Simulating Atmospheric Processes in ESMs and Quantifying Uncertainties with Deep Learning Multi-Member and Stochastic Parameterizations

Abstract

Deep learning has proven to be a valuable tool to represent subgrid processes in climate models, but most application cases have so far used idealized settings and deterministic approaches. Here, we develop ensemble and stochastic param- eterizations with calibrated uncertainty quantification to learn subgrid convective and turbulent processes and surface radiative fluxes of a superparameterization embedded in an Earth System Model (ESM). We explore three methods to con- struct stochastic parameterizations: 1) a single Deep Neural Network (DNN) with Monte Carlo Dropout; 2) a multi-network ensemble; and 3) a Variational Encoder Decoder with latent space perturbation. We show that the multi-network ensem- bles improve the representation of convective processes in the planetary boundary layer compared to individual DNNs. The respective uncertainty quantification illus- trates that the two latter methods are advantageous compared to a dropout-based DNN ensemble regarding the spread of convective processes. We develop a novel partial coupling strategy to sidestep issues in condensate emulation to evaluate the multi-network parameterizations in online runs coupled to the ESM. We can conduct Earth-like stable runs over more than 5 months with the ensemble ap- proach, while such simulations using individual DNNs fail within days. Moreover, we show that our novel ensemble parameterizations improve the representation of extreme precipitation and the underlying diurnal cycle compared to a traditional parameterization, although faithfully representing the mean precipitation pattern remains challenging. Our results pave the way towards a new generation of param- eterizations using machine learning with realistic uncertainty quantification that significantly improve the representation of subgrid effects.