Microphysics of Convective Clouds

There is currently a substantial discrepancy between the degree of sophistication in cloud microphysics in large-scale clouds and the very rudimentary treatment of cloud microphysics in convective clouds. This may reflect the fact that stratiform clouds are generally much more susceptible to indirect effects than convective clouds. Recently, however, evidence has emerged to show that biomass burning may affect convective clouds, thus necessitating improvements in the treatment of microphysical processes in convective clouds. In the first global study, Nober et al. (2003) accounted for this effect by decreasing the precipitation effi ciency for warm cloud formation in convective clouds, and making it dependent on the cloud droplet number concentration. This approach was taken a step further by Lohmann (2007), who introduced the same microphysical processes (e.g., nucleation, autoconversion, freezing, aggregation) considered in large-scale clouds into convective clouds.

Another option, though considerably more computationally intensive, is to use so-called "super-parameterizations," in which cloud-resolving models (CRMs) are embedded within the normal GCM grid cells, but at only a small fraction of the area of the parent GCM grid cell (e.g., Randall et al. 2003). These models have the capability of calculating cloud-scale vertical velocities and liquid water content (LWC) and thus represent explicitly precipitation processes. They have yet, however, to be applied to aerosol effects on precipitation. If the representation of aerosol and clouds can be improved in such models, or in others through new and innovative techniques for representing subgrid processes, this should increase the accuracy of calculations of the influence of aerosols on the amount and distribution of clouds and precipitation as well as on radiation.

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