Cloud Parameterization Frameworks

Colorado State University, Fort Collins CO

Investigators

Abstract

The PI will continue his development of parameterizations of convective cloud systems and the planetary boundary layer (PBL). His new PBL parameterization will include a mechanistic representation of the vertical transport of horizontal momentum by roll circulations. The model predicts the width and orientation of the rolls, and the perturbation wind field is diagnosed. This information is used to compute the perturbation pressure field from the anelastic pressure equation, and this in turn is used to compute the pressure correlations needed to predict the vertical velocity variance and other statistics. The parameterization will be tested using large-eddy simulation (LES) results. The PBL parameterization will be altered to use an explicit PBL depth, with about 10 or fewer layers inside. The PI's intention is to test this parameterization in the Colorado State University general circulation model and eventually in the Community Climate System Model. In order to predict the depth, he must parameterize the entrainment rate. He will develop an entrainment parameterization that is designed for use with a vertically resolved PBL and predicted statistics such as the skewness of the vertical velocity field. This fresh look at the entrainment problem, from a different perspective, may lead to an improvement in the understanding of the basic physics. Some emphasis will be placed on the role of evaporative cooling in enhancing the entrainment rate and reducing the fractional cloudiness. The top-hat probability density function (PDF) used in his earlier work will be replaced by a more realistic and flexible "spatial distribution function." Finally, the new parameterization will include a representation of precipitation processes. The parameterization of deep convection will be altered to abandon the Arakawa-Schubert approach based on an entraining plume cloud model with a spectrum of cloud types. In its place the PI will put a single convective updraft at each level, but with a resolved internal structure. This will improve the scaling of the parameterization as the vertical resolution of the model is increased. Convective downdrafts and stratiform clouds will also be included in this framework. The PI's revised parameterization of deep convection and the attendant stratiform clouds will make use of a new cloud model for parameterization. In the Arakawa-Schubert parameterization there are many cloud types, each with a crudely idealized internal structure. The PI will replace this by a single "cloud type" with a more realistic internal structure, including joint variations (at a given level) of the vertical velocity and the thermodynamic variables. Convective downdrafts will be represented through a second PDF. The PDF of the cloud model can include both convective and stratiform clouds in a unified framework. In fact, the PDF can even include such things as spatial variability of the water vapor mixing ratio in clear air. It has been suggested that humid mesoscale regions (surrounded by much drier air) can provide nurturing environments for the growth of deep cumuli, and that in the absence of such mesoscale humid regions deep convection is suppressed. By parameterizing the mesoscale variability of water vapor in clear air, the PI can explore this idea in the context of a large-scale model. His work will be guided by the results obtained with high-resolution cloud models. To complete the parameterization, his new cloud model will be combined with a prognostic closure. He will predict multiple moments of the vertical velocity as functions of height, as well as multiple moments of the thermodynamic variables. This research will represent a step towards unification of the parameterizations of the PBL and deep convection. Broader Impacts: The research will pave the way for improved weather forecasts and improved simulations of climate change. Deficiencies in cloud parameterizations are widely acknowledged to be among the most serious obstacles standing in the way of more reliable simulations of climate change. The research will also contribute to the training of graduate students and postdoctoral researchers for careers in atmospheric science.