DOI: 10.5281/zenodo.14704314
title: Alpine-wide climate indices from reanalyses and EURO-CORDEX projections for different Global Warming Levels

description: This data set contains calculated and aggregated climate indices for Reanalysis and EURO-CORDEX data on Global Warming Levels (GWLs) for the Alpine Space.

Data

The reanalysis data used was CERRA (Ridal, et al. 2024) and ERA5 (Hersbach, et al. 2020) on a daily basis. CERRA was used for precipitation, temperature and wind related indices, while drought and heat-drought compound indices are based on a downscaled version of ERA5 from the ADO project (https://www.alpine-space.eu/project/ado/).

EURO-CORDEX (Jacob, et al. 2014) data is listed in Tab. 1 and includes two different Regional Climate Models that are driven by 5 to 6 different Global Climate Models. Only the scenario RCP8.5 has been used, because it contains the most Global Warming Levels out of all scenarios.

All data was reprojected to the Alpine Space projection EPSG:32632. The period 1991 to 2020 has been used as historical reference for the reanalysis data. For EURO-CORDEX data, the GWL +1°C has been used as reference.

 

Table 1: Models from EURO-CORDEX that have been used to calculate climate indices.










Regional Climate Model 









Global Climate Model 









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 







Methods

Climate indices (see Tab. 2) were first calculated on either an annual, or seasonal basis and are then averaged over different GWLs given by Hauser et al. (2022), however the therein 20-year GWL periods were extended by 5 years in each direction, such that the used GWLs here span 30 years. All projected changes for each GWL are calculated as differences, either absolute or relative, with respect to the 30-year period correspoding to GWL 1°C. Projected changes are calculated for each EURO-CORDEX simulation and are then reported as ensemble percentiles. The following percentiles are contained: 5, 25, 50, 75, 95.

Table 2: Climate indices calculated, containing the name, abbreviation and use-case:










Climate index name 









Abbreviation 









Use-case 











Maximum 1-day precipitation  









Rx1d 









Extreme 1-day precipitation intensity 











Maximum 3-day precipitation  









Rx3d 









Extreme multi-day precipitation intensity 











Number of days with 1-day precipitation exceeding the 97th percentile 









R97pN_1d 









Extreme 1-day precipitation frequency 











Sum of 1-day precipitation exceeding the 97th percentile 









R97pTOT_1d 









Extreme 1-day precipitation cumulative intensity 











Number of days with 3-day precipitation exceeding the 97th percentile 









R97pN_3d 









Extreme multi-day precipitation frequency 











Sum of 3-day precipitation exceeding the 97th percentile 









R97pTOT_3d 









Extreme multi-day precipitation cumulative intensity 











Convective indicator orange warning 









conv_ind_orange 









Extreme convective precipitation 











Convective indicator red warning 









conv_ind_red 









Very extreme convective precipitation 











Maximum 1-day wind speed 









WSx1d 









Extreme wind speed intensity 











Number of days with 1-day wind speed exceeding the 97th percentile 









WS97pN_1d 









Extreme wind speed frequency 











Mean of maximum wind speed for days exceeding the 97th percentile 









WS97pMEAN_1d 









Wind speed on very windy days 











Number of days with extreme 3-day precipitation and 1-day wind speed maximum 









RWS97pN_3d 









Compound extreme precipitation and wind frequency 











One-month drought peak intensity 









SPEI1_int 









Peak intensity of one-month drought 











One-month drought frequency 









SPEI1_freq 









Frequency of one-month severe drought conditions 











Three-month drought peak intensity 









SPEI3_int 









Peak intensity of three-month drought 











Three-month drought frequency 









SPEI3_freq 









Frequency of three-month severe drought conditions 











Heatwave days 









HWD 









Heatwave frequency 











Heatwave magnitude 









HWM 









Heatwave intensity 











Magnitude of compound one-month drought and heatwave events 









CDHW_1 









Compound heatwave and one-month drought magnitude 











Magnitude of compound three-month drought and heatwave events 









CDHW_3 









Compound heatwave and three-month drought magnitude 







Visualization and more detailed information

The data can be viewed interactively via a WebGIS platform, which also contains more information about data processing and index definitions: https://cct.eurac.edu/x-risk-cc

Acknowledgements

The research leading to these results has received funding from Interreg Alpine Space Program 2021-27 under the project number ASP0100101, “How to adapt to changing weather eXtremes and associated compound and cascading RISKs in the context of Climate Change” (X-RISK-CC).

References

Hauser, M., Engelbrecht, F., & Fischer, E. M. (2022). Transient global warming levels for CMIP5 and CMIP6 (v0.3.0). Zenodo. https://doi.org/10.5281/zenodo.7390473

Hersbach, H., Bell, B., Berrisford, P., et al. (2020). The ERA5 global reanalysis. Q. J. R. Meteorol. Soc., 146, 1999–2049. https://doi.org/10.1002/qj.3803  

Jacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O.B., Bouwer, L.M., Braun, A., Colette, A., Déqué, M., Georgievski, G. and Georgopoulou, E., (2014). EURO-CORDEX: new high-resolution climate change projections for European impact research, Reg. Environ. Change, 14, 563-578. https://doi.org/10.1007/s10113-013-0499-2 

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