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: M. Popov, J.-M. Brankart, A. Capet, E. Cosme, P. Brasseur
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2024
• Subjects: Geography. Anthropology. Recreation; G; Environmental sciences; GE1-350; North Atlantic
• Online Access: https://doi.org/10.5194/os-20-155-2024; https://doaj.org/article/847cd7c7f9104c04b7b0e7fafb54a526

This study is anchored in the H2020 SEAMLESS project ( https://www.seamlessproject.org , last access: 29 January 2024), 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 and 21 ∘ W, between 44 and 50.5 ∘ N, between 15 March and 15 June 2019, at a 1 / 4 <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="20pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="b5e3e9140aa7b06ea772efeaf94fa8b6"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="os-20-155-2024-ie00001.svg" width="20pt" height="14pt" src="os-20-155-2024-ie00001.png"/></svg:svg> ∘ and a 1 d resolution). The ensemble analysis is based on prior ensemble statistics from a stochastic NEMO (Nucleus for European Modelling of the Ocean)–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 is possible to produce a multivariate ensemble analysis continuous in time and consistent with the observations. Furthermore, we study how the method can be used to extrapolate analyses calculated from past observations into the future. The resulting 4D ensemble statistical forecast is shown to contain valuable ...