Articles | Volume 20, issue 5
https://doi.org/10.5194/os-20-1149-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/os-20-1149-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Sep 2024
Research article |
| 19 Sep 2024
| 19 Sep 2024
Predicting particle catchment areas of deep-ocean sediment traps using machine learning
Laboratoire des Sciences de l'Environnement Marin (LEMAR), Univ Brest, CNRS, IRD, Ifremer, IUEM, Plouzané, France
Jonathan Gula
Laboratoire d’Océanographie Physique et Spatiale (LOPS), Univ Brest, CNRS, IRD, Ifremer, IUEM, Plouzané, France
Institut Universitaire de France (IUF), Paris, France
ODYSSEY, Inria, Brest, France
Ronan Fablet
IMT Atlantique, Lab-STICC, Plouzané, France
ODYSSEY, Inria, Brest, France
Jeremy Collin
Laboratoire des Sciences de l'Environnement Marin (LEMAR), Univ Brest, CNRS, IRD, Ifremer, IUEM, Plouzané, France
Laurent Mémery
Laboratoire des Sciences de l'Environnement Marin (LEMAR), Univ Brest, CNRS, IRD, Ifremer, IUEM, Plouzané, France
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Ocean sediment traps measure the sequestrated sinking organic carbon. While sinking, the particles are affected by local currents, which presents a challenge in linking the deep flux with the surface. We present a machine learning tool that predicts the source location of the sinking particles based on satellite data. The predictions demonstrate a stronger correlation between surface and deep carbon fluxes, allowing a more comprehensive understanding of the deep carbon sequestration drivers.
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Ocean sediment traps measure the sequestrated sinking organic carbon. While sinking, the particles are affected by local currents, which presents a challenge in linking the deep flux with the surface. We present a machine learning tool that predicts the source location of the sinking particles based on satellite data. The predictions demonstrate a stronger correlation between surface and deep carbon fluxes, allowing a more comprehensive understanding of the deep carbon sequestration drivers.
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Mathieu Morvan, Pierre L'Hégaret, Xavier Carton, Jonathan Gula, Clément Vic, Charly de Marez, Mikhail Sokolovskiy, and Konstantin Koshel
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Climate change vastly affects the ocean carbon cycle, demanding methods to assess and monitor ocean carbon uptake. In this study, we devised a machine learning tool to detect and track ocean carbon biomes from 1958 to 2018. These biomes show consistent relationships between surface CO2 fugacity and its drivers. Using ML methods, we identified and monitored carbon biomes over time, displaying meaningful responses to seasonal and long-term shifts and providing insights into climate change impacts.
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Short summary
The biological carbon pump plays a key role in the climate system. Plankton absorb and transform CO2 into organic carbon, forming particles that sink to the ocean floor. Sediment traps catch these particles and measure the carbon stored in the abyss. However, the particles' surface origin is unknown as ocean currents alter their paths. Here, we train an AI model to predict the origin of these particles. This new tool enables a better link between deep-ocean observations and satellite images.
The biological carbon pump plays a key role in the climate system. Plankton absorb and transform...
