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Volume 8, issue 18
Atmos. Chem. Phys., 8, 5649-5667, 2008
https://doi.org/10.5194/acp-8-5649-2008
© Author(s) 2008. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Chem. Phys., 8, 5649-5667, 2008
https://doi.org/10.5194/acp-8-5649-2008
© Author(s) 2008. This work is distributed under
the Creative Commons Attribution 3.0 License.

  26 Sep 2008

26 Sep 2008

The influence of chemical composition and mixing state of Los Angeles urban aerosol on CCN number and cloud properties

M. J. Cubison1, B. Ervens1,2,3, G. Feingold3, K. S. Docherty1, I. M. Ulbrich1,4, L. Shields5, K. Prather5, S. Hering6, and J. L. Jimenez1,4 M. J. Cubison et al.
  • 1Cooperative Institute for Research in the Environmental Sciences (CIRES), University of Colorado, Boulder, CO, USA
  • 2Atmospheric Science Department, Colorado State University, Fort Collins, CO, USA
  • 3NOAA Earth System Research Laboratory, Boulder, CO, USA
  • 4Dept. of Chemistry and Biochemistry, University of Colorado, Boulder, CO, USA
  • 5University of California at San Diego, CA, USA
  • 6Aerosol Dynamics, Inc., Berkeley, CA, USA

Abstract. The relationship between cloud condensation nuclei (CCN) number and the physical and chemical properties of the atmospheric aerosol distribution is explored for a polluted urban data set from the Study of Organic Aerosols at Riverside I (SOAR-1) campaign conducted at Riverside, California, USA during summer 2005. The mixing state and, to a lesser degree, the average chemical composition are shown to be important parameters in determining the activation properties of those particles around the critical activation diameters for atmospherically-realistic supersaturation values. Closure between predictions and measurements of CCN number at several supersaturations is attempted by modeling a number of aerosol chemical composition and mixing state cases of increasing complexity. It is shown that a realistic treatment of the state of mixing of the urban aerosol distribution is critical in order to eliminate model bias. Fresh emissions such as elemental carbon and small organic particles must be treated as non-activating and explicitly accounted for in the model. The relative number concentration of these particles compared to inorganics and oxygenated organic compounds of limited hygroscopicity plays an important role in determining the CCN number. Furthermore, expanding the different composition/mixing state cases to predictions of cloud droplet number concentration in a cloud parcel model highlights the dependence of cloud optical properties on the state of mixing and hygroscopic properties of the different aerosol modes, but shows that the relative differences between the different cases are reduced compared to those from the CCN model.

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