About the Data
The X-RISK-CC project developed a set of 20 indices for weather extremes covering the Alpine Space and they are made available under open data license. The indices provided in the WebGIS are calculated starting from existing climate datasets:
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The Copernicus European Regional ReAnalysis (CERRA) and the Copernicus European Regional ReAnalysis Land (CERRA-Land) for historical conditions (1991-2020). The original data are freely available from the Climate Data Store of the Copernicus Climate Change Service (Climate Data Store).
Ridal, M., Bazile, E., Le Moigne, P., Randriamampianina, R., Schimanke, S., Andrae, U., et al. (2024) CERRA, the Copernicus European Regional Reanalysis system. Quarterly Journal of the Royal Meteorological Society, 150(763), 3385–3411, https://doi.org/10.1002/qj.4764 -
The Alpine Drought Observatory (https://ado.eurac.edu/) providing drought indices and downscaled temperature data used for the calculation of compound heat and drought indices in the historical period.
Slovenian Environment Agency, & Central Institution for Meteorology and Geodynamics. (2022). Standardised Precipitation-Evapotranspiration Index - ERA5_QM SPEI-1 (Version 1.0) [Data set]. Eurac Research. https://doi.org/10.48784/166e51ee-534a-11ec-9143-02000a08f41d -
The EURO-CORDEX dataset (EURO-CORDEX Data) for the future projections. Original EURO-CORDEX data are freely available from the Climate Data Store of the Copernicus Climate Change Service (Climate Data Store).
Jacob, D., Petersen, J., Eggert, B. et al. (2014). EURO-CORDEX: new high-resolution climate change projections for European impact research. Regional Environmental Change, 14, 563–578, https://doi.org/10.1007/s10113-013-0499-2
All indices are calculated at the spatial resolution of the original datasets. The maps of indices are at 5.5-km resolution for the historical period 1991-2020 and at 12-km resolution for the future conditions. The geographical reference is EPSG: 32632 (https://epsg.io/32632).
Data license and access
All data shown on this webpage are CC-BY-4.0 licensed.
All data can be downloaded from the X-RISK-CC WebGIS directly as PNG, NetCDF or GeoJSON with NUTS3 aggregations or accessed through the Zenodo repository: https://doi.org/10.5281/zenodo.14704314
Citations
Lehner, S., Crespi, A., Žun, M., Haslinger, K., Pistotnik, G., Maines, E., Vlahović, Ž., Campalani, P., Honzak, L., Bertalanič, R., & Lokošek, N. (2025). Alpine-wide climate indices from reanalyses and EURO-CORDEX projections for different Global Warming Levels (1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14704314
Technical details about the data collection and processing
CERRA and CERRA-Land data were downloaded using the cdsapi (https://cds.climate.copernicus.eu/how-to-api) in their original grib format and preprocessed. CERRA/CERRA-Land fields were reprojected from original projection (proj string: "+proj=lcc +lat_1=50 +lat_2=50 +lat_0=50 +lon_0=8 +x_0=0 +y_0=0 +a=6371229 +b=6371229 +units=m +no_defs") to the target Alpine Space projection (EPSG:32632) using rioxarray (https://pypi.org/project/rioxarray/). Finally, the reprojected data were sliced to the following bounds: x = [87637, 1108500] and y = [4763678, 5601958] and stored as NetCDF files.
The Standardized Precipitation and Evapotranspiration Index (SPEI) for the historical period was retrieved from the precalculated indices derived from downscaled ERA5 reanalysis data within the ADO project (https://www.alpine-space.eu/project/ado/). SPEI data were available at daily resolution in ETRS89 Lambert Azimuthal Equal Area projection (EPSG:3035). The fields were downsampled to monthly resolution by using the last entry of each month and then reprojected to the target AS projection (EPSG:32632). To ensure consistency when calculating indices for compound heatwave and drought events, requiring both SPEI and maximum temperature, 2-m maximum temperature fields were retrieved at daily resolution from the same downscaled ERA5 source provided by the ADO project. The downscaled ERA5 data for temperature were reprojected from the original projection (proj string: "+proj=lcc +lat_1=50 +lat_2=50 +lat_0=50 +lon_0=8 +x_0=2937018.5829291 +y_0=2937031.41074803 +a=6371229 +b=6371229 +units=m +no_defs") to EPSG:32632, while no further temporal processing was applied.
In X-RISK-CC WebGIS the historical fields are derived from the processed reanalysis data as 30-year averages over 1991-2020. This period is used as reference for all index calculation with the only exception of SPEI-based indices, since SPEI values were precalculated in the ADO project wherein 1981-2020 was adopted as calibration period.
A selection of EURO-CORDEX model simulations from CMIP5 (Jacob et al., 2014) providing daily gridded fields at about 12-km spatial resolution was used for the calculation of future climate indices across the AS. The models to be used were constrained by requiring several available variables, that are needed to calculate the whole set of indices (see the document with index descriptions in the X-RISK-CC Digital Library). This limited the calculation of climate indices to two Regional Climate Models (RCMs), COSMO-crCLIM-v1-1 and RCA4, driven by 5 and 6 Global Climate Models (GCMs), respectively, for a total of 19 model simulations. All selected models are listed in Table 1. Since the climate indices were then aggregated to Global Warming Levels (GWLs), only the scenario RCP8.5 was used, to cover as many GWLs as possible. Historical EURO-CORDEX simulations were used together with the scenario data to derive historical reference values to use for the calculation of projected changes. All EURO-CORDEX data were downloaded from ESGF (https://esgf-data.dkrz.de/) by using wget downloading scripts (https://esgf.github.io/esgf-user-support/user_guide.html#download-data-from-esgf-using-wget). The data were reprojected from the source projection (proj string: "+proj=ob_tran +o_proj=longlat +o_lon_p=0 +o_lat_p=39.25 +lon_0=18 +to_meter=0.01745329") to the AS projection (EPSG:32632) and then sliced to the following bounds: x = [87637, 1108500] and y = [4763678, 5601958] (similar to the preprocessing of reanalysis data, albeit with a different spatial resolution). For SPEI calculation, 2-m minimum and maximum temperatures were additionally aggregated to monthly means and used as input to calculate the potential evapotranspiration based on the Hargreaves formulation (Hargreaves and Samani, 1985) as implemented in xclim (hargreaves85, https://pypi.org/project/xclim/). The derived potential evapotranspiration was combined with monthly accumulated precipitation totals to calculate SPEI, through xclim, using a log-logistic distribution.
Table 1: EURO-CORDEX simulations used for the calculation of climate indices for the Alpine Space.
| RCM | GCM | Ensemble Member |
|---|---|---|
| COSMO-crCLIM-v1-1 | CNRM-CM5 | r1i1p1 |
| COSMO-crCLIM-v1-1 | EC-EARTH | r1i1p1 |
| COSMO-crCLIM-v1-1 | EC-EARTH | r3i1p1 |
| COSMO-crCLIM-v1-1 | EC-EARTH | r12i1p1 |
| COSMO-crCLIM-v1-1 | HadGEM2-ES | r1i1p1 |
| COSMO-crCLIM-v1-1 | MPI-ESM-LR | r1i1p1 |
| COSMO-crCLIM-v1-1 | MPI-ESM-LR | r2i1p1 |
| COSMO-crCLIM-v1-1 | MPI-ESM-LR | r3i1p1 |
| COSMO-crCLIM-v1-1 | NorESM1-M | r1i1p1 |
| RCA4 | CNRM-CM5 | r1i1p1 |
| RCA4 | EC-EARTH | r1i1p1 |
| RCA4 | EC-EARTH | r3i1p1 |
| RCA4 | EC-EARTH | r12i1p1 |
| RCA4 | HadGEM2-ES | r1i1p1 |
| RCA4 | MPI-ESM-LR | r1i1p1 |
| RCA4 | MPI-ESM-LR | r2i1p1 |
| RCA4 | MPI-ESM-LR | r3i1p1 |
| RCA4 | IPSL-CM5A-MR | r1i1p1 |
| RCA4 | NorESM1-M | r1i1p1 |
Future changes are calculated and presented following the approach based onGlobal Warming Levels (GWLs). GWLs represent different global temperature increases with respect to the preindustrial period (1850-1900). The time periods corresponding to GWL +1.5, +2, +3, +4 °C for each selected GCM were identified following the IPCC methodology (https://github.com/mathause/cmip_warming_levels?tab=readme-ov-file, https://zenodo.org/records/7390473). However, since those are 20-year intervals, we prolonged the IPCC GWL periods by 5 years in each direction, thereby covering 30 years, while keeping the centre of the period unchanged. The resulting periods for each GWL and GCM simulation are listed in Table 2. All climate indices at their annual/seasonal temporal resolution were averaged over the corresponding GWL 30-year period, yielding one value for each grid cell per GWL and model.
Table 2: Global Warming Levels for the driving GCMs of the used EURO-CORDEX simulations, extended to 30-year periods. Model-GWL combinations without available GWLs are marked with "-" and are not used for the calculation of changes for the missing GWL.
| GCM | Ensemble Member | GWL +1 °C | GWL +1.5 °C | GWL +2 °C | GWL +3 °C | GWL +4 °C |
|---|---|---|---|---|---|---|
| CNRM-CM5 | r1i1p1 | 1997-2026 | 2016-2045 | 2031-2060 | 2053-2082 | 2073-2102 |
| EC-EARTH | r1i1p1 | 1987-2016 | 2005-2034 | 2021-2050 | 2047-2076 | 2068-2097 |
| EC-EARTH | r3i1p1 | - | - | - | - | - |
| EC-EARTH | r12i1p1 | 1989-2018 | 2004-2033 | 2020-2049 | 2046-2075 | 2068-2097 |
| HadGEM2-ES | r1i1p1 | 1995-2024 | 2009-2038 | 2021-2050 | 2040-2069 | 2057-2086 |
| MPI-ESM-LR | r1i1p1 | 1988-2017 | 2003-2032 | 2023-2052 | 2047-2076 | 2067-2096 |
| MPI-ESM-LR | r2i1p1 | 1984-2013 | 2002-2031 | 2018-2047 | 2045-2074 | 2066-2095 |
| MPI-ESM-LR | r3i1p1 | 1989-2018 | 2006-2035 | 2021-2050 | 2045-2074 | 2066-2095 |
| NorESM1-M | r1i1p1 | 2002-2031 | 2018-2047 | 2034-2063 | 2059-2088 | - |
| IPSL-CM5A-MR | r1i1p1 | 1987-2016 | 2001-2030 | 2016-2045 | 2036-2065 | 2052-2081 |
For each index, the projected future changes are then expressed as differences of the 30-year averaged GWL values with respect to GWL +1 °C fields. GWL +1 °C, which roughly coincides with the 1991-2020 period, was adopted as reference for EURO-CORDEX data and used for the derivation of indices in the historical period. The only exception is for SPEI-based indices, for which the period corresponding to GWL +0.61 °C (~ 1981-2010) was used for the index calibration.
The convective indicators cover a slightly different domain not including the south-western section of the AS since they were derived from a precalculated dataset based on ERA5 and reprojected to the AS projection (EPSG: 32632). In the X-RISK-CC WebGIS, aggregated information at NUTS3 level for the convective indicators is provided only for NUTS3 regions fully covered by data.