A Southern Ocean supergyre as a unifying dynamical framework identified by physics-informed machine learning

Southern Ocean circulation can be viewed as a supergyre-"several inter-connected, leaky sub-gyres-"as opposed to isolated gyres in the Weddell and Ross Seas, according to new analysis using machine learning techniques The Southern Ocean closes the global overturning circulation and is key...

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Main Authors: Sonnewald, M., Reeve, K. A., /Lguensat, Redouane
Format: Text
Language:English
Published: 2023
Subjects:
Online Access:https://www.documentation.ird.fr/hor/fdi:010087720
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spelling ftird:oai:ird.fr:fdi:010087720 2024-09-15T17:46:36+00:00 A Southern Ocean supergyre as a unifying dynamical framework identified by physics-informed machine learning Sonnewald, M. Reeve, K. A. /Lguensat, Redouane OCEAN AUSTRAL 2023 https://www.documentation.ird.fr/hor/fdi:010087720 EN eng https://www.documentation.ird.fr/hor/fdi:010087720 oai:ird.fr:fdi:010087720 Sonnewald M., Reeve K. A., Lguensat Redouane. A Southern Ocean supergyre as a unifying dynamical framework identified by physics-informed machine learning. 2023, 4 (1), p. 153 [20 p.] text 2023 ftird 2024-08-15T05:57:40Z Southern Ocean circulation can be viewed as a supergyre-"several inter-connected, leaky sub-gyres-"as opposed to isolated gyres in the Weddell and Ross Seas, according to new analysis using machine learning techniques The Southern Ocean closes the global overturning circulation and is key to the regulation of carbon, heat, biological production, and sea level. However, the dynamics of the general circulation and upwelling pathways remain poorly understood. Here, a physics-informed unsupervised machine learning framework using principled constraints is used. A unifying framework is proposed invoking a semi-circumpolar supergyre south of the Antarctic circumpolar current: a massive series of leaking sub-gyres spanning the Weddell and Ross seas that are connected and maintained via rough topography that acts as scaffolding. The supergyre framework challenges the conventional view of having separate circulation structures in the Weddell and Ross seas and suggests that idealized models and zonally-averaged frameworks may be of limited utility for climate applications. Machine learning was used to reveal areas of coherent driving forces within a vorticity-based analysis. Predictions from the supergyre framework are supported by available observations and could aid observational and modelling efforts to study this climatologically key region undergoing rapid change. Text Antarc* Antarctic Southern Ocean IRD (Institute de recherche pour le développement): Horizon
institution Open Polar
collection IRD (Institute de recherche pour le développement): Horizon
op_collection_id ftird
language English
description Southern Ocean circulation can be viewed as a supergyre-"several inter-connected, leaky sub-gyres-"as opposed to isolated gyres in the Weddell and Ross Seas, according to new analysis using machine learning techniques The Southern Ocean closes the global overturning circulation and is key to the regulation of carbon, heat, biological production, and sea level. However, the dynamics of the general circulation and upwelling pathways remain poorly understood. Here, a physics-informed unsupervised machine learning framework using principled constraints is used. A unifying framework is proposed invoking a semi-circumpolar supergyre south of the Antarctic circumpolar current: a massive series of leaking sub-gyres spanning the Weddell and Ross seas that are connected and maintained via rough topography that acts as scaffolding. The supergyre framework challenges the conventional view of having separate circulation structures in the Weddell and Ross seas and suggests that idealized models and zonally-averaged frameworks may be of limited utility for climate applications. Machine learning was used to reveal areas of coherent driving forces within a vorticity-based analysis. Predictions from the supergyre framework are supported by available observations and could aid observational and modelling efforts to study this climatologically key region undergoing rapid change.
format Text
author Sonnewald, M.
Reeve, K. A.
/Lguensat, Redouane
spellingShingle Sonnewald, M.
Reeve, K. A.
/Lguensat, Redouane
A Southern Ocean supergyre as a unifying dynamical framework identified by physics-informed machine learning
author_facet Sonnewald, M.
Reeve, K. A.
/Lguensat, Redouane
author_sort Sonnewald, M.
title A Southern Ocean supergyre as a unifying dynamical framework identified by physics-informed machine learning
title_short A Southern Ocean supergyre as a unifying dynamical framework identified by physics-informed machine learning
title_full A Southern Ocean supergyre as a unifying dynamical framework identified by physics-informed machine learning
title_fullStr A Southern Ocean supergyre as a unifying dynamical framework identified by physics-informed machine learning
title_full_unstemmed A Southern Ocean supergyre as a unifying dynamical framework identified by physics-informed machine learning
title_sort southern ocean supergyre as a unifying dynamical framework identified by physics-informed machine learning
publishDate 2023
url https://www.documentation.ird.fr/hor/fdi:010087720
op_coverage OCEAN AUSTRAL
genre Antarc*
Antarctic
Southern Ocean
genre_facet Antarc*
Antarctic
Southern Ocean
op_relation https://www.documentation.ird.fr/hor/fdi:010087720
oai:ird.fr:fdi:010087720
Sonnewald M., Reeve K. A., Lguensat Redouane. A Southern Ocean supergyre as a unifying dynamical framework identified by physics-informed machine learning. 2023, 4 (1), p. 153 [20 p.]
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