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Newly reconstructed Arctic surface air temperatures for 1979–2021 with deep learning method
A precise Arctic surface air temperature (SAT) dataset, that is regularly updated, has more complete spatial and temporal coverage, and is based on instrumental observations, is critically important for timely monitoring and improving understanding of the rapid change in the Arctic climate. In this...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10017794/ https://www.ncbi.nlm.nih.gov/pubmed/36922501 http://dx.doi.org/10.1038/s41597-023-02059-5 |
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author | Ma, Ziqi Huang, Jianbin Zhang, Xiangdong Luo, Yong Ding, Minghu Wen, Jun Jin, Weixin Qiao, Chen Yin, Yifu |
author_facet | Ma, Ziqi Huang, Jianbin Zhang, Xiangdong Luo, Yong Ding, Minghu Wen, Jun Jin, Weixin Qiao, Chen Yin, Yifu |
author_sort | Ma, Ziqi |
collection | PubMed |
description | A precise Arctic surface air temperature (SAT) dataset, that is regularly updated, has more complete spatial and temporal coverage, and is based on instrumental observations, is critically important for timely monitoring and improving understanding of the rapid change in the Arctic climate. In this study, a new monthly gridded Arctic SAT dataset dated back to 1979 was reconstructed with a deep learning method by combining surface air temperatures from multiple data sources. The source data include the observations from land station of GHCN (Global Historical Climatology Network), ICOADS (International Comprehensive Ocean-Atmosphere Data Set) over the oceans, drifting ice station of Russian NP (North Pole), and buoys of IABP (International Arctic Buoy Programme). The last two are crucial for improving the representation of the in-situ observed temperatures within the Arctic. The newly reconstructed dataset includes monthly Arctic SAT beginning in 1979 and daily Arctic SAT beginning in 2011. This dataset would represent a new improvement in developing observational temperature datasets and can be used for a variety of applications. |
format | Online Article Text |
id | pubmed-10017794 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-100177942023-03-17 Newly reconstructed Arctic surface air temperatures for 1979–2021 with deep learning method Ma, Ziqi Huang, Jianbin Zhang, Xiangdong Luo, Yong Ding, Minghu Wen, Jun Jin, Weixin Qiao, Chen Yin, Yifu Sci Data Data Descriptor A precise Arctic surface air temperature (SAT) dataset, that is regularly updated, has more complete spatial and temporal coverage, and is based on instrumental observations, is critically important for timely monitoring and improving understanding of the rapid change in the Arctic climate. In this study, a new monthly gridded Arctic SAT dataset dated back to 1979 was reconstructed with a deep learning method by combining surface air temperatures from multiple data sources. The source data include the observations from land station of GHCN (Global Historical Climatology Network), ICOADS (International Comprehensive Ocean-Atmosphere Data Set) over the oceans, drifting ice station of Russian NP (North Pole), and buoys of IABP (International Arctic Buoy Programme). The last two are crucial for improving the representation of the in-situ observed temperatures within the Arctic. The newly reconstructed dataset includes monthly Arctic SAT beginning in 1979 and daily Arctic SAT beginning in 2011. This dataset would represent a new improvement in developing observational temperature datasets and can be used for a variety of applications. Nature Publishing Group UK 2023-03-15 /pmc/articles/PMC10017794/ /pubmed/36922501 http://dx.doi.org/10.1038/s41597-023-02059-5 Text en © The Author(s) 2023 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/) . |
spellingShingle | Data Descriptor Ma, Ziqi Huang, Jianbin Zhang, Xiangdong Luo, Yong Ding, Minghu Wen, Jun Jin, Weixin Qiao, Chen Yin, Yifu Newly reconstructed Arctic surface air temperatures for 1979–2021 with deep learning method |
title | Newly reconstructed Arctic surface air temperatures for 1979–2021 with deep learning method |
title_full | Newly reconstructed Arctic surface air temperatures for 1979–2021 with deep learning method |
title_fullStr | Newly reconstructed Arctic surface air temperatures for 1979–2021 with deep learning method |
title_full_unstemmed | Newly reconstructed Arctic surface air temperatures for 1979–2021 with deep learning method |
title_short | Newly reconstructed Arctic surface air temperatures for 1979–2021 with deep learning method |
title_sort | newly reconstructed arctic surface air temperatures for 1979–2021 with deep learning method |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10017794/ https://www.ncbi.nlm.nih.gov/pubmed/36922501 http://dx.doi.org/10.1038/s41597-023-02059-5 |
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