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A virtual climate library of surface temperature over North America for 1979–2015
The most comprehensive continuous-coverage modern climatic data sets, known as reanalyses, come from combining state-of-the-art numerical weather prediction (NWP) models with diverse available observations. These reanalysis products estimate the path of climate evolution that actually happened, and...
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/PMC5644371/ https://www.ncbi.nlm.nih.gov/pubmed/29039842 http://dx.doi.org/10.1038/sdata.2017.155 |
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author | Kravtsov, Sergey Roebber, Paul Brazauskas, Vytaras |
author_facet | Kravtsov, Sergey Roebber, Paul Brazauskas, Vytaras |
author_sort | Kravtsov, Sergey |
collection | PubMed |
description | The most comprehensive continuous-coverage modern climatic data sets, known as reanalyses, come from combining state-of-the-art numerical weather prediction (NWP) models with diverse available observations. These reanalysis products estimate the path of climate evolution that actually happened, and their use in a probabilistic context—for example, to document trends in extreme events in response to climate change—is, therefore, limited. Free runs of NWP models without data assimilation can in principle be used for the latter purpose, but such simulations are computationally expensive and are prone to systematic biases. Here we produce a high-resolution, 100-member ensemble simulation of surface atmospheric temperature over North America for the 1979–2015 period using a comprehensive spatially extended non-stationary statistical model derived from the data based on the North American Regional Reanalysis. The surrogate climate realizations generated by this model are independent from, yet nearly statistically congruent with reality. This data set provides unique opportunities for the analysis of weather-related risk, with applications in agriculture, energy development, and protection of human life. |
format | Online Article Text |
id | pubmed-5644371 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-56443712017-10-23 A virtual climate library of surface temperature over North America for 1979–2015 Kravtsov, Sergey Roebber, Paul Brazauskas, Vytaras Sci Data Data Descriptor The most comprehensive continuous-coverage modern climatic data sets, known as reanalyses, come from combining state-of-the-art numerical weather prediction (NWP) models with diverse available observations. These reanalysis products estimate the path of climate evolution that actually happened, and their use in a probabilistic context—for example, to document trends in extreme events in response to climate change—is, therefore, limited. Free runs of NWP models without data assimilation can in principle be used for the latter purpose, but such simulations are computationally expensive and are prone to systematic biases. Here we produce a high-resolution, 100-member ensemble simulation of surface atmospheric temperature over North America for the 1979–2015 period using a comprehensive spatially extended non-stationary statistical model derived from the data based on the North American Regional Reanalysis. The surrogate climate realizations generated by this model are independent from, yet nearly statistically congruent with reality. This data set provides unique opportunities for the analysis of weather-related risk, with applications in agriculture, energy development, and protection of human life. Nature Publishing Group 2017-10-17 /pmc/articles/PMC5644371/ /pubmed/29039842 http://dx.doi.org/10.1038/sdata.2017.155 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 Kravtsov, Sergey Roebber, Paul Brazauskas, Vytaras A virtual climate library of surface temperature over North America for 1979–2015 |
title | A virtual climate library of surface temperature over North America for 1979–2015 |
title_full | A virtual climate library of surface temperature over North America for 1979–2015 |
title_fullStr | A virtual climate library of surface temperature over North America for 1979–2015 |
title_full_unstemmed | A virtual climate library of surface temperature over North America for 1979–2015 |
title_short | A virtual climate library of surface temperature over North America for 1979–2015 |
title_sort | virtual climate library of surface temperature over north america for 1979–2015 |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5644371/ https://www.ncbi.nlm.nih.gov/pubmed/29039842 http://dx.doi.org/10.1038/sdata.2017.155 |
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