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Size-resolved long-term measurements of atmospheric aerosol and\udcloud condensation nuclei (CCN) concentrations and hygroscopicity were\udconducted at the remote Amazon Tall Tower Observatory (ATTO) in the central\udAmazon Basin over a 1-year period and full seasonal cycle (March 2014–February 2015). The measurements provide a climatology of CCN properties\udcharacteristic of a remote central Amazonian rain forest site. The CCN measurements were continuously cycled through 10 levels of\udsupersaturation (S  =  0.11 to 1.10 %) and span the aerosol\udparticle size range from 20 to 245 nm. The mean critical diameters of CCN\udactivation range from 43 nm at S  =  1.10 % to 172 nm at\udS  =  0.11 %. The particle hygroscopicity exhibits a pronounced\udsize dependence with lower values for the Aitken mode (κAit  =  0.14 ± 0.03), higher values for the accumulation mode\ud(κAcc  =  0.22 ± 0.05), and an overall mean\udvalue of κmean  =  0.17 ± 0.06, consistent with\udhigh fractions of organic aerosol. The hygroscopicity parameter, κ, exhibits remarkably little\udtemporal variability: no pronounced diurnal cycles, only weak seasonal\udtrends, and few short-term variations during long-range transport events. In\udcontrast, the CCN number concentrations exhibit a pronounced seasonal cycle,\udtracking the pollution-related seasonality in total aerosol concentration.\udWe find that the variability in the CCN concentrations in the central Amazon\udis mostly driven by aerosol particle number concentration and size\uddistribution, while variations in aerosol hygroscopicity and chemical\udcomposition matter only during a few episodes. For modeling purposes, we compare different approaches of predicting CCN\udnumber concentration and present a novel parametrization, which allows\udaccurate CCN predictions based on a small set of input data

atmospheric chemistry and physics

Long-term observations of cloud condensation nuclei in the Amazon rain forest – Part 1: Aerosol size distribution, hygroscopicity, and new model parametrizations for CCN prediction