Abstract
<strong class="journal-contentHeaderColor">Abstract.</strong> Current changes in the world’s climate increasingly impact a wide variety of sectors globally, from agricul-ture, ecosystems, to water and energy supply or human health. Many impacts of climate on these sectors hap-pen at high spatio-temporal resolutions that are not covered by current global climate datasets. Here we pre-sent Climatologies at high resolution for the Earth’s land surface areas - WFDE5 over land merged with ERA5 over the ocean data (CHELSA-W5E5, <a href="https://doi.org/10.48364/ISIMIP.836809.3" target="_blank" rel="noopener">https://doi.org/10.48364/ISIMIP.836809.3</a>, Karger et al., 2022): a cli-mate forcing dataset at daily temporal resolution and 30 arcsec spatial resolution for air-temperatures, precipi-tation rates, and downwelling shortwave solar radiation. This dataset is a spatially downscaled version of the 0.5° W5E5 dataset using the CHELSA V2 topographic downscaling algorithm. We show that the downscaling generally increases the accuracy of climate data by decreasing the bias, and increasing the correlation with measurements from meteorological stations. Bias reductions are largest in topographically complex terrain. Limitations arise for minimum near surface air temperatures in regions that are prone to cold air pooling, or at the upper extreme end of surface downwelling shortwave radiation. We further show that our topographically downscaled climate data compare well with the results of dynamical downscaling using the regional climate model Weather Research and Forecasting Model (WRF), as time series from both sources are similarly well correlated to station observations. This is remarkable given the lower computational cost of the CHELSA V2 algorithm compared to WRF and similar models. Overall, we conclude that the downscaling can provide high-er resolution climate data with increased accuracy. Hence, the dataset will be of value for a wide range of climate change impact studies both at global level but also as for applications that cover more than one region and benefit from using a consistent dataset across these regions.
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