Causally-informed deep learning to improve climate models and projections

Understand

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.