Seasonal Arctic sea ice forecasting with probabilistic deep learning
Abstract: 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 d...
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ftuglasgow:oai:eprints.gla.ac.uk:250358 2023-05-15T14:25:16+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://eprints.gla.ac.uk/250358/ http://eprints.gla.ac.uk/250358/1/250358.pdf en eng Nature Research http://eprints.gla.ac.uk/250358/1/250358.pdf Andersson, T. R. et al. (2021) Seasonal Arctic sea ice forecasting with probabilistic deep learning. Nature Communications <http://eprints.gla.ac.uk/view/journal_volume/Nature_Communications.html>, 12, 5124. (doi:10.1038/s41467-021-25257-4 <http://dx.doi.org/10.1038/s41467-021-25257-4>) (PMID:34446701) (PMCID:PMC8390499) cc_by_4 CC-BY Articles PeerReviewed 2021 ftuglasgow https://doi.org/10.1038/s41467-021-25257-4 2022-03-03T23:15:40Z Abstract: 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 University of Glasgow: Enlighten - Publications Arctic Nature Communications 12 1 |
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Open Polar |
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University of Glasgow: Enlighten - Publications |
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ftuglasgow |
language |
English |
description |
Abstract: 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://eprints.gla.ac.uk/250358/ http://eprints.gla.ac.uk/250358/1/250358.pdf |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Arctic Sea ice |
genre_facet |
Arctic Arctic Sea ice |
op_relation |
http://eprints.gla.ac.uk/250358/1/250358.pdf Andersson, T. R. et al. (2021) Seasonal Arctic sea ice forecasting with probabilistic deep learning. Nature Communications <http://eprints.gla.ac.uk/view/journal_volume/Nature_Communications.html>, 12, 5124. (doi:10.1038/s41467-021-25257-4 <http://dx.doi.org/10.1038/s41467-021-25257-4>) (PMID:34446701) (PMCID:PMC8390499) |
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 |
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1766297696236208128 |