Online test of a data-driven parameterization of deep-convection: evaluation in present and future climate

This study presents a data-driven parameterization of deep convection, implemented and tested within the global climate model ARP-GEM at 50 km resolution. Initially, a 'naive' neural network was used to replace ARP-GEM's traditional physical parameterization. A 30-year simulation with this data-driven approach revealed significant biases, particularly in the representation of high clouds.
To adress these biases, we developed a two-fold neural network architecture: one component responsible for detecting the triggering of the convection and another responsible for computing convective tendency terms. This refined parameterization substantially improved performance compared to the initial version. Furthermore, the enhanced parameterization was evaluated under warmer climate conditions, demonstrating online stability and consistent overall fidelity.