Hydrofluorocarbons (HFCs) are potent greenhouse gases widely used in refrigeration, air-conditioning, and heat pump systems. Accurate monitoring of HFC emissions is essential to evaluate compliance with climate regulations and inform mitigation strategies. This study presents trends of HFC emissions across north-western Europe between 2013 and 2024, derived from atmospheric inverse modelling combining atmospheric measurements at eleven monitoring stations with two transport models (NAME and FLEXPART) and three Bayesian inversion systems (InTEM, ELRIS, RHIME). Although global emissions continue to rise for most HFCs, in north-western Europe our results show an overall steady decline in total HFC emissions from 40 +/- 3 Tg CO2-eq yr-1 in 2016 (prior to enhanced regulation) to 29 +/- 2 Tg CO2-eq yr-1 in 2023, following EU F-gas Regulations. This reduction is driven primarily by decreasing emissions of HFC-134a, HFC-143a and HFC-125 despite increasing HFC-32 emissions due to its adoption as a lower-global-warming-potential alternative refrigerant. Comparisons with national inventories reported to the United Nations Framework Convention on Climate Change (UNFCCC) show generally good agreement over north-western Europe but reveal discrepancies for specific compounds and countries, particularly for HFC-134a and HFC-125 in France and Germany during the earlier years of the study period. The recent expansion of the European measurement network demonstrates potential to improve spatial coverage and resolution of inverse emission estimates, especially in southern and central Europe. This study highlights the value of multi-model inversions to provide robust emission estimates with realistic, hence actionable, uncertainty characterisation.

Isotopic measurements of atmospheric methane are valuable for the verification of bottom-up atmospheric emissions inventories. The balance of sources in emissions inventories must be consistent with the delta C-13-CH4 isotopic record in the air. Long-term records of both methane mole fraction and delta C-13 from five sites across the UK are presented, showing post-2007 growth in CH4 and negative trend in delta C-13, consistent with global background sites. Miller-Tans analyses of atmospheric measurements identified that the delta C-13 signature of the methane source mix varied between -50.1 and -56.1 parts per thousand, with less depleted delta C-13 signatures at sites receiving air from urban areas, consistent with an increased proportion of thermogenic sources. Isotopic signatures calculated for all sites are more enriched than those expected from the bottom-up emissions inventory, suggesting that inventories for the UK either underestimate contributions of thermogenic/pyrogenic emissions or overestimate biogenic sources.

Abstract. We present a method for estimating fossil fuel methane emissions using observations of methane and ethane, accounting for uncertainty in their emission ratio. The ethane:methane emission ratio is incorporated as a variable parameter in a Bayesian model, with its own prior distribution and uncertainty. We find that using an emission ratio distribution mitigates bias from using a fixed, potentially incorrect emission ratio and that uncertainty in this ratio is propagated into posterior estimates of emissions. A synthetic data test is used to show the impact of assuming an incorrect ethane:methane emission ratio and demonstrate how our variable parameter model can better quantify overall uncertainty. We also use this method to estimate UK methane emissions from high-frequency observations of methane and ethane from the UK Deriving Emissions linked to Climate Change (DECC) network. Using the joint methane-ethane inverse model, we estimate annual mean UK methane emissions of approximately 0.27 (95 % uncertainty interval 0.26–0.29) Tg per year from fossil fuel sources and 2.06 (1.99–2.15) Tg per year from non-fossil fuel sources, during the period 2015–2019. Uncertainties in UK fossil fuel emissions estimates are reduced on average by 15 %, and up to 35 %, when incorporating ethane into the inverse model, in comparison to results from the methane-only inversion.

Any decrease in global methane emissions will contribute towards reducing the impacts of climate change. Recently, many nations around the world enacted the Global Methane Pledge to make substantial reductions in methane emissions over the coming decade. A good understanding of methane and its sources is required to effectively target emission reduction policies in anthropogenic sectors and meet these pledges. Total emissions of methane at both global and regional scales can be estimated from atmospheric observations of methane using inverse modelling techniques. However, the attribution of these total emissions estimates to their sources can be difficult when sources are closely located or when there is uncertainty in the spatial distribution of sources in bottom-up inventories. This is the case for many regions of the world, limiting our ability to understand specific sources. The method presented in this work aims to improve on this issue and reduce the overall uncertainties involved with quantifying sector-level emissions by using a co-emitted tracer and its emissions ratio relative to methane to partition methane emissions by source. The emission ratios are included as spatially and temporally varying parameters, with their own uncertainties, and are jointly estimated along with emissions. This allows for any variability and uncertainty in the ratio to be statistically propagated through the inverse model and incorporated into the final estimates of sectoral methane emissions. This is a critical step when employing tracers, as they can bias source sector results if not applied accurately. In this work, we use this novel method with ACT-America aircraft observations of methane and ethane to estimate monthly methane emissions from oil and gas basins across the USA. We show that trends in oil and gas methane emissions varies between basins. We also find that ethane:methane ratios vary largely between basins, which highlights the importance of including the uncertainty in these ratios in any model using ethane as a tracer for fossil fuel emissions.

Source characteristics of methane emissions in Africa are not well understood, despite methane's role as the second largest anthropogenic contributor to climate change. Here, we present monthly methane emission estimates from Algeria, Egypt, Libya, Morocco, and Tunisia between 2010 and 2017, a region dominated by anthropogenic emissions. Emissions are estimated using observations from the GOSAT satellite and a Markov chain Monte Carlo inverse algorithm. Our top-down North African methane emissions are generally in line with inventory estimates and national reporting to the United Nations Framework Convention on Climate Change (UNFCCC). An exception is that summertime emissions from the Nile Delta region are considerably higher than those predicted by inventory estimates, possibly due to agricultural practices and the influence of the Nile.

Methane is an important greenhouse gas with a range of anthropogenic sources, including livestock farming and fossil fuel production. It is important that methane emissions can be correctly attributed to their source, to aid climate change policy and emissions mitigation efforts. For source attribution, many ‘top-down’ models of atmospheric methane use spatial maps of sources from emissions inventory data coupled with an atmospheric transport model. However, this can cause difficulties if sources are co-located or if there is uncertainty in the sources’ spatial distributions. To help with this issue and reduce overall uncertainty in estimates of methane emissions, recent methods have used observations of a secondary trace gas and its correlation with methane to infer methane emissions from a target sector. Most previous work has assumed a fixed emissions ratio between the two gases, which often does not reflect the true range of possible emission ratios. In this work, measurements of atmospheric ethane and its emissions ratio relative to methane are used to infer emissions of methane from fossil fuel sources. Instead of assuming a fixed emission ratio, our method allows for uncertainty in the emission ratio to be statistically propagated through the inverse model and incorporated into the sectoral estimates of methane emissions. We further demonstrate the inaccuracies that can result in an assessment of fossil fuel methane emissions if this uncertainty is not considered. We present this novel method for modelling sectoral methane emissions with examples from a synthetic data experiment and give results from a case study of UK methane emissions. Methane and ethane observations from a tall tower network across the UK were used with this model to produce monthly estimates of UK fossil fuel methane emissions with improved uncertainty characterisation.