Atmospheric Chemistry and Physics
Long-term observations of cloud condensation nuclei over the Amazon rain forest – Part 2: Variability and characteristics of biomass burning, long-range transport, and pristine rain forest aerosols
Mira L. Pöhlker, Florian Ditas, Jorge Saturno, Thomas Klimach, Isabella Hrabě de Angelis, Alessandro C. Araùjo, Joel Brito, Samara Carbone, Yafang Cheng, Xuguang Chi, Reiner Ditz, Sachin S. Gunthe, Bruna A. Holanda, Konrad Kandler, Jürgen Kesselmeier, Tobias Könemann, Ovid O. Krüger, Jošt V. Lavrič, Scot T. Martin, Eugene Mikhailov, Daniel Moran-Zuloaga, Luciana V. Rizzo, Diana Rose, Hang Su, Ryan Thalman, David Walter, Jian Wang, Stefan Wolff, Henrique M. J. Barbosa, Paulo Artaxo, Meinrat O. Andreae, Ulrich Pöschl, Christopher Pöhlker
journal-article
July 19, 2018

Abstract. 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< 100 nm  0.14, κ>100nm 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<150nm 0.10, κ>150nm 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.