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========================================================== Global Downscaled Projections for Climate Impacts Research

.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.6403794.svg :target: https://doi.org/10.5281/zenodo.6403794

.. image:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/ClimateImpactLab/downscaleCMIP6-binder-env/main?urlpath=git-pull%3Frepo%3Dhttps%253A%252F%252Fgithub.com%252FClimateImpactLab%252FPlanetaryComputerExamples%26urlpath%3Dlab%252Ftree%252FPlanetaryComputerExamples%252Fdatasets%252Fcil-gdpcir%252FREADME.md%26branch%3Dgdpcir-additional-notebooks

The World Climate Research Programme's 6th Coupled Model Intercomparison Project (CMIP6) <https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6>_ represents an enormous advance in the quality, detail, and scope of climate modeling.

The Global Downscaled Projections for Climate Impacts Research dataset makes this modeling more applicable to understanding the impacts of changes in the climate on humans and society with two key developments: trend-preserving bias correction and downscaling. In this dataset, the Climate Impact Lab <https://impactlab.org>_ provides global, daily minimum and maximum air temperature at the surface (tasmin and tasmax) and daily cumulative surface precipitation (pr) corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 scenarios for 25 global climate models on a 1/4-degree regular global grid.

Contents:

• Accessing the data <#accessing-the-data>_
• Data format & contents <#data-format--contents>_
• Example use <#example-use>_
• Project methods <#project-methods>_
• The downscaleCMIP6 repository <#the-downscalecmip6-repository>_
• Citing, licensing, and using data produced by this project <#citing-licensing-and-using-data-produced-by-this-project>_
• Acknowledgements <#acknowledgements>_
• Financial support_

Additional links:

• CIL GDPCIR project homepage: github.com/ClimateImpactLab/downscaleCMIP6 <https://github.com/ClimateImpactLab/downscaleCMIP6>_
• Climate Impact Lab homepage: impactlab.org <https://impactlab.org>_
• Project listing on Microsoft Planetary Computer: planetarycomputer.microsoft.com/dataset/group/cil-gdpcir <https://planetarycomputer.microsoft.com/dataset/group/cil-gdpcir>_

.. _Accessing the data:

Accessing the data

GDPCIR data can be accessed on the Microsoft Planetary Computer: planetarycomputer.microsoft.com/dataset/group/cil-gdpcir <https://planetarycomputer.microsoft.com/dataset/group/cil-gdpcir>_

The dataset is made of collections distinguished by data license at the time of publication:

• Public domain (CC0-1.0) collection <https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0>_
• Attribution (CC BY 4.0) collection <https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by>_

Each modeling center with bias corrected and downscaled data in this collection falls into one of these license categories - see the table below <#available-institutions-models-and-scenarios-by-license-collection>_ to see which model is in each collection, and see the section below on Citing, Licensing, and using data produced by this project <#citing-licensing-and-using-data-produced-by-this-project>_ for citations and additional information about each license. For examples of how to browse the collections and load the data using python, see the example use <#example-use>_ section below.

Data format & contents

The data is stored as partitioned zarr stores (see https://zarr.readthedocs.io <https://zarr.readthedocs.io>_), each of which includes thousands of data and metadata files covering the full time span of the experiment. Historical zarr stores contain just over 50 GB, while SSP zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with dimensions time (daily datetime), lat (float latitude), and lon (float longitude). The data is chunked at each interval of 365 days and 90 degree interval of latitude and longitude. Therefore, each chunk is (365, 360, 360), with each chunk occupying approximately 180MB in memory.

Historical data is daily, excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily, excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100, depending on data availability in the source GCM.

The spatial domain covers all 0.25-degree grid cells, indexed by the grid center, with grid edges on the quarter-degree, using a -180 to 180 longitude convention. Thus, the “lon” coordinate extends from -179.875 to 179.875, and the “lat” coordinate extends from -89.875 to 89.875, with intermediate values at each 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).

Available institutions, models, and scenarios by license collection

==================== ================= ========================================== ========================= Modeling institution Source model Available experiments License collection ==================== ================= ========================================== ========================= CAS FGOALS-g3 []_ SSP2-4.5, SSP3-7.0, and SSP5-8.5 Public domain datasets_ INM INM-CM4-8 SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 Public domain datasets_ INM INM-CM5-0 SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 Public domain datasets_ BCC BCC-CSM2-MR SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 CC-BY-4.0_ CMCC CMCC-CM2-SR5 ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 CC-BY-4.0_ CMCC CMCC-ESM2 ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 CC-BY-4.0_ CSIRO-ARCCSS ACCESS-CM2 SSP2-4.5 and SSP3-7.0 CC-BY-4.0_ CSIRO ACCESS-ESM1-5 SSP1-2.6, SSP2-4.5, and SSP3-7.0 CC-BY-4.0_ MIROC MIROC-ES2L SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 CC-BY-4.0_ MIROC MIROC6 SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 CC-BY-4.0_ MOHC HadGEM3-GC31-LL SSP1-2.6, SSP2-4.5, and SSP5-8.5 CC-BY-4.0_ MOHC UKESM1-0-LL SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 CC-BY-4.0_ MPI-M MPI-ESM1-2-LR SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 CC-BY-4.0_ MPI-M/DKRZ []_ MPI-ESM1-2-HR SSP1-2.6 and SSP5-8.5 CC-BY-4.0_ NCC NorESM2-LM SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 CC-BY-4.0_ NCC NorESM2-MM SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 CC-BY-4.0_ NOAA-GFDL GFDL-CM4 SSP2-4.5 and SSP5-8.5 CC-BY-4.0_ NOAA-GFDL GFDL-ESM4 SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 CC-BY-4.0_ NUIST NESM3 SSP1-2.6, SSP2-4.5, and SSP5-8.5 CC-BY-4.0_ EC-Earth-Consortium EC-Earth3 ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 CC-BY-4.0_ EC-Earth-Consortium EC-Earth3-AerChem ssp370 CC-BY-4.0_ EC-Earth-Consortium EC-Earth3-CC ssp245 and ssp585 CC-BY-4.0_ EC-Earth-Consortium EC-Earth3-Veg ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 CC-BY-4.0_ EC-Earth-Consortium EC-Earth3-Veg-LR ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 CC-BY-4.0_ CCCma CanESM5 ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 CC-BY-4.0_ ==================== ================= ========================================== =========================

Notes:

.. [*] At the time of running, no ssp1-2.6 precipitation data was available for the FGOALS-g3 model. Therefore, we provide tasmin and tamax for this model and experiment, but not pr. All other model/experiment combinations in the above table include all three variables.

.. [*] The institution which ran MPI-ESM1-2-HR’s historical (CMIP) simulations is MPI-M, while the future (ScenarioMIP) simulations were run by DKRZ. Therefore, the institution component of MPI-ESM1-2-HR filepaths differ between historical and SSP scenarios.

.. _Example Use:

Example Use

See the following examples on github: github.com/microsoft/PlanetaryComputerExamples <https://github.com/microsoft/PlanetaryComputerExamples/blob/main/datasets/cil-gdpcir/>_

• Exploring the GDPCIR dataset with the STAC API: cil-gdpcir-example.ipynb <https://github.com/microsoft/PlanetaryComputerExamples/blob/main/datasets/cil-gdpcir/cil-gdpcir-example.ipynb>_
• Selecting STAC collections and building an ensemble: ensemble.ipynb <https://github.com/microsoft/PlanetaryComputerExamples/blob/main/datasets/cil-gdpcir/ensemble.ipynb>_
• Computing climate & impact indicators with xclim: indicators.ipynb <https://github.com/microsoft/PlanetaryComputerExamples/blob/main/datasets/cil-gdpcir/indicators.ipynb>_

You can try these out in a live example on Binder:

.. image:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/ClimateImpactLab/downscaleCMIP6-binder-env/main?urlpath=git-pull%3Frepo%3Dhttps%253A%252F%252Fgithub.com%252FClimateImpactLab%252FPlanetaryComputerExamples%26urlpath%3Dlab%252Ftree%252FPlanetaryComputerExamples%252Fdatasets%252Fcil-gdpcir%252FREADME.md%26branch%3Dgdpcir-additional-notebooks

.. _Project methods:

Project methods

This project makes use of statistical bias correction and downscaling algorithms, which are specifically designed to accurately re