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The growth rate of the atmospheric abundance of methane (CH4) reached a record high of 15.4 ppb yr-1 between 2020 and 2022, but the mechanisms driving the accelerated CH4 growth have so far been unclear. In this work, we use measurements of the 13C:12C ratio of CH4 (expressed as δ13CCH4) from NOAA's Global Greenhouse Gas Reference Network and a box model to investigate potential drivers for the rapid CH4 growth. These measurements show that the record-high CH4 growth in 2020-2022 was accompanied by a sharp decline in δ13CCH4, indicating that the increase in CH4 abundance was mainly driven by increased emissions from microbial sources such as wetlands, waste, and agriculture. We use our box model to reject increasing fossil fuel emissions or decreasing hydroxyl radical sink as the dominant driver for increasing global methane abundance. [ABSTRACT FROM AUTHOR]

Author(s): Stefan Schwietzke (corresponding author) [1, 2]; Owen A. Sherwood [3]; Lori M. P. Bruhwiler [2]; John B. Miller [1, 2]; Giuseppe Etiope [4, 5]; Edward J. Dlugokencky [2]; Sylvia [...]

Assessment of bottom-up greenhouse gas emissions estimates through independent methods is needed to demonstrate whether reported values are accurate or if bottom-up methodologies need to be refined. We report atmospheric methane (CH4) mole fractions and δ13CH4 measurements from Imperial College London since early 2018 using a Picarro G2201-i analyser. Measurements from March 2018 to October 2020 were compared to simulations of CH4 mole fractions and δ13CH4 produced using the NAME dispersion model coupled with the UK National Atmospheric Emissions Inventory, UK NAEI, and the global inventory, EDGAR, with model spatial resolutions of ~2 km, ~10 km, and ~25 km. Observed mole fractions were underestimated by 30-35 % in the NAEI simulations. In contrast, a good correspondence between observations and EDGAR simulations was seen. There was no correlation between the measured and simulated δ13CH4 values for either NAEI or EDGAR, however, suggesting the inventories' sectoral attributions are incorrect. On average, natural gas sources accounted for 20-28 % of the above background CH4 in the NAEI simulations, and only 6-9 % in the EDGAR simulations. In contrast, nearly 84 % of isotopic source values calculated by Keeling plot analysis (using measurement data from the afternoon) of individual pollution events were higher than -45 ‰, suggesting the primary CH4 sources in London are actually natural gas leaks. The simulation-observation comparison of CH4 mole fractions suggests that total emissions in London are much higher than the NAEI estimate (0.04 Tg CH4 yr-1) but close to, or slightly lower than the EDGAR estimate (0.10 Tg CH4 yr-1). However, the simulation-observation comparison of δ13CH4 and the Keeling plot results indicate that emissions due to natural gas leaks in London are being underestimated in both the UK NAEI and EDGAR. [ABSTRACT FROM AUTHOR]

Atmospheric CH4 mixing ratios resumed their increase in 2007 after a plateau during the period 1999-2006, suggesting varying sources and sinks as main drivers. Estimating sources by exploiting observations within an inverse modeling framework (top-down approaches) is a powerful approach. It is nevertheless challenging to efficiently differentiate co-located emission categories and sinks by using CH4 observations alone. As a result, top-down approaches are limited when it comes to fully understanding CH4 burden changes and attribute these changes to specific source variations. CH4 source isotopic signatures differ between emission categories (biogenic, thermogenic and pyrogenic), and can therefore be used to address this limitation. Here, a new 3-D variational inverse modeling framework designed to assimilate δ13C(CH4) observations together with CH4 observations is presented. This system is capable of optimizing both emissions and associated source signatures of multiple emission categories. We present the technical implementation of joint CH4 and δ13C(CH4) constraints in a variational system, and analyze how sensitive the system is to the setup controlling the optimization using the 3-D Chemistry-Transport Model LMDz-SACS. We find that assimilating δ13C(CH4) observations and allowing the system to adjust source isotopic signatures provide relatively large differences in global flux estimates for wetlands (5 Tg yr−1), microbial (6 Tg yr−1), fossil fuels (8 Tg yr−1) and biofuels-biomass burning (4 Tg yr−1) categories compared to the results inferred without assimilating δ13C(CH4) observations. More importantly, when assimilating both CH4 and δ13C(CH4) observations, but assuming source signatures are perfectly known increase these differences between the system with CH4 and the enhanced one with δ13C(CH4) by a factor 3 or 4, strengthening the importance of having as accurate as possible signatures. Initial conditions, uncertainties on δ13C(CH4) observations or the number of optimized categories have a much smaller impact (less than 2 Tg yr−1). [ABSTRACT FROM AUTHOR]

The El Niño - Southern Oscillation (ENSO) has been suggested as a strong forcing in the methane cycle and as a driver of recent trends in global atmospheric methane levels. Such a sensitivity of the global CH4 budget to climate events would have important repercussions for climate change mitigation strategies and the accuracy of projections for future greenhouse forcing. Here, we test the impact of ENSO on the CH4 cycle in a correlation analysis. We use local and global records of methane mixing ratio [CH4], as well as stable carbon isotopic records of atmospheric CH4 (δ13CH4), which are particularly sensitive to the combined ENSO effects on CH4 production from wetlands and biomass burning. We use a variety of nominal, smoothed and detrended time series including growth rate records. We find that at most 38 % of the variability in [CH4] and δ13CH4 is attributable to ENSO, but only for detrended records in the Southern tropics. Trend-bearing records from the Southern tropics, as well as all studied hemispheric and global records show a minor impact of ENSO, i.e. < 25 % of variability explained. Additional analyses using hydrogen cyanide (HCN) records show a detectable ENSO influence on biomass burning (up to 51 %-55 %), suggesting that it is wetland CH4 production that responds less to ENSO than previously suggested. It is possible that other processes obscure the ENSO signal, which itself indicates a minor influence of the latter on CH4 emissions. Trends like the recent rise in atmospheric [CH4] can therefore not be attributed to ENSO. This leaves anthropogenic methane sources as the likely driver, which must be mitigated to reduce anthropogenic climate change. [ABSTRACT FROM AUTHOR]