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High-resolution and bias-corrected CMIP5 projections for climate change impact assessments
Projections of climate change are available at coarse scales (70–400 km). But agricultural and species models typically require finer scale climate data to model climate change impacts. Here, we present a global database of future climates developed by applying the delta method –a method for climate...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6971081/ https://www.ncbi.nlm.nih.gov/pubmed/31959765 http://dx.doi.org/10.1038/s41597-019-0343-8 |
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author | Navarro-Racines, Carlos Tarapues, Jaime Thornton, Philip Jarvis, Andy Ramirez-Villegas, Julian |
author_facet | Navarro-Racines, Carlos Tarapues, Jaime Thornton, Philip Jarvis, Andy Ramirez-Villegas, Julian |
author_sort | Navarro-Racines, Carlos |
collection | PubMed |
description | Projections of climate change are available at coarse scales (70–400 km). But agricultural and species models typically require finer scale climate data to model climate change impacts. Here, we present a global database of future climates developed by applying the delta method –a method for climate model bias correction. We performed a technical evaluation of the bias-correction method using a ‘perfect sibling’ framework and show that it reduces climate model bias by 50–70%. The data include monthly maximum and minimum temperatures and monthly total precipitation, and a set of bioclimatic indices, and can be used for assessing impacts of climate change on agriculture and biodiversity. The data are publicly available in the World Data Center for Climate (WDCC; cera-www.dkrz.de), as well as in the CCAFS-Climate data portal (http://ccafs-climate.org). The database has been used up to date in more than 350 studies of ecosystem and agricultural impact assessment. |
format | Online Article Text |
id | pubmed-6971081 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-69710812020-01-28 High-resolution and bias-corrected CMIP5 projections for climate change impact assessments Navarro-Racines, Carlos Tarapues, Jaime Thornton, Philip Jarvis, Andy Ramirez-Villegas, Julian Sci Data Data Descriptor Projections of climate change are available at coarse scales (70–400 km). But agricultural and species models typically require finer scale climate data to model climate change impacts. Here, we present a global database of future climates developed by applying the delta method –a method for climate model bias correction. We performed a technical evaluation of the bias-correction method using a ‘perfect sibling’ framework and show that it reduces climate model bias by 50–70%. The data include monthly maximum and minimum temperatures and monthly total precipitation, and a set of bioclimatic indices, and can be used for assessing impacts of climate change on agriculture and biodiversity. The data are publicly available in the World Data Center for Climate (WDCC; cera-www.dkrz.de), as well as in the CCAFS-Climate data portal (http://ccafs-climate.org). The database has been used up to date in more than 350 studies of ecosystem and agricultural impact assessment. Nature Publishing Group UK 2020-01-20 /pmc/articles/PMC6971081/ /pubmed/31959765 http://dx.doi.org/10.1038/s41597-019-0343-8 Text en © The Author(s) 2020 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 associated with this article. |
spellingShingle | Data Descriptor Navarro-Racines, Carlos Tarapues, Jaime Thornton, Philip Jarvis, Andy Ramirez-Villegas, Julian High-resolution and bias-corrected CMIP5 projections for climate change impact assessments |
title | High-resolution and bias-corrected CMIP5 projections for climate change impact assessments |
title_full | High-resolution and bias-corrected CMIP5 projections for climate change impact assessments |
title_fullStr | High-resolution and bias-corrected CMIP5 projections for climate change impact assessments |
title_full_unstemmed | High-resolution and bias-corrected CMIP5 projections for climate change impact assessments |
title_short | High-resolution and bias-corrected CMIP5 projections for climate change impact assessments |
title_sort | high-resolution and bias-corrected cmip5 projections for climate change impact assessments |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6971081/ https://www.ncbi.nlm.nih.gov/pubmed/31959765 http://dx.doi.org/10.1038/s41597-019-0343-8 |
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