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High-Resolution Simulation of Shallow-to-Deep Convection Transition over Land

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High-Resolution Simulation of Shallow-to-Deep Convection Transition over Land Marat Khairoutdinov1 and David Randall Department of Atmospheric Science Colorado State University Accepted for publication in Journal of the Atmospheric Sciences Revised version March 2006 1 Corresponding author address: Marat Khairoutdinov, Department of Atmospheric Science, Colorado State University, Fort Collins, CO 80523 e-mail: [email protected] Abstract Results are presented from a high-resolution three-dimensional simulation of shallow-to-deep convection transition based on idealization of observations made during the LBA experiment in Amazonia, Brazil during the TRMM-LBA mission, on 23 February. The doubly periodic grid has 1536 x 1536 x 256 grid cells with horizontal grid spacing of 100 meters, thus covering an area of 154 x 154 km2. The vertical resolution varies from 50 m in the boundary layer to 100 m in the free troposphere, and gradually coarsens to 250 m near the domain top at 25.4 km. The length of the simulation is 6 hours, starting from an early morning sounding corresponding to 7:30 local time. Convection is forced by prescribed surface latent and sensible heat fluxes and prescribed horizontally uniform radiative . Despite a considerable amount of convective available potential energy (CAPE) in the range 1600 to 2400 J/kg, and despite virtually no convective inhibition (CIN) in the mean sounding throughout the simulation, the cumulus convection starts as shallow, gradually developing into congestus, and becomes deep only toward the end of simulation. Analysis shows that the reason is that the shallow clouds generated by the boundary layer turbulence are too small to penetrate deep into the troposphere, as they are quickly diluted by mixing with the environment. Precipitation and the associated cold pools are needed to generate thermals big enough to support the growth of deep clouds. This positive feedback involving precipitation is supported by a sensitivity experiment in which the cold pools are effectively eliminated by artificially switching off the evaporation of precipitation; in the experiment, the convection remains shallow throughout the entire simulation, with a few congestus but no deep clouds. The probability distribution function (PDF) of cloud size during the shallow, congestus and deep phases is analyzed using a new method. During each of the three phases, the shallow3 clouds dominate the mode of the PDFs at about 1 km diameter. During the deep phase, the PDFs show cloud bases as wide as 4 km. Analysis of the joint PDFs of cloud size and in-cloud variables demonstrates that, as expected, the bigger clouds are far less diluted above their bases than their smaller counterparts. Also, thermodynamic properties at cloud bases are found to be nearly identical for all cloud sizes, with the moist static energy exceeding the mean value by as much as 4 kJ kg-1. The width of the moist static energy distribution in the boundary layer is mostly due to variability of water vapor; therefore, clouds appear to grow from the air with the highest water vapor content available. No undiluted cloudy parcels are found near the level of neutral buoyancy. It appears that a simple entraining-plume model explains the entrainment rates rather well. The least diluted plumes in the simulation correspond to an entrainment parameter of about 0.1 km-1.4 1. Introduction Despite substantial progress made over the past decades, the representation of convection in large-scale models remains a difficult problem (e.g., Randall et al. 2003; Arakawa, 2004). It is well known that general circulation models (GCMs) with parameterized convection have considerable difficulty in representing the diurnal cycle of convection and precipitation over the summertime continents (e.g., Betts and Jakob 2002, Bechtold et al. 2004). The problem manifests itself most noticeably as a tendency for precipitation to reach its maximum several hours too early, compared to observations. One of the reasons for this bias may be the fact that convective parameterizations in GCMs generally use the large-scale (which is the same as the grid-scale in most GCMs) characteristics such as convectively available potential energy (CAPE), among others, to predict the timing and strength of convection. The diurnal cycle, on the other hand, is a more localized response of rapidly developing convection to solar heating of the surface in the form of unresolved-by-GCM (sub grid-scale) circulations associated with non uniform heating in the presence of terrain (e.g., Redelsperger and Clark 1990) and other land surface heterogeneity (e.g., Avissar 1995), sea breeze (e.g, Wakimoto and Atkins 1994), cold pools and gust fronts due to evaporating precipitation (e.g., Wakimoto 1982; Kingsmill 1995), drylines (e.g., Bluestein and Parker 1993), horizontal convective rolls (e.g., Weckwerth et al. 1996), etc. The relatively new “super-parameterization” approach to general circulation modeling (Grabowski 2001; Khairoutdinov and Randall, 2001; Khairoutdinov et al., 2005) has the advantage of explicitly including the sub-GCM-grid-scale dynamics by replacing of the cloud parameterization with a cloud-resolving model inserted into each GCM grid. It has been demonstrated (Khairoutdinov et al. 2005) that such a modified GCM appears to considerably5 improve the simulated diurnal cycle of precipitation over summertime continents when compared to the standard version of the same GCM. Such an approach, however, comes with very high computational cost, and has not yet been used for prolonged climate-change simulations. In any case, the representation of the diurnal cycle in conventional GCMs still needs to be improved. Over the past two decades or so, major progress in our undestanding of convection has been driven through the widespread use of cloud-resolving models (CRMs). They have extensively been used to evaluate and improve existing paramaterizations, as well as to inspire new approaches. Without doubt, CRMs have their own share of problems and


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