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Impact of transport model errors on the global and regional methane emissions estimated by inverse modelling

机译：逆模型估计的运输模型误差对全球和区域甲烷排放量的影响

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A modelling experiment has been conceived to assess the impact of transport model errors on methane emissions estimated in an atmospheric inversion system. Synthetic methane observations, obtained from 10 different model outputs from the international TransCom-CH4 model inter-comparison exercise, are combined with a prior scenario of methane emissions and sinks, and integrated into the three-component PYVAR-LMDZ-SACS (PYthon VARiational-Laboratoire de Météorologie Dynamique model with Zooming capability-Simplified Atmospheric Chemistry System) inversion system to produce 10 different methane emission estimates at the global scale for the year 2005. The same methane sinks, emissions and initial conditions have been applied to produce the 10 synthetic observation datasets. The same inversion set-up (statistical errors, prior emissions, inverse procedure) is then applied to derive flux estimates by inverse modelling. Consequently, only differences in the modelling of atmospheric transport may cause differences in the estimated fluxes. In our framework, we show that transport model errors lead to a discrepancy of 27 Tg yr-1 at the global scale, representing 5% of total methane emissions. At continental and annual scales, transport model errors are proportionally larger than at the global scale, with errors ranging from 36 Tg yr-1 in North America to 7 Tg yr-1 in Boreal Eurasia (from 23 to 48%, respectively). At the model grid-scale, the spread of inverse estimates can reach 150% of the prior flux. Therefore, transport model errors contribute significantly to overall uncertainties in emission estimates by inverse modelling, especially when small spatial scales are examined. Sensitivity tests have been carried out to estimate the impact of the measurement network and the advantage of higher horizontal resolution in transport models. The large differences found between methane flux estimates inferred in these different configurations highly question the consistency of transport model errors in current inverse systems. Future inversions should include more accurately prescribed observation covariances matrices in order to limit the impact of transport model errors on estimated methane fluxes.
机译：已经构想出一个模拟实验来评估运输模型误差对大气反演系统中甲烷排放量的影响。从国际TransCom-CH4模型比对活动的10种不同模型输出中获得的合成甲烷观测值与甲烷排放和汇的先前情景相结合，并整合到三分量PYVAR-LMDZ-SACS（PYthon VARiational-具有缩放功能的化工厂实验室动力学模型-简化大气化学系统）反演系统可在2005年全球范围内产生10种不同的甲烷排放量估算值。相同的甲烷汇，排放量和初始条件也已应用于产生10种合成观测值数据集。然后使用相同的反演设置（统计误差，先验排放，反演程序）通过反演建模得出通量估计。因此，仅大气传输模型的差异可能会导致估算通量的差异。 在我们的框架中，我们表明，运输模型错误导致全球范围内27 Tg yr-1的差异，占甲烷总排放量的5％。在大陆和年度尺度上，运输模型误差成比例地大于全球尺度，其误差范围从北美的36 Tg yr-1到北欧亚大陆的7 Tg yr-1（分别为23％至48％）。在模型网格规模上，逆估计的分布可以达到先前通量的150％。因此，运输模型错误通过逆建模对排放估算中的总体不确定性有重大影响，尤其是在检查小空间尺度时。已经进行了敏感性测试，以估计测量网络的影响以及运输模型中更高的水平分辨率的优势。在这些不同的配置中推断出的甲烷通量估计值之间发现的巨大差异，极大地质疑了当前逆系统中输运模型误差的一致性。 未来的反演应包括更准确地规定的观测协方差矩阵，以限制传输模型误差对估计的甲烷通量的影响。

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