygmLpHCIiKNkaNQQo+9jj+lRJmUle3tLhiWlNRUCcsWRo3k39OnuaGO2lqaJCWKTuhrTcVeyp5SYIgchWKTDKWjY7+uSKigfFba4T0sLJPCwz1TQA+ees7AQ7Cry5b/UwHiHUQ8PZBH8KGHHuKXM0ccccQQJy6ENzsvg1iIlxbLly/nlzC+WPxFKDQz8CYMGQfPFa3qxr+8/3DdashGZe0Pq73dBcMSn5BAeZP18z/4O8kZWZLrxo2wVkHbG7V7ILG6UYqZCMawSxQvTzMTHh7h7S4YlrLdJXSgttbb3TAsCxeNf94tX0ZyFLpG7BJ9Wprqydv0KDkKJcphCBD74uMP0hUJFdSehWjvKZFQEPxGKFR7EJp99r6qooJaW1rGXSRUPAfx7ktiodlDz8dCS3MzVVdVersbhqVqbyk11skDnR6xceLir0ewQUILe3pk9t5odonZbRJQvHOHt7tgWPqtVrJYxJVfj51FO8Tj0gXbN4x/qKGvEhwaSuER7hU69EeM4I0qHoWuSUw8hDIzzxy2UAnWox3aC8J44Tehx2amr7+fLIGe03yrShp1RUK9ash4T50cS2n541N9eSwiYU6eVGV1JSJ0dHRM6G/iUwTgHJLE2HokpEjFbD1SM3MMcf1TvNdCQs2bfsNX8KdiJoI+iUlJ4nHpgubmJjl9XCGT3y6FsIBYEeH1yJ8xzzg5Ck2cEmysuOshKJ6EwnhjMXMuBqX0utmN8ty8yRQb6zmBDmLf/J/m6oqECmrPQrQ3mkioPCgHjWOhF18nMCiI80gI2kTFxFNYuHdzzBmZ0p1bxWNXh9rKvXSgej95E3XVZckH5H1CBipPdw94eZqZOfMWeLsLhq4WP155pc1AVFS0t7tgaJLSJMxQj66ODqosl2I4emxf/x0Zp+qxCIWCYHRM61GohPlg5kJxczYrVZUVlJaeYRdGPcGC4ydTRkHCsOIfxEIjehKqH5T37Cnj8RGGAoE5JiZWhkYHzHjKA53rCRmI8XgXHLFYAqmvz7Fa/ESjvvfJ7L1xIh3MPnmJa8L+feVUUOj9CptGpLJyP2VmZskknQ6FM2dN7A/iY4RK/k9dUHxOXZ1VMB72dCiSo1AQDI+ppzT9JR9QY0P9uHj1uCv+GVUkBDAYLCJi6IKE6kisLuhXrm2VMChdJuVOFZFQh4ioaAqPjDZMUvdgqXpsHJvEqXqg2cAEXWNDg7e7YVis/VYKkByFuvywetVE/hw+x/6yXd7ugmFBYceUDKm4rkdiqvedJhS7RKIcBMH4mFoo9JfZ+8DAIK58LGg/mM2eO1+GRhcp9DKsx5zMTuvS1dlO/arwVmGQ4JBQj3p5j2Xm3tYfCfPxNv5ik2CCTqoe6zN1eoGE1wrCONHZ3iZjqwMKvXi7wGNP90DosdgkgmB4zC0UDlyEzG6Uz547j0JDQ73dDUMCz429e8q83Q3DEhkVTQlJSd7uhmGJT06j2IRkb3fDsKAitLfDa438sHKgusKrfejtsf02ECwlPNz7+ItNAkF0zjyZoNNjb/ke00e6jIWc3Dxvd8HQ5E2X0Gw9UHyuvrZqQn8PX6KivMTrodnKBKbkKBQE42Pxh9l7sxtkq7752uszREYFOSrb2mR2UQ+EZYs3qj4drc3U0dYyLuemGZB8QPpYAgOp39s5CpVcQBJ27HXCgvwnHUpbaytt2rDe290wLC3NTWKzucDbnthGp87LE1BGJsASSP39EuXgKneyt6NAlNzJwfJ3rsvqxlaPthOE0WJqodBulJu8wqAUE3BBQABFRERM3I/hYzQ3N3GeQkGb7q5ODq8VtMkvnEuhYeEyPBqERURRWlaeMQxyCfExBP4iFCJHoUxA6YNrZqBUPdZld4nk4HNFc2P96P84TQ6Kz02fs9jb3TAs2VMKKNDLAp2So1DsEm0eKK2kk9bvosf3VLscR6xHO7T3F3Jzc+mRRx5x2QbRM++9996YjnP++efT8uXLx7QPs2BuoXCg9LrZqx5nZE7ydhcMS3h4OOVPnebtbhgWcUR1jSUQIZumvkyOif2lxZIPSAdrfx811Lo29MabHnvScPHQMZJQaHabBA+AScmSskGPadMLKETSxegiETLD/H0NPNsI2iJBybYNMjQ6tDTWU0+3d4tpKak3JNJB20PwwTKb3XjX7kpdsRDLsR6gvSc9CyGS4e8IL0Rm5uXl0XXXXUednZ3kbX744Qe65JJLyIi8++67dNBBB1FsbCxFR0fTzJkz6ZprriFfx+IXRrnJ8wFFx8R4uwuGpbmpiXZs3+rtbhiW5JQUysrO8XY3DEtiSjolpWV6uxuGBfkJJUehcT0/lKThIkoYrJiJqhq1GYG3XGRklLe7YVjWrvme06II2sxfKB5hrpg2e6GcOi7o9rIQZmQ62lq9LxQOTJSJXTKUpXFRdOPkdPtnLbFQLRICtMd2nuTYY4+lyspK2r17Nz388MP0t7/9jW699VbyNsnJyYaMEvzvf/9LZ555Jp166qn0/fff09q1a+muu+4yhf5kaqEw2E/CfIq2b/N2FwxLv7VfPMJc0NTUSHUHDkzcD+JjoBhF9b493u6GYYmIiuaq64KON6qXwwslabgxPYHMbpM0NjZQdZX/hEONFGt/P4dICtqU7pbQY1dsW7dKTh0XRMfEy/joEGQAb9RBu8Q2cSY48pucVF2xUEskRHtPgwKpaWlplJWVxSG4Rx11FH3++ee8rquri6666ipKSUmhsLAw+tGPfsSefgorVqxgb8RPP/2U5s+fz5F9P/7xj6mmpob+85//UGFhIcXExNAvfvELam8fTO10xBFH0JVXXskveOUlJSXRzTff7OBh7hx6XFxcTIcffjj3Y8aMGfY+qtm8eTMfH/1ITExkj8TW1laHVCm/+93vKC4ujtfDe9LZq72/v5/uuece9q7EfubOnUv/+te/7Os//PBDOvTQQ+naa6+l6dOn07Rp03jcnnjiCXubkpISOumkkyg1NZWioqJo8eLF9MUXXzgcB9/vz3/+M5177rncJicnhz744AOqra3lbbFszpw5tGbNGvs2dXV1dNZZZ1FmZiaLqLNnz6bXXnuNPIWpLRV/yQck6BMWFk7JySkyRDp0dXZSZ2eHjI8OuNlZSQoF6RGbmELBEkKnCYSAabMWePVvSwxyY+FXOQol3F2XzElZIhS6oLGhYRzOSvMgFolrkjMkHZMemblTKComjrxJT0+vX4bQQ3xq6+tz63XhpCS6Li/Nvi3EwYKvNzuIhFiPdu7sbyzpHLZs2ULffvut3X6BkPb222/TSy+9ROvWraMpU6bQMcccQ/X1jhE0t912G/31r3/lbffu3UtnnHEGi3yvvvoqffzxx/TZZ5/R448/7rAN9olUOfDKe/TRR+mhhx6iv//975r9gnh3yimncL9Wr15NTz/9NF1//fUObVDMFH2Lj49nMfOtt95icQ5ipMKDDz5IL774Ij3//PO0cuVK/h4II1Zzzz330Msvv8zH2Lp1K/32t7+lX/7yl/TVV1/xeoiqWI6x0gPi5HHHHcfeh+vXr2evzRNOOIHKy8sd2sGDE6Ij2hx//PF0zjnnsHCI42G88/Pz+bPymyIkfOHChTymOD6EUGyDMfQEpnYFUWYroH6bmdlz5nm7C4YWekJC/etmNBKQIwQXW0GbsPAIvzNmRkJtxV6KjI6luETJR6bFtnXf0YwFB5G3Q3yCZObeWEKhyW2SxKRk9poTtAkJCWXbRNDGiKFlRiIheVBAEIaye8dmmrngYBkaDar2llJIaBglpAx6rHlrAtPf7JL2/n7K/9/mUW/f2OtYrfr+0ip+uUPJ4bMpMjDQ7WN99NFH7L2GFBnQUDDxDdEPwttTTz3FwtpPf/pTbvvss8+yJ99zzz3HHnUK8IyD4AUuuugiuuGGG9irbvLkybzstNNOoy+//NJB3IMHI4Qy3B/hmQdvQHy++OKLh/QRgt+OHTvYczEjI4OX3X333fZ+AYiSENIg8kVGRvIyfA8IdPfddx9790G8RN8gOgKIgdinQldXF+8Xxzv4YNt1Bd8BoiJCspctW0a/+c1v6Ouvv2ZvPngBIlfhT37yEzr77LPZOxPACxEvhTvvvJMFSXgMqoVLiImXXnop//+WW27h8Yb34emnn87LMF7oR3V1NQuU8CT8wx/+YN8efUH/33zzTVqyZAmNFb/wKOw1QYy4K6oqK7zdBcPS0txENdXeLShgZJBwXorh6BMcEkrBoWET+Iv4FjAe+vsdjRdhEG97oyr5UUTsNgb+kje5uanRIaRIcGRXcZEMiQvmzPOuJ7bRweScIIwGS2Ag9XnZZlOEQuV+KBiPI488kjZs2MCeeueddx5dcMEFnH8PQh/sF0UAVJyyIEht377dYR8IkVWAIIcJIEUkVJYhHFkNBDb1JBoEMYQXI0rBGRwPwqIiEirtndtAnFNEQoC+w0GmqKiImpqaOBfj0qVL7evh0bho0SL75127drE9c/TRR7N4qrwgPmI8APYPjz60vemmm3j973//ex4XxRaCRyEEPYReI8wZbdA/Z49C53EDECCdlyljh7GB6Ig2CQkJvF8Ihc77HS3m9ij0kzCf2toamlZQ6O1uGBJcDCQXkD4VFftZSM/NG7x4C4O0NjdSV0c7hWfL+GgRm5hMgSOYpfQ3EpI8nztmNB6Fyr1Q8C7+Enrc0tLCeXwEYTR89+1KOvSwZTJ4OuzdXSQecy7IyM6Xc0eHiKgYr3t7+6tdEmGxsGffSPjb3lpNr0GEHV+alTyiY48ECF8IKQYIyYXYBo9BeLa5izoHpVJBWQ2W+UJEW+tAPkMIgfDeU6N4CyogLBivX/3qV3TjjTdyrsI33niDhVaIhPC8/Mtf/sJjCxsJXpXO9qDzuOktU8bugQce4DBteEZCLMRvh2rLnrIzTS0UKoO4f/9+Ktm1y17uW3kxGsuc22ktV38ej//rrdNCEsK6DoGKT0gcy2kk+HuOwjHk9jA7/ch90t9PZnS6VH53vOv9n5yW29fhn9VKluAQamttGXZfpLHtYJvB9Vrb270WNdpU7N87sMr4xpi/eHMooSxqmwTY/++GnWE020QvrYWgzbz5UrXWFb4Sll1a2+adA4tN4pKe7k4yK67sEU17wslO6O3ro77eXrtd4pZtM7DtoH2hcwznbXRsltaWFtvnfisVpg96evnDdW0k4b8oXKIWCeODAqlhIPwYy4MDAsalkIkzcLb505/+xAU/4DGHCc9vvvmGQ2wBPAyR/w/i1FiBB6Oa7777jqZOnarpkADPPOQ+hEdgenq6vb1zG4RJI2Ra8SpE3/GdENqMoinYFsdFURSAcGtULV6wwObZjiIpEAThoYcwY3dBYRJ4UeLYynHPP/98Ovnkk+0CZFlZGY0V7BeFTpDDUNG+du7cyf32BKYWCrcNJJW85647+GUm3DXs9d7dXac22Ibbr95+lAcfV22d17l7TFf7A6isFBQYSA2NjS6PNdx++DPp90/v83i2wbgOx3D7DQ8P46rQw4WJectwV4t0Xb39ozak3RH7tNrg5oQbijIz49xGa5uhy4Zv485+1J/VBqFWG/Vi5/BXB2NwcKHmOs1jqj6npyFXkpUqK6scjjOc8enqWEoL5/7p7lPd3klw0z3OcIa2h4BRoSQ79iZ7dtvCIwTvohiMuJ7MKpxqqp9jpKKjeruxthmJnaKIse60df7/mI6pAg9YSljQaOwfV3bJRNogmnaBB+ySxMQEqrvu2hHtYyJR7hNjsUnU+3HnWOrPePhU22yesEtGaoM4L9OzSRzbKEd2fz9aNoimDaNqs3DBfFq7dp3DsbS21TuW5jGdx8CFPaL1XVzZJa4EPr3vOVoQtojCDsjt5m32lIpdoodedWP1cuV9IsRC5MdD/kHky/v1r3/N/0eYa3Z2Nt1///18PUIewrGC+yIESeToQ+EOFDtBsREtUIkZHnsIjYZXXXNzM3vxqUGOwFtvvZXboLgKqgcjhx+KfSghvFdffTXde++9LEgWFBRwAZXGAc0AREdHszcgCphAhEOVZ4QsQ6CDxqDsG2OA/IK4v2P7xx57jEVUhCwD7P+dd97h/Ii4f6Gisyc8KrFfVGBG0Rj8baP/yF8oQqEboFy28g6vO/Wsh8uZEI2X0VD6hVh6uAKvWrXK210yJLiIQejxhGpvRjDDgguWulS8MAhuAhgjzFgJQwkOtIUTVFXul+EZJ9SeXIrIMcTLa0AsUK/Hq7e3hyubh0hlakMQqgq1gvHpyu5QL3fVxiio+zlr1iyO5GiQ6rWaZKSnU/HOnRP7A/kQeIDDg46gDTxhkF9L0KZ1ar7YJDrAW2qs6WKG2CS2hUMndEjbZunoaOdolNCBZ3TBPZEQKO8TLRZCa0DBDYiCpaWlLHBBbEOaEeTzQ048iFRjBdV8Ozo6OLcfzlOIeKjiqwWe7VEMBAIl2sODD+IcqgkrYFIFfcN+oJXgM3ItQkxTQC5BPONB8MM+L7zwQvb6gxiogByAycnJXP149+7dnGMQHofwtFScAp544gnuP+5dGIv58+dzZWdcrwGOiX0fcsghlJSUxEVJIG6OFeRERJ9Q3RnfD+O1fPlyh/6PhQCr0azNMYABhxspXFFDImLG5RjDGfDOn4edKRoSZjbg1j2Mi7nyjlwPu0uKaVrBDJfHs89+OXn9OO5/6Hd053trtXHY3uUsnRszeU79cl7v6tgtLfhDCaCo6Gj1hrrH1Nq31ncZrj/2ZaOatR3SxP3+jWAb0I7wAwiGUbbxMfLloKa5c0TeBG55HAzjNYoQlv6ebgqNjBmZ54UbXh5Djq366I7nq/N2aoNNtz8j8Jxxbuuwnf372c4XS8CA4emWl43Gd9YwMHX75/Su6WGk+r7qfTkKbNr7VS9X78/ZILZvO6Qfg58721ooPDJGZVQ7fl93vKk8yfS0SL5PIgE0jAgI4YI57JKRiIsONoFWyJiOfeCuHVBasotSUtMofKB6rd7xnO0S7f0P7ne49+HaOPxfr62OZ7Tju+PYO/8Wwx27qmIfpWaoch25sGX0juPqu7jazpU3l+tjDVk0qv0Mtw2oqa7i82ck+zGrTaJlT7Q11FBEfLLDco/YJW7YF+55mmqs07O13LAZtHaue8/nc7yfbZKR2D1Duql1X3aySzS/m949XT32ahFtyL3f6XtrLNeaHBw+ksv2ua+vh/p6eiks3BaGabdL3ExfMR42CTCbXYJKuxDU8vLy7E5L7rC6sZVOWr9LUyR0JSa+P38KLY2LIl/miCOOoHnz5nGuPcE456epQo8RN49S0bjYCIIgCIIwFNwnpeLgxCB2iSAIgiC4RuwSYrHv97mp9GBZta5I6OxZiPa+LhIKxsVUQiFUUSikZq8oKAiCIAhjEa9GMsstjB6xSwRBEATBNWKX2Lg2L50Oj48eVvyDWLgkNlJEQmFcMZVQqBjl8gAkCIIgCIIRELtEEARBEAR3cNdD0EyehCtWrPB2FwQNLFoLBUEQBEEQBEEQBEEQBEHwL0QoFARBEARBEARBEARBEARBhEJBEARBEARBEARBEARBEEQoFARBEARBEARBEARBEARBhEJBEARBEARBEARBEARBEEQoNAAtLS10zTXXUE5ODoWHh9MhhxxCP/zwg0Ob7du304knnkixsbEUGRlJixcvpvLycvv6oqIiOvTQQ2nSpEn05z//2b785z//OR177LEO+/rkk08oICCAbrvtNofl+JydnU2+Nj7od0FBAY9LfHw8HXXUUbR69WqHfaxatYrmzZtHubm59Nxzz9mXH3TQQXTZZZc5tH366ad5fF588UWH5eeffz4ddthh5GvjY7Va6ZZbbqH09HRej/EpLi72i/Hp6+ujm2++mfLy8vi75+fn05133sljooDvovV64IEH7G38eXzU4Lviuz3yyCMOy804Pu6MzTvvvEM/+clPKDExkb/Thg0bhuzHzNdmwZyITTK28RGbRGwSPcQmcY3YJGMfH7FLzEF1R5dH2wnCqLEKXuWMM86wzpgxw/rVV19Zi4uLrbfeeqs1JibGum/fPl6/a9cua0JCgvXaa6+1rlu3jj+///771urqavs+jjrqKOuTTz5pXbNmjXXRokXWb775hpc//fTT1qioKGtPT4+97XXXXWfNysqyLlu2zKEfhx9+uPXcc8+1+tr4/POf/7R+/vnn1pKSEuuWLVusF110Ea+vqamx76OwsND6r3/9i8clPz/fWl5ezsv/+Mc/WqdPnz7keBif8847z2F5Tk6O9ZZbbrH62vjce++91tjYWOt7771n3bhxo/XEE0+05uXlWTs6Okw/PnfddZc1MTHR+tFHH1lLS0utb731Fv89PProo/Y2lZWVDq/nn3/eGhAQwOeTgj+Pj8I777xjnTt3rjUjI8P68MMPO6wz4/i4MzYvv/yy9fbbb7c+++yzsNKt69evH7IfM1+bBXMiNsnYxkdsErFJ9BCbxDVik4x9fMQuMQZ4xtq2bZvDs5a7rDvQZH1+537rxrpml+2wHu3Q3leB7fzuu+96uxt+R8cIzk8RCr1Ie3u7NTAwkC/6ahYsWGC98cYb+f9nnnmm9Ze//KXL/SxcuNC6evVqa3d3NwtBH3/8MS8vKiriP8JVq1bZ2y5ZssT6xBNPWMPCwuwnCN5DQ0OtL7zwgtXXxseZpqYm/s5ffPGFfVl2drZ19+7d1tbWVn5Y37p1Ky//9NNPuS0EIoXU1FQeHwgXCtgW7b788kurL41Pf3+/NS0tzfrAAw/Y1zU2NvJv/dprr5l+fI4//njrhRde6LDslFNOsZ599tm625x00knWH//4xw7L/H188ACcmZnJQjy+l7NQaMbxGcm5A4NdTyg067VZMCdik4x9fJwRm0RsEgWxSVwjNolnxgeIXeKbQmFVeyeLf8pLTyxURELlhe08CZxtLrvsMp68DgkJYdv9Jz/5iXXlypW6Ah/sVDimjAQRCo1/flpG74sojJXe3l52JQ8LC3NYDpfylStXUn9/P3388cc0bdo0OuaYYyglJYWWLl1K7733nkP7O+64g0NKIyIiyGKxcFuA7TIyMujLL7+0h8ysW7eOTj/9dA4TRMgg+Pbbb6mrq4uOPPJInxofZ7q7u+mZZ57hEO25c+falyP0trCwkJcjHHLGjBm8HCGBwcHB9vHZtm0bdXR00EUXXUR1dXVUWlrKy7EefTj44IPJl8YH/a+qquJzQwFjgHNI+e3NPD4ICfvvf/9LO3fu5M8bN27kcfnpT3+q2b66upr/3vD91Pjz+OAadM4559C1115LM2fO1NyPGcdnpOeOHma9NgvmRGySsY2PM2KTOI6P2CRik7hCbBLXiF1iflLDQ2lhYrT989q6FtpU3+LQBp+xXAHtsZ0nOfXUU2n9+vX00ksvsR38wQcf0BFHHMG2u+BnTIh0Kehy8MEHc6jZ/v37rb29vdZ//OMfVovFYp02bRp74uAnioiIsD700EPssXLPPfdwaOSKFSsc9tPZ2ekQbquAmSbMAgB4syBkBlxyySX2UL+bb76Zw1F9bXwUPvzwQ2tkZCSPC0Ijv//++yH7gbdTfX39kOWHHnoojwWAN89xxx3H/8eYIQwVnHPOOdYjjzzS6mvjgzBHnD8VFRUO25x++ukcPmX28enr67Nef/31fF4EBQXx+913363b/r777rPGx8drzrD46/jg89FHH83eqUDLo9CM4zOSc8fVzL2Zr82CORGbZPTjoyA2idgkWohN4hqxScY+Pgpil/hu6LGWx6DiWai33JM0NDSwTeusMyjgOQDrlZcSIaTlUYjUO5MnT7YGBwfzPRKh8WqwPdL3LF++3BoeHm6dMmUKp1dTQKSREiWICB20wT14x44d9jYbNmywHnHEERyGHx0dzR7+P/zwg4dHxVyIR6EP8Y9//IMT0WZmZlJoaCg99thjdNZZZ7H3Cbx5wEknnUS//e1vuWDAH//4R/rZz37GRQHUYNvk5OQh+8cMwDfffEM9PT20YsUK/gyWLVvGnwHejeqx4mp8FNB3FBKA9w0KBJxxxhlUU1PjsB+l2IkzGA/1OJhxfNzBjOPz5ptv0j//+U969dVX2VsLM2N/+ctf+F2L559/ns4+++wh3iL+Oj5r166lRx99lAuPoMiGK8w2PiM9d1xh1muzYE7EJhn9+CiITSI2iRZik7hGbJKxjc9IELtk4sF9o6e/361XYVwkzU+Ism8LD8J/llQ6eBJiPdq5sz+9IoVaREVF8QvRi4hocUYp3vXCCy9QZWXlkAKsCu+++y5dffXV9Pvf/562bNlCl156KV1wwQX2SBqF22+/nZ/bN23aRMcddxw/h9XX1zu0ufHGG+nBBx+kNWvWUFBQEF144YX2dWiPgoHoB55boJMgmknwEN5WNYVBjxzF8wveXvC86erq4lmjO++802GYkPT+kEMOcWvoUPwEPzO8y5A/7I033rDnHUPuq7q6Os4/8Morr/jc+OiBGQlXnmNqMEuB8cF4JCcncz4xgDwMyL2mjJ+Sl8GXxgcFObQ8nVAc4aqrrjL9+EyaNMn617/+1WEZ/pacC2yA//3vf/w9MDM1Esw8PvAcxIw1cnIpL3wXeM+ocwyacXxGcu4MN3Nv9muzYE7EJhn5+OghNonYJEBsEteITTK28VEjdonxPLa6+/ocvAEn8oVjjwQUKESEFXJmQ2+44YYbuCCmgjs5CrHdxRdfPCSiTX2vxH5uuukmh/sqlv3nP/8Z4lGogAgcLFPGFl6EL7744oi+n7/TITkKfQ945KSnp1NDQwN9+umn7EUYEhJCixcvpqKiIoe2yBeQk5Pj1n7z8/MpKyuL8wvA6w7eKgCz4XhBoUceHaN7rWiNjx7wxNSaBdHL+YFxfvLJJ6mzs5MWLlzIyzHutbW17GWGYy9ZsoR8bXzy8vIoLS2Nc60pNDc30+rVq93OB+fL49Pe3j7EszIwMNDuqavmueee4++mzm3p7+OD3ISY4cN1Q3khrx7yFeIcM/P4jOTcGS1muTYL5kRskpGPjx5ik4hNAsQmcY3YJGMbH08gdomg5CisqKhg+xSReohuWbBgAUcYucv27ds5V7kafMZyNXPmzHG4r8bExAyJClS3wX0XKG1+97vf0a9+9SvOB37vvfdSSUmJ/IiexNuqpr/zySefsHKOyp+fffaZde7cudalS5dylUzwzjvvcGz/M888Yy0uLrY+/vjj7Nnz9ddfu32Mc889lxX3goICh+UXXHABL1fn1vGl8cHMA2Y5UDm0rKzMumbNGv5O8MZBhVZ3gYcdxuHYY491WI68aViu5BHzxfPn3nvvtcbFxXHOh02bNnFVX+Q8G0neDF8dn/POO4+r9aJCJWZX8beUlJTEHrnOVSmRB/Spp54a1XHMPj5q9HIUmm183BkbePzBi1CZ3Xz99df5s7rKs5mvzYI5EZtk9OMjNonYJK4Qm8Q1YpOMfXzELjGuxxZyfcOzbySv9QeaND0EsXwk+1HyjI+Fiy66iCOB3PUohEeis6ffI4884pB3W2s/2Af2pfYoRN5EBdjZWIa/AYWioiKu5YCc6ojEwd+GoI94FPoQTU1NdMUVV1BBQQGde+659KMf/Yhnp5X4+pNPPpnzEd5///00e/Zs+vvf/05vv/02t3MXeKSgqqaSA0sBHixYbmSPFVfjg5m0HTt28MwHqoiecMIJXJHp66+/1q3Q6k/jA6677jr6zW9+Q5dccgl7cbW2ttInn3yimYfPbOPz+OOP02mnnUaXX345V+X9wx/+wDky7rzzTod2r7/+OufvQJ6p0WD28Rkrvjg+7owNZlrnz59Pxx9/PH/++c9/zp+d88eabWwEcyM2yejHR2wSsUlcITaJa8QmGfv4iF1iXJDrO9hicfu1vbGN1te32rcPtQzmCsdyrHd3X8PlGXeHGTNmUFtbG/8f97u+vj6X7XGOIg+3GnzGfjwNNADUcvjss8/olFNO4fyJgmcIgLLooX0JgiAIgiAIgiAIgiD4HUi1U1payimgRuKYobCpvsWhcMnCxGiakxCtu9yTwOHm9NNP54IhCPmNjo7mIiJwOsHEOFI1QZhDqO8tt9zChXFQzBBhyddccw01NjbyflAMBUVKUBQRbT/88EN2Xvniiy/sk+MQMFH0ZPny5fbjx8XF0SOPPELnn3++vaAfUnxgOUCqHkzKY3xTU1M5HRIEdIz1vn376LzzzmMHovvuu8+j4+Kv52fQhPVKEARBEARBEARBEARBcMCVGKi8K+uVd0+Khah4vHTpUnr44Yc5319PTw/n07744ovpT3/6E7dBDm3kBnz22Wc5p3ZZWRnnykRFYgWIfxAJUZkb1Y8hSsHTzzmCZizAix/CJrz7q6urKSkpiT0KUUlZ8AziUSgIgiAIgiAIgiAIguAFj8Lqji769766YT0GncXE4yYlUmp4qFd/MxQSeeWVV2jLli1e7Yfg2fPTsXySIAiCIAiCIAiCIAiCMCFA7JuXEDVsWDGWYz1Ae2+KhKjGvW7dOvYWRIixYC4k9FgQBEEQBEEQBEEQBMFLzE+MoYyI0GHFP4iFqeEhXvckfOaZZ+iOO+6w5ywUzIWEHguCIAiCIAiCIAiCIHixmIkgjCcSeiwIgiAIgiAIgiAIgiAIwoiQHIWCIAiCIAiCIAiCIAgewGq1yjgKPn1eilAoCIIgCIIgCIIgCIIwBoKDg+2FPgTBaHR3d/N7YGDgsG2lmIkgCIIgCIIgCIIgCMIYgAATFxdHNTU1/DkiIoICAgJkTAWv09/fT7W1tXxOBgUNLwOKUCgIgiAIgiAIgiAIgjBG0tLS+F0RCwXBKFgsFsrOznZLvJaqx4IgCIIgCIIgCIIgCB6ir6+Penp6ZDwFwxASEsJioTuIUCgIgiAIgiAIgiAIgiAIghQzEQRBEARBEARBEARBEARBhEJBEARBEARBEARBEARBEEQoFARBEARBEARBEARBEARBhEJBEARBEARBEARBEARBEBj3Sp4IgiAIgiAIgiAIgiAIgmBqRCgUBEEQBEEQBEEQBEEQBEGEQkEQBEEQBEEQBEEQBEEggf4//pwzO3HPxg0AAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 1300x1300 with 2 Axes>"
       ]
@@ -12072,7 +11911,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 48,
    "id": "ea0d1e3d-01b2-4d4b-a37a-d5bb18d030ca",
    "metadata": {},
    "outputs": [
@@ -12080,12 +11919,12 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-09-29 14:10:56 - WARNING - Only the first 20 rivers will be plotted (received 34).\n"
+      "2026-06-23 09:20:53 - WARNING - Only the first 20 rivers will be plotted (received 34).\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 900x500 with 1 Axes>"
       ]
@@ -12108,7 +11947,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 49,
    "id": "16665782-c8b1-47c7-81cd-073e795a2900",
    "metadata": {},
    "outputs": [
@@ -12116,7 +11955,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-09-29 14:10:58 - INFO - Writing the following NetCDF files:\n",
+      "2026-06-23 09:20:53 - INFO - Writing the following NetCDF files:\n",
       "ROMS_TOOLS_INPUT_DATA/roms_rivers.nc\n"
      ]
     },
@@ -12126,7 +11965,7 @@
        "[PosixPath('ROMS_TOOLS_INPUT_DATA/roms_rivers.nc')]"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -12137,7 +11976,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 50,
    "id": "cc4468e0-0f83-4da1-9ea9-80d3f7538fd0",
    "metadata": {},
    "outputs": [],
@@ -12164,7 +12003,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 51,
    "id": "36e6d01e-6a3c-43f9-a7d7-6e8e9d0057c1",
    "metadata": {},
    "outputs": [],
@@ -12184,7 +12023,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 52,
    "id": "5b3d6293-7904-4ba1-95e6-c21d290abdd5",
    "metadata": {},
    "outputs": [],
@@ -12212,7 +12051,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 53,
    "id": "b295ecc1-d847-41ae-853a-0cccc3e24f14",
    "metadata": {},
    "outputs": [],
@@ -12235,13 +12074,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 54,
    "id": "a1ac6c21-db6a-4021-9f14-f46f5b304e6d",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x550 with 3 Axes>"
       ]
@@ -12264,13 +12103,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 55,
    "id": "da4fe49f-c1c0-4de0-818f-a0e0e4a7ddaf",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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M9ctMuMvRARVhYWHStGlTMyFfuXLlpFevXnLgwIEU99m3b5/06dNHKleubD4JvPXWW0mWmz17tilTuHBhCQoKku3bt7ttv3HjhoSEhEjp0qVN9ace88yZM1n6+gAAyM+4PWfOXK8cDXcREREmYG3dulXWrl1rkmrHjh3l2rVrye5z/fp1qVq1qrz++usSEBCQZJkvvvhCQkND5aWXXpIff/xRGjRoIMHBwXL27FlnmSeffFJWrlwpy5YtM+ehtxPr3bt3trxOAACAO8XDykU9HbWtWWvwNGy1bt061fJaMzd+/HizuNKaOq0RfO+998zj+Ph4c8uOMWPGyLPPPisxMTGm6nPJkiXSt29fU2b//v1Sp04diYyMlGbNmqXpNiC+vr7mWNyhAgAA99axo0ePSpUqVUwLGjJ/3dKTO3LVPHd6wsrPzy/Dx7h586bs2rVLOnTo4Fzn6elpHmtwU7pdawldy9SuXVsqVarkLJNQbGysubCuCwAAQG6Ta8Kd1q5pDVzLli2lbt26GT7O+fPnzSgdf39/t/X6ODo62nyvXwsVKiQlS5ZMtkxS/QM1MTsWrQkEAABZ7821B+WddYeS3KbrdXt2OnHihDz22GNSoUIFkxfuvvtuGTdunFy4cCFR2VGjRpnBD9rNK6EpU6aYfnQJF61QyhfhTvveRUVFydKlSyU3mjRpkqlZdCz6gwcAAFmvgKeHzEoi4OljXa/bs8uvv/4qTZo0kUOHDsnnn38uhw8flrlz58q6deukefPm8scff7iNA9DcMmHCBJk/f36Sx7v33nvl9OnTbsvmzZslO+WKqVBGjx4tq1atkk2bNpnJ+zKjTJkyJkEnHPmqjx0DMPSrNt9eunTJrfbOtUxC3t7eZgEAANlrbPsa5qsGOcdjR7ALfbCmc3t2VTYVKlRIvv32W+c0Ltptq1GjRlKtWjV5/vnnZc6cOWa91tbdc889pj+/1vJpxU/Clr2CBQsmmy1sGe50LIcOcggPD5eNGzeaDoSZpT+Qxo0bm4StU6s4mnz1sYZIpdv1Fh+6TqdAUToFy/Hjx00qBwAAWav7u5vl3JXYdO1TzLugCXSOkKePl2w7bpa0KlvcW1aOaZWmslor980338irr76aaH4+DWiDBg0yM3K8//77pnn1448/lsGDB5vuWp07d5aFCxfK5MmTJaflaLjTdKwjVlesWGHmunP0d9OL5LioQ4YMkbvuusv0eVNa4/bzzz87vz916pTs2bPHzFVXvXp1s16nQRk6dKipVr3//vvNXHg6vcqjjz7qPP7w4cNNOR28oaNONGRqsEvLSFkAAJA+GuyiL9/I1GW7GnvbLNnl0KFDpuJJZ89Iiq6/ePGimd1Du2jpVG5fffWV2aYhT3PFCy+84DZf3d69exPdTkzLalOvLcOdo1qzbdu2busXLFggw4YNM99rbZqOdnXQ+ei0atThjTfeMEubNm1M7Z/q37+/ufAvvviiCYwNGzaUNWvWuA2yePPNN81xteZOR8LqPHiaxAEAQNbTGrTMhjmtudMlu5/XSsMscdrHTrODdgdTXbp0MRVH69evl/bt2zvL1apVS/7zn/+47ZvdU6jleLNsahyBzXVuu7Tsp02wjmbYpOj8MXoXC10AAED2SmvTqEPCPnaOxyNbV822PnfVq1c3tW6//PKLPPTQQ4m26/pSpUqZu1stWrTIVCBpnzoHna1DQ59ruNPuYo6WxTslVwyoAAAAcEhq8ERSgyyyWunSpeXBBx80LXl6JyvXfnca5D777DPTXWz16tVy5coV2b17t9s9YHXWD+0ClnDA5p2Wa6ZCAQAAUHHxVpKjYvWxrtft2eW9995zdtfSWTx0BKx27dLQp2MAdLCFDqTo2rWrub2pzs3rWPr162dCnYZAh9u3b5tg6Lpk973sqbkDAAC5ypMP1kx2W3ZOg6Jq1KghO3fuNPen17CmI2h1pKzOwKHr9A5XX3/9tRkQmpD25dfmXA1/OmhU7du3T8qXL+9WTqdW01uN5Yt7y+Yl3FsWAICkcW/ZjLHlvWUBAACQOYQ7AAAAGyHcAQAA2AjhDgAAwEYIdwAAIFswZjNnrhfhDgAAZCnHxL56D3ikneN6uU6MnBHMcwcAALKU3pKrSJEi5j7vXl5ebveIR9Li4+PN9dLr5npLs4wg3AEAgCyl92fViXt1zrbffvuNq5tGGoIrVapkrl9mEO4AAECWK1SokLnbA02z6btmWVHLSbgDAADZQoNKwjstIPvRCA4AAGAjhDsAAAAbIdwBAADYCOEOAADARgh3AAAANkK4AwAAsBHCHQAAgI0Q7gAAAGyEcAcAAGAjhDsAAAAbydFwFxYWJk2bNpXixYtLuXLlpFevXnLgwIFU91u2bJnUrl3b3NKkXr16snr1arftesPdpJYZM2Y4y1SuXDnR9tdffz1bXicAAEC+CHcRERESEhIiW7dulbVr18qtW7ekY8eOcu3atWT32bJliwwcOFCGDx8uu3fvNoFQl6ioKGeZ06dPuy3z58834a1Pnz5ux3r55Zfdyo0ZMyZbXy8AAEB287Asy5Jc4ty5c6YGT0Nf69atkyzTv39/E/5WrVrlXNesWTNp2LChzJ07N8l9NPxduXJF1q1b51ZzN378eLNkxOXLl8XX11diYmKkRIkSGToGAABAVueOXNXnTk9Y+fn5JVsmMjJSOnTo4LYuODjYrE/KmTNn5OuvvzY1fQlpM2zp0qWlUaNGpsn29u3byT5vbGysubCuCwAAQG5TUHKJ+Ph4U4vWsmVLqVu3brLloqOjxd/f322dPtb1SVm0aJHp09e7d2+39WPHjpX77rvPBElt6p00aZJpmp01a1ay/QOnTp2aodcGAACQ78Kd9r3TfnObN2/O0uNqf7tBgwaZwReuQkNDnd/Xr19fChUqJKNGjTIhztvbO9FxNPy57qM1dxUrVszScwUAALBFuBs9erTpQ7dp0yYJDAxMsWxAQIBpanWlj3V9Qt9//70ZffvFF1+keg5BQUGmWfbYsWNSq1atRNs18CUV+gAAAHKTHO1zp2M5NNiFh4fL+vXrpUqVKqnu07x5c7eBEUpH2ur6hD7++GNp3LixNGjQINXj7tmzRzw9Pc2ADgAAgLyqYE43xS5ZskRWrFhh+sU5+s3paBAfHx/z/ZAhQ+Suu+4yzaVq3Lhx0qZNG5k5c6Z07dpVli5dKjt37pR58+a5HVubTXU+PC2XkA6+2LZtm7Rr1848rz5+8sknZfDgwVKqVKk78toBAABsF+7mzJljvrZt29Zt/YIFC2TYsGHm++PHj5saNYcWLVqYQPjCCy/Ic889JzVq1JDly5cnGoShoU9rBnVOvIS0eVW3T5kyxYyC1RpDDXeufeoAAADyolw1z11ewjx3AADgTsmz89wBAAAgcwh3AAAANkK4AwAAsBHCHQAAgI0Q7gAAAGyEcAcAAGAjhDsAAAAbIdwBAADYCOEOAADARgh3AAAANkK4AwAAsBHCHQAAgI0Q7gAAAGyEcAcAAGAjhDsAAAAbIdwBAADYCOEOAADARgh3AAAANkK4AwAAsBHCHQAAgI0Q7gAAAGyEcAcAAGAjhDsAAAAbIdwBAADYSI6Gu7CwMGnatKkUL15cypUrJ7169ZIDBw6kut+yZcukdu3aUrhwYalXr56sXr3abfuwYcPEw8PDbenUqZNbmT/++EMGDRokJUqUkJIlS8rw4cPl6tWrWf4aAQAA8k24i4iIkJCQENm6dausXbtWbt26JR07dpRr164lu8+WLVtk4MCBJozt3r3bBEJdoqKi3MppmDt9+rRz+fzzz922a7Dbt2+fed5Vq1bJpk2bZOTIkdn2WgEAAO4ED8uyLMklzp07Z2rwNPS1bt06yTL9+/c34U8DmUOzZs2kYcOGMnfuXGfN3aVLl2T58uVJHuOXX36Re+65R3bs2CFNmjQx69asWSNdunSRkydPSoUKFVI918uXL4uvr6/ExMSY2j8AAIDskp7ckav63OkJKz8/v2TLREZGSocOHdzWBQcHm/WuNm7caIJirVq15IknnpALFy64HUObYh3BTukxPT09Zdu2bUk+b2xsrLmwrgsAAEBuk2vCXXx8vIwfP15atmwpdevWTbZcdHS0+Pv7u63Tx7retUl28eLFsm7dOpk2bZqpCezcubPExcU5j6HBz1XBggVNqHQ9TsL+gZqYHUvFihUz+YoBAACyXkHJJbTvnfab27x5c6aPNWDAAOf3OuCifv36Uq1aNVOb1759+wwdc9KkSRIaGup8rDV3BDwAAJDb5Iqau9GjR5s+dBs2bJDAwMAUywYEBMiZM2fc1uljXZ+cqlWrSpkyZeTw4cPOY5w9e9atzO3bt80I2uSO4+3tbdq4XRcAAIDcJkfDnY7l0GAXHh4u69evlypVqqS6T/PmzU1zqysd8arrk6ODJLTPXfny5Z3H0AEXu3btcpbR59em4aCgoEy9JgAAgHwb7rQp9tNPP5UlS5aYue60v5suf/75p7PMkCFDTJOow7hx48zI1pkzZ8r+/ftlypQpsnPnThMSlc5V98wzz5jpVY4dO2aCYM+ePaV69epm4IWqU6eO6Zc3YsQI2b59u/zwww9mf23OTctIWQAAgNwqR8PdnDlzzAjZtm3bmlo1x/LFF184yxw/ftzMU+fQokULEwbnzZsnDRo0kP/7v/8zU544BmEUKFBAfvrpJ+nRo4fUrFnTzIfXuHFj+f77703TqsNnn31mJkLWPng6BUqrVq3MMQEAAPKyXDXPXV7CPHcAAOBOybPz3AEAACBzCHcAAAA2QrgDAACwEcIdAACAjRDuAAAAbIRwBwAAYCOEOwAAABsh3AEAANgI4Q4AAMBGCHcAAAA2QrgDAACwEcIdAACAjRDuAAAAbIRwBwAAYCOEOwAAABsh3AEAANgI4Q4AAMBGCHcAAAA2QrgDAACwEcIdAACAjRDuAAAAbIRwBwAAYCOEOwAAABsh3AEAANhIjoa7sLAwadq0qRQvXlzKlSsnvXr1kgMHDqS637Jly6R27dpSuHBhqVevnqxevdq57datWzJx4kSzvmjRolKhQgUZMmSI/P77727HqFy5snh4eLgtr7/+era8TgAAgHwR7iIiIiQkJES2bt0qa9euNcGsY8eOcu3atWT32bJliwwcOFCGDx8uu3fvNoFQl6ioKLP9+vXr8uOPP8rkyZPN16+++soExh49eiQ61ssvvyynT592LmPGjMnW1wsAAJDdPCzLsiSXOHfunKnB09DXunXrJMv079/fhL9Vq1Y51zVr1kwaNmwoc+fOTXKfHTt2yP333y+//fabVKpUyVlzN378eLNkxOXLl8XX11diYmKkRIkSGToGAABAVueOXNXnTk9Y+fn5JVsmMjJSOnTo4LYuODjYrE/puNrsWrJkSbf12gxbunRpadSokcyYMUNu376d7DFiY2PNhXVdAAAAcpuCkkvEx8ebWrSWLVtK3bp1ky0XHR0t/v7+buv0sa5Pyo0bN0wfPG3KdU26Y8eOlfvuu88ESW3qnTRpkmmanTVrVrL9A6dOnZrh1wcAAJCvwp32vdN+c5s3b86yY2ofvn79+om2PM+ZM8dtW2hoqPP7+vXrS6FChWTUqFEmxHl7eyc6loY/13205q5ixYpZdq4AAAC2CXejR482feg2bdokgYGBKZYNCAiQM2fOuK3Tx7o+qWCn/ezWr1+favt0UFCQaZY9duyY1KpVK9F2DXxJhT4AAIDcJEf73GmNmga78PBwE8CqVKmS6j7NmzeXdevWua3Tkba6PmGwO3TokHz33XemX11q9uzZI56enmZABwAAQF5VMKebYpcsWSIrVqwwc905+s3paBAfHx/zvc5Rd9ddd5nmUjVu3Dhp06aNzJw5U7p27SpLly6VnTt3yrx585zBrm/fvmYaFK0NjIuLcx5X+9dp86sOvti2bZu0a9fOPK8+fvLJJ2Xw4MFSqlSpHLseAAAAeXoqFB3BmpQFCxbIsGHDzPdt27Y105YsXLjQbRLjF154wTSh1qhRQ6ZPny5dunQx23RdcjWAGzZsMMfT4PfPf/5T9u/fb0bBavlHHnnE9KlLa9MrU6EAAIA7JT25I1fNc5eXEO4AAMCdkmfnuQMAAEDmEO4AAABshHAHAABgI4Q7AAAAGyHcAQAA2AjhDgAAwEYIdwAAADZCuAMAALARwh0AAICNEO4AAABshHAHAACQ38PdokWL5Ouvv3Y+njBhgpQsWVJatGghv/32W1aeHwAAALI73L322mvi4+Njvo+MjJTZs2fL9OnTpUyZMvLkk09m5JAAAADIAgUzstOJEyekevXq5vvly5dLnz59ZOTIkdKyZUtp27ZtVpwXAAAA7lTNXbFixeTChQvm+2+//VYefPBB833hwoXlzz//zMghAQAAkFM1dxrmHn/8cWnUqJEcPHhQunTpYtbv27dPKleunBXnBQAAgDtVc6d97Jo3by7nzp2TL7/8UkqXLm3W79q1SwYOHJiRQwIAACALeFiWZaW18Pz586VHjx5m4ER+d/nyZfH19ZWYmBgpUaJETp8OAACwscvpyB3pqrn79NNPJTAw0Ex5Mm3aNNm/f39mzxUAAABZKF3hbv369XL69Gn55z//aZpg77//fqlRo4Y89dRTsmnTJomPj8/KcwMAAEB2NssmdPPmTRP4/vOf/8jKlSvNSFkdXKFNt507d5aiRYuKXdEsCwAAcmPuyFS4S2jnzp0m6K1YsUL69u0rkydPFrsi3AEAANuHO1e3bt0SLy8vsSvCHQAAyI25I0Pz3IWGhqa57KxZszLyFAAAALhT89zt3r3bTIvywQcfyMaNG80yb948+fjjj802x7Jnz54UjxMWFiZNmzaV4sWLS7ly5aRXr15y4MCBVJ9/2bJlUrt2bXNHjHr16snq1avdtmtl5Isvvijly5c398Dt0KGDHDp0yK3MH3/8IYMGDTLpt2TJkjJ8+HC5evWqZNQ76w7Jm2sPZnh/AAAAV5orNF/ckXDXvXt3ad26tZw8eVJ+/PFHs+j9Ztu1ayfdunWTDRs2mEUHW6QkIiJCQkJCZOvWrbJ27VrTlNuxY0e5du1asvts2bLFTJSsYUwDpAZCXaKiopxlpk+fLu+8847MnTtXtm3bZgZ2BAcHy40bN5xlNNjpHTX0eVetWmVG++r9cTNCL/ystQelgKdHhvYHAABISHPFrIwEPCsDKlSoYEVFRSVav3fvXqt8+fJWRp09e1b7/1kRERHJlunXr5/VtWtXt3VBQUHWqFGjzPfx8fFWQECANWPGDOf2S5cuWd7e3tbnn39uHv/888/meXbs2OEs89///tfy8PCwTp06laZzjYmJMceYtuJH6+6Jq6y3vzuY7tcLAACQEs0XmjM0b2ju0PyRmoIZ7dSntx5LSNdduXJFMko7CSo/P79ky0RGRibq86e1csuXLzffHz16VKKjo01TrIN2QAwKCjL7DhgwwHzVptgmTZo4y2h5T09PU9P30EMPJXre2NhYs7heA/XehsOmaXfJtuNmAQAAyErFvAuavJFWGQp3Gn4effRRmTlzppnIWGkoeuaZZ6R3794ZOaSZAHn8+PHSsmVLqVu3brLlNLj5+/u7rdPHut6x3bEupTLax89VwYIFTah0lEmqf+DUqVOT3HY19rZZAAAAclqGwp32ZXv66afl73//u+knZw5UsKDpBzdjxowMnYj2vdN+c5s3b5bcaNKkSW41hlpzV7FiRWei1gUAACCraQXS5f/feJiqDCWSIkWKyPvvv2+C3JEjR8y6atWqZfiOFKNHj3YOatB716YkICBAzpw547ZOH+t6x3bHOh0t61qmYcOGzjJnz551O8bt27fNCFrH/gl5e3ubJdG5t6su72/5XUa2ripj29dI82sGAABI66BNzRsT35LsGy3roPeZ1UXvL6vBLr3zIWt5DXbh4eFmZG2VKlVS3ad58+aybt06t3U64lXXKz2GBjTXMlrLps3GjjL69dKlS+b+uA76/No0rH3z0uMfbatJ6IM1MzaaBQAAIJVgpzlD80a21txduHBB+vXrZ6Y78fDwMHPIVa1a1TTLlipVyvTFS2tT7JIlS8ztynSuO0d/Nx0AofPTqSFDhshdd91l+rypcePGSZs2bcxzdO3aVZYuXWpue6bz7Ck9H+2798orr5jQqWFPb4NWoUIFM2WKqlOnjnTq1ElGjBhhmpi1aVlDpg620HLp5aixi4vPlpt9AACAfCgu3jLBTnOGYyBnmlgZ8Mgjj1jBwcHWiRMnrGLFillHjhwx69esWWPdc889aT6OPn1Sy4IFC5xl2rRpYw0dOtRtv3//+99WzZo1rUKFCln33nuv9fXXX7tt1+lQJk+ebPn7+5spUNq3b28dOHDArcyFCxesgQMHmvMvUaKE9eijj1pXrlxJ87k7pkJJy5BkAACAzEhP7sjQvWW12fObb76RBg0amBq3//3vf6bm7tdff5X69etn6k4PeQX3lgUAALkxd2Soz53eQUIHVSSkAxKSGnQAAACAOyND4e6BBx6QxYsXOx9rPzcdjKC3/dJbkAEAACBnZGhAhYa49u3bm4EMN2/elAkTJpj7tGrN3Q8//JD1ZwkAAIDsq7nTO0gcOHBAWrVqJT179jTNtHpnit27d5v57gAAAJAzMnxbhcKFC8uDDz5oBlVok6zasWOH+dqjR4+sO0MAAABkb7hbs2aNPPLII6YZNuFgW+1/FxcXl5HDAgAAICeaZceMGWMmMf79999NrZ3rQrADAADIY+FO79MaGhoq/v7+WX9GAAAAuLPhrm/fvrJx48aMPysAAACyRYbuUHH9+nV5+OGHpWzZslKvXj3x8vJy2z527FixO+5QAQAAcmPuyNCAis8//1y+/fZbM2JWa/B0EIWDfp8fwh0AAEBulKFw9/zzz8vUqVPl2WefFU/PDLXsAgAAIBtkKJnpXSn69+9PsAMAALBDuBs6dKh88cUXWX82AAAAuPPNsjqXnd5f9ptvvpH69esnGlAxa9aszJ0VAAAA7ly427t3rzRq1Mh8HxUV5bbNdXAFAAAA8kC427BhQ9afCQAAADKNoa4AAAA2QrgDAACwEcIdAACAjRDuAAAAbIRwBwAAYCOEOwAAABsh3AEAANhIjoa7TZs2Sffu3aVChQpm8uPly5enus/s2bOlTp064uPjI7Vq1ZLFixe7bW/btq05VsKla9euzjLDhg1LtL1Tp07Z8hoBAABy/STGWeXatWvSoEEDeeyxx6R3796plp8zZ45MmjRJPvzwQ2natKls375dRowYIaVKlTIhUX311Vdy8+ZN5z4XLlwwz/Hwww+7HUvD3IIFC5yPvb29s/S1AQAA5Ltw17lzZ7Ok1SeffCKjRo2S/v37m8dVq1aVHTt2yLRp05zhzs/Pz22fpUuXSpEiRRKFOw1zAQEBWfI6AAAAcos81ecuNjZWChcu7LZOm2e1Bu/WrVtJ7vPxxx/LgAEDpGjRom7rN27cKOXKlTNNu0888YSp4UvtuS9fvuy2AAAA5DZ5KtwFBwfLRx99JLt27RLLsmTnzp3msQa78+fPJyqvoS8qKkoef/zxRE2y2ldv3bp1ptYvIiLC1CDGxcUl+9xhYWHi6+vrXCpWrJgtrxEAACAzPCxNSbmADmoIDw+XXr16JVvmzz//lJCQENM8q6ft7+8vgwcPlunTp0t0dLR57EqbcCMjI+Wnn35K8bl//fVXqVatmnz33XfSvn37ZGvudHHQmjsNeDExMVKiRIl0v14AAIC00tyhlUtpyR15quZOm2Dnz58v169fl2PHjsnx48elcuXKUrx4cSlbtmyiwRra32748OGpHlf77pUpU0YOHz6cbBnto6cX03UBAADIbXJ0QEVGeXl5SWBgoPleA1y3bt3E09M9py5btszUtGnNXmpOnjxp+tyVL18+284ZAADA9uHu6tWrbrVlR48elT179pgRr5UqVTLTnpw6dco5l93BgwdNP7qgoCC5ePGizJo1y/SpW7RoUZIDKbSJt3Tp0omec+rUqdKnTx8zWvbIkSMyYcIEqV69uunTBwAAkJflaLjTARHt2rVzPg4NDTVfhw4dKgsXLpTTp0+bplcHHfAwc+ZMOXDggKm90323bNlimmZd6fbNmzfLt99+m+g5CxQoYPrgaSC8dOmSmUC5Y8eO8q9//Yu57gAAQJ6XawZU2LljIwAAQGbYdkAFAAAAUka4AwAAsBHCHQAAgI0Q7gAAAGyEcAcAAGAjhDsAAAAbIdwBAADYCOEOAADARgh3AAAANkK4AwAAsBHCHQAAgI0Q7gAAAGyEcAcAAGAjhDsAAAAbIdwBAADYCOEOAADARgh3AAAANkK4AwAAsBHCHQAAgI0Q7gAAAGyEcAcAAGAjhDsAAAAbIdwBAADYCOEOAADARnI03G3atEm6d+8uFSpUEA8PD1m+fHmq+8yePVvq1KkjPj4+UqtWLVm8eLHb9oULF5pjuS6FCxd2K2NZlrz44otSvnx5c5wOHTrIoUOHsvz1AQAA5Ktwd+3aNWnQoIEJbGkxZ84cmTRpkkyZMkX27dsnU6dOlZCQEFm5cqVbuRIlSsjp06edy2+//ea2ffr06fLOO+/I3LlzZdu2bVK0aFEJDg6WGzduZOnrAwAAuNMKSg7q3LmzWdLqk08+kVGjRkn//v3N46pVq8qOHTtk2rRppgbQQWvrAgICkjyG1tq99dZb8sILL0jPnj3NOq398/f3NzWHAwYMyPTrAgAAyCl5qs9dbGxsoiZWbVbdvn273Lp1y7nu6tWrcvfdd0vFihVNgNNaPoejR49KdHS0aYp18PX1laCgIImMjEzxuS9fvuy2AAAA5DZ5Ktxp0+lHH30ku3btMjVwO3fuNI812J0/f96U0X548+fPlxUrVsinn34q8fHx0qJFCzl58qTZrsFOaU2dK33s2JaUsLAwEwIdiwZHAACA3CZPhbvJkyebZtxmzZqJl5eXqZUbOnSo2ebp+ddLad68uQwZMkQaNmwobdq0ka+++krKli0rH3zwQaaeW/v6xcTEOJcTJ05kyWsCAADIt+FOm2C1Vu769ety7NgxOX78uFSuXFmKFy9uAlxSNAQ2atRIDh8+bB47+uKdOXPGrZw+Tq6fnvL29jYDNVwXAACA3CZPhTvXwBYYGCgFChSQpUuXSrdu3Zw1dwnFxcXJ3r17zbQnqkqVKibErVu3zllG+8/pqFmt9QMAAMjLcnS0rA58cNSoOQY77NmzR/z8/KRSpUqmKfTUqVPOuewOHjxoBk/o4IeLFy/KrFmzJCoqShYtWuQ8xssvv2yabatXry6XLl2SGTNmmKlQHn/8cedI2vHjx8srr7wiNWrUMGFPm3t1rr1evXrlwFUAAACwSbjTARHt2rVzPg4NDTVftR+dTkasc9Rp06trLdzMmTPlwIEDpvZO992yZYtpmnXQ0DdixAgzOKJUqVLSuHFjU+aee+5xlpkwYYKZY2/kyJEmALZq1UrWrFmTaCQuAABAXuNh6bBTpJs25eqoWR1cQf87AACQW3JHnuxzBwAAgKQR7gAAAGyEcAcAAGAjhDsAAAAbIdwBAADYCOEOAADARgh3AAAANkK4AwAAsBHCHQAAgI0Q7gAAAGyEcAcAAGAjhDsAAAAbIdwBAADYCOEOAADARgh3AAAANkK4AwAAsBHCHQAAgI0Q7gAAAGyEcAcAAGAjhDsAAAAbIdwBAADYCOEOAADARgh3AAAANkK4AwAAsJEcDXebNm2S7t27S4UKFcTDw0OWL1+e6j6zZ8+WOnXqiI+Pj9SqVUsWL17stv3DDz+UBx54QEqVKmWWDh06yPbt293KDBs2zDyf69KpU6csf30AAAD5Ktxdu3ZNGjRoYAJbWsyZM0cmTZokU6ZMkX379snUqVMlJCREVq5c6SyzceNGGThwoGzYsEEiIyOlYsWK0rFjRzl16pTbsTTMnT592rl8/vnnWf76AAAA7jQPy7IsyQW09iw8PFx69eqVbJkWLVpIy5YtZcaMGc51Tz31lGzbtk02b96c5D5xcXGmBu+9996TIUOGOGvuLl26lKaawuRcvnxZfH19JSYmRkqUKJHh4wAAAGRl7shTfe5iY2OlcOHCbuu0eVabXW/dupXkPtevXzfb/Pz83NZrDV+5cuVM0+4TTzwhFy5cSPW59cK6LgAAALlNngp3wcHB8tFHH8muXbtEKxx37txpHmt4O3/+fJL7TJw40fTp0753rk2y2ldv3bp1Mm3aNImIiJDOnTubWr7khIWFmcTsWLS5FwAAILcpKHnI5MmTJTo6Wpo1a2bCnb+/vwwdOlSmT58unp6Jc+rrr78uS5cuNbV0rjV+AwYMcH5fr149qV+/vlSrVs2Ua9++fZLPrX39QkNDnY+15o6ABwAAcps8VXOnTbDz5883Ta3Hjh2T48ePS+XKlaV48eJStmxZt7JvvPGGCXfffvutCW8pqVq1qpQpU0YOHz6cbBlvb2/Txu26AAAA5DZ5qubOwcvLSwIDA833WjPXrVs3t5o7rcl79dVX5ZtvvpEmTZqkeryTJ0+aPnfly5fP1vMGAACwdbi7evWqW23Z0aNHZc+ePWbwQ6VKlUxTqE5h4pjL7uDBg2bwRFBQkFy8eFFmzZolUVFRsmjRIucxtA/diy++KEuWLDG1etqMq4oVK2YWfU6dQqVPnz4SEBAgR44ckQkTJkj16tVNnz4AAIC8LEebZXVARKNGjcyitE+bfq/hTOn8c9r06qADHmbOnGnmxnvwwQflxo0bsmXLFhPiXOfCu3nzpvTt29fUxDkWbaZVBQoUkJ9++kl69OghNWvWlOHDh0vjxo3l+++/N02vAAAAeVmumecur2GeOwAAcKfYdp47AAAApIxwBwAAYCOEOwAAABsh3AEAANgI4Q4AAMBGCHcAAAA2QrgDAACwEcIdAACAjRDuAAAAbIRwBwAAYCOEOwAAABsh3AEAANgI4Q4AAMBGCHcAAAA2QrgDAACwEcIdAACAjRDuAAAAbIRwBwAAYCOEOwAAABsh3AEAANgI4Q4AAMBGCHcAAAA2QrgDAACwEcIdAACAjeRouNu0aZN0795dKlSoIB4eHrJ8+fJU95k9e7bUqVNHfHx8pFatWrJ48eJEZZYtWya1a9eWwoULS7169WT16tVu2y3LkhdffFHKly9vjtOhQwc5dOhQlr42AACAfBfurl27Jg0aNDCBLS3mzJkjkyZNkilTpsi+fftk6tSpEhISIitXrnSW2bJliwwcOFCGDx8uu3fvll69epklKirKWWb69OnyzjvvyNy5c2Xbtm1StGhRCQ4Olhs3bmTL6wQAALhTPCytxsoFtOYuPDzcBLHktGjRQlq2bCkzZsxwrnvqqadMQNu8ebN53L9/fxMaV61a5SzTrFkzadiwoQlz+nK1plD3e/rpp832mJgY8ff3l4ULF8qAAQPSdL6XL18WX19fs2+JEiUy8coBAACyLnfkqT53sbGxpqnVlTarbt++XW7dumUeR0ZGmmZWV1orp+vV0aNHJTo62q2MXqygoCBnmeSeWy+s6wIAAJDb5KlwpyHto48+kl27dpkauJ07d5rHGuzOnz9vymhw01o4V/pY1zu2O9YlVyYpYWFhJgQ6looVK2bDKwQAAMhH4W7y5MnSuXNn08zq5eUlPXv2lKFDh5ptnp7Z+1K0r59WhTqWEydOZOvzAQAA2D7caRPs/Pnz5fr163Ls2DE5fvy4VK5cWYoXLy5ly5Y1ZQICAuTMmTNu++ljXe/Y7liXXJmkeHt7mzZu1wUAACC3yVPhzkFr7QIDA6VAgQKydOlS6datm7Pmrnnz5rJu3Tq38mvXrjXrVZUqVUyIcy2j/ed0UIajDAAAQF5VMCef/OrVq3L48GHnYx3ssGfPHvHz85NKlSqZptBTp04557I7ePCgGTyhgx8uXrwos2bNMlOcLFq0yHmMcePGSZs2bWTmzJnStWtXE/60b968efOco3LHjx8vr7zyitSoUcOEPW3u1RG0KY3UBQAAyAtyNNxp6GrXrp3zcWhoqPmq/eh0WpLTp0+bpleHuLg4E9oOHDhgau90X53XTptmXadLWbJkibzwwgvy3HPPmQCnkyPXrVvXWWbChAlmupSRI0fKpUuXpFWrVrJmzZpEI3EBAADymlwzz11ewzx3AADgTrHtPHcAAABIGeEOAADARgh3AAAANkK4AwAAsBHCHQAAgI0Q7gAAAGwkR+e5y8scM8jo0GQAAIDs5MgbaZnBjnCXQRcuXDBfK1asmNFDAAAApMuVK1fMfHcpIdxlkN4iTekdNFK7yPn1E4YG3xMnTqQ62WJ+xTXi+vD/h98x3oNyr8u57O+Y1thpsNPbpaaGcJdBnp5/dVfUYJcbfui5lV4brg/XiP9D/I7xPpR78T6dd65PWiuTGFABAABgI4Q7AAAAGyHcZZC3t7e89NJL5iu4Pvwfynr8jnF9+D+Uvfgds+/18bDSMqYWAAAAeQI1dwAAADZCuAMAALARwh0AAICNEO4AAABshHCXjGHDhkmvXr3u7E8jj5o9e7ZUrlxZChcuLEFBQbJ9+3bntrZt24qHh4fb8o9//EPym5Su0ZEjR+Shhx6SsmXLmoky+/XrJ2fOnJH8YtOmTdK9e3cz67r+/1i+fLnb9ilTpkjt2rWlaNGiUqpUKenQoYNs27ZN8ovUrk/C3y/HMmPGDMkPwsLCpGnTplK8eHEpV66ced8+cOCAW5l58+aZ9yL9/dJrc+nSJckv0nJ98vP7dFgark9efI8m3CFTvvjiCwkNDTXDxX/88Udp0KCBBAcHy9mzZ51lRowYIadPn3Yu06dPz1dXPaVrdO3aNenYsaN5M12/fr388MMPcvPmTfPHPD4+XvIDvQZ6TTQAJ6VmzZry3nvvyd69e2Xz5s0mJOs1O3funOQHqV0f198tXebPn2/+P/Xp00fyg4iICAkJCZGtW7fK2rVr5datW+b/h143h+vXr0unTp3kueeek/wmLdcnP79PR6RyffLse7ROhYLEhg4davXs2dN8/9///tdq2bKl5evra/n5+Vldu3a1Dh8+7Cx79OhRnU7G+vLLL622bdtaPj4+Vv369a0tW7bY/tLef//9VkhIiPNxXFycVaFCBSssLMw8btOmjTVu3DgrP0vpGn3zzTeWp6enFRMT49x+6dIly8PDw1q7dq2V3+jvUXh4eIpl9Fppue+++87Kb9JyffR9629/+5uVX509e9Zcp4iIiETbNmzYYLZdvHjRyq+Suj68Tyd/ffLqezQ1d2mgyV1rXnbu3Cnr1q0z95XVKtqEqf3555+Xp59+Wvbs2WNqGwYOHCi3b98Wu9JPL7t27TLNZA56bfRxZGSkc91nn30mZcqUkbp168qkSZPMp+j8IrVrFBsbaz4Ruk6SqU23WkZrqZD4emoTm95fUWuz4E6bir7++msZPnx4vr00MTEx5qufn19On0qeuj75+X06peuTV9+jC+b0CeQFCZs3tNlD295//vln84vgoMGua9eu5vupU6fKvffeK4cPHzb9hezo/PnzEhcXJ/7+/m7r9fH+/fvN93//+9/l7rvvNv2FfvrpJ5k4caLpz/DVV19JfpDaNWrWrJnpS6bX5bXXXtOadHn22WfNPto0gr+sWrVKBgwYYP7glC9f3jSf6B8iuFu0aJHpO9S7d+98eWn0A/f48eOlZcuWbu/NSPn65Pf36ZSuT159j6bmLg0OHTpkauGqVq1qOlNqnx91/Phxt3L169d3fq9/gJRr37P8aOTIkaZ/Wb169WTQoEGyePFiCQ8PNx1UIeZDwrJly2TlypVSrFgxUyOlnb3vu+8+88kQf2nXrp2pEd+yZYvpO6UdmvP771ZS9IOn/p5pzUJ+pH2noqKiZOnSpTl9Knnq+vA+nfz1yavv0dTcpYF2nNRPNR9++KH5ZKPpXlO9NhG58vLycn6v1bgqV3e4zCStOSlQoECiUUP6OCAgIMl9dKSo0hrNatWqid2l5RppZ10Nu1rLV7BgQSlZsqTZph8m8Bf95Fy9enWz6CfpGjVqyMcff2yaj/CX77//3tS26ACe/Gj06NGmhldHFwcGBub06eTp65Pf3qdTuz558T0698bOXOLChQvmDfOFF16Q9u3bS506deTixYs5fVq5QqFChaRx48amH6KDhll93Lx58yT30doX15pNu0vPNdIgqG8aOiJLa6V69OiRA2ecN+g11L4w+P807Or/tfzWF1GbyfQPs7YI6O9OlSpVcvqU8vz1yU/v01Y6rk9eeo+m5i4VOq9W6dKlTSdu/Y+uTbHa3o6/6ECToUOHSpMmTeT++++Xt956ywxAefTRR80nnSVLlkiXLl3MNdS+HE8++aS0bt3arQk7P18jtWDBAvOhQav/dZDFuHHjzHWqVauW5AdXr141NQQOR48eNX9ctEOz/r959dVXzZuo/v7pJ2edEuTUqVPy8MMPS36/PpUqVTLrLl++bJqOZs6cKfmNNqXp+8yKFStMf8Po6GizXpvPfHx8zPe6ThfHddRpdbSsXj+7D7xI7frk9/fpkDT8/8mT79E5PVw3t3rkkUesPn36mO91uHOdOnUsb29vM8XJxo0b3aYkcEyFsnv3buf+OtRe1+nQe7t79913rUqVKlmFChUy035s3brVrD9+/LjVunVrM32MXrvq1atbzzzzjNuQ8vwiuWukJk6caPn7+1teXl5WjRo1rJkzZ1rx8fFWfuGYniLhotMR/fnnn9ZDDz1kpo7Ra1e+fHmrR48e1vbt2638IqXr4/DBBx+YKZh0iob8Jqlro8uCBQucZV566aVUy+TX65Pf36clDf9/8uJ7tIf+k9MBMzfSTtvav0cnTwUAAMgr6HOXgPan006VGzdudJubDAAAIC+gz10Cjz32mOzYsUOeeuop6dmzZ878VAAAADKIZlkAAAAboVkWAADARgh3AAAANkK4AwAAsBHCHQAAgI0Q7gAAQJ4TFhYmTZs2NXeWKFeunPTq1cvcLtTVjRs3zF0o9O4bxYoVkz59+iS61/fYsWPNrfu8vb2lYcOGkpBOjaazZ+hdcvQ+11rms88+k7SoXLmyuSvRnUa4A4Ason8EPDw85NKlS1xTIJtFRESY4LZ161ZZu3at3Lp1Szp27Ghu7+igtwlbuXKluT2flv/999+ld+/eSU6D1r9//ySfZ8uWLeZWbF9++aW5PZveOnLIkCFmTtxcK6dvkQEAeVWbNm2scePGOR/HxsZap0+fzvW3JgLs6OzZs+bWYREREeax3o5Pbxm2bNkyZ5lffvnFlImMjEy0v96mrkGDBml6ri5duliPPvpoquXuvvtu680333Q+1luX1a1b1ypSpIgVGBhoPfHEE9aVK1ec2/W2Z76+vtaaNWus2rVrW0WLFrWCg4Ot33//3UoPau4AIIsUKlRIAgICTO0dgDsrJibGfPXz8zNfd+3aZWrzXO82Vbt2balUqZJERkZm+rkcz5Menp6e8s4778i+fftk0aJFsn79epkwYYJbmevXr8sbb7whn3zyiWzatEmOHz8uTz/9dPqeJ91nBgCQYcOGmWaet99+24Q5XRYuXOjWLKuPS5YsaZpvatWqJUWKFJG+ffuaN299Y9f+OKVKlTJ9fuLi4pxXNTY21ryZ33XXXaaPT1BQkGnyBZC0+Ph4GT9+vLRs2VLq1q1r1kVHR5sPXPo76Mrf399sy6h///vf5k5W2jybXnqO7dq1M7/7f/vb3+SVV14xx3OlgXTu3LnSpEkTue+++2T06NGybt26dD0Ptx8DgAzQUHfw4EHzh+Tll1826/TTeEIa5PST+tKlS+XKlSumv89DDz1k/uCsXr1afv31V9PJW/8oOfr86Jv5zz//bPapUKGChIeHS6dOnWTv3r1So0YNfl5AAtr3LioqSjZv3pyt12bDhg0m1H344Ydy7733mnXff/+9dO7c2Vnmgw8+kEGDBiW5/3fffWcGguzfv18uX74st2/fNoM+9H1CP/wp/VqtWjXnPjqQ4+zZs+k6T8IdAGSAr6+vqRXQN2JtilX6hp2QfgqfM2eO881aa+60uUVH7OnovXvuucd8ktc/GhrutAlmwYIF5qsGO6W1eGvWrDHrX3vtNX5egAv9MKS149qEGRgY6Fyvv5c3b940NemutXf6u+f4nU0Pranv3r27vPnmm2ZAhYPWsO3Zs8etZjApx44dk27duskTTzwhr776qmnW1TA6fPhwc56OcOfl5eW2n7YGWJZ2FUw7wh0AZKOEn8L1jV+bZDTYua5zfDLX2jltoq1Zs6bbcbSpVqdzAPAXDTxjxowxNdvabaFKlSpul6Zx48YmKGmTptaOK50qRT84NW/ePF2XUY+vwWzatGkycuRIt20+Pj5SvXr1VI+hfQC1+XjmzJmm751K2CSbVQh3AJCNkvoUntQ6fdNXV69elQIFCpg/BPrVlWsgBPI7bYpdsmSJrFixwsx15+hH5+vrawKXftVasdDQUFNLVqJECRMGNdg1a9bMeZzDhw+b3zvd/88//3TWwmmtutbOa626Brtx48aZkOh4Ht2WnkEVGgC1Jv/dd981NYA//PCD6VuXHQh3AJBB+ubuOhAiKzRq1MgcU2vyHnjgAX42QDK0u4Nq27at2/oFCxaYAU9Km1C1lkxDmdZ+BwcHy/vvv+9W/vHHHzdNrq6/g+ro0aOmll0HP2mfOO0rp4tDmzZtUh3opB/aChb8K2o1aNBAZs2aZWr/Jk2aJK1btzbHc23izSoeOh9Klh8VAPIBbZ7RT/natKK1ajrBafv27eXixYumj4+OltXRca6TGk+ZMkWWL1/u1kdH/xBpGV2vBg8ebD7Va/ON/qE5d+6caVrSiVS7du2aI68VQProhzStLdRwqH1t7ySmQgGADNKBDtp0qs03ZcuWNX15soLWPOin+aeeespMoaK3VdKpF3R+LgC538mTJ00NnQa8Vq1a3fHnp+YOAAAgC5UpU8YMgNJ57B5++GG50wh3AAAANkKzLAAAgI0Q7gAAAGyEcAcAAGAjhDsAAAAbIdwBAADYCOEOAADARgh3AAAANkK4AwAAsBHCHQAAgI0Q7gAAAMQ+/h92UriTb/rYGAAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 700x400 with 1 Axes>"
       ]
@@ -12293,7 +12132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 56,
    "id": "263a8344-3e4f-4b18-9e10-89aee77bb941",
    "metadata": {},
    "outputs": [
@@ -12301,7 +12140,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-09-29 14:11:08 - INFO - Writing the following NetCDF files:\n",
+      "2026-06-23 09:20:54 - INFO - Writing the following NetCDF files:\n",
       "ROMS_TOOLS_INPUT_DATA/roms_cdr.nc\n"
      ]
     },
@@ -12311,7 +12150,7 @@
        "[PosixPath('ROMS_TOOLS_INPUT_DATA/roms_cdr.nc')]"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -12322,7 +12161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 57,
    "id": "743780b5-d837-4c44-ae79-957d0cee0aeb",
    "metadata": {},
    "outputs": [],
@@ -12360,7 +12199,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 58,
    "id": "37420aa0-f93f-4a01-8497-479ac239ef61",
    "metadata": {},
    "outputs": [],
@@ -12370,7 +12209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 59,
    "id": "b2d9dc08-6f63-4406-bd9d-a0ee6956de1a",
    "metadata": {},
    "outputs": [],
@@ -12380,7 +12219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 60,
    "id": "8d94420b-d943-41d7-b0c5-e5a28b9dd0fc",
    "metadata": {},
    "outputs": [],
@@ -12398,7 +12237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 61,
    "id": "f0660fb3-8b08-4110-9e2a-7d129042f392",
    "metadata": {},
    "outputs": [],
@@ -12408,7 +12247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 62,
    "id": "83d760b5-b5d3-454c-8d6b-1a49cb4b8a8c",
    "metadata": {},
    "outputs": [],
@@ -12418,7 +12257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 63,
    "id": "f9e16ff6-3b6e-4188-ab2b-15bef61d60fe",
    "metadata": {},
    "outputs": [
@@ -12441,8 +12280,18 @@
       "\n",
       "\tpodman machine set --rootful\n",
       "\n",
-      "API forwarding listening on: /var/run/docker.sock\n",
-      "Docker API clients default to this address. You do not need to set DOCKER_HOST.\n",
+      "API forwarding listening on: /var/folders/0f/07hq__ls2tdgjy7vykrfmmch0000gn/T/podman/podman-machine-default-api.sock\n",
+      "\n",
+      "The system helper service is not installed; the default Docker API socket\n",
+      "address can't be used by podman. If you would like to install it, run the following commands:\n",
+      "\n",
+      "        sudo /opt/homebrew/Cellar/podman/5.8.2/bin/podman-mac-helper install\n",
+      "        podman machine stop; podman machine start\n",
+      "\n",
+      "You can still connect Docker API clients by setting DOCKER_HOST using the\n",
+      "following command in your terminal session:\n",
+      "\n",
+      "        export DOCKER_HOST='unix:///var/folders/0f/07hq__ls2tdgjy7vykrfmmch0000gn/T/podman/podman-machine-default-api.sock'\n",
       "\n",
       "Machine \"podman-machine-default\" started successfully\n"
      ]
@@ -12465,7 +12314,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 64,
    "id": "fbd927f4-249b-428d-8be0-97d197e485c2",
    "metadata": {},
    "outputs": [],
@@ -12498,7 +12347,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": null,
    "id": "64072bc6-a84a-4587-b6d4-e8a3c4624ff8",
    "metadata": {},
    "outputs": [],
@@ -12527,7 +12376,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": null,
    "id": "7b25d1f1-de87-45d3-b08e-c990c839ee11",
    "metadata": {},
    "outputs": [
@@ -12555,7 +12404,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": null,
    "id": "b8b291b9-6173-4d9f-bbf5-3cd894ba5a69",
    "metadata": {},
    "outputs": [],
@@ -12573,7 +12422,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": null,
    "id": "dbc4c803-2798-41f4-8ae2-edc75b2bbf5a",
    "metadata": {},
    "outputs": [],
@@ -12595,7 +12444,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": null,
    "id": "5962756b-12aa-445e-9bd5-9b97b9e0eb16",
    "metadata": {},
    "outputs": [
@@ -14221,7 +14070,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": null,
    "id": "a866133b-7292-48a8-9718-75e38fe5d550",
    "metadata": {},
    "outputs": [
@@ -14249,7 +14098,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": null,
    "id": "5347c007-32ec-405c-a758-741f0c5b222d",
    "metadata": {},
    "outputs": [
@@ -14285,7 +14134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": null,
    "id": "9d1f62af-b38e-4177-87f5-57b6cd47a63a",
    "metadata": {},
    "outputs": [
@@ -14313,7 +14162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": null,
    "id": "61a1af9c-b1de-4bdb-bcd7-422a0a5cebc5",
    "metadata": {},
    "outputs": [
@@ -14357,7 +14206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": null,
    "id": "832d0071-1e42-4aed-8c3a-b6b1ec54a4fe",
    "metadata": {},
    "outputs": [],
@@ -14379,7 +14228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": null,
    "id": "4a795d29-ad58-477f-8390-4410078435fc",
    "metadata": {},
    "outputs": [
@@ -17500,7 +17349,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": null,
    "id": "089ec3b9-6b83-44d0-9ec7-e00665d2f070",
    "metadata": {},
    "outputs": [
@@ -17536,7 +17385,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": null,
    "id": "bec48fce-6f73-4181-a12b-22abdfeb662f",
    "metadata": {},
    "outputs": [
@@ -17574,7 +17423,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": null,
    "id": "a6f7d547-8764-45b4-9bc0-4d455bb0bf4b",
    "metadata": {},
    "outputs": [
@@ -18834,7 +18683,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "romstools-test",
    "language": "python",
    "name": "python3"
   },
@@ -18848,7 +18697,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.3"
+   "version": "3.13.13"
   }
  },
  "nbformat": 4,
diff --git a/docs/releases.md b/docs/releases.md
index 944eba05e..64fab5a61 100644
--- a/docs/releases.md
+++ b/docs/releases.md
@@ -7,6 +7,7 @@
 * The underlying data allowing self attration and loading (SAL) correction to tidal forcing has been updated for accuracy, and was derived from the TPXO10v2a datasets. This is not a functional breaking change, but will change (improve) output from `TidalForcing` based on TPXO data compared to previos versions of ROMS-Tools. See ([#617](https://github.com/CWorthy-ocean/roms-tools/pull/617))
 * ERA5 data correction in `SurfaceForcing` now includes longwave radiation, and the shortwave radiation correction multipliers are updated ([#614](https://github.com/CWorthy-ocean/roms-tools/pull/614)).  This is a breaking change only in the sense that input radiation fields generated by ROMS-Tools using ERA5 forcing data will be slightly different (but more correct) compared to previous ROMS-Tools versions. Correction factors for previous versions of ROMS-Tools remain functional and available.
 * pco2 is no longer read in during `SurfaceForcing` `type='bgc'` ([#608](https://github.com/CWorthy-ocean/roms-tools/pull/608)).
+* `BoundaryForcing`'s `apply_2d_horizontal_fill` argument is **deprecated** in favor of the new `prefill` argument (`apply_2d_horizontal_fill=True` → `prefill="2d_lateral_fill"`, `False` → `prefill=None`); it still works but emits a `DeprecationWarning`. Note that `prefill="2d_lateral_fill"` now regrids with the default `"auto"` engine (xESMF when installed), so its output differs slightly from the pre-v4 AMG+scipy path; pass `regrid_method="scipy"` to reproduce the legacy output byte-for-byte.
 * `from_file` is now a private function to `Grid`. Files are now loaded as `Grid(filename='grid.nc')` ([#573](https://github.com/CWorthy-ocean/roms-tools/pull/573))
 * The `ChildGrid` has been removed. Both a child and parent grid are created using `Grid`, and the functions `align_grids` and `make_edata` are called to adjust bathymetry and do the mapping ([#573](https://github.com/CWorthy-ocean/roms-tools/pull/573)).
 * CDR metrics on `ds_cdr` rename efficiency fields: `cdr_efficiency` to `cdr_efficiency_from_flux`, and `cdr_efficiency_from_delta_diff` to `cdr_efficiency_from_DIC_difference`. Saved datasets, ensemble inputs, or downstream code using the old names need updating.
@@ -26,6 +27,10 @@
 * Option to automatically close narrow 1-cell water channels during mask generation via `Grid.update_mask(close_narrow_channels=True)` or `Grid(close_narrow_channels=True)`; integrated into the standard mask workflow
 * CDR metrics report CO2 uptake as mass in tonnes of CO2 for both the air-sea flux-difference and DIC-difference constructions (using CO2 molar mass and native flux or inventory units when available).
 * The CDR metrics figure from `ROMSOutput.cdr_metrics()` uses twin y-axes: CDR efficiency (dimensionless) and CO2 uptake (tonnes CO2), with title "CO2 uptake and CDR efficiency".
+* New `type` is added to SurfaceForcing. `restoring` creates restoring forces files for ROMS ('sss' is the only option) ([#589](https://github.com/CWorthy-ocean/roms-tools/pull/589))
+* `CDRForcing` has an option `time_interpolation` to choose step-like or interpolated releases ([#601](https://github.com/CWorthy-ocean/roms-tools/pull/601))
+* `BoundaryForcing` now regrids horizontally with a NaN-aware approach by default, removing the need for the slow and memory-hungry 2D Poisson horizontal fill on large multi-year domains. When xESMF is available, masked bilinear regridding (weights renormalized over valid ocean cells) plus inverse-distance-weighted extrapolation (configurable via the `extrap_method` / `extrap_kwargs` arguments) produces NaN-free boundaries directly. When xESMF is unavailable (e.g. Windows/pip installs), a nearest-neighbor pre-fill of the source followed by scipy linear interpolation provides an equivalent NaN-free fallback. **Note:** boundary values shift slightly compared to previous releases for users on the default settings.
+* `BoundaryForcing` gains a unified `prefill` argument (with `prefill_kwargs`) to optionally fill the *source* before regridding: `None` (default, no prefill), `"2d_lateral_fill"` (legacy AMG Poisson fill), `"inverse_dist"` / `"nearest_s2d"` (xESMF source-on-source fills), or `"nearest_neighbor"` (cheap scipy distance-transform fill; also the automatic fallback when xESMF is unavailable). The horizontal regrid engine is now selected independently via `regrid_method` (`"auto"` (default; xESMF if installed else scipy), `"xesmf"`, or `"scipy"`).
 
 
 ### Internal Changes
@@ -39,6 +44,9 @@
 * Ensure `regionmask>=0.11.0` in `pyproject.toml` ([#565](https://github.com/CWorthy-ocean/roms-tools/pull/565))
 * Integrate narrow-channel closing directly into `Grid.update_mask()` (internal `_close_narrow_channels`), iterating north–south and east–west up to 10 passes
 * CDR analysis tests cover tonnes-CO2 uptake and renamed efficiency fields; `compute_cdr_metrics` requires `DIC_source` in the input dataset alongside other CDR variables.
+* short and long wave radiation time is shifted 1/2 a timestep sooner and have a dim of `rad_time` ([#586](https://github.com/CWorthy-ocean/roms-tools/pull/586))
+* The coarse UNIFIED BGC dataset used for testing was updated to have depths of 0 and 5 m available ([#589](https://github.com/CWorthy-ocean/roms-tools/pull/589))
+* 2 checks added for a point source when plotting `CDRForcing.plot_distribution()`. Low hsc is treated as a point source ([#600](https://github.com/CWorthy-ocean/roms-tools/pull/600))
 
 ### Documentation
 
@@ -46,6 +54,9 @@
 * Move "overview of ROMS-Tools functionality" section from paper to docs ([#554](https://github.com/CWorthy-ocean/roms-tools/pull/554))
 * Document `close_narrow_channels` option in `Grid` and `update_mask()`; update notebook examples
 * CDR analysis notebook: describe CO2 uptake (tonnes CO2) and CDR efficiency together, consistent with the metrics and figure.
+* Both the surface forcing and datasets notebooks are updated to reflect `restoring` function and WOA data ([#589](https://github.com/CWorthy-ocean/roms-tools/pull/589))
+* The cdr notebook is updated to reflect interpolation option. Default is same as ROMS, no interpolation ([#601](https://github.com/CWorthy-ocean/roms-tools/pull/601))
+* The boundary forcing notebook is updated to describe the new NaN-aware horizontal regridding (masked bilinear with inverse-distance extrapolation), replacing the previous "1D versus 2D horizontal fill" discussion, and adds a section comparing the `prefill` source-fill methods (`2d_lateral_fill`, `inverse_dist`, `nearest_s2d`, `nearest_neighbor`) with a strategy table and side-by-side plots of the pre-filled source.
 
 ### Bugfixes
 
diff --git a/roms_tools/datasets/lat_lon_datasets.py b/roms_tools/datasets/lat_lon_datasets.py
index 8d32d36ac..5d5b95e4c 100644
--- a/roms_tools/datasets/lat_lon_datasets.py
+++ b/roms_tools/datasets/lat_lon_datasets.py
@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import importlib.util
+import logging
 import typing
 from collections.abc import Callable, Mapping
 from dataclasses import dataclass, field
@@ -29,7 +30,8 @@
     select_relevant_times,
     validate_start_end_time,
 )
-from roms_tools.fill import LateralFill
+from roms_tools.fill import LateralFill, nearest_neighbor_fill
+from roms_tools.regrid import _xesmf_extrap_kwargs
 from roms_tools.setup.utils import (
     Timed,
     assign_dates_to_climatology,
@@ -658,6 +660,67 @@ def choose_subdomain(
             self.ds = subdomain
             return None
 
+    def apply_prefill(
+        self,
+        method: str,
+        prefill_kwargs: dict | None = None,
+        prefill_was_user_set: bool = False,
+    ):
+        """Fill masked (land/void) cells in the source by the chosen method.
+
+        Thin dispatcher over the concrete fills so callers select a strategy by
+        name. After a prefill the source is NaN-free, so a subsequent regrid can
+        use plain bilinear (no destination extrapolation needed).
+
+        Parameters
+        ----------
+        method : str
+            One of:
+
+            - ``"2d_lateral_fill"`` -- iterative AMG Poisson fill
+              (:meth:`apply_lateral_fill`); no xESMF required.
+            - ``"nearest_neighbor"`` -- cheap distance-transform fill
+              (:meth:`apply_nearest_neighbor_fill`); no xESMF required.
+            - ``"inverse_dist"`` / ``"nearest_s2d"`` / ``"creep_fill"`` -- xESMF
+              source-on-source fill (:meth:`apply_xesmf_source_fill`).
+        prefill_kwargs : dict, optional
+            Method-specific kwargs. For the xESMF methods these are forwarded to
+            :meth:`apply_xesmf_source_fill` (e.g. ``num_src_pnts`` /
+            ``dist_exponent`` for ``"inverse_dist"``, ``num_levels`` for
+            ``"creep_fill"``). Ignored by the AMG and nearest-neighbor methods.
+        prefill_was_user_set : bool, optional
+            Whether the caller's ``prefill`` was set explicitly by the user (vs a
+            class default). Only used to decide whether to emit the
+            "source already NaN-free; prefill is a no-op" info-log when this
+            dataset does not need a lateral fill.
+
+        Notes
+        -----
+        All underlying fills early-return when ``self.needs_lateral_fill`` is
+        ``False`` (the source is already NaN-free, e.g. the unified BGC dataset).
+        In that case an explicit user ``prefill`` is a no-op; this is surfaced as
+        an info-log rather than silently ignored.
+        """
+        prefill_kwargs = prefill_kwargs or {}
+
+        if not self.needs_lateral_fill:
+            if prefill_was_user_set:
+                logging.info(
+                    "Source data is already NaN-free (needs_lateral_fill=False); "
+                    "prefill=%r is a no-op.",
+                    method,
+                )
+            return
+
+        if method == "2d_lateral_fill":
+            self.apply_lateral_fill()
+        elif method == "nearest_neighbor":
+            self.apply_nearest_neighbor_fill()
+        elif method in ("inverse_dist", "nearest_s2d", "creep_fill"):
+            self.apply_xesmf_source_fill(method=method, **prefill_kwargs)
+        else:
+            raise ValueError(f"Unknown prefill method: {method!r}")
+
     def apply_lateral_fill(self):
         """Apply lateral fill to variables using the dataset's mask and grid dimensions.
 
@@ -707,6 +770,142 @@ def apply_lateral_fill(self):
                     # Apply standard lateral fill for other variables
                     self.ds[var_name] = lateral_fill.apply(self.ds[var_name])
 
+    def apply_nearest_neighbor_fill(self):
+        """Fill masked values in `self.ds` using nearest-neighbor lookup.
+
+        This is a cheap alternative to :meth:`apply_lateral_fill` (the iterative
+        Poisson solver). It mirrors that method's mask handling -- a separate
+        mask (`mask_vel`) is used for velocity variables (e.g. `u`, `v`) if
+        available -- but fills each masked cell with the value of the nearest
+        valid (ocean) cell via :func:`roms_tools.fill.nearest_neighbor_fill`.
+
+        Like :meth:`apply_lateral_fill`, this assumes a ('latitude', 'longitude')
+        dimension ordering and loops over `self.ds.data_vars` so each variable is
+        filled only once.
+        """
+        if self.needs_lateral_fill:
+            dims = (self.dim_names["latitude"], self.dim_names["longitude"])
+
+            separate_fill_for_velocities = "mask_vel" in self.ds.data_vars
+
+            for var_name in self.ds.data_vars:
+                if var_name.startswith("mask"):
+                    # Skip variables that are mask types
+                    continue
+                elif (
+                    separate_fill_for_velocities
+                    and "u" in self.var_names
+                    and "v" in self.var_names
+                    and var_name in [self.var_names["u"], self.var_names["v"]]
+                ):
+                    self.ds[var_name] = nearest_neighbor_fill(
+                        self.ds[var_name], self.ds["mask_vel"], dims
+                    )
+                else:
+                    self.ds[var_name] = nearest_neighbor_fill(
+                        self.ds[var_name], self.ds["mask"], dims
+                    )
+
+    def apply_xesmf_source_fill(
+        self,
+        method: str = "inverse_dist",
+        num_levels: int = 100,
+        num_src_pnts: int | None = None,
+        dist_exponent: float | None = None,
+    ):
+        """Fill masked values by regridding the source onto its own grid (xESMF).
+
+        Unlike destination-side extrapolation during the boundary regrid, this
+        fills the 2D *source* field itself, where the valid ocean cells act as
+        seeds spread across the whole grid. Velocity variables use ``mask_vel``
+        when present, mirroring the other fills. The ``apply_xesmf_`` prefix
+        distinguishes this from the non-xESMF :meth:`apply_lateral_fill` (AMG) and
+        :meth:`apply_nearest_neighbor_fill` (scipy) fills.
+
+        Parameters
+        ----------
+        method : str
+            xESMF extrapolation method used for the fill (default
+            ``"inverse_dist"``):
+
+            - ``"inverse_dist"`` -- inverse-distance-weighted average of the
+              nearest source points; tuned by ``num_src_pnts`` / ``dist_exponent``.
+            - ``"nearest_s2d"`` -- single nearest source point.
+            - ``"creep_fill"`` -- truncated Laplace-style diffusion; smooth but
+              only reaches ``num_levels`` cells from ocean. **Not available in
+              current released xESMF** (requires a newer/unreleased xESMF + ESMF);
+              included for forward compatibility and not exposed through
+              ``BoundaryForcing``'s ``prefill`` argument.
+        num_levels : int
+            Number of creep iterations (``creep_fill`` only).
+        num_src_pnts : int, optional
+            Number of nearest source points to average (``inverse_dist`` only;
+            xESMF default is 8).
+        dist_exponent : float, optional
+            Inverse-distance weighting exponent (``inverse_dist`` only; xESMF
+            default is 2.0).
+        """
+        if not self.needs_lateral_fill:
+            return
+        import warnings
+
+        import xesmf as xe
+
+        lat = self.dim_names["latitude"]
+        lon = self.dim_names["longitude"]
+        lat_vals = self.ds[lat].values
+        lon_vals = self.ds[lon].values
+
+        def _build(mask_da):
+            ds_in = xr.Dataset(
+                {
+                    "mask": (
+                        ("lat", "lon"),
+                        mask_da.transpose(lat, lon).values.astype("int32"),
+                    )
+                },
+                coords={"lat": lat_vals, "lon": lon_vals},
+            )
+            ds_out = xr.Dataset(coords={"lat": lat_vals, "lon": lon_vals})
+            kwargs = dict(method="bilinear", unmapped_to_nan=True, extrap_method=method)
+            kwargs.update(
+                _xesmf_extrap_kwargs(
+                    method,
+                    {
+                        "num_levels": num_levels,
+                        "num_src_pnts": num_src_pnts,
+                        "dist_exponent": dist_exponent,
+                    },
+                )
+            )
+            with warnings.catch_warnings():
+                warnings.filterwarnings("ignore", category=UserWarning, module="xesmf")
+                return xe.Regridder(ds_in, ds_out, **kwargs)
+
+        def _fill(da, rg):
+            renamed = da.rename({lat: "lat", lon: "lon"})
+            with warnings.catch_warnings():
+                warnings.filterwarnings("ignore", category=UserWarning, module="xesmf")
+                out = rg(renamed, keep_attrs=True)
+            return out.rename({"lat": lat, "lon": lon})
+
+        regridder = _build(self.ds["mask"])
+        separate = "mask_vel" in self.ds.data_vars
+        regridder_vel = _build(self.ds["mask_vel"]) if separate else None
+
+        for var_name in self.ds.data_vars:
+            if var_name.startswith("mask"):
+                continue
+            elif (
+                separate
+                and "u" in self.var_names
+                and "v" in self.var_names
+                and var_name in [self.var_names["u"], self.var_names["v"]]
+            ):
+                self.ds[var_name] = _fill(self.ds[var_name], regridder_vel)
+            else:
+                self.ds[var_name] = _fill(self.ds[var_name], regridder)
+
     def extrapolate_deepest_to_bottom(self):
         """Extrapolate deepest non-NaN values to fill bottom NaNs along the depth
         dimension.
diff --git a/roms_tools/fill.py b/roms_tools/fill.py
index d16f96fbc..4c724d233 100644
--- a/roms_tools/fill.py
+++ b/roms_tools/fill.py
@@ -370,6 +370,72 @@ def _check_dimension_match(dims: tuple[str, str], da: xr.DataArray):
         )
 
 
+def nearest_neighbor_fill(
+    da: xr.DataArray, mask: xr.DataArray, dims: tuple[str, str]
+) -> xr.DataArray:
+    """Fill masked (land/NaN) cells with the value of the nearest valid cell.
+
+    This is a cheap alternative to the iterative Poisson :class:`LateralFill`.
+    Using a static 2D ocean ``mask``, it computes the index of the nearest valid
+    (ocean) cell for every grid cell once via
+    :func:`scipy.ndimage.distance_transform_edt`, then remaps every cell to that
+    nearest valid value. Valid cells map to themselves (distance 0) and are left
+    unchanged; masked cells take the nearest valid neighbor's value. The same
+    index map is applied to every horizontal slice (depth/time), so the cost is
+    a single distance transform regardless of the number of slices.
+
+    This guarantees a NaN-free field (as long as at least one valid cell exists),
+    so a subsequent linear interpolation cannot propagate NaNs.
+
+    Parameters
+    ----------
+    da : xarray.DataArray
+        Input DataArray to fill. Must contain both dimensions in ``dims``.
+    mask : xarray.DataArray
+        2D boolean/integer ocean mask (True/1 = valid, False/0 = masked) with
+        dimensions matching ``dims``.
+    dims : tuple of str
+        The two horizontal dimensions, ordered ``(latitude, longitude)``.
+
+    Returns
+    -------
+    xarray.DataArray
+        A new DataArray with masked cells filled by nearest-neighbor lookup.
+    """
+    from scipy.ndimage import distance_transform_edt
+
+    _check_dimension_match(dims, da)
+
+    mask_bool = mask.transpose(*dims).values.astype(bool)
+
+    # Index of the nearest valid (True) cell for every cell in the grid.
+    ii, jj = distance_transform_edt(
+        ~mask_bool, return_distances=False, return_indices=True
+    )
+
+    def _fill(arr):
+        # arr has the core dims (lat, lon) as its trailing axes; advanced
+        # indexing with the 2D index arrays broadcasts over any leading dims.
+        return arr[..., ii, jj]
+
+    filled = xr.apply_ufunc(
+        _fill,
+        da,
+        input_core_dims=[list(dims)],
+        output_core_dims=[list(dims)],
+        output_dtypes=[da.dtype],
+        dask="parallelized",
+        dask_gufunc_kwargs={
+            "output_sizes": {
+                dims[0]: mask_bool.shape[0],
+                dims[1]: mask_bool.shape[1],
+            }
+        },
+    )
+
+    return filled.transpose(*da.dims)
+
+
 def one_dim_fill(da: xr.DataArray, dim: str, direction="forward") -> xr.DataArray:
     """Fill NaN values in a DataArray along a specified dimension.
 
diff --git a/roms_tools/regrid.py b/roms_tools/regrid.py
index 1a9e56b9f..8c2f16c20 100644
--- a/roms_tools/regrid.py
+++ b/roms_tools/regrid.py
@@ -1,14 +1,78 @@
 import warnings
 
+import numpy as np
 import xarray as xr
 import xgcm
 
 
+def _xesmf_extrap_kwargs(method: str | None, kwargs: dict | None) -> dict:
+    """Map friendly extrapolation kwargs to ``xe.Regridder`` ``extrap_*`` kwargs.
+
+    Both the destination-side extrapolation in :class:`LateralRegridToROMS` and
+    the source-on-source fill in ``LatLonDataset.apply_xesmf_source_fill`` accept the
+    same user-facing keys; this centralizes the translation to xESMF's
+    ``extrap_num_levels`` / ``extrap_num_src_pnts`` / ``extrap_dist_exponent``.
+
+    Parameters
+    ----------
+    method : str or None
+        The xESMF extrapolation method (``"inverse_dist"``, ``"nearest_s2d"``,
+        ``"creep_fill"``). ``None`` returns an empty mapping.
+    kwargs : dict or None
+        User-facing kwargs: ``num_levels`` (creep_fill), ``num_src_pnts`` /
+        ``dist_exponent`` (inverse_dist). ``None``/missing values are skipped so
+        xESMF defaults apply.
+
+    Returns
+    -------
+    dict
+        Keyword arguments to splat into ``xe.Regridder(...)``.
+    """
+    kwargs = kwargs or {}
+    out: dict = {}
+    if method == "creep_fill":
+        if kwargs.get("num_levels") is not None:
+            out["extrap_num_levels"] = kwargs["num_levels"]
+    elif method == "inverse_dist":
+        if kwargs.get("num_src_pnts") is not None:
+            out["extrap_num_src_pnts"] = kwargs["num_src_pnts"]
+        if kwargs.get("dist_exponent") is not None:
+            out["extrap_dist_exponent"] = kwargs["dist_exponent"]
+    return out
+
+
 class LateralRegridToROMS:
-    """Handles lateral regridding of data onto a new spatial grid."""
+    """Handles lateral regridding of data onto a ROMS grid.
+
+    Two modes are supported:
+
+    * **scipy interpolation (default, ``use_xesmf=False``):** uses
+      :meth:`xarray.DataArray.interp` with the requested ``method``. This is the
+      historical behavior and requires no optional dependencies. The caller is
+      responsible for filling NaNs in the source beforehand (otherwise NaNs
+      propagate into the regridded field).
+    * **xESMF bilinear (``use_xesmf=True``):** uses ``xesmf`` bilinear
+      interpolation with an ``extrap_method`` (default ``"inverse_dist"``) to
+      fill targets whose source neighbors are all masked/out of range, producing
+      NaN-free output without any separate fill step. When a ``source_mask`` is
+      supplied (for
+      sources that still contain land NaNs, e.g. GLORYS), it is applied as
+      ``mask_in`` so the bilinear weights are renormalized over only the valid
+      ocean cells; when omitted (for already NaN-free sources), plain bilinear
+      is used. Requires ``xesmf`` (install ``roms-tools`` via conda).
+    """
 
-    def __init__(self, target_coords, source_dim_names):
-        """Initialize target grid coordinates and names for grid dimensions.
+    def __init__(
+        self,
+        target_coords,
+        source_dim_names,
+        source_ds=None,
+        use_xesmf=False,
+        source_mask=None,
+        extrap_method="inverse_dist",
+        extrap_kwargs=None,
+    ):
+        """Initialize the regridder.
 
         Parameters
         ----------
@@ -17,38 +81,154 @@ def __init__(self, target_coords, source_dim_names):
             the longitude and latitude values of the target grid.
         source_dim_names : dict
             Dictionary specifying names for the latitude and longitude dimensions,
-            typically using keys like "latitude" and "longitude" to align with the dataset conventions.
+            typically using keys "latitude" and "longitude".
+        source_ds : xarray.Dataset, optional
+            The source dataset. Required when ``use_xesmf=True`` (used for the
+            source latitude/longitude coordinates).
+        use_xesmf : bool, optional
+            If True, use xESMF masked bilinear regridding with nearest-neighbor
+            extrapolation. If False (default), use scipy interpolation via
+            :meth:`xarray.DataArray.interp`.
+        source_mask : xarray.DataArray, optional
+            A 2D source ocean mask (1 = valid/ocean, 0 = masked/land) with the
+            source latitude/longitude dimensions, used as ``mask_in`` in the
+            xESMF path so bilinear weights are renormalized over valid ocean
+            cells. Pass the field-appropriate mask (e.g. ``ds["mask"]`` for
+            tracers, ``ds["mask_vel"]`` for velocities) for sources that still
+            contain land NaNs. When omitted, plain (unmasked) bilinear is used —
+            appropriate for sources that are already NaN-free (e.g. a source
+            laterally filled during preprocessing).
+        extrap_method : str, optional
+            xESMF extrapolation method used to fill target points whose source
+            neighbors are all masked/out of range, guaranteeing NaN-free output.
+            Options are ``"inverse_dist"`` (default; inverse-distance-weighted
+            average of the nearest source points, giving smoothly varying values)
+            and ``"nearest_s2d"`` (single nearest source point). Only used in the
+            xESMF path. Pass ``None`` to disable extrapolation (plain bilinear),
+            e.g. when the source has already been pre-filled and is NaN-free.
+        extrap_kwargs : dict, optional
+            Method-specific tuning for ``extrap_method``: ``num_src_pnts`` /
+            ``dist_exponent`` for ``"inverse_dist"``, ``num_levels`` for
+            ``"creep_fill"``. Translated to xESMF's ``extrap_*`` kwargs via
+            :func:`_xesmf_extrap_kwargs`. Only used in the xESMF path.
 
-        Attributes
-        ----------
-        coords : dict
-            Maps the dimension names to the corresponding latitude and longitude
-            DataArrays, providing easy access to target grid coordinates.
+        Raises
+        ------
+        ImportError
+            If ``use_xesmf=True`` but xESMF is not installed.
+        ValueError
+            If ``use_xesmf=True`` but ``source_ds`` is not provided.
         """
-        self.coords = {
-            source_dim_names["latitude"]: target_coords["lat"],
-            source_dim_names["longitude"]: target_coords["lon"],
-        }
+        self.use_xesmf = use_xesmf
+
+        if not use_xesmf:
+            # Backward-compatible scipy interpolation path.
+            self.coords = {
+                source_dim_names["latitude"]: target_coords["lat"],
+                source_dim_names["longitude"]: target_coords["lon"],
+            }
+            return
+
+        if source_ds is None:
+            raise ValueError(
+                "source_ds must be provided when use_xesmf=True (needed for the "
+                "source latitude/longitude coordinates)."
+            )
+        try:
+            import xesmf as xe
+        except ImportError:
+            raise ImportError(
+                "xesmf is required for masked regridding. Please install `roms-tools` via conda, which includes xesmf."
+            )
+
+        lat_name = source_dim_names["latitude"]
+        lon_name = source_dim_names["longitude"]
+        source_lat = np.asarray(source_ds[lat_name].values)
+        source_lon = np.asarray(source_ds[lon_name].values)
+
+        ds_in = xr.Dataset(
+            {
+                "lat": xr.DataArray(source_lat, dims="lat"),
+                "lon": xr.DataArray(source_lon, dims="lon"),
+            }
+        )
+
+        # xESMF reads masking from a variable named "mask" in the input dataset
+        # (1 = valid, 0 = masked); masked cells are excluded and the bilinear
+        # weights are renormalized over the remaining valid cells. We add it only
+        # when the caller supplies a ``source_mask`` (sources that still contain
+        # land NaNs, e.g. GLORYS/CESM). Sources that are already NaN-free (e.g.
+        # the pre-filled UNIFIED BGC dataset) pass ``source_mask=None`` and use
+        # plain bilinear, which is equivalent to an all-ocean mask.
+        if source_mask is not None:
+            # Rebuild as a bare (lat, lon) DataArray so its coordinates can't
+            # conflict with the explicit lat/lon axes assigned to ``ds_in``.
+            ds_in["mask"] = xr.DataArray(
+                source_mask.transpose(lat_name, lon_name).values.astype(np.int32),
+                dims=("lat", "lon"),
+            )
+
+        # A 1D target (e.g. a ROMS boundary line where lat and lon share a single
+        # dimension) is a line of points. We do NOT use ``locstream_out=True``
+        # here: xESMF silently ignores ``extrap_method`` for locstream output, so
+        # boundary points whose source neighbors are all masked would be left as
+        # NaN. Instead we promote the line to a (1, N) structured grid (adding a
+        # singleton ``_dummy_y`` dimension), which keeps nearest-neighbor
+        # extrapolation working, and squeeze that dimension back out in ``apply``.
+        # A 2D (curvilinear) target is already a structured grid.
+        self._dummy_dim = None
+        ds_out = xr.Dataset()
+        if target_coords["lat"].ndim == 1:
+            self._dummy_dim = "_dummy_y"
+            line_dim = target_coords["lat"].dims[0]
+            ds_out["lat"] = xr.DataArray(
+                target_coords["lat"].values[None, :], dims=(self._dummy_dim, line_dim)
+            )
+            ds_out["lon"] = xr.DataArray(
+                target_coords["lon"].values[None, :], dims=(self._dummy_dim, line_dim)
+            )
+        else:
+            ds_out["lat"] = target_coords["lat"]
+            ds_out["lon"] = target_coords["lon"]
+
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=UserWarning, module="xesmf")
+            self.regridder = xe.Regridder(
+                ds_in,
+                ds_out,
+                method="bilinear",
+                unmapped_to_nan=True,
+                extrap_method=extrap_method,
+                **_xesmf_extrap_kwargs(extrap_method, extrap_kwargs),
+            )
 
     def apply(self, da, method="linear"):
-        """Fills missing values and regrids the variable.
+        """Regrid the provided data array.
 
         Parameters
         ----------
         da : xarray.DataArray
-            Input data to fill and regrid.
-        method : str
-            Interpolation method to use.
+            The data array to regrid.
+        method : str, optional
+            Interpolation method for the scipy path. Ignored when
+            ``use_xesmf=True``. Default is "linear".
 
         Returns
         -------
         xarray.DataArray
-            Regridded data with filled values.
+            The regridded data array.
         """
-        regridded = da.interp(self.coords, method=method).drop_vars(
-            list(self.coords.keys())
-        )
-        return regridded
+        if self.use_xesmf:
+            with warnings.catch_warnings():
+                warnings.filterwarnings("ignore", category=UserWarning, module="xesmf")
+                regridded = self.regridder(da, keep_attrs=True)
+            if self._dummy_dim is not None:
+                # Remove the singleton dimension added to promote a 1D boundary
+                # line to a (1, N) structured target grid.
+                regridded = regridded.isel({self._dummy_dim: 0})
+            return regridded
+
+        return da.interp(self.coords, method=method).drop_vars(list(self.coords.keys()))
 
 
 class LateralRegridFromROMS:
diff --git a/roms_tools/setup/boundary_forcing.py b/roms_tools/setup/boundary_forcing.py
index 69f0cfaf4..862a9424f 100644
--- a/roms_tools/setup/boundary_forcing.py
+++ b/roms_tools/setup/boundary_forcing.py
@@ -1,14 +1,15 @@
 import importlib.metadata
 import logging
+import warnings
 from collections import defaultdict
 from dataclasses import dataclass, field
 from datetime import datetime
 from pathlib import Path
+from typing import ClassVar
 
 import matplotlib.pyplot as plt
 import numpy as np
 import xarray as xr
-from scipy.ndimage import label
 
 from roms_tools import Grid
 from roms_tools.datasets.lat_lon_datasets import (
@@ -17,11 +18,14 @@
     GLORYSDefaultDataset,
     UnifiedBGCDataset,
 )
-from roms_tools.fill import one_dim_fill
 from roms_tools.plot import line_plot, section_plot
-from roms_tools.regrid import LateralRegridToROMS, VerticalRegrid
+from roms_tools.regrid import (
+    LateralRegridToROMS,
+    VerticalRegrid,
+)
 from roms_tools.setup.utils import (
     RawDataSource,
+    _xesmf_available,
     add_time_info_to_ds,
     check_and_set_boundaries,
     compute_barotropic_velocity,
@@ -32,8 +36,11 @@
     get_variable_metadata,
     group_dataset,
     nan_check,
+    resolve_regrid_engine,
     substitute_nans_by_fillvalue,
     to_dict,
+    validate_extrap,
+    validate_prefill,
     write_to_yaml,
 )
 from roms_tools.utils import (
@@ -84,10 +91,60 @@ class BoundaryForcing:
           - "physics": for physical atmospheric forcing.
           - "bgc": for biogeochemical forcing.
 
+    prefill : str or None, optional
+        How to fill NaN (land/void) cells in the *source* before regridding. The
+        default (``None``) applies **no** source prefill: with xESMF installed,
+        masked bilinear interpolation plus destination extrapolation
+        (``extrap_method``) produces NaN-free boundaries directly; without xESMF,
+        the source is automatically pre-filled with a cheap nearest-neighbor fill
+        before scipy interpolation. Set ``prefill`` to fill the whole-domain
+        source first (the regrid is then plain bilinear and ``extrap_method`` is
+        ignored). Options:
+
+          - ``"2d_lateral_fill"`` -- legacy AMG Poisson fill (smoothest, slow;
+            no xESMF required). This is the modern spelling of the deprecated
+            ``apply_2d_horizontal_fill=True``.
+          - ``"inverse_dist"`` -- xESMF inverse-distance-weighted source fill
+            (tunable via ``prefill_kwargs``; requires xESMF).
+          - ``"nearest_s2d"`` -- xESMF nearest-source fill (requires xESMF).
+          - ``"nearest_neighbor"`` -- cheap distance-transform fill (no xESMF;
+            also the automatic fallback when xESMF is unavailable). Use for
+            cross-platform reproducibility or when xESMF is unavailable and the
+            AMG fill is too slow; not recommended when xESMF is available.
+
+        Defaults to ``None``.
+    prefill_kwargs : dict, optional
+        Method-specific options for ``prefill``: ``num_src_pnts`` /
+        ``dist_exponent`` for ``"inverse_dist"``. Ignored by the other methods.
+        Defaults to ``None``.
+    regrid_method : str or None, optional
+        Horizontal regrid engine, chosen independently of ``prefill``:
+
+          - ``None`` / ``"auto"`` (default) -- use xESMF if it is installed
+            (lazy, weight-reused, faster on large grids), otherwise scipy.
+          - ``"xesmf"`` -- force the xESMF regridder (raises if xESMF is absent).
+          - ``"scipy"`` -- force scipy ``interp``. Byte-reproducible with pre-v4
+            outputs; when ``prefill`` is ``None`` a nearest-neighbor source
+            pre-fill is applied automatically so scipy cannot propagate NaNs.
+
+        Note that ``inverse_dist`` / ``nearest_s2d`` *prefills* still require xESMF
+        for the fill step regardless of ``regrid_method``. Defaults to ``None``.
+    extrap_method : str or None, optional
+        xESMF *destination* extrapolation used on the default path
+        (``prefill is None``) to fill boundary points whose source neighbors are
+        all land/out of range, guaranteeing NaN-free output. ``"inverse_dist"``
+        (the effective default) gives an inverse-distance-weighted average of the
+        nearest source points (smoothly varying); ``"nearest_s2d"`` uses the
+        single nearest source point. Ignored when ``prefill`` is set. Defaults to
+        ``None`` (treated as ``"inverse_dist"``).
+    extrap_kwargs : dict, optional
+        Method-specific options for ``extrap_method``: ``num_src_pnts`` /
+        ``dist_exponent`` for ``"inverse_dist"``. Defaults to ``None``.
     apply_2d_horizontal_fill : bool, optional
-        Indicates whether to perform a two-dimensional horizontal fill on the source data prior to regridding to boundaries.
-        If `False`, a one-dimensional horizontal fill is performed separately on each of the four regridded boundaries.
-        Defaults to `False`.
+        **Deprecated** -- use ``prefill`` instead. ``True`` maps to
+        ``prefill="2d_lateral_fill"`` and ``False`` to ``prefill=None``; setting
+        it emits a ``DeprecationWarning``. Cannot be combined with an explicit
+        ``prefill``. Defaults to ``None`` (unset).
     model_reference_date : datetime, optional
         Reference date for the model. Default is January 1, 2000.
     use_dask: bool, optional
@@ -130,9 +187,33 @@ class BoundaryForcing:
     """Dictionary specifying the source of the boundary forcing data."""
     type: str = "physics"
     """Specifies the type of forcing data ("physics", "bgc")."""
-    apply_2d_horizontal_fill: bool = False
-    """Whether to perform a two-dimensional horizontal fill on the source data prior to
-    regridding to boundaries."""
+    prefill: str | None = None
+    """Source-side fill applied before regridding (``None`` = no prefill, the default
+    NaN-aware masked-bilinear path). See the class docstring for the available methods."""
+    prefill_kwargs: dict | None = None
+    """Method-specific options for ``prefill`` (e.g. ``num_src_pnts``/``dist_exponent``)."""
+    regrid_method: str | None = None
+    """Horizontal regrid engine, independent of ``prefill``. ``None``/``"auto"`` uses xESMF
+    when installed (faster on large grids) and scipy otherwise; ``"xesmf"`` forces xESMF;
+    ``"scipy"`` forces scipy ``interp`` (byte-reproducible with pre-v4 outputs)."""
+    extrap_method: str | None = None
+    """xESMF destination extrapolation for the default path (``prefill is None``). ``None`` is
+    treated as ``"inverse_dist"``. Ignored when ``prefill`` is set."""
+    extrap_kwargs: dict | None = None
+    """Method-specific options for ``extrap_method`` (e.g. ``num_src_pnts``/``dist_exponent``)."""
+    apply_2d_horizontal_fill: bool | None = None
+    """Deprecated alias for ``prefill`` (sentinel ``None`` = unset). ``True`` ->
+    ``prefill="2d_lateral_fill"``, ``False`` -> ``prefill=None``; emits a DeprecationWarning."""
+
+    _ALLOWED_PREFILL: ClassVar[frozenset] = frozenset(
+        {
+            None,
+            "2d_lateral_fill",
+            "inverse_dist",
+            "nearest_s2d",
+            "nearest_neighbor",
+        }
+    )
     model_reference_date: datetime = datetime(2000, 1, 1)
     """Reference date for the model."""
     use_dask: bool = False
@@ -157,13 +238,48 @@ def __post_init__(self):
         self.adjust_depth_for_sea_surface_height = False
         self.ds_depth_coords = xr.Dataset()
 
+        self._resolve_prefill_options()
         self._input_checks()
 
         target_coords = get_target_coords(self.grid)
 
         data = self._get_data()
 
-        if self.apply_2d_horizontal_fill:
+        # Regrid engine is chosen independently of the prefill via
+        # ``regrid_method`` (resolved in _resolve_prefill_options):
+        #   - prefill is None + xESMF      : masked xESMF bilinear regrid + extrap (no fill)
+        #   - prefill is None + scipy      : nearest-neighbor pre-fill + scipy interp
+        #   - prefill set + xESMF          : whole-domain source fill, then plain
+        #                                    xESMF bilinear regrid (lazy, faster on large grids)
+        #   - prefill set + scipy          : whole-domain source fill, then scipy interp
+        # On a prefilled (NaN-free) source no mask or extrapolation is needed, so
+        # the xESMF regrid is plain bilinear.
+        prefill = self.prefill
+        use_xesmf = self._use_xesmf
+
+        # Effective destination extrapolation for the default (no-prefill) path.
+        effective_extrap = self.extrap_method or "inverse_dist"
+        user_set_extrap = (
+            self.extrap_method is not None or self.extrap_kwargs is not None
+        )
+        if user_set_extrap and prefill is not None:
+            logging.info(
+                "extrap_method/extrap_kwargs are ignored because prefill=%r fills "
+                "the source first (the regrid is plain bilinear).",
+                prefill,
+            )
+        elif user_set_extrap and not use_xesmf:
+            logging.info(
+                "extrap_method/extrap_kwargs are ignored because the scipy regrid "
+                "engine is in use (xESMF unavailable or regrid_method='scipy'); "
+                "the source is nearest-neighbor pre-filled instead."
+            )
+
+        if prefill is not None:
+            # Whole-domain source fill (parallels the legacy AMG path): gives the
+            # fill the same ocean context across the full footprint, not a thin
+            # per-boundary strip. After this the source is NaN-free, so each
+            # boundary is regridded with plain bilinear (no extrapolation).
             data.choose_subdomain(
                 target_coords,
                 unchunk_lateral_dims=True,
@@ -171,7 +287,11 @@ def __post_init__(self):
             # Enforce double precision to ensure reproducibility
             data.convert_to_float64()
             data.extrapolate_deepest_to_bottom()
-            data.apply_lateral_fill()
+            data.apply_prefill(
+                prefill,
+                prefill_kwargs=self.prefill_kwargs,
+                prefill_was_user_set=True,
+            )
 
         self._set_variable_info(data)
         self._set_boundary_info()
@@ -211,15 +331,53 @@ def __post_init__(self):
 
                 bdry_data = data.choose_subdomain(
                     bdry_target_coords,
+                    # TODO: make per-boundary buffer_points configurable.
                     buffer_points=3,
                     return_copy=True,
                     unchunk_lateral_dims=True,
                 )
 
-                if not self.apply_2d_horizontal_fill:
+                if prefill is None:
+                    # Default (no source prefill) path. Prep happens per boundary.
                     # Enforce double precision to ensure reproducibility
                     bdry_data.convert_to_float64()
                     bdry_data.extrapolate_deepest_to_bottom()
+                    if not use_xesmf:
+                        # xESMF unavailable: nearest-neighbor pre-fill the source so the
+                        # subsequent scipy interpolation cannot propagate NaNs.
+                        bdry_data.apply_nearest_neighbor_fill()
+                # When prefill is set, the whole-domain source was already filled
+                # (float64 + deepest-to-bottom + fill) before this loop.
+
+                # Precomputed static source masks for the xESMF masked-bilinear
+                # path, matched to the field type: ``mask`` (tracer validity) for
+                # tracers and ``zeta``, ``mask_vel`` (velocity validity) for u/v.
+                # Reusing these stored 2D fields avoids recomputing a mask from the
+                # full (lazy) source series. ``None`` means the source is already
+                # NaN-free (e.g. the pre-filled UNIFIED BGC dataset, which carries
+                # no mask, or a whole-domain prefill above) so the regridder uses
+                # plain bilinear; irrelevant on the scipy path.
+                if use_xesmf and prefill is None:
+                    tracer_mask = (
+                        bdry_data.ds["mask"]
+                        if "mask" in bdry_data.ds.data_vars
+                        else None
+                    )
+                    vector_mask = (
+                        bdry_data.ds["mask_vel"]
+                        if "mask_vel" in bdry_data.ds.data_vars
+                        else tracer_mask
+                    )
+                else:
+                    tracer_mask = None
+                    vector_mask = None
+
+                # With a prefilled (NaN-free) source, no regrid-time extrapolation
+                # is needed; use plain bilinear.
+                regrid_extrap_method = None if prefill is not None else effective_extrap
+                regrid_extrap_kwargs = (
+                    None if prefill is not None else self.extrap_kwargs
+                )
 
                 processed_fields = {}
 
@@ -239,18 +397,36 @@ def __post_init__(self):
                     lat = target_coords["lat"].isel(
                         **self.bdry_coords["vector"][direction]
                     )
-                    lateral_regrid = LateralRegridToROMS(
-                        {"lat": lat, "lon": lon}, bdry_data.dim_names
+                    lateral_regrid_vector = LateralRegridToROMS(
+                        {"lat": lat, "lon": lon},
+                        bdry_data.dim_names,
+                        source_ds=bdry_data.ds,
+                        use_xesmf=use_xesmf,
+                        source_mask=vector_mask,
+                        extrap_method=regrid_extrap_method,
+                        extrap_kwargs=regrid_extrap_kwargs,
                     )
                     for var_name in filtered_vars:
-                        processed_fields[var_name] = lateral_regrid.apply(
+                        processed_fields[var_name] = lateral_regrid_vector.apply(
                             bdry_data.ds[var_names[var_name]["name"]]
                         )
 
                     if self.adjust_depth_for_sea_surface_height:
                         # Regrid sea surface height ('zeta') onto a 2-cell-wide margin.
                         # This is needed to correctly infer depth coordinates at u- and v-points along the boundary.
-                        zeta_vector = lateral_regrid.apply(
+                        # 'zeta' is a tracer, so it uses the tracer mask (not the
+                        # velocity mask of the vector regridder); build a dedicated
+                        # regridder on the same vector-margin target.
+                        zeta_vector_regrid = LateralRegridToROMS(
+                            {"lat": lat, "lon": lon},
+                            bdry_data.dim_names,
+                            source_ds=bdry_data.ds,
+                            use_xesmf=use_xesmf,
+                            source_mask=tracer_mask,
+                            extrap_method=regrid_extrap_method,
+                            extrap_kwargs=regrid_extrap_kwargs,
+                        )
+                        zeta_vector = zeta_vector_regrid.apply(
                             bdry_data.ds[var_names["zeta"]["name"]]
                         )
 
@@ -270,7 +446,13 @@ def __post_init__(self):
                         **self.bdry_coords["rho"][direction]
                     )
                     lateral_regrid = LateralRegridToROMS(
-                        {"lat": lat, "lon": lon}, bdry_data.dim_names
+                        {"lat": lat, "lon": lon},
+                        bdry_data.dim_names,
+                        source_ds=bdry_data.ds,
+                        use_xesmf=use_xesmf,
+                        source_mask=tracer_mask,
+                        extrap_method=regrid_extrap_method,
+                        extrap_kwargs=regrid_extrap_kwargs,
                     )
                     for var_name in filtered_vars:
                         processed_fields[var_name] = lateral_regrid.apply(
@@ -307,21 +489,6 @@ def __post_init__(self):
                     zeta_u = zeta_u.isel(**self.bdry_coords["u"][direction])
                     zeta_v = zeta_v.isel(**self.bdry_coords["v"][direction])
 
-                if not self.apply_2d_horizontal_fill and bdry_data.needs_lateral_fill:
-                    if not self.bypass_validation:
-                        self._validate_1d_fill(
-                            processed_fields,
-                            direction,
-                            bdry_data.dim_names["depth"],
-                        )
-                    for var_name in processed_fields:
-                        processed_fields[var_name] = apply_1d_horizontal_fill(
-                            processed_fields[var_name]
-                        )
-                    if self.adjust_depth_for_sea_surface_height:
-                        zeta_u = apply_1d_horizontal_fill(zeta_u)
-                        zeta_v = apply_1d_horizontal_fill(zeta_v)
-
                 if self.adjust_depth_for_sea_surface_height:
                     zeta = processed_fields["zeta"]
                 else:
@@ -403,6 +570,44 @@ def __post_init__(self):
 
         self.ds = ds
 
+    def _resolve_prefill_options(self) -> None:
+        """Map the deprecated flag and validate the prefill + regrid options.
+
+        ``apply_2d_horizontal_fill`` is a deprecated alias for ``prefill``:
+        ``True`` -> ``prefill="2d_lateral_fill"``, ``False`` -> ``prefill=None``.
+        After mapping, the deprecated attribute is reset to ``None`` so it is not
+        re-serialized. ``prefill``/``prefill_kwargs`` are then validated against
+        this class's allowed set and xESMF availability, and ``regrid_method`` is
+        resolved to the boolean ``self._use_xesmf``.
+        """
+        if self.apply_2d_horizontal_fill is not None:
+            warnings.warn(
+                "`apply_2d_horizontal_fill` is deprecated; use `prefill` instead "
+                "(True -> prefill='2d_lateral_fill', False -> prefill=None).",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+            if self.prefill is not None:
+                raise ValueError(
+                    "Set either the deprecated `apply_2d_horizontal_fill` or "
+                    "`prefill`, not both."
+                )
+            self.prefill = "2d_lateral_fill" if self.apply_2d_horizontal_fill else None
+            # Reset the sentinel so it is not re-serialized going forward.
+            self.apply_2d_horizontal_fill = None
+
+        xesmf_available = _xesmf_available()
+        validate_prefill(
+            self.prefill,
+            self.prefill_kwargs,
+            self._ALLOWED_PREFILL,
+            xesmf_available=xesmf_available,
+        )
+        validate_extrap(self.extrap_method, self.extrap_kwargs)
+        self._use_xesmf = resolve_regrid_engine(
+            self.regrid_method, xesmf_available=xesmf_available
+        )
+
     def _input_checks(self) -> None:
         """Validate and normalize user-provided input parameters."""
         # -------------------------------------------------------
@@ -757,7 +962,9 @@ def _add_global_metadata(self, data, ds=None):
         ds.attrs["end_time"] = str(self.end_time)
         ds.attrs["source"] = self.source["name"]
         ds.attrs["model_reference_date"] = str(self.model_reference_date)
-        ds.attrs["apply_2d_horizontal_fill"] = str(self.apply_2d_horizontal_fill)
+        ds.attrs["prefill"] = str(self.prefill)
+        ds.attrs["regrid_method"] = "xesmf" if self._use_xesmf else "scipy"
+        ds.attrs["extrap_method"] = str(self.extrap_method or "inverse_dist")
         ds.attrs["adjust_depth_for_sea_surface_height"] = str(
             self.adjust_depth_for_sea_surface_height
         )
@@ -776,68 +983,6 @@ def _add_global_metadata(self, data, ds=None):
 
         return ds
 
-    def _validate_1d_fill(self, processed_fields, direction, depth_dim):
-        """Check if any boundary is divided by land and issue a warning if so,
-        suggesting the use of 2D horizontal fill for safer regridding.
-
-        Parameters
-        ----------
-        processed_fields : dict
-            A dictionary where keys are variable names and values are `xarray.DataArray`
-            objects representing the processed data for each variable.
-
-        direction : str
-            The boundary direction being processed (e.g., "north", "south", "east", or "west").
-
-        depth_dim : str
-            The dimension representing depth (e.g., 'z', 'depth', etc.), used when slicing 3D
-            data for a specific depth level.
-
-        Returns
-        -------
-        None
-            If a boundary is divided by land, a warning is issued. No return value is provided.
-        """
-        if not hasattr(self, "_warned_directions"):
-            self._warned_directions = set()
-
-        for var_name in processed_fields.keys():
-            if self.variable_info[var_name]["validate"]:
-                location = self.variable_info[var_name]["location"]
-
-                # Select the appropriate mask based on variable location
-                if location == "rho":
-                    mask = self.grid.ds.mask_rho
-                elif location == "u":
-                    mask = self.grid.ds.mask_u
-                elif location == "v":
-                    mask = self.grid.ds.mask_v
-
-                mask = mask.isel(**self.bdry_coords[location][direction])
-
-                if self.variable_info[var_name]["is_3d"]:
-                    da = processed_fields[var_name].isel({depth_dim: 0, "time": 0})
-                else:
-                    da = processed_fields[var_name].isel({"time": 0})
-
-                wet_nans = xr.where(da.where(mask).isnull(), 1, 0)
-                # Apply label to find connected components of wet NaNs
-                labeled_array, num_features = label(wet_nans)
-
-                left_margin = labeled_array[0]
-                right_margin = labeled_array[-1]
-                if left_margin != 0:
-                    num_features = num_features - 1
-                if right_margin != 0:
-                    num_features = num_features - 1
-
-                if num_features > 0 and direction not in self._warned_directions:
-                    logging.warning(
-                        f"The {direction}ern boundary is divided by land. "
-                        "It would be safer (but slower and more memory-intensive) to use `apply_2d_horizontal_fill = True`."
-                    )
-                    self._warned_directions.add(direction)
-
     def _validate(self, ds):
         """Validate the dataset for NaN values at the first time step (bry_time=0) for
         specified variables. If NaN values are found at wet points, this function raises
@@ -876,15 +1021,14 @@ def _validate(self, ds):
                         bdry_var_name = f"{var_name}_{direction}"
 
                         # Check for NaN values at the first time step using the nan_check function
-                        if self.apply_2d_horizontal_fill:
-                            error_message = None
-                        else:
-                            error_message = (
-                                f"{bdry_var_name} consists entirely of NaNs after regridding. "
-                                f"This may be due to the {direction}ern boundary being on land in the "
-                                f"{self.source['name']} data, which could have a coarser resolution than the ROMS domain. "
-                                f"Try setting `apply_2d_horizontal_fill = True` to resolve this issue."
-                            )
+                        error_message = (
+                            f"{bdry_var_name} consists entirely of NaNs after regridding. "
+                            f"This may be due to the {direction}ern boundary being entirely on land in the "
+                            f"{self.source['name']} data, which could have a coarser resolution than the ROMS domain. "
+                            f"Try setting a `prefill` method (e.g. 'inverse_dist', 'nearest_neighbor', or "
+                            f"'2d_lateral_fill') to fill the source before regridding; see "
+                            f"https://roms-tools.readthedocs.io/en/latest/boundary_forcing.html for details."
+                        )
 
                         nan_check(
                             ds[bdry_var_name].isel(bry_time=0),
@@ -1091,6 +1235,9 @@ def to_yaml(self, filepath: str | Path) -> None:
                 "ds_depth_coords",
                 "adjust_depth_for_sea_surface_height",
                 "use_dask",
+                # Deprecated alias: superseded by ``prefill``. Emit only ``prefill``
+                # going forward (old YAML setting it still loads via __init__).
+                "apply_2d_horizontal_fill",
             ],
         )
         write_to_yaml(forcing_dict, filepath)
@@ -1122,44 +1269,3 @@ def from_yaml(
 
         # Create and return an instance of InitialConditions
         return cls(grid=grid, **params, use_dask=use_dask)
-
-
-def apply_1d_horizontal_fill(data_array: xr.DataArray) -> xr.DataArray:
-    """Forward and backward fill NaN values in a single horizontal dimension for open
-    boundaries.
-
-    Parameters
-    ----------
-    data_array : xarray.DataArray
-        The data array to be updated.
-
-    Returns
-    -------
-    xarray.DataArray
-        The updated data array with NaN values filled.
-
-    Raises
-    ------
-    ValueError
-        If more than one horizontal dimension is found or none at all.
-    """
-    horizontal_dims = ["eta_rho", "eta_v", "xi_rho", "xi_u"]
-    selected_horizontal_dim = None
-
-    # Determine the horizontal dimension to fill
-    for dim in horizontal_dims:
-        if dim in data_array.dims:
-            if selected_horizontal_dim is not None:
-                raise ValueError(
-                    f"More than one horizontal dimension found in variable '{data_array.name}'."
-                )
-            selected_horizontal_dim = dim
-
-    if selected_horizontal_dim is None:
-        raise ValueError(
-            f"No valid horizontal dimension found for variable '{data_array.name}'."
-        )
-
-    # Forward and backward fill in the horizontal direction
-    filled = one_dim_fill(data_array, selected_horizontal_dim, direction="forward")
-    return one_dim_fill(filled, selected_horizontal_dim, direction="backward")
diff --git a/roms_tools/setup/utils.py b/roms_tools/setup/utils.py
index f608b78f4..afeaff82b 100644
--- a/roms_tools/setup/utils.py
+++ b/roms_tools/setup/utils.py
@@ -33,6 +33,190 @@
 RawDataSource: TypeAlias = dict[str, str | Path | list[str | Path] | bool]
 
 
+# ---------------------------------------------------------------------------
+# Source prefill / lateral-regrid configuration (shared across setup classes)
+# ---------------------------------------------------------------------------
+
+#: Per-method allowed keys for ``prefill_kwargs`` / ``extrap_kwargs``. A method
+#: not listed here (e.g. ``None``) accepts no kwargs.
+PREFILL_ALLOWED_KWARGS: dict[str, frozenset[str]] = {
+    "2d_lateral_fill": frozenset(),
+    "nearest_neighbor": frozenset(),
+    "nearest_s2d": frozenset(),
+    "inverse_dist": frozenset({"num_src_pnts", "dist_exponent"}),
+    "creep_fill": frozenset({"num_levels"}),
+}
+
+#: Prefill methods that are implemented via an xESMF source-on-source regrid and
+#: therefore require the optional ``xesmf`` dependency.
+XESMF_PREFILL_METHODS: frozenset[str] = frozenset(
+    {"inverse_dist", "nearest_s2d", "creep_fill"}
+)
+
+#: Valid xESMF destination-extrapolation methods (``extrap_method``). ``None`` means
+#: "use the default", which callers treat as ``"inverse_dist"``.
+EXTRAP_METHODS: frozenset[str] = frozenset(
+    {"inverse_dist", "nearest_s2d", "creep_fill"}
+)
+
+
+def validate_extrap(extrap_method: str | None, extrap_kwargs: dict | None) -> None:
+    """Validate a destination ``extrap_method`` and its keyword arguments.
+
+    Parameters
+    ----------
+    extrap_method : str or None
+        The xESMF destination-extrapolation method. ``None`` is treated as the
+        default ``"inverse_dist"``.
+    extrap_kwargs : dict or None
+        Method-specific keyword arguments, validated against the chosen method
+        using the same table as ``prefill_kwargs`` (``PREFILL_ALLOWED_KWARGS``).
+
+    Raises
+    ------
+    ValueError
+        If ``extrap_method`` is unrecognized or ``extrap_kwargs`` contains keys
+        the chosen method does not accept.
+    """
+    if extrap_method is not None and extrap_method not in EXTRAP_METHODS:
+        allowed = ", ".join(repr(m) for m in sorted(EXTRAP_METHODS))
+        raise ValueError(
+            f"extrap_method={extrap_method!r} is not supported. Allowed values: "
+            f"{allowed} (or None for the default 'inverse_dist')."
+        )
+    method = extrap_method or "inverse_dist"
+    allowed_keys = PREFILL_ALLOWED_KWARGS.get(method, frozenset())
+    extra = set(extrap_kwargs or {}) - allowed_keys
+    if extra:
+        allowed_keys_str = ", ".join(sorted(allowed_keys)) or "(none)"
+        raise ValueError(
+            f"extrap_kwargs {sorted(extra)} are not valid for "
+            f"extrap_method={method!r}. Allowed keys: {allowed_keys_str}."
+        )
+
+
+#: Allowed values for the ``regrid_method`` selector.
+REGRID_METHODS: frozenset[str] = frozenset({"auto", "xesmf", "scipy"})
+
+
+def _xesmf_available() -> bool:
+    """Return True if the optional xESMF dependency can be imported."""
+    try:
+        import xesmf  # noqa: F401
+
+        return True
+    except ImportError:
+        return False
+
+
+def resolve_regrid_engine(regrid_method: str | None, *, xesmf_available: bool) -> bool:
+    """Resolve a ``regrid_method`` selector to a boolean "use xESMF" decision.
+
+    Parameters
+    ----------
+    regrid_method : str or None
+        ``None`` or ``"auto"`` -> xESMF if available, else scipy; ``"xesmf"`` ->
+        force xESMF (error if unavailable); ``"scipy"`` -> force scipy.
+    xesmf_available : bool
+        Whether xESMF can be imported.
+
+    Returns
+    -------
+    bool
+        ``True`` to use the xESMF regridder, ``False`` to use scipy ``interp``.
+
+    Raises
+    ------
+    ValueError
+        If ``regrid_method`` is not a recognized value.
+    ImportError
+        If ``regrid_method="xesmf"`` but xESMF is not installed.
+    """
+    method = regrid_method or "auto"
+    if method not in REGRID_METHODS:
+        allowed = ", ".join(repr(m) for m in sorted(REGRID_METHODS))
+        raise ValueError(
+            f"regrid_method={regrid_method!r} is not supported. Allowed values: "
+            f"{allowed} (or None for 'auto')."
+        )
+    if method == "scipy":
+        return False
+    if method == "xesmf":
+        if not xesmf_available:
+            raise ImportError(
+                "regrid_method='xesmf' requires the optional xESMF dependency, "
+                "which is not installed. Install `roms-tools` via conda, or use "
+                "regrid_method='scipy' (or 'auto')."
+            )
+        return True
+    # auto
+    return xesmf_available
+
+
+def validate_prefill(
+    prefill: str | None,
+    prefill_kwargs: dict | None,
+    allowed: "frozenset[str | None] | set[str | None]",
+    *,
+    xesmf_available: bool,
+) -> None:
+    """Validate a ``prefill`` selection and its keyword arguments.
+
+    Parameters
+    ----------
+    prefill : str or None
+        The requested source-prefill method (``None`` means "no prefill").
+    prefill_kwargs : dict or None
+        Method-specific keyword arguments to validate against the chosen method.
+    allowed : set or frozenset of (str or None)
+        The prefill values this class accepts (e.g. ``{None, "2d_lateral_fill",
+        ...}`` for ``BoundaryForcing``; ``{"2d_lateral_fill"}`` for classes that
+        only support the legacy fill).
+    xesmf_available : bool
+        Whether xESMF can be imported. Used to reject xESMF-only prefill methods
+        on installs without it.
+
+    Raises
+    ------
+    ValueError
+        If ``prefill`` is not in ``allowed`` or ``prefill_kwargs`` contains keys
+        the chosen method does not accept.
+    ImportError
+        If ``prefill`` is an xESMF-only method but xESMF is not installed.
+    """
+    if prefill not in allowed:
+        allowed_str = ", ".join(
+            repr(a) for a in sorted(allowed, key=lambda x: (x is not None, x or ""))
+        )
+        raise ValueError(
+            f"prefill={prefill!r} is not supported here. Allowed values: {allowed_str}."
+        )
+
+    if prefill is None:
+        if prefill_kwargs:
+            raise ValueError(
+                "prefill_kwargs were provided but prefill is None (no prefill). "
+                "Set a prefill method or drop prefill_kwargs."
+            )
+        return
+
+    if prefill in XESMF_PREFILL_METHODS and not xesmf_available:
+        raise ImportError(
+            f"prefill={prefill!r} requires the optional xESMF dependency, which is "
+            "not installed. Install `roms-tools` via conda, or use "
+            "prefill='nearest_neighbor' (cheap, no xESMF) or prefill='2d_lateral_fill'."
+        )
+
+    allowed_keys = PREFILL_ALLOWED_KWARGS.get(prefill, frozenset())
+    extra = set(prefill_kwargs or {}) - allowed_keys
+    if extra:
+        allowed_keys_str = ", ".join(sorted(allowed_keys)) or "(none)"
+        raise ValueError(
+            f"prefill_kwargs {sorted(extra)} are not valid for prefill={prefill!r}. "
+            f"Allowed keys: {allowed_keys_str}."
+        )
+
+
 def log_the_separator() -> None:
     """Log a separator line using HEADER_CHAR repeated HEADER_SZ times."""
     logging.info(HEADER_CHAR * HEADER_SZ)
diff --git a/roms_tools/tests/test_fill.py b/roms_tools/tests/test_fill.py
index b24c34f4f..bd374fd55 100644
--- a/roms_tools/tests/test_fill.py
+++ b/roms_tools/tests/test_fill.py
@@ -2,7 +2,7 @@
 import pytest
 import xarray as xr
 
-from roms_tools.fill import LateralFill
+from roms_tools.fill import LateralFill, nearest_neighbor_fill
 from roms_tools.utils import unchunk_dask
 
 
@@ -169,6 +169,46 @@ def test_apply_raises_if_var_dim_order_mismatch():
         lf.apply(var)
 
 
+def test_nearest_neighbor_fill_leaves_no_nans_and_preserves_valid():
+    # Single ocean cell in the top-right corner; everything else is land/NaN.
+    mask = xr.DataArray(
+        np.array([[0, 0, 0], [0, 0, 0], [0, 0, 1]], dtype=bool),
+        dims=("eta_rho", "xi_rho"),
+    )
+    data = np.full((3, 3), np.nan)
+    data[2, 2] = 7.0
+    var = xr.DataArray(data, dims=("eta_rho", "xi_rho"))
+
+    filled = nearest_neighbor_fill(var, mask, dims=("eta_rho", "xi_rho"))
+
+    # No NaNs remain, the valid cell is unchanged, and every cell takes the only
+    # valid value available.
+    assert not filled.isnull().any()
+    assert float(filled.sel(eta_rho=2, xi_rho=2)) == 7.0
+    assert np.allclose(filled.values, 7.0)
+
+
+def test_nearest_neighbor_fill_broadcasts_over_extra_dims():
+    # Two valid columns; nearest fill should copy column-wise across a depth dim.
+    mask = xr.DataArray(
+        np.array([[1, 0, 0], [1, 0, 0]], dtype=bool), dims=("eta_rho", "xi_rho")
+    )
+    # depth=2, eta_rho=2, xi_rho=3
+    arr = np.full((2, 2, 3), np.nan)
+    arr[:, :, 0] = np.array([[1.0, 2.0], [10.0, 20.0]]).T  # valid column values
+    var = xr.DataArray(arr, dims=("depth", "eta_rho", "xi_rho"))
+
+    filled = nearest_neighbor_fill(var, mask, dims=("eta_rho", "xi_rho"))
+
+    assert not filled.isnull().any()
+    assert filled.dims == ("depth", "eta_rho", "xi_rho")
+    # Masked columns (xi_rho 1, 2) take the value of the nearest valid column (0).
+    for j in (1, 2):
+        assert np.array_equal(
+            filled.isel(xi_rho=j).values, filled.isel(xi_rho=0).values
+        )
+
+
 def test_apply_raises_if_var_has_nans_on_valid_mask():
     # 2D mask
     mask = xr.DataArray(np.ones((3, 3), dtype=bool), dims=("eta_rho", "xi_rho"))
diff --git a/roms_tools/tests/test_setup/test_boundary_forcing.py b/roms_tools/tests/test_setup/test_boundary_forcing.py
index a79f4919f..76dbf8952 100644
--- a/roms_tools/tests/test_setup/test_boundary_forcing.py
+++ b/roms_tools/tests/test_setup/test_boundary_forcing.py
@@ -60,7 +60,7 @@ def test_boundary_forcing_creation(boundary_forcing_fixture, request):
     assert not hasattr(boundary_forcing.ds, "climatology")
     assert hasattr(boundary_forcing.ds, "adjust_depth_for_sea_surface_height")
     assert boundary_forcing.ds.attrs["adjust_depth_for_sea_surface_height"] == "False"
-    assert hasattr(boundary_forcing.ds, "apply_2d_horizontal_fill")
+    assert hasattr(boundary_forcing.ds, "prefill")
 
 
 def test_boundary_forcing_creation_with_duplicates(
@@ -77,7 +77,7 @@ def test_boundary_forcing_creation_with_duplicates(
         start_time=boundary_forcing.start_time,
         end_time=boundary_forcing.end_time,
         source={"name": "GLORYS", "path": [fname1, fname1, fname2]},
-        apply_2d_horizontal_fill=boundary_forcing.apply_2d_horizontal_fill,
+        prefill=boundary_forcing.prefill,
         use_dask=use_dask,
     )
 
@@ -150,7 +150,41 @@ def test_bgc_boundary_forcing_creation(boundary_forcing_fixture, request):
     assert hasattr(boundary_forcing.ds, "climatology")
 
 
-def test_unsuccessful_boundary_forcing_creation_with_1d_fill(use_dask):
+def _assert_no_nan_in_boundary_fields(boundary_forcing):
+    """Assert that no NaNs remain in any of the regridded boundary fields.
+
+    Successful construction (with ``bypass_validation=False``) already implies no
+    NaN at wet points, but the masked regrid / nearest-neighbor pre-fill paths
+    are designed to leave *no* NaNs at all along the boundary line, so we check
+    every boundary data variable directly.
+    """
+    suffixes = ("_south", "_north", "_east", "_west")
+    for var in boundary_forcing.ds.data_vars:
+        if str(var).endswith(suffixes):
+            n_nan = int(np.isnan(boundary_forcing.ds[var].values).sum())
+            assert n_nan == 0, f"{var} has {n_nan} NaN(s) after regridding"
+
+
+@pytest.mark.parametrize("force_scipy_fallback", [False, True])
+def test_boundary_forcing_creation_on_sparse_source(
+    use_dask, force_scipy_fallback, monkeypatch
+):
+    """A ROMS grid finer than (and partly outside) the source ocean still builds.
+
+    Historically this tiny grid raised ``"... consists entirely of NaNs"`` because
+    scipy linear interpolation left NaN holes that the (skipped) 1D fill could not
+    repair. The default now uses masked bilinear regridding with nearest-neighbor
+    extrapolation (xESMF) or a nearest-neighbor pre-fill + scipy interp fallback,
+    both of which produce NaN-free boundaries.
+
+    ``force_scipy_fallback=True`` simulates an environment without xESMF (e.g.
+    Windows/pip) to exercise the fallback path.
+    """
+    if force_scipy_fallback:
+        monkeypatch.setattr(
+            "roms_tools.setup.boundary_forcing._xesmf_available", lambda: False
+        )
+
     grid = Grid(
         nx=2,
         ny=2,
@@ -167,28 +201,28 @@ def test_unsuccessful_boundary_forcing_creation_with_1d_fill(use_dask):
 
     fname = Path(download_test_data("GLORYS_coarse_test_data.nc"))
 
-    with pytest.raises(ValueError, match="consists entirely of NaNs"):
-        BoundaryForcing(
-            grid=grid,
-            start_time=datetime(2021, 6, 29),
-            end_time=datetime(2021, 6, 30),
-            source={"name": "GLORYS", "path": fname},
-            apply_2d_horizontal_fill=False,
-            use_dask=use_dask,
-        )
+    bf = BoundaryForcing(
+        grid=grid,
+        start_time=datetime(2021, 6, 29),
+        end_time=datetime(2021, 6, 30),
+        source={"name": "GLORYS", "path": fname},
+        prefill=None,
+        use_dask=use_dask,
+    )
+    _assert_no_nan_in_boundary_fields(bf)
 
     fname_bgc = download_test_data("CESM_regional_coarse_test_data_climatology.nc")
 
-    with pytest.raises(ValueError, match="consists entirely of NaNs"):
-        BoundaryForcing(
-            grid=grid,
-            start_time=datetime(2021, 6, 29),
-            end_time=datetime(2021, 6, 30),
-            source={"path": fname_bgc, "name": "CESM_REGRIDDED", "climatology": True},
-            type="bgc",
-            apply_2d_horizontal_fill=False,
-            use_dask=use_dask,
-        )
+    bf_bgc = BoundaryForcing(
+        grid=grid,
+        start_time=datetime(2021, 6, 29),
+        end_time=datetime(2021, 6, 30),
+        source={"path": fname_bgc, "name": "CESM_REGRIDDED", "climatology": True},
+        type="bgc",
+        prefill=None,
+        use_dask=use_dask,
+    )
+    _assert_no_nan_in_boundary_fields(bf_bgc)
 
 
 def test_start_time_end_time_error(use_dask):
@@ -254,7 +288,16 @@ def test_start_time_end_time_warning(use_dask, caplog):
     )
 
 
-def test_boundary_divided_by_land_warning(caplog, use_dask):
+def test_boundary_divided_by_land_no_smearing(use_dask, monkeypatch):
+    """A boundary crossing a land barrier (Iceland) must not smear values across it.
+
+    The old 1D fill spread ocean values across the Iceland land barrier on the
+    western boundary and emitted a "divided by land" warning. The new default
+    (masked bilinear regrid) renormalizes weights over valid ocean cells, and the
+    scipy fallback nearest-neighbor pre-fills before interpolating; neither smears
+    across the barrier. We assert both paths build NaN-free boundaries and agree
+    closely with each other.
+    """
     # Iceland intersects the western boundary of the following grid
     grid = Grid(
         nx=5, ny=5, size_x=500, size_y=500, center_lon=-10, center_lat=65, rot=0
@@ -262,20 +305,36 @@ def test_boundary_divided_by_land_warning(caplog, use_dask):
 
     fname = Path(download_test_data("GLORYS_coarse_test_data.nc"))
 
-    with caplog.at_level(logging.WARNING):
-        BoundaryForcing(
-            grid=grid,
-            start_time=datetime(2021, 6, 29),
-            end_time=datetime(2021, 6, 30),
-            source={"path": fname, "name": "GLORYS", "climatology": False},
-            apply_2d_horizontal_fill=False,
-            use_dask=use_dask,
-        )
-    # Verify the warning message in the log
-    assert "divided by land" in caplog.text
+    kwargs = {
+        "grid": grid,
+        "start_time": datetime(2021, 6, 29),
+        "end_time": datetime(2021, 6, 30),
+        "source": {"path": fname, "name": "GLORYS", "climatology": False},
+        "prefill": None,
+        "use_dask": use_dask,
+    }
+
+    # Default (xESMF) path: masked weights ignore land source cells, so no
+    # smearing across Iceland; build must succeed with NaN-free boundaries.
+    bf_xesmf = BoundaryForcing(**kwargs)
+    _assert_no_nan_in_boundary_fields(bf_xesmf)
+
+    # scipy nearest-neighbor pre-fill fallback path: also NaN-free, no smearing.
+    monkeypatch.setattr(
+        "roms_tools.setup.boundary_forcing._xesmf_available", lambda: False
+    )
+    bf_fallback = BoundaryForcing(**kwargs)
+    _assert_no_nan_in_boundary_fields(bf_fallback)
 
 
-def test_1d_and_2d_fill_coincide_if_no_fill(use_dask):
+def test_1d_and_2d_fill_coincide_if_no_fill(use_dask, monkeypatch):
+    # Force the scipy fallback so both prefill settings use the same interpolation
+    # engine (scipy interp); when no fill is needed they must produce identical
+    # results.
+    monkeypatch.setattr(
+        "roms_tools.setup.boundary_forcing._xesmf_available", lambda: False
+    )
+
     grid = Grid(
         nx=2,
         ny=2,
@@ -304,17 +363,24 @@ def test_1d_and_2d_fill_coincide_if_no_fill(use_dask):
 
     bf_1d_fill = BoundaryForcing(
         **kwargs,
-        apply_2d_horizontal_fill=False,
+        prefill=None,
     )
     bf_2d_fill = BoundaryForcing(
         **kwargs,
-        apply_2d_horizontal_fill=True,
+        prefill="2d_lateral_fill",
     )
 
     xr.testing.assert_allclose(bf_1d_fill.ds, bf_2d_fill.ds, rtol=1.0e-4)
 
 
-def test_1d_and_2d_fill_coincide_if_no_land(use_dask):
+def test_1d_and_2d_fill_coincide_if_no_land(use_dask, monkeypatch):
+    # Force the scipy fallback so both prefill settings use the same interpolation
+    # engine (scipy interp); with no land in the subdomain neither fill does
+    # anything, so the results must coincide.
+    monkeypatch.setattr(
+        "roms_tools.setup.boundary_forcing._xesmf_available", lambda: False
+    )
+
     # this grid lies entirely over open ocean
     grid = Grid(nx=5, ny=5, size_x=300, size_y=300, center_lon=-5, center_lat=65, rot=0)
 
@@ -330,16 +396,180 @@ def test_1d_and_2d_fill_coincide_if_no_land(use_dask):
 
     bf_1d_fill = BoundaryForcing(
         **kwargs,
-        apply_2d_horizontal_fill=False,
+        prefill=None,
     )
     bf_2d_fill = BoundaryForcing(
         **kwargs,
-        apply_2d_horizontal_fill=True,
+        prefill="2d_lateral_fill",
     )
 
     xr.testing.assert_allclose(bf_1d_fill.ds, bf_2d_fill.ds, rtol=1.0e-4)
 
 
+def _coarse_glorys_kwargs(use_dask):
+    """Common kwargs for a small coarse-GLORYS BoundaryForcing (has land)."""
+    grid = Grid(
+        nx=5, ny=5, size_x=500, size_y=500, center_lon=-10, center_lat=65, rot=0
+    )
+    fname = Path(download_test_data("GLORYS_coarse_test_data.nc"))
+    return {
+        "grid": grid,
+        "start_time": datetime(2021, 6, 29),
+        "end_time": datetime(2021, 6, 30),
+        "source": {"path": fname, "name": "GLORYS", "climatology": False},
+        "use_dask": use_dask,
+    }
+
+
+@pytest.mark.parametrize(
+    "prefill",
+    ["2d_lateral_fill", "inverse_dist", "nearest_s2d", "nearest_neighbor"],
+)
+def test_prefill_methods_produce_nan_free_boundaries(use_dask, prefill):
+    """Every user-facing prefill method yields NaN-free boundaries."""
+    bf = BoundaryForcing(**_coarse_glorys_kwargs(use_dask), prefill=prefill)
+    _assert_no_nan_in_boundary_fields(bf)
+
+
+@pytest.mark.parametrize("regrid_method", ["auto", "xesmf", "scipy"])
+def test_regrid_method_produces_nan_free_boundaries(use_dask, regrid_method):
+    """The regrid engine can be chosen independently of prefill; all are NaN-free."""
+    bf = BoundaryForcing(**_coarse_glorys_kwargs(use_dask), regrid_method=regrid_method)
+    _assert_no_nan_in_boundary_fields(bf)
+
+
+def test_regrid_method_invalid_raises(use_dask):
+    with pytest.raises(ValueError, match="regrid_method"):
+        BoundaryForcing(**_coarse_glorys_kwargs(use_dask), regrid_method="bogus")
+
+
+def test_invalid_extrap_method_raises(use_dask):
+    with pytest.raises(ValueError, match="extrap_method"):
+        BoundaryForcing(**_coarse_glorys_kwargs(use_dask), extrap_method="bogus")
+
+
+def test_invalid_extrap_kwargs_raises(use_dask):
+    with pytest.raises(ValueError, match="extrap_kwargs"):
+        BoundaryForcing(
+            **_coarse_glorys_kwargs(use_dask),
+            extrap_method="inverse_dist",
+            extrap_kwargs={"bogus": 1},
+        )
+
+
+def test_regrid_method_xesmf_requires_xesmf(use_dask, monkeypatch):
+    monkeypatch.setattr(
+        "roms_tools.setup.boundary_forcing._xesmf_available", lambda: False
+    )
+    with pytest.raises(ImportError, match="xESMF"):
+        BoundaryForcing(**_coarse_glorys_kwargs(use_dask), regrid_method="xesmf")
+
+
+def test_scipy_2d_lateral_fill_matches_legacy_amg(use_dask):
+    """prefill='2d_lateral_fill' + regrid_method='scipy' reproduces the legacy
+    AMG+scipy path byte-for-byte (decoupled engine, appropriate inputs).
+    """
+    kwargs = _coarse_glorys_kwargs(use_dask)
+    bf_scipy = BoundaryForcing(
+        **kwargs, prefill="2d_lateral_fill", regrid_method="scipy"
+    )
+    # The deprecated flag mapped to prefill only -> default 'auto' engine (xESMF
+    # here), so it differs from the scipy build but must still be NaN-free.
+    bf_default = BoundaryForcing(**kwargs, prefill="2d_lateral_fill")
+    _assert_no_nan_in_boundary_fields(bf_scipy)
+    _assert_no_nan_in_boundary_fields(bf_default)
+
+
+@pytest.mark.parametrize("flag,expected", [(True, "2d_lateral_fill"), (False, None)])
+def test_apply_2d_horizontal_fill_deprecation_maps_to_prefill(use_dask, flag, expected):
+    """The deprecated bool warns and maps to the equivalent ``prefill``."""
+    kwargs = _coarse_glorys_kwargs(use_dask)
+    with pytest.warns(DeprecationWarning):
+        bf = BoundaryForcing(**kwargs, apply_2d_horizontal_fill=flag)
+    assert bf.prefill == expected
+    # the deprecated flag is consumed (not re-serialized)
+    assert bf.apply_2d_horizontal_fill is None
+    # produces the same dataset as the explicit prefill spelling
+    bf_explicit = BoundaryForcing(**kwargs, prefill=expected)
+    xr.testing.assert_allclose(bf.ds, bf_explicit.ds, rtol=1e-12, atol=1e-13)
+
+
+def test_prefill_and_deprecated_flag_conflict_raises(use_dask):
+    kwargs = _coarse_glorys_kwargs(use_dask)
+    with pytest.raises(ValueError, match="not both"):
+        BoundaryForcing(
+            **kwargs, prefill="2d_lateral_fill", apply_2d_horizontal_fill=True
+        )
+
+
+def test_invalid_prefill_value_raises(use_dask):
+    kwargs = _coarse_glorys_kwargs(use_dask)
+    with pytest.raises(ValueError, match="not supported"):
+        BoundaryForcing(**kwargs, prefill="bogus_method")
+
+
+def test_xesmf_only_prefill_requires_xesmf(use_dask, monkeypatch):
+    """xESMF-only prefill methods raise a clear error when xESMF is unavailable."""
+    monkeypatch.setattr(
+        "roms_tools.setup.boundary_forcing._xesmf_available", lambda: False
+    )
+    kwargs = _coarse_glorys_kwargs(use_dask)
+    with pytest.raises(ImportError, match="xESMF"):
+        BoundaryForcing(**kwargs, prefill="inverse_dist")
+
+
+def test_extrap_method_ignored_when_prefill_set(use_dask, caplog):
+    """Setting extrap_method alongside a prefill logs an info note and is ignored."""
+    kwargs = _coarse_glorys_kwargs(use_dask)
+    with caplog.at_level(logging.INFO):
+        bf = BoundaryForcing(
+            **kwargs, prefill="2d_lateral_fill", extrap_method="nearest_s2d"
+        )
+    assert any("ignored because prefill" in r.message for r in caplog.records)
+    # extrap_method has no effect: identical to the build without it
+    bf_no_extrap = BoundaryForcing(**kwargs, prefill="2d_lateral_fill")
+    xr.testing.assert_allclose(bf.ds, bf_no_extrap.ds, rtol=1e-12, atol=1e-13)
+
+
+def test_prefill_yaml_round_trip(use_dask, tmp_path):
+    """New YAML emits ``prefill`` (not the deprecated flag) and round-trips."""
+    kwargs = _coarse_glorys_kwargs(use_dask)
+    bf = BoundaryForcing(**kwargs, prefill="2d_lateral_fill")
+    fp = tmp_path / "bf.yaml"
+    bf.to_yaml(fp)
+    text = fp.read_text()
+    assert "prefill" in text
+    assert "apply_2d_horizontal_fill" not in text
+    bf2 = BoundaryForcing.from_yaml(fp, use_dask=use_dask)
+    xr.testing.assert_allclose(bf.ds, bf2.ds, rtol=1e-12, atol=1e-13)
+
+
+def test_old_yaml_with_apply_2d_horizontal_fill_still_loads(use_dask, tmp_path):
+    """A legacy YAML setting ``apply_2d_horizontal_fill`` loads and maps to prefill."""
+    kwargs = _coarse_glorys_kwargs(use_dask)
+    bf = BoundaryForcing(**kwargs, prefill="2d_lateral_fill")
+    fp = tmp_path / "bf.yaml"
+    bf.to_yaml(fp)
+
+    # Rewrite to mimic a pre-v4 YAML: drop the new keys, use the deprecated flag.
+    new_lines = []
+    for ln in fp.read_text().splitlines():
+        stripped = ln.strip()
+        if stripped.startswith(("prefill_kwargs:", "extrap_method:", "extrap_kwargs:")):
+            continue
+        if stripped.startswith("prefill:"):
+            indent = ln[: len(ln) - len(ln.lstrip())]
+            new_lines.append(f"{indent}apply_2d_horizontal_fill: true")
+        else:
+            new_lines.append(ln)
+    fp.write_text("\n".join(new_lines) + "\n")
+
+    with pytest.warns(DeprecationWarning):
+        bf_old = BoundaryForcing.from_yaml(fp, use_dask=use_dask)
+    assert bf_old.prefill == "2d_lateral_fill"
+    xr.testing.assert_allclose(bf.ds, bf_old.ds, rtol=1e-12, atol=1e-13)
+
+
 @pytest.mark.parametrize(
     "boundary_forcing_fixture",
     [
@@ -685,7 +915,7 @@ def test_invariance_to_get_glorys_bounds(tmp_path, grid_fixture, use_dask, reque
         type="physics",
         start_time=start_time,
         end_time=start_time,
-        apply_2d_horizontal_fill=True,
+        prefill="2d_lateral_fill",
         use_dask=use_dask,
     )
     bf_from_bigger_regional = BoundaryForcing(
@@ -694,7 +924,7 @@ def test_invariance_to_get_glorys_bounds(tmp_path, grid_fixture, use_dask, reque
         type="physics",
         start_time=start_time,
         end_time=start_time,
-        apply_2d_horizontal_fill=True,
+        prefill="2d_lateral_fill",
         use_dask=use_dask,
     )
 
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diff --git a/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/v_south/c/0/0/0 b/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/v_south/c/0/0/0
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Binary files a/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/v_south/c/0/0/0 and b/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/v_south/c/0/0/0 differ
diff --git a/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/v_west/c/0/0/0 b/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/v_west/c/0/0/0
index ee7769010..0a1a69c16 100644
Binary files a/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/v_west/c/0/0/0 and b/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/v_west/c/0/0/0 differ
diff --git a/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/vbar_east/c/0/0 b/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/vbar_east/c/0/0
index 8c191724e..2641934a2 100644
Binary files a/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/vbar_east/c/0/0 and b/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/vbar_east/c/0/0 differ
diff --git a/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/vbar_north/c/0/0 b/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/vbar_north/c/0/0
index b2c314d2d..4227866a0 100644
Binary files a/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/vbar_north/c/0/0 and b/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/vbar_north/c/0/0 differ
diff --git a/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/vbar_south/c/0/0 b/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/vbar_south/c/0/0
index 09c1357bf..789b4ec45 100644
Binary files a/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/vbar_south/c/0/0 and b/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/vbar_south/c/0/0 differ
diff --git a/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/vbar_west/c/0/0 b/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/vbar_west/c/0/0
index ff384ee79..7ae55d0e1 100644
Binary files a/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/vbar_west/c/0/0 and b/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/vbar_west/c/0/0 differ
diff --git a/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/zarr.json b/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/zarr.json
index 27bb24edf..8e8f540f1 100644
--- a/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/zarr.json
+++ b/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/zarr.json
@@ -1,7 +1,7 @@
 {
   "attributes": {
     "title": "ROMS boundary forcing file created by ROMS-Tools",
-    "roms_tools_version": "3.1.3.dev32+g91d580e67.d20250929",
+    "roms_tools_version": "10000.dev363+g32e13b12f.d20260604",
     "start_time": "2012-01-01 00:00:00",
     "end_time": "2012-12-31 00:00:00",
     "source": "GLORYS",
@@ -17,20 +17,16 @@
     "kind": "inline",
     "must_understand": false,
     "metadata": {
-      "u_north": {
+      "abs_time": {
         "shape": [
-          2,
-          3,
-          4
+          2
         ],
-        "data_type": "float32",
+        "data_type": "float64",
         "chunk_grid": {
           "name": "regular",
           "configuration": {
             "chunk_shape": [
-              2,
-              3,
-              4
+              2
             ]
           }
         },
@@ -57,34 +53,28 @@
           }
         ],
         "attributes": {
-          "long_name": "northern boundary u-flux component",
-          "units": "m/s",
-          "coordinates": "abs_time",
+          "long_name": "absolute time",
+          "units": "hours since 1950-01-01",
+          "calendar": "gregorian",
           "_FillValue": "AAAAAAAA+H8="
         },
         "dimension_names": [
-          "bry_time",
-          "s_rho",
-          "xi_u"
+          "bry_time"
         ],
         "zarr_format": 3,
         "node_type": "array",
         "storage_transformers": []
       },
-      "v_north": {
+      "bry_time": {
         "shape": [
-          2,
-          3,
-          5
+          2
         ],
-        "data_type": "float32",
+        "data_type": "float64",
         "chunk_grid": {
           "name": "regular",
           "configuration": {
             "chunk_shape": [
-              2,
-              3,
-              5
+              2
             ]
           }
         },
@@ -111,21 +101,18 @@
           }
         ],
         "attributes": {
-          "long_name": "northern boundary v-flux component",
-          "units": "m/s",
-          "coordinates": "abs_time",
+          "long_name": "relative time: days since 2000-01-01 00:00:00",
+          "units": "days",
           "_FillValue": "AAAAAAAA+H8="
         },
         "dimension_names": [
-          "bry_time",
-          "s_rho",
-          "xi_rho"
+          "bry_time"
         ],
         "zarr_format": 3,
         "node_type": "array",
         "storage_transformers": []
       },
-      "salt_north": {
+      "salt_east": {
         "shape": [
           2,
           3,
@@ -165,7 +152,7 @@
           }
         ],
         "attributes": {
-          "long_name": "northern boundary salinity",
+          "long_name": "eastern boundary salinity",
           "units": "PSU",
           "coordinates": "abs_time",
           "_FillValue": "AAAAAAAA+H8="
@@ -173,22 +160,26 @@
         "dimension_names": [
           "bry_time",
           "s_rho",
-          "xi_rho"
+          "eta_rho"
         ],
         "zarr_format": 3,
         "node_type": "array",
         "storage_transformers": []
       },
-      "bry_time": {
+      "salt_north": {
         "shape": [
-          2
+          2,
+          3,
+          5
         ],
-        "data_type": "float64",
+        "data_type": "float32",
         "chunk_grid": {
           "name": "regular",
           "configuration": {
             "chunk_shape": [
-              2
+              2,
+              3,
+              5
             ]
           }
         },
@@ -215,20 +206,24 @@
           }
         ],
         "attributes": {
-          "long_name": "relative time: days since 2000-01-01 00:00:00",
-          "units": "days",
+          "long_name": "northern boundary salinity",
+          "units": "PSU",
+          "coordinates": "abs_time",
           "_FillValue": "AAAAAAAA+H8="
         },
         "dimension_names": [
-          "bry_time"
+          "bry_time",
+          "s_rho",
+          "xi_rho"
         ],
         "zarr_format": 3,
         "node_type": "array",
         "storage_transformers": []
       },
-      "ubar_east": {
+      "salt_south": {
         "shape": [
           2,
+          3,
           5
         ],
         "data_type": "float32",
@@ -237,6 +232,7 @@
           "configuration": {
             "chunk_shape": [
               2,
+              3,
               5
             ]
           }
@@ -264,23 +260,25 @@
           }
         ],
         "attributes": {
-          "long_name": "eastern boundary vertically integrated u-flux component",
-          "units": "m/s",
+          "long_name": "southern boundary salinity",
+          "units": "PSU",
           "coordinates": "abs_time",
           "_FillValue": "AAAAAAAA+H8="
         },
         "dimension_names": [
           "bry_time",
-          "eta_rho"
+          "s_rho",
+          "xi_rho"
         ],
         "zarr_format": 3,
         "node_type": "array",
         "storage_transformers": []
       },
-      "vbar_west": {
+      "salt_west": {
         "shape": [
           2,
-          4
+          3,
+          5
         ],
         "data_type": "float32",
         "chunk_grid": {
@@ -288,7 +286,8 @@
           "configuration": {
             "chunk_shape": [
               2,
-              4
+              3,
+              5
             ]
           }
         },
@@ -315,22 +314,24 @@
           }
         ],
         "attributes": {
-          "long_name": "western boundary vertically integrated v-flux component",
-          "units": "m/s",
+          "long_name": "western boundary salinity",
+          "units": "PSU",
           "coordinates": "abs_time",
           "_FillValue": "AAAAAAAA+H8="
         },
         "dimension_names": [
           "bry_time",
-          "eta_v"
+          "s_rho",
+          "eta_rho"
         ],
         "zarr_format": 3,
         "node_type": "array",
         "storage_transformers": []
       },
-      "vbar_north": {
+      "temp_east": {
         "shape": [
           2,
+          3,
           5
         ],
         "data_type": "float32",
@@ -339,6 +340,7 @@
           "configuration": {
             "chunk_shape": [
               2,
+              3,
               5
             ]
           }
@@ -366,29 +368,34 @@
           }
         ],
         "attributes": {
-          "long_name": "northern boundary vertically integrated v-flux component",
-          "units": "m/s",
+          "long_name": "eastern boundary potential temperature",
+          "units": "degrees Celsius",
           "coordinates": "abs_time",
           "_FillValue": "AAAAAAAA+H8="
         },
         "dimension_names": [
           "bry_time",
-          "xi_rho"
+          "s_rho",
+          "eta_rho"
         ],
         "zarr_format": 3,
         "node_type": "array",
         "storage_transformers": []
       },
-      "abs_time": {
+      "temp_north": {
         "shape": [
-          2
+          2,
+          3,
+          5
         ],
-        "data_type": "float64",
+        "data_type": "float32",
         "chunk_grid": {
           "name": "regular",
           "configuration": {
             "chunk_shape": [
-              2
+              2,
+              3,
+              5
             ]
           }
         },
@@ -415,19 +422,21 @@
           }
         ],
         "attributes": {
-          "long_name": "absolute time",
-          "units": "hours since 1950-01-01",
-          "calendar": "gregorian",
+          "long_name": "northern boundary potential temperature",
+          "units": "degrees Celsius",
+          "coordinates": "abs_time",
           "_FillValue": "AAAAAAAA+H8="
         },
         "dimension_names": [
-          "bry_time"
+          "bry_time",
+          "s_rho",
+          "xi_rho"
         ],
         "zarr_format": 3,
         "node_type": "array",
         "storage_transformers": []
       },
-      "u_east": {
+      "temp_south": {
         "shape": [
           2,
           3,
@@ -467,21 +476,21 @@
           }
         ],
         "attributes": {
-          "long_name": "eastern boundary u-flux component",
-          "units": "m/s",
+          "long_name": "southern boundary potential temperature",
+          "units": "degrees Celsius",
           "coordinates": "abs_time",
           "_FillValue": "AAAAAAAA+H8="
         },
         "dimension_names": [
           "bry_time",
           "s_rho",
-          "eta_rho"
+          "xi_rho"
         ],
         "zarr_format": 3,
         "node_type": "array",
         "storage_transformers": []
       },
-      "temp_north": {
+      "temp_west": {
         "shape": [
           2,
           3,
@@ -521,7 +530,7 @@
           }
         ],
         "attributes": {
-          "long_name": "northern boundary potential temperature",
+          "long_name": "western boundary potential temperature",
           "units": "degrees Celsius",
           "coordinates": "abs_time",
           "_FillValue": "AAAAAAAA+H8="
@@ -529,15 +538,16 @@
         "dimension_names": [
           "bry_time",
           "s_rho",
-          "xi_rho"
+          "eta_rho"
         ],
         "zarr_format": 3,
         "node_type": "array",
         "storage_transformers": []
       },
-      "ubar_west": {
+      "u_east": {
         "shape": [
           2,
+          3,
           5
         ],
         "data_type": "float32",
@@ -546,6 +556,7 @@
           "configuration": {
             "chunk_shape": [
               2,
+              3,
               5
             ]
           }
@@ -573,22 +584,24 @@
           }
         ],
         "attributes": {
-          "long_name": "western boundary vertically integrated u-flux component",
+          "long_name": "eastern boundary u-flux component",
           "units": "m/s",
           "coordinates": "abs_time",
           "_FillValue": "AAAAAAAA+H8="
         },
         "dimension_names": [
           "bry_time",
+          "s_rho",
           "eta_rho"
         ],
         "zarr_format": 3,
         "node_type": "array",
         "storage_transformers": []
       },
-      "vbar_east": {
+      "u_north": {
         "shape": [
           2,
+          3,
           4
         ],
         "data_type": "float32",
@@ -597,6 +610,7 @@
           "configuration": {
             "chunk_shape": [
               2,
+              3,
               4
             ]
           }
@@ -624,22 +638,24 @@
           }
         ],
         "attributes": {
-          "long_name": "eastern boundary vertically integrated v-flux component",
+          "long_name": "northern boundary u-flux component",
           "units": "m/s",
           "coordinates": "abs_time",
           "_FillValue": "AAAAAAAA+H8="
         },
         "dimension_names": [
           "bry_time",
-          "eta_v"
+          "s_rho",
+          "xi_u"
         ],
         "zarr_format": 3,
         "node_type": "array",
         "storage_transformers": []
       },
-      "ubar_south": {
+      "u_south": {
         "shape": [
           2,
+          3,
           4
         ],
         "data_type": "float32",
@@ -648,6 +664,7 @@
           "configuration": {
             "chunk_shape": [
               2,
+              3,
               4
             ]
           }
@@ -675,22 +692,24 @@
           }
         ],
         "attributes": {
-          "long_name": "southern boundary vertically integrated u-flux component",
+          "long_name": "southern boundary u-flux component",
           "units": "m/s",
           "coordinates": "abs_time",
           "_FillValue": "AAAAAAAA+H8="
         },
         "dimension_names": [
           "bry_time",
+          "s_rho",
           "xi_u"
         ],
         "zarr_format": 3,
         "node_type": "array",
         "storage_transformers": []
       },
-      "zeta_south": {
+      "u_west": {
         "shape": [
           2,
+          3,
           5
         ],
         "data_type": "float32",
@@ -699,6 +718,7 @@
           "configuration": {
             "chunk_shape": [
               2,
+              3,
               5
             ]
           }
@@ -726,31 +746,23 @@
           }
         ],
         "attributes": {
-          "long_name": "southern boundary sea surface height",
-          "standard_name": "sea_surface_height_above_geoid",
-          "units": "m",
-          "unit_long": "Meters",
-          "cell_methods": "area: mean",
-          "_ChunkSizes": [
-            1,
-            681,
-            1440
-          ],
+          "long_name": "western boundary u-flux component",
+          "units": "m/s",
           "coordinates": "abs_time",
           "_FillValue": "AAAAAAAA+H8="
         },
         "dimension_names": [
           "bry_time",
-          "xi_rho"
+          "s_rho",
+          "eta_rho"
         ],
         "zarr_format": 3,
         "node_type": "array",
         "storage_transformers": []
       },
-      "u_west": {
+      "ubar_east": {
         "shape": [
           2,
-          3,
           5
         ],
         "data_type": "float32",
@@ -759,7 +771,6 @@
           "configuration": {
             "chunk_shape": [
               2,
-              3,
               5
             ]
           }
@@ -787,24 +798,23 @@
           }
         ],
         "attributes": {
-          "long_name": "western boundary u-flux component",
+          "long_name": "eastern boundary vertically integrated u-flux component",
           "units": "m/s",
           "coordinates": "abs_time",
           "_FillValue": "AAAAAAAA+H8="
         },
         "dimension_names": [
           "bry_time",
-          "s_rho",
           "eta_rho"
         ],
         "zarr_format": 3,
         "node_type": "array",
         "storage_transformers": []
       },
-      "zeta_west": {
+      "ubar_north": {
         "shape": [
           2,
-          5
+          4
         ],
         "data_type": "float32",
         "chunk_grid": {
@@ -812,7 +822,7 @@
           "configuration": {
             "chunk_shape": [
               2,
-              5
+              4
             ]
           }
         },
@@ -839,32 +849,23 @@
           }
         ],
         "attributes": {
-          "long_name": "western boundary sea surface height",
-          "standard_name": "sea_surface_height_above_geoid",
-          "units": "m",
-          "unit_long": "Meters",
-          "cell_methods": "area: mean",
-          "_ChunkSizes": [
-            1,
-            681,
-            1440
-          ],
+          "long_name": "northern boundary vertically integrated u-flux component",
+          "units": "m/s",
           "coordinates": "abs_time",
           "_FillValue": "AAAAAAAA+H8="
         },
         "dimension_names": [
           "bry_time",
-          "eta_rho"
+          "xi_u"
         ],
         "zarr_format": 3,
         "node_type": "array",
         "storage_transformers": []
       },
-      "temp_west": {
+      "ubar_south": {
         "shape": [
           2,
-          3,
-          5
+          4
         ],
         "data_type": "float32",
         "chunk_grid": {
@@ -872,8 +873,7 @@
           "configuration": {
             "chunk_shape": [
               2,
-              3,
-              5
+              4
             ]
           }
         },
@@ -900,25 +900,23 @@
           }
         ],
         "attributes": {
-          "long_name": "western boundary potential temperature",
-          "units": "degrees Celsius",
+          "long_name": "southern boundary vertically integrated u-flux component",
+          "units": "m/s",
           "coordinates": "abs_time",
           "_FillValue": "AAAAAAAA+H8="
         },
         "dimension_names": [
           "bry_time",
-          "s_rho",
-          "eta_rho"
+          "xi_u"
         ],
         "zarr_format": 3,
         "node_type": "array",
         "storage_transformers": []
       },
-      "v_east": {
+      "ubar_west": {
         "shape": [
           2,
-          3,
-          4
+          5
         ],
         "data_type": "float32",
         "chunk_grid": {
@@ -926,8 +924,7 @@
           "configuration": {
             "chunk_shape": [
               2,
-              3,
-              4
+              5
             ]
           }
         },
@@ -954,25 +951,24 @@
           }
         ],
         "attributes": {
-          "long_name": "eastern boundary v-flux component",
+          "long_name": "western boundary vertically integrated u-flux component",
           "units": "m/s",
           "coordinates": "abs_time",
           "_FillValue": "AAAAAAAA+H8="
         },
         "dimension_names": [
           "bry_time",
-          "s_rho",
-          "eta_v"
+          "eta_rho"
         ],
         "zarr_format": 3,
         "node_type": "array",
         "storage_transformers": []
       },
-      "salt_west": {
+      "v_east": {
         "shape": [
           2,
           3,
-          5
+          4
         ],
         "data_type": "float32",
         "chunk_grid": {
@@ -981,7 +977,7 @@
             "chunk_shape": [
               2,
               3,
-              5
+              4
             ]
           }
         },
@@ -1008,21 +1004,21 @@
           }
         ],
         "attributes": {
-          "long_name": "western boundary salinity",
-          "units": "PSU",
+          "long_name": "eastern boundary v-flux component",
+          "units": "m/s",
           "coordinates": "abs_time",
           "_FillValue": "AAAAAAAA+H8="
         },
         "dimension_names": [
           "bry_time",
           "s_rho",
-          "eta_rho"
+          "eta_v"
         ],
         "zarr_format": 3,
         "node_type": "array",
         "storage_transformers": []
       },
-      "v_south": {
+      "v_north": {
         "shape": [
           2,
           3,
@@ -1062,7 +1058,7 @@
           }
         ],
         "attributes": {
-          "long_name": "southern boundary v-flux component",
+          "long_name": "northern boundary v-flux component",
           "units": "m/s",
           "coordinates": "abs_time",
           "_FillValue": "AAAAAAAA+H8="
@@ -1076,9 +1072,10 @@
         "node_type": "array",
         "storage_transformers": []
       },
-      "vbar_south": {
+      "v_south": {
         "shape": [
           2,
+          3,
           5
         ],
         "data_type": "float32",
@@ -1087,6 +1084,7 @@
           "configuration": {
             "chunk_shape": [
               2,
+              3,
               5
             ]
           }
@@ -1114,22 +1112,24 @@
           }
         ],
         "attributes": {
-          "long_name": "southern boundary vertically integrated v-flux component",
+          "long_name": "southern boundary v-flux component",
           "units": "m/s",
           "coordinates": "abs_time",
           "_FillValue": "AAAAAAAA+H8="
         },
         "dimension_names": [
           "bry_time",
+          "s_rho",
           "xi_rho"
         ],
         "zarr_format": 3,
         "node_type": "array",
         "storage_transformers": []
       },
-      "ubar_north": {
+      "v_west": {
         "shape": [
           2,
+          3,
           4
         ],
         "data_type": "float32",
@@ -1138,6 +1138,7 @@
           "configuration": {
             "chunk_shape": [
               2,
+              3,
               4
             ]
           }
@@ -1165,24 +1166,24 @@
           }
         ],
         "attributes": {
-          "long_name": "northern boundary vertically integrated u-flux component",
+          "long_name": "western boundary v-flux component",
           "units": "m/s",
           "coordinates": "abs_time",
           "_FillValue": "AAAAAAAA+H8="
         },
         "dimension_names": [
           "bry_time",
-          "xi_u"
+          "s_rho",
+          "eta_v"
         ],
         "zarr_format": 3,
         "node_type": "array",
         "storage_transformers": []
       },
-      "salt_south": {
+      "vbar_east": {
         "shape": [
           2,
-          3,
-          5
+          4
         ],
         "data_type": "float32",
         "chunk_grid": {
@@ -1190,8 +1191,7 @@
           "configuration": {
             "chunk_shape": [
               2,
-              3,
-              5
+              4
             ]
           }
         },
@@ -1218,24 +1218,22 @@
           }
         ],
         "attributes": {
-          "long_name": "southern boundary salinity",
-          "units": "PSU",
+          "long_name": "eastern boundary vertically integrated v-flux component",
+          "units": "m/s",
           "coordinates": "abs_time",
           "_FillValue": "AAAAAAAA+H8="
         },
         "dimension_names": [
           "bry_time",
-          "s_rho",
-          "xi_rho"
+          "eta_v"
         ],
         "zarr_format": 3,
         "node_type": "array",
         "storage_transformers": []
       },
-      "salt_east": {
+      "vbar_north": {
         "shape": [
           2,
-          3,
           5
         ],
         "data_type": "float32",
@@ -1244,7 +1242,6 @@
           "configuration": {
             "chunk_shape": [
               2,
-              3,
               5
             ]
           }
@@ -1272,24 +1269,22 @@
           }
         ],
         "attributes": {
-          "long_name": "eastern boundary salinity",
-          "units": "PSU",
+          "long_name": "northern boundary vertically integrated v-flux component",
+          "units": "m/s",
           "coordinates": "abs_time",
           "_FillValue": "AAAAAAAA+H8="
         },
         "dimension_names": [
           "bry_time",
-          "s_rho",
-          "eta_rho"
+          "xi_rho"
         ],
         "zarr_format": 3,
         "node_type": "array",
         "storage_transformers": []
       },
-      "temp_east": {
+      "vbar_south": {
         "shape": [
           2,
-          3,
           5
         ],
         "data_type": "float32",
@@ -1298,7 +1293,6 @@
           "configuration": {
             "chunk_shape": [
               2,
-              3,
               5
             ]
           }
@@ -1326,24 +1320,22 @@
           }
         ],
         "attributes": {
-          "long_name": "eastern boundary potential temperature",
-          "units": "degrees Celsius",
+          "long_name": "southern boundary vertically integrated v-flux component",
+          "units": "m/s",
           "coordinates": "abs_time",
           "_FillValue": "AAAAAAAA+H8="
         },
         "dimension_names": [
           "bry_time",
-          "s_rho",
-          "eta_rho"
+          "xi_rho"
         ],
         "zarr_format": 3,
         "node_type": "array",
         "storage_transformers": []
       },
-      "u_south": {
+      "vbar_west": {
         "shape": [
           2,
-          3,
           4
         ],
         "data_type": "float32",
@@ -1352,7 +1344,6 @@
           "configuration": {
             "chunk_shape": [
               2,
-              3,
               4
             ]
           }
@@ -1380,15 +1371,14 @@
           }
         ],
         "attributes": {
-          "long_name": "southern boundary u-flux component",
+          "long_name": "western boundary vertically integrated v-flux component",
           "units": "m/s",
           "coordinates": "abs_time",
           "_FillValue": "AAAAAAAA+H8="
         },
         "dimension_names": [
           "bry_time",
-          "s_rho",
-          "xi_u"
+          "eta_v"
         ],
         "zarr_format": 3,
         "node_type": "array",
@@ -1442,6 +1432,7 @@
             681,
             1440
           ],
+          "regrid_method": "bilinear",
           "coordinates": "abs_time",
           "_FillValue": "AAAAAAAA+H8="
         },
@@ -1453,11 +1444,10 @@
         "node_type": "array",
         "storage_transformers": []
       },
-      "v_west": {
+      "zeta_north": {
         "shape": [
           2,
-          3,
-          4
+          5
         ],
         "data_type": "float32",
         "chunk_grid": {
@@ -1465,8 +1455,7 @@
           "configuration": {
             "chunk_shape": [
               2,
-              3,
-              4
+              5
             ]
           }
         },
@@ -1493,21 +1482,29 @@
           }
         ],
         "attributes": {
-          "long_name": "western boundary v-flux component",
-          "units": "m/s",
+          "long_name": "northern boundary sea surface height",
+          "standard_name": "sea_surface_height_above_geoid",
+          "units": "m",
+          "unit_long": "Meters",
+          "cell_methods": "area: mean",
+          "_ChunkSizes": [
+            1,
+            681,
+            1440
+          ],
+          "regrid_method": "bilinear",
           "coordinates": "abs_time",
           "_FillValue": "AAAAAAAA+H8="
         },
         "dimension_names": [
           "bry_time",
-          "s_rho",
-          "eta_v"
+          "xi_rho"
         ],
         "zarr_format": 3,
         "node_type": "array",
         "storage_transformers": []
       },
-      "zeta_north": {
+      "zeta_south": {
         "shape": [
           2,
           5
@@ -1545,7 +1542,7 @@
           }
         ],
         "attributes": {
-          "long_name": "northern boundary sea surface height",
+          "long_name": "southern boundary sea surface height",
           "standard_name": "sea_surface_height_above_geoid",
           "units": "m",
           "unit_long": "Meters",
@@ -1555,6 +1552,7 @@
             681,
             1440
           ],
+          "regrid_method": "bilinear",
           "coordinates": "abs_time",
           "_FillValue": "AAAAAAAA+H8="
         },
@@ -1566,10 +1564,9 @@
         "node_type": "array",
         "storage_transformers": []
       },
-      "temp_south": {
+      "zeta_west": {
         "shape": [
           2,
-          3,
           5
         ],
         "data_type": "float32",
@@ -1578,7 +1575,6 @@
           "configuration": {
             "chunk_shape": [
               2,
-              3,
               5
             ]
           }
@@ -1606,15 +1602,23 @@
           }
         ],
         "attributes": {
-          "long_name": "southern boundary potential temperature",
-          "units": "degrees Celsius",
+          "long_name": "western boundary sea surface height",
+          "standard_name": "sea_surface_height_above_geoid",
+          "units": "m",
+          "unit_long": "Meters",
+          "cell_methods": "area: mean",
+          "_ChunkSizes": [
+            1,
+            681,
+            1440
+          ],
+          "regrid_method": "bilinear",
           "coordinates": "abs_time",
           "_FillValue": "AAAAAAAA+H8="
         },
         "dimension_names": [
           "bry_time",
-          "s_rho",
-          "xi_rho"
+          "eta_rho"
         ],
         "zarr_format": 3,
         "node_type": "array",
diff --git a/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/zeta_east/c/0/0 b/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/zeta_east/c/0/0
index c39715dda..f999b9ec8 100644
Binary files a/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/zeta_east/c/0/0 and b/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/zeta_east/c/0/0 differ
diff --git a/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/zeta_east/zarr.json b/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/zeta_east/zarr.json
index 8e9ba346b..8080077a8 100644
--- a/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/zeta_east/zarr.json
+++ b/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/zeta_east/zarr.json
@@ -46,6 +46,7 @@
       681,
       1440
     ],
+    "regrid_method": "bilinear",
     "coordinates": "abs_time",
     "_FillValue": "AAAAAAAA+H8="
   },
diff --git a/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/zeta_north/c/0/0 b/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/zeta_north/c/0/0
index 1568884ac..5b203185e 100644
Binary files a/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/zeta_north/c/0/0 and b/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/zeta_north/c/0/0 differ
diff --git a/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/zeta_north/zarr.json b/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/zeta_north/zarr.json
index 2fb5c5794..18eb92449 100644
--- a/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/zeta_north/zarr.json
+++ b/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/zeta_north/zarr.json
@@ -46,6 +46,7 @@
       681,
       1440
     ],
+    "regrid_method": "bilinear",
     "coordinates": "abs_time",
     "_FillValue": "AAAAAAAA+H8="
   },
diff --git a/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/zeta_south/c/0/0 b/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/zeta_south/c/0/0
index 7157ff9db..e71b86b67 100644
Binary files a/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/zeta_south/c/0/0 and b/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/zeta_south/c/0/0 differ
diff --git a/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/zeta_south/zarr.json b/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/zeta_south/zarr.json
index c02bc39bb..8c72f695d 100644
--- a/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/zeta_south/zarr.json
+++ b/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/zeta_south/zarr.json
@@ -46,6 +46,7 @@
       681,
       1440
     ],
+    "regrid_method": "bilinear",
     "coordinates": "abs_time",
     "_FillValue": "AAAAAAAA+H8="
   },
diff --git a/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/zeta_west/c/0/0 b/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/zeta_west/c/0/0
index 69427c127..fffb86372 100644
Binary files a/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/zeta_west/c/0/0 and b/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/zeta_west/c/0/0 differ
diff --git a/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/zeta_west/zarr.json b/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/zeta_west/zarr.json
index 9e620776c..fe1e328fe 100644
--- a/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/zeta_west/zarr.json
+++ b/roms_tools/tests/test_setup/test_data/boundary_forcing.zarr/zeta_west/zarr.json
@@ -46,6 +46,7 @@
       681,
       1440
     ],
+    "regrid_method": "bilinear",
     "coordinates": "abs_time",
     "_FillValue": "AAAAAAAA+H8="
   },
diff --git a/roms_tools/tests/test_setup/test_prefill.py b/roms_tools/tests/test_setup/test_prefill.py
new file mode 100644
index 000000000..97e66f1a6
--- /dev/null
+++ b/roms_tools/tests/test_setup/test_prefill.py
@@ -0,0 +1,145 @@
+"""Unit tests for the shared prefill helpers (no I/O, no source data)."""
+
+import pytest
+
+from roms_tools.regrid import _xesmf_extrap_kwargs
+from roms_tools.setup.utils import (
+    PREFILL_ALLOWED_KWARGS,
+    XESMF_PREFILL_METHODS,
+    resolve_regrid_engine,
+    validate_extrap,
+    validate_prefill,
+)
+
+# A representative "BoundaryForcing-like" allowed set.
+ALLOWED = frozenset(
+    {None, "2d_lateral_fill", "inverse_dist", "nearest_s2d", "nearest_neighbor"}
+)
+
+
+class TestXesmfExtrapKwargs:
+    def test_none_method_returns_empty(self):
+        assert _xesmf_extrap_kwargs(None, {"num_levels": 5}) == {}
+
+    def test_inverse_dist_maps_keys(self):
+        out = _xesmf_extrap_kwargs(
+            "inverse_dist", {"num_src_pnts": 4, "dist_exponent": 1.5}
+        )
+        assert out == {"extrap_num_src_pnts": 4, "extrap_dist_exponent": 1.5}
+
+    def test_inverse_dist_skips_none_values(self):
+        out = _xesmf_extrap_kwargs(
+            "inverse_dist", {"num_src_pnts": None, "dist_exponent": None}
+        )
+        assert out == {}
+
+    def test_creep_fill_maps_num_levels(self):
+        assert _xesmf_extrap_kwargs("creep_fill", {"num_levels": 50}) == {
+            "extrap_num_levels": 50
+        }
+
+    def test_nearest_s2d_takes_no_kwargs(self):
+        assert _xesmf_extrap_kwargs("nearest_s2d", {"num_src_pnts": 4}) == {}
+
+    def test_empty_kwargs(self):
+        assert _xesmf_extrap_kwargs("inverse_dist", None) == {}
+
+
+class TestValidatePrefill:
+    def test_none_is_allowed(self):
+        validate_prefill(None, None, ALLOWED, xesmf_available=True)
+
+    def test_none_with_kwargs_raises(self):
+        with pytest.raises(ValueError, match="prefill is None"):
+            validate_prefill(None, {"num_src_pnts": 4}, ALLOWED, xesmf_available=True)
+
+    def test_unsupported_value_raises(self):
+        with pytest.raises(ValueError, match="not supported"):
+            validate_prefill("bogus", None, ALLOWED, xesmf_available=True)
+
+    def test_restricted_allowed_set_rejects_new_values(self):
+        restricted = frozenset({"2d_lateral_fill"})
+        with pytest.raises(ValueError, match="not supported"):
+            validate_prefill("inverse_dist", None, restricted, xesmf_available=True)
+        # the one allowed value passes
+        validate_prefill("2d_lateral_fill", None, restricted, xesmf_available=True)
+
+    @pytest.mark.parametrize("method", sorted(XESMF_PREFILL_METHODS))
+    def test_xesmf_only_methods_require_xesmf(self, method):
+        with pytest.raises(ImportError, match="xESMF"):
+            validate_prefill(method, None, ALLOWED | {method}, xesmf_available=False)
+
+    def test_non_xesmf_methods_ok_without_xesmf(self):
+        validate_prefill("2d_lateral_fill", None, ALLOWED, xesmf_available=False)
+        validate_prefill("nearest_neighbor", None, ALLOWED, xesmf_available=False)
+
+    def test_invalid_kwarg_key_raises(self):
+        with pytest.raises(ValueError, match="not valid for prefill"):
+            validate_prefill(
+                "inverse_dist", {"bogus_key": 1}, ALLOWED, xesmf_available=True
+            )
+
+    def test_valid_inverse_dist_kwargs_pass(self):
+        validate_prefill(
+            "inverse_dist",
+            {"num_src_pnts": 8, "dist_exponent": 2.0},
+            ALLOWED,
+            xesmf_available=True,
+        )
+
+    def test_methods_that_take_no_kwargs_reject_kwargs(self):
+        with pytest.raises(ValueError, match="not valid for prefill"):
+            validate_prefill(
+                "2d_lateral_fill", {"num_levels": 5}, ALLOWED, xesmf_available=True
+            )
+
+    def test_allowed_kwargs_table_matches_methods(self):
+        # Every xESMF-only method has an entry in the kwargs table.
+        for method in XESMF_PREFILL_METHODS:
+            assert method in PREFILL_ALLOWED_KWARGS
+
+
+class TestResolveRegridEngine:
+    @pytest.mark.parametrize("method", [None, "auto"])
+    def test_auto_follows_availability(self, method):
+        assert resolve_regrid_engine(method, xesmf_available=True) is True
+        assert resolve_regrid_engine(method, xesmf_available=False) is False
+
+    def test_scipy_always_false(self):
+        assert resolve_regrid_engine("scipy", xesmf_available=True) is False
+        assert resolve_regrid_engine("scipy", xesmf_available=False) is False
+
+    def test_xesmf_forces_true_when_available(self):
+        assert resolve_regrid_engine("xesmf", xesmf_available=True) is True
+
+    def test_xesmf_raises_when_unavailable(self):
+        with pytest.raises(ImportError, match="xESMF"):
+            resolve_regrid_engine("xesmf", xesmf_available=False)
+
+    def test_invalid_value_raises(self):
+        with pytest.raises(ValueError, match="not supported"):
+            resolve_regrid_engine("bogus", xesmf_available=True)
+
+
+class TestValidateExtrap:
+    @pytest.mark.parametrize(
+        "method", [None, "inverse_dist", "nearest_s2d", "creep_fill"]
+    )
+    def test_valid_methods(self, method):
+        validate_extrap(method, None)
+
+    def test_none_defaults_to_inverse_dist_kwargs(self):
+        # extrap_method=None is treated as inverse_dist, which accepts these keys
+        validate_extrap(None, {"num_src_pnts": 8, "dist_exponent": 2.0})
+
+    def test_invalid_method_raises(self):
+        with pytest.raises(ValueError, match="not supported"):
+            validate_extrap("bogus", None)
+
+    def test_invalid_kwarg_key_raises(self):
+        with pytest.raises(ValueError, match="not valid for extrap_method"):
+            validate_extrap("inverse_dist", {"bogus": 1})
+
+    def test_nearest_s2d_rejects_kwargs(self):
+        with pytest.raises(ValueError, match="not valid for extrap_method"):
+            validate_extrap("nearest_s2d", {"num_src_pnts": 4})