Articles | Volume 20, issue 5
https://doi.org/10.5194/os-20-1149-2024
https://doi.org/10.5194/os-20-1149-2024
Research article
 | 
19 Sep 2024
Research article |  | 19 Sep 2024

Predicting particle catchment areas of deep-ocean sediment traps using machine learning

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

Related authors

Integrating wide swath altimetry data into Level-4 multi-mission maps
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
EGUsphere, https://doi.org/10.5194/egusphere-2024-2345,https://doi.org/10.5194/egusphere-2024-2345, 2024
Short summary
Reconstructing the ocean's mesopelagic zone carbon budget: sensitivity and estimation of parameters associated with prokaryotic remineralization
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
Short summary
4DVarNet-SSH: end-to-end learning of variational interpolation schemes for nadir and wide-swath satellite altimetry
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
Short summary
Four-dimensional temperature, salinity and mixed-layer depth in the Gulf Stream, reconstructed from remote-sensing and in situ observations with neural networks
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
Short summary
Cloud-based framework for inter-comparing submesoscale-permitting realistic ocean models
Takaya Uchida, Julien Le Sommer, Charles Stern, Ryan P. Abernathey, Chris Holdgraf, Aurélie Albert, Laurent Brodeau, Eric P. Chassignet, Xiaobiao Xu, Jonathan Gula, Guillaume Roullet, Nikolay Koldunov, Sergey Danilov, Qiang Wang, Dimitris Menemenlis, Clément Bricaud, Brian K. Arbic, Jay F. Shriver, Fangli Qiao, Bin Xiao, Arne Biastoch, René Schubert, Baylor Fox-Kemper, William K. Dewar, and Alan Wallcraft
Geosci. Model Dev., 15, 5829–5856, https://doi.org/10.5194/gmd-15-5829-2022,https://doi.org/10.5194/gmd-15-5829-2022, 2022
Short summary

Cited articles

Alldredge, A. L. and Gotschalk, C.: In situ settling behavior of marine snow, Limnol. Oceanogr., 33, 339–351, https://doi.org/10.4319/lo.1988.33.3.0339, 1988. a
Alonso-González, I. J., Arístegui, J., Lee, C., and Calafat, A.: Regional and temporal variability of sinking organic matter in the subtropical northeast Atlantic Ocean: a biomarker diagnosis, Biogeosciences, 7, 2101–2115, https://doi.org/10.5194/bg-7-2101-2010, 2010. a
Armstrong, R. A., Lee, C., Hedges, J. I., Honjo, S., and Wakeham, S. G.: A new, mechanistic model for organic carbon fluxes in the ocean based on the quantitative association of POC with ballast minerals, Deep-Sea Res. Pt. II, 49, 219–236, https://doi.org/10.1016/S0967-0645(01)00101-1, 2001. a, b
Asper, V. L., Deuser, W. G., Knauer, G. A., and Lohrenz, S. E.: Rapid coupling of sinking particle fluxes between surface and deep ocean waters, Nature, 357, 670–672, https://doi.org/10.1038/357670a0, 1992. a
Aumont, O., Ethé, C., Tagliabue, A., Bopp, L., and Gehlen, M.: PISCES-v2: An ocean biogeochemical model for carbon and ecosystem studies, Geosci. Model Dev., 8, 2465–2513, https://doi.org/10.5194/gmd-8-2465-2015, 2015. a
Download
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.