Data‐Driven Probabilistic Air‐Sea Flux Parameterization

Abstract Accurately quantifying air‐sea fluxes is important for understanding air‐sea interactions and improving coupled weather and climate models. This study introduces a probabilistic framework to represent the highly variable nature of air‐sea fluxes, which is missing in deterministic bulk algorithms. Assuming Gaussian distributions conditioned on the input variables, we use artificial neural networks and eddy‐covariance measurement data to estimate the mean and variance by minimizing negative log‐likelihood loss. The trained neural networks provide alternative mean flux estimates to existing bulk algorithms, and quantify the uncertainty around the mean estimates. A stochastic parameterization of air‐sea turbulent fluxes can be constructed by sampling from the predicted distributions. Tests in a single‐column forced upper‐ocean model suggest that changes in flux algorithms influence sea surface temperature and mixed layer depth seasonally. The ensemble spread in stochastic runs is most pronounced during spring restratification.