Synthetic observations, emission sensitivity, and inversed emission datasets

In this study, we developed a new inverse framework, in which the atmospheric concentration of gases was decomposed into two parts: “enhancement” caused by emissions during the study period and “background” from decay of initial conditions. This inversion framework consists of a 3D CTM to simulate the emissions sensitivity for reference species, an Interspecies Emission Sensitivity Conversion (IESC) module to calculate emission sensitivity for any target species from that of the reference species, and a 2D box model to simulate the decay of initial conditions. This inversion framework eliminates the need to run 3D CTM for each target species, thereby significantly reducing computational costs compared to traditional approaches. We tested this inversion system using observing system simulation experiments on reference species (CH₄) and six target species (SF₆, H-1301, HCFC-141b, CH₃CCl₃, HCFC-132b, and HFC-152a), and found it performed well for long-lived gases. This new inversion method can help scientists and policymakers better monitor emissions at global and continental scales, while saving significant computing time and resources.本研究构建了一种新型反演框架，将大气中气体浓度分解为两部分：一是研究时段内排放引发的浓度增量，二是由初始条件衰减产生的本底浓度。该反演框架包含三个核心组件：用于模拟参考物种排放敏感性的三维化学传输模型（3D CTM）、可基于参考物种排放敏感性推算任意目标物种排放敏感性的种间排放敏感性转换（Interspecies Emission Sensitivity Conversion, IESC）模块，以及用于模拟初始条件衰减过程的二维箱式模型（2D box model）。该框架无需针对每个目标物种单独运行三维化学传输模型，相较传统方法可显著降低计算成本。本研究通过针对参考物种（甲烷，CH₄）与6种目标物种（六氟化硫SF₆、H-1301、HCFC-141b、三氯甲烷CH₃CCl₃、HCFC-132b及氢氟烃HFC-152a）的观测系统模拟试验，对该反演系统开展了测试，结果显示其对长寿命气体的反演表现优异。该新型反演方法可助力科研人员与政策制定者在全球及大陆尺度下更高效地开展排放监测工作，同时大幅节省计算时间与资源。