Challenges of parameterizing CCN due to changes in particle physicochemical properties: implications from observations at a suburban site in China

Abstract

This study is concerned with the challenges of parameterizing cloud condensation nuclei (CCN) when changes in particle physicochemical properties occur, based on field measurements made at two distinct locations in China. The CCN nucleation efficiency of aerosols produced by local biomass burning was low. This is because the particles were freshly emitted with low oxidation level organics and thus are less hygroscopic. The CCN activation efficiency was enhanced significantly when the site was under the influence of air transported from far away, during which aerosol properties changed with more hygroscopic secondary organic and inorganic components. The influence of the variation in particle number size distribution (PSD) on estimating CCN number concentrations ( N CCN) was examined, showing poor correlation (slope = 0.8, R 2 = 0.35) of predicted and measured N CCN. While the PSD is found to play a dominant role in predicting ( N CCN), a strong dependence of N CCN on the mass fraction of organics ( x org) was also noted. N CCN was underestimated by 52 and 13 % at supersaturation levels of 0.13 and 0.76 %, respectively, when x org = 66 %. N CCN was slightly overestimated, or in good agreement, with observations when x org was reduced to 35 % ( R 2 = 0.94). The applicability of the CCN activation spectrum obtained at Xinzhou to the Xianghe site, about 400 km to the northeast of Xinzhou, was investigated, with the goal of further examining the sensitivity of CCN to aerosol type. Overall, the mean CCN efficiency spectrum derived from Xinzhou performs well at Xianghe when the supersaturation levels are > 0.2 % (overestimation of 2–4 %). However, N CCN was overestimated by ∼ 20 % at supersaturation levels of < 0.1 %. This suggests that the overestimation is mainly due to the smaller proportion of aged and oxidized organic aerosols present at Xianghe compared with Xinzhou.