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Articles | Volume 20, issue 1
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.
https://doi.org/10.5194/os-20-155-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 20, issue 1
Research article
 | 
16 Feb 2024
Research article |  | 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, Jean-Michel Brankart, Arthur Capet, Emmanuel Cosme, and Pierre Brasseur

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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
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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.
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