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| Titel |
On the attribution of contributions of atmospheric trace gases to emissions in atmospheric model applications |
| VerfasserIn |
V. Grewe, E. Tsati, P. Hoor |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1991-959X
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| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 3, no. 2 ; Nr. 3, no. 2 (2010-10-12), S.487-499 |
| Datensatznummer |
250000952
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| Publikation (Nr.) |
 copernicus.org/gmd-3-487-2010.pdf |
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| Zusammenfassung |
| We present an improved tagging method, which describes the combined effect of
emissions of various species from individual emission categories, e.g. the
impact of both, nitrogen oxides and non-methane hydrocarbon emissions on
ozone. This method is applied to two simplified chemistry schemes, which
represent the main characteristics of atmospheric ozone chemistry. Analytical
solutions are presented for this tagging approach. In the past, besides
tagging approaches, sensitivity methods were used, which estimate the
contributions from individual sources based on differences in two
simulations, a base case and a simulation with a perturbation in the
respective emission category. We apply both methods to our simplified
chemical systems and demonstrate that potentially large errors (factor of 2)
occur with the sensitivity method, which depend on the degree of linearity of
the chemical system. This error depends on two factors, the ability to
linearise the chemical system around a base case, and second the completeness
of the contributions, which means that all contributions should principally
add up to 100%. For some chemical regimes the first error can be minimised
by employing only small perturbations of the respective emission, e.g. 5%.
The second factor depends on the chemical regime and cannot be minimized by a
specific experimental set-up. It is inherent to the sensitivity method. Since
a complete tagging algorithm for global chemistry models is difficult to
achieve, we present two error metrics, which can be applied for sensitivity
methods in order to estimate the potential error of this approach for
a specific application. |
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