1. Data-Driven Probabilistic Air-Sea Flux Parameterization
- Author
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Wu, Jiarong, Perezhogin, Pavel, Gagne, David John, Reichl, Brandon, Subramanian, Aneesh C., Thompson, Elizabeth, and Zanna, Laure
- Subjects
Physics - Atmospheric and Oceanic Physics ,Computer Science - Machine Learning ,Statistics - Applications ,Statistics - Machine Learning - Abstract
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.
- Published
- 2025