Abstract
Clouds are one of the most critical yet uncertain aspects of weather and
climate prediction. The complex nature of sub-grid scale cloud processes
makes traceable simulation of clouds across scales difficult (or
impossible). Often models and measurements are used to develop empirical
relationships for large-scale models to be computationally efficient.
Machine learning provides another potential tool to improve our
empirical parameterizations of clouds. To explore these opportunities,
we replace the warm rain formation process in a General Circulation
Model (GCM) with a detailed treatment from a bin microphysical model
that causes a 400\% slowdown in the GCM. We analyze the
changes in climate that result from the use of the bin microphysical
calculation and find improvements in the rain onset and frequency of
light rain compared to detailed models and observations. We also find a
resulting change in the cloud feedback response of the model to warming,
which will significantly impact the climate sensitivity. We then emulate
this process with an emulator consisting of multiple neural networks
that predict whether specific tendencies will be nonzero and the
magnitude of the nonzero tendencies. We describe the risks of
over-fitting, extrapolation, and linearization of a non-linear problem
by using perfect model experiments with and without the emulator and
show we can recover the solutions with the emulators in almost all
respects, and recover nearly all the speed to get simulations that
perform as the detailed model, but with the computational cost of the
control simulation.