Influence of common assumptions regarding aerosol composition and mixing state on predicted CCN concentration

Abstract

Abstract.A 4-year record of aerosol size and hygroscopic growth factor distributions measured at the Department of Energy's SGP ARM site in Oklahoma, U.S. were used to estimate supersaturation (S)-dependent cloud condensation nuclei concentrations (NCCN). Baseline or reference NCCN(S) spectra were estimated by using the data to create a matrix of size-and 10 hygroscopicity-dependent number concentration (N)and then integrating for S > critical supersaturation (Sc) calculated for the same size and hygroscopicity pairs using -Ko hler Theory. The accuracy of those estimates was assessed through comparison with the directly measured NCCN at the same site. Subsequently, NCCN was calculated using the same dataset but with an array of simplified treatments in which the aerosol was assumed to be either an internal or an external mixture and the hygroscopicity either assumed or based on averages derived from the growth factor distributions. The CCN spectra calculated using the 15 simplified treatments were compared with those from the baseline approach to evaluate the impact of commonly used approximations. Among the simplified approaches, assuming the aerosol is an internal mixture with size-dependent hygroscopicity parameter () resulted in estimates closest to those from the baseline approach over the range in S considered.

Aerosol Activation in Radiation Fog at the Atmospheric Radiation Program Southern Great Plains Site

“…Mahish et al. (2018) tested the impact of several assumptions about mixing state on the predicted concentration of CCN at the ARM SGP site, finding that the assumption of internally mixed aerosols with size‐dependent κ (as used here) resulted in the best agreement with the measured CCN concentrations. As such, we believe it is reasonable to assume that the aerosols are internally mixed and that κ increases with particle size.…”

“…We extend κ beyond a dry particle diameter of 250 nm by extrapolating based on the derived values of κ at 200 and 250 nm, following Mahish et al. (2018). The black diamond in the upper right panel of Figure 2 illustrates this extrapolation for a sample dry particle diameter of 375 nm (in this case, κ at 375 nm is 0.22).…”

Revisiting matrix-based inversion of scanning mobility particle sizer (SMPS) and humidified tandem differential mobility analyzer (HTDMA) data

“…Number concentrations fluctuate in response to the nitrate and organic aerosol cycle on short timescales and synoptic weather variability on longer timescales. During winter months, the inorganic aerosol composition at the site is dominated by nitrate aerosol (Jefferson et al, 2017;Mahish et al, 2018), and hygroscopicity derived from scattering measurements is largest during those months (Jefferson et al, 2017).…”

“…Examples include the characterization of the evolution of the aerosol mixing state as a function of time, characterization of changes in the growth factor with the dry diameter and its relationship to chemical composition, or characterization of the growth factor at the mode diameter of particles during modal growth events (Park et al, 2008;Wu et al, 2013;Jung and Kawamura, 2014). Additional examples include the decomposition of the hygroscopicity frequency distributions into distinct growth factor classes (Swietlicki et al, 2008), evaluation of the temporal trends of spectral concentration for hygroscopicity-resolved data (Royalty et al, 2017), evaluation of the accuracy of (organic) mass concentration measured by aerosol mass spectrometers through hygroscopicity constraints (Jimenez et al, 2016), and inclusion of growth factor frequency distributions to account for the mixing state in aerosol hygroscopicity to cloud condensation nuclei closure (Mahish et al, 2018).…”

Manuscript Review

“…The authors do not provide any information on how their study is different from the published ones, do not clearly state their objectives taking into account the already existing knowledge, and, therefore, fail to convince me that the presented study is new or important. It has long been known that aerosol mixing state plays a minor role in determining the ambient CCN and, even more so, cloud droplet number concentration CDNC, especially so in non-pristine regions (Moore et al, 2013). The effects of the total particle number and the size distribution are of much higher importance than the particle hygroscopicity or the mixing state (e.g.…”