Data-driven probabilistic air-sea flux parameterization
Wu, J., Perezhogin, P., Gagne, D. J., Reichl, B. G., Subramanian, A. C., et al. (2026). Data-driven probabilistic air-sea flux parameterization. Geophysical Research Letters, doi:https://doi.org/10.1029/2025gl120472
| Title | Data-driven probabilistic air-sea flux parameterization |
|---|---|
| Genre | Article |
| Author(s) | J. Wu, P. Perezhogin, David John Gagne, B. G. Reichl, A. C. Subramanian, E. J. Thompson, L. Zanna |
| 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. |
| Publication Title | Geophysical Research Letters |
| Publication Date | Mar 28, 2026 |
| Publisher's Version of Record | https://doi.org/10.1029/2025gl120472 |
| OpenSky Citable URL | https://n2t.net/ark:/85065/d7w95f85 |
| OpenSky Listing | View on OpenSky |
| CISL Affiliations | MILES |