It is well known that the contrast between land and water originates breezes (i.e., sea, lake, and land breezes), which are mesoscale circulations. Several papers and textbooks describe in length the mechanism involved in the generation of these circulations, and their impact on the weather and the climate [e.g., Pielke, 1984, among many others]. More recent investigations have indicated that other landscape discontinuities, e.g., irrigated land in arid areas, deforestation, and afforestation, provide also an environment appropriate for the development of mesoscale circulations.
Pielke et al. [1991] used a set of numerical simulations to demonstrate that the surface heterogeneity created by alternating strips of land and water generates mesoscale heat fluxes often more significant than turbulent fluxes. Further analysis by Avissar and Chen [1993], Chen and Avissar [1994a,b], and Lynn et al. [1995a] indicated that mesoscale heat fluxes are created by various types of landscape discontinuity, and are affected by different background conditions, e.g., wind velocity, thermal stratification and humidity profile of the atmosphere, latitude, and day of the year. For instance, an increase of the background wind tends to reduce the importance of mesoscale fluxes, but even with a moderate wind these fluxes remain significant. While the humidity has only a minor direct impact on the generation of mesoscale circulations, this parameter has a dominant role in the formation of cloud and precipitation. As already well known, the latitude and day of the year affect significantly the amount of radiative energy received at the ground surface. Consequently, they affect the turbulent fluxes near the surface, which in turn affect the entire PBL and mesoscale fluxes, as discussed above.
These mesoscale fluxes are likely to form clouds, which affect the radiation and the precipitation regimes, and, as a result, the hydrologic cycle. For instance, Bougeault et al. [1991] simulated with the French Weather Service limited-area numerical model the formation of clouds next to the boundary between a forest and a crop area. Their results were supported by satellite images and observations of various meteorological parameters in the PBL. Similarly, Lyons et al. [1993] presented a satellite photograph of convective clouds which clearly developed at the boundary between irrigated land and native vegetation in Southwestern Australia.
Chen and Avissar [1994a] investigated in more details the impact of such
landscape discontinuities on clouds and precipitation. They showed that
significantly affects the timing of onset of clouds, and the
intensity and distribution of precipitation. In general, they found
that landscape discontinuities enhance shallow convective precipitation.
They emphasized that the two mechanisms which are strongly modulated by
the spatial distribution of 
, namely turbulence and mesoscale
circulations, also determine the horizontal distribution of maximum
precipitation. They found that interactions between shallow cumulus and
are highly nonlinear and complicated by different factors,
such as atmospheric thermodynamic structure, background (large-scale)
wind, and the characteristic scale of the landscape discontinuity
created by the spatial distribution of 
.
So far, these mesoscale fluxes are not represented in GCMs. Because of their significant contribution to subgrid-scale fluxes, clouds, and precipitation, appropriate parameterizations need to be developed. Avissar and Chen [1993] proposed a framework to introduce the mesoscale fluxes within the set of prognostic equations used in GCMs. They suggested to develop a parameterization based on the mesoscale kinetic energy. While this approach is currently being investigated, Lynn et al. [1995b] developed a primary parameterization of mesoscale fluxes for GCMs using similarity theory concepts. The similarity theory consists of (i) identify the relevant variables to the problem; (ii) organize these variables into dimensionless form; (iii) gather observations or perform experiments to determine their values; and (iv) find an empirical relationship between the dimensionless variables. Ideally, this empirical relationship is ``universal,'' i.e., it applies for all possible cases. Such an empirical relationship was found between mesoscale fluxes and a combination of a land-surface heterogeneity characteristic length scale, the standard deviation of turbulent sensible heat fluxes at the ground surface, and other variables representative of the macroscale atmospheric background, e.g., wind intensity, moist static energy, stability, and the coriolis force. However, moist processes and topographical features were not considered in this relationship.