Articles | Volume 16, issue 24
https://doi.org/10.5194/acp-16-15709-2016
https://doi.org/10.5194/acp-16-15709-2016
Research article
 | 
20 Dec 2016
Research article |  | 20 Dec 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

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

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Cited articles

Almeida, G. P., Borrmann, S., and Leal Junior, J. B. V.: Cloud condensation nuclei (CCN) concentration in the Brazilian northeast semi-arid region: the influence of local circulation, Meteorol. Atmos. Phys., 125, 159–176, https://doi.org/10.1007/s00703-014-0329-1, 2014.
Andreae, M. O.: Aerosols before pollution, Science, 315, 50–51, https://doi.org/10.1126/science.1136529, 2007.
Andreae, M. O.: Correlation between cloud condensation nuclei concentration and aerosol optical thickness in remote and polluted regions, Atmos. Chem. Phys., 9, 543–556, https://doi.org/10.5194/acp-9-543-2009, 2009.
Andreae, M. O. and Rosenfeld, D.: Aerosol-cloud-precipitation interactions. Part 1. The nature and sources of cloud-active aerosols, Earth-Sci. Rev., 89, 13–41, https://doi.org/10.1016/j.earscirev.2008.03.001, 2008.
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The paper presents a systematic characterization of cloud condensation nuclei (CCN) concentration in the central Amazonian atmosphere. Our results show that the CCN population in this globally important ecosystem follows a pollution-related seasonal cycle, in which it mainly depends on changes in total aerosol size distribution and to a minor extent in the aerosol chemical composition. Our results allow an efficient modeling and prediction of the CCN population based on a novel approach.
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