Articles | Volume 20, issue 7
https://doi.org/10.5194/bg-20-1405-2023
© Author(s) 2023. 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/bg-20-1405-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Apr 2023
Research article |
| 12 Apr 2023
| 12 Apr 2023
Using machine learning and Biogeochemical-Argo (BGC-Argo) floats to assess biogeochemical models and optimize observing system design
Alexandre Mignot
CORRESPONDING AUTHOR
Mercator Ocean International, 31400 Toulouse, France
Hervé Claustre
Laboratoire d'Océanographie de Villefranche, CNRS, Sorbonne Université, 06230
Villefranche-sur-Mer, France
Institut de la Mer de Villefranche, CNRS, Sorbonne Université,
06230 Villefranche-sur-Mer, France
Gianpiero Cossarini
National Institute of Oceanography and Applied Geophysics – OGS,
34010 Trieste, Italy
Fabrizio D'Ortenzio
Laboratoire d'Océanographie de Villefranche, CNRS, Sorbonne Université, 06230
Villefranche-sur-Mer, France
Institut de la Mer de Villefranche, CNRS, Sorbonne Université,
06230 Villefranche-sur-Mer, France
Elodie Gutknecht
Mercator Ocean International, 31400 Toulouse, France
Julien Lamouroux
Mercator Ocean International, 31400 Toulouse, France
Paolo Lazzari
National Institute of Oceanography and Applied Geophysics – OGS,
34010 Trieste, Italy
Coralie Perruche
Mercator Ocean International, 31400 Toulouse, France
Stefano Salon
National Institute of Oceanography and Applied Geophysics – OGS,
34010 Trieste, Italy
Raphaëlle Sauzède
Institut de la Mer de Villefranche, CNRS, Sorbonne Université,
06230 Villefranche-sur-Mer, France
Vincent Taillandier
Laboratoire d'Océanographie de Villefranche, CNRS, Sorbonne Université, 06230
Villefranche-sur-Mer, France
Institut de la Mer de Villefranche, CNRS, Sorbonne Université,
06230 Villefranche-sur-Mer, France
Anna Teruzzi
National Institute of Oceanography and Applied Geophysics – OGS,
34010 Trieste, Italy
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- Adaptive foraging strategies of Adélie penguins in the Ross Sea Region: balancing chick feeding and body condition in changing marine environments Y. Kim et al. https://doi.org/10.1007/s00227-024-04575-3
- Numerical models for monitoring and forecasting ocean biogeochemistry: a short description of present status G. Cossarini et al. https://doi.org/10.5194/sp-5-opsr-12-2025
- Towards a sustained and fit-for-purpose European ocean observing and forecasting system T. Tanhua et al. https://doi.org/10.3389/fmars.2024.1394549
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- High-dimensional parameter optimization of a biogeochemical model: a multi-variable BGC-Argo data assimilation approach Q. Hyvernat et al. https://doi.org/10.5194/bg-23-4967-2026
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- A method for quantifying correlation in the shape of oceanographic profile data M. Taylor & S. Henson https://doi.org/10.5194/os-22-1377-2026
- Global 3-D Chlorophyll-a Retrieval via Profile Classification and Structural Parameterization H. Cho et al. https://doi.org/10.1109/TGRS.2026.3723399
- Advancing ocean monitoring and knowledge for societal benefit: the urgency to expand Argo to OneArgo by 2030 V. Thierry et al. https://doi.org/10.3389/fmars.2025.1593904
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- Machine learning for the physics of climate A. Bracco et al. https://doi.org/10.1038/s42254-024-00776-3
- Connecting ocean observations with prediction P. Le Traon et al. https://doi.org/10.5194/sp-5-opsr-7-2025
15 citations as recorded by crossref.
- Differences in bloom phenology and seasonal chlorophyll-a variability in the Fram Strait's hydrographic subregions resolved by a machine learning approach V. Lampe et al. https://doi.org/10.1016/j.dsr2.2026.105603
- Adaptive foraging strategies of Adélie penguins in the Ross Sea Region: balancing chick feeding and body condition in changing marine environments Y. Kim et al. https://doi.org/10.1007/s00227-024-04575-3
- Numerical models for monitoring and forecasting ocean biogeochemistry: a short description of present status G. Cossarini et al. https://doi.org/10.5194/sp-5-opsr-12-2025
- Towards a sustained and fit-for-purpose European ocean observing and forecasting system T. Tanhua et al. https://doi.org/10.3389/fmars.2024.1394549
- Biogeochemical and Physical Assessment of CMIP5 and CMIP6 Ocean Components for the Southwest Pacific Ocean G. Rickard et al. https://doi.org/10.1029/2022JG007123
- Deep learning-based chlorophyll prediction: comparison with a dynamic model and applications to fish catch forecasting J. Park et al. https://doi.org/10.5194/esd-17-795-2026
- High-dimensional parameter optimization of a biogeochemical model: a multi-variable BGC-Argo data assimilation approach Q. Hyvernat et al. https://doi.org/10.5194/bg-23-4967-2026
- Comparing satellite and BGC-Argo chlorophyll estimation: A phenological study A. Baudena et al. https://doi.org/10.1016/j.rse.2025.114743
- A method for quantifying correlation in the shape of oceanographic profile data M. Taylor & S. Henson https://doi.org/10.5194/os-22-1377-2026
- Global 3-D Chlorophyll-a Retrieval via Profile Classification and Structural Parameterization H. Cho et al. https://doi.org/10.1109/TGRS.2026.3723399
- Advancing ocean monitoring and knowledge for societal benefit: the urgency to expand Argo to OneArgo by 2030 V. Thierry et al. https://doi.org/10.3389/fmars.2025.1593904
- From traditional observation to intelligent monitoring of estuarine, coastal, and shelf-sea environments: A review Q. Li et al. https://doi.org/10.1016/j.ecss.2026.110067
- A synthesis of ocean total alkalinity and dissolved inorganic carbon measurements from 1993 to 2022: the SNAPO-CO2-v1 dataset N. Metzl et al. https://doi.org/10.5194/essd-16-89-2024
- Machine learning for the physics of climate A. Bracco et al. https://doi.org/10.1038/s42254-024-00776-3
- Connecting ocean observations with prediction P. Le Traon et al. https://doi.org/10.5194/sp-5-opsr-7-2025
Saved (final revised paper)
Latest update: 30 Sep 2026
Short summary
Numerical models of ocean biogeochemistry are becoming a major tool to detect and predict the impact of climate change on marine resources and monitor ocean health. Here, we demonstrate the use of the global array of BGC-Argo floats for the assessment of biogeochemical models. We first detail the handling of the BGC-Argo data set for model assessment purposes. We then present 23 assessment metrics to quantify the consistency of BGC model simulations with respect to BGC-Argo data.
Numerical models of ocean biogeochemistry are becoming a major tool to detect and predict the...
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