  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 while retaining
many features at a fraction of computational cost. 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. Here, we show that the combination of
causality with deep learning helps removing spurious correlations and
optimizing the neural network algorithm. To resolve this, we apply a causal
discovery method to unveil causal drivers in the set of input predictors of
atmospheric subgrid-scale processes of a superparameterized climate model in
which deep convection is explicitly resolved. The resulting causally-informed
neural networks are coupled to the climate model, hence, replacing the
superparameterization and radiation scheme. We show that the climate
simulations with causally-informed neural network parameterizations retain many
convection-related properties and accurately generate the climate of the
original high-resolution climate model, while retaining similar generalization
capabilities to unseen climates compared to the non-causal approach. The
combination of causal discovery and deep learning is a new and promising
approach that leads to stable and more trustworthy climate simulations and
paves the way towards more physically-based causal deep learning approaches
also in other scientific disciplines.
