Articles | Volume 23, issue 19
https://doi.org/10.5194/bg-23-6879-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-23-6879-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Oct 2026
Research article |
| 06 Oct 2026
| 06 Oct 2026
Solving calibration and reanalysis challenges of ocean biogeochemical dynamics with neural schemes: a 1D vertical model case-study
IMT Atlantique, UMR Lab-STICC, Brest, France
Laboratoire des sciences de l'Environnement MARin, UBO/CNRS/IRD/Ifremer, Plouzané, France
Laurent Memery
Laboratoire des sciences de l'Environnement MARin, UBO/CNRS/IRD/Ifremer, Plouzané, France
Ronan Fablet
IMT Atlantique, UMR Lab-STICC, Brest, France
ODYSSEY Team, INRIA, Brest/Rennes, France
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Théo Picard, Chelsey A. Baker, Jonathan Gula, Ronan Fablet, Laurent Mémery, and Richard Lampitt
Biogeosciences, 22, 4309–4331, https://doi.org/10.5194/bg-22-4309-2025, https://doi.org/10.5194/bg-22-4309-2025, 2025
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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 with regard to 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.
Maxime Ballarotta, Clément Ubelmann, Valentin Bellemin-Laponnaz, Florian Le Guillou, Guillaume Meda, Cécile Anadon, Alice Laloue, Antoine Delepoulle, Yannice Faugère, Marie-Isabelle Pujol, Ronan Fablet, and Gérald Dibarboure
Ocean Sci., 21, 63–80, https://doi.org/10.5194/os-21-63-2025, https://doi.org/10.5194/os-21-63-2025, 2025
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The Surface Water and Ocean Topography (SWOT) mission provides unprecedented swath altimetry data. This study examines SWOT's impact on mapping systems, showing a moderate effect with the current nadir altimetry constellation and a stronger impact with a reduced one. Integrating SWOT with dynamic mapping techniques improves the resolution of satellite-derived products, offering promising solutions for studying and monitoring sea-level variability at finer scales.
Théo Picard, Jonathan Gula, Ronan Fablet, Jeremy Collin, and Laurent Mémery
Ocean Sci., 20, 1149–1165, https://doi.org/10.5194/os-20-1149-2024, https://doi.org/10.5194/os-20-1149-2024, 2024
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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.
Chloé Baumas, Robin Fuchs, Marc Garel, Jean-Christophe Poggiale, Laurent Memery, Frédéric A. C. Le Moigne, and Christian Tamburini
Biogeosciences, 20, 4165–4182, https://doi.org/10.5194/bg-20-4165-2023, https://doi.org/10.5194/bg-20-4165-2023, 2023
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Through the sink of particles in the ocean, carbon (C) is exported and sequestered when reaching 1000 m. Attempts to quantify C exported vs. C consumed by heterotrophs have increased. Yet most of the conducted estimations have led to C demands several times higher than C export. The choice of parameters greatly impacts the results. As theses parameters are overlooked, non-accurate values are often used. In this study we show that C budgets can be well balanced when using appropriate values.
Maxime Beauchamp, Quentin Febvre, Hugo Georgenthum, and Ronan Fablet
Geosci. Model Dev., 16, 2119–2147, https://doi.org/10.5194/gmd-16-2119-2023, https://doi.org/10.5194/gmd-16-2119-2023, 2023
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4DVarNet is a learning-based method based on traditional data assimilation (DA). This new class of algorithms can be used to provide efficient reconstructions of a dynamical system based on single observations. We provide a 4DVarNet application to sea surface height reconstructions based on nadir and future Surface Water and Ocean and Topography data. It outperforms other methods, from optimal interpolation to sophisticated DA algorithms. This work is part of on-going AI Chair Oceanix projects.
Etienne Pauthenet, Loïc Bachelot, Kevin Balem, Guillaume Maze, Anne-Marie Tréguier, Fabien Roquet, Ronan Fablet, and Pierre Tandeo
Ocean Sci., 18, 1221–1244, https://doi.org/10.5194/os-18-1221-2022, https://doi.org/10.5194/os-18-1221-2022, 2022
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Temperature and salinity profiles are essential for studying the ocean’s stratification, but there are not enough of these data. Satellites are able to measure daily maps of the surface ocean. We train a machine to learn the link between the satellite data and the profiles in the Gulf Stream region. We can then use this link to predict profiles at the high resolution of the satellite maps. Our prediction is fast to compute and allows us to get profiles at any locations only from surface data.
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Short summary
A realistic representation of ocean carbon exchanges through a biogeochemical (BGC) model depends heavily on its parameterisation. However, this calibration is often hindered by an inaccurate representation of small-scale ocean physical dynamics, which are common in physical reanalysis. Here, a novel learning-based method enables a robust estimation of BGC states and parameters, and correction of physical forcing, despite physical forcing uncertainties and sparse observations.
A realistic representation of ocean carbon exchanges through a biogeochemical (BGC) model...
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