Climate model tuning with adaptive supermodeling

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 that has shown the potential for reducing climate model biases by dynamically coupling multiple models together and training the coupling strength 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.