Towards Physically Consistent Deep Learning For Climate Model
Parameterizations
Towards Physically Consistent Deep Learning For Climate Model
Parameterizations
Climate models play a critical role in understanding and projecting climate change. Due to their complexity, their horizontal resolution of ~40-100 km remains too coarse to resolve processes such as clouds and convection, which need to be approximated via parameterizations. These parameterizations are a major source of systematic errors and …