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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ftnerc:oai:nora.nerc.ac.uk:529437 2023-05-15T14:28:01+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 text http://nora.nerc.ac.uk/id/eprint/529437/ https://nora.nerc.ac.uk/id/eprint/529437/1/s41467-021-25257-4.pdf https://www.nature.com/articles/s41467-021-25257-4 en eng Nature Research https://nora.nerc.ac.uk/id/eprint/529437/1/s41467-021-25257-4.pdf Andersson, Tom R. orcid:0000-0002-1556-9932 Hosking, J. Scott orcid:0000-0002-3646-3504 Pérez-Ortiz, María; Paige, Brooks; Elliott, Andrew; Russell, Chris; Law, Stephen; Jones, Daniel C. orcid:0000-0002-8701-4506 Wilkinson, Jeremy; Phillips, Tony orcid:0000-0002-3058-9157 Byrne, James orcid:0000-0003-3731-2377 Tietsche, Steffen; Sarojini, Beena Balan; Blanchard-Wrigglesworth, Eduardo; Aksenov, Yevgeny orcid:0000-0001-6132-3434 Downie, Rod; Shuckburgh, Emily orcid:0000-0001-9206-3444 . 2021 Seasonal Arctic sea ice forecasting with probabilistic deep learning. Nature Communications, 12, 5124. 12, pp. https://doi.org/10.1038/s41467-021-25257-4 <https://doi.org/10.1038/s41467-021-25257-4> cc_by_4 CC-BY Publication - Article PeerReviewed 2021 ftnerc https://doi.org/10.1038/s41467-021-25257-4 2023-02-04T19:51:38Z 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. Article in Journal/Newspaper Arctic Arctic Sea ice Natural Environment Research Council: NERC Open Research Archive Arctic Nature Communications 12 1 |
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Open Polar |
collection |
Natural Environment Research Council: NERC Open Research Archive |
op_collection_id |
ftnerc |
language |
English |
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 |
Article in Journal/Newspaper |
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 |
spellingShingle |
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 |
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 Research |
publishDate |
2021 |
url |
http://nora.nerc.ac.uk/id/eprint/529437/ https://nora.nerc.ac.uk/id/eprint/529437/1/s41467-021-25257-4.pdf https://www.nature.com/articles/s41467-021-25257-4 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Arctic Sea ice |
genre_facet |
Arctic Arctic Sea ice |
op_relation |
https://nora.nerc.ac.uk/id/eprint/529437/1/s41467-021-25257-4.pdf Andersson, Tom R. orcid:0000-0002-1556-9932 Hosking, J. Scott orcid:0000-0002-3646-3504 Pérez-Ortiz, María; Paige, Brooks; Elliott, Andrew; Russell, Chris; Law, Stephen; Jones, Daniel C. orcid:0000-0002-8701-4506 Wilkinson, Jeremy; Phillips, Tony orcid:0000-0002-3058-9157 Byrne, James orcid:0000-0003-3731-2377 Tietsche, Steffen; Sarojini, Beena Balan; Blanchard-Wrigglesworth, Eduardo; Aksenov, Yevgeny orcid:0000-0001-6132-3434 Downie, Rod; Shuckburgh, Emily orcid:0000-0001-9206-3444 . 2021 Seasonal Arctic sea ice forecasting with probabilistic deep learning. Nature Communications, 12, 5124. 12, pp. https://doi.org/10.1038/s41467-021-25257-4 <https://doi.org/10.1038/s41467-021-25257-4> |
op_rights |
cc_by_4 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.1038/s41467-021-25257-4 |
container_title |
Nature Communications |
container_volume |
12 |
container_issue |
1 |
_version_ |
1766302143223955456 |