Inverse Estimation of Urban Methane Emissions Using Both Column and Surface Observations: An OSSE Study

Abstract The magnitudes, trends, and source contributions of CH 4 emissions are still highly uncertain, especially at an urban scale. Here, we present an observing system simulation experiment framework to quantitatively evaluate a potential measurement network that combines column and surface observations for estimating urban CH 4 emissions. We evaluate the observing systems from multiple perspectives, focusing on their ability to estimate monthly and interannual variability, and to attribute emissions to specific sectors. A multivariate linear regression analysis was performed to identify the sources of uncertainties in the posterior fluxes. A 10% annual rise in CH 4 emissions from 2021 to 2023 was assumed to evaluate the capability of the observing system to capture interannual variability and trends. We found that MUCCnet, the world's first permanent urban ground‐based column greenhouse gas network with 5 stations, was able to capture the monthly and interannual variability and trends of CH 4 emissions. The significant uncertainties in emission estimates from MUCCnet‐only inversions can be attributed to strong emissions from the Agriculture sector, which are far from the network deployment sites, or to limited observation coverage due to meteorological conditions. Surface in situ observations within the joint network can effectively reduce these uncertainties. The uncertainties in total CH 4 emission estimates can be reduced by using observations from MUCCnet and a surface in situ network, leveraging the complementarity between the two platforms. Using the joint network configuration, the interannual variability and trend of CH 4 emissions can be detected with low uncertainties.