Data-Driven Probabilistic Air-Sea Flux Parameterization

Accurately quantifying air-sea fluxes is important for understanding air-sea interactions and improving coupled weather and climate systems. 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. 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.

@article{wu2025_2503.03990,
  title={ Data-Driven Probabilistic Air-Sea Flux Parameterization },
  author={ Jiarong Wu and Pavel Perezhogin and David John Gagne and Brandon Reichl and Aneesh C. Subramanian and Elizabeth Thompson and Laure Zanna },
  journal={arXiv preprint arXiv:2503.03990},
  year={ 2025 }
}