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

Dirk Nikolaus KargerDepartment of Systematic and Evolutionary Botany, University of Zurich, Zollikerstrasse 107, Zurich 8008, SwitzerlandOlaf ConradInstitute of Geography, University of Hamburg, Bundesstrasse 55, Hamburg 20146, GermanyJürgen BöhnerInstitute of Geography, University of Hamburg, Bundesstrasse 55, Hamburg 20146, GermanyTobias KawohlInstitute of Geography, University of Hamburg, Bundesstrasse 55, Hamburg 20146, GermanyHolger KreftBiodiversity, Macroecology &Conservation Biogeography Group, University of Göttingen, Göttingen 37077, GermanyRodrigo Wilber Soria-AuzaAsociación Armonía, Av. Lomas de Arena # 400, Zona Palmasola, Santa Cruz de la Sierra 10260, BoliviaNiklaus E. ZimmermannSwiss Federal Research Institute WSL, Zürcherstr 111, Birmensdorf 8903, SwitzerlandH. Peter LinderDepartment of Systematic and Evolutionary Botany, University of Zurich, Zollikerstrasse 107, Zurich 8008, SwitzerlandMichael KesslerDepartment of Systematic and Evolutionary Botany, University of Zurich, Zollikerstrasse 107, Zurich 8008, Switzerland
2017en
ABI

Аннотация

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.

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