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
Item
Title (Dublin Core)
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
Description (Dublin Core)
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.<br><br>The CCN measurements were continuously cycled through 10 levels of supersaturation (<i>S</i> = 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 <i>S</i> = 1.10 % to 172 nm at <i>S</i> = 0.11 %. The particle hygroscopicity exhibits a pronounced size dependence with lower values for the Aitken mode (<i>κ</i><sub>Ait</sub> = 0.14 ± 0.03), higher values for the accumulation mode (<i>κ</i><sub>Acc</sub> = 0.22 ± 0.05), and an overall mean value of <i>κ</i><sub>mean</sub> = 0.17 ± 0.06, consistent with high fractions of organic aerosol.<br><br>The hygroscopicity parameter, <i>κ</i>, 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.<br><br>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.
Creator (Dublin Core)
Pöhlker, Mira L.
Pöhlker, Christopher
Ditas, Florian
Klimach, Thomas
Hrabe de Angelis, Isabella
Araújo, Alessandro
Brito, Joel
Carbone, Samara
Cheng, Yafang
Chi, Xuguang
Ditz, Reiner
Gunthe, Sachin S.
Kesselmeier, Jürgen
Könemann, Tobias
Lavrič, Jošt V.
Martin, Scot T.
Mikhailov, Eugene
Moran-Zuloaga, Daniel
Rose, Diana
Saturno, Jorge
Su, Hang
Thalman, Ryan
Walter, David
Wang, Jian
Wolff, Stefan
Barbosa, Henrique M. J.
Artaxo, Paulo
Andreae, Meinrat O.
Pöschl, Ulrich
Date (Dublin Core)
2018-09-09
Type (Dublin Core)
Text
Format (Dublin Core)
application/pdf
Identifier (Dublin Core)
10.5194/acp-16-15709-2016
https://acp.copernicus.org/articles/16/15709/2016/
Source (Dublin Core)
eISSN: 1680-7324
Language (Dublin Core)
eng
