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Optimizing global CO emission estimates using a four-dimensional variational data assimilation system and surface network observations

机译:使用四维变分数据同化系统和表面网络观测优化全局CO发射估计

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We apply a four-dimensional variational (4D-VAR) data assimilation system to optimize carbon monoxide (CO) emissions for 2003 and 2004 and to reduce the uncertainty of emission estimates from individual sources using the chemistry transport model TM5. The system is designed to assimilate large (satellite) datasets, but in the current study only a limited amount of surface network observations from the National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA/ESRL) Global Monitoring Division (GMD) is used to test the 4D-VAR system. By design, the system is capable to adjust the emissions in such a way that the posterior simulation reproduces background CO mixing ratios and large-scale pollution events at background stations. Uncertainty reduction up to 60 % in yearly emissions is observed over well-constrained regions and the inferred emissions compare well with recent studies for 2004. However, with the limited amount of data from the surface network, the system becomes data sparse resulting in a large solution space. Sensitivity studies have shown that model uncertainties (e.g., vertical distribution of biomass burning emissions and the OH field) and the prior inventories used, influence the inferred emission estimates. Also, since the observations only constrain total CO emissions, the 4D-VAR system has difficulties in separating anthropogenic and biogenic sources in particular. The inferred emissions are validated with NOAA aircraft data over North America and the agreement is significantly improved from the prior to posterior simulation. Validation with the Measurements Of Pollution In The Troposphere (MOPITT) instrument version 4 (V4) shows a slight improved agreement over the well-constrained Northern Hemisphere and in the tropics (except for the African continent). However, the model simulation with posterior emissions underestimates MOPITT CO total columns on the remote Southern Hemisphere (SH) by about 10 %. This is caused by a reduction in SH CO sources mainly due to surface stations on the high southern latitudes.
机译:我们应用四维变分(4D-VAR)数据同化系统,以优化2003年和2004年的一氧化碳(CO)排放,并使用化学传输模型TM5降低各种来源的排放估计的不确定性。该系统旨在同化大型(卫星)数据集,但在目前的研究中,只有来自国家海洋和大气管理地球系统研究实验室(NOAA / ESRL)全球监测部门(GMD)的有限量的表面网络观测用于测试4D-VAR系统。通过设计,该系统能够以后仿真再现后部模拟在背景站中的背景CO混合比和大规模污染事件的方式调整排放。在受良好约束的地区观察到年度排放量高达60%,推断排放与2004年最近的研究相比。但是,从地面网络的数据有限,系统变得稀疏导致大量的数据解决方案空间。敏感性研究表明,模型不确定性(例如,生物质燃烧排放和OH场的垂直分布)以及所用的现有清单,影响推断的排放估计。此外,由于观察结果仅限于总共共同排放,因此4D-VAR系统特别难以分离人为和生物源。通过北美的Noaa飞机数据验证了推断的排放,协议从后模拟之前显着改善。通过对流层(MOPITT)仪器版本4(v4)污染测量的验证显示了在良好约束的北半球和热带地区(非洲大陆除外)的略微改进的协议。然而,具有后排放的模型模拟低估了远程南半球(SH)上的MOPITT CO总柱约10%。这是由SH CO来源的减少引起的,主要是由于高南部纬度的表面站。

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