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Using measurements for evaluation of black carbon modeling

by S. Gilardoni, E. Vignati, J. Wilson
Atmospheric Chemistry and Physics ()
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The ever increasing use of air quality and climate model assessments to underpin economic, public health, and environmental policy decisions makes effective model eval- uation 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 ob- servations. 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 Is- pra). Equivalent black carbon (EBC) concentrations are cal- culated using light attenuation measurements from January 2000 to December 2005. Seasonal trends in the measure- ments 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 rel- ative 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 carbonmeasurements at Alert, Bondville and Trinidad Head are compared to light absorp- tion and elemental carbon measurements performed at dif- ferent sites inside the same model grid cells. Comparison of these equivalent black carbon and elemental carbon mea- surements 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 simu- late the variability in the observations over different integra- tion periods as this will help to identify the most appropri- ate timescales. The agreement between model and observation is accurately described by the overlap of probability distribution (PD) curves. Simple monthly median compar- isons, 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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