Causally-informed deep learning to improve climate models and projections

Abstract

Climate models are essential to understand and project climate change, yet long-standing biases and uncertainties in their projections remain. This is largely associated with the representation of subgrid-scale processes, particularly clouds and convection. Deep learning can learn these subgrid-scale processes from computationally expensive storm-resolving models. Yet, climate simulations with embedded neural network parameterizations are still challenging and highly depend on the deep learning solution. This is likely associated with spurious non-physical correlations learned by the neural networks due to the complexity of the physical dynamical system. We apply a causal discovery method to unveil key physical drivers in the set of input predictors of atmospheric subgrid-scale processes of a superparameterized climate model. We show that the climate simulations with causally-informed neural network parameterizations clearly outperform the non-causal approach. These results demonstrate that the combination of causal discovery and deep learning helps removing spurious correlations and optimizing the neural network algorithm