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. Here we demonstrate the potential of a parameter estimation algorithm which makes use of synchronization to tune a global atmospheric model at modest computational costs. We first use it to directly optimize internal model parameters. We then apply the algorithm to the weights of each member of a supermodel ensemble to optimize the overall predictions. In both cases, the algorithm is able to find parameters which result in reduced errors in the climatology of the model. Finally, we introduce a novel approach which combines both methods called adaptive supermodeling, where the internal parameters of the members of a supermodel are tuned simultaneously with the model weights such that the supermodel predictions are optimized. For a case designed to challenge the two previous methods, adaptive supermodeling achieves a performance similar to a perfect model.

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