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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
- Source :
- Atmospheric Chemistry and Physics, Vol 18, Pp 10289-10331 (2018), Atmospheric Chemistry and Physics, Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA-Alice), Empresa Brasileira de Pesquisa Agropecuária (Embrapa), instacron:EMBRAPA, Atmospheric Chemistry and Physics, European Geosciences Union, 2018, 18 (14), pp.10289-10331. ⟨10.5194/acp-18-10289-2018⟩, Atmospheric Chemistry and Physics, 2018, 18 (14), pp.10289-10331. ⟨10.5194/acp-18-10289-2018⟩
- Publication Year :
- 2018
- Publisher :
- Copernicus GmbH, 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 ≈ 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.
- Subjects :
- Atmospheric Science
Materials science
010504 meteorology & atmospheric sciences
[SDE.MCG]Environmental Sciences/Global Changes
Biomassa
Mineral dust
010502 geochemistry & geophysics
Atmospheric sciences
01 natural sciences
lcsh:Chemistry
chemistry.chemical_compound
Floresta Tropical
Cloud condensation nuclei
Sulfate
Chemical composition
0105 earth and related environmental sciences
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere
[PHYS.PHYS.PHYS-AO-PH]Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph]
Supersaturation
15. Life on land
Sea spray
lcsh:QC1-999
Aerosol
lcsh:QD1-999
chemistry
13. Climate action
lcsh:Physics
Water vapor
Subjects
Details
- ISSN :
- 16807324 and 16807316
- Volume :
- 18
- Database :
- OpenAIRE
- Journal :
- Atmospheric Chemistry and Physics
- Accession number :
- edsair.doi.dedup.....edda95dbb6b12d0d6fa87b9b236454a7
- Full Text :
- https://doi.org/10.5194/acp-18-10289-2018