Assessing the Potential of Deep Learning for Emulating Cloud Superparameterization in Climate Models With Real‐Geography Boundary Conditions.

We explore the potential of feed‐forward deep neural networks (DNNs) for emulating cloud superparameterization in realistic geography, using offline fits to data from the superparameterized community atmospheric model. To identify the network architecture of greatest skill, we formally optimize hyperparameters using ∼250 trials. Our DNN explains over 70% of the temporal variance at the 15‐min sampling scale throughout the mid‐to‐upper troposphere. Autocorrelation timescale analysis compared against DNN skill suggests the less good fit in the tropical, marine boundary layer is driven by neural network difficulty emulating fast, stochastic signals in convection. However, spectral analysis in the temporal domain indicates skillful emulation of signals on diurnal to synoptic scales. A closer look at the diurnal cycle reveals correct emulation of land‐sea contrasts and vertical structure in the heating and moistening fields, but some distortion of precipitation. Sensitivity tests targeting precipitation skill reveal complementary effects of adding positive constraints versus hyperparameter tuning, motivating the use of both in the future. A first attempt to force an offline land model with DNN emulated atmospheric fields produces reassuring results further supporting neural network emulation viability in real‐geography settings. Overall, the fit skill is competitive with recent attempts by sophisticated Residual and Convolutional Neural Network architectures trained on added information, including memory of past states. Our results confirm the parameterizability of superparameterized convection with continents through machine learning and we highlight the advantages of casting this problem locally in space and time for accurate emulation and hopefully quick implementation of hybrid climate models. Plain Language Summary: Machine learning methods have been previously used to replace parameterizations (approximations) of atmospheric convection under very idealized scenarios (aqua‐planets). The hope is that these machine learning emulators can help power the next generation of climate models with similar accuracy but at a fraction of the computational cost. But important questions remain about how learnable more realistic convection (over both land and ocean) is. Recently, the first attempt at machine learning replicated convection was made under these Earth‐like conditions. But it required a highly specialized neural network as well as memory of the previous behavior of the atmosphere. This design would make using these machine learning emulators with climate models very difficult. This motivates learning convection under realistic geography with a simpler network. Our results are reassuring because our simple neural network learns realistic convection over land as well as a more complicated model. But even harder tests involving full coupling with a host climate model will be needed to truly test this method's potential. Key Points: Feed‐forward neural networks can emulate explicit convection including geographic complexityAfter tuning, our neural network can fit 70% of the temporal variance in the mid‐to‐upper troposphereDeep convection over land is parameterizable in neural networks locally in time [ABSTRACT FROM AUTHOR]