Climatologies at high resolution for the earth's land surface areas.
- Karger DN Department of Systematic and Evolutionary Botany, University of Zurich, Zollikerstrasse 107, Zurich 8008, Switzerland.
- Conrad O Institute of Geography, University of Hamburg, Bundesstrasse 55, Hamburg 20146, Germany.
- Böhner J Institute of Geography, University of Hamburg, Bundesstrasse 55, Hamburg 20146, Germany.
- Kawohl T Institute of Geography, University of Hamburg, Bundesstrasse 55, Hamburg 20146, Germany.
- Kreft H Biodiversity, Macroecology &Conservation Biogeography Group, University of Göttingen, Göttingen 37077, Germany.
- Soria-Auza RW Biodiversity, Macroecology &Conservation Biogeography Group, University of Göttingen, Göttingen 37077, Germany.
- Zimmermann NE Swiss Federal Research Institute WSL, Zürcherstr 111, Birmensdorf 8903, Switzerland.
- Linder HP Department of Systematic and Evolutionary Botany, University of Zurich, Zollikerstrasse 107, Zurich 8008, Switzerland.
- Kessler M Department of Systematic and Evolutionary Botany, University of Zurich, Zollikerstrasse 107, Zurich 8008, Switzerland.
- 2017-09-06
Published in:
- Scientific data. - 2017
English
High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present the CHELSA (Climatologies at high resolution for the earth's land surface areas) data of downscaled model output temperature and precipitation estimates of the ERA-Interim climatic reanalysis to a high resolution of 30 arc sec. The temperature algorithm is based on statistical downscaling of atmospheric temperatures. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height, with a subsequent bias correction. The resulting data consist of a monthly temperature and precipitation climatology for the years 1979-2013. We compare the data derived from the CHELSA algorithm with other standard gridded products and station data from the Global Historical Climate Network. We compare the performance of the new climatologies in species distribution modelling and show that we can increase the accuracy of species range predictions. We further show that CHELSA climatological data has a similar accuracy as other products for temperature, but that its predictions of precipitation patterns are better.
- Language
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- English
- Open access status
- gold
- Identifiers
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- DOI 10.1038/sdata.2017.122
- PMID 28872642
- Persistent URL
- https://sonar.ch/global/documents/258463
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