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Subgrid parameterizations, which represent physical processes occurring belowthe resolution of current climate models, are an important component inproducing accurate, long-term predictions for the climate. A variety ofapproaches have been tested to design these components, including deep learningmethods. In this work, we evaluate a proof of concept illustrating a multiscaleapproach to this prediction problem. We train neural networks to predictsubgrid forcing values on a testbed model and examine improvements inprediction accuracy that can be obtained by using additional information inboth fine-to-coarse and coarse-to-fine directions.

Data-driven multiscale modeling of subgrid parameterizations in climate models