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| Titel |
Cloud condensation nuclei in pristine tropical rainforest air of Amazonia: size-resolved measurements and modeling of atmospheric aerosol composition and CCN activity |
| VerfasserIn |
S. S. Gunthe, S. M. King, D. Rose, Q. Chen, P. Roldin, D. K. Farmer, J. L. Jimenez, P. Artaxo, M. O. Andreae, S. T. Martin, U. Pöschl |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7316
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 9, no. 19 ; Nr. 9, no. 19 (2009-10-09), S.7551-7575 |
| Datensatznummer |
250007678
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| Publikation (Nr.) |
 copernicus.org/acp-9-7551-2009.pdf |
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| Zusammenfassung |
Atmospheric aerosol particles serving as cloud condensation nuclei (CCN) are
key elements of the hydrological cycle and climate. We have measured and
characterized CCN at water vapor supersaturations in the range of S=0.10–0.82%
in pristine tropical rainforest air during the AMAZE-08 campaign in central Amazonia.
The effective hygroscopicity parameters describing the influence of chemical
composition on the CCN activity of aerosol particles varied in the range of
κ≈0.1–0.4 (0.16±0.06 arithmetic mean and standard deviation).
The overall median value of κ≈0.15 was by a factor of two lower
than the values typically observed for continental aerosols in other regions
of the world. Aitken mode particles were less hygroscopic than accumulation
mode particles (κ≈0.1 at D≈50 nm; κ≈0.2 at
D≈200 nm), which is in agreement with earlier hygroscopicity tandem
differential mobility analyzer (H-TDMA) studies.
The CCN measurement results are consistent with aerosol mass spectrometry
(AMS) data, showing that the organic mass fraction (forg) was
on average as high as ~90% in the Aitken mode (D≤100 nm) and
decreased with increasing particle diameter in the accumulation mode
(~80% at D≈200 nm). The κ values exhibited a negative linear
correlation with forg (R2=0.81), and extrapolation yielded the
following effective hygroscopicity parameters for organic and inorganic
particle components: κorg≈0.1 which can be regarded as the
effective hygroscopicity of biogenic secondary organic aerosol (SOA) and
κinorg≈0.6 which is characteristic for ammonium sulfate and
related salts. Both the size dependence and the temporal variability of
effective particle hygroscopicity could be parameterized as a function of
AMS-based organic and inorganic mass fractions (κp=κorg×forg
+κinorg×finorg).
The CCN number concentrations
predicted with κp were in fair agreement with the measurement results
(~20% average deviation). The median CCN number concentrations at
S=0.1–0.82% ranged from NCCN,0.10≈35 cm−3 to
NCCN,0.82≈160 cm−3, the median concentration of aerosol
particles larger than 30 nm was NCN,30≈200 cm−3, and the
corresponding integral CCN efficiencies were in the range of
NCCN,0.10/NCN,30≈0.1 to NCCN,0.82/NCN,30≈0.8.
Although the number concentrations and hygroscopicity parameters were much
lower in pristine rainforest air, the integral CCN efficiencies observed
were similar to those in highly polluted megacity air. Moreover, model
calculations of NCCN,S assuming an approximate global average value of
κ≈0.3 for continental aerosols led to systematic overpredictions,
but the average deviations exceeded ~50% only at low water vapor
supersaturation (0.1%) and low particle number concentrations (≤100 cm−3).
Model calculations assuming a constant aerosol size distribution
led to higher average deviations at all investigated levels of
supersaturation: ~60% for the campaign average distribution and
~1600% for a generic remote continental size distribution. These
findings confirm earlier studies suggesting that aerosol particle number and
size are the major predictors for the variability of the CCN concentration
in continental boundary layer air, followed by particle composition and
hygroscopicity as relatively minor modulators.
Depending on the required and applicable level of detail, the information
and parameterizations presented in this paper should enable efficient
description of the CCN properties of pristine tropical rainforest aerosols
of Amazonia in detailed process models as well as in large-scale atmospheric
and climate models. |
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