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| Titel |
Estimating bacteria emissions from inversion of atmospheric transport: sensitivity to modelled particle characteristics |
| VerfasserIn |
S. M. Burrows, P. J. Rayner, T. Butler, M. G. Lawrence |
| 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 ; 13, no. 11 ; Nr. 13, no. 11 (2013-06-04), S.5473-5488 |
| Datensatznummer |
250018683
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| Publikation (Nr.) |
 copernicus.org/acp-13-5473-2013.pdf |
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| Zusammenfassung |
Model-simulated transport of atmospheric trace components can be
combined with observed concentrations to obtain estimates of
ground-based sources using various inversion techniques. These
approaches have been applied in the past primarily to obtain source
estimates for long-lived trace gases such as CO2. We
consider the application of similar techniques to source estimation
for atmospheric aerosols, using as a case study the estimation of
bacteria emissions from different ecosystem regions in the global
atmospheric chemistry and climate model ECHAM5/MESSy-Atmospheric
Chemistry (EMAC).
Source estimation via Markov Chain Monte Carlo is applied to a suite
of sensitivity simulations, and the global mean emissions are
estimated for the example problem of bacteria-containing aerosol
particles. We present an analysis of the uncertainties in the
global mean emissions, and a partitioning of the uncertainties that
are attributable to particle size, activity as cloud condensation
nuclei (CCN), the ice nucleation scavenging ratios for mixed-phase
and cold clouds, and measurement error.
For this example, uncertainty due to CCN activity or to
a 1 μm error in particle size is typically between
10% and 40% of the uncertainty due to observation
uncertainty, as measured by the 5–95th percentile range of
the Monte Carlo ensemble. Uncertainty attributable to the ice
nucleation scavenging ratio in mixed-phase clouds is as high as
10–20% of that attributable to observation
uncertainty. Taken together, the four model parameters examined
contribute about half as much to the uncertainty in the estimated
emissions as do the observations. This was a surprisingly large
contribution from model uncertainty in light of the substantial
observation uncertainty, which ranges from 81–870% of
the mean for each of ten ecosystems for this case study. The
effects of these and other model parameters in contributing to the
uncertainties in the transport of atmospheric aerosol particles
should be treated explicitly and systematically in both forward and
inverse modelling studies. |
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