On the sensitivity of droplet size relative dispersion to warm cumulus cloud evolution
Article first published online: 12 JUL 2012
©2012. American Geophysical Union. All Rights Reserved.
Geophysical Research Letters
Volume 39, Issue 13, July 2012
How to Cite
2012), On the sensitivity of droplet size relative dispersion to warm cumulus cloud evolution, Geophys. Res. Lett., 39, L13807, doi:10.1029/2012GL052157., , and (
- Issue published online: 12 JUL 2012
- Article first published online: 12 JUL 2012
- Manuscript Accepted: 7 JUN 2012
- Manuscript Revised: 1 JUN 2012
- Manuscript Received: 24 APR 2012
- cloud aerosol interaction;
- cloud evolution;
- droplet size relative dispersion;
 Relative dispersion (ε), defined as the ratio between cloud droplet size distribution width (σ) and cloud droplet average radius (〈r〉), is a key factor used to parameterize various cloud processes in global circulation models (GCMs) and bulk microphysical scheme models (BSMs). Recent studies indicate that the impact of aerosol loading (N) and atmospheric thermodynamic conditions on ε are far from fully understood. Currently, a fixed value per hydrometeor type is used in most BSMs and GCMs, which imposes significant limitations on our ability to model and predict cloud processes and their impact on the environment, on regional to global scales. In this study, we use a detailed bin microphysics single cloud model to investigate the combined impact of atmospheric thermodynamic conditions and N on ε, in warm cumulus clouds. As initial conditions, we used different lapse-rates combined with 8 scenarios of aerosol loading, representing very clean (N = 25 cm−3) to heavily polluted (N = 1600 cm−3) conditions. Moreover, the results are analyzed per cloud evolutionary stage according to the dominance of microphysical processes. The use of this method indicated a different pattern of ε at each stage. Specifically, during the mature stage fitting of ε to rv is relatively resilient to changes in the environmental conditions. Such findings suggest a new view of the effect of aerosols on clouds, via changes in the cloud evolution patterns and a new approach to parameterization of ε based on rv, which can significantly improve the prediction of cloud processes by GCMs and BSMs.