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Climatologies at high resolution for the earth’s land surface areas
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 t...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5584396/ https://www.ncbi.nlm.nih.gov/pubmed/28872642 http://dx.doi.org/10.1038/sdata.2017.122 |
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author | Karger, Dirk Nikolaus Conrad, Olaf Böhner, Jürgen Kawohl, Tobias Kreft, Holger Soria-Auza, Rodrigo Wilber Zimmermann, Niklaus E. Linder, H. Peter Kessler, Michael |
author_facet | Karger, Dirk Nikolaus Conrad, Olaf Böhner, Jürgen Kawohl, Tobias Kreft, Holger Soria-Auza, Rodrigo Wilber Zimmermann, Niklaus E. Linder, H. Peter Kessler, Michael |
author_sort | Karger, Dirk Nikolaus |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5584396 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-55843962017-09-12 Climatologies at high resolution for the earth’s land surface areas Karger, Dirk Nikolaus Conrad, Olaf Böhner, Jürgen Kawohl, Tobias Kreft, Holger Soria-Auza, Rodrigo Wilber Zimmermann, Niklaus E. Linder, H. Peter Kessler, Michael Sci Data Data Descriptor 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. Nature Publishing Group 2017-09-05 /pmc/articles/PMC5584396/ /pubmed/28872642 http://dx.doi.org/10.1038/sdata.2017.122 Text en Copyright © 2017, The Author(s) http://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files made available in this article. |
spellingShingle | Data Descriptor Karger, Dirk Nikolaus Conrad, Olaf Böhner, Jürgen Kawohl, Tobias Kreft, Holger Soria-Auza, Rodrigo Wilber Zimmermann, Niklaus E. Linder, H. Peter Kessler, Michael Climatologies at high resolution for the earth’s land surface areas |
title | Climatologies at high resolution for the earth’s land surface areas |
title_full | Climatologies at high resolution for the earth’s land surface areas |
title_fullStr | Climatologies at high resolution for the earth’s land surface areas |
title_full_unstemmed | Climatologies at high resolution for the earth’s land surface areas |
title_short | Climatologies at high resolution for the earth’s land surface areas |
title_sort | climatologies at high resolution for the earth’s land surface areas |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5584396/ https://www.ncbi.nlm.nih.gov/pubmed/28872642 http://dx.doi.org/10.1038/sdata.2017.122 |
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