Learning-based prediction of the particles catchment area of deep ocean sediment traps
International audience Abstract. The ocean biological carbon pump plays a major role in climate and biogeochemical cycles. Photosynthesis at the surface produces particles that are exported to the deep ocean by gravity. Sediment traps, which measure the deep carbon fluxes, help to quantify the carbo...
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Language: | English |
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HAL CCSD
2024
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Online Access: | https://imt-atlantique.hal.science/hal-04672626 https://doi.org/10.5194/egusphere-2023-2777 |
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institution |
Open Polar |
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Institut national des sciences de l'Univers: HAL-INSU |
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language |
English |
topic |
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean Atmosphere |
spellingShingle |
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean Atmosphere Picard, Théo Gula, Jonathan Fablet, Ronan Collin, Jeremy Mémery, Laurent Learning-based prediction of the particles catchment area of deep ocean sediment traps |
topic_facet |
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean Atmosphere |
description |
International audience Abstract. The ocean biological carbon pump plays a major role in climate and biogeochemical cycles. Photosynthesis at the surface produces particles that are exported to the deep ocean by gravity. Sediment traps, which measure the deep carbon fluxes, help to quantify the carbon stored by this process. However, it is challenging to precisely identify the surface origin of particles trapped thousands of meters deep because of the influence of ocean circulation on the carbon sinking path. In this study, we conducted a series of numerical Lagrangian experiments in the Porcupine Abyssal Plain region of the North Atlantic and developed a machine learning approach to predict the surface origin of particles trapped in a deep sediment trap. Our numerical experiments support its predictive performance, and surface conditions appear to be sufficient to accurately predict the source area, suggesting a potential application with satellite data. We also identify potential factors that affect the prediction efficiency and we show that the best predictions are associated with low kinetic energy and the presence of mesoscale eddies above the trap. This new tool could provide a better link between satellite-derived sea surface observations and deep sediment trap measurements, ultimately improving our understanding of the biological carbon pump mechanism. |
author2 |
Université de Brest (UBO) 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) Institut universitaire de France (IUF) Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.) Océan Dynamique Observations Analyse (ODYSSEY) Université de Rennes (UR)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Brest (UBO)-Inria Rennes – Bretagne Atlantique Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-IMT Atlantique (IMT Atlantique) Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT) Département Mathematical and Electrical Engineering (IMT Atlantique - MEE) IMT Atlantique (IMT Atlantique) Equipe Observations Signal & Environnement (Lab-STICC_OSE) Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC) École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom Paris (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique) Institut Mines-Télécom Paris (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom Paris (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique) Institut Mines-Télécom Paris (IMT) FEDER through AIDA GPU resources ANR-19-CHIA-0016,OceaniX,Physics-Informed AI for Observation-driven Ocean AnalytiX(2019) |
format |
Article in Journal/Newspaper |
author |
Picard, Théo Gula, Jonathan Fablet, Ronan Collin, Jeremy Mémery, Laurent |
author_facet |
Picard, Théo Gula, Jonathan Fablet, Ronan Collin, Jeremy Mémery, Laurent |
author_sort |
Picard, Théo |
title |
Learning-based prediction of the particles catchment area of deep ocean sediment traps |
title_short |
Learning-based prediction of the particles catchment area of deep ocean sediment traps |
title_full |
Learning-based prediction of the particles catchment area of deep ocean sediment traps |
title_fullStr |
Learning-based prediction of the particles catchment area of deep ocean sediment traps |
title_full_unstemmed |
Learning-based prediction of the particles catchment area of deep ocean sediment traps |
title_sort |
learning-based prediction of the particles catchment area of deep ocean sediment traps |
publisher |
HAL CCSD |
publishDate |
2024 |
url |
https://imt-atlantique.hal.science/hal-04672626 https://doi.org/10.5194/egusphere-2023-2777 |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
ISSN: 1812-0784 EISSN: 1812-0792 Ocean Science https://imt-atlantique.hal.science/hal-04672626 Ocean Science, 2024, ⟨10.5194/egusphere-2023-2777⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.5194/egusphere-2023-2777 hal-04672626 https://imt-atlantique.hal.science/hal-04672626 doi:10.5194/egusphere-2023-2777 |
op_rights |
http://creativecommons.org/licenses/by/ |
op_doi |
https://doi.org/10.5194/egusphere-2023-2777 |
_version_ |
1809928241832525824 |
spelling |
ftinsu:oai:HAL:hal-04672626v1 2024-09-09T19:57:20+00:00 Learning-based prediction of the particles catchment area of deep ocean sediment traps Picard, Théo Gula, Jonathan Fablet, Ronan Collin, Jeremy Mémery, Laurent Université de Brest (UBO) 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) Institut universitaire de France (IUF) Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.) Océan Dynamique Observations Analyse (ODYSSEY) Université de Rennes (UR)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Brest (UBO)-Inria Rennes – Bretagne Atlantique Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-IMT Atlantique (IMT Atlantique) Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT) Département Mathematical and Electrical Engineering (IMT Atlantique - MEE) IMT Atlantique (IMT Atlantique) Equipe Observations Signal & Environnement (Lab-STICC_OSE) Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC) École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom Paris (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique) Institut Mines-Télécom Paris (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom Paris (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique) Institut Mines-Télécom Paris (IMT) FEDER through AIDA GPU resources ANR-19-CHIA-0016,OceaniX,Physics-Informed AI for Observation-driven Ocean AnalytiX(2019) 2024 https://imt-atlantique.hal.science/hal-04672626 https://doi.org/10.5194/egusphere-2023-2777 en eng HAL CCSD European Geosciences Union info:eu-repo/semantics/altIdentifier/doi/10.5194/egusphere-2023-2777 hal-04672626 https://imt-atlantique.hal.science/hal-04672626 doi:10.5194/egusphere-2023-2777 http://creativecommons.org/licenses/by/ ISSN: 1812-0784 EISSN: 1812-0792 Ocean Science https://imt-atlantique.hal.science/hal-04672626 Ocean Science, 2024, ⟨10.5194/egusphere-2023-2777⟩ [SDU.OCEAN]Sciences of the Universe [physics]/Ocean Atmosphere info:eu-repo/semantics/article Journal articles 2024 ftinsu https://doi.org/10.5194/egusphere-2023-2777 2024-08-21T23:45:56Z International audience Abstract. The ocean biological carbon pump plays a major role in climate and biogeochemical cycles. Photosynthesis at the surface produces particles that are exported to the deep ocean by gravity. Sediment traps, which measure the deep carbon fluxes, help to quantify the carbon stored by this process. However, it is challenging to precisely identify the surface origin of particles trapped thousands of meters deep because of the influence of ocean circulation on the carbon sinking path. In this study, we conducted a series of numerical Lagrangian experiments in the Porcupine Abyssal Plain region of the North Atlantic and developed a machine learning approach to predict the surface origin of particles trapped in a deep sediment trap. Our numerical experiments support its predictive performance, and surface conditions appear to be sufficient to accurately predict the source area, suggesting a potential application with satellite data. We also identify potential factors that affect the prediction efficiency and we show that the best predictions are associated with low kinetic energy and the presence of mesoscale eddies above the trap. This new tool could provide a better link between satellite-derived sea surface observations and deep sediment trap measurements, ultimately improving our understanding of the biological carbon pump mechanism. Article in Journal/Newspaper North Atlantic Institut national des sciences de l'Univers: HAL-INSU |