In order to use in situ measurements to constrain urban anthropogenic emissions of carbon dioxide (CO2), we use a Lagrangian methodology based on diffusive backward trajectory tracer reconstructions and Bayesian inversion. The observations of atmospheric CO2 were collected within the Tokyo Bay Area during the Comprehensive Observation Network for TRace gases by AIrLiner (CONTRAIL) flights, from the Tsukuba tall tower of the Meteorological Research Institute (MRI) of the Japan Meteorological Agency and at two surface sites (Dodaira and Kisai) from the World Data Center for Greenhouse Gases (WDCGG). We produce gridded estimates of the CO2 emissions and calculate the averages for different areas within the Kanto plain where Tokyo is located. Using these inversions as reference we investigate the impact of perturbing different elements in the inversion system. We modified the observations amount and location (surface only sparse vs. including aircraft CO2 observations), the background representation, the wind data used to drive the transport model, the prior emissions magnitude and time resolution and error parameters of the inverse model. Optimized fluxes were consistent with other estimates for the unperturbed simulations. Inclusion of CONTRAIL measurements resulted in significant differences in the magnitude of the retrieved fluxes, 13% on average for the whole domain and of up to 21% for the spatiotemporal cells with the highest fluxes. Changes in the background yielded differences in the retrieved fluxes of up to 50% and more. Simulated biases in the modelled transport cause differences in the retrieved fluxes of up to 30% similar to those obtained using different meteorological winds to advect the Lagrangian trajectories. Perturbations to the prior inventory can impact the fluxes by ~ 10% or more depending on the assumptions on the error covariances. All of these factors can cause significant differences in the estimated flux, and highlight the challenges in estimating regional CO2 fluxes from atmospheric observations.

中文翻译：

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使用东京地区的现场飞机和地面测量评估城市人为二氧化碳通量的拉格朗日逆模型。


为了利用现场测量来限制城市人为二氧化碳（CO2）排放，我们使用基于扩散后向轨迹示踪重建和贝叶斯反演的拉格朗日方法。大气二氧化碳观测数据是在 AIrLiner (CONTRAIL) 航班的 TRace 气体综合观测网络期间从日本气象厅气象研究所 (MRI) 的筑波高塔和两个地面站点收集的东京湾地区大气二氧化碳观测数据（Dodaira 和 Kisai）来自世界温室气体数据中心 (WDCGG)。我们对二氧化碳排放量进行网格化估算，并计算东京所在关东平原内不同地区的平均值。使用这些反演作为参考，我们研究了反演系统中不同元素的扰动的影响。我们修改了观测数量和位置（仅表面稀疏观测与包括飞机二氧化碳观测）、背景表示、用于驱动传输模型的风数据、先前的排放量级和时间分辨率以及逆模型的误差参数。优化后的通量与未受扰动模拟的其他估计值一致。纳入 CONTRAIL 测量结果会导致检索到的通量大小存在显着差异，整个域的平均差异为 13%，通量最高的时空细胞的差异高达 21%。背景的变化导致检索到的通量差异高达 50% 甚至更多。模拟输运中的模拟偏差导致检索到的通量差异高达 30%，与使用不同气象风平流拉格朗日轨迹获得的通量相似。 对先前清单的扰动可能会对通量产生约 10% 或更多的影响，具体取决于误差协方差的假设。所有这些因素都可能导致估计通量的显着差异，并凸显了根据大气观测估计区域二氧化碳通量的挑战。