HR: 12:05h
AN: A42B-08    [Abstracts]
TI: Model Intercomparison of Aerosol Indirect Effect
AU: * Penner, J E
EM: penner@umich.edu
AF: Atmospheric, Oceanic, and Space Sciences University of Michigan, 2455 Hayward St., Ann Arbor, MI 48109-2143 United States
AU: Quaas, J
EM: quaas@dkrz.de
AF: Max-Planck Institute for Meteorology, Bundesstrasse 53, Hamburg, 20146 Germany
AU: Storelvmo, T
EM: trude.storelvmo@geo.uio.no
AF: Department of Geosciences University of Oslo, Boks 1072 Blindern, Oslo, NO-0316 Norway
AU: Takemura, T
EM: toshi@riam.kyushu-u.ac.jp
AF: Research Institute for Applied Mechanics, Kyushu University, 6-1 Kasuga-koen, Kasuga, Fukuoka, 816-8580 Japan
AU: Guo, H
EM: hguo@engin.umich.edu
AF: Atmospheric, Oceanic, and Space Sciences University of Michigan, 2455 Hayward St., Ann Arbor, MI 48109-2143 United States
AB: We present model results from six different model experiments to quantify the effect of various model assumptions in determining the aerosol indirect effect. The six experiments are conducted using three different aerosol/general circulation models (GCMs). The purpose of the first experiment is to examine the effect of the basic cloud fields in the GCMs, and so specifies the aerosol fields as well as the parameterization of the formation of cloud droplets and uses an autoconversion scheme that does not depend on the cloud droplet concentration or size. The second experiment is similar, except that each model group has used their own preferred method of parameterization for cloud droplets. The third experiment introduces the effect of cloud droplet number concentration on the rate of autoconversion, but each group uses the same scheme. The fourth experiment is similar except that each model group has used their own preferred autoconversion parameterization. In the fifth experiment, each group also uses their own model-predicted aerosol fields from a common set of sources and in the sixth experiment, the effect of aerosol heating on the meteorological fields is included. We find similar cloud forcing from the first experiment despite vastly different liquid water paths in the GCMs. The results between the groups begin to diverge with the 2nd experiment. We discuss the reasons for differences between the models and from one experiment to the next. The largest differences between the models are introduced when each model uses their own modeled aerosol fields to predict the aerosol indirect effect. Our study suggests that it is important to improve the basic cloud fields within the GCMs, the prediction of cloud cover or cloud fraction, and the prediction of aerosol abundance in order to improve the prediction of the aerosol indirect effect.
DE: 0305 Aerosols and particles (0345, 4801, 4906)
DE: 0319 Cloud optics
DE: 0321 Cloud/radiation interaction
SC: Atmospheric Sciences [A]
MN: Fall Meeting 2005