Convective parametrization of dry atmospheric boundary layer by generative machine learning model

Even though global simulations of the Earth system on monthly timescales reach now resolutions of 2.5 kilometers essential turbulent transport processes in the lowest part of the atmosphere still have to be modeled. Here, we implement a machine learning-based mass-flux parametrization of the subgrid-scale heat flux for a shear-free dry atmospheric atmospheric boundary layer by a generative adversarial network. Training data and prediction capability of the algorithm are increased by incorporating the physics of boundary layer growth following from classical mixed layer similarity theory. Our model is compared successfully to standard mass-flux parametrizations. It is additionally found to reproduce the intermittent fluctuations of the convective buoyancy flux and the horizontal organisation of mesoscale turbulence correctly.