Portfolio of future climate change scenarios

Extreme wildfire events (EWE) have significant socioeconomic and environmental impacts, with many concerns about their increasing occurrence under climate change effects. We aimed to generate new climate projections critical for predicting EWE in Europe. Using state-of-the-art climate models, we provided long-term projections of key climatic drivers, such as the Continuous Haines Index (CHI) and FIRE Weather Index (FWI), which assess atmospheric instability and fire danger. Projections were made at both pan-European and Living Lab scales, with daily/montly data from CMIP6-SSP scenarios, offering essential insights for wildfire preparedness and management. 

The data is presented in NetCDF format.

The EU_DATA.zip includes two subfolders: one containing the raw daily model data for various variables, and another with the ensemble means of the models for several indices, over the time period 1995-2014; 2041-2060; 2081-2100.

The fwi_daily.zip contains both the FWI and the daily values of the variables needed to calculate the FWI from various CMIP6 models, over the time period 1995-2014; 2041-2060; 2081-2100.

The Catalonia_LL_FWI, Aquitane_LL_FWW, Peloponnese_GREECE_LL_FWI, Germany_Netherlands_LL_FWI and Portugal_LL_FWI only include the daily FWI values. The LLs statistical downscaling has been performed to a higher resolution (9km) using ERA5-Land as the reference dataset following the methodology of Varotsos 2023*.

Within each folder, there is a NetCD file corresponding to calculated indices and variables, generated under various climate models (five in total) and climatic scenarios (three different scenarios) during different time windows (three periods).

Example:

Index: FWI

Model name: cmcc_esm2

Scenario: ssp2.6

Period: 2081-2100

Resolution: Monthly

The file: FWI_cmcc_esm2_2081-2100_ssp26_monthly.nc

*Varotsos, K.V., Dandou, A., Papangelis, G. et al. Using a new local high resolution daily gridded dataset for Attica to statistically downscale climate projections. Clim Dyn 60, 2931–2956 (2023). https://doi.org/10.1007/s00382-022-06482-z