Articles | Volume 16, issue 24
Atmos. Chem. Phys., 16, 15709–15740, 2016

Special issue: Amazon Tall Tower Observatory (ATTO) Special Issue

Special issue: Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5)...

Atmos. Chem. Phys., 16, 15709–15740, 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. Pöhlker1, Christopher Pöhlker1, Florian Ditas1, Thomas Klimach1, Isabella Hrabe de Angelis1, Alessandro Araújo2, Joel Brito3,a, Samara Carbone3,b, Yafang Cheng1, Xuguang Chi1,c, Reiner Ditz1, Sachin S. Gunthe4, Jürgen Kesselmeier1, Tobias Könemann1, Jošt V. Lavrič5, Scot T. Martin6, Eugene Mikhailov7, Daniel Moran-Zuloaga1, Diana Rose8, Jorge Saturno1, Hang Su1, Ryan Thalman9,d, David Walter1, Jian Wang9, Stefan Wolff1,10, Henrique M. J. Barbosa3, Paulo Artaxo3, Meinrat O. Andreae1,11, and Ulrich Pöschl1 Mira L. Pöhlker et al.
  • 1Multiphase Chemistry and Biogeochemistry Departments, Max Planck Institute for Chemistry, 55020 Mainz, Germany
  • 2Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA), Trav. Dr. Enéas Pinheiro, Belém, PA, 66095-100, Brazil
  • 3Institute of Physics, University of São Paulo, São Paulo, 05508-900, Brazil
  • 4EWRE Division, Department of Civil Engineering, Indian Institute of Technology Madras, Chennai 600036, India
  • 5Department of Biogeochemical Systems, Max Planck Institute for Biogeochemistry, 07701 Jena, Germany
  • 6School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
  • 7St. Petersburg State University, 7/9 Universitetskaya nab, St. Petersburg, 199034, Russia
  • 8Institute for Atmospheric and Environmental Research, Goethe University Frankfurt am Main, 60438 Frankfurt, Germany
  • 9Biological, Environmental & Climate Sciences Department, Brookhaven National Laboratory, Upton, NY 11973, USA
  • 10Instituto Nacional de Pesquisas da Amazonia (INPA), Manaus, 69083-000, Brazil
  • 11Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA 92037, USA
  • anow at: Laboratory for Meteorological Physics, University Blaise Pascal, Clermont-Ferrand, France
  • bnow at: Federal University of Uberlândia, Uberlândia-MG, 38408-100, Brazil
  • cnow at: Institute for Climate and Global Change Research & School of Atmospheric Sciences, Nanjing University, Nanjing, 210093, China
  • dnow at: Department of Chemistry, Snow College, Richfield, UT 84701, USA

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 (κAit  =  0.14 ± 0.03), higher 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, 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.

Short summary
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.
Final-revised paper