Climatologies at high resolution for the earth's land surface areas

2017 | journal article. A publication with affiliation to the University of Göttingen.

Jump to: Cite & Linked | Documents & Media | Details | Version history

Cite this publication

​Climatologies at high resolution for the earth's land surface areas​
Karger, D. N.; Conrad, O.; Böhner, J.; Kawohl, T.; Kreft, H. ; Soria-Auza, R. W. & Zimmermann, N. E. et al.​ (2017) 
Scientific data4 art. 170122​.​ DOI: https://doi.org/10.1038/sdata.2017.122 

Documents & Media

sdata2017122.pdf3.28 MBAdobe PDF

License

Published Version

Attribution 4.0 CC BY 4.0

Details

Authors
Karger, Dirk Nikolaus; Conrad, Olaf; Böhner, Jürgen; Kawohl, Tobias; Kreft, Holger ; Soria-Auza, Rodrigo Wilber; Zimmermann, Niklaus E.; Linder, H. Peter; Kessler, Michael
Abstract
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.
Issue Date
2017
Journal
Scientific data 
Organization
Fakultät für Forstwissenschaften und Waldökologie ; Burckhardt-Institut ; Abteilung Biodiversität, Makroökologie und Biogeographie 
eISSN
2052-4463
Language
English

Reference

Citations


Social Media