This article was corrected on 01 AUG 2014. See the end of the full text for details.
Aerosol effect on the warm rain formation process: Satellite observations and modeling
Article first published online: 16 JAN 2013
©2013. American Geophysical Union. All Rights Reserved.
Journal of Geophysical Research: Atmospheres
Volume 118, Issue 1, pages 170–184, 16 January 2013
How to Cite
2013), Aerosol effect on the warm rain formation process: Satellite observations and modeling, J. Geophys. Res. Atmos., 118, 170–184, doi:10.1002/jgrd.50043., , and (
- Issue published online: 29 JAN 2013
- Article first published online: 16 JAN 2013
- Manuscript Accepted: 17 NOV 2012
- Manuscript Revised: 29 OCT 2012
- Manuscript Received: 21 AUG 2012
- National Aeronautics and Space Administration (NASA). Grant Numbers: NNN13D771T, NNN13D968T
- aerosol-cloud interaction;
- cloud microphysics;
- precipitation process
 This study demonstrates how aerosols influence the liquid precipitation formation process. This demonstration is provided by the combined use of satellite observations and global high-resolution model simulations. Methodologies developed to examine the warm cloud microphysical processes are applied to both multi-sensor satellite observations and aerosol-coupled global cloud-resolving model (GCRM) results to illustrate how the warm rain formation process is modulated under different aerosol conditions. The observational analysis exhibits process-scale signatures of rain suppression due to increased aerosols, providing observational evidence of the aerosol influence on precipitation. By contrast, the corresponding statistics obtained from the model show a much faster rain formation even for polluted aerosol conditions and much weaker reduction of precipitation in response to aerosol increase. It is then shown that this reduced sensitivity points to a fundamental model bias in the warm rain formation process that in turn biases the influence of aerosol on precipitation. A method of improving the model bias is introduced in the context of a simplified single-column model (SCM) that represents the cloud-to-rain water conversion process in a manner similar to the original GCRM. Sensitivity experiments performed by modifying the model assumptions in the SCM and their comparisons to satellite statistics both suggest that the auto-conversion scheme has a critical role in determining the precipitation response to aerosol perturbations and also provide a novel way of constraining key parameters in the auto-conversion schemes of global models.