CHELSA-W5E5: daily 1 km meteorological forcing data for climate impact studies

作者：Dirk Nikolaus Karger, Stefan Lange, Chantal Hari, Christopher Reyer, Olaf Conrad, Niklaus E. Zimmermann, Katja Frieler · 发表于：Earth system science data · 年份：2023 · DOI：10.5194/essd-15-2445-2023 · 被引用次数：97 · 研究领域：Climate variability and models、Meteorological Phenomena and Simulations、Cryospheric studies and observations

Abstract. Current changes in the world's climate increasingly impact a wide variety of sectors globally, from agriculture and ecosystems to water and energy supply or human health. Many impacts of climate on these sectors happen at high spatio-temporal resolutions that are not covered by current global climate datasets. Here we present CHELSA-W5E5 (https://doi.org/10.48364/ISIMIP.836809.3, Karger et al., 2022): a climate forcing dataset at daily temporal resolution and 30 arcsec spatial resolution for air temperatures, precipitation 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 Weather Research and Forecasting (WRF) regional climate model, 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 ...