Ensemble analysis and forecast of ecosystem indicators in the North Atlantic using ocean colour observations and prior statistics from a stochastic NEMO/PISCES simulator

• Published in: Ocean Science
• Main Authors: Popov, Mikhail, Brankart, Jean-Michel, Capet, Arthur, Cosme, Emmanuel, Brasseur, Pierre
• Other Authors: Institut des Géosciences de l’Environnement (IGE), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Observatoire des Sciences de l'Univers de Grenoble (Fédération OSUG)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP), Université Grenoble Alpes (UGA), Université de Liège = University of Liège = Universiteit van Luik = Universität Lüttich (ULiège)
• Format: Article in Journal/Newspaper
• Language: English
• Published: CCSD 2023
• Subjects: [SDE]Environmental Sciences; North Atlantic
• Online Access: https://hal.science/hal-04307146; https://hal.science/hal-04307146v1/document; https://hal.science/hal-04307146v1/file/egusphere-2023-2026.pdf; https://doi.org/10.5194/os-20-155-2024

International audience This study is anchored in the H2020 SEAMLESS project (www.seamlessproject.org), which aims to develop ensemble assimilation methods to be implemented in Copernicus Marine Service monitoring and forecasting systems, in order to operationally estimate a set of targeted ecosystem indicators in various regions, including uncertainty estimates. In this paper, a simplified approach is introduced to perform a 4D (space-time) ensemble analysis describing the evolution of the ocean ecosystem. An example application is provided, which covers a limited time period in a limited subregion of the North Atlantic (between 31 • W and 21 • W, between 44 • N and 50.5 • N, between March 15 and June 15, 2019, at a 1/4 • and a 1 day resolution). The ensemble analysis is based on prior ensemble statistics from a stochastic NEMO/PISCES simulator. Ocean colour observations are used as constraints to condition the 4D prior probability distribution. As compared to classic data assimilation, the simplification comes from the decoupling between the forward simulation using the complex modelling system and the update of the 4D ensemble to account for the observation constraint. The shortcomings and possible advantages of this approach for biogeochemical applications are discussed in the paper. The results show that it