资源环境科技发展态势分析平台

Abstract. Understanding aerosol particle activation is essential for evaluating aerosol indirect effects (AIEs) on climate. Long-term measurements on aerosol particle activation help to understand the AIEs and narrow down the uncertainties of AIEs simulation; however, they are still scarce. In this study, more than 4-year aerosol comprehensive measurements were utilized at the central European research station Melpitz, Germany, to gain insight into the aerosol particle activation and provide recommendations on improving prediction. The overall characteristics of aerosol particle activation at Melpitz are first summarized. For supersaturation (SS) levels of 0.1 %, 0.2 %, 0.3 %, 0.5 %, and 0.7 %, the mean cloud condensation nuclei (CCN) number concentration (N_CCN) increases with the increase of SS from 513 to 2477 cm^-3, which represents 11 % to 52 % of the total particle number concentration with diameter ranging from 10 to 800 nm, while the hygroscopicity factor (k) and the critical diameter (D_c) decrease from 0.28±0.08 (mean value ± one standard deviation) to 0.20±0.09 and from 177±19 to 54±8 nm, respectively. Aerosol particle activation is highly variable across seasons, especially at low SS conditions. At SS = 0.1 %, the seasonal mean N_CCN is 681 cm^-3 in winter, which is almost twice higher than the summer value (347 cm^-3); the seasonal mean activation ratio (AR) in winter (0.18) is three times higher than the summer one. Subsequently, size dependency of both k and the state of mixing were investigated. As the particle diameter (D_p) increases, k increases at D_p of ~40 to 100 nm and almost stays constant at D_p of 100 to 200 nm, whereas the degree of the external mixture keeps decreasing at D_p of ~40 to 200 nm. The relationships of k vs. D_p and mixture degree vs. D_p were both fitted well by the power-law function for each season. Finally, we recommend applying the k - D_p power-law fit for N_CCN prediction, which can narrow down the median uncertainty within 10 % for different SS conditions and seasons at Melpitz; it also could be applied to predict N_CCN at other rural and continental regions with a similar aerosol background. Additionally, the mean k value over D_p of 100 to 200 nm also works well on the N_CCN prediction when SS is less than 0.2 %.