2025/10/07 by Jordan Seneca, Suzanne Bintanja, Seneca, Jordan +3
Earth and Planetary Sciences · Computer Science · #Meteorological Phenomena and Simulations #Neural Networks and Applications
paper · pdf · doi:10.48550/arxiv.2510.06180
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 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.