Aerosol activation characteristics and prediction at the central European ACTRIS research station of Melpitz, Germany

Understanding aerosol particle activation is essential for evaluating aerosol indirect effects (AIEs) on climate. Long-term measurements of 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 years of comprehensive aerosol measurements were utilized at the central European research station of Melpitz, Germany, to gain insight into the aerosol particle activation and provide recommendations on improving the prediction of number concentration of cloud condensation nuclei (CCN, N-CCN). (1) The overall CCN activation characteristics at Melpitz are provided. As supersaturation (SS) increases from 0.1 % to 0.7 %, the median NCCN increases from 399 to 2144 cm(-3), which represents 10 % to 48 % of the total particle number concentration with a diameter range of 10-800 nm, while the median hygroscopicity factor (kappa) and critical diameter (D-c) decrease from 0.27 to 0.19 and from 176 to 54 nm, respectively. (2) Aerosol particle activation is highly variable across seasons, especially at low-SS conditions. At SS = 0.1 %, the median NCCN and activation ratio (AR) in winter are 1.6 and 2.3 times higher than the summer values, respectively. (3) Both kappa and the mixing state are size-dependent. As the particle diameter (D-p) increases, kappa increases at D-p of similar to 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 similar to 40 to 200 nm. The relationships of kappa vs. D-p and degree of mixing vs. Dp were both fitted well by a power-law function. (4) Size-resolved kappa improves the N-CCN prediction. We recommend applying the kappa-D-p power-law fit for N-CCN prediction at Melpitz, which performs better than using the constant kappa of 0.3 and the kappa derived from particle chemical compositions and much better than using the N-CCN (AR) vs. SS relationships. The kappa-D-p power-law fit measured at Melpitz could be applied to predict N-CCN for other rural regions. For the purpose of improving the prediction of N-CCN, longterm monodisperse CCN measurements are still needed to obtain the kappa-D-p relationships for different regions and their seasonal variations.