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Long-term observations of cloud condensation nuclei in the Amazon rain forest – Part 2: Variability and characteristic differences under near-pristine, biomass burning, and long-range transport conditions parameterizations for CCN prediction

MPS-Authors
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Pöhlker,  M. L.
Multiphase Chemistry, Max Planck Institute for Chemistry, Max Planck Society;

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Pöhlker,  C.
Multiphase Chemistry, Max Planck Institute for Chemistry, Max Planck Society;

/persons/resource/persons187726

Ditas,  F.
Biogeochemistry, Max Planck Institute for Chemistry, Max Planck Society;

/persons/resource/persons101066

Klimach,  T.
Particle Chemistry, Max Planck Institute for Chemistry, Max Planck Society;

/persons/resource/persons194739

Hrabe de Angelis,  I.
Biogeochemistry, Max Planck Institute for Chemistry, Max Planck Society;

/persons/resource/persons127588

Cheng,  Yafang
Multiphase Chemistry, Max Planck Institute for Chemistry, Max Planck Society;

/persons/resource/persons145117

Chi,  X.
Biogeochemistry, Max Planck Institute for Chemistry, Max Planck Society;

/persons/resource/persons100911

Ditz,  R.
Biogeochemistry, Max Planck Institute for Chemistry, Max Planck Society;

/persons/resource/persons100971

Gunthe,  S. S.
Biogeochemistry, Max Planck Institute for Chemistry, Max Planck Society;

/persons/resource/persons101057

Kesselmeier,  J.
Biogeochemistry, Max Planck Institute for Chemistry, Max Planck Society;

/persons/resource/persons203107

Könemann,  T.
Multiphase Chemistry, Max Planck Institute for Chemistry, Max Planck Society;

/persons/resource/persons194737

Moran-Zuloaga,  D.
Biogeochemistry, Max Planck Institute for Chemistry, Max Planck Society;

/persons/resource/persons140322

Saturno,  J.
Biogeochemistry, Max Planck Institute for Chemistry, Max Planck Society;

/persons/resource/persons101295

Su,  H.
Multiphase Chemistry, Max Planck Institute for Chemistry, Max Planck Society;

/persons/resource/persons140184

Walter,  D.
Atmospheric Chemistry, Max Planck Institute for Chemistry, Max Planck Society;

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Andreae,  M. O.
Biogeochemistry, Max Planck Institute for Chemistry, Max Planck Society;

/persons/resource/persons101189

Pöschl,  U.
Multiphase Chemistry, Max Planck Institute for Chemistry, Max Planck Society;

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Citation

Pöhlker, M. L., Pöhlker, C., Ditas, F., Klimach, T., Hrabe de Angelis, I., Araujo, A., et al. (2017). Long-term observations of cloud condensation nuclei in the Amazon rain forest – Part 2: Variability and characteristic differences under near-pristine, biomass burning, and long-range transport conditions parameterizations for CCN prediction. Atmospheric Chemistry and Physics Discussions, 17. doi:10.5194/acp-2017-847.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002E-032F-7
Abstract
Size-resolved measurements of atmospheric aerosol and cloud condensation nuclei (CCN) concentrations and hygroscopicity were conducted at the remote Amazon Tall Tower Observatory (ATTO) in the central Amazon Basin over a full seasonal cycle (Mar 2014–Feb 2015). In a companion part 1 paper, we presented an in-depth CCN characterization based on annually as well as seasonally averaged time intervals and discuss different parametrization strategies to represent the Amazonian CCN cycling in modelling studies (M. Pöhlker et al., 2016b). The present part 2 study analyzes the aerosol and CCN variability in original time resolution and, thus, resolves aerosol advection and transformation for the following case studies, which represent the most characteristic states of the Amazonian atmosphere: 1. Near-pristine (NP) conditions, defined as the absence of detectable black carbon (< 0.01 µg m−3), showed their highest occurrence (up to 30 %) in the wet season (i.e., Mar–May). On average, the NP episodes are characterized by a bimodal aerosol size distribution (strong Aitken mode: DAit = 70 nm, NAit = ~ 200 cm−3 vs. weaker accumulation mode: Dacc = 170 nm, Nacc = ~ 60 cm−3), a mostly organic particle composition, and relatively low hygroscopicity levels (κAit = 0.12 vs. κacc = 0.18). The NP CCN efficiency spectrum shows that the CCN population is sensitive to changes in supersaturation (S) over a wide S range. 2. Long-range transport (LRT) conditions frequently mix Saharan dust, African combustion smoke, and sea spray aerosols into the Amazonian wet season atmosphere. The LRT episodes (i.e., Feb–Apr) are characterized by an accumulation mode dominated size distribution (DAit = 80 nm, NAit = 120 cm−3 vs. Dacc = 180 nm, Nacc = 300 cm−3), a clearly increased abundance of dust and salt compounds, and relatively high hygroscopicity levels (κAit = 0.18, κacc = 0.34). The LRT CCN efficiency spectrum shows that the CCN population is highly sensitive to changes in S in the low S regime. 3. Biomass burning (BB) conditions dominate the Amazonian dry season. A selected characteristic BB episode shows a very strong accumulation mode (DAit = 70 nm, NAit = ~ 140 cm−3 vs. Dacc = 170 nm, Nacc = ~ 3400 cm−3), particles with very high organic fractions (> 90 %), and correspondingly low hygroscopicity levels (κAit = 0.14, κacc = 0.17). The BB CCN efficiency spectrum shows that the CCN population is highly sensitive to changes in S in the low S regime. 4. Mixed pollution conditions show the superposition of African (i.e., volcanic) and Amazonian (i.e., biomass burning) aerosol emissions during the dry season. The African aerosols showed a broad monomodal distribution (D = 130 nm, N = ~ 1300 cm−3), with very high sulfate fractions (20 %), and correspondingly high hygroscopicity (κAit = 0.14, κacc = 0.22). This was superimposed by fresh smoke from nearby fires with one strong mode (D = 113 nm, Nacc = ~ 2800 cm−3), an organic-dominated aerosol, and sharply decreased hygroscopicity (κAit = 0.10, κacc = 0.20). These conditions underline the rapidly changing pollution regimes with clear impacts on the aerosol and CCN properties. Overall, this study provides detailed insights into the CCN cycling in relation to aerosol-cloud interaction in the vulnerable and climate-relevant Amazon region. The detailed analysis of aerosol and CCN key properties and particularly the extracted CCN efficiency spectra with the associated fit parameters provide a basis for an in-depth analysis of aerosol-cloud interaction in the Amazon and beyond.