Cumulus parameterization (CP) in state-of-the-art global climate models (GCM) is based on the quasi-equilibrium assumption (QEA), which views convection as the action of an ensemble of cumulus clouds, in a state of equilibrium with respect to a slowly varying atmospheric large-scale state. This view is not compatible with the organization and dynamical interactions across multiple scales of cloud systems in the tropics and progress in this research area was slow over decades despite the widely recognized major shortcomings. Novel ideas on how to represent key physical processes of moist convection-large-scale interaction to overcome the QEA have surged recently. The stochastic multicloud model (SMCM) CP in particular mimics the dynamical interactions of multiple cloud types that characterize organized tropical convection. Here, the SMCM is used to modify the Zhang-McFarlane (ZM) CP by changing the way in which the bulk mass flux is calculated. This is done by introducing a stochastic ensemble of plumes characterized by randomly varying detrainment level distributions based on the cloud area fraction (CAF) of the SMCM. The SMCM is here extended to include shallow cumulus clouds resulting in a unified shallow-deep CP. The new stochastic multicloud plume CP is validated against the control ZM scheme in the context of the single column Community Climate Model of the National Center for Atmospheric Research using six test-cases including both tropical ocean and midladitude land convection. Some key features of the SMCM SP such as it capability to represent the tri-modal nature of organized convection are emphasized.

Many biases in global climate models (GCMs) have been associated with the poor representation of unresolved variability due to organised convection in the tropics by the underlying cumulus parameterizations (CP). Many researchers have suggested that the quasi-equilibrium assumption (QEA) on which these CP’s are based is to blame. This is even more problematic with the recent and future increases in grid resolutions as the cloud large ensemble requirement for QEA breaks down. The stochastic multi-cloud model (SMCM) of Khouider et al. 2010 was proposed as a cheap alternative of overcoming this QEA dilemma by emulating the variability of the three cloud types which characterize tropical convection via a Markov jump birth-death process. The SMCM has proven to be very successful in terms of the simulation of the main modes of tropical variability when used as a simple alternative CP in a GCM. Here, we propose to incorporate the SMCM directly into the Zhang-McFarlane scheme (ZMS; Zhang and McFarlane 1995) to break the QEA dead end by using instead a stochastic plume ensemble and generalise the SMCM framework to cumulus schemes. The new stochastic ZMS (SZMS) uses a random number of plumes that are launched for each one of the three cloud types, shallow, congestus and deep, and that detrain at random levels, according to the SMCM. The new approach somehow combines the idea of Cohen and Craig (2006) of assuming a Poisson process for the number of plumes and that of Gentine et al. (2013) of prescribing a distribution of plume detrainment levels. Here we shall show the results of our experiment, for the single column version of the Community Earth System Model. Bibliography: Cohen BG, Craig GC. Fluctuations in an equilibrium convective ensemble. Part II: Numerical experiments. J. Atmos. Sci. 2006;63(8) Gentine P, Betts AK, Lintner BR, Findell KL, Van Heerwaarden CC, D’andrea F. A probabilistic bulk model of coupled mixed layer and convection. Part II: Shallow convection case. J. Atmos. Sci. 2013;70(6) Khouider B, Biello J, Majda AJ. A stochastic multicloud model for tropical convection. Comm. Math. Sci. 2010;8(1) Zhang GJ, McFarlane NA. Sensitivity of climate simulations to the parameterization of cumulus convection in the Canadian Climate Centre general circulation model. Atmos-ocean. 1995;33(3)