Causally‐Informed Deep Learning to Improve Climate Models and Projections
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 while retaining many features …