Articles | Volume 20, issue 1
https://doi.org/10.5194/os-20-155-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-20-155-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Feb 2024
Research article |
| 16 Feb 2024
| 16 Feb 2024
Ensemble analysis and forecast of ecosystem indicators in the North Atlantic using ocean colour observations and prior statistics from a stochastic NEMO–PISCES simulator
Mikhail Popov
Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, Grenoble, France
Jean-Michel Brankart
CORRESPONDING AUTHOR
Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, Grenoble, France
Arthur Capet
Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, Grenoble, France
Operational Directorate Natural Environment, Royal Belgian Institute of Natural Sciences, Brussels, Belgium
Emmanuel Cosme
Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, Grenoble, France
Pierre Brasseur
Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, Grenoble, France
Related authors
No articles found.
Mauro Cirano, Enrique Alvarez-Fanjul, Arthur Capet, Stefania Ciliberti, Emanuela Clementi, Boris Dewitte, Matias Dinápoli, Ghada El Serafy, Patrick Hogan, Sudheer Joseph, Yasumasa Miyazawa, Ivonne Montes, Diego Narvaez, Heather Regan, Claudia G. Simionato, Clemente A. S. Tanajura, Pramod Thupaki, Claudia Urbano-Latorre, and Jennifer Veitch
State Planet Discuss., https://doi.org/10.5194/sp-2024-26, https://doi.org/10.5194/sp-2024-26, 2024
Preprint under review for SP
Short summary
Short summary
Predicting the ocean state in support of human activities, environmental monitoring and policymaking across different regions worldwide is fundamental. The status of operational ocean forecasting systems (OOFS) in 8 key regions worldwide is provided. A discussion follows on the numerical strategy and available OOFS, pointing out the straightness and the ways forward to improve the essential ocean variables predictability from regional to coastal scales, products reliability and accuracy.
Florian Le Guillou, Lucile Gaultier, Maxime Ballarotta, Sammy Metref, Clément Ubelmann, Emmanuel Cosme, and Marie-Helène Rio
Ocean Sci., 19, 1517–1527, https://doi.org/10.5194/os-19-1517-2023, https://doi.org/10.5194/os-19-1517-2023, 2023
Short summary
Short summary
Altimetry provides sea surface height (SSH) data along one-dimensional tracks. For many applications, the tracks are interpolated in space and time to provide gridded SSH maps. The operational SSH gridded products filter out the small-scale signals measured on the tracks. This paper evaluates the performances of a recently implemented dynamical method to retrieve the small-scale signals from real SSH data. We show a net improvement in the quality of SSH maps when compared to independent data.
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
Sammy Metref, Emmanuel Cosme, Matthieu Le Lay, and Joël Gailhard
Hydrol. Earth Syst. Sci., 27, 2283–2299, https://doi.org/10.5194/hess-27-2283-2023, https://doi.org/10.5194/hess-27-2283-2023, 2023
Short summary
Short summary
Predicting the seasonal streamflow supply of water in a mountainous basin is critical to anticipating the operation of hydroelectric dams and avoiding hydrology-related hazard. This quantity partly depends on the snowpack accumulated during winter. The study addresses this prediction problem using information from streamflow data and both direct and indirect snow measurements. In this study, the prediction is improved by integrating the data information into a basin-scale hydrological model.
Stephanie Leroux, Jean-Michel Brankart, Aurélie Albert, Laurent Brodeau, Jean-Marc Molines, Quentin Jamet, Julien Le Sommer, Thierry Penduff, and Pierre Brasseur
Ocean Sci., 18, 1619–1644, https://doi.org/10.5194/os-18-1619-2022, https://doi.org/10.5194/os-18-1619-2022, 2022
Short summary
Short summary
The goal of the study is to evaluate the predictability of the ocean circulation
at a kilometric scale, in order to anticipate the requirements of the future operational forecasting systems. For that purpose, ensemble experiments have been performed with a regional model for the Western Mediterranean (at 1/60° horizontal resolution). From these ensemble experiments, we show that it is possible to compute targeted predictability scores, which depend on initial and model uncertainties.
Bertrand Cluzet, Matthieu Lafaysse, Emmanuel Cosme, Clément Albergel, Louis-François Meunier, and Marie Dumont
Geosci. Model Dev., 14, 1595–1614, https://doi.org/10.5194/gmd-14-1595-2021, https://doi.org/10.5194/gmd-14-1595-2021, 2021
Short summary
Short summary
In the mountains, the combination of large model error and observation sparseness is a challenge for data assimilation. Here, we develop two variants of the particle filter (PF) in order to propagate the information content of observations into unobserved areas. By adjusting observation errors or exploiting background correlation patterns, we demonstrate the potential for partial observations of snow depth and surface reflectance to improve model accuracy with the PF in an idealised setting.
Florian Ricour, Arthur Capet, Fabrizio D'Ortenzio, Bruno Delille, and Marilaure Grégoire
Biogeosciences, 18, 755–774, https://doi.org/10.5194/bg-18-755-2021, https://doi.org/10.5194/bg-18-755-2021, 2021
Short summary
Short summary
This paper addresses the phenology of the deep chlorophyll maximum (DCM) in the Black Sea (BS). We show that the DCM forms in March at a density level set by the winter mixed layer. It maintains this location until June, suggesting an influence of the DCM on light and nutrient profiles rather than mere adaptation to external factors. In summer, the DCM concentrates ~55 % of the chlorophyll in a 10 m layer at ~35 m depth and should be considered a major feature of the BS phytoplankton dynamics.
Arthur Capet, Luc Vandenbulcke, and Marilaure Grégoire
Biogeosciences, 17, 6507–6525, https://doi.org/10.5194/bg-17-6507-2020, https://doi.org/10.5194/bg-17-6507-2020, 2020
Short summary
Short summary
The Black Sea is 2000 m deep, but, due to limited ventilation, only about the upper 100 m contains enough oxygen to support marine life such as fish. This oxygenation depth has been shown to be decreasing (1955–2019). Here, we evidence that atmospheric warming induced a clear shift in an important ventilation mechanism. We highlight the impact of this shift on oxygenation. There are important implications for marine life and carbon and nutrient cycling if this new ventilation regime persists.
Yeray Santana-Falcón, Pierre Brasseur, Jean Michel Brankart, and Florent Garnier
Ocean Sci., 16, 1297–1315, https://doi.org/10.5194/os-16-1297-2020, https://doi.org/10.5194/os-16-1297-2020, 2020
Short summary
Short summary
Data assimilation is the most comprehensive strategy to estimate the biogeochemical state of the ocean. Here, surface Chl a data are daily assimilated into a 24-member NEMO–PISCES ensemble configuration to implement a complete 4D assimilation system. Results show the assimilation increases the skills of the ensemble, though a regional diagnosis suggests that the description of model and observation uncertainties needs to be refined according to the biogeochemical characteristics of each region.
Ann-Sophie Tissier, Jean-Michel Brankart, Charles-Emmanuel Testut, Giovanni Ruggiero, Emmanuel Cosme, and Pierre Brasseur
Ocean Sci., 15, 443–457, https://doi.org/10.5194/os-15-443-2019, https://doi.org/10.5194/os-15-443-2019, 2019
Short summary
Short summary
To better exploit the observational information available for all scales in data assimilation systems, we investigate a new method to introduce scale separation in the algorithm. It consists in carrying out the analysis with spectral localisation for the large scales and spatial localisation for the residual scales. The performance is then checked explicitly and separately for all scales. Results show that accuracy can be improved for the large scales while preserving reliability at all scales.
Florent Garnier, Pierre Brasseur, Jean-Michel Brankart, Yeray Santana-Falcon, and Emmanuel Cosme
Ocean Sci. Discuss., https://doi.org/10.5194/os-2018-153, https://doi.org/10.5194/os-2018-153, 2019
Publication in OS not foreseen
Fanny Larue, Alain Royer, Danielle De Sève, Alexandre Roy, and Emmanuel Cosme
Hydrol. Earth Syst. Sci., 22, 5711–5734, https://doi.org/10.5194/hess-22-5711-2018, https://doi.org/10.5194/hess-22-5711-2018, 2018
Short summary
Short summary
A data assimilation scheme was developed to improve snow water equivalent (SWE) simulations by updating meteorological forcings and snowpack states using passive microwave satellite observations. A chain of models was first calibrated to simulate satellite observations over northeastern Canada. The assimilation was then validated over 12 stations where daily SWE measurements were acquired during 4 winters (2012–2016). The overall SWE bias is reduced by 68 % compared to original SWE simulations.
Bàrbara Barceló-Llull, Evan Mason, Arthur Capet, and Ananda Pascual
Ocean Sci., 12, 1003–1011, https://doi.org/10.5194/os-12-1003-2016, https://doi.org/10.5194/os-12-1003-2016, 2016
Short summary
Short summary
Vertical velocity in the ocean makes an important contribution to the modulation of marine ecosystems through its impact on fluxes of nutrients and phytoplankton. Here, we estimate full 3-D current velocity fields from an observation-based data product. The 3-D currents are used to force a set of particle-tracking (Lagrangian) experiments. The Lagrangian results show that vertical motions induce local increases in nitrate uptake reaching up to 30 %.
Luc Charrois, Emmanuel Cosme, Marie Dumont, Matthieu Lafaysse, Samuel Morin, Quentin Libois, and Ghislain Picard
The Cryosphere, 10, 1021–1038, https://doi.org/10.5194/tc-10-1021-2016, https://doi.org/10.5194/tc-10-1021-2016, 2016
Short summary
Short summary
This study investigates the assimilation of optical reflectances, snowdepth data and both combined into a multilayer snowpack model. Data assimilation is performed with an ensemble-based method, the Sequential Importance Resampling Particle filter. Experiments assimilating only synthetic data are conducted at one point in the French Alps, the Col du Lautaret, over five hydrological years. Results of the assimilation experiments show improvements of the snowpack bulk variables estimates.
Arthur Capet, Emil V. Stanev, Jean-Marie Beckers, James W. Murray, and Marilaure Grégoire
Biogeosciences, 13, 1287–1297, https://doi.org/10.5194/bg-13-1287-2016, https://doi.org/10.5194/bg-13-1287-2016, 2016
Short summary
Short summary
We show that the Black Sea oxygen inventory has decreased by 44 % from 1955 to 2015, while oxygen penetration depth decreased from 140 to 90 m. A transient increase of the oxygen inventory during 1985–1995 supported the perception of a stable oxic interface and of a general recovery of the Black Sea after a strong eutrophication phase (1970–1990). Instead, we show that ongoing high oxygen consumption was masked by high ventilation rates, which are now limited by atmospheric warming.
G. A. Ruggiero, Y. Ourmières, E. Cosme, J. Blum, D. Auroux, and J. Verron
Nonlin. Processes Geophys., 22, 233–248, https://doi.org/10.5194/npg-22-233-2015, https://doi.org/10.5194/npg-22-233-2015, 2015
S. Metref, E. Cosme, C. Snyder, and P. Brasseur
Nonlin. Processes Geophys., 21, 869–885, https://doi.org/10.5194/npg-21-869-2014, https://doi.org/10.5194/npg-21-869-2014, 2014
Cited articles
Armengol, L., Calbet, A., Franchy, G., Rodríguez-Santos, A., and Hernández-León, S.: Planktonic food web structure and trophic transfer efficiency along a productivity gradient in the tropical and subtropical Atlantic Ocean, Sci. Rep., 9, 2044, https://doi.org/10.1038/s41598-019-38507-9, 2019. 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
Bishop, C. H., Etherton, B. J., and Majumdar, S. J.: Adaptive sampling with the ensemble transform Kalman filter, Part I: theoretical aspects, Mon. Weather Rev., 129, 420–436, https://doi.org/10.1175/1520-0493(2001)129<0420:ASWTET>2.0.CO;2, 2001. a, b
Bishop, C. H., Whitaker, J. S., and Lei, L.: Gain Form of the Ensemble Transform Kalman Filter and Its Relevance to Satellite Data Assimilation with Model Space Ensemble Covariance Localization?, Mon. Wheather Rev., 145, 4575–4592, https://doi.org/10.1175/MWR-D-17-0102.1, 2017. a, b
Brankart, J.-M.: brankart/ensdam: ensdam (v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.10617516, 2024a. a
Brankart, J.-M.: brankart/sesam: sesam (v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.10617526, 2024b. a
Brankart, J.-M.: brankart/ensemble-ocean-colour: ensemble-ocean-color (v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.10617498, 2024c. a
Brankart, J.-M., Testut, C.-E., Brasseur, P., and Verron, J.: Implementation of a multivariate data assimilation scheme for isopycnic coordinate ocean models: Application to a 1993–96 hindcast of the North Atlantic Ocean circulation, J. Geophys. Res., 108, 3074, https://doi.org/10.1029/2001JC001198, 2003. a
Brankart, J.-M., Testut, C.-E., Béal al, D., Doron, M., Fontana, C., Meinvielle, M., Brasseur, P., and Verron, J.: Towards an improved description of ocean uncertainties: effect of local anamorphic transformations on spatial correlations, Ocean Sci., 8, 121–142, https://doi.org/10.5194/os-8-121-2012, 2012. a
Brankart, J.-M., Candille, G., Garnier, F., Calone, C., Melet, A., Bouttier, P.-A., Brasseur, P., and Verron, J.: A generic approach to explicit simulation of uncertainty in the NEMO ocean model, Geosci. Model Dev., 8, 1285–1297, https://doi.org/10.5194/gmd-8-1285-2015, 2015. a
Buehner, M., Morneau, J., and Charette, C.: Four-dimensional ensemble-variational data assimilation for global deterministic weather prediction, Nonlin. Processes Geophys., 20, 669–682, https://doi.org/10.5194/npg-20-669-2013, 2013. a
Candille, G., Côté, C., Houtekamer, P. L., and Pellerin, G.: Verification of an ensemble prediction system against observations, Mon. Weather Rev., 135, 2688–2699, https://doi.org/10.1175/MWR3414.1, 2007. a
Candille, G., Brankart, J.-M., and Brasseur, P.: Assessment of an ensemble system that assimilates Jason-1/Envisat altimeter data in a probabilistic model of the North Atlantic ocean circulation, Ocean Sci., 11, 425–438, https://doi.org/10.5194/os-11-425-2015, 2015. a
Claustre, H., Legendre, L., Boyd, P. W., and Levy, M.: The Oceans' Biological Carbon Pumps: Framework for a Research Observational Community Approach, Front. Mar. Sci., 8, 780052, https://doi.org/10.3389/fmars.2021.780052, 2021. a
Cosme, E., Verron, J., Brasseur, P., Blum, J., and Auroux, D.: Smoothing Problems in a Bayesian Framework and Their Linear Gaussian Solutions, Mon. Weather Rev., 140, 683–695, https://doi.org/10.1175/mwr-d-10-05025.1, 2012. a
Eddy, T. D., Bernhardt, J. R., Blanchard, J. L., Cheung, W. W. L., Colléter, M., du Pontavice, H., Fulton, E. A., Gascuel, D., Kearney, K. A., Petrik, C. M., Roy, T., Rykaczewski, R. R., Selden, R., Stock, C. A., Wabnitz, C. C. C., and Watson, R. A.: Energy Flow Through Marine Ecosystems: Confronting Transfer Efficiency, Trend. Ecol. Evol., 36, 76–86, https://doi.org/10.1016/j.tree.2020.09.006, 2021. a, b
Evensen, G.: The Ensemble Kalman Filter: theoretical formulation and practical implementation, Ocean Dynam., 53, 343–367, https://doi.org/10.1007/s10236-003-0036-9, 2003. a, b
Fennel, K., Gehlen, M., Brasseur, P., Brown, C., Ciavatta, S., Cossarini, G., Crise, A., Edwards, C., Ford, D., Friedrichs, MAM., Gregoire, M., Jones, E., Kim, H.-C., Lamouroux, J., Murtugudde, R., Perruche, C., and the GODAE OceanView Marine Ecosystem Analysis and Prediction Task Team: Advancing marine biogeochemical and ecosystem reanalyses and forecasts as tools for monitoring and managing ecosystem health, Front. Mar. Sci., 6, 89, https://doi.org/10.3389/fmars.2019.00089, 2019. a
Galí, M., Falls, M., Claustre, H., Aumont, O., and Bernardello, R.: Bridging the gaps between particulate backscattering measurements and modeled particulate organic carbon in the ocean, Biogeosciences, 19, 1245–1275, https://doi.org/10.5194/bg-19-1245-2022, 2022. a
Garcia, H., Weathers, K., Paver, C., Smolyar, I., Boyer, T., Locarnini, M., Zweng, M., Mishonov, A., Baranova, O., Seidov, D., and Reagan, J.: World ocean atlas 2018, Vol. 4, Dissolved inorganic nutrients (phosphate, nitrate and nitrate + nitrite, silicate), NOAA Atlas NESDIS 84, 35 pp., https://archimer.ifremer.fr/doc/00651/76336/ (last access: 29 January 2024), 2018. a
Garcia, H., Weathers, K., Paver, C., Smolyar, I., Boyer, T., Locarnini, M., Zweng, M., Mishonov, A., Baranova, O., Seidov, D., and Reagan, J.: World ocean atlas 2018, Vol. 3, Dissolved oxygen, apparent oxygen utilization, and dissolved oxygen saturation, NOAA Atlas NESDIS 83, 38 pp., https://archimer.ifremer.fr/doc/00651/76337/ , 2018. a
Garnier, F., Brankart, J.-M., Brasseur, P., and Cosme, E.: Stochastic parameterizations of biogeochemical uncertainties in a
Gehlen, M., Barciela, R., Bertino, L., Brasseur, P., Butenschön, M., Chai, F., Crise, A., Drillet, Y., Ford, D., Lavoie, D., Lehodey, P., Perruche, C., Samuelsen, A. and Simon, E.: Building the capacity for forecasting marine biogeochemistry and ecosystems: recent advances and future developments, J. Operat. Oceanogr., 8, s168–s187, https://doi.org/10.1080/1755876X.2015.1022350, 2015. a
Gutknecht, E., Bertino, L., Brasseur, P., Ciavatta, S., Cossarini, G., Fennel, K., Ford, D., Gregoire, M., Lavoie, D., and Lehodey, P.: Biogeochemical modelling, Chap. 9, Implementing Operational Ocean Monitoring and Forecasting Systems, IOC/UNESCO, GOOS-275, 392 pp., https://doi.org/10.48670/ETOOFS, 2022. a
Houtekamer, P. L. and Mitchell, H. L.: Data assimilation using an Ensemble Kalman Filter technique, Mon. Weather Rev., 126, 796–811, https://doi.org/10.1175/1520-0493(1998)126<0796:DAUAEK>2.0.CO;2, 1998. a
Janjic Pfander, T., Nerger, L., Albertella, A., Schröter, J., and Skachko, S.: On Domain Localization in Ensemble-Based Kalman Filter Algorithms, Mon. Weather Rev., 139, 2046–2060, https://doi.org/10.1175/2011MWR3552.1, 2011. a, b, c
Lellouche, J.-M., Greiner, E., Bourdallé-Badie, R., Garric, G., Melet, A., Drévillon, M., Bricaud, C., Hamon, M., Le Galloudec, O., Regnier, C., Candela, T., Testut, C.-E., Gasparin, F., Ruggiero, G., Benkiran, M., Drillet, Y., and Le Traon, P.-Y.: The Copernicus Global
Leroux, S., Brankart, J.-M., Albert, A., Brodeau, L., Molines, J.-M., Jamet, Q., Le Sommer, J., Penduff, T., and Brasseur, P.: Ensemble quantification of short-term predictability of the ocean dynamics at a kilometric-scale resolution: a Western Mediterranean test case, Ocean Sci., 18, 1619–1644, https://doi.org/10.5194/os-18-1619-2022, 2022. a
Madec, G., Bourdallé-Badie, R., Bouttier, P.-A., Bricaud, C., Bruciaferri, D., Calvert, D., Chanut, J., Clementi, E., Coward, A., Delrosso, D., Ethé, C., Flavoni, S., Graham, T., Harle, J., Iovino, D., Lea, D., Lévy, C., Lovato, T., Martin, N., Masson, S., Mocavero, S., Paul, J., Rousset, R., Storkey, D., Storto, A., and Vancoppenolle, M.: Nemo ocean engine, Zenodo, https://doi.org/10.5281/zenodo.1472492, 2017. a
Mattern, J. P. and Edwards, C. A.: Ensemble optimal interpolation for adjoint-free biogeochemical data assimilation, PLoS ONE, 18, e0291039, https://doi.org/10.1371/journal.pone.0291039, 2023. a
Modi, A., Roxy, M., and Ghosh, S.: Gap-filling of ocean color over the tropical Indian Ocean using Monte-Carlo method, Sci. Rep., 12, 18395, https://doi.org/10.1038/s41598-022-22087-2, 2022. a
Negrete-García, G., Luo, J. Y.,Long, M. C., Lindsay, K., Levy, M., and Barton, A. D.: Plankton energy flows using a global size-structured and trait-based model, Prog. Oceanogr., 209, 102898, https://doi.org/10.1016/j.pocean.2022.102898, 2022. a
Oke, P. R., Brassington, G. B., Griffin, D. A., and Schiller, A.: Ocean data assimilation: A case for ensemble optimal interpolation, Aust. Meteorol. Ocean., 59, 67–76, https://doi.org/10.22499/2.5901.008, 2010. a
Olsen, A., Lange, N., Key, R., Tanhua, T., Bittig, H., Kozyr, A., Alvarez, M., Azetsu-Scott, K., Becker, S., Brown, P., Carter, B., Cotrim da Cunha, L., Feely, R., van Heuven, S., Hoppema, M., Ishii, M., Jeansson, E., Jutterström, S., Landa, C., Lauvsetand, S., Michaelis, P., Murata, A., Pérez, F., Pfeil, B., Schirnick, C., Steinfeldt, R., Suzuki, T., Tilbrook, B., Velo, A., Wanninkhof, R., and Woosley, R.: An updated version of the global interior ocean biogeochemical data product, GLODAPv2.2020, Earth Syst. Sci. Data, 12, 3653–3678, https://doi.org/10.5194/essd-12-3653-2020, 2020. a
Pham, D. T., Verron, J., and Roubaud, M. C.: Singular evolutive extended Kalman filter with EOF initialization for data assimilation in oceanography, J. Mar. Syst., 16, 323–340, https://doi.org/10.1016/S0924-7963(97)00109-7, 1998. a, b
Popov, M., Brankart, J.-M., Molines, J.-M., Leroux, S., Penduff, T., and Brasseur, P.: Development of a probabilistic ocean modelling system based on NEMO4.0: a contribution to the SEAMLESS project (deliverable 3.3), Zenodo [code], https://doi.org/10.5281/zenodo.6303007, 2022. a
Robert, C. and Casella, P.: Monte Carlo Statistical Methods, Springer, 645 pp., ISBN 0-387-21239-6, 2004. a
Testut, C.-E., Brasseur, P., Brankart, J.-M., and Verron, J.: Assimilation of sea-surface temperature and altimetric observations during 1992–1993 into an eddy permitting primitive equation model of the North Atlantic Ocean, J. Mar. Syst., 40/41, 291–316, https://doi.org/10.1016/S0924-7963(03)00022-8, 2003. a, b
Van Leeuwen, P.-J. and Evensen, G.: Data assimilation and inverse methods in terms of a probabilistic formulation, Mon. Weather Rev., 124, 2898–2913, https://doi.org/10.1175/1520-0493(1996)124<2898:DAAIMI>2.0.CO;2, 1996. a
Short summary
This study contributes to the development of methods to estimate targeted ocean ecosystem indicators, including their uncertainty, in the framework of the Copernicus Marine Service. A simplified approach is introduced to perform a 4D ensemble analysis and forecast, directly targeting selected biogeochemical variables and indicators (phenology, trophic efficiency, downward flux of organic matter). Care is taken to present the methods and discuss the reliability of the solution proposed.
This study contributes to the development of methods to estimate targeted ocean ecosystem...
