Open Access

Mira L. Pöhlker, Christopher Pöhlker, Thomas Klimach, Isabella Hrabe de Angelis, Henrique M. J. Barbosa, Joel Brito, Samara Carbone, Yafang Cheng, Xuguang Chi, Florian Ditas, Reiner Ditz, Sachin S. Gunthe, Jürgen Kesselmeier, Tobias Könemann, Jost-Valentin Lavrič, Scot Turnbull Martin, Daniel Moran-Zuloaga, Diana Rose, Jorge Saturno, Hang Su, Ryan Thalman, David Walter, Jian Wang, Stefan Wolff, Paulo Artaxo, Meinrat O. Andreae, Ulrich Pöschl

• Size-resolved long-term measurements of atmospheric aerosol and cloud condensation nuclei (CCN) concentrations as well as hygroscopicity were conducted at the remote Amazon Tall Tower Observatory (ATTO) in the central Amazon Basin over a one-year period and full seasonal cycle (March 2014 - February 2015). The presented measurements provide a climatology of CCN properties for a characteristic central Amazonian rain forest site. The CCN measurements were continuously cycled through 10 levels of supersaturation (S = 0.11 to 1.10 %) and span the aerosol particle size range from 20 to 245 nm. The observed mean critical diameters of CCN activation range from 43 nm at S = 1.10 % to 172 nm at S = 0.11 %. The particle hygroscopicity exhibits a pronounced size dependence with lower values for the Aitken mode (κAit = 0.14 ± 0.03), elevated values for the accumulation mode (κAcc = 0.22 ± 0.05), and an overall mean value of κmean = 0.17 ± 0.06, consistent with high fractions of organic aerosol. The hygroscopicity parameter κ exhibits remarkably little temporal variability: no pronounced diurnal cycles, weak seasonal trends, and few short-term variations during long-range transport events. In contrast, the CCN number concentrations exhibit a pronounced seasonal cycle, tracking the pollution-related seasonality in total aerosol concentration. We find that the variability in the CCN concentrations in the central Amazon is mostly driven by aerosol particle number concentration and size distribution, while variations in aerosol hygroscopicity and chemical composition matter only during a few episodes. For modelling purposes, we compare different approaches of predicting CCN number concentration and present a novel parameterization, which allows accurate CCN predictions based on a small set of input data.