Articles | Volume 23, issue 7
https://doi.org/10.5194/bg-23-2601-2026
© 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-23-2601-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Apr 2026
Research article |
| 17 Apr 2026
| 17 Apr 2026
Forecasting seasonal global sea surface chlorophyll a with a lightweight data-driven approach
Gabriela Martinez Balbontin
CORRESPONDING AUTHOR
Mercator Océan International, Toulouse, France
Julien Jouanno
Laboratoire d'Etudes en Géophysique et Océanographie Spatiales, Toulouse, France
Institut de Recherche pour le Développement, Toulouse, France
Rachid Benshila
Laboratoire d'Etudes en Géophysique et Océanographie Spatiales, Toulouse, France
Julien Lamouroux
Mercator Océan International, Toulouse, France
Coralie Perruche
Mercator Océan International, Toulouse, France
Stefano Ciavatta
Mercator Océan International, Toulouse, France
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Marco Larrañaga, Julien Jouanno, Eric P. Chassignet, Giovanni Durante, Ilkyeong Ma, Julio Sheinbaum, and Lionel Renault
Ocean Sci., 22, 821–841, https://doi.org/10.5194/os-22-821-2026, https://doi.org/10.5194/os-22-821-2026, 2026
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We analyze 29 years of satellite altimetry to investigate the detachment of Loop Current Eddies in the Gulf of Mexico. Over half of the Loop Current eddies reattach within a month, while 42 % separate and drift westward. Detachment requires the Loop Current to reach the Mississippi Fan and is strongly influenced by cyclonic eddies, whose configuration determines whether an eddy separates or reattaches to the Loop Current.
Ieuan Higgs, Ross Bannister, Jozef Skákala, Alberto Carrassi, and Stefano Ciavatta
Biogeosciences, 23, 315–344, https://doi.org/10.5194/bg-23-315-2026, https://doi.org/10.5194/bg-23-315-2026, 2026
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We explored how machine learning can improve computer models that simulate ocean ecosystems. These models help us understand how the ocean works, but they often struggle due to limited observations and complex processes. Our approach uses machine learning to better connect the parts of the system we can observe with those we cannot. This leads to more accurate and efficient predictions, offering a promising way to improve future ocean monitoring and forecasting tools.
Jozef Skákala, Shubha Sathyendranath, Yuri Artioli, Deep S. Banerjee, Heather Bouman, Robert J. W. Brewin, Momme Butenschön, Stefano Ciavatta, Stephanie Dutkiewicz, Yanna Fidai, David Ford, Grinson George, Karen Guihou, Bror Jönsson, Marija Bačeković Koloper, Žarko Kovač, Lekshmi Krishnakumary, Gemma Kulk, Charlotte Laufkötter, Gennadi Lessin, Jann Paul Mattern, Angélique Melet, Alexandre Mignot, David Moffat, Fanny Monteiro, Mayra Rodriguez Bennadji, Cécile Rousseaux, Ranjini Swaminathan, Osvaldo Ulloa, and Jerry Tjiputra
EGUsphere, https://doi.org/10.5194/egusphere-2025-6256, https://doi.org/10.5194/egusphere-2025-6256, 2025
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Marine primary production (PP) is a key component of the Earth's climate system, but its current estimates and future projections are highly uncertain. We review the PP uncertainties and discuss their sources both across the ecosystem and satellite models. We propose to reduce the PP uncertainties by better addressing the PP model structures and parametrizations. We also argue that for many models it is desirable to consider spatial and temporal variability in the model parameter values.
Quentin Hyvernat, Alexandre Mignot, Elodie Gutknecht, Giovanni Ruggiero, Coralie Perruche, Guillaume Samson, Raphaëlle Sauzède, Olivier Aumont, Hervé Claustre, and Fabrizio D'Ortenzio
EGUsphere, https://doi.org/10.5194/egusphere-2025-4369, https://doi.org/10.5194/egusphere-2025-4369, 2025
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We introduce an iterative Importance Sampling (iIS) framework to optimize the PISCES model using BGC-Argo data. Using these data, 20 metrics are applied to better constrain parameter values. Three parameter selection strategies are compared: 29 main effects parameters, 66 parameters including interaction effects, and all 95 parameters. All yield statistically indistinguishable but significant skill gains, reducing NRMSE by 54–56% in median across assimilated metrics in the productive layer.
Marc Kakante Mendy, Florent Gasparin, Manon Gévaudan, Moussa Diakhaté, Issa Sakho, and Julien Jouanno
EGUsphere, https://doi.org/10.5194/egusphere-2025-4429, https://doi.org/10.5194/egusphere-2025-4429, 2025
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The North Tropical Atlantic plays an important role in shaping climate in the region. In our study we examined how African Easterly Waves influence the ocean surface. Using numerical modelling and buoy records, we found that these waves can warm or cool the sea by more than half a degree. The faster waves have the strongest impact. Because sea temperature affects rainfall and storms, understanding these waves can help improve weather and climate forecasts.
Jozef Skákala, David Ford, Keith Haines, Amos Lawless, Matthew J. Martin, Philip Browne, Marcin Chrust, Stefano Ciavatta, Alison Fowler, Daniel Lea, Matthew Palmer, Andrea Rochner, Jennifer Waters, Hao Zuo, Deep S. Banerjee, Mike Bell, Davi M. Carneiro, Yumeng Chen, Susan Kay, Dale Partridge, Martin Price, Richard Renshaw, Georgy Shapiro, and James While
Ocean Sci., 21, 1709–1734, https://doi.org/10.5194/os-21-1709-2025, https://doi.org/10.5194/os-21-1709-2025, 2025
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UK marine data assimilation (MDA) involves a closely collaborating research community. In this paper, we offer both an overview of the state of the art and a vision for the future across all of the main areas of UK MDA, ranging from physics to biogeochemistry to coupled DA. We discuss the current UK MDA stakeholder applications, highlight theoretical developments needed to advance our systems, and reflect upon upcoming opportunities with respect to hardware and observational missions.
Rosmery Sosa-Gutierrez, Julien Jouanno, and Leo Berline
Ocean Sci., 21, 1505–1514, https://doi.org/10.5194/os-21-1505-2025, https://doi.org/10.5194/os-21-1505-2025, 2025
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Since 2010, pelagic Sargassum spp. blooms have increased in several tropical Atlantic regions, causing socioeconomic and ecosystem impacts. Offshore structuration of Sargassum by mesoscale dynamics may influence transport and growth. Sargassum stays afloat, constantly interacting with currents, waves, winds, and mesoscale eddies. We find that anticyclones and cyclones effectively trap Sargassum throughout its propagation, with a greater tendency for cyclones to accumulate Sargassum.
Gianpiero Cossarini, Andrew Moore, Stefano Ciavatta, and Katja Fennel
State Planet, 5-opsr, 12, https://doi.org/10.5194/sp-5-opsr-12-2025, https://doi.org/10.5194/sp-5-opsr-12-2025, 2025
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Marine biogeochemistry refers to the cycling of chemical elements resulting from physical transport, chemical reaction, uptake, and processing by living organisms. Biogeochemical models can have a wide range of complexity, from a single nutrient to fully explicit representations of multiple nutrients, trophic levels, and functional groups. Uncertainty sources are the lack of knowledge about the parameterizations, the initial and boundary conditions, and the lack of observations.
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev., 17, 5619–5639, https://doi.org/10.5194/gmd-17-5619-2024, https://doi.org/10.5194/gmd-17-5619-2024, 2024
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To understand and predict the ocean’s capacity for carbon sequestration, its ability to supply food, and its response to climate change, we need the best possible estimate of its physical and biogeochemical properties. This is obtained through data assimilation which blends numerical models and observations. We present the Ensemble and Assimilation Tool (EAT), a flexible and efficient test bed that allows any scientist to explore and further develop the state of the art in data assimilation.
Ieuan Higgs, Jozef Skákala, Ross Bannister, Alberto Carrassi, and Stefano Ciavatta
Biogeosciences, 21, 731–746, https://doi.org/10.5194/bg-21-731-2024, https://doi.org/10.5194/bg-21-731-2024, 2024
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A complex network is a way of representing which parts of a system are connected to other parts. We have constructed a complex network based on an ecosystem–ocean model. From this, we can identify patterns in the structure and areas of similar behaviour. This can help to understand how natural, or human-made, changes will affect the shelf sea ecosystem, and it can be used in multiple future applications such as improving modelling, data assimilation, or machine learning.
Eva Álvarez, Gianpiero Cossarini, Anna Teruzzi, Jorn Bruggeman, Karsten Bolding, Stefano Ciavatta, Vincenzo Vellucci, Fabrizio D'Ortenzio, David Antoine, and Paolo Lazzari
Biogeosciences, 20, 4591–4624, https://doi.org/10.5194/bg-20-4591-2023, https://doi.org/10.5194/bg-20-4591-2023, 2023
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Chromophoric dissolved organic matter (CDOM) interacts with the ambient light and gives the waters of the Mediterranean Sea their colour. We propose a novel parameterization of the CDOM cycle, whose parameter values have been optimized by using the data of the monitoring site BOUSSOLE. Nutrient and light limitations for locally produced CDOM caused aCDOM(λ) to covary with chlorophyll, while the above-average CDOM concentrations observed at this site were maintained by allochthonous sources.
Stefania A. Ciliberti, Enrique Alvarez Fanjul, Jay Pearlman, Kirsten Wilmer-Becker, Pierre Bahurel, Fabrice Ardhuin, Alain Arnaud, Mike Bell, Segolene Berthou, Laurent Bertino, Arthur Capet, Eric Chassignet, Stefano Ciavatta, Mauro Cirano, Emanuela Clementi, Gianpiero Cossarini, Gianpaolo Coro, Stuart Corney, Fraser Davidson, Marie Drevillon, Yann Drillet, Renaud Dussurget, Ghada El Serafy, Katja Fennel, Marcos Garcia Sotillo, Patrick Heimbach, Fabrice Hernandez, Patrick Hogan, Ibrahim Hoteit, Sudheer Joseph, Simon Josey, Pierre-Yves Le Traon, Simone Libralato, Marco Mancini, Pascal Matte, Angelique Melet, Yasumasa Miyazawa, Andrew M. Moore, Antonio Novellino, Andrew Porter, Heather Regan, Laia Romero, Andreas Schiller, John Siddorn, Joanna Staneva, Cecile Thomas-Courcoux, Marina Tonani, Jose Maria Garcia-Valdecasas, Jennifer Veitch, Karina von Schuckmann, Liying Wan, John Wilkin, and Romane Zufic
State Planet, 1-osr7, 2, https://doi.org/10.5194/sp-1-osr7-2-2023, https://doi.org/10.5194/sp-1-osr7-2-2023, 2023
Peter Brandt, Gaël Alory, Founi Mesmin Awo, Marcus Dengler, Sandrine Djakouré, Rodrigue Anicet Imbol Koungue, Julien Jouanno, Mareike Körner, Marisa Roch, and Mathieu Rouault
Ocean Sci., 19, 581–601, https://doi.org/10.5194/os-19-581-2023, https://doi.org/10.5194/os-19-581-2023, 2023
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Tropical upwelling systems are among the most productive ecosystems globally. The tropical Atlantic upwelling undergoes a strong seasonal cycle that is forced by the wind. Local wind-driven upwelling and remote effects, particularly via the propagation of equatorial and coastal trapped waves, lead to an upward and downward movement of the nitracline. Turbulent mixing results in upward supply of nutrients. Here, we review the different physical processes responsible for biological productivity.
Roy Dorgeless Ngakala, Gaël Alory, Casimir Yélognissè Da-Allada, Olivia Estelle Kom, Julien Jouanno, Willi Rath, and Ezinvi Baloïtcha
Ocean Sci., 19, 535–558, https://doi.org/10.5194/os-19-535-2023, https://doi.org/10.5194/os-19-535-2023, 2023
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Surface heat flux is the main driver of the heat budget in the Senegal, Angola, and Benguela regions but not in the equatorial region. In the Senegal and Benguela regions, freshwater flux governs the salt budget, while in equatorial and Angola regions, oceanic processes are the main drivers. Results from numerical simulation show the important role of mesoscale advection for temperature and salinity variations in the mixed layer. Nonlinear processes unresolved by observations play a key role.
Sarah Berthet, Julien Jouanno, Roland Séférian, Marion Gehlen, and William Llovel
Earth Syst. Dynam., 14, 399–412, https://doi.org/10.5194/esd-14-399-2023, https://doi.org/10.5194/esd-14-399-2023, 2023
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Phytoplankton absorbs the solar radiation entering the ocean surface and contributes to keeping the associated energy in surface waters. This natural effect is either not represented in the ocean component of climate models or its representation is simplified. An incomplete representation of this biophysical interaction affects the way climate models simulate ocean warming, which leads to uncertainties in projections of oceanic emissions of an important greenhouse gas (nitrous oxide).
Alexandre Mignot, Hervé Claustre, Gianpiero Cossarini, Fabrizio D'Ortenzio, Elodie Gutknecht, Julien Lamouroux, Paolo Lazzari, Coralie Perruche, Stefano Salon, Raphaëlle Sauzède, Vincent Taillandier, and Anna Teruzzi
Biogeosciences, 20, 1405–1422, https://doi.org/10.5194/bg-20-1405-2023, https://doi.org/10.5194/bg-20-1405-2023, 2023
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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.
Michel Tchilibou, Ariane Koch-Larrouy, Simon Barbot, Florent Lyard, Yves Morel, Julien Jouanno, and Rosemary Morrow
Ocean Sci., 18, 1591–1618, https://doi.org/10.5194/os-18-1591-2022, https://doi.org/10.5194/os-18-1591-2022, 2022
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This high-resolution model-based study investigates the variability in the generation, propagation, and sea height signature (SSH) of the internal tide off the Amazon shelf during two contrasted seasons. ITs propagate further north during the season characterized by weak currents and mesoscale eddies and a shallow and strong pycnocline. IT imprints on SSH dominate those of the geostrophic motion for horizontal scales below 200 km; moreover, the SSH is mainly incoherent below 70 km.
Pierre Damien, Julio Sheinbaum, Orens Pasqueron de Fommervault, Julien Jouanno, Lorena Linacre, and Olaf Duteil
Biogeosciences, 18, 4281–4303, https://doi.org/10.5194/bg-18-4281-2021, https://doi.org/10.5194/bg-18-4281-2021, 2021
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The Gulf of Mexico deep waters are relatively poor in phytoplankton biomass due to low levels of nutrients in the upper layers. Using modeling techniques, we find that the long-living anticyclonic Loop Current eddies that are shed episodically from the Yucatan Channel strongly shape the distribution of phytoplankton and, more importantly, stimulate their growth. This results from the contribution of multiple mechanisms of physical–biogeochemical interactions discussed in this study.
Julien Jouanno, Rachid Benshila, Léo Berline, Antonin Soulié, Marie-Hélène Radenac, Guillaume Morvan, Frédéric Diaz, Julio Sheinbaum, Cristele Chevalier, Thierry Thibaut, Thomas Changeux, Frédéric Menard, Sarah Berthet, Olivier Aumont, Christian Ethé, Pierre Nabat, and Marc Mallet
Geosci. Model Dev., 14, 4069–4086, https://doi.org/10.5194/gmd-14-4069-2021, https://doi.org/10.5194/gmd-14-4069-2021, 2021
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The tropical Atlantic has been facing a massive proliferation of Sargassum since 2011, with severe environmental and socioeconomic impacts. We developed a modeling framework based on the NEMO ocean model, which integrates transport by currents and waves, and physiology of Sargassum with varying internal nutrient quota, and considers stranding at the coast. Results demonstrate the ability of the model to reproduce and forecast the seasonal cycle and large-scale distribution of Sargassum biomass.
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Short summary
This study uses machine learning to predict global sea surface chlorophyll a, which is important for monitoring marine ecosystems and the carbon cycle. Using forecasts of sea surface temperature, salinity, height, and mixed layer depth, we generate global predictions up to six months ahead in just minutes. Our approach matches state-of-the-art numerical methods while being faster and more resource-efficient.
This study uses machine learning to predict global sea surface chlorophyll a, which is important...
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