Calibrating subgrid parametrizations of single-column ocean models via simulation-based inference

תקציר מקורי באנגליתarXiv:2609.13242v1 Announce Type: cross Abstract: Subgrid parametrizations of vertical mixing in ocean models depend on free coefficients that cannot be measured directly and must be calibrated against high-fidelity references such as large-eddy simulations (LES). Existing approaches return point estimates and leave the associated uncertainty unquantified, a limitation when the inverse problem is ill-posed or when distinct parameter configurations fit the data comparably well. Simulation-based inference (SBI) addresses exactly this: given a prior and access to the simulator, it approximates the full posterior over parameters without requiring a tractable likelihood, at a cost set by the number of simulator evaluations. We apply it to \texttt{tunax}, a JAX-based single-column ocean model, t