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Cosmic rays entering the Earth's atmosphere produce showers of secondary particles such as protons, neutrons, and muons. The interaction of these particles with oxygen-16 (16O) in minerals such as ice and quartz can produce carbon-14 (14C). In glacial ice, 14C is also incorporated through trapping of 14C-containing atmospheric gases (14CO2, 14CO, and 14CH4). Understanding the production rates of in situ cosmogenic 14C is important to deconvolve the in situ cosmogenic and atmospheric 14C signals in ice, both of which contain valuable paleoenvironmental information. Unfortunately, the in situ 14C production rates by muons (which are the dominant production mechanism at depths of >6 m solid ice equivalent) are uncertain. In this study, we use measurements of in situ 14C in ancient ice (>50 ka) from the Taylor Glacier, an ablation site in Antarctica, in combination with a 2D ice flow model to better constrain the compound-specific rates of 14C production by muons and the partitioning of in situ 14C between CO2, CO, and CH4. Our measurements show that 33.7 % (±11.4 %; 95 % confidence interval) of the produced cosmogenic 14C forms 14CO and 66.1 % (±11.5 %; 95 % confidence interval) of the produced cosmogenic 14C forms 14CO2. 14CH4 represents a very small fraction (%) of the total. Assuming that the majority of in situ muogenic 14C in ice forms 14CO2, 14CO, and 14CH4, we also calculated muogenic 14C production rates that are lower by factors of 5.7 (3.6–13.9; 95 % confidence interval) and 3.7 (2.0–11.9; 95 % confidence interval) for negative muon capture and fast muon interactions, respectively, when compared to values determined in quartz from laboratory studies (Heisinger et al., 2002a, b) and in a natural setting (Lupker et al., 2015). This apparent discrepancy in muogenic 14C production rates in ice and quartz currently lacks a good explanation and requires further investigation.

Atmospheric CH4 mole fractions resumed their increase in 2007 after a plateau during the 1999–2006 period, indicating relative changes in the sources and sinks. 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 attributing these changes to specific source variations. δ13C(CH4)source isotopic signatures of CH4 sources 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 the emissions and the associated source signatures of multiple emission categories at the pixel scale. To our knowledge, this represents the first attempt to carry out variational inversion assimilating δ13C(CH4) with a 3-D chemistry transport model (CTM) and to independently optimize isotopic 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 LMDz-SACS 3-D CTM. We find that assimilating δ13C(CH4) observations and allowing the system to adjust isotopic source signatures provide relatively large differences in global flux estimates for wetlands (−5.7 Tg CH4 yr−1), agriculture and waste (−6.4 Tg CH4 yr−1), fossil fuels (+8.6 Tg CH4 yr−1) and biofuels–biomass burning (+3.2 Tg CH4 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 that the source signatures are perfectly known, these differences increase by a factor of 3–4, strengthening the importance of having as accurate signature estimates as possible. Initial conditions, uncertainties in δ13C(CH4) observations, or the number of optimized categories have a much smaller impact (less than 2 Tg CH4 yr−1).

Atmospheric methane’s rapid growth from 2006 to the present is unprecedented in the observational record. Isotopic evidence implies the growth is mainly driven by an increase in biogenically-sourced emissions, both from wetlands and ruminants, and waste. A significant part of methane’s current rise may come not from direct anthropogenic emissions and land use changes, but rather from a combination of natural biogenic feedback responses, occurring in response to the anthropogenic forcing. Although microbial emissions from agricultural and waste have increased between 2006-2020 by about 35 Tg/yr, perhaps 35-40 Tg/yr of the recent net growth in methane emissions may have been driven by natural biogenic processes, especially wetland feedbacks to climate change. Modelling comparison between the biogenic component of methane’s growth and isotopic shift in the 15 years from 2007-2022, and the global-scale climate reorganisations during the transitions from glacial to interglacial periods in the Pleistocene, shows that the modern growth event is comparable to or greater than the scale and speed of methane’s growth and isotopic shift during past glacial/interglacial termination events. It remains possible that current changes are related to decadal- or centennial-scale variability in precipitation and temperature and remain within the range of Holocene variability, or due to direct anthropogenic actions. But, though any current transition will differ greatly from the past glacial-interglacial changes, it is also possible methane’s remarkable growth and isotopic shift that began in 2006 may be a first indicator that a very large-scale reorganisation of the natural climate and biosphere system is under way.

The causes of methane's renewed rise since 2007, accelerated growth from 2014 and record rise in 2020, concurrent with an isotopic shift to values more depleted in 13 C, remain poorly understood. This rise is the dominant departure from greenhouse gas scenarios that limit global heating to less than 2°C. Thus a comprehensive understanding of methane sources and sinks, their trends and inter-annual variations are becoming more urgent. Efforts to quantify both sources and sinks and understand latitudinal and seasonal variations will improve our understanding of the methane cycle and its anthropogenic component. Nationally declared emissions inventories under the UN Framework Convention on Climate Change (UNFCCC) and promised contributions to emissions reductions under the UNFCCC Paris Agreement need to be verified independently by top-down observation. Furthermore, indirect effects on natural emissions, such as changes in aquatic ecosystems, also need to be quantified. Nitrous oxide is even more poorly understood. Despite this, options for mitigating methane and nitrous oxide emissions are improving rapidly, both in cutting emissions from gas, oil and coal extraction and use, and also from agricultural and waste sources. Reductions in methane and nitrous oxide emission are arguably among the most attractive immediate options for climate action. This article is part of a discussion meeting issue 'Rising methane: is warming feeding warming? (part 1)'.

This study investigates the contribution of different CH4 sources to the seasonal cycle of 𝛿13C during years 2000–2012 using the TM5 atmospheric transport model. The seasonal cycles of anthropogenic emissions from two versions of the EDGAR inventories, v4.3.2 and v5.0 are examined. Those includes emissions from Enteric Fermentation and Manure Management (EFMM), rice cultivation and residential sources. Those from wetlands obtained from LPX-Bern v1.4 are also examined in addition to other sources such as fires and ocean sources. We use spatially varying isotopic source signatures for EFMM, coal, oil and gas, wetlands, fires and geological emission and for other sources a global uniform value. We analysed the results as zonal means for 30° latitudinal bands. Seasonal cycles of 𝛿13C are found to be an inverse of CH4 cycles in general, with a peak-to-peak amplitude of 0.07–0.26 ‰. However, due to emissions, the phase ellipses do not form straight lines, and the anti-correlations between CH4 and 𝛿13C are weaker (−0.35 to −0.91) in north of 30° S. We found that wetland emissions are the dominant driver in the 𝛿13C seasonal cycle in the Northern Hemisphere and Tropics, such that the timing of 𝛿13C seasonal minimum is shifted by ∼90 days in 60° N–90° N from the end of the year to the beginning of the year when seasonality of wetland emissions is removed. The results also showed that in the Southern Hemisphere Tropics, emissions from fires contribute to the enrichment of 𝛿13C in July–October. In addition, we also compared the results against observations from the South Pole, Antarctica, Alert, Nunavut, Canada and Niwot Ridge, Colorado, USA. In light of this research, comparison to the observation showed that the seasonal cycle of EFMM emissions in EDGAR v5.0 inventory is more realistic than in v4.3.2. In addition, the comparison at Alert showed that modelled 𝛿13C amplitude was approximately half of the observations, mainly because the model could not reproduce the strong depletion in autumn. This indicates that CH4 emission magnitude and seasonal cycle of wetlands may need to be revised. Results from Niwot Ridge indicate that in addition to biogenic emissions, the proportion of biogenic to fossil based emissions may need to be revised.

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.

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).

Around the world laboratories that are part of the Global Atmospheric Watch (GAW) community conduct atmospheric trace gas measurements under the auspices of the World Meteorological Organisation (WMO). The GAW-WMO defines the inter-laboratory compatibility goals for these measurements, that is the maximum tolerable bias these measurements may have in order to still be useful for modelling and flux studies. The GAW-WMO network compatibility goals for δ13C- and δ18O-CO2(atm) measurements are 0.01 ‰ and 0.05 ‰ respectively, and for δ13C- and δ2H-CH4(atm) measurements they are 0.02 ‰ and 1 ‰, respectively. It has to be noted that these goals are very ambitious and at the precision limit of current analytical techniques. Nevertheless, in particular the isotopic measurements of atmospheric methane have suffered from considerable inter-laboratory biases of up to 0.5 ‰ and 13 ‰ for δ13C- and δ2H-CH4(atm) measurements in the past (Umezawa et al., 2018).These inter-laboratory measurement biases have been, and still are in part due to the different standardisation strategies that are used in different laboratories. In order to tackle this problem the stable isotope laboratory at the Max-Planck-Institute for Biogeochemistry (BGC-IsoLab) developed the Jena Reference Air Scale (JRAS-06) that has been in use since 2006. JRAS-06 is the scale realisation of the VPDB-CO2 scale, and its use is recommended by the GAW-WMO community to standardise δ13C- and δ18O-CO2(atm) measurements. The JRAS-06 scale is based on CO2 in air standards where the CO2 is evolved from standard calcium carbonates (e.g. NBS 19). Using an example dataset of δ13C- and δ18O-CO2(atm) measurements we show the improved inter-laboratory compatibility that results from using the JRAS-06 standards and scale at two laboratories, the stable isotope laboratory at the Institute of Arctic and Alpine Research (INSTAAR) and BGC-IsoLab.The BGC-IsoLab is now collaborating with the National Institute of Water and Atmospheric Research (NIWA) in New Zealand in order to develop standards and a unifying scale for δ13C- and δ2H-CH4(atm) measurements analogous to the JRAS-06 scale realisation. Here we show the first results of this collaborative effort towards a JRAS-M(ethane) scale that aims to improve the inter-laboratory compatibility and closely link δ13C- and δ2H-CH4(atm) measurements to the international VPDB-CO2 and VSMOW scales, respectively.