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The unique physical and chemical characteristics of radon make it an excellent tracer of atmospheric mixing and transport processes. As such, it has long been a species of interest to the climate change research communities. However, reliable, high resolution radon flux maps are essential for the use of atmospheric radon in climate studies.Validation of the existing radon flux maps and inventories is currently limited by the availability of systematic measurements of radon fluxes and other process-relevant parameters (e.g., physical characteristics and moisture content of soil). Localised measurements of radon flux, soil moisture and soil physical properties can provide some information to validate and improve flux maps. On the other hand, high sensitivity atmospheric radon measurements in conjunction with atmospheric transport models would determine the relative representativeness of radon flux models over larger scales.To tackle the validation of radon flux maps, atmospheric radon measurements were compared to the results of modelled radon concentrations calculated using the Lagrangian particle model, the Met Office Numerical Atmospheric Modelling Environment (NAME) and two available versions of European radon flux maps1. For this purpose, radon data from three UK greenhouse gases atmospheric monitoring stations located in Heathfield (an inland, 100 m tall tower), Tacolneston (an inland, 185 m tall tower), and Weybourne (a coastal site, 10 m tower) were used. The differences between measured and modelled radon concentrations on diurnal and monthly scales will be presented and discussed. The ratio of measured-modelled radon concentrations shows the potential to objectively assess the reliability of radon flux maps under different wind directions and atmospheric mixing conditions. 1 2015: doi:10.1594/PANGAEA.854715 and 2022: ICOS data search (icos-cp.eu).

The vertical mixing state of the atmosphere, as well as the atmospheric boundary layer (ABL) height, are important atmospheric transport model parameters for the accurate simulation of greenhouse gas concentrations. In order to use tall tower greenhouse gas measurements to quantify regional scale emissions (top-down, inverse estimates) an estimate of the atmospheric transport model uncertainty across the time series of study is needed. Several methods have been used to estimate this, often relying on arbitrary thresholds or a combination of parameters (such as vertical gradients if a gas is measured at multiple points). Here we study if radon has potential as an independent measurement to assess model uncertainty.Radon is a radioactive noble gas present in our atmosphere and is a good tracer of mixing processes in the ABL due to its properties. Hence, measurements of atmospheric radon concentration can provide useful insights into the vertical mixing state of the atmosphere, and in turn may help to calibrate and validate atmospheric dispersion models.In this study, we use high temporal resolution atmospheric measurements of radon and CH4 from four tall tower sites in the UK, which are part of the Deriving Emissions linked to Climate Change (DECC) network: Heathfield (HFD), Ridge Hill (RGH), Tacolneston (TAC) and Weybourne (WAO). At each site, CH4 is measured at two or three different heights, while radon is measured at one height.To determine a metric whereby single-height measurements of radon can provide a proxy for vertical mixing states, we compare the diurnal cycle of the measured radon concentration with the modelled radon (calculated by the Met Office Numerical Atmospheric Modelling Environment (NAME) dispersion model and radon flux maps). The largest uncertainties are shown to be before sunrise and after sunset right before the inversion layer was formed/destroyed. The diurnal CH4 vertical gradient at these times is also compared with the modelled CH4 vertical gradient.

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.

Atmospheric methane mole fractions have been rising globally since 2007, with 2020 exhibiting the largest annual growth since intensive measurements began globally in 1983. Quantifying methane emissions from Arctic regions is crucial as temperatures have been increasing twice as fast over the Arctic as the global mean. Temperature is an important driver of methane emissions from Arctic soils, controlling rates of microbial production and permafrost thaw. We use a range of methods to quantify emissions and assess trends from the North Slope of Alaska and Northeast Siberia using the most recent data from near-coastal Arctic surface sites. Methods include: the analysis of trends in methane mole fraction enhancements over background from the land sector and as the quantification of emissions at monthly resolution through Bayesian inversions using the NAME atmospheric transport model. This study employs data from the surface sites at Barrow, Alaska and Ambarchik. Preliminary analyses at Barrow using data up to 2020 show that emissions from Alaska’s North Slope have increased substantially in recent years, reflecting a change from previous analyses, which showed no significant increase in summertime methane emissions between 1986-2014. (Sweeney et al., 2016). We show that methane enhancements over background from the land sector increased most prominently from July to September, and that late Autumn enhancements continue to rise at a rate similar to the previous decades. We also find substantial terrestrial emissions from Northeast Siberia occurring both in the Summer as well as in the Autumn months, suggesting important “zero-curtain” emissions. We investigate correlations between surface air temperature and surface inundation with methane enhancements to determine the likely drivers of these enhanced emissions. Our findings can be used to assess whether important changes to the Arctic cryosphere are beginning to be observed.

Atmospheric measurements can be used as a tool to evaluate national greenhouse gas inventories through inverse modelling. Using eight years of continuous methane (CH4) concentration data, this work assesses the United Kingdom's (UK) CH4 emissions over the period 2013–2020. Using two different inversion methods, we find mean emissions of 2.10 ± 0.09 Tg yr−1 and 2.12 ± 0.26 Tg yr−1 between 2013–2020, and an overall trend of −0.05 ± 0.01 Tg yr−2 and −0.06 ± 0.04 Tg yr−2, a 2–3 % decrease each year. This compares with the mean emissions of 2.23 Tg yr−1 and trend of −0.03 Tg yr−2 (1 % annual decrease) reported in the UK's 2021 inventory between 2013–2019. We examine how sensitive these estimates are to various components of the inversion set-up, such as the measurement network configuration, the prior emissions estimate, the inversion method, and the atmospheric transport model used. We find the decreasing trend to be due primarily to a reduction of emissions from England, which accounts for 70 % of the UK CH4 emissions. Comparisons during 2015 demonstrate consistency when different atmospheric transport models are used to map the relationship between sources and atmospheric observations at the aggregation level of the UK. The posterior annual national means and negative trend are found to be consistent across changes in network configuration. We show, using only two monitoring sites, the same conclusions on mean UK emissions and negative trend would be reached as using the full six-site network, albeit with larger posterior uncertainties. However, emissions estimates from Scotland fail to converge on the same posterior under different inversion setups, highlighting a shortcoming of the current observation network in monitoring all of the UK. Although CH4 emissions in 2020 are estimated to have declined relative to previous years, this decrease is in line with the longer-term emissions trend, and is not necessarily a response to national lockdowns.

Airborne measurements of methane (CH4) were recorded over three major wetland areas in Zambia in February 2019 during the MOYA (Methane Observations and Yearly Assessments) ZWAMPS field campaign. Enhancements of up to 600 ppb CH4 were measured over the Bangweulu (11°36’ S, 30°05’ E), Kafue (15°43’ S, 27°17’ E), and Lukanga (14°29’ S, 27°47’ E) wetlands. Three independent methods were used to quantify methane emission fluxes; aircraft mass balance, aircraft eddy covariance, and atmospheric inversion modelling. Results yielded methane emission fluxes of 10-20 mg CH4 m-2 hr-1, which were up to an order of magnitude greater than the emission fluxes simulated by various wetland process models (WetCHARTs ensemble and LPX-Bern). Independent column CH4 observations from the TROPOMI instrument were used to verify these fluxes, and investigate their applicability for timescales longer than the duration of the MOYA flight campaign.

Methane is a potent greenhouse gas with concentrations that are rising in the atmosphere in unexpected ways. Because of its radiative efficiency and because its lifetime in the atmosphere is only around a decade, reducing atmospheric methane concentration is a major component of most pathways designed to meet climate targets. Over the past two decades, observations indicate that there have been substantial changes in the emissions and removal of methane. Yet, years later, we still do not definitively know why methane concentrations plateaued in the 2000s, increased globally after 2007 and then continued to increase at an even faster rate after 2014. This limited understanding impacts our ability to carry out targeted emissions reductions. Here, I discuss two areas of my work in addressing gaps in our knowledge. First, I discuss how high-resolution modeling can extract information from satellite data to quantify long-term changes in emissions and the underlying drivers of these changes. I show that Brazil is a unique example where major sources such as wetlands and cattle are geographically distinct and thus satellite data can be used to examine changes from particular processes. I show how in the absence of this separation, which is the case for many other parts of world, additional information such as isotopic ratios can be used to contribute to the partitioning of methane emissions into underlying sources. I also discuss the limitations in current capability to effectively use isotopic ratio measurements. I show how field experiments and simple models can be used to derive global distributions in the isotopic signatures of major sources such as wetlands, providing more consistency against observations. I discuss how incorrect assumptions about source signature distributions have a major impact on our ability to interpret atmospheric isotopic ratio measurements and that this may be one reason why we have not been able to conclusively interpret the recent atmospheric methane record.

We reconstruct atmospheric abundances of the potent greenhouse gas c-C4F8 (perfluorocyclobutane, perfluorocarbon PFC-318) from measurements of in situ, archived, firn, and aircraft air samples with precisions of ~ 1–2 % reported on the SIO-14 gravimetric calibration scale. Combined with inverse methods, we found near zero atmospheric abundances from the early 1900s to the early 1960s, after which they rose sharply, reaching 1.66 ppt (parts per trillion dry-air mole fraction) in 2017. Global c-C4F8 emissions rose from near zero in the 1960s to ~ 1.2 Gg yr−1 in the late 1970s to late 1980s, then declined to ~ 0.8 Gg yr−1 in the mid-1990s to early 2000s, followed by a rise since the early 2000s to ~ 2.2 Gg yr−1 in 2017. These emissions are significantly larger than inventory based emission estimates. Estimated emissions from eastern Asia rose from 0.36 Gg yr−1 in 2010 to 0.73 Gg yr−1 in 2016 and 2017, 31 % of global emissions, mostly from eastern China. We estimate emissions of 0.14 Gg yr−1 from Northern and Central India in 2016 and find evidence for significant emissions from Russia. In contrast, recent emissions from North Western Europe and Australia are estimated to be small (≤ 1 % each). We conclude that emissions from China, India and Russia are likely related to production of polytetrafluoroethylene (PTFE, “Teflon”) and other fluoropolymers that are based on the pyrolysis of hydrochlorofluorocarbon HCFC-22 (CHClF2) in which c-C4F8 is a known by-product. The semiconductor sector, where c-C4F8 is used, is estimated to be a small source. Without an obvious correlation with population density, incineration of waste containing fluoropolymers is probably a minor source, and we find no evidence of emissions from electrolytic production of aluminum in Australia. While many possible emissive uses of c-C4F8 are known, the start of significant emissions may well be related to the advent of commercial PTFE production in 1947. Process controls or abatement to reduce c-C4F8 by-product were probably not in place in the early decades, explaining the increase in emissions. With the advent of by-product reporting requirements to the United Nations Framework Convention on Climate Change (UNFCCC) in the 1990s, concern about climate change and product stewardship, abatement, and perhaps the collection of c-C4F8 by-product for use in the semiconductor industry where it can be easily abated, it is conceivable that emissions in developed countries were stabilized and then reduced, explaining the observed emission reduction in the 1980s and 1990s. Concurrently, production of PTFE in China began to increase rapidly. Without emission reduction requirements, it is plausible that global emissions today are dominated by China and other developing countries, in agreement with our analysis. We predict that c-C4F8 emissions will continue to rise and that c-C4F8 will become the second most important emitted PFC in terms of CO2-equivalent emissions within a year or two. The 2017 radiative forcing of c-C4F8 (0.52 mW m−2) is small but emissions of c-C4F8 and other PFCs, due to their very long atmospheric lifetimes, essentially permanently alter Earth's radiative budget and should be reduced. Significant emissions outside of the investigated regions clearly show that observational capabilities and reporting requirements need to be improved to understand global and country scale emissions of PFCs and other synthetic greenhouse gases and ozone depleting substances.