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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ftunivcam:oai:www.repository.cam.ac.uk:1810/327266 2023-07-30T04:01:20+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 2021-08-26T18:38:33Z text/xml application/pdf https://doi.org/10.17863/CAM.74715 https://www.repository.cam.ac.uk/handle/1810/327266 en eng Nature Publishing Group UK Nature Communications doi:10.17863/CAM.74715 https://www.repository.cam.ac.uk/handle/1810/327266 Article /704/106/125 /704/172/4081 /639/705/117 /639/705/531 /139 /141 /129 /119 Article 2021 ftunivcam https://doi.org/10.17863/CAM.74715 2023-07-10T21:17:29Z 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 Sea ice Apollo - University of Cambridge Repository Arctic |
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Open Polar |
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Apollo - University of Cambridge Repository |
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ftunivcam |
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English |
topic |
Article /704/106/125 /704/172/4081 /639/705/117 /639/705/531 /139 /141 /129 /119 |
spellingShingle |
Article /704/106/125 /704/172/4081 /639/705/117 /639/705/531 /139 /141 /129 /119 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 |
topic_facet |
Article /704/106/125 /704/172/4081 /639/705/117 /639/705/531 /139 /141 /129 /119 |
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 |
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 Publishing Group UK |
publishDate |
2021 |
url |
https://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_relation |
doi:10.17863/CAM.74715 https://www.repository.cam.ac.uk/handle/1810/327266 |
op_doi |
https://doi.org/10.17863/CAM.74715 |
_version_ |
1772812076766461952 |