CondensNet: Enabling stable long-term climate simulations via hybrid deep learning models with adaptive physical constraints

Understand

Accurate and efficient climate simulations are crucial for understanding Earth's evolving climate.

• However, current general circulation models (GCMs) face challenges in capturing unresolved physical processes, such as cloud and convection.
• A common solution is to adopt cloud resolving models, that provide more accurate results than the standard subgrid parametrisation schemes typically used in GCMs.
• However, cloud resolving models, also referred to as super paramtetrizations, remain computationally prohibitive.