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| Titel |
Spatial distributions and seasonal cycles of aerosol climate effects in India seen in a global climate–aerosol model |
| VerfasserIn |
S. V. Henriksson, J.-P. Pietikäinen, A.-P. Hyvärinen, P. Räisänen, K. Kupiainen, J. Tonttila, R. Hooda, H. Lihavainen, D. O'Donnell, L. Backman, Z. Klimont, A. Laaksonen |
| 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 ; 14, no. 18 ; Nr. 14, no. 18 (2014-09-24), S.10177-10192 |
| Datensatznummer |
250119063
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| Publikation (Nr.) |
 copernicus.org/acp-14-10177-2014.pdf |
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| Zusammenfassung |
| Climate–aerosol interactions in India are studied
by employing the global climate–aerosol model ECHAM5-HAM and the GAINS
inventory for anthropogenic aerosol emissions. Model validation is done for
black carbon surface concentrations in Mukteshwar and for features of the
monsoon circulation. Seasonal cycles and spatial distributions of radiative
forcing and the temperature and rainfall responses are presented for
different model setups. While total aerosol radiative forcing is strongest in
the summer, anthropogenic forcing is considerably stronger in winter than in
summer. Local seasonal temperature anomalies caused by aerosols are mostly
negative with some exceptions, e.g., parts of northern India in March–May.
Rainfall increases due to the elevated heat pump (EHP) mechanism and
decreases due to solar dimming mechanisms (SDMs) and the relative strengths
of these effects during different seasons and for different model setups are
studied. Aerosol light absorption does increase rainfall in northern India,
but effects due to solar dimming and circulation work to cancel the increase.
The total aerosol effect on rainfall is negative for northern India in the
months of June–August, but during March–May the effect is positive for most
model setups. These differences between responses in different seasons might
help converge the ongoing debate on the EHPs and SDMs. Due to the complexity
of the problem and known or potential sources for error and bias, the results
should be interpreted cautiously as they are completely dependent on how
realistic the model is. Aerosol–rainfall correlations and anticorrelations
are shown not to be a reliable sole argument for deducing causality. |
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