Southern Ocean Dynamics Under Climate Change: New Knowledge Through Physics-Guided Machine Learning ...
Complex ocean systems such as the Antarctic Circumpolar Current play key roles in the climate, and current models predict shifts in their strength and area under climate change. However, the physical processes underlying these changes are not well understood, in part due to the difficulty of charact...
Main Authors: | , , , |
---|---|
Format: | Article in Journal/Newspaper |
Language: | unknown |
Published: |
arXiv
2023
|
Subjects: | |
Online Access: | https://dx.doi.org/10.48550/arxiv.2310.13916 https://arxiv.org/abs/2310.13916 |
id |
ftdatacite:10.48550/arxiv.2310.13916 |
---|---|
record_format |
openpolar |
spelling |
ftdatacite:10.48550/arxiv.2310.13916 2024-02-04T09:54:55+01:00 Southern Ocean Dynamics Under Climate Change: New Knowledge Through Physics-Guided Machine Learning ... Yik, William Sonnewald, Maike Clare, Mariana C. A. Lguensat, Redouane 2023 https://dx.doi.org/10.48550/arxiv.2310.13916 https://arxiv.org/abs/2310.13916 unknown arXiv Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 Atmospheric and Oceanic Physics physics.ao-ph Machine Learning cs.LG FOS Physical sciences FOS Computer and information sciences Article Preprint CreativeWork article 2023 ftdatacite https://doi.org/10.48550/arxiv.2310.13916 2024-01-05T02:12:45Z Complex ocean systems such as the Antarctic Circumpolar Current play key roles in the climate, and current models predict shifts in their strength and area under climate change. However, the physical processes underlying these changes are not well understood, in part due to the difficulty of characterizing and tracking changes in ocean physics in complex models. Using the Antarctic Circumpolar Current as a case study, we extend the method Tracking global Heating with Ocean Regimes (THOR) to a mesoscale eddy permitting climate model and identify regions of the ocean characterized by similar physics, called dynamical regimes, using readily accessible fields from climate models. To this end, we cluster grid cells into dynamical regimes and train an ensemble of neural networks, allowing uncertainty quantification, to predict these regimes and track them under climate change. Finally, we leverage this new knowledge to elucidate the dynamical drivers of the identified regime shifts as noted by the neural network ... : 14 pages, 11 figures, NeurIPS 2023 Workshop: Tackling Climate Change with Machine Learning ... Article in Journal/Newspaper Antarc* Antarctic Southern Ocean DataCite Metadata Store (German National Library of Science and Technology) Antarctic Southern Ocean The Antarctic |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
unknown |
topic |
Atmospheric and Oceanic Physics physics.ao-ph Machine Learning cs.LG FOS Physical sciences FOS Computer and information sciences |
spellingShingle |
Atmospheric and Oceanic Physics physics.ao-ph Machine Learning cs.LG FOS Physical sciences FOS Computer and information sciences Yik, William Sonnewald, Maike Clare, Mariana C. A. Lguensat, Redouane Southern Ocean Dynamics Under Climate Change: New Knowledge Through Physics-Guided Machine Learning ... |
topic_facet |
Atmospheric and Oceanic Physics physics.ao-ph Machine Learning cs.LG FOS Physical sciences FOS Computer and information sciences |
description |
Complex ocean systems such as the Antarctic Circumpolar Current play key roles in the climate, and current models predict shifts in their strength and area under climate change. However, the physical processes underlying these changes are not well understood, in part due to the difficulty of characterizing and tracking changes in ocean physics in complex models. Using the Antarctic Circumpolar Current as a case study, we extend the method Tracking global Heating with Ocean Regimes (THOR) to a mesoscale eddy permitting climate model and identify regions of the ocean characterized by similar physics, called dynamical regimes, using readily accessible fields from climate models. To this end, we cluster grid cells into dynamical regimes and train an ensemble of neural networks, allowing uncertainty quantification, to predict these regimes and track them under climate change. Finally, we leverage this new knowledge to elucidate the dynamical drivers of the identified regime shifts as noted by the neural network ... : 14 pages, 11 figures, NeurIPS 2023 Workshop: Tackling Climate Change with Machine Learning ... |
format |
Article in Journal/Newspaper |
author |
Yik, William Sonnewald, Maike Clare, Mariana C. A. Lguensat, Redouane |
author_facet |
Yik, William Sonnewald, Maike Clare, Mariana C. A. Lguensat, Redouane |
author_sort |
Yik, William |
title |
Southern Ocean Dynamics Under Climate Change: New Knowledge Through Physics-Guided Machine Learning ... |
title_short |
Southern Ocean Dynamics Under Climate Change: New Knowledge Through Physics-Guided Machine Learning ... |
title_full |
Southern Ocean Dynamics Under Climate Change: New Knowledge Through Physics-Guided Machine Learning ... |
title_fullStr |
Southern Ocean Dynamics Under Climate Change: New Knowledge Through Physics-Guided Machine Learning ... |
title_full_unstemmed |
Southern Ocean Dynamics Under Climate Change: New Knowledge Through Physics-Guided Machine Learning ... |
title_sort |
southern ocean dynamics under climate change: new knowledge through physics-guided machine learning ... |
publisher |
arXiv |
publishDate |
2023 |
url |
https://dx.doi.org/10.48550/arxiv.2310.13916 https://arxiv.org/abs/2310.13916 |
geographic |
Antarctic Southern Ocean The Antarctic |
geographic_facet |
Antarctic Southern Ocean The Antarctic |
genre |
Antarc* Antarctic Southern Ocean |
genre_facet |
Antarc* Antarctic Southern Ocean |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_doi |
https://doi.org/10.48550/arxiv.2310.13916 |
_version_ |
1789958743433150464 |