02 Dec 2022
02 Dec 2022
Status: this preprint is currently under review for the journal ESSD.

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

Dirk Nikolaus Karger1, Stefan Lange2, Chantal Hari1,3, Christopher P. O. Reyer2, Olaf Conrad4, Niklaus E. Zimmermann1, and Katja Frieler2 Dirk Nikolaus Karger et al.
  • 1Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903 Birmensdorf, Switzerland
  • 2Potsdam Institute for Climate Impact Research (PIK), Member of Leibniz Association, P.O.Box 601203, 14412, Potsdam, Germany
  • 3Wyss Academy for Nature at the University of Bern, Kochergasse 4, 3011 Bern, Switzerland
  • 4University of Hamburg, Bundesstraße 55, 20146 Hamburg, Germany

Abstract. 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,, 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.

Dirk Nikolaus Karger et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on essd-2022-367', ali jaan, 02 Dec 2022
  • RC1: 'Comment on essd-2022-367', Anonymous Referee #1, 08 Dec 2022
  • RC2: 'Comment on essd-2022-367', Anonymous Referee #2, 24 Dec 2022

Dirk Nikolaus Karger et al.

Data sets

CHELSA-W5E5 v1.0: W5E5 v1.0 downscaled with CHELSA v2.0 Dirk N. Karger, Stefan Lange, Chantal Hari, Christopher P. O. Reyer, Niklaus E. Zimmermann

Dirk Nikolaus Karger et al.


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Short summary
We present the first 1km, daily, global climate dataset for climate impact studies. We show that the high resolution data has a decreased bias, and higher correlation with measurements from meteorological stations than coarser data. The dataset will be of value for a wide range of climate change impact studies both at global and regional level that benefit from using a consistent global dataset.