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A statistics-based reconstruction of high-resolution global terrestrial climate for the last 800,000 years
Curated global climate data have been generated from climate model outputs for the last 120,000 years, whereas reconstructions going back even further have been lacking due to the high computational cost of climate simulations. Here, we present a statistically-derived global terrestrial climate data...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8397735/ https://www.ncbi.nlm.nih.gov/pubmed/34453060 http://dx.doi.org/10.1038/s41597-021-01009-3 |
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author | Krapp, Mario Beyer, Robert M. Edmundson, Stephen L. Valdes, Paul J. Manica, Andrea |
author_facet | Krapp, Mario Beyer, Robert M. Edmundson, Stephen L. Valdes, Paul J. Manica, Andrea |
author_sort | Krapp, Mario |
collection | PubMed |
description | Curated global climate data have been generated from climate model outputs for the last 120,000 years, whereas reconstructions going back even further have been lacking due to the high computational cost of climate simulations. Here, we present a statistically-derived global terrestrial climate dataset for every 1,000 years of the last 800,000 years. It is based on a set of linear regressions between 72 existing HadCM3 climate simulations of the last 120,000 years and external forcings consisting of CO(2), orbital parameters, and land type. The estimated climatologies were interpolated to 0.5° resolution and bias-corrected using present-day climate. The data compare well with the original HadCM3 simulations and with long-term proxy records. Our dataset includes monthly temperature, precipitation, cloud cover, and 17 bioclimatic variables. In addition, we derived net primary productivity and global biome distributions using the BIOME4 vegetation model. The data are a relevant source for different research areas, such as archaeology or ecology, to study the long-term effect of glacial-interglacial climate cycles for periods beyond the last 120,000 years. |
format | Online Article Text |
id | pubmed-8397735 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-83977352021-09-15 A statistics-based reconstruction of high-resolution global terrestrial climate for the last 800,000 years Krapp, Mario Beyer, Robert M. Edmundson, Stephen L. Valdes, Paul J. Manica, Andrea Sci Data Data Descriptor Curated global climate data have been generated from climate model outputs for the last 120,000 years, whereas reconstructions going back even further have been lacking due to the high computational cost of climate simulations. Here, we present a statistically-derived global terrestrial climate dataset for every 1,000 years of the last 800,000 years. It is based on a set of linear regressions between 72 existing HadCM3 climate simulations of the last 120,000 years and external forcings consisting of CO(2), orbital parameters, and land type. The estimated climatologies were interpolated to 0.5° resolution and bias-corrected using present-day climate. The data compare well with the original HadCM3 simulations and with long-term proxy records. Our dataset includes monthly temperature, precipitation, cloud cover, and 17 bioclimatic variables. In addition, we derived net primary productivity and global biome distributions using the BIOME4 vegetation model. The data are a relevant source for different research areas, such as archaeology or ecology, to study the long-term effect of glacial-interglacial climate cycles for periods beyond the last 120,000 years. Nature Publishing Group UK 2021-08-27 /pmc/articles/PMC8397735/ /pubmed/34453060 http://dx.doi.org/10.1038/s41597-021-01009-3 Text en © The Author(s) 2021 https://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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) applies to the metadata files associated with this article. |
spellingShingle | Data Descriptor Krapp, Mario Beyer, Robert M. Edmundson, Stephen L. Valdes, Paul J. Manica, Andrea A statistics-based reconstruction of high-resolution global terrestrial climate for the last 800,000 years |
title | A statistics-based reconstruction of high-resolution global terrestrial climate for the last 800,000 years |
title_full | A statistics-based reconstruction of high-resolution global terrestrial climate for the last 800,000 years |
title_fullStr | A statistics-based reconstruction of high-resolution global terrestrial climate for the last 800,000 years |
title_full_unstemmed | A statistics-based reconstruction of high-resolution global terrestrial climate for the last 800,000 years |
title_short | A statistics-based reconstruction of high-resolution global terrestrial climate for the last 800,000 years |
title_sort | statistics-based reconstruction of high-resolution global terrestrial climate for the last 800,000 years |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8397735/ https://www.ncbi.nlm.nih.gov/pubmed/34453060 http://dx.doi.org/10.1038/s41597-021-01009-3 |
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