{"ID":2859499,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.06180","arxiv_id":"2510.06180","title":"Climate Model Tuning with Online Synchronization-Based Parameter Estimation","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.","short_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...","url_abs":"https://arxiv.org/abs/2510.06180","url_pdf":"https://arxiv.org/pdf/2510.06180v2","authors":"[\"Jordan Seneca\",\"Suzanne Bintanja\",\"Frank M. Selten\"]","published":"2025-10-07T17:43:11Z","proceeding":"nlin.CD","tasks":"[\"nlin.CD\",\"cs.LG\",\"physics.ao-ph\"]","methods":"[]","has_code":false}