A closure study of cloud condensation nuclei in the North China Plain using droplet kinetic condensational growth model

Abstract

Abstract. Aerosol size distribution and cloud condensation nucleus (CCN) number concentration were measured in the North China Plain from 31 December 2009 to 20 January 2010. The CCN closure study was performed using these data and droplet kinetic condensational growth model. The calculated CCN concentration with the assumption of pure ammonium sulfate aerosol is 40-140 % higher than that observed for the supersaturations in this study. A sensitivity test on aerosol solubility and mixing state indicates that 0.2-0.5 mass fraction of ammonium sulfate for internal mixture can lead to a ratio of 0.82-1.30 for the calculated to observed CCN concentrations, and that 0.4-0.7 mass fraction of ammonium sulfate for external mixture results in a ratio of 0.74-1.25 in the North China Plain during the time period of the field observations, suggesting that a relatively simple scheme may be used for CCN prediction in climate models for this region. Finally, we compare the calculated CCN concentrations from the kinetic condensational growth model and the equilibrium model. The kinetic condensational growth model can simulate droplet growth in a time period under a certain supersaturation, while the equilibrium model only predicts whether a certain aerosol can be activated as CCN under that supersaturation. The CCN concentration calculated with the kinetic model is higher than that with the equilibrium model at supersaturations of 0.056 % and 0.083 %, because some particles that are not activated from the equilibrium pointof-view can grow large enough to be considered as CCN in the kinetic model. While at a supersaturation of 0.17 %, CCN concentration calculated with the kinetic model is lower than that with the equilibrium model, due to the limitation of droplet kinetic growth. The calculated CCN concentrations using the kinetic model and the equilibrium model are the same at supersaturations of 0.35 % and 0.70 %.

Estimation of cloud condensation nuclei concentration from aerosol optical quantities: influential factors and uncertainties

“…Aerosol particles in the ambient environment are often very complex and are comprised of inorganic and organic species (Kanakidou et al, 2005;Zhang et al, 2007), so the Köhler theory has been extended to include the influence of these species (Shulman et al, 1996;Facchini et al, 1999;Svenningsson et al, 2006). The mixing state and a detailed knowledge of how different compounds interact with water also matter (McFiggans et al, 2006;Andreae and Rosenfeld, 2008;Ward et al, 2010;Yang et al, 2012). A modified Köhler theory, called the "κ-Köhler" theory, was proposed by Petter and Kriedenweis (2007), which uses a single parameter, κ, to describe the solubility effect on CCN activation.…”

Reply to RC2

“…Previous studies show that although the mean lifetime of cloud droplets is usually less than half an hour, the residence time for some lucky cloud droplets can be longer than 1 h (e.g., Feingold et al, 1996;Kogan, 2006;Andrejczuk et al, 2008). Those long-lifetime cloud droplets might contribute to large droplets in the cloud, similar to long-lifetime ice particles in mixed-phase clouds (Yang et al, 2015).…”

Cloud droplet size distribution broadening during diffusional growth: ripening amplified by deactivation and reactivation

“…The original version of the model was designed to study cirrus clouds by Heymsfield and Sabin (1989), and then warm clouds (Feingold and Heymsfield, 1992;Feingold et al, 1998). In recent years, this model has been modified and applied to investigate various microphysical problems (e.g., Feingold and Kreidenweis, 2000;Xue and Feingold, 2004;Ervens and Feingold, 2012;Yang et al, 2012Yang et al, , 2016Li et al, 2013). In the current version of the parcel model, air pressure (p), parcel height (h), air temperature (T ), water vapor mixing ratio (q v ) and radii of haze and cloud droplets (r i ) are prognostic variables, which are calculated using the variable-coefficient ordinary differential equation solver (VODE) (Brown et al, 1989).…”