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| Titel |
The scale problem in quantifying aerosol indirect effects |
| VerfasserIn |
A. McComiskey, G. Feingold |
| 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. 2 ; Nr. 12, no. 2 (2012-01-23), S.1031-1049 |
| Datensatznummer |
250010546
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| Publikation (Nr.) |
 copernicus.org/acp-12-1031-2012.pdf |
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| Zusammenfassung |
| A wide range of estimates exists for the radiative forcing of the aerosol
effect on cloud albedo. We argue that a component of this uncertainty
derives from the use of a wide range of observational scales and platforms.
Aerosol influences cloud properties at the microphysical scale, or the
"process scale", but observations are most often made of bulk properties
over a wide range of resolutions, or "analysis scales". We show that
differences between process and analysis scales incur biases in
quantification of the albedo effect through the impact that data aggregation
and computational approach have on statistical properties of the aerosol or
cloud variable, and their covariance. Measures made within this range of
scales are erroneously treated as equivalent, leading to a large uncertainty
in associated radiative forcing estimates. Issues associated with the
coarsening of observational resolution particular to quantifying the albedo
effect are discussed. Specifically, the omission of the constraint on cloud
liquid water path and the separation in space of cloud and aerosol
properties from passive, space-based remote sensors dampen the measured
strength of the albedo effect. We argue that, because of this lack of
constraints, many of these values are in fact more representative of the
full range of aerosol-cloud interactions and their associated feedbacks.
Based on our understanding of these biases we propose a new
observationally-based and process-model-constrained, method for estimating
aerosol-cloud interactions that can be used for radiative forcing estimates
as well as a better characterization of the uncertainties associated with
those estimates. |
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