Abstract
A promising approach to improve cloud parameterizations within climate
models and thus climate projections is to use deep learning in
combination with training data from storm-resolving model (SRM)
simulations. The Icosahedral Non-Hydrostatic (ICON) modeling framework
permits simulations ranging from numerical weather prediction to climate
projections, making it an ideal target to develop neural network (NN)
based parameterizations for sub-grid scale processes. Within the ICON
framework, we train NN based cloud cover parameterizations with
coarse-grained data based on realistic regional and global ICON SRM
simulations. We set up three different types of NNs that differ in the
degree of vertical locality they assume for diagnosing cloud cover from
coarse-grained atmospheric state variables. The NNs accurately estimate
sub-grid scale cloud cover from coarse-grained data that has similar
geographical characteristics as their training data. Additionally,
globally trained NNs can reproduce sub-grid scale cloud cover of the
regional SRM simulation. Using the game-theory based interpretability
library SHapley Additive exPlanations, we identify an overemphasis on
specific humidity and cloud ice as the reason why our column-based NN
cannot perfectly generalize from the global to the regional
coarse-grained SRM data. The interpretability tool also helps visualize
similarities and differences in feature importance between regionally
and globally trained column-based NNs, and reveals a local relationship
between their cloud cover predictions and the thermodynamic environment.
Our results show the potential of deep learning to derive accurate yet
interpretable cloud cover parameterizations from global SRMs, and
suggest that neighborhood-based models may be a good compromise between
accuracy and generalizability.