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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.03990 |
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| _version_ | 1866917229155581952 |
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| author | Wu, Jiarong Perezhogin, Pavel Gagne, David John Reichl, Brandon Subramanian, Aneesh C. Thompson, Elizabeth Zanna, Laure |
| author_facet | Wu, Jiarong Perezhogin, Pavel Gagne, David John Reichl, Brandon Subramanian, Aneesh C. Thompson, Elizabeth Zanna, Laure |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_03990 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-Driven Probabilistic Air-Sea Flux Parameterization Wu, Jiarong Perezhogin, Pavel Gagne, David John Reichl, Brandon Subramanian, Aneesh C. Thompson, Elizabeth Zanna, Laure Atmospheric and Oceanic Physics Machine Learning Applications 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. |
| title | Data-Driven Probabilistic Air-Sea Flux Parameterization |
| topic | Atmospheric and Oceanic Physics Machine Learning Applications |
| url | https://arxiv.org/abs/2503.03990 |