These sites are interactive web maps of hydro-climate data. The sites are based on CarbonPlan's maps.
| Viewing Option | Descriptin | Variables |
|---|---|---|
| Ave. | Maps of metrics averaged over time epochs | Year Range , Downscaling Method, Climate Model, Metrics |
| Dif. | Map the difference between datasets of climate or observational data | Dif. Obs. Data, Downscaling Method, Climate Model, Metrics |
| Climate Signal: Method & Model | View climate signal of specific method and model combinations | Downscaling Method, Climate Model, Metrics, RCP Scenario |
| Climate Signal: Metric Performance | View climate signal, averaged over best performing datasets | Selecting Performance Metrics |
| Name | Description |
|---|---|
| 1981-2016 | Time range past yearly data averaged over |
| 2016-2099 | Time range future yearly data averaged over |
See this detailed downscaling methods matrix document for more information on some of the datasets mapped.
| Name | URL |
|---|---|
| ICAR | Intermediate Complexity Atmospheric Research Model |
| GARD_puv | GARD analog regression on precipitation and 500mb horizontal wind |
| GARD_quv | GARD analog regression on 500mb water vapor and 500mb horizontal wind |
| LOCA_8th | LOcalized Constructed Analog (LOCA) |
| MACA | Multivariate Adaptive Constructed Analogs |
| NASA-NEX | NCCS NASA |
| Name | URL |
|---|---|
| ACCESS1-3 | Australian Community Climate and Earth System Simulator |
| CanESM2 | Canadian Earth System Model |
| CCSM4 | Community Climate System Model |
| MIROC5 | Model for Interdisciplinary Research on Climate |
| NorESM-M | Norwegian Earth System Model |
| Short Name | Long Name | Units | Description |
|---|---|---|---|
ann_p |
Annual Precipiation | mm | Mean of yearly annual total precipitation |
ann_t |
Annual Temperature | °C | Mean of yearly annual mean temperature |
ann_p_iav |
Standard Deviation Annual Precipitation | °C | Standard deviation of yearly annual total precipitation |
ann_t_iav |
Standard Deviation Annual Temperature | °C | Standard deviation of yearly annual mean temperature |
ann_snow |
Annual Snow Accumulation | mm | Mean of yearly annual total snow accumulation (Pr when daily mean T < 1°C) |
ann_snow_iav |
Standard Deviation Annual Snow Accummulation | mm | Standard deviation of yearly annual total snow accumulation (Pr when daily mean T < 1°C) |
djf_p |
Seasonal Precipitation | mm | Mean of yearly seasonal Dec/Jan/Feb total precipitation |
djf_t |
Seasonal Temperature | °C | Mean of yearly seasonal Dec/Jan/Feb mean temperature |
djf_p_iav |
Standard Deviation Seasonal Precipitation | °C | Standard deviation of Dec/Jan/Feb seasonal total precipitation |
djf_t_iav |
Standard Deviation Seasonal Temperature | °C | Standard deviation of Dec/Jan/Feb seasonal mean temperature |
freezethaw |
Freeze-Thaw Cycles | Days | Mean of annual total freeze-thaw cycles (days with Tmin < 0 and Tmax > 0) |
jja_p |
Seasonal Precipitation | mm | Mean of yearly seasonal Jun/Jul/Aug total precipitation |
jja_t |
Seasonal Temperature | °C | Mean of yearly seasonal Jun/Jul/Aug mean temperature |
jja_p_iav |
Standard Deviation Seasonal Precipitation | °C | Standard deviation of Jun/Jul/Aug seasonal total precipitation |
jja_t_iav |
Standard Deviation Seasonal Temperature | °C | Standard deviation of Jun/Jul/Aug seasonal mean temperature |
mam_p |
Seasonal Precipitation | mm | Mean of yearly seasonal Mar/Apr/May total precipitation |
mam_t |
Seasonal Temperature | °C | Mean of yearly seasonal Mar/Apr/May mean temperature |
mam_p_iav |
Standard Deviation Seasonal Precipitation | °C | Standard deviation of Mar/Apr/May seasonal total precipitation |
mam_t_iav |
Standard Deviation Seasonal Temperature | °C | Standard deviation of Mar/Apr/May seasonal mean temperature |
n34pr |
Nino3.4 Precipitation | mm | Temporal correlation between yearly DJF Nino 3.4 Index and DJF precipitation |
n34t |
Nino3.4 Temperature | °C | Temporal correlation between yearly DJF Nino 3.4 Index and DJF temperature |
pr90 |
Precipitation 90th Percentile | mm | 90th percentile daily precipitation accumulation |
pr99 |
Precipitation 99th Percentile | mm | 99th percentile daily precipitation accumulation |
pr_gev-#yr |
Precipitation GEV #Yr | Return level of annual precipitation maximum for {20, 50, 100} year return period, estimated from GEV function fit to annual precipitation maxima | |
ptrend |
Precipitation Trend | mm | Linear trend of annual mean precipitation by least squares regression |
son_p |
Seasonal Precipitation | mm | Mean of yearly seasonal Sep/Oct/Nov total precipitation |
son_t |
Seasonal Temperature | °C | Mean of yearly seasonal Sep/Oct/Nov mean temperature |
son_p_iav |
Standard Deviation Seasonal Precipitation | °C | Standard deviation of Sep/Oct/Nov seasonal total precipitation |
son_t_iav |
Standard Deviation Seasonal Temperature | °C | Standard deviation of Sep/Oct/Nov seasonal mean temperature |
SPI#year |
Standardized Precipitation Index | Total count of months with SPI < -1.5 computed from smoothed precipitation using {1, 2, 5} year window | |
t90 |
Temperature 90th Percentile | °C | 90th percentile daily temperature extremes |
t99 |
Temperature 99th Percentile | °C | 99th percentile daily temperature extremes |
tpcorr |
Temperature and Precipitation Temporal Correlation | Temporal correlation of annual mean temperature and annual total precipitation | |
ttrend |
Temperature Trend | °C | Linear trend of annual mean temperature by least squares regression |
wet_day_frac |
Wet Day Fraction | Wet day fraction (Fraction of days with Pr > 0) | |
wt_day_to_day |
Weather typing spacial correlation | ||
wt_clim |
Weather typing climatologies |
Observational dataset used to compute the difference against.
| Name | Description |
|---|---|
| CONUS404 | Four-kilometer long-term regional hydroclimate reanalysis |
| GMET | Gridded Meteorological Ensemble Tool |
| Livneh | Livneh hydrometeorological dataset |
| Maurer | Maurer hydrometeorological dataset |
| NLDAS | North American Land Data Assimilation System |
| PRISM | PRISM Climate Group |
| Name | Description |
|---|---|
| 4.5 | Radiative forcing levels of 4.5 W/m² above pre-industrial levels by 2100 |
| 8.5 | Radiative forcing levels of 8.5 W/m² above pre-industrial levels by 2100 |
| Steps | Description |
|---|---|
| 1. Select Metrics | Select metrics to use as the criteria for choicing the best performing maps |
| 2. Select Future RCP Scenario | RCP scenario to map |
| 3. Number of Datasets | Number of climate signal datasets to average over |
| 4. Compute Climate Signal | Compute climate signal map after completing previous steps |
| Plot Metric | Plot selected metrics |
To build the website the user will need to
Note, this repository uses Git Submodules.
$ git clone --recurse-submodules git@github.com:NCAR/hydro-climate-evaluation.git
$ cd hydro-climate-evaluation
The simplest way is to use a Conda Environment to install the NodeJS prerequisite.
First Setup of Conda Environment
$ conda env create -f conda-environment.yml
or
$ conda activate maps
$ conda env update -f conda-environment.yml
Future Use After Setup
$ conda activate maps
Create datasets using the Zarr datasets repository to change NetCDF files to Zarr files that can be mapped.
Or download datasets from https://hydro.rap.ucar.edu/hydro-climate-eval/data/refactor/. Note, the size of these files range from 50MB to 1.7GB.
$ mkdir -p data/refactor
$ cd data/refactor
$ wget https://hydro.rap.ucar.edu/hydro-climate-eval/data/refactor/basemaps.tar.gz
$ wget https://hydro.rap.ucar.edu/hydro-climate-eval/data/refactor/climateSignal.tar.gz
$ wget https://hydro.rap.ucar.edu/hydro-climate-eval/data/refactor/map.tar.gz
$ wget https://hydro.rap.ucar.edu/hydro-climate-eval/data/refactor/obs.tar.gz
$ wget https://hydro.rap.ucar.edu/hydro-climate-eval/data/refactor/refactor_conus.tar.gz IS THIS NEEDED???
$ wget https://hydro.rap.ucar.edu/hydro-climate-eval/data/refactor/regionmaps.tar.gz
$ for f in *.tar.gz; do tar zxf "$f"; done
The Production and Development builds are for a hosted website while the Local builds a local web server.
For setting up the URL redirect to point to the hosted website the user will need to talk to their Sys Admins.
The production build is more reliable and faster, but changes will only show up after a user rebuilds. This is for hosting at hydro.rap.ucar.edu/hydro-climate-eval
$ npm install .
$ npm run build
$ npm run start
Or use Makefile
$ make install
$ make build
$ make run
The development build will host the site at hydro.rap.ucar.edu/hydro-climate-eval but compiles on-the-fly as the user loads the site. This is nice for development but leads to slow page-loads and random reloads.
$ npm install .
$ npm run build
$ npm run dev
Or use Makefile
$ make install
$ make build
$ make dev
This is for building locally, the data will be read from
https://hydro.rap.ucar.edu/hydro-climate-eval/data/ unless the user changes
the value of the bucket variable in the files of the initialConditions
directory. The variable bucket would need to be changed to
http://localhost:8080/hydro-climate-eval/data/refactor/.
$ npm install .
$ npm run build
$ npm run local
Or use Makefile
$ make install
$ make build
$ make local
License is based on CarbonPlan's maps. All the original code in this repository is MIT licensed. The library contains code from mapbox-gl-js version 1.13 (3-Clause BSD licensed). Please provide attribution if reusing any of our digital content (graphics, logo, copy, etc.).