J7.6 EVALUTION OF A NEW PARAMETERIZATION FOR FAIR-WEATHER CUMULUS Larry K. Berg* 1 and Roland B. Stull 2 1 Pacific Northwest National Laboratory, Richland, Washington 2 The University of British Columbia, Vancouver, BC, Canada 1. INTRODUCTION A new scheme for predicting the cloud cover, cloud- base height, and cloud-top height of fair-weather cumuli has been developed. The scheme, called the Cumulus Potential (CuP) scheme couples the fair-weather clouds with the boundary-layer turbulence. The CuP scheme does a better job predicting the cloud cover, cloud-base height, and cloud-top height than three other methods. 2. DESCRIPTION OF THE SCHEME The CuP parameterization consists of two inde- pendent modules (Fig. 1), and was originally presented by Berg and Stull (2005). In the scheme, one module represents boundary-layer physics, and the other repre- sents clouds. The boundary-layer physics module com- bines the virtual potential temperature ( " v ) and water vapor mixing ratio ( r ) to form a Joint Probability Density Function (JPDF). The JPDF can be compared to the mean environmental profile of " v . Parcels with " v greater than the mean mixed layer value of " v are as- sumed to rise. If a parcel rises to its lifting condensation level then it forms a cloud, and the parcels thermody- namic properties are passed to the cloud module. The size and shape of the JPDF must be prescribed. Berg and Stull (2004) developed a parameterization that treats the distribution of " v and r as a mixing diagram, with the distribution of parcels reaching along mixing lines connecting the boundary layer mean value to both the surface and the entrainment zone properties. Figure 1. Schematic of the CuP parameterization show- ing the boundary-layer turbulence and cloud modules. Arrows indicate the flow of information through the pa- rameterization. *Corresponding Author Address: Dr. Larry K. Berg, Pacific Northwest National Lab, PO Box 999, Richland, WA 99352. e-mail: larry.berg@pnl.gov. In the CuP scheme, the thermodynamic properties at cloud base are determined from the " v and r of the parcels that rise to form clouds. The cloud processes are represented in the cloud module. A simple entrain- ing-detraining cloud model is used here. In this model mixing between the cloud and the environment occurs at a constant rate as the cloud rises (e.g. Malkus 1958). The entrainment and detrainment rates selected for use with the CuP scheme were 1.0 X 10 -3 m -1 , and 3.0 X 10 -3 m -1 , respectively. These values are consistent with estimates found using LES of the trade wind boundary layer for a population of cumuli (Siebesma and Cuijpers 1995; Siebesma and Holtslag 1996). The cloud top height is predicted by determining the level at which all of the parcels convective available potential energy (CAPE) is dissipated. The cloud cover is determined using the prognostic equation, d" cloud dt = " active t active # " cloud $ cloud , (1) where " cloud is the cloud-cover fraction at time, t , and " active is the fraction of parcels that form clouds, as de- termined from the JPDF. The active cloud time scale is defined to be t active = z top w " , where z top is the average cloud-top height. The cloud lifetime is modeled after the work of Albrecht (1981) and Haiden (1996), as: " cloud = t # ln 1 + 1 + $ ( ) l cloud dz % &r s dz % ’ ( ) * + , , (2) where t " is the boundary layer time scale, l cloud is the cloud liquid water, "r s is the saturation deficit of the en- vironment ( r s, env " r env ), and " = LC p ( ) # r s, env # T env ( ) . 3. RESULTS FOR BLX96 The scheme has been tested using data collected during Boundary Layer Experiment 1996 (BLX96; Stull et al. 1997). In this test, the CuP scheme is used as a stand-alone model driven with thermodynamic profiles measured by the University of Wyoming King Air air- craft. Parameterized JPDFs were created using the methods of Berg and Stull (2004). Equation (1) was then integrated forward in time using Runge-Kutta methods and assuming that " cloud , " active , and t active changed linearly with time between the individual profiles. In addition to the results computed using the CuP scheme, cloud properties simulated using three other schemes will also be presented for comparison: 1) the relative humidity-based scheme of the ECHAM4 global climate model (Roeckner et al. 1996), 2) the classical statistical scheme suggested by Sommeria and Dear-