HR: 11:08h
AN: A22B-04    [Abstracts]
TI: On the Determination of CCN from Satellite: Challenges and Possibilities
AU: Kapustin, V
EM: kapustin@soest.hawaii.edu
AF: University of Hawaii, 1000 Pope Rd., MSB500 Department of Oceanography, Honolulu, HI 96822 United States
AU: * Clarke, A
EM: tclarke@soest.hawaii.edu
AF: University of Hawaii, 1000 Pope Rd., MSB500 Department of Oceanography, Honolulu, HI 96822 United States
AU: Shinozuka, Y
EM: yohei@hawaii.edu
AF: University of Hawaii, 1000 Pope Rd., MSB500 Department of Oceanography, Honolulu, HI 96822 United States
AU: Howell, S
EM: showell@soest.hawaii.edu
AF: University of Hawaii, 1000 Pope Rd., MSB500 Department of Oceanography, Honolulu, HI 96822 United States
AU: Brekhovskikh, V
EM: verab@soest.hawaii.edu
AF: University of Hawaii, 1000 Pope Rd., MSB500 Department of Oceanography, Honolulu, HI 96822 United States
AU: Nakajima, T
EM: teruyuki@ccsr.u-tokyo.ac.jp
AF: University of Tokyo Center for Climate System Research Center, 4-6-1 Komaba, Meguto-ku, Tokyo, 153-8904 Japan
AU: Higurashi, A
EM: hakiko@nies.go.jp
AF: National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki, 305-8505 Japan
AB: We are using aerosol size distributions measured in the size range from 0.01 to 10+ um during TRACE-P and ACE-Asia, results of chemical analysis, measured/modeled humidity growth and stratification by air mass types to explore correlation between aerosol optical parameters and aerosol number concentration. Size distributions allow us to integrate aerosol number over any size range expected to be effective cloud condensation nuclei (CCN) and provide definition of a proxy for CCN (CCNproxy). Because of the mixed nature of the accumulation mode aerosol and the link between volatility and solubility this CCNproxy can be linked to the optical properties of these size distributions at ambient conditions. This allows examination of the relationship between CCNproxy and the aerosol optical properties expected to be seen by satellites. Relative increases in coarse aerosol (e.g. dust) generally add little particle number to effective CCN but significantly increase scattering detected by satellite and drives the Angstrom exponent to approach zero. This has prompted the use of a so-called aerosol index (AI) based upon the product of the scattering and the non-dimensional Angstrom exponent, both capable of being inferred from satellite observations. This biases the AI to be closer to scattering values generated by particles in the accumulation mode (Angstrom exponent about 1 to 2) that dominate particle number and is therefore dominated by sizes commonly effective as CCN. Our measurements demonstrate that AI does not generally relate well to measured CCN proxy unless the data is suitably stratified. Multiple layers, complex humidity profiles, dust with very low Angstrom etc. mixed with pollution and size distributions differences in pollution and biomass emissions appear to contribute most to method limitations. However, we demonstrate that these characteristic differences result in predictable influences on AI. These results suggest that new satellite and model capabilities can be integrated to improve on satellite retrieval of CCN.
DE: 0305 Aerosols and particles (0345, 4801)
DE: 0320 Cloud physics and chemistry
SC: Atmospheric Sciences [A]
MN: 2004 AGU Fall Meeting