Climate Model Tuning with Online Synchronization-Based Parameter Estimation
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915935622791168 |
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| author | Seneca, Jordan Bintanja, Suzanne Selten, Frank M. |
| author_facet | Seneca, Jordan Bintanja, Suzanne Selten, Frank M. |
| contents | In climate science, the tuning of climate models is a computationally intensive problem due to the combination of the high-dimensionality of the system state and long integration times. Supermodelling is a technique which has shown the potential for reducing climate model biases by dynamically coupling multiple models together, and training their coupling on a short timescale. Here, we introduce a new approach called \emph{adaptive supermodeling}, where the internal model parameters of the member of a supermodel are tuned. We perform three experiments. We first directly optimize the internal parameters of a climate model. We then optimize the weights between two members of a supermodel in a classical supermodel approach. For a case designed to challenge the two previous methods, we implement adaptive supermodeling, which achieves a performance similar to a perfect model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_06180 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Climate Model Tuning with Online Synchronization-Based Parameter Estimation Seneca, Jordan Bintanja, Suzanne Selten, Frank M. Chaotic Dynamics Machine Learning Atmospheric and Oceanic Physics In climate science, the tuning of climate models is a computationally intensive problem due to the combination of the high-dimensionality of the system state and long integration times. Supermodelling is a technique which has shown the potential for reducing climate model biases by dynamically coupling multiple models together, and training their coupling on a short timescale. Here, we introduce a new approach called \emph{adaptive supermodeling}, where the internal model parameters of the member of a supermodel are tuned. We perform three experiments. We first directly optimize the internal parameters of a climate model. We then optimize the weights between two members of a supermodel in a classical supermodel approach. For a case designed to challenge the two previous methods, we implement adaptive supermodeling, which achieves a performance similar to a perfect model. |
| title | Climate Model Tuning with Online Synchronization-Based Parameter Estimation |
| topic | Chaotic Dynamics Machine Learning Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2510.06180 |