Data-Driven Probabilistic Air-Sea Flux Parameterization

• URL: http://arxiv.org/abs/2503.03990v1
• Date: Thu, 06 Mar 2025 00:40:49 GMT
• Title: Data-Driven Probabilistic Air-Sea Flux Parameterization
• Authors: Jiarong Wu, Pavel Perezhogin, David John Gagne, Brandon Reichl, Aneesh C. Subramanian, Elizabeth Thompson, Laure Zanna,
• Abstract summary: This study introduces a probabilistic framework to represent the highly variable nature of air-sea flux.<n>We use artificial neural networks and eddy-covariance measurement data to estimate the mean and variance.
• Score: 1.5126645360354214
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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