  A promising approach to improve climate-model simulations is to replace
traditional subgrid parameterizations based on simplified physical models by
machine learning algorithms that are data-driven. However, neural networks
(NNs) often lead to instabilities and climate drift when coupled to an
atmospheric model. Here we learn an NN parameterization from a high-resolution
atmospheric simulation in an idealized domain by coarse graining the model
equations and output. The NN parameterization has a structure that ensures
physical constraints are respected, and it leads to stable simulations that
replicate the climate of the high-resolution simulation with similar accuracy
to a successful random-forest parameterization while needing far less memory.
We find that the simulations are stable for a variety of NN architectures and
horizontal resolutions, and that an NN with substantially reduced numerical
precision could decrease computational costs without affecting the quality of
simulations.

Atmospheric and Oceanic Physics