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Emissions inventories of black carbon (BC), which are traditionallyconstructed using a <i>bottom-up</i> approach based on activity data andemissions factors, are considered to contain a large level of uncertainty.In this paper, an ensemble optimal interpolation (EnOI) data assimilationtechnique is used to investigate the possibility of optimally recovering thespatially resolved emissions bias of BC. An inverse modeling system foremissions is established for an atmospheric chemistry aerosol model and twokey problems related to ensemble data assimilation in the <i>top-down</i> emissions estimation are discussed: (1) how to obtain reasonable ensembles of prior emissions and (2) establishing a scheme to localize the background-errormatrix. An experiment involving 1-year-long simulation cycle with EnOIinversion of BC emissions is performed for 2008. The bias of the BCemissions intensity in China at each grid point is corrected by this inversesystem. The inverse emission over China in January is 240.1 Gg, and annualemission is about 2539.3 Gg, which is about 1.8 times of bottom-up emissioninventory. The results show that, even though only monthly mean BCmeasurements are employed to inverse the emissions, the accuracy of thedaily model simulation improves. Using top-down emissions, the averageroot mean square error of simulated daily BC is decreased by nearly 30 %.These results are valuable and promising for a better understanding ofaerosol emissions and distributions, as well as aerosol forecasting

Inverse modeling of black carbon emissions over China using ensemble data assimilation