Abstract
Abstract In this paper, an idealized, high-resolution simulation of a gradually forced transition from shallow, nonprecipitating to deep, precipitating cumulus convection is described; how the cloud and transport statistics evolve as the convection deepens is explored; and the collected statistics are used to evaluate assumptions in current cumulus schemes. The statistical analysis methodologies that are used do not require tracing the history of individual clouds or air parcels; instead they rely on probing the ensemble characteristics of cumulus convection in the large model dataset. They appear to be an attractive way for analyzing outputs from cloud-resolving numerical experiments. Throughout the simulation, it is found that 1) the initial thermodynamic properties of the updrafts at the cloud base have rather tight distributions; 2) contrary to the assumption made in many cumulus schemes, nearly undiluted air parcels are too infrequent to be relevant to any stage of the simulated convection; and 3) a simple model with a spectrum of entraining plumes appears to reproduce most features of the cloudy updrafts, but significantly overpredicts the mass flux as the updrafts approach their levels of zero buoyancy. A buoyancy-sorting model was suggested as a potential remedy. The organized circulations of cold pools seem to create clouds with larger-sized bases and may correspondingly contribute to their smaller lateral entrainment rates. Our results do not support a mass-flux closure based solely on convective available potential energy (CAPE), and are in general agreement with a convective inhibition (CIN)-based closure. The general similarity in the ensemble characteristics of shallow and deep convection and the continuous evolution of the thermodynamic structure during the transition provide justification for developing a single unified cumulus parameterization that encompasses both shallow and deep convection.
Highlights
Parameterizations of cumulus convection in largescale models currently employ a wide variety of assumptions about how the cumulus cloud ensemble and associated fluxes of heat, moisture, and momentum relate to large-scale variables
In a recent study of shallow cumulus convection that includes a feedback between entrainment rate and vertical velocity, the distributions of thermodynamic properties at the cloud base, while narrow, were sufficient to explain the variability in the cumulus clouds (Neggers et al 2002), implying no role for the cloud sizes
While alternative CRM-based approaches to large-scale modeling are being introduced to eliminate the need for traditional cumulus parameterizations for some problems (Arakawa 2004; Grabowski 2001; Kuang et al 2005), most global weather and climate models continue to rely on cumulus parameterizations
Summary
Parameterizations of cumulus convection in largescale models currently employ a wide variety of assumptions about how the cumulus cloud ensemble and associated fluxes of heat, moisture, and momentum relate to large-scale variables Many of these assumptions have not yet been adequately evaluated. For shallow (by which we mean almost nonprecipitating) cumulus convection, studies by Siebesma and Cuijpers (1995), Siebesma et al (2003), Zhao and Austin (2005a,b), and others compared CRM results with mass-flux schemes, the most widely used type of cumulus parameterizations. We describe an idealized, highresolution CRM simulation of a gradually forced transition from shallow, nonprecipitating to deep, precipitating cumulus convection; explore how the cloud and transport statistics evolve as the convection deepens; and use the collected statistics to evaluate assumptions in current cumulus schemes, including cloud-base properties, the utility of an entraining plume perspective, and the mass-flux closure and partitioning problems. Analyses of the model output and implications for cumulus schemes are presented in section 3, followed by a brief summary (section 4)
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