xclim-testdata

NetCDF example files used for the testing suite of xclim

Contributing

In order to add a new dataset to the xclim testing data, please ensure you perform the following:

1. Create a new branch: git checkout -b my_new_testdata_branch
2. Place your dataset within an appropriate subdirectory (or create a new one: mkdir data/my_testdata_contribution).
3. Run the registry generation script: python make_check_sums.py
4. Commit your changes: git add testdata_contribution && git commit -m "added my_new_testdata"
5. Open a Pull Request.

To modify an existing dataset, be sure to remove the existing checksum file before running the make_check_sums.py script.

If you wish to perform preliminary tests against the dataset using xclim, this can be done with the following procedure:

from xclim.testing import open_dataset


ds = open_dataset(
    "testdata_contribution/my_netcdf.nc",
    github_url="https://github.com/my_username/xclim-testdata/data",
    branch="my_new_testdata_branch",
    checksum="sha256:1234567890abcdef",
)

Note

The following options only work for branches based on Ouranosinc/xclim-testdata, not forks.

If you wish to run the entire xclim testing suite locally against your branch, this can be set via an environment variable:

$ export XCLIM_TESTDATA_BRANCH="my_new_testdata_branch"

$ pytest xclim
# or, alternatively:
$ tox

If you wish to run the entire xclim testing suite on the Ouranosinc/xclim GitHub Workflows (CI) against your branch, this can be set via an environment variable default in the .github/workflows/main.yml workflow configuration:

env:
  XCLIM_TESTDATA_BRANCH: my_new_testdata_branch

![WARNING] Be aware that modifying this variable to a value other than the latest tagged version of xclim-testdata will trigger a GitHub Workflow that will block merging of your Pull Request until changes are effected.

Versioning

When updating a dataset in xclim-testdata using a development branch and Pull Request, once changes have been merged to the main branch, you should tag a new version of xclim-testdata.

The version tag of xclim-testdata should follow a calendar versioning scheme (i.e. version string follows from vYYYY.MM.DD-r#) reflecting the date of the tag creation, with modifiers if required.

Data Information

CMIP(3/5/6)

• About the CMIP3 project: https://wcrp-cmip.org/cmip3/

• About the CMIP5 project: https://wcrp-cmip.org/cmip5/

• About the CMIP6 project: https://wcrp-cmip.org/cmip6/

• For raw data access: https://pcmdi.llnl.gov/mips/cmip5/data-access-getting-started.html

• Data access: https://pcmdi.llnl.gov/mips/cmip5/data-portal.html

• For information about the NASA GISS-ER model: https://data.giss.nasa.gov/modelE/cmip3/

ECCC (AHCCD: Adjusted and Homogenized Canadian Climate Data)

• About the data: https://www.canada.ca/en/environment-climate-change/services/climate-change/canadian-centre-climate-services/display-download/technical-documentation-adjusted-climate-data.html
• Data license: https://open.canada.ca/en/open-government-licence-canada

ERA5

• About ERA5: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview
• Copernicus Data license: https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf

NA-CORDEX (CRCM5)

• About NA-CORDEX: https://na-cordex.org/
• For more information about NA-CORDEX models: https://na-cordex.org/rcm-characteristics.html

NASA (GFWED)

• About the GFWED project: https://data.giss.nasa.gov/impacts/gfwed/
• GFWED data access: https://portal.nccs.nasa.gov/datashare/GlobalFWI/

NRCAN (NRCan Canada Daily Gridded 10Km and CFFDRS)

• Gridded Daily 10Km data and data access: https://cfs.nrcan.gc.ca/projects/3/4
• About the CFFDRS project: https://cwfis.cfs.nrcan.gc.ca/background/summary/fdr

OURANOS (Generic Climate Scenarios)

• For data access and terms of use: https://www.ouranos.ca/portraits-climatiques/#/
• About the Generic Scenarios project: https://www.ouranos.ca/wp-content/uploads/FicheLoganGauvin2016_EN.pdf

PCIC (BCCAQv2)

• About the data and data access information: https://www.pacificclimate.org/data/statistically-downscaled-climate-scenarios

Regional Variance Dataset

• Data Repository: https://github.com/thenaomig/regionalVariance/
• Goldenson, N., Mauger, G., Leung, L. R., Bitz, C. M., & Rhines, A. (2018). Effects of Ensemble Configuration on Estimates of Regional Climate Uncertainties. Geophysical Research Letters, 45(2), 926–934. https://doi.org/10.1002/2017GL076297

Lafferty-Sriver Uncertainty Dataset

• Data Repository: https://github.com/david0811/lafferty-sriver_2023_npjCliAtm
  • DOI: https://doi.org/10.5281/zenodo.8244794
• Lafferty, D.C. & Sriver, R.L. Downscaling and bias-correction contribute considerable uncertainty to local climate projections in CMIP6. npj Clim Atmos Sci 6, 158 (2023). https://doi.org/10.1038/s41612-023-00486-0