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| Titel |
Inverse modelling of cloud-aerosol interactions – Part 2: Sensitivity tests on liquid phase clouds using a Markov chain Monte Carlo based simulation approach |
| VerfasserIn |
D. G. Partridge, J. A. Vrugt, P. Tunved, A. M. L. Ekman, H. Struthers, A. Sorooshian |
| 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 ; 12, no. 6 ; Nr. 12, no. 6 (2012-03-16), S.2823-2847 |
| Datensatznummer |
250010939
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| Publikation (Nr.) |
 copernicus.org/acp-12-2823-2012.pdf |
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| Zusammenfassung |
This paper presents a novel approach to investigate cloud-aerosol
interactions by coupling a Markov chain Monte Carlo (MCMC) algorithm to an
adiabatic cloud parcel model. Despite the number of numerical cloud-aerosol
sensitivity studies previously conducted few have used statistical analysis
tools to investigate the global sensitivity of a cloud model to input
aerosol physiochemical parameters. Using numerically generated cloud droplet
number concentration (CDNC) distributions (i.e. synthetic data) as cloud
observations, this inverse modelling framework is shown to successfully
estimate the correct calibration parameters, and their underlying posterior
probability distribution.
The employed analysis method provides a new, integrative framework to
evaluate the global sensitivity of the derived CDNC distribution to the
input parameters describing the lognormal properties of the accumulation
mode aerosol and the particle chemistry. To a large extent, results from
prior studies are confirmed, but the present study also provides some
additional insights. There is a transition in relative sensitivity from very
clean marine Arctic conditions where the lognormal aerosol parameters
representing the accumulation mode aerosol number concentration and mean
radius and are found to be most important for determining the CDNC
distribution to very polluted continental environments (aerosol
concentration in the accumulation mode >1000 cm−3) where particle
chemistry is more important than both number concentration and size of the
accumulation mode.
The competition and compensation between the cloud model input parameters
illustrates that if the soluble mass fraction is reduced, the aerosol number
concentration, geometric standard deviation and mean radius of the
accumulation mode must increase in order to achieve the same CDNC
distribution.
This study demonstrates that inverse modelling provides a flexible,
transparent and integrative method for efficiently exploring cloud-aerosol
interactions with respect to parameter sensitivity and
correlation. |
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