CHELSA Future Climate Predictors for SDMs - OneSTOP Task 5.1

This entry groups the six future CHELSA climate-scenario datasets prepared for Species Distribution Model (SDM) projections within the OneSTOP Project (Task 5.1). The datasets provide high-resolution (~1 km) bioclimatic and environmental predictor variables derived from the CHELSA v2.1 BIOCLIM+ dataset, which is based on downscaled and bias-corrected climate data.

For each future period and SSP/RCP scenario, CHELSA v2.1 climate layers were obtained for the five available CMIP6 Global Circulation Models (GCMs): GFDL-ESM4, UKESM1-0-LL, MPI-ESM1-2-HR, IPSL-CM6A-LR, and MRI-ESM2-0. The GCM layers were averaged into a single ensemble mean for each time period and scenario to reduce dependence on any individual climate model and provide a robust representation of mid- and late-century climatic conditions for SDMs.

The ensemble predictors were prepared to be compatible with the land-cover projections of Chen et al. (2022), enabling integration between future climate suitability projections and 1 km plant functional type land-cover scenarios. Raster data were internally scaled and reprojected using bilinear resampling in the terra R package.

All raster layers are provided as GeoTIFF float files in EPSG:6933, WGS 1984 / NSIDC EASE-Grid 2.0 Global (Cylindrical Equal Area projection). The variables include the standard BIOCLIM temperature and precipitation predictors and additional BIOCLIM+ metrics, including growing-season length, growing-season precipitation, growing-season mean temperature, growing-degree days above 0 °C, 5 °C, and 10 °C, and net primary productivity.

These future predictor datasets complement the historical CHELSA baseline used for model training and are intended for assessing climatic suitability and potential species distributions under changing environmental conditions, including OneSTOP analyses of Invasive Alien Species and integration with future land-cover projections.

Chen, G., Li, X., & Liu, X. (2022). Global land projection based on plant functional types with a 1-km resolution under socio-climatic scenarios. Scientific Data, 9, 125. https://doi.org/10.1038/s41597-022-01208-6

Notes

List of variables in the dataset