Water Mass and Biogeochemical Variability in the Kerguelen Sector of the Southern Ocean: A Machine Learning Approach for a Mixing Hotspot
International audience The Southern Ocean (SO) is one of the most energetic regions in the world, where strong air‐sea fluxes, oceanic instabilities, and flow‐topography interactions yield complex dynamics. The Kerguelen Plateau (KP) region in the Indian sector of the SO is a hotspot for these energ...
Published in: | Journal of Geophysical Research: Oceans |
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Online Access: | https://hal.science/hal-04202539 https://hal.science/hal-04202539/document https://hal.science/hal-04202539/file/JGR%20Oceans%20-%202020%20-%20Rosso%20-%20Water%20Mass%20and%20Biogeochemical%20Variability%20in%20the%20Kerguelen%20Sector%20of%20the%20Southern%20Ocean%20A.pdf https://doi.org/10.1029/2019JC015877 |
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ftunivbrest:oai:HAL:hal-04202539v1 2024-04-14T08:19:58+00:00 Water Mass and Biogeochemical Variability in the Kerguelen Sector of the Southern Ocean: A Machine Learning Approach for a Mixing Hotspot Rosso, Isabella Mazloff, Matthew R. Talley, Lynne D. Purkey, Sarah G. Freeman, Natalie M. Maze, Guillaume Laboratoire d'Océanographie Physique et Spatiale (LOPS) Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS) 2020-03 https://hal.science/hal-04202539 https://hal.science/hal-04202539/document https://hal.science/hal-04202539/file/JGR%20Oceans%20-%202020%20-%20Rosso%20-%20Water%20Mass%20and%20Biogeochemical%20Variability%20in%20the%20Kerguelen%20Sector%20of%20the%20Southern%20Ocean%20A.pdf https://doi.org/10.1029/2019JC015877 en eng HAL CCSD Wiley-Blackwell info:eu-repo/semantics/altIdentifier/doi/10.1029/2019JC015877 hal-04202539 https://hal.science/hal-04202539 https://hal.science/hal-04202539/document https://hal.science/hal-04202539/file/JGR%20Oceans%20-%202020%20-%20Rosso%20-%20Water%20Mass%20and%20Biogeochemical%20Variability%20in%20the%20Kerguelen%20Sector%20of%20the%20Southern%20Ocean%20A.pdf doi:10.1029/2019JC015877 http://hal.archives-ouvertes.fr/licences/copyright/ info:eu-repo/semantics/OpenAccess ISSN: 2169-9275 EISSN: 2169-9291 Journal of Geophysical Research. Oceans https://hal.science/hal-04202539 Journal of Geophysical Research. Oceans, 2020, 125 (3), e2019JC015877 (23p.). ⟨10.1029/2019JC015877⟩ [SDU]Sciences of the Universe [physics] info:eu-repo/semantics/article Journal articles 2020 ftunivbrest https://doi.org/10.1029/2019JC015877 2024-03-21T16:22:40Z International audience The Southern Ocean (SO) is one of the most energetic regions in the world, where strong air‐sea fluxes, oceanic instabilities, and flow‐topography interactions yield complex dynamics. The Kerguelen Plateau (KP) region in the Indian sector of the SO is a hotspot for these energetic dynamics, which result in large spatio‐temporal variability of physical and biogeochemical (BGC) properties throughout the water column.Data from Argo floats (including biogeochemical) are used to investigate the spatial variability of intermediate and deep water physical and BGC properties. An unsupervised machine learning classification approach is used to organize the float profiles into five SO frontal zones based on their temperature and salinity structure between 300 and 900 m, revealing not only the location of frontal zones and their boundaries, but also the variability of water mass properties relative to the zonal mean state. We find that the variability is property‐dependent and can be more than twice as large as the mean zonal variability in intense eddy fields. In particular, we observe this intense variability in the intermediate and deep waters of the Subtropical Zone; in the Subantarctic Zone just west of and at KP; east of KP in the Polar Frontal Zone, associated with intense eddy variability that enhances deep waters convergence and mixing; and, as the deep waters upwell to the upper 500 m and mix with the surface waters in the southernmost regimes, each property shows a large variability.Plain Language SummaryThe Southern Ocean strongly influences the global climate system, by absorbing, storing and redistributing heat and carbon across the different ocean basins. Thanks to an increasing number of observations from autonomous instruments, called Argo floats, our understanding of this harsh environment has deepened in the last two decades. Here we use a machine learning technique to automatically classify the float measurements and sort them in regimes with similar properties based on their ... Article in Journal/Newspaper Southern Ocean Université de Bretagne Occidentale: HAL Indian Kerguelen Southern Ocean Journal of Geophysical Research: Oceans 125 3 |
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Université de Bretagne Occidentale: HAL |
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ftunivbrest |
language |
English |
topic |
[SDU]Sciences of the Universe [physics] |
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[SDU]Sciences of the Universe [physics] Rosso, Isabella Mazloff, Matthew R. Talley, Lynne D. Purkey, Sarah G. Freeman, Natalie M. Maze, Guillaume Water Mass and Biogeochemical Variability in the Kerguelen Sector of the Southern Ocean: A Machine Learning Approach for a Mixing Hotspot |
topic_facet |
[SDU]Sciences of the Universe [physics] |
description |
International audience The Southern Ocean (SO) is one of the most energetic regions in the world, where strong air‐sea fluxes, oceanic instabilities, and flow‐topography interactions yield complex dynamics. The Kerguelen Plateau (KP) region in the Indian sector of the SO is a hotspot for these energetic dynamics, which result in large spatio‐temporal variability of physical and biogeochemical (BGC) properties throughout the water column.Data from Argo floats (including biogeochemical) are used to investigate the spatial variability of intermediate and deep water physical and BGC properties. An unsupervised machine learning classification approach is used to organize the float profiles into five SO frontal zones based on their temperature and salinity structure between 300 and 900 m, revealing not only the location of frontal zones and their boundaries, but also the variability of water mass properties relative to the zonal mean state. We find that the variability is property‐dependent and can be more than twice as large as the mean zonal variability in intense eddy fields. In particular, we observe this intense variability in the intermediate and deep waters of the Subtropical Zone; in the Subantarctic Zone just west of and at KP; east of KP in the Polar Frontal Zone, associated with intense eddy variability that enhances deep waters convergence and mixing; and, as the deep waters upwell to the upper 500 m and mix with the surface waters in the southernmost regimes, each property shows a large variability.Plain Language SummaryThe Southern Ocean strongly influences the global climate system, by absorbing, storing and redistributing heat and carbon across the different ocean basins. Thanks to an increasing number of observations from autonomous instruments, called Argo floats, our understanding of this harsh environment has deepened in the last two decades. Here we use a machine learning technique to automatically classify the float measurements and sort them in regimes with similar properties based on their ... |
author2 |
Laboratoire d'Océanographie Physique et Spatiale (LOPS) Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS) |
format |
Article in Journal/Newspaper |
author |
Rosso, Isabella Mazloff, Matthew R. Talley, Lynne D. Purkey, Sarah G. Freeman, Natalie M. Maze, Guillaume |
author_facet |
Rosso, Isabella Mazloff, Matthew R. Talley, Lynne D. Purkey, Sarah G. Freeman, Natalie M. Maze, Guillaume |
author_sort |
Rosso, Isabella |
title |
Water Mass and Biogeochemical Variability in the Kerguelen Sector of the Southern Ocean: A Machine Learning Approach for a Mixing Hotspot |
title_short |
Water Mass and Biogeochemical Variability in the Kerguelen Sector of the Southern Ocean: A Machine Learning Approach for a Mixing Hotspot |
title_full |
Water Mass and Biogeochemical Variability in the Kerguelen Sector of the Southern Ocean: A Machine Learning Approach for a Mixing Hotspot |
title_fullStr |
Water Mass and Biogeochemical Variability in the Kerguelen Sector of the Southern Ocean: A Machine Learning Approach for a Mixing Hotspot |
title_full_unstemmed |
Water Mass and Biogeochemical Variability in the Kerguelen Sector of the Southern Ocean: A Machine Learning Approach for a Mixing Hotspot |
title_sort |
water mass and biogeochemical variability in the kerguelen sector of the southern ocean: a machine learning approach for a mixing hotspot |
publisher |
HAL CCSD |
publishDate |
2020 |
url |
https://hal.science/hal-04202539 https://hal.science/hal-04202539/document https://hal.science/hal-04202539/file/JGR%20Oceans%20-%202020%20-%20Rosso%20-%20Water%20Mass%20and%20Biogeochemical%20Variability%20in%20the%20Kerguelen%20Sector%20of%20the%20Southern%20Ocean%20A.pdf https://doi.org/10.1029/2019JC015877 |
geographic |
Indian Kerguelen Southern Ocean |
geographic_facet |
Indian Kerguelen Southern Ocean |
genre |
Southern Ocean |
genre_facet |
Southern Ocean |
op_source |
ISSN: 2169-9275 EISSN: 2169-9291 Journal of Geophysical Research. Oceans https://hal.science/hal-04202539 Journal of Geophysical Research. Oceans, 2020, 125 (3), e2019JC015877 (23p.). ⟨10.1029/2019JC015877⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1029/2019JC015877 hal-04202539 https://hal.science/hal-04202539 https://hal.science/hal-04202539/document https://hal.science/hal-04202539/file/JGR%20Oceans%20-%202020%20-%20Rosso%20-%20Water%20Mass%20and%20Biogeochemical%20Variability%20in%20the%20Kerguelen%20Sector%20of%20the%20Southern%20Ocean%20A.pdf doi:10.1029/2019JC015877 |
op_rights |
http://hal.archives-ouvertes.fr/licences/copyright/ info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.1029/2019JC015877 |
container_title |
Journal of Geophysical Research: Oceans |
container_volume |
125 |
container_issue |
3 |
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1796298126639759360 |