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| Titel |
Using measurements for evaluation of black carbon modeling |
| VerfasserIn |
S. Gilardoni, E. Vignati, J. Wilson |
| 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 (2011-01-17), S.439-455 |
| Datensatznummer |
250009175
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| Publikation (Nr.) |
 copernicus.org/acp-11-439-2011.pdf |
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| Zusammenfassung |
| The ever increasing use of air quality and climate model
assessments to underpin economic, public health, and environmental
policy decisions makes effective model evaluation critical. This paper
discusses the properties of black carbon and light attenuation and absorption
observations that are the key to a reliable evaluation of black carbon model and
compares parametric and nonparametric statistical tools for the quantification
of the agreement between models and observations. Black carbon concentrations
are simulated with TM5/M7 global model from July 2002 to June 2003 at four remote
sites (Alert, Jungfraujoch, Mace Head, and Trinidad Head) and two regional background sites
(Bondville and Ispra). Equivalent black carbon (EBC) concentrations are calculated using
light attenuation measurements from January 2000 to December 2005. Seasonal trends in
the measurements are determined by fitting sinusoidal functions and the representativeness
of the period simulated by the model is verified based on the scatter of the experimental
values relative to the fit curves. When the resolution of the model grid is larger than
1° × 1°, it is recommended to verify that the measurement site is representative
of the grid cell. For this purpose, equivalent black carbon measurements at Alert,
Bondville and Trinidad Head are compared to light absorption and elemental carbon
measurements performed at different sites inside the same model grid cells.
Comparison of these equivalent black carbon and elemental carbon measurements
indicates that uncertainties in black carbon optical properties can compromise
the comparison between model and observations. During model evaluation it is
important to examine the extent to which a model is able to simulate the variability
in the observations over different integration periods as this will help to identify
the most appropriate timescales. The agreement between model and observation is
accurately described by the overlap of probability distribution (PD) curves. Simple
monthly median comparisons, the Student's t-test, and the Mann-Whitney test are
discussed as alternative statistical tools to evaluate the model performance.
The agreement measured by the Student's t-test, when applied to the logarithm
of EBC concentrations, overestimates the higher PD agreements and underestimates
the lower PD agreements; the Mann-Whitney test can be employed to evaluate model
performance on a relative scale when the shape of model and experimental distributions are similar. |
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