Improving the Tropical Modes of Variability in CAM through a Stochastic Mulitcloud/Unified Shallow-Deep Convection scheme

In this study, we thoroughly evaluate large-scale features of the global climate and tropical modes of variability in the National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM) version 5, using a stochastic modification of the Zhang and McFarlane cumulus parameterization that also unifies the representation of deep and shallow convection. The results from a 15-year simulation of the unified-stochastic CAM (USM-CAM) are examined and compared with the CAM simulations using the default Zhang and McFarlane parameterization (referred to here as the control, CTRL for short). The Stochastic Multi-cloud model (SMCM) is based on an interacting particle system on a lattice whose microscopic configuration represents various cloud types that characterize organized tropical convection (namely, shallow cumulus, cumulus congestus, deep, and stratiform), as they interact with each other and with the environment. The SMCM’s stochasticity and equilibrium measure are by design dependent on a set of large-scale predictors such as CAPE, CIN, and mid-tropospheric humidity, and modulated by a set of “transition timescale” parameters that can be inferred from radar data using machine learning. Here, three sets of experiments have been conducted based on three different sets of transition timescale parameters, one of which is inferred from the Dynamics of the MJO (DYNAMO) field experiment. Although, the SMCM itself is highly dependent on the transition time-scale parameters, overall all three USM-CAM simulations show promising results in capturing MJO, and convectively coupled waves. This improvement might be due in part to the overall improvement in the ability of the USM-CAM simulations to produce realistic proportions of large/stratiform versus convective rainfall while the CTRL simulation is dominated by convective precipitation—the Zhang McFarlane scheme seems to rain too much and too often.