N. Brenowitz, Vulcan Inc. (USA)
Abstract: Climate models cannot affordably simulate small-scale physical processes so they must approximate the effect of important phenomena like clouds, precipitation, and turbulence with parameterizations. Unfortunately, uncertainty in these sub-grid-scale parameterizations limits our faith in our coarse-resolution climate models and contributes to well-known climate model biases. Traditional parameterizations designed by human experts have changed little in decades despite a recent explosion in our ability to observe the Earth-system and run explicitly-resolved simulations. The brute-force solution to this problem is to decrease the grid-spacing of atmospheric models until they can resolve individual thunderstorms, but this is not yet feasible for climate-scale simulations. Machine learning (ML) presents another path forward by replacing traditional schemes with nonlinear regression models trained from shorter high-resolution simulations or observations. This talk introduced the attempts to build machine learning parameterizations for use in increasingly complex simulations of the atmosphere. The talk was particularly focused on how ML parameterizations can play nicely with fluid-mechanics simulations and on interpreting their behavior. While these efforts are targeted on improving atmospheric models, similar techniques could be applied to sub-grid-scale problems throughout the Earth sciences.