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

Abstract ∘ 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 compared to WRF and similar models. Overall, we conclude that the downscaling can provide higher-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 and for applications that cover more than one region and benefit from using a consistent dataset across these regions.

Standort
Deutsche Nationalbibliothek Frankfurt am Main
Umfang
Online-Ressource
Sprache
Englisch

Erschienen in
CHELSA-W5E5: daily 1 km meteorological forcing data for climate impact studies ; volume:15 ; number:6 ; year:2023 ; pages:2445-2464 ; extent:20
Earth system science data ; 15, Heft 6 (2023), 2445-2464 (gesamt 20)

Urheber
Karger, Dirk Nikolaus
Lange, Stefan
Hari, Chantal
Reyer, Christopher
Conrad, Olaf
Zimmermann, Niklaus E.
Frieler, Katja

DOI
10.5194/essd-15-2445-2023
URN
urn:nbn:de:101:1-2023061504292028928074
Rechteinformation
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
Letzte Aktualisierung
14.08.2025, 11:01 MESZ

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