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Titel |
An integrated modeling study on the effects of mineral dust and sea salt particles on clouds and precipitation |
VerfasserIn |
S. Solomos, G. Kallos, J. Kushta, M. Astitha, C. Tremback, A. Nenes, Z. Levin |
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 ; 11, no. 2 ; Nr. 11, no. 2 (2011-01-31), S.873-892 |
Datensatznummer |
250009204
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Publikation (Nr.) |
copernicus.org/acp-11-873-2011.pdf |
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Zusammenfassung |
This report addresses the effects of pollution on the development of
precipitation in clean ("pristine") and polluted ("hazy") environments
in the Eastern Mediterranean by using the Integrated Community Limited Area
Modeling System (ICLAMS) (an extended version of the Regional Atmospheric
Modeling System, RAMS). The use of this model allows one to investigate the
interactions of the aerosols with cloud development. The simulations show
that the onset of precipitation in hazy clouds is delayed compared to
pristine conditions. Adding small concentrations of GCCN to polluted clouds
promotes early-stage rain. The addition of GCCN to pristine clouds has no
effect on precipitation amounts. Topography was found to be more important
for the distribution of precipitation than aerosol properties. Increasing by
15% the concentration of hygroscopic dust particles for a case study over
the Eastern Mediterranean resulted in more vigorous convection and more
intense updrafts. The clouds that were formed extended about three
kilometers higher, delaying the initiation of precipitation by one hour.
Prognostic treatment of the aerosol concentrations in the explicit cloud
droplet nucleation scheme of the model, improved the model performance for
the twenty-four hour accumulated precipitation. The spatial distribution and
the amounts of precipitation were found to vary greatly between the
different aerosol scenarios. These results indicate the large uncertainty
that remains and the need for more accurate description of aerosol feedbacks
in atmospheric models and climate change predictions. |
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