Published on 10 August 2018 |
Data from: Climatologies at high resolution for the earth's land surface areas
View DatasetDescription
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.
Citations (236)
Cited on 01 January 2026
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Cited on 02 December 2025
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Cited on 12 November 2025
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Cited on 01 November 2025
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Cited on 01 August 2025
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Cited on 10 July 2025
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Mentions (4)
- https://github.com/UCAS-fxy0v0/dd_forecastSoftware Heritage
Mentioned on 08 December 2024
Weight: 1.64
- https://github.com/Amaris91/Species_Occurence_-BioClim_Data_BoxplotsSoftware Heritage
Mentioned on 12 May 2023
Weight: 1.59
- https://github.com/scrameri/DalbergiaTaxonomySoftware Heritage
Mentioned on 18 April 2023
Weight: 1.59
- https://github.com/jannebor/dd_forecastSoftware Heritage
Mentioned on 15 April 2023
Weight: 1.59
Metrics Over Time
Publication Details
Subfield
Global and Planetary Change
Field
Environmental Science
Domain
Physical Sciences
Confidence Score
79%
Source
Open Alex