Seasonal Arctic sea ice forecasting with probabilistic deep learning

Accurate seasonal forecasts of sea ice are highly valuable, particularly in the context of sea ice loss due to global warming. A new machine learning tool for sea ice forecasting offers a substantial increase in accuracy over current physics-based dynamical model predictions.

Bibliographic Details
Published in:Nature Communications
Main Authors: Tom R. Andersson, J. Scott Hosking, María Pérez-Ortiz, Brooks Paige, Andrew Elliott, Chris Russell, Stephen Law, Daniel C. Jones, Jeremy Wilkinson, Tony Phillips, James Byrne, Steffen Tietsche, Beena Balan Sarojini, Eduardo Blanchard-Wrigglesworth, Yevgeny Aksenov, Rod Downie, Emily Shuckburgh
Format: Article in Journal/Newspaper
Language:English
Published: Nature Portfolio 2021
Subjects:
Science
Q
Arctic
Global warming
Sea ice
Online Access:https://doi.org/10.1038/s41467-021-25257-4
https://doaj.org/article/5d37269ed2734f7bac2303b89ff00149
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spelling ftdoajarticles:oai:doaj.org/article:5d37269ed2734f7bac2303b89ff00149 2023-05-15T14:52:56+02:00 Seasonal Arctic sea ice forecasting with probabilistic deep learning Tom R. Andersson J. Scott Hosking María Pérez-Ortiz Brooks Paige Andrew Elliott Chris Russell Stephen Law Daniel C. Jones Jeremy Wilkinson Tony Phillips James Byrne Steffen Tietsche Beena Balan Sarojini Eduardo Blanchard-Wrigglesworth Yevgeny Aksenov Rod Downie Emily Shuckburgh 2021-08-01T00:00:00Z https://doi.org/10.1038/s41467-021-25257-4 https://doaj.org/article/5d37269ed2734f7bac2303b89ff00149 EN eng Nature Portfolio https://doi.org/10.1038/s41467-021-25257-4 https://doaj.org/toc/2041-1723 doi:10.1038/s41467-021-25257-4 2041-1723 https://doaj.org/article/5d37269ed2734f7bac2303b89ff00149 Nature Communications, Vol 12, Iss 1, Pp 1-12 (2021) Science Q article 2021 ftdoajarticles https://doi.org/10.1038/s41467-021-25257-4 2022-12-31T09:23:09Z Accurate seasonal forecasts of sea ice are highly valuable, particularly in the context of sea ice loss due to global warming. A new machine learning tool for sea ice forecasting offers a substantial increase in accuracy over current physics-based dynamical model predictions. Article in Journal/Newspaper Arctic Global warming Sea ice Directory of Open Access Journals: DOAJ Articles Arctic Nature Communications 12 1
institution Open Polar
collection Directory of Open Access Journals: DOAJ Articles
op_collection_id ftdoajarticles
language English
topic Science
Q
spellingShingle Science
Q
Tom R. Andersson
J. Scott Hosking
María Pérez-Ortiz
Brooks Paige
Andrew Elliott
Chris Russell
Stephen Law
Daniel C. Jones
Jeremy Wilkinson
Tony Phillips
James Byrne
Steffen Tietsche
Beena Balan Sarojini
Eduardo Blanchard-Wrigglesworth
Yevgeny Aksenov
Rod Downie
Emily Shuckburgh
Seasonal Arctic sea ice forecasting with probabilistic deep learning
topic_facet Science
Q
description Accurate seasonal forecasts of sea ice are highly valuable, particularly in the context of sea ice loss due to global warming. A new machine learning tool for sea ice forecasting offers a substantial increase in accuracy over current physics-based dynamical model predictions.
format Article in Journal/Newspaper
author Tom R. Andersson
J. Scott Hosking
María Pérez-Ortiz
Brooks Paige
Andrew Elliott
Chris Russell
Stephen Law
Daniel C. Jones
Jeremy Wilkinson
Tony Phillips
James Byrne
Steffen Tietsche
Beena Balan Sarojini
Eduardo Blanchard-Wrigglesworth
Yevgeny Aksenov
Rod Downie
Emily Shuckburgh
author_facet Tom R. Andersson
J. Scott Hosking
María Pérez-Ortiz
Brooks Paige
Andrew Elliott
Chris Russell
Stephen Law
Daniel C. Jones
Jeremy Wilkinson
Tony Phillips
James Byrne
Steffen Tietsche
Beena Balan Sarojini
Eduardo Blanchard-Wrigglesworth
Yevgeny Aksenov
Rod Downie
Emily Shuckburgh
author_sort Tom R. Andersson
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 Portfolio
publishDate 2021
url https://doi.org/10.1038/s41467-021-25257-4
https://doaj.org/article/5d37269ed2734f7bac2303b89ff00149
geographic Arctic
geographic_facet Arctic
genre Arctic
Global warming
Sea ice
genre_facet Arctic
Global warming
Sea ice
op_source Nature Communications, Vol 12, Iss 1, Pp 1-12 (2021)
op_relation https://doi.org/10.1038/s41467-021-25257-4
https://doaj.org/toc/2041-1723
doi:10.1038/s41467-021-25257-4
2041-1723
https://doaj.org/article/5d37269ed2734f7bac2303b89ff00149
op_doi https://doi.org/10.1038/s41467-021-25257-4
container_title Nature Communications
container_volume 12
container_issue 1
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