  Subgrid parameterizations, which represent physical processes occurring below
the resolution of current climate models, are an important component in
producing accurate, long-term predictions for the climate. A variety of
approaches have been tested to design these components, including deep learning
methods. In this work, we evaluate a proof of concept illustrating a multiscale
approach to this prediction problem. We train neural networks to predict
subgrid forcing values on a testbed model and examine improvements in
prediction accuracy that can be obtained by using additional information in
both fine-to-coarse and coarse-to-fine directions.
