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A monthly global paleo-reanalysis of the atmosphere from 1600 to 2005 for studying past climatic variations
Climatic variations at decadal scales such as phases of accelerated warming or weak monsoons have profound effects on society and economy. Studying these variations requires insights from the past. However, most current reconstructions provide either time series or fields of regional surface climate...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5460593/ https://www.ncbi.nlm.nih.gov/pubmed/28585926 http://dx.doi.org/10.1038/sdata.2017.76 |
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author | Franke, Jörg Brönnimann, Stefan Bhend, Jonas Brugnara, Yuri |
author_facet | Franke, Jörg Brönnimann, Stefan Bhend, Jonas Brugnara, Yuri |
author_sort | Franke, Jörg |
collection | PubMed |
description | Climatic variations at decadal scales such as phases of accelerated warming or weak monsoons have profound effects on society and economy. Studying these variations requires insights from the past. However, most current reconstructions provide either time series or fields of regional surface climate, which limit our understanding of the underlying dynamics. Here, we present the first monthly paleo-reanalysis covering the period 1600 to 2005. Over land, instrumental temperature and surface pressure observations, temperature indices derived from historical documents and climate sensitive tree-ring measurements were assimilated into an atmospheric general circulation model ensemble using a Kalman filtering technique. This data set combines the advantage of traditional reconstruction methods of being as close as possible to observations with the advantage of climate models of being physically consistent and having 3-dimensional information about the state of the atmosphere for various variables and at all points in time. In contrast to most statistical reconstructions, centennial variability stems from the climate model and its forcings, no stationarity assumptions are made and error estimates are provided. |
format | Online Article Text |
id | pubmed-5460593 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-54605932017-06-15 A monthly global paleo-reanalysis of the atmosphere from 1600 to 2005 for studying past climatic variations Franke, Jörg Brönnimann, Stefan Bhend, Jonas Brugnara, Yuri Sci Data Data Descriptor Climatic variations at decadal scales such as phases of accelerated warming or weak monsoons have profound effects on society and economy. Studying these variations requires insights from the past. However, most current reconstructions provide either time series or fields of regional surface climate, which limit our understanding of the underlying dynamics. Here, we present the first monthly paleo-reanalysis covering the period 1600 to 2005. Over land, instrumental temperature and surface pressure observations, temperature indices derived from historical documents and climate sensitive tree-ring measurements were assimilated into an atmospheric general circulation model ensemble using a Kalman filtering technique. This data set combines the advantage of traditional reconstruction methods of being as close as possible to observations with the advantage of climate models of being physically consistent and having 3-dimensional information about the state of the atmosphere for various variables and at all points in time. In contrast to most statistical reconstructions, centennial variability stems from the climate model and its forcings, no stationarity assumptions are made and error estimates are provided. Nature Publishing Group 2017-06-06 /pmc/articles/PMC5460593/ /pubmed/28585926 http://dx.doi.org/10.1038/sdata.2017.76 Text en Copyright © 2017, The Author(s) http://creativecommons.org/licenses/by/4.0 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 Franke, Jörg Brönnimann, Stefan Bhend, Jonas Brugnara, Yuri A monthly global paleo-reanalysis of the atmosphere from 1600 to 2005 for studying past climatic variations |
title | A monthly global paleo-reanalysis of the atmosphere from 1600 to 2005 for studying past climatic variations |
title_full | A monthly global paleo-reanalysis of the atmosphere from 1600 to 2005 for studying past climatic variations |
title_fullStr | A monthly global paleo-reanalysis of the atmosphere from 1600 to 2005 for studying past climatic variations |
title_full_unstemmed | A monthly global paleo-reanalysis of the atmosphere from 1600 to 2005 for studying past climatic variations |
title_short | A monthly global paleo-reanalysis of the atmosphere from 1600 to 2005 for studying past climatic variations |
title_sort | monthly global paleo-reanalysis of the atmosphere from 1600 to 2005 for studying past climatic variations |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5460593/ https://www.ncbi.nlm.nih.gov/pubmed/28585926 http://dx.doi.org/10.1038/sdata.2017.76 |
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