Subseasonal Prediction of Regional Antarctic Sea Ice by a Deep Learning Model
Abstract Antarctic sea ice concentration (SIC) prediction at seasonal scale has been documented, but a gap remains at subseasonal scale (1–8 weeks) due to limited understanding of ice‐related physical mechanisms. To overcome this limitation, we developed a deep learning model named Sea Ice Predictio...
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Online Access: | https://doi.org/10.1029/2023GL104347 https://doaj.org/article/e7f78b85b7834e6e825fde8d702ef1f4 |
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ftdoajarticles:oai:doaj.org/article:e7f78b85b7834e6e825fde8d702ef1f4 2024-09-15T17:48:11+00:00 Subseasonal Prediction of Regional Antarctic Sea Ice by a Deep Learning Model Yunhe Wang Xiaojun Yuan Yibin Ren Mitchell Bushuk Qi Shu Cuihua Li Xiaofeng Li 2023-09-01T00:00:00Z https://doi.org/10.1029/2023GL104347 https://doaj.org/article/e7f78b85b7834e6e825fde8d702ef1f4 EN eng Wiley https://doi.org/10.1029/2023GL104347 https://doaj.org/toc/0094-8276 https://doaj.org/toc/1944-8007 1944-8007 0094-8276 doi:10.1029/2023GL104347 https://doaj.org/article/e7f78b85b7834e6e825fde8d702ef1f4 Geophysical Research Letters, Vol 50, Iss 17, Pp n/a-n/a (2023) Antarctic sea ice prediction Geophysics. Cosmic physics QC801-809 article 2023 ftdoajarticles https://doi.org/10.1029/2023GL104347 2024-08-05T17:49:23Z Abstract Antarctic sea ice concentration (SIC) prediction at seasonal scale has been documented, but a gap remains at subseasonal scale (1–8 weeks) due to limited understanding of ice‐related physical mechanisms. To overcome this limitation, we developed a deep learning model named Sea Ice Prediction Network (SIPNet) that can predict SIC without the need to account for complex physical processes. Compared to mainstream dynamical models like European Centre for Medium‐Range Weather Forecasts, National Centers for Environmental Prediction, and Seamless System for Prediction and Earth System Research developed at Geophysical Fluid Dynamics Laboratory, as well as a relatively advanced statistical model like the linear Markov model, SIPNet outperforms them all, effectively filling the gap in subseasonal Antarctic SIC prediction capability. SIPNet results indicate that autumn SIC variability contributes the most to sea ice predictability, whereas spring contributes the least. In addition, the Weddell Sea displays the highest sea ice predictability, while predictability is low in the West Pacific. SIPNet can also capture the signal of ENSO and SAM on sea ice. Article in Journal/Newspaper Antarc* Antarctic Sea ice Weddell Sea Directory of Open Access Journals: DOAJ Articles Geophysical Research Letters 50 17 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Antarctic sea ice prediction Geophysics. Cosmic physics QC801-809 |
spellingShingle |
Antarctic sea ice prediction Geophysics. Cosmic physics QC801-809 Yunhe Wang Xiaojun Yuan Yibin Ren Mitchell Bushuk Qi Shu Cuihua Li Xiaofeng Li Subseasonal Prediction of Regional Antarctic Sea Ice by a Deep Learning Model |
topic_facet |
Antarctic sea ice prediction Geophysics. Cosmic physics QC801-809 |
description |
Abstract Antarctic sea ice concentration (SIC) prediction at seasonal scale has been documented, but a gap remains at subseasonal scale (1–8 weeks) due to limited understanding of ice‐related physical mechanisms. To overcome this limitation, we developed a deep learning model named Sea Ice Prediction Network (SIPNet) that can predict SIC without the need to account for complex physical processes. Compared to mainstream dynamical models like European Centre for Medium‐Range Weather Forecasts, National Centers for Environmental Prediction, and Seamless System for Prediction and Earth System Research developed at Geophysical Fluid Dynamics Laboratory, as well as a relatively advanced statistical model like the linear Markov model, SIPNet outperforms them all, effectively filling the gap in subseasonal Antarctic SIC prediction capability. SIPNet results indicate that autumn SIC variability contributes the most to sea ice predictability, whereas spring contributes the least. In addition, the Weddell Sea displays the highest sea ice predictability, while predictability is low in the West Pacific. SIPNet can also capture the signal of ENSO and SAM on sea ice. |
format |
Article in Journal/Newspaper |
author |
Yunhe Wang Xiaojun Yuan Yibin Ren Mitchell Bushuk Qi Shu Cuihua Li Xiaofeng Li |
author_facet |
Yunhe Wang Xiaojun Yuan Yibin Ren Mitchell Bushuk Qi Shu Cuihua Li Xiaofeng Li |
author_sort |
Yunhe Wang |
title |
Subseasonal Prediction of Regional Antarctic Sea Ice by a Deep Learning Model |
title_short |
Subseasonal Prediction of Regional Antarctic Sea Ice by a Deep Learning Model |
title_full |
Subseasonal Prediction of Regional Antarctic Sea Ice by a Deep Learning Model |
title_fullStr |
Subseasonal Prediction of Regional Antarctic Sea Ice by a Deep Learning Model |
title_full_unstemmed |
Subseasonal Prediction of Regional Antarctic Sea Ice by a Deep Learning Model |
title_sort |
subseasonal prediction of regional antarctic sea ice by a deep learning model |
publisher |
Wiley |
publishDate |
2023 |
url |
https://doi.org/10.1029/2023GL104347 https://doaj.org/article/e7f78b85b7834e6e825fde8d702ef1f4 |
genre |
Antarc* Antarctic Sea ice Weddell Sea |
genre_facet |
Antarc* Antarctic Sea ice Weddell Sea |
op_source |
Geophysical Research Letters, Vol 50, Iss 17, Pp n/a-n/a (2023) |
op_relation |
https://doi.org/10.1029/2023GL104347 https://doaj.org/toc/0094-8276 https://doaj.org/toc/1944-8007 1944-8007 0094-8276 doi:10.1029/2023GL104347 https://doaj.org/article/e7f78b85b7834e6e825fde8d702ef1f4 |
op_doi |
https://doi.org/10.1029/2023GL104347 |
container_title |
Geophysical Research Letters |
container_volume |
50 |
container_issue |
17 |
_version_ |
1810289328467738624 |