Cloud-Resolving Model Approach for Understanding Clouds in Climate Problems

The representation of convection and clouds in general circulation models (GCMs) remains one of most challenging and long-standing problems in atmospheric research. The slow progress is largely due to the lack of global observations of cloud properties. Yanai et al. (1973) developed a diagnostic method to obtain cloud properties such as the vertical mass flux using the observed large-scale heat and moisture budgets together with a cumulus ensemble model. The theory of cumulus-environment interaction from this paper has influenced the cumulus parameterization development for decades. Broader impacts of this paper on atmospheric sciences have also been documented by many studies in the last forty years. Partly motivated by this paper, the cloud-resolving model (CRM) approach, more than twenty years later, was constructed to produce cloud properties using the large-scale forcing (i.e., temperature and moisture advection) and the traditional cloud-scale numerical model that resolves cloud dynamics. The application of CRM-simulated cloud properties has shown some encouraging impacts on GCM simulations of climate mean state and variability.