HR: 0800h
AN: A11A-0039    [Abstracts]
TI: Aerosol / Cloud Interactions Using The Nasa Global Modeling Initiative
AU: * Sotiropoulou, R P
EM: rsot@eas.gatech.edu
AF: School of Earth and Atmospheric Sciences, Georgia Institute of Technology, 311 Ferst Drive NW, Atlanta, GA 30332, United States
AU: Meskhidze, N
EM: nmeskhidze@ncsu.edu
AF: School of Earth and Atmospheric Sciences, Georgia Institute of Technology, 311 Ferst Drive NW, Atlanta, GA 30332, United States
AU: Meskhidze, N
EM: nmeskhidze@ncsu.edu
AF: School of Marine Earth and Atmospheric Sciences, North Carolina State University, Box 8208, Raleigh, NC 27685, United States
AU: Nenes, A
EM: nenes@eas.gatech.edu
AF: School of Earth and Atmospheric Sciences, Georgia Institute of Technology, 311 Ferst Drive NW, Atlanta, GA 30332, United States
AU: Nenes, A
EM: nenes@eas.gatech.edu
AF: School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst Drive NW, Atlanta, GA 30332, United States
AB: The aerosol indirect effect (AIE) is one of the largest sources of uncertainty in assessments of anthropogenic climate change. The objective of this study is to assess the uncertainties in indirect forcing and autoconversion of cloud water to rain from differences in meteorological fields, emission scenarios, parameterizations of cloud droplet formation, and aerosol microphysics. The uncertainty in AIE and autoconversion is assessed with the NASA Global Modeling Initiative (GMI). GMI is ideal for this study as different model components (such as meteorological fields and chemical mechanisms) can easily be interchanged under the same model framework to capture the first AIE, and its sensitivity to parameterizations, meteorological fields, emission scenario and aerosol microphysics. "Present day" and "preindustrial" simulations were carried out using the University of Michigan and AEROCOM emission inventories. Meteorological fields are provided by two global climate models (the NASA GEOS4 finite volume and the Goddard Institute for Space Studies version II') and the NASA Data Assimilation Office. Cloud droplet number concentration (CDNC) was calculated by implementing both diagnostic and physically based droplet parameterizations. Computed CDNC is used to calculate the cloud optical depth, the autoconversion rate and the mean net whole-sky shortwave incoming flux at the surface using a modified version of the FAST-J algorithm. Derived cloud properties, such as cloud optical thickness and effective radius are compared with satellite products from MODIS platform. Our results suggest that differences in meteorological fields, cloud droplet activation parameterizations, emission scenarios and aerosol microphysics could account for more than 30% variability in forcing estimates for the first indirect effect and up to 50% in autoconversion rates. AIE is mostly sensitive to CDNC parameterization; meteorology is of lesser importance.
DE: 0305 Aerosols and particles (0345, 4801, 4906)
DE: 0321 Cloud/radiation interaction
DE: 1620 Climate dynamics (0429, 3309)
DE: 1626 Global climate models (3337, 4928)
DE: 1640 Remote sensing (1855)
SC: Atmospheric Sciences [A]
MN: 2007 Fall Meeting