Articles | Volume 22, issue 4
https://doi.org/10.5194/bg-22-841-2025
© Author(s) 2025. 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-22-841-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Toward more robust net primary production projections in the North Atlantic Ocean
Stéphane Doléac
CORRESPONDING AUTHOR
Laboratoire d’Océanographie et du Climat: Expérimentations et Analyses Numériques de l’Institut Pierre Simon Laplace (LOCEAN-IPSL) – Sorbonne Université, CNRS, IRD, MHNH, Paris, France
École des Ponts, Marne-la-Vallée, France
Marina Lévy
Laboratoire d’Océanographie et du Climat: Expérimentations et Analyses Numériques de l’Institut Pierre Simon Laplace (LOCEAN-IPSL) – Sorbonne Université, CNRS, IRD, MHNH, Paris, France
Roy El Hourany
Laboratoire d'Océanologie et de Géosciences (LOG) – Université du Littoral Côte d'Opale, Université de Lille, CNRS, IRD, Wimereux, France
Laurent Bopp
Laboratoire de Météorologie Dynamique de l'Institut Pierre-Simon Laplace (LMD-IPSL), Ecole Normale Supérieure – Université PSL, CNRS, Ecole Polytechnique, Sorbonne Université, Paris, France
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Mathieu Delteil, Marina Lévy, and Laurent Bopp
EGUsphere, https://doi.org/10.5194/egusphere-2025-2805, https://doi.org/10.5194/egusphere-2025-2805, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
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The ocean is losing oxygen due to climate change, threatening ecosystems, especially in naturally low-oxygen areas called Oxygen Minimum Zones (OMZs). Using the IPSL-CM6A-LR Large Ensemble, this study identifies when climate-driven changes in OMZ volumes and regional deoxygenation emerge from natural variability. We highlight hemispheric asymmetries due to ocean ventilation and provide model-based estimates for the timing of detectable OMZ evolution.
Marina Lévy, Karina von Schuckmann, Patrick Vincent, Bruno Blanke, Joachim Claudet, Patrice Guillotreau, Audrey Hasson, Claire Jolly, Yunne Shin, Olivier Thébaud, Adrien Vincent, and Pierre Bahurel
State Planet, 6-osr9, 1, https://doi.org/10.5194/sp-6-osr9-1-2025, https://doi.org/10.5194/sp-6-osr9-1-2025, 2025
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The Ocean is vital to humanity, but humans are putting it at risk. The Starfish Barometer is a new yearly civic rendezvous that shows how people and the Ocean affect each other. Using science-based facts, it highlights major trends in ocean health, the pressures it faces, the harm to people, and current protection efforts and opportunities. The goal is to raise awareness to secure a better future for the Ocean and humanity.
Madhavan Girijakumari Keerthi, Olivier Aumont, Lester Kwiatkowski, and Marina Levy
Biogeosciences, 22, 2163–2180, https://doi.org/10.5194/bg-22-2163-2025, https://doi.org/10.5194/bg-22-2163-2025, 2025
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We assessed how well climate models replicate sub-seasonal changes in ocean chlorophyll observed by satellites. Models struggle to capture these variations accurately. Some overestimate fluctuations and their impact on annual chlorophyll variability, while others underestimate them. The underestimation is likely due to limited model resolution, while the overestimation may come from internal model oscillations.
Georges Baaklini, Julien Brajard, Leila Issa, Gina Fifani, Laurent Mortier, and Roy El Hourany
Ocean Sci., 20, 1707–1720, https://doi.org/10.5194/os-20-1707-2024, https://doi.org/10.5194/os-20-1707-2024, 2024
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Understanding the flow of the Levantine Sea surface current is not straightforward. We propose a study based on learning techniques to follow interactions between water near the shore and further out at sea. Our results show changes in the coastal currents past 33.8° E, with frequent instances of water breaking away along the Lebanese coast. These events happen quickly and sometimes lead to long-lasting eddies. This study underscores the need for direct observations to improve our knowledge.
Roy El Hourany, Juan Pierella Karlusich, Lucie Zinger, Hubert Loisel, Marina Levy, and Chris Bowler
Ocean Sci., 20, 217–239, https://doi.org/10.5194/os-20-217-2024, https://doi.org/10.5194/os-20-217-2024, 2024
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Satellite observations offer valuable information on phytoplankton abundance and community structure. Here, we employ satellite observations to infer seven phytoplankton groups at a global scale based on a new molecular method from Tara Oceans. The link has been established using machine learning approaches. The output of this work provides excellent tools to collect essential biodiversity variables and a foundation to monitor the evolution of marine biodiversity.
Inès Mangolte, Marina Lévy, Clément Haëck, and Mark D. Ohman
Biogeosciences, 20, 3273–3299, https://doi.org/10.5194/bg-20-3273-2023, https://doi.org/10.5194/bg-20-3273-2023, 2023
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Ocean fronts are ecological hotspots, associated with higher diversity and biomass for many marine organisms, from bacteria to whales. Using in situ data from the California Current Ecosystem, we show that far from being limited to the production of diatom blooms, fronts are the scene of complex biophysical couplings between biotic interactions (growth, competition, and predation) and transport by currents that generate planktonic communities with an original taxonomic and spatial structure.
Saeed Hariri, Sabrina Speich, Bruno Blanke, and Marina Lévy
Ocean Sci., 19, 1183–1201, https://doi.org/10.5194/os-19-1183-2023, https://doi.org/10.5194/os-19-1183-2023, 2023
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This work presents a series of studies conducted by the authors on the application of the Lagrangian approach for the connectivity analysis between different ocean locations in an idealized open-ocean model. We assess how the connectivity properties of typical oceanic flows are affected by the fine-scale circulation and discuss the challenges facing ocean connectivity estimates related to the spatial resolution. Our results are important to improve the understanding of marine ecosystems.
Clément Haëck, Marina Lévy, Inès Mangolte, and Laurent Bopp
Biogeosciences, 20, 1741–1758, https://doi.org/10.5194/bg-20-1741-2023, https://doi.org/10.5194/bg-20-1741-2023, 2023
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Phytoplankton vary in abundance in the ocean over large regions and with the seasons but also because of small-scale heterogeneities in surface temperature, called fronts. Here, using satellite imagery, we found that fronts enhance phytoplankton much more where it is already growing well, but despite large local increases the enhancement for the region is modest (5 %). We also found that blooms start 1 to 2 weeks earlier over fronts. These effects may have implications for ecosystems.
Georges Baaklini, Roy El Hourany, Milad Fakhri, Julien Brajard, Leila Issa, Gina Fifani, and Laurent Mortier
Ocean Sci., 18, 1491–1505, https://doi.org/10.5194/os-18-1491-2022, https://doi.org/10.5194/os-18-1491-2022, 2022
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We use machine learning to analyze the long-term variation of the surface currents in the Levantine Sea, located in the eastern Mediterranean Sea. We decompose the circulation into groups based on their physical characteristics and analyze their spatial and temporal variability. We show that most structures of the Levantine Sea are becoming more energetic over time, despite those of the western area remaining the most dominant due to their complex bathymetry and strong currents.
Alain de Verneil, Zouhair Lachkar, Shafer Smith, and Marina Lévy
Biogeosciences, 19, 907–929, https://doi.org/10.5194/bg-19-907-2022, https://doi.org/10.5194/bg-19-907-2022, 2022
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The Arabian Sea is a natural CO2 source to the atmosphere, but previous work highlights discrepancies between data and models in estimating air–sea CO2 flux. In this study, we use a regional ocean model, achieve a flux closer to available data, and break down the seasonal cycles that impact it, with one result being the great importance of monsoon winds. As demonstrated in a meta-analysis, differences from data still remain, highlighting the great need for further regional data collection.
Zouhair Lachkar, Michael Mehari, Muchamad Al Azhar, Marina Lévy, and Shafer Smith
Biogeosciences, 18, 5831–5849, https://doi.org/10.5194/bg-18-5831-2021, https://doi.org/10.5194/bg-18-5831-2021, 2021
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This study documents and quantifies a significant recent oxygen decline in the upper layers of the Arabian Sea and explores its drivers. Using a modeling approach we show that the fast local warming of sea surface is the main factor causing this oxygen drop. Concomitant summer monsoon intensification contributes to this trend, although to a lesser extent. These changes exacerbate oxygen depletion in the subsurface, threatening marine habitats and altering the local biogeochemistry.
Damien Couespel, Marina Lévy, and Laurent Bopp
Biogeosciences, 18, 4321–4349, https://doi.org/10.5194/bg-18-4321-2021, https://doi.org/10.5194/bg-18-4321-2021, 2021
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An alarming consequence of climate change is the oceanic primary production decline projected by Earth system models. These coarse-resolution models parameterize oceanic eddies. Here, idealized simulations of global warming with increasing resolution show that the decline in primary production in the eddy-resolved simulations is half as large as in the eddy-parameterized simulations. This stems from the high sensitivity of the subsurface nutrient transport to model resolution.
Clément Bricaud, Julien Le Sommer, Gurvan Madec, Christophe Calone, Julie Deshayes, Christian Ethe, Jérôme Chanut, and Marina Levy
Geosci. Model Dev., 13, 5465–5483, https://doi.org/10.5194/gmd-13-5465-2020, https://doi.org/10.5194/gmd-13-5465-2020, 2020
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In order to reduce the cost of ocean biogeochemical models, a multi-grid approach where ocean dynamics and tracer transport are computed with different spatial resolution has been developed in the NEMO v3.6 OGCM. Different experiments confirm that the spatial resolution of hydrodynamical fields can be coarsened without significantly affecting the resolved passive tracer fields. This approach leads to a factor of 7 reduction of the overhead associated with running a full biogeochemical model.
Cited articles
Abot, L., Provost, C., and Poli, L.: Recent Convection Decline in the Greenland Sea: Insights From the Mercator Ocean System Over 2008–2020, J. Geophys. Res.-Oceans, 128, e2022JC019320, https://doi.org/10.1029/2022JC019320, 2023. a
Alexander, K. and Easterbrook, S. M.: The software architecture of climate models: a graphical comparison of CMIP5 and EMICAR5 configurations, Geosci. Model Dev., 8, 1221–1232, https://doi.org/10.5194/gmd-8-1221-2015, 2015. a
Baaklini, G., El Hourany, R., Fakhri, M., Brajard, J., Issa, L., Fifani, G., and Mortier, L.: Surface circulation properties in the eastern Mediterranean emphasized using machine learning methods, Ocean Sci., 18, 1491–1505, https://doi.org/10.5194/os-18-1491-2022, 2022. a
Behrenfeld, M. J. and Falkowski, P. G.: Photosynthetic rates derived from satellite-based chlorophyll concentration, Limnol. Oceanogr., 42, 1–20, https://doi.org/10.4319/lo.1997.42.1.0001, 1997. a
Benedetti, F., Vogt, M., Elizondo, U. H., Righetti, D., Zimmermann, N. E., and Gruber, N.: Major restructuring of marine plankton assemblages under global warming, Nat. Commun., 12, 5226, https://doi.org/10.1038/s41467-021-25385-x, 2021. a
Bishop, C. H. and Abramowitz, G.: Climate model dependence and the replicate Earth paradigm, Clim. Dynam., 41, 885–900, https://doi.org/10.1007/s00382-012-1610-y, 2013. a
Bopp, L., Resplandy, L., Orr, J. C., Doney, S. C., Dunne, J. P., Gehlen, M., Halloran, P., Heinze, C., Ilyina, T., Séférian, R., Tjiputra, J., and Vichi, M.: Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models, Biogeosciences, 10, 6225–6245, https://doi.org/10.5194/bg-10-6225-2013, 2013. a, b
Bopp, L., Aumont, O., Kwiatkowski, L., Clerc, C., Dupont, L., Ethé, C., Gorgues, T., Séférian, R., and Tagliabue, A.: Diazotrophy as a key driver of the response of marine net primary productivity to climate change, Biogeosciences, 19, 4267–4285, https://doi.org/10.5194/bg-19-4267-2022, 2022. a, b, c
Boucher, O., Servonnat, J., Albright, A. L., Aumont, O., Balkanski, Y., Bastrikov, V., Bekki, S., Bonnet, R., Bony, S., Bopp, L., Braconnot, P., Brockmann, P., Cadule, P., Caubel, A., Cheruy, F., Codron, F., Cozic, A., Cugnet, D., D'Andrea, F., Davini, P., de Lavergne, C., Denvil, S., Deshayes, J., Devilliers, M., Ducharne, A., Dufresne, J.-L., Dupont, E., Éthé, C., Fairhead, L., Falletti, L., Flavoni, S., Foujols, M.-A., Gardoll, S., Gastineau, G., Ghattas, J., Grandpeix, J.-Y., Guenet, B., Guez, Lionel, E., Guilyardi, E., Guimberteau, M., Hauglustaine, D., Hourdin, F., Idelkadi, A., Joussaume, S., Kageyama, M., Khodri, M., Krinner, G., Lebas, N., Levavasseur, G., Lévy, C., Li, L., Lott, F., Lurton, T., Luyssaert, S., Madec, G., Madeleine, J.-B., Maignan, F., Marchand, M., Marti, O., Mellul, L., Meurdesoif, Y., Mignot, J., Musat, I., Ottlé, C., Peylin, P., Planton, Y., Polcher, J., Rio, C., Rochetin, N., Rousset, C., Sepulchre, P., Sima, A., Swingedouw, D., Thiéblemont, R., Traore, A. K., Vancoppenolle, M., Vial, J., Vialard, J., Viovy, N., and Vuichard, N.: Presentation and Evaluation of the IPSL-CM6A-LR Climate Model, J. Adv. Model. Earth Sy., 12, e2019MS002010, https://doi.org/10.1029/2019MS002010, 2020. a
Canadell, J. G., Scheel Monteiro, P., Costa, M. H., Cotrim da Cunha, L., Cox, P. M., Eliseev, A. V., Henson, S., Ishii, M., Jaccard, S., Koven, C., Lohila, A., Patra, P. K., Piao, S., Rogelj, J., Syampungani, S., Zaehle, S., and Zickfeld, K.: Global carbon and other biogeochemical cycles and feedbacks, in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, R., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 673–816, https://doi.org/10.1017/9781009157896.001, 2021. a
Carozza, D. A., Bianchi, D., and Galbraith, E. D.: The ecological module of BOATS-1.0: a bioenergetically constrained model of marine upper trophic levels suitable for studies of fisheries and ocean biogeochemistry, Geosci. Model Dev., 9, 1545–1565, https://doi.org/10.5194/gmd-9-1545-2016, 2016. a
Cheung, W. W. L., Frölicher, T. L., Asch, R. G., Jones, M. C., Pinsky, M. L., Reygondeau, G., Rodgers, K. B., Rykaczewski, R. R., Sarmiento, J. L., Stock, C., and Watson, J. R.: Building confidence in projections of the responses of living marine resources to climate change, ICES J. Mar. Sci., 73, 1283–1296, https://doi.org/10.1093/icesjms/fsv250, 2016. a
Christian, J. R., Denman, K. L., Hayashida, H., Holdsworth, A. M., Lee, W. G., Riche, O. G. J., Shao, A. E., Steiner, N., and Swart, N. C.: Ocean biogeochemistry in the Canadian Earth System Model version 5.0.3: CanESM5 and CanESM5-CanOE, Geosci. Model Dev., 15, 4393–4424, https://doi.org/10.5194/gmd-15-4393-2022, 2022. a, b, c, d, e
Copernicus Climate Change Service (C3S): Sea ice concentration daily gridded data from 1978 to present derived from satellite observations, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.3cd8b812, 2020. a
Cox, P. M., Huntingford, C., and Williamson, M. S.: Emergent constraint on equilibrium climate sensitivity from global temperature variability, Nature, 553, 319–322, https://doi.org/10.1038/nature25450, 2018. a
Derouiche, S., Mallet, C., Hannachi, A., and Bargaoui, Z.: Characterisation of rainfall events in northern Tunisia using self-organising maps, J. Hydrol., 42, 101159, https://doi.org/10.1016/j.ejrh.2022.101159, 2022. a, b
DeVries, T., Primeau, F., and Deutsch, C.: The sequestration efficiency of the biological pump, Geophys. Res. Lett., 39, L13601, https://doi.org/10.1029/2012GL051963, 2012. a
Dilmi, M. D., Mallet, C., Barthes, L., and Chazottes, A.: Data-driven clustering of rain events: microphysics information derived from macro-scale observations, Atmos. Meas. Tech., 10, 1557–1574, https://doi.org/10.5194/amt-10-1557-2017, 2017. a, b
Earth System Grid Federation (ESGF): https://esgf.llnl.gov/, last access: January 2024. a
El Hourany, R., Mejia, C., Faour, G., Crépon, M., and Thiria, S.: Evidencing the Impact of Climate Change on the Phytoplankton Community of the Mediterranean Sea Through a Bioregionalization Approach, J. Geophys. Res.-Oceans, 126, e2020JC016808, https://doi.org/10.1029/2020JC016808, 2021. a, b
Eppley, R. W.: Temperature and phytoplankton growth in the sea, Fishery Bulletin, 70, 1972. a
E.U. Copernicus Marine Service Information (CMEMS): Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997–ongoing), Marine Data Store (MDS) [data set], https://doi.org/10.48670/moi-00281, 2023a. a
E.U. Copernicus Marine Service Information (CMEMS): Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD, Marine Data Store (MDS) [data set], https://doi.org/10.48670/moi-00052, 2023b. a
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a
Eyring, V., Cox, P. M., Flato, G. M., Gleckler, P. J., Abramowitz, G., Caldwell, P., Collins, W. D., Gier, B. K., Hall, A. D., Hoffman, F. M., Hurtt, G. C., Jahn, A., Jones, C. D., Klein, S. A., Krasting, J. P., Kwiatkowski, L., Lorenz, R., Maloney, E., Meehl, G. A., Pendergrass, A. G., Pincus, R., Ruane, A. C., Russell, J. L., Sanderson, B. M., Santer, B. D., Sherwood, S. C., Simpson, I. R., Stouffer, R. J., and Williamson, M. S.: Taking climate model evaluation to the next level, Nat. Clim. Change, 9, 102–110, https://doi.org/10.1038/s41558-018-0355-y, 2019. a, b, c
Farikou, O., Sawadogo, S., Niang, A., Diouf, D., Brajard, J., Mejia, C., Dandonneau, Y., Gasc, G., Crepon, M., and Thiria, S.: Inferring the seasonal evolution of phytoplankton groups in the Senegalo-Mauritanian upwelling region from satellite ocean-color spectral measurements, J. Geophys. Res.-Oceans, 120, 6581–6601, https://doi.org/10.1002/2015JC010738, 2015. a
Fox-Kemper, B., Hewitt, H. T., Xiao, C., Aðalgeirsdóttir, G., Drijfhout, S. S., Edwards, T. L., Golledge, N. R., Hemer, M., Kopp, R. E., Krinner, G., Mix, A., Notz, D., Nowicki, S., Nurhati, I. S., Ruiz, L., Sallée, J.-B., Slangen, A. B. A., and Yu, Y.: Ocean, cryosphere, and sea level change, in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, R., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1211–1362, https://doi.org/10.1017/9781009157896.001, 2021. a
Frieler, K., Volkholz, J., Lange, S., Schewe, J., Mengel, M., del Rocío Rivas López, M., Otto, C., Reyer, C. P. O., Karger, D. N., Malle, J. T., Treu, S., Menz, C., Blanchard, J. L., Harrison, C. S., Petrik, C. M., Eddy, T. D., Ortega-Cisneros, K., Novaglio, C., Rousseau, Y., Watson, R. A., Stock, C., Liu, X., Heneghan, R., Tittensor, D., Maury, O., Büchner, M., Vogt, T., Wang, T., Sun, F., Sauer, I. J., Koch, J., Vanderkelen, I., Jägermeyr, J., Müller, C., Rabin, S., Klar, J., Vega del Valle, I. D., Lasslop, G., Chadburn, S., Burke, E., Gallego-Sala, A., Smith, N., Chang, J., Hantson, S., Burton, C., Gädeke, A., Li, F., Gosling, S. N., Müller Schmied, H., Hattermann, F., Wang, J., Yao, F., Hickler, T., Marcé, R., Pierson, D., Thiery, W., Mercado-Bettín, D., Ladwig, R., Ayala-Zamora, A. I., Forrest, M., and Bechtold, M.: Scenario setup and forcing data for impact model evaluation and impact attribution within the third round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a), Geosci. Model Dev., 17, 1–51, https://doi.org/10.5194/gmd-17-1-2024, 2024. a
Fu, W., Randerson, J. T., and Moore, J. K.: Climate change impacts on net primary production (NPP) and export production (EP) regulated by increasing stratification and phytoplankton community structure in the CMIP5 models, Biogeosciences, 13, 5151–5170, https://doi.org/10.5194/bg-13-5151-2016, 2016. a
Garcia, H. E., Weathers, K. W., Paver, C. R., Smolyar, I., Boyer, T. P., Locarnini, R. A., Zweng, M. M., Mishonov, A. V., Baranova, O. K., Seidov, D., and Reagan, J. R.: World Ocean Atlas 2018, Volume 4: Dissolved Inorganic Nutrients (phosphate, nitrate and nitrate + nitrite, silicate), edited by: Mishonov, A. (Technical Ed.), NOAA Atlas NESDIS, 84, 35 pp., https://www.ncei.noaa.gov/sites/default/files/2020-04/woa18_vol4.pdf (last access: October 2023), 2018. a
Guèye, A. K., Janicot, S., Niang, A., Sawadogo, S., Sultan, B., Diongue-Niang, A., and Thiria, S.: Weather regimes over Senegal during the summer monsoon season using self-organizing maps and hierarchical ascendant classification. Part I: synoptic time scale, Clim. Dynam., 36, 1–18, https://doi.org/10.1007/s00382-010-0782-6, 2011. a
Guinehut, S., Dhomps, A.-L., Larnicol, G., and Le Traon, P.-Y.: High resolution 3-D temperature and salinity fields derived from in situ and satellite observations, Ocean Sci., 8, 845–857, https://doi.org/10.5194/os-8-845-2012, 2012. a, b
Hátún, H., Azetsu-Scott, K., Somavilla, R., Rey, F., Johnson, C., Mathis, M., Mikolajewicz, U., Coupel, P., Tremblay, J.-R., Hartman, S., Pacariz, S. V., Salter, I., and Ólafsson, J.: The subpolar gyre regulates silicate concentrations in the North Atlantic, Sci. Rep., 7, 14576, https://doi.org/10.1038/s41598-017-14837-4, 2017. a
Hofmann Elizondo, U., Righetti, D., Benedetti, F., and Vogt, M.: Biome partitioning of the global ocean based on phytoplankton biogeography, Prog. Oceanogr., 194, 102530, https://doi.org/10.1016/j.pocean.2021.102530, 2021. a
ilarinieminen: SOM-Toolbox, GitHub [code], https://github.com/ilarinieminen/SOM-Toolbox, last access: October 2023. a
Ilyina, T., Six, K. D., Segschneider, J., Maier-Reimer, E., Li, H., and Núñez-Riboni, I.: Global ocean biogeochemistry model HAMOCC: Model architecture and performance as component of the MPI-Earth system model in different CMIP5 experimental realizations, J. Adv. Model. Earth Sy., 5, 287–315, https://doi.org/10.1029/2012MS000178, 2013. a
Jouini, M., Béranger, K., Arsouze, T., Beuvier, J., Thiria, S., Crépon, M., and Taupier-Letage, I.: The Sicily Channel surface circulation revisited using a neural clustering analysis of a high-resolution simulation, J. Geophys. Res.-Oceans, 121, 4545–4567, https://doi.org/10.1002/2015JC011472, 2016. a, b
Kearney, K. A., Bograd, S. J., Drenkard, E., Gomez, F. A., Haltuch, M., Hermann, A. J., Jacox, M. G., Kaplan, I. C., Koenigstein, S., Luo, J. Y., Masi, M., Muhling, B., Pozo Buil, M., and Woodworth-Jefcoats, P. A.: Using Global-Scale Earth System Models for Regional Fisheries Applications, Front. Mar. Sci., 8, 622206, https://doi.org/10.3389/fmars.2021.622206, 2021. a, b
Kléparski, L., Beaugrand, G., Edwards, M., and Ostle, C.: Phytoplankton life strategies, phenological shifts and climate change in the North Atlantic Ocean from 1850 to 2100, Glob. Change Biol., 29, 3833–3849, https://doi.org/10.1111/gcb.16709, 2023. a
Knutti, R.: The end of model democracy?, Climatic Change, 102, 395–404, https://doi.org/10.1007/s10584-010-9800-2, 2010. a
Knutti, R., Sedláček, J., Sanderson, B. M., Lorenz, R., Fischer, E. M., and Eyring, V.: A climate model projection weighting scheme accounting for performance and interdependence, Geophys. Res. Lett., 44, 1909–1918, https://doi.org/10.1002/2016GL072012, 2017. a, b
Kohonen, T.: Essentials of the self-organizing map, Neural Networks, 37, 52–65, https://doi.org/10.1016/j.neunet.2012.09.018, 2013. a
Kwiatkowski, L., Bopp, L., Aumont, O., Ciais, P., Cox, P. M., Laufkötter, C., Li, Y., and Séférian, R.: Emergent constraints on projections of declining primary production in the tropical oceans, Nat. Clim. Change, 7, 355–358, https://doi.org/10.1038/nclimate3265, 2017. a, b
Kwiatkowski, L., Torres, O., Bopp, L., Aumont, O., Chamberlain, M., Christian, J. R., Dunne, J. P., Gehlen, M., Ilyina, T., John, J. G., Lenton, A., Li, H., Lovenduski, N. S., Orr, J. C., Palmieri, J., Santana-Falcón, Y., Schwinger, J., Séférian, R., Stock, C. A., Tagliabue, A., Takano, Y., Tjiputra, J., Toyama, K., Tsujino, H., Watanabe, M., Yamamoto, A., Yool, A., and Ziehn, T.: Twenty-first century ocean warming, acidification, deoxygenation, and upper-ocean nutrient and primary production decline from CMIP6 model projections, Biogeosciences, 17, 3439–3470, https://doi.org/10.5194/bg-17-3439-2020, 2020. a, b, c, d
Laufkötter, C., Vogt, M., Gruber, N., Aita-Noguchi, M., Aumont, O., Bopp, L., Buitenhuis, E., Doney, S. C., Dunne, J., Hashioka, T., Hauck, J., Hirata, T., John, J., Le Quéré, C., Lima, I. D., Nakano, H., Seferian, R., Totterdell, I., Vichi, M., and Völker, C.: Drivers and uncertainties of future global marine primary production in marine ecosystem models, Biogeosciences, 12, 6955–6984, https://doi.org/10.5194/bg-12-6955-2015, 2015. a
Li, G., Cheng, L., Zhu, J., Trenberth, K. E., Mann, M. E., and Abraham, J. P.: Increasing ocean stratification over the past half-century, Nat. Clim. Change, 10, 1116–1123, https://doi.org/10.1038/s41558-020-00918-2, 2020. a
Llort, J., Lévy, M., Sallée, J. B., and Tagliabue, A.: Nonmonotonic Response of Primary Production and Export to Changes in Mixed-Layer Depth in the Southern Ocean, Geophys. Res. Lett., 46, 3368–3377, https://doi.org/10.1029/2018GL081788, 2019. a
Longhurst, A. R.: Chapter 1 – Toward an ecological geography of the sea, in: Ecological Geography of the Sea (Second Edition), edited by: Longhurst, A. R., Academic Press, Burlington, 1–17, https://doi.org/10.1016/B978-012455521-1/50002-4, 2007. a
Masson, D. and Knutti, R.: Climate model genealogy: CLIMATE MODEL GENEALOGY, Geophys. Res. Lett., 38, L08703, https://doi.org/10.1029/2011GL046864, 2011. a
Mauritsen, T., Bader, J., Becker, T., Behrens, J., Bittner, M., Brokopf, R., Brovkin, V., Claussen, M., Crueger, T., Esch, M., Fast, I., Fiedler, S., Fläschner, D., Gayler, V., Giorgetta, M., Goll, D. S., Haak, H., Hagemann, S., Hedemann, C., Hohenegger, C., Ilyina, T., Jahns, T., Jimenéz-de-la Cuesta, D., Jungclaus, J., Kleinen, T., Kloster, S., Kracher, D., Kinne, S., Kleberg, D., Lasslop, G., Kornblueh, L., Marotzke, J., Matei, D., Meraner, K., Mikolajewicz, U., Modali, K., Möbis, B., Müller, W. A., Nabel, J. E. M. S., Nam, C. C. W., Notz, D., Nyawira, S.-S., Paulsen, H., Peters, K., Pincus, R., Pohlmann, H., Pongratz, J., Popp, M., Raddatz, T. J., Rast, S., Redler, R., Reick, C. H., Rohrschneider, T., Schemann, V., Schmidt, H., Schnur, R., Schulzweida, U., Six, K. D., Stein, L., Stemmler, I., Stevens, B., von Storch, J.-S., Tian, F., Voigt, A., Vrese, P., Wieners, K.-H., Wilkenskjeld, S., Winkler, A., and Roeckner, E.: Developments in the MPI-M Earth System Model version 1.2 (MPI-ESM1.2) and Its Response to Increasing CO2, J. Adv. Model. Earth Sy., 11, 998–1038, https://doi.org/10.1029/2018MS001400, 2019. a
Mousing, E. A., Ellingen, I., Hjøllo, S. S., Husson, B., Skogen, M. D., and Wallhead, P.: Why do regional biogeochemical models produce contrasting future projections of primary production in the Barents Sea?, J. Sea Res., 192, 102366, https://doi.org/10.1016/j.seares.2023.102366, 2023. a, b
Mulet, S., Rio, M. H., Mignot, A., Guinehut, S., and Morrow, R.: A new estimate of the global 3D geostrophic ocean circulation based on satellite data and in-situ measurements, Deep-Sea Res. Pt. II, 77–80, 70–81, https://doi.org/10.1016/j.dsr2.2012.04.012, 2012. a, b
Nowicki, M., DeVries, T., and Siegel, D. A.: Quantifying the Carbon Export and Sequestration Pathways of the Ocean's Biological Carbon Pump, Global Biogeochem. Cy., 36, e2021GB007083, https://doi.org/10.1029/2021GB007083, 2022. a, b
Oke, P. R., Griffin, D. A., Schiller, A., Matear, R. J., Fiedler, R., Mansbridge, J., Lenton, A., Cahill, M., Chamberlain, M. A., and Ridgway, K.: Evaluation of a near-global eddy-resolving ocean model, Geosci. Model Dev., 6, 591–615, https://doi.org/10.5194/gmd-6-591-2013, 2013. a
O'Gorman, P. A.: Sensitivity of tropical precipitation extremes to climate change, Nat. Geosci., 5, 697–700, https://doi.org/10.1038/ngeo1568, 2012. a
O'Neill, B. C., Kriegler, E., Riahi, K., Ebi, K. L., Hallegatte, S., Carter, T. R., Mathur, R., and van Vuuren, D. P.: A new scenario framework for climate change research: the concept of shared socioeconomic pathways, Climatic Change, 122, 387–400, https://doi.org/10.1007/s10584-013-0905-2, 2014. a
O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., and Sanderson, B. M.: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6, Geosci. Model Dev., 9, 3461–3482, https://doi.org/10.5194/gmd-9-3461-2016, 2016. a, b
Palter, J. B. and Lozier, M. S.: On the source of Gulf Stream nutrients, J. Geophys. Res.-Oceans, 113, C06018, https://doi.org/10.1029/2007JC004611, 2008. a
Paulsen, H., Ilyina, T., Six, K. D., and Stemmler, I.: Incorporating a prognostic representation of marine nitrogen fixers into the global ocean biogeochemical model HAMOCC, J. Adv. Model. Earth Sy., 9, 438–464, https://doi.org/10.1002/2016MS000737, 2017. a, b
Qu, X. and Hall, A.: On the persistent spread in snow-albedo feedback, Clim. Dynam., 42, 69–81, https://doi.org/10.1007/s00382-013-1774-0, 2014. a
Räisänen, J., Ruokolainen, L., and Ylhäisi, J.: Weighting of model results for improving best estimates of climate change, Clim. Dynam., 35, 407–422, https://doi.org/10.1007/s00382-009-0659-8, 2010. a
Rohr, T., Richardson, A. J., Lenton, A., Chamberlain, M. A., and Shadwick, E. H.: Zooplankton grazing is the largest source of uncertainty for marine carbon cycling in CMIP6 models, Commun. Earth Environ., 4, 1–22, https://doi.org/10.1038/s43247-023-00871-w, 2023. a, b, c, d
Sanderson, B. M., Knutti, R., and Caldwell, P.: Addressing Interdependency in a Multimodel Ensemble by Interpolation of Model Properties, J. Climate, 28, 5150–5170, https://doi.org/10.1175/JCLI-D-14-00361.1, 2015. a
Sanderson, B. M., Wehner, M., and Knutti, R.: Skill and independence weighting for multi-model assessments, Geosci. Model Dev., 10, 2379–2395, https://doi.org/10.5194/gmd-10-2379-2017, 2017. a, b
Sanderson, B. M., Pendergrass, A. G., Koven, C. D., Brient, F., Booth, B. B. B., Fisher, R. A., and Knutti, R.: The potential for structural errors in emergent constraints, Earth Syst. Dynam., 12, 899–918, https://doi.org/10.5194/esd-12-899-2021, 2021. a, b
Sauterey, B., Gland, G. L., Cermeño, P., Aumont, O., Lévy, M., and Vallina, S. M.: Phytoplankton adaptive resilience to climate change collapses in case of extreme events – A modeling study, Ecol Model., 483, 110437, https://doi.org/10.1016/j.ecolmodel.2023.110437, 2023. a
Séférian, R., Berthet, S., Yool, A., Palmiéri, J., Bopp, L., Tagliabue, A., Kwiatkowski, L., Aumont, O., Christian, J., Dunne, J., Gehlen, M., Ilyina, T., John, J. G., Li, H., Long, M. C., Luo, J. Y., Nakano, H., Romanou, A., Schwinger, J., Stock, C., Santana-Falcón, Y., Takano, Y., Tjiputra, J., Tsujino, H., Watanabe, M., Wu, T., Wu, F., and Yamamoto, A.: Tracking Improvement in Simulated Marine Biogeochemistry Between CMIP5 and CMIP6, Current Climate Change Reports, 6, 95–119, https://doi.org/10.1007/s40641-020-00160-0, 2020. a
Sellar, A. A., Jones, C. G., Mulcahy, J. P., Tang, Y., Yool, A., Wiltshire, A., O'Connor, F. M., Stringer, M., Hill, R., Palmieri, J., Woodward, S., de Mora, L., Kuhlbrodt, T., Rumbold, S. T., Kelley, D. I., Ellis, R., Johnson, C. E., Walton, J., Abraham, N. L., Andrews, M. B., Andrews, T., Archibald, A. T., Berthou, S., Burke, E., Blockley, E., Carslaw, K., Dalvi, M., Edwards, J., Folberth, G. A., Gedney, N., Griffiths, P. T., Harper, A. B., Hendry, M. A., Hewitt, A. J., Johnson, B., Jones, A., Jones, C. D., Keeble, J., Liddicoat, S., Morgenstern, O., Parker, R. J., Predoi, V., Robertson, E., Siahaan, A., Smith, R. S., Swaminathan, R., Woodhouse, M. T., Zeng, G., and Zerroukat, M.: UKESM1: Description and Evaluation of the U.K. Earth System Model, J. Adv. Model. Earth Sy., 11, 4513–4558, https://doi.org/10.1029/2019MS001739, 2019. a
Sieracki, M. E., Verity, P. G., and Stoecker, D. K.: Plankton community response to sequential silicate and nitrate depletion during the 1989 North Atlantic spring bloom, Deep-Sea Res. Pt. II, 40, 213–225, https://doi.org/10.1016/0967-0645(93)90014-E, 1993. a
Swart, N. C., Cole, J. N. S., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Hanna, S., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Sigmond, M., Solheim, L., von Salzen, K., Yang, D., and Winter, B.: The Canadian Earth System Model version 5 (CanESM5.0.3), Geosci. Model Dev., 12, 4823–4873, https://doi.org/10.5194/gmd-12-4823-2019, 2019. a, b
Tagliabue, A., Kwiatkowski, L., Bopp, L., Butenschön, M., Cheung, W., Lengaigne, M., and Vialard, J.: Persistent Uncertainties in Ocean Net Primary Production Climate Change Projections at Regional Scales Raise Challenges for Assessing Impacts on Ecosystem Services, Frontiers in Climate, 3, 738224, https://doi.org/10.3389/fclim.2021.738224, 2021. a, b, c, d, e, f, g
Tittensor, D. P., Novaglio, C., Harrison, C. S., Heneghan, R. F., Barrier, N., Bianchi, D., Bopp, L., Bryndum-Buchholz, A., Britten, G. L., Büchner, M., Cheung, W. W. L., Christensen, V., Coll, M., Dunne, J. P., Eddy, T. D., Everett, J. D., Fernandes-Salvador, J. A., Fulton, E. A., Galbraith, E. D., Gascuel, D., Guiet, J., John, J. G., Link, J. S., Lotze, H. K., Maury, O., Ortega-Cisneros, K., Palacios-Abrantes, J., Petrik, C. M., du Pontavice, H., Rault, J., Richardson, A. J., Shannon, L., Shin, Y.-J., Steenbeek, J., Stock, C. A., and Blanchard, J. L.: Next-generation ensemble projections reveal higher climate risks for marine ecosystems, Nat. Clim. Change, 11, 973–981, https://doi.org/10.1038/s41558-021-01173-9, 2021. a
Vancoppenolle, M., Bopp, L., Madec, G., Dunne, J., Ilyina, T., Halloran, P. R., and Steiner, N.: Future Arctic Ocean primary productivity from CMIP5 simulations: Uncertain outcome, but consistent mechanisms: FUTURE ARCTIC OCEAN PRIMARY PRODUCTIVITY, Global Biogeochem. Cy., 27, 605–619, https://doi.org/10.1002/gbc.20055, 2013. a, b
Vatanen, T., Osmala, M., Raiko, T., Lagus, K., Sysi-Aho, M., Orešič, M., Honkela, T., and Lähdesmäki, H.: Self-organization and missing values in SOM and GTM, Neurocomputing, 147, 60–70, https://doi.org/10.1016/j.neucom.2014.02.061, 2015. a
Wang, W.-L., Moore, J. K., Martiny, A. C., and Primeau, F. W.: Convergent estimates of marine nitrogen fixation, Nature, 566, 205–211, https://doi.org/10.1038/s41586-019-0911-2, 2019. a
Westberry, T., Behrenfeld, M. J., Siegel, D. A., and Boss, E.: Carbon-based primary productivity modeling with vertically resolved photoacclimation, Global Biogeochem. Cy., 22, 2007GB003078, https://doi.org/10.1029/2007GB003078, 2008. a
Whitt, D. B.: On the Role of the Gulf Stream in the Changing Atlantic Nutrient Circulation During the 21st Century, in: Geophysical Monograph Series, 1 edn., edited by: Nagai, T., Saito, H., Suzuki, K., and Takahashi, M., Wiley, 51–82, https://doi.org/10.1002/9781119428428.ch4, 2019. a, b
Williams, R. G., Roussenov, V., and Follows, M. J.: Nutrient streams and their induction into the mixed layer: NUTRIENT STREAMS AND INDUCTION, Global Biogeochem. Cy., 20, GB1016, https://doi.org/10.1029/2005GB002586, 2006. a
Williams, R. G., McDonagh, E., Roussenov, V. M., Torres-Valdes, S., King, B., Sanders, R., and Hansell, D. A.: Nutrient streams in the North Atlantic: Advective pathways of inorganic and dissolved organic nutrients, Global Biogeochem. Cy., 25, GB4008, https://doi.org/10.1029/2010GB003853, 2011. a
Wilson, J. D., Andrews, O., Katavouta, A., de Melo Viríssimo, F., Death, R. M., Adloff, M., Baker, C. A., Blackledge, B., Goldsworth, F. W., Kennedy-Asser, A. T., Liu, Q., Sieradzan, K. R., Vosper, E., and Ying, R.: The biological carbon pump in CMIP6 models: 21st century trends and uncertainties, P. Natl. Acad. Sci. USA, 119, e2204369119, https://doi.org/10.1073/pnas.2204369119, 2022. a
Xiu, P., Chai, F., Curchitser, E. N., and Castruccio, F. S.: Future changes in coastal upwelling ecosystems with global warming: The case of the California Current System, Sci. Rep., 8, 2866, https://doi.org/10.1038/s41598-018-21247-7, 2018. a
Yahi, H., Marticorena, B., Thiria, S., Chatenet, B., Schmechtig, C., Rajot, J. L., and Crepon, M.: Statistical relationship between surface PM10 concentration and aerosol optical depth over the Sahel as a function of weather type, using neural network methodology, J. Geophys. Res.-Atmos., 118, 13265–13281, https://doi.org/10.1002/2013JD019465, 2013. a
Yool, A., Popova, E. E., and Anderson, T. R.: MEDUSA-2.0: an intermediate complexity biogeochemical model of the marine carbon cycle for climate change and ocean acidification studies, Geosci. Model Dev., 6, 1767–1811, https://doi.org/10.5194/gmd-6-1767-2013, 2013. a, b
Zahariev, K., Christian, J. R., and Denman, K. L.: Preindustrial, historical, and fertilization simulations using a global ocean carbon model with new parameterizations of iron limitation, calcification, and N2 fixation, Prog. Oceanogr., 77, 56–82, https://doi.org/10.1016/j.pocean.2008.01.007, 2008. a, b
Ziehn, T., Chamberlain, M. A., Law, R. M., Lenton, A., Bodman, R. W., Dix, M., Stevens, L., Wang, Y.-P., and Srbinovsky, J.: The Australian Earth System Model: ACCESS-ESM1.5, Journal of Southern Hemisphere Earth Systems Science, 70, 193–214, https://doi.org/10.1071/ES19035, 2020. a
Co-editor-in-chief
This study presents a new methodology to address the divergent projections of Net Primary Production in the North Atlantic Ocean in the last phase 6 of CMIP. The method is based on the process-based selection of models in the different ocean regions. This method can contribute to improve the reliability of climate change impact assessments on ecosystems and human societies.
This study presents a new methodology to address the divergent projections of Net Primary...
Short summary
The marine biogeochemistry components of Coupled Model Intercomparison Project phase 6 (CMIP6) models vary widely in their process representations. Using an innovative bioregionalization of the North Atlantic, we reveal that this model diversity largely drives the divergence in net primary production projections under a high-emission scenario. The identification of the most mechanistically realistic models allows for a substantial reduction in projection uncertainty.
The marine biogeochemistry components of Coupled Model Intercomparison Project phase 6 (CMIP6)...
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