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| Titel |
Mapping the uncertainty in global CCN using emulation |
| VerfasserIn |
L. A. Lee, K. S. Carslaw, K. J. Pringle, G. W. Mann |
| 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. 20 ; Nr. 12, no. 20 (2012-10-25), S.9739-9751 |
| Datensatznummer |
250011532
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| Publikation (Nr.) |
 copernicus.org/acp-12-9739-2012.pdf |
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| Zusammenfassung |
| In the last two IPCC assessments aerosol radiative forcings
have been given the largest uncertainty range of all forcing agents
assessed. This forcing range is really a diversity of simulated
forcings in different models. An essential step towards reducing model uncertainty
is to quantify and attribute the sources of uncertainty at the
process level. Here, we use statistical emulation techniques to
quantify uncertainty in simulated concentrations of July-mean cloud
condensation nuclei (CCN) from a complex global aerosol microphysics
model. CCN was chosen because it is the aerosol property that controls
cloud drop concentrations, and therefore the aerosol indirect radiative forcing
effect. We use Gaussian process emulation to perform a full
variance-based sensitivity analysis and quantify, for each model grid
box, the uncertainty in simulated CCN that results from 8 uncertain
model parameters. We produce global maps of absolute and relative CCN
sensitivities to the 8 model parameter ranges and derive probability
density functions for simulated CCN. The approach also allows us to
include the uncertainty from interactions between these parameters,
which cannot be quantified in traditional one-at-a-time sensitivity
tests. The key findings from our analysis are that model CCN in
polluted regions and the Southern Ocean are mostly only sensitive to
uncertainties in emissions parameters but in all other regions CCN
uncertainty is driven almost exclusively by uncertainties in
parameters associated with model processes. For example, in marine regions between
30° S and 30° N model CCN uncertainty is driven
mainly by parameters associated with cloud-processing of Aitken-sized
particles whereas in polar regions uncertainties in scavenging
parameters dominate. In these two regions a single parameter dominates
but in other regions up to 50% of the variance can be due to
interaction effects between different parameters. Our analysis
provides direct quantification of the reduction in variance that would
result if a parameter could be specified precisely. When extended to
all process parameters the approach presented here will therefore
provide a clear global picture of how improved knowledge of aerosol
processes would translate into reduced model uncertainty. |
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