Test data from SPCAM for machine learning in moist physics

Current moist physics parameterization schemes in general circulation
models (GCMs) are the main source of biases in simulated precipitation and
atmospheric circulation. Recent advances in machine learning make it
possible to explore data-driven approaches to developing parameterization
for moist physics processes such as convection and clouds. This study aims
to develop a new moist physics parameterization scheme based on deep
learning. We use a residual convolutional neural network (ResNet) for this
purpose. It is trained with one-year simulation from a superparameterized
GCM, SPCAM. An independent year of SPCAM simulation is used for
evaluation. In the design of the neural network, referred to as ResCu, the
moist static energy conservation during moist processes is considered. In
addition, the past history of the atmospheric states, convection and
clouds are also considered. The predicted variables from the neural
network are GCM grid-scale heating and drying rates by convection and
clouds, and cloud liquid and ice water contents. Precipitation is derived
from predicted moisture tendency. In the independent-data test, ResCu can
accurately reproduce the SPCAM simulation in both time-mean and temporal
variance. Comparison with other neural networks demonstrates the superior
performance of ResNet architecture. ResCu is further tested in a single
column model for both continental midlatitude warm season convection and
tropical monsoonal convection. In both cases, it simulates the timing and
intensity of convective events well. In the prognostic test of tropical
convection case, the simulated temperature and moisture biases with ResCu
are smaller than those using conventional convection and cloud
parameterizations.