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

Sonnewald, Maike; Reeve, Krissy Anne; Lguensat, Redouane

doi:10.1038/s43247-023-00793-7

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The NOAA IR serves as an archival repository of NOAA-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by NOAA or funded partners. As a repository, the NOAA IR retains documents in their original published format to ensure public access to scientific information.

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

2023
By Sonnewald, Maike ; Reeve, Krissy Anne ; Lguensat, Redouane
Source: Communications Earth & Environment, 4(1)

[PDF-4.46 MB]

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Journal Title:

Communications Earth & Environment
Personal Author:

Sonnewald, Maike ; Reeve, Krissy Anne ; Lguensat, Redouane

Sonnewald, Maike ; Reeve, Krissy Anne ; Lguensat, Redouane Less -
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; GFDL (Geophysical Fluid Dynamics Laboratory)

OAR (Oceanic and Atmospheric Research) ; GFDL (Geophysical Fluid Dynamics Laboratory) Less -
Description:

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.
Keywords:

[+]

General Earth And Planetary Sciences General Environmental Science
Source:

Communications Earth & Environment, 4(1)
DOI:

https://doi.org/10.1038/s43247-023-00793-7
ISSN:

2662-4435
Format:

PDF
Publisher:

Springer Science and Business Media LLC
Document Type:

Journal Article
Funding:

Grant no. NA18OAR4320123
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https://creativecommons.org/licenses/by/4.0
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CC BY
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Library
Main Document Checksum:

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urn:sha256:61c213bbb58ce344eda0c5c5dc4959a7dca283f383c9c35ead73a1dc56780162
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https://repository.library.noaa.gov/view/noaa/54734/noaa_54734_DS1.pdf
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A Southern Ocean supergyre as a unifying dynamical framework identified by physics-informed machine learning

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