Data-driven surrogate modeling of high-resolution sea-ice thickness in the Arctic
A novel generation of sea-ice models with elasto-brittle rheologies, such as neXtSIM, can represent sea-ice processes with an unprecedented accuracy at the mesoscale for resolutions of around 10 km. As these models are computationally expensive, we introduce supervised deep learning techniques for s...
Published in: | The Cryosphere |
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Format: | Article in Journal/Newspaper |
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Copernicus Publications
2024
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ftnonlinearchiv:oai:noa.gwlb.de:cop_mods_00072951 2024-05-19T07:35:50+00:00 Data-driven surrogate modeling of high-resolution sea-ice thickness in the Arctic Durand, Charlotte Finn, Tobias Sebastian Farchi, Alban Bocquet, Marc Boutin, Guillaume Ólason, Einar 2024-04 electronic https://doi.org/10.5194/tc-18-1791-2024 https://noa.gwlb.de/receive/cop_mods_00072951 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00071139/tc-18-1791-2024.pdf https://tc.copernicus.org/articles/18/1791/2024/tc-18-1791-2024.pdf eng eng Copernicus Publications The Cryosphere -- ˜Theœ Cryosphere -- http://www.bibliothek.uni-regensburg.de/ezeit/?2393169 -- http://www.the-cryosphere.net/ -- 1994-0424 https://doi.org/10.5194/tc-18-1791-2024 https://noa.gwlb.de/receive/cop_mods_00072951 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00071139/tc-18-1791-2024.pdf https://tc.copernicus.org/articles/18/1791/2024/tc-18-1791-2024.pdf https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess article Verlagsveröffentlichung article Text doc-type:article 2024 ftnonlinearchiv https://doi.org/10.5194/tc-18-1791-2024 2024-04-22T23:50:58Z A novel generation of sea-ice models with elasto-brittle rheologies, such as neXtSIM, can represent sea-ice processes with an unprecedented accuracy at the mesoscale for resolutions of around 10 km. As these models are computationally expensive, we introduce supervised deep learning techniques for surrogate modeling of the sea-ice thickness from neXtSIM simulations. We adapt a convolutional U-Net architecture to an Arctic-wide setup by taking the land–sea mask with partial convolutions into account. Trained to emulate the sea-ice thickness at a lead time of 12 h, the neural network can be iteratively applied to predictions for up to 1 year. The improvements of the surrogate model over a persistence forecast persist from 12 h to roughly 1 year, with improvements of up to 50 % in the forecast error. Moreover, the predictability gain for the sea-ice thickness measured against the daily climatology extends to over 6 months. By using atmospheric forcings as additional input, the surrogate model can represent advective and thermodynamical processes which influence the sea-ice thickness and the growth and melting therein. While iterating, the surrogate model experiences diffusive processes which result in a loss of fine-scale structures. However, this smoothing increases the coherence of large-scale features and thereby the stability of the model. Therefore, based on these results, we see huge potential for surrogate modeling of state-of-the-art sea-ice models with neural networks. Article in Journal/Newspaper Arctic Sea ice The Cryosphere Niedersächsisches Online-Archiv NOA The Cryosphere 18 4 1791 1815 |
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Niedersächsisches Online-Archiv NOA |
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language |
English |
topic |
article Verlagsveröffentlichung |
spellingShingle |
article Verlagsveröffentlichung Durand, Charlotte Finn, Tobias Sebastian Farchi, Alban Bocquet, Marc Boutin, Guillaume Ólason, Einar Data-driven surrogate modeling of high-resolution sea-ice thickness in the Arctic |
topic_facet |
article Verlagsveröffentlichung |
description |
A novel generation of sea-ice models with elasto-brittle rheologies, such as neXtSIM, can represent sea-ice processes with an unprecedented accuracy at the mesoscale for resolutions of around 10 km. As these models are computationally expensive, we introduce supervised deep learning techniques for surrogate modeling of the sea-ice thickness from neXtSIM simulations. We adapt a convolutional U-Net architecture to an Arctic-wide setup by taking the land–sea mask with partial convolutions into account. Trained to emulate the sea-ice thickness at a lead time of 12 h, the neural network can be iteratively applied to predictions for up to 1 year. The improvements of the surrogate model over a persistence forecast persist from 12 h to roughly 1 year, with improvements of up to 50 % in the forecast error. Moreover, the predictability gain for the sea-ice thickness measured against the daily climatology extends to over 6 months. By using atmospheric forcings as additional input, the surrogate model can represent advective and thermodynamical processes which influence the sea-ice thickness and the growth and melting therein. While iterating, the surrogate model experiences diffusive processes which result in a loss of fine-scale structures. However, this smoothing increases the coherence of large-scale features and thereby the stability of the model. Therefore, based on these results, we see huge potential for surrogate modeling of state-of-the-art sea-ice models with neural networks. |
format |
Article in Journal/Newspaper |
author |
Durand, Charlotte Finn, Tobias Sebastian Farchi, Alban Bocquet, Marc Boutin, Guillaume Ólason, Einar |
author_facet |
Durand, Charlotte Finn, Tobias Sebastian Farchi, Alban Bocquet, Marc Boutin, Guillaume Ólason, Einar |
author_sort |
Durand, Charlotte |
title |
Data-driven surrogate modeling of high-resolution sea-ice thickness in the Arctic |
title_short |
Data-driven surrogate modeling of high-resolution sea-ice thickness in the Arctic |
title_full |
Data-driven surrogate modeling of high-resolution sea-ice thickness in the Arctic |
title_fullStr |
Data-driven surrogate modeling of high-resolution sea-ice thickness in the Arctic |
title_full_unstemmed |
Data-driven surrogate modeling of high-resolution sea-ice thickness in the Arctic |
title_sort |
data-driven surrogate modeling of high-resolution sea-ice thickness in the arctic |
publisher |
Copernicus Publications |
publishDate |
2024 |
url |
https://doi.org/10.5194/tc-18-1791-2024 https://noa.gwlb.de/receive/cop_mods_00072951 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00071139/tc-18-1791-2024.pdf https://tc.copernicus.org/articles/18/1791/2024/tc-18-1791-2024.pdf |
genre |
Arctic Sea ice The Cryosphere |
genre_facet |
Arctic Sea ice The Cryosphere |
op_relation |
The Cryosphere -- ˜Theœ Cryosphere -- http://www.bibliothek.uni-regensburg.de/ezeit/?2393169 -- http://www.the-cryosphere.net/ -- 1994-0424 https://doi.org/10.5194/tc-18-1791-2024 https://noa.gwlb.de/receive/cop_mods_00072951 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00071139/tc-18-1791-2024.pdf https://tc.copernicus.org/articles/18/1791/2024/tc-18-1791-2024.pdf |
op_rights |
https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.5194/tc-18-1791-2024 |
container_title |
The Cryosphere |
container_volume |
18 |
container_issue |
4 |
container_start_page |
1791 |
op_container_end_page |
1815 |
_version_ |
1799474857808756736 |