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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

MPS-Authors
/persons/resource/persons203102

Pöhlker,  M. L.
Multiphase Chemistry, Max Planck Institute for Chemistry, Max Planck Society;

/persons/resource/persons104597

Pöhlker,  C.
Multiphase Chemistry, Max Planck Institute for Chemistry, Max Planck Society;

/persons/resource/persons187726

Ditas,  F.
Biogeochemistry, Max Planck Institute for Chemistry, Max Planck Society;

/persons/resource/persons194739

Hrabe de Angelis,  I.
Biogeochemistry, Max Planck Institute for Chemistry, Max Planck Society;

/persons/resource/persons127588

Cheng,  Yafang
Multiphase Chemistry, Max Planck Institute for Chemistry, Max Planck Society;

/persons/resource/persons145117

Chi,  X.
Biogeochemistry, Max Planck Institute for Chemistry, Max Planck Society;

/persons/resource/persons100911

Ditz,  R.
Biogeochemistry, Max Planck Institute for Chemistry, Max Planck Society;

/persons/resource/persons100971

Gunthe,  S. S.
Biogeochemistry, Max Planck Institute for Chemistry, Max Planck Society;

/persons/resource/persons101057

Kesselmeier,  J.
Biogeochemistry, Max Planck Institute for Chemistry, Max Planck Society;

/persons/resource/persons203107

Könemann,  T.
Multiphase Chemistry, Max Planck Institute for Chemistry, Max Planck Society;

/persons/resource/persons194737

Moran-Zuloaga,  D.
Biogeochemistry, Max Planck Institute for Chemistry, Max Planck Society;

/persons/resource/persons140322

Saturno,  J.
Biogeochemistry, Max Planck Institute for Chemistry, Max Planck Society;

/persons/resource/persons101295

Su,  H.
Multiphase Chemistry, Max Planck Institute for Chemistry, Max Planck Society;

/persons/resource/persons140184

Walter,  D.
Atmospheric Chemistry, Max Planck Institute for Chemistry, Max Planck Society;

/persons/resource/persons100833

Andreae,  M. O.
Biogeochemistry, Max Planck Institute for Chemistry, Max Planck Society;

/persons/resource/persons101189

Pöschl,  U.
Multiphase Chemistry, Max Planck Institute for Chemistry, Max Planck Society;

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Citation

Pöhlker, M. L., Pöhlker, C., Ditas, F., Klimach, T., Hrabe de Angelis, I., Araujo, A., et al. (2016). 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. Atmospheric Chemistry and Physics, 16(24), 15709-15740. doi:10.5194/acp-16-15709-2016.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002C-89B2-0
Abstract
Size-resolved long-term measurements of atmospheric aerosol and cloud condensation nuclei (CCN) concentrations and hygroscopicity were conducted at the remote Amazon Tall Tower Observatory (ATTO) in the central Amazon Basin over a 1-year period and full seasonal cycle (March 2014-February 2015). The measurements provide a climatology of CCN properties characteristic of a remote 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 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 (kappa(Ait) = 0.14 +/- 0.03), higher values for the accumulation mode (kappa(Acc) = 0.22 +/- 0.05), and an overall mean value of kappa(mean) = 0.17 +/- 0.06, consistent with high fractions of organic aerosol. The hygroscopicity parameter, kappa, exhibits remarkably little temporal variability: no pronounced diurnal cycles, only 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 modeling purposes, we compare different approaches of predicting CCN number concentration and present a novel parametrization, which allows accurate CCN predictions based on a small set of input data.