M. L. Pöhlker, F. Ditas, J. Saturno, T. Klimach, I. Hrabě de Angelis, A. C. Araùjo, J. Brito, S. Carbone, Y. Cheng, X. Chi, R. Ditz, S. S. Gunthe, B. A. Holanda, K. Kandler, J. Kesselmeier, T. Könemann, O. O. Krüger, J. V. Lavrič, S. T. Martin, E. Mikhailov, D. Moran-Zuloaga, L. V. Rizzo, D. Rose, H. Su, R. Thalman, D. Walter, J. Wang, S. Wolff, H. M. J. Barbosa, P. Artaxo, M. O. Andreae, U. Pöschl, C. Pöhlker, Mira L. Pöhlker, Max Planck Institute for Chemistry, Reiner Ditz, Max Planck Institute for Chemistry, Sachin S. Gunthe, Indian Institute of Technology Madras, Bruna A. Holanda, Max Planck Institute for Chemistry, Konrad Kandler, Technische Universität Darmstadt, Ovid O. Krüger, Max Planck Institute for Chemistry, Jost V. Lavric, Max Planck Institute for Biogeochemistry, Scot T. Martin, Harvard University, Eugene Mikhailov, St. Petersburg State University, Daniel Moran-Zuloaga, Max Planck Institute for Chemistry, Luciana V. Rizzo, UNIFESP, Diana Rose, Goethe Universität / Hessian Agency for Nature Conservation, Environment and Geology, Hang Su, Max Planck Institute for Chemistry, Ryan Thalman, Brookhaven National Laboratory / Snow College, David Walter, Max Planck Institute for Chemistry, Jian Wang, Brookhaven National Laboratory, Stefan Wolff, Max Planck Institute for Chemistry, Henrique M. J. Barbosa, USP, Paulo Artaxo, colaborador CPATU, Meinrat O. Andreae, Max Planck Institute for Chemistry / University of California San Diego, Ulrich Pöschl, Max Planck Institute for Chemistry, Christopher Pöhlker, Max Planck Institute for Chemistry., ALESSANDRO CARIOCA DE ARAUJO, CPATU, Florian Ditas, Max Planck Institute for Chemistry, Jorge Saturno, Max Planck Institute for Chemistry, Thomas Klimach, Max Planck Institute for Chemistry, Isabella Hrabe de Angelis, Max Planck Institute for Chemistry, Joel Brito, USP / Université Clermont Auvergne, Samara Carbone, USP / UNIVERSIDADE FEDERAL DE UBERLÂNDIA, Yafang Cheng, Max Planck Institute for Chemistry, Jürgen Kesselmeier, Max Planck Institute for Chemistry, Tobias Könemann, Max Planck Institute for Chemistry, Xuguang Chi, Max Planck Institute for Chemistry / Nanjing University, Biogeochemistry Department [Mainz], Max Planck Institute for Chemistry (MPIC), Max-Planck-Gesellschaft-Max-Planck-Gesellschaft, Embrapa Amazônia Oriental, Centre for Energy and Environment (CERI EE), Ecole nationale supérieure Mines-Télécom Lille Douai (IMT Lille Douai), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Laboratoire de Météorologie Physique (LaMP), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA), Universidade de São Paulo (USP), Max-Planck-Gesellschaft, Nanjing University (NJU), Centre for Energy and Environment (CERI EE - IMT Nord Europe), Ecole nationale supérieure Mines-Télécom Lille Douai (IMT Nord Europe), and Universidade de São Paulo = University of São Paulo (USP)
Size-resolved measurements of atmospheric aerosol and cloud condensation nuclei (CCN) concentrations and hygroscopicity were conducted over a full seasonal cycle at the remote Amazon Tall Tower Observatory (ATTO, March 2014–February 2015). In a preceding companion paper, we presented annually and seasonally averaged data and parametrizations (Part 1; Pöhlker et al., 2016a). In the present study (Part 2), we analyze key features and implications of aerosol and CCN properties for the following characteristic atmospheric conditions:Empirically pristine rain forest (PR) conditions, where no influence of pollution was detectable, as observed during parts of the wet season from March to May. The PR episodes are characterized by a bimodal aerosol size distribution (strong Aitken mode with DAit ≈ 70 nm and NAit ≈ 160 cm−3, weak accumulation mode with Dacc ≈ 160 nm and Nacc ≈ 90 cm−3), a chemical composition dominated by organic compounds, and relatively low particle hygroscopicity (κAit ≈ 0.12, κacc ≈ 0.18).Long-range-transport (LRT) events, which frequently bring Saharan dust, African biomass smoke, and sea spray aerosols into the Amazon Basin, mostly during February to April. The LRT episodes are characterized by a dominant accumulation mode (DAit ≈ 80 nm, NAit ≈ 120 cm−3 vs. Dacc ≈ 180 nm, Nacc ≈ 310 cm−3), an increased abundance of dust and salt, and relatively high hygroscopicity (κAit ≈ 0.18, κacc ≈ 0.35). The coarse mode is also significantly enhanced during these events.Biomass burning (BB) conditions characteristic for the Amazonian dry season from August to November. The BB episodes show a very strong accumulation mode (DAit ≈ 70 nm, NAit ≈ 140 cm−3 vs. Dacc ≈ 170 nm, Nacc ≈ 3400 cm−3), very high organic mass fractions ( ∼ 90 %), and correspondingly low hygroscopicity (κAit ≈ 0.14, κacc ≈ 0.17).Mixed-pollution (MPOL) conditions with a superposition of African and Amazonian aerosol emissions during the dry season. During the MPOL episode presented here as a case study, we observed African aerosols with a broad monomodal distribution (D ≈ 130 nm, NCN, 10 ≈ 1300 cm−3), with high sulfate mass fractions (∼ 20 %) from volcanic sources and correspondingly high hygroscopicity (κ ≈ 0.14, κ > 100 nm ≈ 0.22), which were periodically mixed with fresh smoke from nearby fires (D ≈ 110 nm, NCN, 10 ≈ 2800 cm−3) with an organic-dominated composition and sharply decreased hygroscopicity (κ ≈ 0.10, κ > 150 nm ≈ 0.20).Insights into the aerosol mixing state are provided by particle hygroscopicity (κ) distribution plots, which indicate largely internal mixing for the PR aerosols (narrow κ distribution) and more external mixing for the BB, LRT, and MPOL aerosols (broad κ distributions).The CCN spectra (CCN concentration plotted against water vapor supersaturation) obtained for the different case studies indicate distinctly different regimes of cloud formation and microphysics depending on aerosol properties and meteorological conditions. The measurement results suggest that CCN activation and droplet formation in convective clouds are mostly aerosol-limited under PR and LRT conditions and updraft-limited under BB and MPOL conditions. Normalized CCN efficiency spectra (CCN divided by aerosol number concentration plotted against water vapor supersaturation) and corresponding parameterizations (Gaussian error function fits) provide a basis for further analysis and model studies of aerosol–cloud interactions in the Amazon.