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
Authors: Pöhlker Mira L. , Ditas Florian , Saturno Jorge , Klimach Thomas , Hrabě de Angelis Isabella , Araùjo Alessandro C. , Brito Joel , Carbone Samara , Cheng Yafang , Chi Xuguang , Ditz Reiner , Gunthe Sachin S. , Holanda Bruna A. , Kandler Konrad , Kesselmeier Jürgen , Könemann Tobias , Krüger Ovid O. , Lavrič 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 
Published: July 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.