Inverse modeling of black carbon emissions over China using ensemble data assimilation
Emissions inventories of black carbon (BC), which are traditionally constructed using a <i>bottom-up</i> approach based on activity data and emissions factors, are considered to contain a large level of uncertainty. In this paper, an ensemble optimal interpolation (EnOI) data assimila...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-01-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://www.atmos-chem-phys.net/16/989/2016/acp-16-989-2016.pdf |
| Summary: | Emissions inventories of black carbon (BC), which are traditionally
constructed using a <i>bottom-up</i> approach based on activity data and
emissions factors, are considered to contain a large level of uncertainty.
In this paper, an ensemble optimal interpolation (EnOI) data assimilation
technique is used to investigate the possibility of optimally recovering the
spatially resolved emissions bias of BC. An inverse modeling system for
emissions is established for an atmospheric chemistry aerosol model and two
key 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-error
matrix. An experiment involving 1-year-long simulation cycle with EnOI
inversion of BC emissions is performed for 2008. The bias of the BC
emissions intensity in China at each grid point is corrected by this inverse
system. The inverse emission over China in January is 240.1 Gg, and annual
emission is about 2539.3 Gg, which is about 1.8 times of bottom-up emission
inventory. The results show that, even though only monthly mean BC
measurements are employed to inverse the emissions, the accuracy of the
daily model simulation improves. Using top-down emissions, the average
root mean square error of simulated daily BC is decreased by nearly 30 %.
These results are valuable and promising for a better understanding of
aerosol emissions and distributions, as well as aerosol forecasting. |
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| ISSN: | 1680-7316 1680-7324 |