An ensemble reconstruction of global monthly sea surface temperature and sea ice concentration 1000–1849
This paper describes a global monthly gridded Sea Surface Temperature (SST) and Sea Ice Concentration (SIC) dataset for the period 1000–1849, which can be used as boundary conditions for atmospheric model simulations. The reconstruction is based on existing coarse-resolution annual temperature ensem...
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ftpubmed:oai:pubmedcentral.nih.gov:8490424 2023-05-15T18:17:46+02:00 An ensemble reconstruction of global monthly sea surface temperature and sea ice concentration 1000–1849 Samakinwa, Eric Valler, Veronika Hand, Ralf Neukom, Raphael Gómez-Navarro, Juan José Kennedy, John Rayner, Nick A. Brönnimann, Stefan 2021-10-04 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8490424/ http://www.ncbi.nlm.nih.gov/pubmed/34608148 https://doi.org/10.1038/s41597-021-01043-1 en eng Nature Publishing Group UK http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8490424/ http://www.ncbi.nlm.nih.gov/pubmed/34608148 http://dx.doi.org/10.1038/s41597-021-01043-1 © 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. CC0 PDM CC-BY Sci Data Data Descriptor Text 2021 ftpubmed https://doi.org/10.1038/s41597-021-01043-1 2021-10-10T00:39:20Z This paper describes a global monthly gridded Sea Surface Temperature (SST) and Sea Ice Concentration (SIC) dataset for the period 1000–1849, which can be used as boundary conditions for atmospheric model simulations. The reconstruction is based on existing coarse-resolution annual temperature ensemble reconstructions, which are then augmented with intra-annual and sub-grid scale variability. The intra-annual component of HadISST.2.0 and oceanic indices estimated from the reconstructed annual mean are used to develop grid-based linear regressions in a monthly stratified approach. Similarly, we reconstruct SIC using analog resampling of HadISST.2.0 SIC (1941–2000), for both hemispheres. Analogs are pooled in four seasons, comprising of 3-months each. The best analogs are selected based on the correlation between each member of the reconstructed SST and its target. For the period 1780 to 1849, We assimilate historical observations of SST and night-time marine air temperature from the ICOADS dataset into our reconstruction using an offline Ensemble Kalman Filter approach. The resulting dataset is physically consistent with information from models, proxies, and observations. Text Sea ice PubMed Central (PMC) Scientific Data 8 1 |
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Data Descriptor Samakinwa, Eric Valler, Veronika Hand, Ralf Neukom, Raphael Gómez-Navarro, Juan José Kennedy, John Rayner, Nick A. Brönnimann, Stefan An ensemble reconstruction of global monthly sea surface temperature and sea ice concentration 1000–1849 |
topic_facet |
Data Descriptor |
description |
This paper describes a global monthly gridded Sea Surface Temperature (SST) and Sea Ice Concentration (SIC) dataset for the period 1000–1849, which can be used as boundary conditions for atmospheric model simulations. The reconstruction is based on existing coarse-resolution annual temperature ensemble reconstructions, which are then augmented with intra-annual and sub-grid scale variability. The intra-annual component of HadISST.2.0 and oceanic indices estimated from the reconstructed annual mean are used to develop grid-based linear regressions in a monthly stratified approach. Similarly, we reconstruct SIC using analog resampling of HadISST.2.0 SIC (1941–2000), for both hemispheres. Analogs are pooled in four seasons, comprising of 3-months each. The best analogs are selected based on the correlation between each member of the reconstructed SST and its target. For the period 1780 to 1849, We assimilate historical observations of SST and night-time marine air temperature from the ICOADS dataset into our reconstruction using an offline Ensemble Kalman Filter approach. The resulting dataset is physically consistent with information from models, proxies, and observations. |
format |
Text |
author |
Samakinwa, Eric Valler, Veronika Hand, Ralf Neukom, Raphael Gómez-Navarro, Juan José Kennedy, John Rayner, Nick A. Brönnimann, Stefan |
author_facet |
Samakinwa, Eric Valler, Veronika Hand, Ralf Neukom, Raphael Gómez-Navarro, Juan José Kennedy, John Rayner, Nick A. Brönnimann, Stefan |
author_sort |
Samakinwa, Eric |
title |
An ensemble reconstruction of global monthly sea surface temperature and sea ice concentration 1000–1849 |
title_short |
An ensemble reconstruction of global monthly sea surface temperature and sea ice concentration 1000–1849 |
title_full |
An ensemble reconstruction of global monthly sea surface temperature and sea ice concentration 1000–1849 |
title_fullStr |
An ensemble reconstruction of global monthly sea surface temperature and sea ice concentration 1000–1849 |
title_full_unstemmed |
An ensemble reconstruction of global monthly sea surface temperature and sea ice concentration 1000–1849 |
title_sort |
ensemble reconstruction of global monthly sea surface temperature and sea ice concentration 1000–1849 |
publisher |
Nature Publishing Group UK |
publishDate |
2021 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8490424/ http://www.ncbi.nlm.nih.gov/pubmed/34608148 https://doi.org/10.1038/s41597-021-01043-1 |
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Sea ice |
genre_facet |
Sea ice |
op_source |
Sci Data |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8490424/ http://www.ncbi.nlm.nih.gov/pubmed/34608148 http://dx.doi.org/10.1038/s41597-021-01043-1 |
op_rights |
© 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. |
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CC0 PDM CC-BY |
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https://doi.org/10.1038/s41597-021-01043-1 |
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