Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions
Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions
Global climate models represent small-scale processes such as clouds and convection using quasi-empirical models known as parameterizations, and these parameterizations are a leading cause of uncertainty in climate projections. A promising alternative approach is to use machine learning to build new parameterizations directly from high-resolution model output. However, parameterizations learned …