Deep learning to represent subgrid processes in climate models.

ABSTRACT: The representation of nonlinear subgrid processes, especially clouds, has been a major source of uncertainty in climate models for decades. Cloud-resolving models better represent many of these processes and can now be run globally but only for short-term simulations of at most a few years because of computational limitations. Here we demonstrate that deep learning can be used to capture many advantages of cloud-resolving modeling at a fraction of the computational cost. We train a deep neural network to represent all atmospheric subgrid processes in a climate model by learning from a multiscale model in which convection is treated explicitly. The trained neural network then replaces the traditional subgrid parameterizations in a global general circulation model in which it freely interact

SUBMITTER: Rasp S 

PROVIDER: S-EPMC6166853 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature