Parametric Sensitivities of a Wind-driven Baroclinic Ocean Using Neural Surrogates
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866929314235154432 |
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| author | Sun, Yixuan Cucuzzella, Elizabeth Brus, Steven Narayanan, Sri Hari Krishna Nadiga, Balasubramanya Van Roekel, Luke Hückelheim, Jan Madireddy, Sandeep Heimbach, Patrick |
| author_facet | Sun, Yixuan Cucuzzella, Elizabeth Brus, Steven Narayanan, Sri Hari Krishna Nadiga, Balasubramanya Van Roekel, Luke Hückelheim, Jan Madireddy, Sandeep Heimbach, Patrick |
| contents | Numerical models of the ocean and ice sheets are crucial for understanding and simulating the impact of greenhouse gases on the global climate. Oceanic processes affect phenomena such as hurricanes, extreme precipitation, and droughts. Ocean models rely on subgrid-scale parameterizations that require calibration and often significantly affect model skill. When model sensitivities to parameters can be computed by using approaches such as automatic differentiation, they can be used for such calibration toward reducing the misfit between model output and data. Because the SOMA model code is challenging to differentiate, we have created neural network-based surrogates for estimating the sensitivity of the ocean model to model parameters. We first generated perturbed parameter ensemble data for an idealized ocean model and trained three surrogate neural network models. The neural surrogates accurately predicted the one-step forward ocean dynamics, of which we then computed the parametric sensitivity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_09950 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Parametric Sensitivities of a Wind-driven Baroclinic Ocean Using Neural Surrogates Sun, Yixuan Cucuzzella, Elizabeth Brus, Steven Narayanan, Sri Hari Krishna Nadiga, Balasubramanya Van Roekel, Luke Hückelheim, Jan Madireddy, Sandeep Heimbach, Patrick Atmospheric and Oceanic Physics Numerical models of the ocean and ice sheets are crucial for understanding and simulating the impact of greenhouse gases on the global climate. Oceanic processes affect phenomena such as hurricanes, extreme precipitation, and droughts. Ocean models rely on subgrid-scale parameterizations that require calibration and often significantly affect model skill. When model sensitivities to parameters can be computed by using approaches such as automatic differentiation, they can be used for such calibration toward reducing the misfit between model output and data. Because the SOMA model code is challenging to differentiate, we have created neural network-based surrogates for estimating the sensitivity of the ocean model to model parameters. We first generated perturbed parameter ensemble data for an idealized ocean model and trained three surrogate neural network models. The neural surrogates accurately predicted the one-step forward ocean dynamics, of which we then computed the parametric sensitivity. |
| title | Parametric Sensitivities of a Wind-driven Baroclinic Ocean Using Neural Surrogates |
| topic | Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2404.09950 |