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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
Date Issued
19-07-2018
Author(s)
Pöhlker, Mira L.
Ditas, Florian
Saturno, Jorge
Klimach, Thomas
HrabÄ› De Angelis, Isabella
Araùjo, Alessandro C.
Brito, Joel
Carbone, Samara
Cheng, Yafang
Chi, Xuguang
Ditz, Reiner
Indian Institute of Technology, Madras
Holanda, Bruna A.
Kandler, Konrad
Kesselmeier, Jürgen
Könemann, Tobias
Krüger, Ovid O.
Lavric, Jošt V.
Martin, Scot T.
Mikhailov, Eugene
Moran-Zuloaga, Daniel
Rizzo, Luciana V.
Rose, Diana
Su, Hang
Thalman, Ryan
Walter, David
Wang, Jian
Wolff, Stefan
Barbosa, Henrique M.J.
Artaxo, Paulo
Andreae, Meinrat O.
Pöschl, Ulrich
Pöhlker, Christopher
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 ≈ 70nm and NAit ≈ 160cm-3, weak accumulation mode with Dacc ≈ 160nm and Nacc ≈ 90cm-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 ≈ 80nm, NAit ≈ 120cm-3 vs. Dacc ≈ 180nm, Nacc ≈ 310cm-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 ≈ 70nm, NAit ≈ 140cm-3 vs. Dacc ≈ 170nm, Nacc ≈ 3400cm-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 ≈ 130nm, NCN, 10 ≈ 1300cm-3), with high sulfate mass fractions (∼ 20%) from volcanic sources and correspondingly high hygroscopicity (Κ < 100 nm ≈ 0.14, Κ > 100 nm ≈ 0.22), which were periodically mixed with fresh smoke from nearby fires (D ≈ 110nm, NCN, 10 ≈ 2800cm-3) with an organic-dominated composition and sharply decreased hygroscopicity (Κ < 150 nm ≈ 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. .
Volume
18