A growing body of evidence from modeling and observations indicates that mesoscale circulations generated by land-surface wetness heterogeneities result in substantial vertical fluxes of sensible heat and moisture. These fluxes are believed to have a strong impact on large-scale mean atmospheric variables such as temperature, humidity, cloudiness, and precipitation. Currently, however, this type of mesoscale convective process is not considered in general circulation models (GCMs). In this study, we develop a parameterization for these landscape-forced fluxes, similar to what might eventually be implemented into a GCM. In addition, we investigate the relationship between the parameterized mesoscale flux and the convective condensation associated with these circulations as a first step toward directly including clouds and precipitation forced by surface heterogeneity effects as one component of a comprehensive GCM convective scheme. To generate the data necessary for this development, we perform a number of simulations with a state-of-the-art mesoscale model to determine the sensitivity of the fluxes and condensation to a variety of background atmospheric conditions and land-surface wetness distributions. We use similarity theory to determine the dependence of the mesoscale sensible heat and moisture fluxes on the parameters relevant to the problem, and we create parameterized vertical flux profiles by fitting with Chebyshev polynomials. The parameterized fluxes are tested against an independent, three-dimensional (3-D) simulation of mesoscale development over a heterogeneous landscape, and general good agreement is found. We propose an empirical form for domain-averaged condensation on the basis of a linear relationship with parameterized mesoscale moisture flux and also demonstrate a reasonable agreement with the results from the 3-D simulation. The methodology of this study, i.e., the use of a numerical model in the preliminary stages of parameterization development, is advantageous for situations where the necessary observational data set is nonexistent. The use of model simulations to fully explore the parameter space of this type of problem should then lead to observational campaigns that focus on only those key processes and variables which are relevant for the further refinement of a given parameterization.
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