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Abstract text
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Concentrations of atmospheric methane (CH4), the second most important greenhouse gas, continue to grow. In recent years this growth rate has increased further (2020: +14.7 ppb), the cause of which remains largely unknown. Accurate estimates of CH4 emissions are key to better understand these observed trends and help implement efficient climate change mitigation policies. New methane observations from the TROPOMI instrument provide unprecedented spatiotemporal constraints on these emissions. Here, we present preliminary results from a new inversion system based on the ECMWF Integrated Forecasting System (IFS) ,which assimilates observations within a 24-hour window cycled 4D-variational algorithm. Specificities of this system include the use of a high-resolution transport model (~9km) combined with online data assimilation (i.e., joint optimization of meteorological and atmospheric composition variables) that provides consistent treatment of atmospheric transport errors. The performance of the system is illustrated by comparing posterior atmospheric concentrations with independent observations, as well as by evaluating posterior emission estimates for regional and point source case studies previously analyzed in the literature. The largest national disagreement found between prior (63.1 Tg yr-1) and posterior (59.8 Tg yr-1) CH4 emissions is from China, mainly attributed to the energy sector. Emissions estimated form our global system agree well with previous basin-wide regional studies and point source specific studies. Emission events (leaks/blowouts) >10 t hr-1 were detected, but without accurate prior uncertainty information, were not well quantified. Our results suggest that global anthropogenic CH4 emissions for 2020 were 5.7 Tg yr-1 (+1.6%) higher than for 2019, mainly attributed to the energy and agricultural sectors. Regionally, the largest increases were seen from China (+2.6 Tg yr-1, 4.3%), with smaller increases from India (+0.8 Tg yr-1, 2.2%) and Indonesia (+0.3 Tg yr-1, 2.6%). Plans to further develop the global IFS inversion system and to extend the 4D-Var window-length using a hybrid ensemble-variational method will also be presented.
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