Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions

27 Aug 2020 Yuval Janni O'Gorman Paul A.

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... (read more)

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  • ATMOSPHERIC AND OCEANIC PHYSICS