A stochastic framework for modeling surface ocean variability in the Southwest Indian Ocean

Le résumé fourni par la source

The Southwest Indian Ocean (SWIO) is characterized by diverse dynamic regimes, with intense energy fluxes and intricate atmospheric interactions (Phillips et al., 2021, OS). The Mascarene area, to the east of Madagascar, is influenced by the South Equatorial Current and the Indian subtropical gyre, the Mozambique Channel presents numerous mesoscale eddies, which play an important role in the biogeochemical dynamics, and the Equatorial zone is affected by the inversion of seasonal Monsoon circulation. Modeling such complex systems requires the consideration of multiple sources of uncertainty. In the context of global warming and climate projections, it is essential to simulate these uncertainties in order to obtain a more accurate representation and understanding of the SWIO ocean dynamics. The objective of this project is to identify and analyze the dominant sources of uncertainty affecting surface circulation in the SWIO. To address this issue, a probabilistic approach is integrated into the CROCO model (Coastal and Regional Ocean Community), following three key steps. Firstly, a realistic regional configuration of the CROCO model is developed for the SWIO region, which is forced and validated by CMEMS and ECMWF operational and satellite products. Then, a stochastic perturbation generator (referred to as STOGEN and originally developed in the NEMO model, Brankart et al., 2015, GMD) is implemented into CROCO, associated with an ensemble generator. Finally, several ensemble simulations are performed using stochastic processes with varying correlation structures in space and time within the defined regional setting. This allows to test the cumulative effect of different sources of uncertainty associated with surface ocean circulation by analyzing the ensemble statistics and variability based on surface variables such as sea surface height, temperature, salinity or velocity fields. We starts with the simulation of an ensemble by perturbing the wind stress. Then, three additional ensemble simulations will be generated by perturbing the vertical mixing, the initial conditions to analyze the intrinsic ocean variability and the open boundary conditions. The integration of stochastic parameterization within CROCO allow to simulate and partially quantify some of the non-deterministic effects of unresolved processes and scales. It enables an objective statistical comparison between model and observations associated with uncertainty description for data assimilation systems (Popov et al., 2024, OS).

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