日本地球惑星科学連合2019年大会

講演情報

[J] ポスター発表

セッション記号 A (大気水圏科学) » A-AS 大気科学・気象学・大気環境

[A-AS04] 大気化学

2019年5月30日(木) 17:15 〜 18:30 ポスター会場 (幕張メッセ国際展示場 8ホール)

コンビーナ:中山 智喜(長崎大学 大学院水産・環境科学総合研究科)、岩本 洋子(広島大学 生物圏科学研究科)、豊田 栄(東京工業大学物質理工学院)、江口 菜穂(Kyushu University)

[AAS04-P14] Inverse modeling of black carbon emissions over China using ensemble data assimilation

*Ping Wang1 (1. Chinese Academy of Meteorological Sciences)

キーワード:Inverse modeling, black carbon, ensemble optimal interpolation

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 one 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 inversed 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.