Inverse modeling of black carbon emissions over China using ensemble data assimilation
Inverse modeling of black carbon emissions over China using ensemble data assimilation
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DOI：
10.5194/acp-16-989-2016
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发表时间：
2015-08
影响因子：
6.3
通讯作者：
P. Wang;Hong Wang;Y. Wang;Xiaoling Zhang;S. Gong;M. Xue;Chenglong Zhou;H. L. Liu;X. An;T. Niu;Y. Cheng
中科院分区：
文献类型：
--
作者：
P. Wang;Hong Wang;Y. Wang;Xiaoling Zhang;S. Gong;M. Xue;Chenglong Zhou;H. L. Liu;X. An;T. Niu;Y. Cheng
抽象。黑碳排放清单传统上采用基于活动数据和排放因素的自下而上的方法构建，被认为包含很大程度的不确定性。在本文中，集合最优插值（EnOI）数据同化技术被用来调查的可能性，最佳恢复的空间分辨的排放偏差的BC。建立了一个大气化学气溶胶模式的排放反演系统，讨论了自上而下排放估算中集合同化的两个关键问题：（1）如何获得合理的先验排放集合;（2）建立背景误差矩阵的局部化方案。实验涉及1年的模拟周期与EnOI反演的BC排放量进行2008年。该反演系统对中国各网格点碳排放强度的偏差进行了修正。中国1月份的逆排放量为240.1Gg，全年排放量约为2539.3Gg，约为自下而上排放清单的1.8倍。结果表明，即使只采用月平均BC测量值来反演排放量，日模型模拟的准确性也有所提高。使用自上而下的排放，模拟的每日BC的平均均方根误差减少了近30%。这些结果对于更好地了解气溶胶的排放和分布，以及气溶胶预报是有价值的和有前途的。
Abstract. Emissions inventories of black carbon (BC), which are traditionally constructed using a bottom-up 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 top-down 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.