CCN Predictions Using Simplified Assumptions of Organic Aerosol Composition and Mixing State: a Synthesis from Six Different Locations

Barbara Ervens NOAA/Coop. Instit. for Research in Environmental Studies

Category: Aerosol-Cloud-Radiation Interactions

Working Group: Cloud-Aerosol-Precipitation Interaction

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Ratio of calculated to measured CCN number concentration for seven different data sets. The symbol size corresponds to the frequency of the respective ratio in the CCN closure. The dashed lines represent CCN(calc)/CCN(meas) = 0.5 and 2, respectively.

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 [RVS] and the Ship Channel in Houston, TX [HSC] cannot be represented without knowledge of more complex (size-resolved) composition. For aerosol that has experienced processing (Mexico City [MEX], Holme Moss (UK) [HOM], Point Reyes (CA) [PYE], and Chebogue Point (Canada) [CBG]), 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. Under typical conditions, a factor of two uncertainty in CCN concentration 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.

This poster will be displayed at ASR Science Team Meeting.

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