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 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 schemes 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 scheme. 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 schemes 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 schemes are reduced compared to those from the CCN model.