| description abstract | The authors examine 3D solar radiative heating rates within tropical convective?cirrus systems to identify the scales that contribute significantly to the spatial average over a climate model?s grid cell (i.e., its grid mean), and determine their relationship to the cloud field properties (e.g., cloud-top height variation). These results are used to understand the spatial resolution and subgrid-scale cloud property information needed in climate models to accurately simulate the grid-mean solar heating of these systems. The 3D heating rates are computed by a broadband Monte Carlo model for several regional-scale cloud fields [(400 km)2] whose properties are retrieved from satellite data over the tropical western Pacific. The analyses discussed in this paper have identified two key subgrid-scale features within these systems that largely govern the grid-mean heating rates: the variability in the cloud-top height, and the structure of the cloud edge. These features give rise to hot spots?regions of intense local heating that occupy a small area but dominate the grid-mean value. For example for the fields considered here, 5%?25% of the grid area can contribute 30%?60% of the total heating rate, respectively. Explicitly resolving the hot spots requires a model grid of about (20 km)2?(30 km)2 which is smaller than that currently used in general circulation models (GCMs) for weather forecasting and about a factor of 20 smaller than that used for climate studies. It is shown that, unless a grid of ?(20 km)2 is used, GCM-style heating rate calculations that employ a standard cloud overlap-type treatment can significantly overestimate the solar heating aloft and underestimate it below. This might enhance the vertical velocity within the cloud layer and suppress it at cloud base. Thus, over the long term, biases in the GCM treatments of the vertical heating rate might have consequences to cloud evolution and feedback, particularly for clouds in weak local dynamical regimes. | |
