This instrument can measure CCN concentrations at supersaturations from 0.06% to 3% (potentially up to 6%), at a 1 Hz sampling rate that is sufficient for airborne operation. Our analysis employs a fully coupled numerical flow model to simulate the water vapor supersaturation, temperature, velocity profiles and CCN growth in the CFSTGC for its entire range of operation (aerosol sample flow rates 0.25-2.0 L min −1 , temperature differences 2-15 K and ambient pressures 100-1000 mb). The model was evaluated by comparing simulated instrument responses for calibration aerosol against actual measurements from an existing CCN instrument. The model was used to evaluate the CCN detection efficiency for a wide range of ambient pressures, flow rates, temperature gradients, and droplet growth kinetics. Simulations overestimate the instrument supersaturation when the thermal resistance across the walls of the flow chamber is not considered. We have developed a methodology to determine the thermal resistance and temperature drop across the wetted walls of the flow chamber, by combining simulations and calibration experiments. Finally, we provide parameterizations for determining the thermal resistance, the instrument supersaturation and the optimal detection threshold for the optical particle counter.

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Measurements of cloud condensation nuclei (CCN), aerosol size distribution and chemical composition were obtained at the UNH‐AIRMAP Thompson Farms site, during the ICARTT 2004 campaign. This work focuses on the analysis of a week of measurements, during which semiurban and continental air were sampled. Predictions of CCN concentrations were carried out using “simple” Köhler theory; the predictions are subsequently compared with CCN measurements at 0.2%, 0.3%, 0.37%, 0.5% and 0.6% supersaturation. Using size‐averaged chemical composition, CCN are substantially overpredicted (by 35.8 ± 28.5%). Introducing size‐dependent chemical composition substantially improved closure (average error 17.4 ± 27.0%). CCN closure is worse during periods of changing wind direction, suggesting that the introduction of aerosol mixing state into CCN predictions may sometimes be required. Finally, knowledge of the soluble salt fraction is sufficient for description of CCN activity.

[1] This study quantitatively assesses the sensitivity of cloud droplet number (CDNC) to errors in cloud condensation nuclei (CCN) predictions that arise from application of Köhler theory. The CDNC uncertainty is assessed by forcing a droplet activation parameterization with a comprehensive dataset of CCN activity and aerosol size and chemical composition obtained during the ICARTT field campaign in August 2004. Our analysis suggests that, for a diverse range of updraft velocity, droplet growth kinetics and airmass origin, the error in predicted CDNC is (at most) half of the CCN prediction error. This means that the typical 20-50% error in ambient CCN closure studies would result in a 10-25% error in CDNC. For the first time, a quantitative link between aerosol and CDNC prediction errors is available, and can be the basis of a robust uncertainty analysis of the first aerosol indirect effect.

[ 1 ] The presence of giant cloud condensation nuclei (GCCN) within stratocumulus clouds can help the formation of drizzle by acting as collector drops. We propose that the presence of film-forming compounds (FFCs) on GCCN may decrease their growth enough to cease this drizzle formation mechanism. We systematically explore the accommodation properties and amount of FFCs necessary to have as ignificant impact on GCCN size under realistic conditions of growth inside typical stratocumulus clouds. It is found that even low mass fractions (as low as 0.2%) of FFCs with amodest effect on water vapor accommodation can significantly reduce GCCN size and their potential to act as collector drops. Our conclusions apply to both pristine and polluted aerosol conditions, which suggest that in the presence of FFCs, GCCN may be influencing the microphysical evolution of clouds to al esser extent than previously thought.