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arXiv · 2510.06180

Climate Model Tuning with Online Synchronization-Based Parameter Estimation

Jordan Seneca·Suzanne Bintanja·Frank M. Selten
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Abstract

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.

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Jordan Seneca, Suzanne Bintanja, Frank M. Selten. 2025-10-07. Climate Model Tuning with Online Synchronization-Based Parameter Estimation. https://arxiv.org/abs/2510.06180

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