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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ftdatacite:10.17863/cam.74715 2023-05-15T14:55:14+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 https://dx.doi.org/10.17863/cam.74715 https://www.repository.cam.ac.uk/handle/1810/327266 unknown Apollo - University of Cambridge Repository Article /704/106/125 /704/172/4081 /639/705/117 /639/705/531 /139 /141 /129 /119 article Text Article article-journal ScholarlyArticle 2021 ftdatacite https://doi.org/10.17863/cam.74715 2021-11-05T12:55:41Z 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. Text Arctic Sea ice DataCite Metadata Store (German National Library of Science and Technology) Arctic |
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Article /704/106/125 /704/172/4081 /639/705/117 /639/705/531 /139 /141 /129 /119 article |
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Article /704/106/125 /704/172/4081 /639/705/117 /639/705/531 /139 /141 /129 /119 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 /704/106/125 /704/172/4081 /639/705/117 /639/705/531 /139 /141 /129 /119 article |
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 |
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 |
Apollo - University of Cambridge Repository |
publishDate |
2021 |
url |
https://dx.doi.org/10.17863/cam.74715 https://www.repository.cam.ac.uk/handle/1810/327266 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_doi |
https://doi.org/10.17863/cam.74715 |
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1766327020118081536 |