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

Abstract. 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, NCCN). (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 (κ) and critical diameter (Dc) 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
κ and the mixing state are size-dependent. As the particle diameter
(Dp) increases, κ increases at Dp of ∼40 to
100 nm and almost stays constant at Dp of 100 to 200 nm, whereas the
degree of the external mixture keeps decreasing at Dp of ∼40 to 200 nm. The relationships of κ vs. Dp and degree of mixing
vs. Dp were both fitted well by a power-law function. (4) Size-resolved
κ improves the NCCN prediction. We recommend applying the
κ–Dp power-law fit for NCCN prediction at Melpitz, which
performs better than using the constant κ of 0.3 and the κ
derived from particle chemical compositions and much better than using the
NCCN (AR) vs. SS relationships. The κ–Dp power-law fit measured
at Melpitz could be applied to predict NCCN for other rural regions. For
the purpose of improving the prediction of NCCN, long-term monodisperse
CCN measurements are still needed to obtain the κ–Dp
relationships for different regions and their seasonal variations.
