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.

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Auteurs principaux:	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
Langue:	EN
Publié:	Nature Portfolio 2021
Sujets:	Science Q
Accès en ligne:	https://doaj.org/article/5d37269ed2734f7bac2303b89ff00149
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spelling	oai:doaj.org-article:5d37269ed2734f7bac2303b89ff001492021-12-02T15:09:18ZSeasonal Arctic sea ice forecasting with probabilistic deep learning10.1038/s41467-021-25257-42041-1723https://doaj.org/article/5d37269ed2734f7bac2303b89ff001492021-08-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-25257-4https://doaj.org/toc/2041-1723Accurate 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.Tom R. AnderssonJ. Scott HoskingMaría Pérez-OrtizBrooks PaigeAndrew ElliottChris RussellStephen LawDaniel C. JonesJeremy WilkinsonTony PhillipsJames ByrneSteffen TietscheBeena Balan SarojiniEduardo Blanchard-WrigglesworthYevgeny AksenovRod DownieEmily ShuckburghNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
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
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
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://doaj.org/article/5d37269ed2734f7bac2303b89ff00149
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Seasonal Arctic sea ice forecasting with probabilistic deep learning

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