Premium
On the sensitivity of droplet size relative dispersion to warm cumulus cloud evolution
Author(s) -
Tas E.,
Koren I.,
Altaratz O.
Publication year - 2012
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2012gl052157
Subject(s) - environmental science , bin , atmospheric sciences , cloud computing , aerosol , effective radius , cloud height , liquid water content , cloud physics , meteorology , climatology , cloud cover , physics , mathematics , geology , computer science , algorithm , quantum mechanics , galaxy , operating system
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 r v 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 r v , which can significantly improve the prediction of cloud processes by GCMs and BSMs.