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

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. Plain Language Summary: Numerical models used for projecting climate change impacts must use ad‐hoc assumptions about the effects of unresolved small‐scale processes. These assumptions contribute to uncertainty in predicting how rainfall and temperature will change in the future. Expensive fine‐grid simulations which eliminate the need for some of these assumptions are possible to run for shorter (month‐to year‐long) duration. We use such a simulation to train a data‐driven representation of the effects of processes, like clouds, which are poorly simulated by a cheaper coarse‐grid model. The data‐driven representation (a neural network) predicts rates of temperature and moisture change in each column using inputs from that grid column. This approach has been previously shown to work for models with idealized boundary conditions, but not for the realistic setting we use. When this neural network is used in a coarse‐resolution model, the realism of many global skill metrics is as good or better than a baseline model with traditional representation of small‐scale processes. However, some features are degraded, such as the time‐evolving pattern of rainfall in the tropics and humidity in the upper atmosphere. This work is a first step toward the use of data‐driven representations of unresolved processes in realistic global atmospheric models. Key Points: Effective sources of heat and moisture are computed from a global storm‐resolving simulation accounting for semi‐resolved dynamicsA neural network is trained to predict columns of the effective sources using profiles of temperature and specific humidityWhen used online, stable month‐long simulations are possible although skill is not yet comparable to a previous corrective approach [ABSTRACT FROM AUTHOR]

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