CCN predictions using simplified assumptions of organic aerosol composition and mixing state: a synthesis from six different locations
1Cooperative Institute for Research in the Environmental Sciences (CIRES), University of Colorado, Boulder, CO, USA
2NOAA Earth System Research Laboratory, Boulder, CO, USA
3Department of Chemistry and Biochemistry, University of Colorado, Boulder, CO, USA
4NOAA Pacific Marine Environmental Laboratory, Seattle, WA, USA
5Brookhaven National Laboratory, 75 Rutherford Drive, Upton, NY, USA
6Department of Environmental Toxicology, University of California, Davis, CA, USA
7School of Earth, Atmospheric and Environmental Science, The University of Manchester, UK
8National Centre for Atmospheric Science, School of Earth, Atmospheric and Environmental Sciences, The University of Manchester, Manchester, UK
Abstract. An accurate but simple quantification of the fraction of aerosol particles that can act as cloud condensation nuclei (CCN) is needed for implementation in large-scale models. Data on aerosol size distribution, chemical composition, and CCN concentration from six different locations have been analyzed to explore the extent to which simple assumptions of composition and mixing state of the organic fraction can reproduce measured CCN number concentrations.
Fresher pollution aerosol as encountered in Riverside, CA, and the ship channel in Houston, TX, cannot be represented without knowledge of more complex (size-resolved) composition. For aerosol that has experienced processing (Mexico City, Holme Moss (UK), Point Reyes (CA), and Chebogue Point (Canada)), CCN can be predicted within a factor of two assuming either externally or internally mixed soluble organics although these simplified compositions/mixing states might not represent the actual properties of ambient aerosol populations, in agreement with many previous CCN studies in the literature. Under typical conditions, a factor of two uncertainty in CCN concentration due to composition assumptions translates to an uncertainty of ~15% in cloud drop concentration, which might be adequate for large-scale models given the much larger uncertainty in cloudiness.