Probabilistic modeling of the Southwest Indian Ocean dynamics to quantify uncertainties in surface currents

The Southwest Indian Ocean (SWIO) is characterized by diverse dynamic regimes, with intense energy fluxes and intricate atmospheric interactions (Phillips et al., 2021). 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 model configuration for the SWIO region is developed, 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) is implemented into CROCO, associated with an ensemble generator. Finally, stochastic processes with varying correlation structures in space and time are generated in the defined regional setting. This allows to test the cumulative effect of different sources of uncertainty associated with surface ocean circulation. 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. These ensemble experiments describing prior uncertainties and the associated modeling testbed will then be used for 4D inversions (Popov et al., 2024) exploiting high-resolution spatial (real or simulated) altimetry, surface currents, high-resolution temperature and ocean colour data to reduce uncertainties in surface ocean Lagrangian transport. The integration of stochastic methodologies within CROCO may facilitate scenario exploration and uncertainty quantification, providing a basis for informed decision-making and collaborations with ocean actors in the SWIO region.

Phillips, H. E., Tandon, A., Furue, R., Hood, R., Ummenhofer, C. C., Benthuysen, J. A., ... & Wiggert, J. (2021). Progress in understanding of Indian Ocean circulation, variability, air–sea exchange, and impacts on biogeochemistry.

Brankart, J.-M., Candille, G., Garnier, F., Calone, C., Melet, A., Bouttier, P. A., Brasseur, P., Verron, J. (2015). A generic approach to explicit simulation of uncertainty in the NEMO ocean model.

Popov, M., Brankart, J.-M., Capet, A., Cosme, E., Brasseur, P. (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.