Seasonal Arctic sea ice forecasting with probabilistic deep learning
Anthropogenic warming has led to an unprecedented year-round reduction in Arctic sea ice extent. This has far-reaching consequences for indigenous and local communities, polar ecosystems, and global climate, motivating the need for accurate seasonal sea ice forecasts. While physics-based dynamical m...
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ftpubmed:oai:pubmedcentral.nih.gov:8390499 2023-05-15T14:54:07+02:00 Seasonal Arctic sea ice forecasting with probabilistic deep learning Andersson, Tom R. Hosking, J. Scott Pérez-Ortiz, María Paige, Brooks Elliott, Andrew Russell, Chris Law, Stephen Jones, Daniel C. Wilkinson, Jeremy Phillips, Tony Byrne, James Tietsche, Steffen Sarojini, Beena Balan Blanchard-Wrigglesworth, Eduardo Aksenov, Yevgeny Downie, Rod Shuckburgh, Emily 2021-08-26 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8390499/ http://www.ncbi.nlm.nih.gov/pubmed/34446701 https://doi.org/10.1038/s41467-021-25257-4 en eng Nature Publishing Group UK http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8390499/ http://www.ncbi.nlm.nih.gov/pubmed/34446701 http://dx.doi.org/10.1038/s41467-021-25257-4 © 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/) . CC-BY Nat Commun Article Text 2021 ftpubmed https://doi.org/10.1038/s41467-021-25257-4 2021-09-26T00:22:35Z Anthropogenic warming has led to an unprecedented year-round reduction in Arctic sea ice extent. This has far-reaching consequences for indigenous and local communities, polar ecosystems, and global climate, motivating the need for accurate seasonal sea ice forecasts. While physics-based dynamical models can successfully forecast sea ice concentration several weeks ahead, they struggle to outperform simple statistical benchmarks at longer lead times. We present a probabilistic, deep learning sea ice forecasting system, IceNet. The system has been trained on climate simulations and observational data to forecast the next 6 months of monthly-averaged sea ice concentration maps. We show that IceNet advances the range of accurate sea ice forecasts, outperforming a state-of-the-art dynamical model in seasonal forecasts of summer sea ice, particularly for extreme sea ice events. This step-change in sea ice forecasting ability brings us closer to conservation tools that mitigate risks associated with rapid sea ice loss. Text Arctic Sea ice PubMed Central (PMC) Arctic Nature Communications 12 1 |
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Article Andersson, Tom R. Hosking, J. Scott Pérez-Ortiz, María Paige, Brooks Elliott, Andrew Russell, Chris Law, Stephen Jones, Daniel C. Wilkinson, Jeremy Phillips, Tony Byrne, James Tietsche, Steffen Sarojini, Beena Balan Blanchard-Wrigglesworth, Eduardo Aksenov, Yevgeny Downie, Rod Shuckburgh, Emily Seasonal Arctic sea ice forecasting with probabilistic deep learning |
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Article |
description |
Anthropogenic warming has led to an unprecedented year-round reduction in Arctic sea ice extent. This has far-reaching consequences for indigenous and local communities, polar ecosystems, and global climate, motivating the need for accurate seasonal sea ice forecasts. While physics-based dynamical models can successfully forecast sea ice concentration several weeks ahead, they struggle to outperform simple statistical benchmarks at longer lead times. We present a probabilistic, deep learning sea ice forecasting system, IceNet. The system has been trained on climate simulations and observational data to forecast the next 6 months of monthly-averaged sea ice concentration maps. We show that IceNet advances the range of accurate sea ice forecasts, outperforming a state-of-the-art dynamical model in seasonal forecasts of summer sea ice, particularly for extreme sea ice events. This step-change in sea ice forecasting ability brings us closer to conservation tools that mitigate risks associated with rapid sea ice loss. |
format |
Text |
author |
Andersson, Tom R. Hosking, J. Scott Pérez-Ortiz, María Paige, Brooks Elliott, Andrew Russell, Chris Law, Stephen Jones, Daniel C. Wilkinson, Jeremy Phillips, Tony Byrne, James Tietsche, Steffen Sarojini, Beena Balan Blanchard-Wrigglesworth, Eduardo Aksenov, Yevgeny Downie, Rod Shuckburgh, Emily |
author_facet |
Andersson, Tom R. Hosking, J. Scott Pérez-Ortiz, María Paige, Brooks Elliott, Andrew Russell, Chris Law, Stephen Jones, Daniel C. Wilkinson, Jeremy Phillips, Tony Byrne, James Tietsche, Steffen Sarojini, Beena Balan Blanchard-Wrigglesworth, Eduardo Aksenov, Yevgeny Downie, Rod Shuckburgh, Emily |
author_sort |
Andersson, Tom R. |
title |
Seasonal Arctic sea ice forecasting with probabilistic deep learning |
title_short |
Seasonal Arctic sea ice forecasting with probabilistic deep learning |
title_full |
Seasonal Arctic sea ice forecasting with probabilistic deep learning |
title_fullStr |
Seasonal Arctic sea ice forecasting with probabilistic deep learning |
title_full_unstemmed |
Seasonal Arctic sea ice forecasting with probabilistic deep learning |
title_sort |
seasonal arctic sea ice forecasting with probabilistic deep learning |
publisher |
Nature Publishing Group UK |
publishDate |
2021 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8390499/ http://www.ncbi.nlm.nih.gov/pubmed/34446701 https://doi.org/10.1038/s41467-021-25257-4 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_source |
Nat Commun |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8390499/ http://www.ncbi.nlm.nih.gov/pubmed/34446701 http://dx.doi.org/10.1038/s41467-021-25257-4 |
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/) . |
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CC-BY |
op_doi |
https://doi.org/10.1038/s41467-021-25257-4 |
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Nature Communications |
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12 |
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1 |
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