Preprints
https://doi.org/10.5194/egusphere-2023-2777
https://doi.org/10.5194/egusphere-2023-2777
05 Dec 2023
 | 05 Dec 2023
Status: this preprint is open for discussion.

Learning-based prediction of the particles catchment area of deep ocean sediment traps

Théo Picard, Jonathan Gula, Ronan Fablet, Jeremy Collin, and Laurent Mémery

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.

Théo Picard, Jonathan Gula, Ronan Fablet, Jeremy Collin, and Laurent Mémery

Status: open (until 06 Mar 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-2777', Anonymous Referee #1, 05 Jan 2024 reply
  • AC1: 'Comment on egusphere-2023-2777', Théo Picard, 31 Jan 2024 reply
Théo Picard, Jonathan Gula, Ronan Fablet, Jeremy Collin, and Laurent Mémery

Data sets

Data for learning-based prediction of the particles catchment area of deep ocean sediment traps Picard Théo, Gula Jonathan, Fablet Ronan, Memery Laurent, Collin Jéremy https://doi.org/10.17882/97556

Model code and software

SPARO Picard Théo, Gula Jonathan, Fablet Ronan, Memery Laurent, Collin Jéremy https://github.com/TheoPcrd/SPARO

Théo Picard, Jonathan Gula, Ronan Fablet, Jeremy Collin, and Laurent Mémery

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Short summary
The biological carbon pump plays a major role in climate. Plankton uptake and transform CO2 into organic carbon, creating particles that sink down to the ocean floor. Sediment traps catch these particles and measure the carbon stored in the abyss. But the surface origin of particles is unknown because ocean currents alter their path. In this study, we train an AI to predict the origin of these particles. This new tool allows a better link between deep ocean observations and satellite images.