Neural Network Parameterization of Subgrid‐Scale Physics From a Realistic Geography Global Storm‐Resolving Simulation

Abstract

Parameterization of subgrid‐scale processes is a major source of uncertainty in global atmospheric model simulations. Global storm‐resolving simulations use a finer grid (less than 5 km) to reduce this uncertainty by explicitly resolving deep convection and details of orography. This study uses machine learning to replace the physical parameterizations of heating and moistening rates, but not wind tendencies, in a coarse‐grid (200 km) global atmosphere model, using training data obtained by spatially coarse‐graining a 40‐day realistic geography global storm‐resolving simulation. The training targets are the three‐dimensional fields of effective heating and moistening rates, including the effect of grid‐scale motions that are resolved but imperfectly simulated by the coarse model. A neural network is trained to predict the time‐dependent heating and moistening rates in each grid column using the coarse‐grained temperature, specific humidity, surface turbulent heat fluxes, cosine of solar zenith angle, land‐sea mask and surface geopotential of that grid column as inputs. The coefficient of determination R2 for offline prediction ranges from 0.4 to 0.8 at most vertical levels and latitudes. Online, we achieve stable 35‐day simulations, with metrics of skill such as the time‐mean pattern of near‐surface temperature and precipitation comparable or slightly better than a baseline simulation with conventional physical parameterizations. However, the structure of tropical circulation and relative humidity in the upper troposphere are unrealistic. Overall, this study shows potential for the replacement of human‐designed parameterizations with data‐driven ones in a realistic setting.

A Machine Learning Parameterization of Clouds in a Coarse‐Resolution Climate Model for Unbiased Radiation

“…Examples include replacing computationally intensive physical parameterizations with ML emulation (Keller & Evans, 2019;Krasnopolsky et al, 2005Krasnopolsky et al, , 2010Lagerquist et al, 2021;O'Gorman & Dwyer, 2018;Perkins et al, 2023) and training ML against observations (Chen et al, 2023;McGibbon & Bretherton, 2019;Watt-Meyer et al, 2021) or more accurate and computationally intensive parameterizations (Chantry et al, 2021). ML parameterizations for coarse-grid models have been trained on coarsened (coarse-grained) outputs of fine-grid or super-parameterized reference simulations, for example, to predict the effect of the full physics parameterization (Brenowitz & Bretherton, 2019;Han et al, 2020;Rasp et al, 2018;Watt-Meyer et al, 2024;Yuval et al, 2021), or a column-wise correction to the coarse-grid model physics (Bretherton et al, 2022;Clark et al, 2022;Kwa et al, 2023). While using ML in coarse-grid models to correct physics tendencies of temperature and humidity can improve aspects of their simulated climates, clouds are often made worse because they are not among the ML target variables (Kwa et al, 2023), creating knock-on biases in surface and top-of-atmosphere radiative fluxes.…”