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We investigate the use of atmospheric oxygen (O2) and carbon dioxide (CO2) measurements for the estimation of the fossil fuel component of atmospheric CO2 in the UK. Atmospheric potential oxygen (APO) – a tracer that combines O2 and CO2, minimizing the influence of terrestrial biosphere fluxes – is simulated at three sites in the UK, two of which make APO measurements. We present a set of model experiments that estimate the sensitivity of APO simulations to key inputs: fluxes from the ocean, fossil fuel flux magnitude and distribution, the APO baseline, and the exchange ratio of O2 to CO2 fluxes from fossil fuel combustion and the terrestrial biosphere. To estimate the influence of uncertainties in ocean fluxes, we compare three ocean O2 flux estimates from the NEMO–ERSEM, the ECCO–Darwin ocean model, and the Jena CarboScope (JC) APO inversion. The sensitivity of APO to fossil fuel emission magnitudes and to terrestrial biosphere and fossil fuel exchange ratios is investigated through Monte Carlo sampling within literature uncertainty ranges and by comparing different inventory estimates. We focus our model–data analysis on the year 2015 as ocean fluxes are not available for later years. As APO measurements are only available for one UK site at this time, our analysis focuses on the Weybourne station. Model–data comparisons for two additional UK sites (Heathfield and Ridge Hill) in 2021, using ocean flux climatologies, are presented in the Supplement. Of the factors that could potentially compromise simulated APO-derived fossil fuel CO2 (ffCO2) estimates, we find that the ocean O2 flux estimate has the largest overall influence at the three sites in the UK. At times, this influence is comparable in magnitude to the contribution of simulated fossil fuel CO2 to simulated APO. We find that simulations using different ocean fluxes differ from each other substantially. No single model estimate, or a model estimate that assumed zero ocean flux, provided a significantly closer fit than any other. Furthermore, the uncertainty in the ocean contribution to APO could lead to uncertainty in defining an appropriate regional background from the data. Our findings suggest that the contribution of non-terrestrial sources needs to be better accounted for in model simulations of APO in the UK to reduce the potential influence on inferred fossil fuel CO2 using APO.

Anthropogenic trace gases often exhibit interhemispheric gradients because of larger emissions in the Northern Hemisphere. Depending on a tracer's emission pattern and sink processes, trace gas observations can thus be used to investigate interhemispheric transport in the atmosphere. Vice versa, understanding interhemispheric transport is important for interpreting spatial tracer distributions and for inferring emissions. We combine several data sets from the upper troposphere (UT) to investigate the interhemispheric gradient of sulfur hexafluoride (SF6) covering latitudes from ∼ 80∘ N to ∼ 60∘ S: canister sampling based measurements from the IAGOS-CARIBIC infrastructure and data from the in-flight gas chromatography instruments GhOST (Gas chromatograph for Observational Studies using Tracers) and UCATS (Unmanned aircraft systems Chromatograph for Atmospheric Trace Species). The interhemispheric gradient of SF6 in the UT is found to be weaker than near the surface. Using the concept of a lag time removes the increasing trend from the time series. At the most southern latitudes, a lag time of over 1 year with respect to the northern mid-latitude surface is derived, and lag times decrease over the period 2006–2020 in the extra-tropics and the southern tropics. Observations are compared to results from the two-dimensional Advanced Global Atmospheric Gases Experiment (AGAGE) 12-box model. Based on Emissions Database for Global Atmospheric Research (EDGAR 7) emissions, fair agreement of lag times is obtained for the Northern Hemisphere, but southern hemispheric air appears too “old”. This is consistent with earlier findings that transport from the northern extra-tropics into the tropics is too slow in many models. The influence of the emission scenario and the model transport scheme are evaluated in sensitivity runs. It is found that EDGAR 7 underestimates emissions of SF6 globally and in the Southern Hemisphere, whereas northern extra-tropical emissions seem overestimated. Faster southward transport from the northern extra-tropics would be needed in the model, but transport from the southern tropics into the southern extra-tropics appears too fast.

Production and consumption of CFC-11 (trichlorofluoromethane, CCl3F), CFC-12 (dichlorodifluoromethane, CCl2F2) and CCl4 (carbon tetrachloride) are controlled under the regulations of the Montreal Protocol and have been phased out globally since 2010. Only CCl4 is still widely produced as a chemical feedstock. After 2010, emissions of CFC-11 and CFC-12 should therefore mostly originate from existing banks (e.g. from foams, mobile air conditioning units and refrigerators); however evidence has emerged of an increase in global emissions of CFC-11 in the last decade, some of which has not been fully accounted for. The motivation for this work was to assess the emissions of CFC-11, CFC-12 and CCl4 from western Europe. All countries in this region have been subject to the controls of the Montreal Protocol since the late 1980s and, as non-Article 5 Parties, have been prohibited from producing CFCs and CCl4 for dispersive use since 1996. Four different inverse modelling systems are used to estimate emissions of these gases from 2008 to 2021 using data from four atmospheric measurement stations: Mace Head (Ireland), Jungfraujoch (Switzerland), Monte Cimone (Italy) and Tacolneston (UK). The average of the four model studies found that western European emissions of CFC-11, CFC-12 and CCl4 between 2008 and 2021 were declining at 3.5 % yr−1 (2.7 % yr−1–4.8 % yr−1), 7.7 % yr−1 (6.3 % yr−1–8.0 % yr−1) and 4.4 % yr−1 (2.6 % yr−1–6.4 % yr−1), respectively. Even though the emissions were declining throughout the period, the area including northern France, Belgium, the Netherlands and Luxembourg showed consistently elevated emissions of CFC-11 compared with the surrounding regions. Emissions of CFC-12 were slightly elevated in the same region. CCl4 emissions were the highest in the south of France. France had the highest emissions of all three gases over the period 2008–2021. Emissions from western Europe (2008–2021) were on average 2.4 ± 0.4 Gg (CFC-11), 1.3 ± 0.3 Gg (CFC-12) and 0.9 ± 0.2 Gg (CCl4). Our estimated decline in emissions of CFC-11 is consistent with a western European bank release rate of 3.4 % (2.6 %–4.5 %). This study concludes that emissions of CFC-11, CFC-12 and CCl4 have all declined from 2008 to 2021 in western Europe. Therefore, no evidence is found that western European emissions contributed to the unexplained part of the global increase in atmospheric concentrations of CFC-11 observed in the last decade.

Lagrangian particle dispersion models (LPDMs) have been used extensively to calculate source-receptor relationships (“footprints”) for use in applications such as greenhouse gas (GHG) flux inversions. Because a single model simulation is required for each data point, LPDMs do not scale well to applications with large data sets such as flux inversions using satellite observations. Here, we develop a proof-of-concept machine learning emulator for LPDM footprints over a ∼ 350 km × 230 km region around an observation point, and test it for a range of in situ measurement sites from around the world. As opposed to previous approaches to footprint approximation, it does not require the interpolation or smoothing of footprints produced by the LPDM. Instead, the footprint is emulated entirely from meteorological inputs. This is achieved by independently emulating the footprint magnitude at each grid cell in the domain using gradient-boosted regression trees with a selection of meteorological variables as inputs. The emulator is trained based on footprints from the UK Met Office's Numerical Atmospheric-dispersion Modelling Environment (NAME) for 2014 and 2015, and the emulated footprints are evaluated against hourly NAME output from 2016 and 2020. When compared to CH4 concentration time series generated by NAME, we show that our emulator achieves a mean R-squared score of 0.69 across all sites investigated between 2016 and 2020. The emulator can predict a footprint in around 10 ms, compared to around 10 min for the 3D simulator. This simple and interpretable proof-of-concept emulator demonstrates the potential of machine learning for LPDM emulation.

In recent years, the concept of Digital Twin (DT) for cities is increasingly at the core of most smart city initiatives, as it has been identified as a critical tool for tackling the challenges of this century. A robust city modelling framework is essential if local, state and national governments are to move towards sustainable built environments and work together across complex multi-sectoral problems to drive impacts that improve urban liveability and climate adaptability. Furthermore, the level of collaboration and interoperability required to address these cannot be achieved without proper standardisation of DT components. The aim of this project is to develop a demonstration DT that integrates existing data using a standardised 3D format based on CityGML and that embeds analytics, such as sun exposure and tree coverage, to assess liveability within a 3D city modelling framework. Common urban features such as buildings, roads, railways, vegetation and water bodies are also processed and incorporated. Additionally, IoT sensors are integrated into the model and all processes are performed using open-source tools to improve accessibility and repeatability. Details of the workflow, including the storage of the city features in a 3D City Database (3DCityDB), the 3D upgrading of urban features commonly available as 2D data as well as a few use cases are illustrated and discussed in this paper.

Nitrous oxide is a potent greenhouse gas (GHG) and ozone-depleting substance, whose atmospheric abundance has risen throughout the contemporary record. In this work, we carry out the first global hierarchical Bayesian inversion to solve for nitrous oxide emissions, which includes prior emissions with truncated Gaussian distributions and Gaussian model errors, in order to examine the drivers of the atmospheric surface growth rate. We show that both emissions and climatic variability are key drivers of variations in the surface nitrous oxide growth rate between 2011 and 2020. We derive increasing global nitrous oxide emissions, which are mainly driven by emissions between 0 and 30∘ N, with the highest emissions recorded in 2020. Our mean global total emissions for 2011–2020 of 17.2 (16.7–17.7 at the 95 % credible intervals) Tg N yr−1, comprising of 12.0 (11.2–12.8) Tg N yr−1 from land and 5.2 (4.5–5.9) Tg N yr−1 from ocean, agrees well with previous studies, but we find that emissions are poorly constrained for some regions of the world, particularly for the oceans. The prior emissions used in this and other previous work exhibit a seasonal cycle in the extra-tropical Northern Hemisphere that is out of phase with the posterior solution, and there is a substantial zonal redistribution of emissions from the prior to the posterior. Correctly characterizing the uncertainties in the system, for example in the prior emission fields, is crucial for deriving posterior fluxes that are consistent with observations. In this hierarchical inversion, the model-measurement discrepancy and the prior flux uncertainty are informed by the data, rather than solely through “expert judgement”. We show cases where this framework provides different plausible adjustments to the prior fluxes compared to inversions using widely adopted, fixed uncertainty constraints.

Global emissions of the ozone-depleting gas HCFC-141b (1,1-dichloro-1-fluoroethane, CH3CCl2F) derived from measurements of atmospheric mole fractions increased between 2017 and 2021 despite a fall in reported production and consumption of HCFC-141b for dispersive uses. HCFC-141b is a controlled substance under the Montreal Protocol, and its phase-out is currently underway, after a peak in reported consumption and production in developing (Article 5) countries in 2013. If reported production and consumption are correct, our study suggests that the 2017–2021 rise is due to an increase in emissions from the bank when appliances containing HCFC-141b reach the end of their life, or from production of HCFC-141b not reported for dispersive uses. Regional emissions have been estimated between 2017–2020 for all regions where measurements have sufficient sensitivity to emissions. This includes the regions of northwestern Europe, east Asia, the United States and Australia, where emissions decreased by a total of 2.3 ± 4.6 Gg yr−1, compared to a mean global increase of 3.0 ± 1.2 Gg yr−1 over the same period. Collectively these regions only account for around 30 % of global emissions in 2020. We are not able to pinpoint the source regions or specific activities responsible for the recent global emission rise.

The emissions of hydrofluorocarbons (HFCs) have increased significantly in the past 2 decades, primarily as a result of the phaseout of ozone-depleting substances under the Montreal Protocol and the use of HFCs as their replacements. In 2015, large increases were projected in HFC use and emissions in this century in the absence of regulations, contributing up to 0.5 ∘C to global surface warming by 2100. In 2019, the Kigali Amendment to the Montreal Protocol came into force with the goal of limiting the use of HFCs globally, and currently, regulations to limit the use of HFCs are in effect in several countries. Here, we analyze trends in HFC emissions inferred from observations of atmospheric abundances and compare them with previous projections. Total CO2 eq. inferred HFC emissions continue to increase through 2019 (to about 0.8 GtCO2eq.yr-1) but are about 20 % lower than previously projected for 2017–2019, mainly because of the lower global emissions of HFC-143a. This indicates that HFCs are used much less in industrial and commercial refrigeration (ICR) applications than previously projected. This is supported by data reported by the developed countries and the lower reported consumption of HFC-143a in China. Because this time period preceded the beginning of the Kigali provisions, this reduction cannot be linked directly to the provisions of the Kigali Amendment. However, it could indicate that companies transitioned away from the HFC-143a with its high global warming potential (GWP) for ICR applications in anticipation of national or global mandates. There are two new HFC scenarios developed based (1) on current trends in HFC use and Kigali-independent (K-I) control policies currently existing in several countries and (2) current HFC trends and compliance with the Kigali Amendment (KA-2022). These current policies reduce projected emissions in 2050 from the previously calculated 4.0–5.3 GtCO2eq.yr-1 to 1.9–3.6 GtCO2eq.yr-1. The added provisions of the Kigali Amendment are projected to reduce the emissions further to 0.9–1.0 GtCO2eq.yr-1 in 2050. Without any controls, projections suggest a HFC contribution of 0.28–0.44 ∘C to global surface warming by 2100, compared to a temperature contribution of 0.14–0.31 ∘C that is projected considering the national K-I policies current in place. Warming from HFCs is additionally limited by the Kigali Amendment controls to a contribution of about 0.04 ∘C by 2100.

Emissions of the potent greenhouse gas perfluorocyclobutane (c-C4F8, PFC-318, octafluorocyclobutane) into the global atmosphere inferred from atmospheric measurements have been increasing sharply since the early 2000s. We find that these inferred emissions are highly correlated with the production of hydrochlorofluorocarbon-22 (HCFC-22, CHClF2) for feedstock (FS) uses, because almost all HCFC-22 FS is pyrolyzed to produce (poly)tetrafluoroethylene ((P)TFE) and hexafluoropropylene (HFP), a process in which c-C4F8 is a known by-product, causing a significant fraction of global c-C4F8 emissions. We find a global emission factor of ∼0.003 kg c-C4F8 per kilogram of HCFC-22 FS pyrolyzed. Mitigation of these c-C4F8 emissions, e.g., through process optimization, abatement, or different manufacturing processes, such as refined methods of electrochemical fluorination and waste recycling, could reduce the climate impact of this industry. While it has been shown that c-C4F8 emissions from developing countries dominate global emissions, more atmospheric measurements and/or detailed process statistics are needed to quantify c-C4F8 emissions at country to facility levels.

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 spatially and temporally 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 yr−1 from fossil fuel sources and 2.06 (1.99–2.15) Tg yr−1 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.

WOMBAT (the WOllongong Methodology for Bayesian Assimilation of Trace-gases) is a fully Bayesian hierarchical statistical framework for flux inversion of trace gases from flask, in situ, and remotely sensed data. WOMBAT extends the conventional Bayesian synthesis framework through the consideration of a correlated error term, the capacity for online bias correction, and the provision of uncertainty quantification on all unknowns that appear in the Bayesian statistical model. We show, in an observing system simulation experiment (OSSE), that these extensions are crucial when the data are indeed biased and have errors that are spatio-temporally correlated. Using the GEOS-Chem atmospheric transport model, we show that WOMBAT is able to obtain posterior means and variances on non-fossil-fuel CO2 fluxes from Orbiting Carbon Observatory-2 (OCO-2) data that are comparable to those from the Model Intercomparison Project (MIP) reported in Crowell et al. (2019). We also find that WOMBAT's predictions of out-of-sample retrievals obtained from the Total Column Carbon Observing Network (TCCON) are, for the most part, more accurate than those made by the MIP participants.

Atmospheric measurements can be used as a tool to evaluate national greenhouse gas inventories through inverse modelling. Using 8 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 and 2.12 ± 0.26 Tg yr−1 between 2013 and 2020, an overall trend of −0.05 ± 0.01 and −0.06 ± 0.04 Tg yr−2 and a 2 %–3 % decrease each year. This compares with the mean emissions of 2.23 Tg yr−1 and the trend of −0.03 Tg yr−2 (1 % annual decrease) reported in the UK's 2021 inventory between 2013 and 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 in 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, that 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 set-ups, 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.

To track progress towards keeping global warming well below 2 ∘C or even 1.5 ∘C, as agreed in the Paris Agreement, comprehensive up-to-date and reliable information on anthropogenic emissions and removals of greenhouse gas (GHG) emissions is required. Here we compile a new synthetic dataset on anthropogenic GHG emissions for 1970–2018 with a fast-track extension to 2019. Our dataset is global in coverage and includes CO2 emissions, CH4 emissions, N2O emissions, as well as those from fluorinated gases (F-gases: HFCs, PFCs, SF6, NF3) and provides country and sector details. We build this dataset from the version 6 release of the Emissions Database for Global Atmospheric Research (EDGAR v6) and three bookkeeping models for CO2 emissions from land use, land-use change, and forestry (LULUCF). We assess the uncertainties of global greenhouse gases at the 90 % confidence interval (5th–95th percentile range) by combining statistical analysis and comparisons of global emissions inventories and top-down atmospheric measurements with an expert judgement informed by the relevant scientific literature. We identify important data gaps for F-gas emissions. The agreement between our bottom-up inventory estimates and top-down atmospheric-based emissions estimates is relatively close for some F-gas species (∼ 10 % or less), but estimates can differ by an order of magnitude or more for others. Our aggregated F-gas estimate is about 10 % lower than top-down estimates in recent years. However, emissions from excluded F-gas species such as chlorofluorocarbons (CFCs) or hydrochlorofluorocarbons (HCFCs) are cumulatively larger than the sum of the reported species. Using global warming potential values with a 100-year time horizon from the Sixth Assessment Report by the Intergovernmental Panel on Climate Change (IPCC), global GHG emissions in 2018 amounted to 58 ± 6.1 GtCO2 eq. consisting of CO2 from fossil fuel combustion and industry (FFI) 38 ± 3.0 GtCO2, CO2-LULUCF 5.7 ± 4.0 GtCO2, CH4 10 ± 3.1 GtCO2 eq., N2O 2.6 ± 1.6 GtCO2 eq., and F-gases 1.3 ± 0.40 GtCO2 eq. Initial estimates suggest further growth of 1.3 GtCO2 eq. in GHG emissions to reach 59 ± 6.6 GtCO2 eq. by 2019. Our analysis of global trends in anthropogenic GHG emissions over the past 5 decades (1970–2018) highlights a pattern of varied but sustained emissions growth. There is high confidence that global anthropogenic GHG emissions have increased every decade, and emissions growth has been persistent across the different (groups of) gases. There is also high confidence that global anthropogenic GHG emissions levels were higher in 2009–2018 than in any previous decade and that GHG emissions levels grew throughout the most recent decade. While the average annual GHG emissions growth rate slowed between 2009 and 2018 (1.2 % yr−1) compared to 2000–2009 (2.4 % yr−1), the absolute increase in average annual GHG emissions by decade was never larger than between 2000–2009 and 2009–2018. Our analysis further reveals that there are no global sectors that show sustained reductions in GHG emissions. There are a number of countries that have reduced GHG emissions over the past decade, but these reductions are comparatively modest and outgrown by much larger emissions growth in some developing countries such as China, India, and Indonesia. There is a need to further develop independent, robust, and timely emissions estimates across all gases. As such, tracking progress in climate policy requires substantial investments in independent GHG emissions accounting and monitoring as well as in national and international statistical infrastructures. The data associated with this article (Minx et al., 2021) can be found at https://doi.org/10.5281/zenodo.5566761.

We present a method to efficiently approximate the response of atmospheric-methane mole fraction and δ13C–CH4 to changes in uncertain emission and loss parameters in a three-dimensional global chemical transport model. Our approach, based on Gaussian process emulation, allows relationships between inputs and outputs in the model to be efficiently explored. The presented emulator successfully reproduces the chemical transport model output with a root-mean-square error of 1.0 ppb and 0.05 ‰ for hemispheric-methane mole fraction and δ13C–CH4, respectively, for 28 uncertain model inputs. The method is shown to outperform multiple linear regression because it captures non-linear relationships between inputs and outputs as well as the interaction between model input parameters. The emulator was used to determine how sensitive methane mole fraction and δ13C–CH4 are to the major source and sink components of the atmospheric budget given current estimates of their uncertainty. We find that our current knowledge of the methane budget, as inferred through hemispheric mole fraction observations, is limited primarily by uncertainty in the global mean hydroxyl radical concentration and freshwater emissions. Our work quantitatively determines the added value of measurements of δ13C–CH4, which are sensitive to some uncertain parameters to which mole fraction observations on their own are not. However, we demonstrate the critical importance of constraining isotopic initial conditions and isotopic source signatures, small uncertainties in which strongly influence long-term δ13C–CH4 trends because of the long timescales over which transient perturbations propagate through the atmosphere. Our results also demonstrate that the magnitude and trend of methane mole fraction and δ13C–CH4 can be strongly influenced by the combined uncertainty in more minor components of the atmospheric budget, which are often fixed and assumed to be well-known in inverse-modelling studies (e.g. emissions from termites, hydrates, and oceans). Overall, our work provides an overview of the sensitivity of atmospheric observations to budget uncertainties and outlines a method which could be employed to account for these uncertainties in future inverse-modelling systems.

Perfluorocarbons (PFCs) are amongst the most potent greenhouse gases listed under the United Nations Framework Convention on Climate Change (UNFCCC). With atmospheric lifetimes on the order of thousands to tens of thousands of years, PFC emissions represent a permanent alteration to the global atmosphere on human timescales. While the industries responsible for the vast majority of these emissions – aluminium smelting and semi-conductor manufacturing – have made efficiency improvements and introduced abatement measures, the global mean mole fractions of three PFCs, namely tetrafluoromethane (CF4, PFC-14), hexafluoroethane (C2F6, PFC-116) and octafluoropropane (C3F8, PFC-218), continue to grow. In this study, we update baseline growth rates using in situ high-frequency measurements from the Advanced Global Atmospheric Gases Experiment (AGAGE) and, using data from four European stations, estimate PFC emissions for northwest Europe. The global growth rate of CF4 decreased from 1.3 ppt yr−1 in 1979 to 0.6 ppt yr−1 around 2010 followed by a renewed steady increase to 0.9 ppt yr−1 in 2019. For C2F6, the growth rate grew to a maximum of 0.125 ppt yr−1 around 1999, followed by a decline to a minimum of 0.075 ppt yr−1 in 2009, followed by weak growth thereafter. The C3F8 growth rate was around 0.007 ppt yr−1 until the early 1990s and then quickly grew to a maximum of 0.03 ppt yr−1 in 2003–2004. Following a period of decline until 2012 to 0.015 ppt yr−1, the growth rate slowly increased again to ∼ 0.017 ppt yr−1 in 2019. We used an inverse modelling framework to infer PFC emissions for northwest Europe. No statistically significant trend in regional emissions was observed for any of the PFCs assessed. For CF4, European emissions in early years were linked predominantly to the aluminium industry. However, we link large emissions in recent years to a chemical manufacturer in northwest Italy. Emissions of C2F6 are linked to a range of sources, including a semi-conductor manufacturer in Ireland and a cluster of smelters in Germany's Ruhr valley. In contrast, northwest European emissions of C3F8 are dominated by a single source in northwest England, raising the possibility of using emissions from this site for a tracer release experiment.

Brazil's CH4 emissions over the period 2010–2018 were derived for the three main sectors of activity: anthropogenic, wetland and biomass burning. Our inverse modelling estimates were derived from GOSAT (Greenhouse gases Observing SATellite) satellite measurements of XCH4 combined with surface data from Ragged Point, Barbados, and the high-resolution regional atmospheric transport model NAME (Numerical Atmospheric-dispersion Modelling Environment). We find that Brazil's mean emissions over 2010–2018 are 33.6±3.6Tgyr-1, which are comprised of 19.0±2.6Tgyr-1 from anthropogenic (primarily related to agriculture and waste), 13.0±1.9Tgyr-1 from wetlands and 1.7±0.3Tgyr-1 from biomass burning sources. In addition, between the 2011–2013 and 2014–2018 periods, Brazil's mean emissions rose by 6.9±5.3Tgyr-1 and this increase may have contributed to the accelerated global methane growth rate observed during the latter period. We find that wetland emissions from the western Amazon increased during the start of the 2015–2016 El Niño by 3.7±2.7Tgyr-1 and this is likely driven by increased surface temperatures. We also find that our estimates of anthropogenic emissions are consistent with those reported by Brazil to the United Framework Convention on Climate Change. We show that satellite data are beneficial for constraining national-scale CH4 emissions, and, through a series of sensitivity studies and validation experiments using data not assimilated in the inversion, we demonstrate that (a) calibrated ground-based data are important to include alongside satellite data in a regional inversion and that (b) inversions must account for any offsets between the two data streams and their representations by models.

Atmospheric inversions have been used for the past two decades to derive large-scale constraints on the sources and sinks of CO2 into the atmosphere. The development of dense in situ surface observation networks, such as ICOS in Europe, enables in theory inversions at a resolution close to the country scale in Europe. This has led to the development of many regional inversion systems capable of assimilating these high-resolution data, in Europe and elsewhere. The EUROCOM (European atmospheric transport inversion comparison) project is a collaboration between seven European research institutes, which aims at producing a collective assessment of the net carbon flux between the terrestrial ecosystems and the atmosphere in Europe for the period 2006–2015. It aims in particular at investigating the capacity of the inversions to deliver consistent flux estimates from the country scale up to the continental scale. The project participants were provided with a common database of in situ-observed CO2 concentrations (including the observation sites that are now part of the ICOS network) and were tasked with providing their best estimate of the net terrestrial carbon flux for that period, and for a large domain covering the entire European Union. The inversion systems differ by the transport model, the inversion approach, and the choice of observation and prior constraints, enabling us to widely explore the space of uncertainties. This paper describes the intercomparison protocol and the participating systems, and it presents the first results from a reference set of inversions, at the continental scale and in four large regions. At the continental scale, the regional inversions support the assumption that European ecosystems are a relatively small sink (-0.21±0.2 Pg C yr−1). We find that the convergence of the regional inversions at this scale is not better than that obtained in state-of-the-art global inversions. However, more robust results are obtained for sub-regions within Europe, and in these areas with dense observational coverage, the objective of delivering robust country-scale flux estimates appears achievable in the near future.

We report a 40-year history of SF6 atmospheric mole fractions measured at the Advanced Global Atmospheric Gases Experiment (AGAGE) monitoring sites, combined with archived air samples, to determine emission estimates from 1978 to 2018. Previously we reported a global emission rate of 7.3±0.6 Gg yr−1 in 2008 and over the past decade emissions have continued to increase by about 24 % to 9.04±0.35 Gg yr−1 in 2018. We show that changing patterns in SF6 consumption from developed (Kyoto Protocol Annex-1) to developing countries (non-Annex-1) and the rapid global expansion of the electric power industry, mainly in Asia, have increased the demand for SF6-insulated switchgear, circuit breakers, and transformers. The large bank of SF6 sequestered in this electrical equipment provides a substantial source of emissions from maintenance, replacement, and continuous leakage. Other emissive sources of SF6 occur from the magnesium, aluminium, and electronics industries as well as more minor industrial applications. More recently, reported emissions, including those from electrical equipment and metal industries, primarily in the Annex-1 countries, have declined steadily through substitution of alternative blanketing gases and technological improvements in less emissive equipment and more efficient industrial practices. Nevertheless, there are still demands for SF6 in Annex-1 countries due to economic growth, as well as continuing emissions from older equipment and additional emissions from newly installed SF6-insulated electrical equipment, although at low emission rates. In addition, in the non-Annex-1 countries, SF6 emissions have increased due to an expansion in the growth of the electrical power, metal, and electronics industries to support their continuing development. There is an annual difference of 2.5–5 Gg yr−1 (1990–2018) between our modelled top-down emissions and the UNFCCC-reported bottom-up emissions (United Nations Framework Convention on Climate Change), which we attempt to reconcile through analysis of the potential contribution of emissions from the various industrial applications which use SF6. We also investigate regional emissions in East Asia (China, S. Korea) and western Europe and their respective contributions to the global atmospheric SF6 inventory. On an average annual basis, our estimated emissions from the whole of China are approximately 10 times greater than emissions from western Europe. In 2018, our modelled Chinese and western European emissions accounted for ∼36 % and 3.1 %, respectively, of our global SF6 emissions estimate.

We present a method to infer spatially and spatio-temporally correlated emissions of greenhouse gases from atmospheric measurements and a chemical transport model. The method allows fast computation of spatial emissions using a hierarchical Bayesian framework as an alternative to Markov chain Monte Carlo algorithms. The spatial emissions follow a Gaussian process with a Matérn correlation structure which can be represented by a Gaussian Markov random field through a stochastic partial differential equation approach. The inference is based on an integrated nested Laplacian approximation (INLA) for hierarchical models with Gaussian latent fields. Combining an autoregressive temporal correlation and the Matérn field provides a full spatio-temporal correlation structure. We first demonstrate the method on a synthetic data example and follow this using a well-studied test case of inferring UK methane emissions from tall tower measurements of atmospheric mole fraction. Results from these two test cases show that this method can accurately estimate regional greenhouse gas emissions, accounting for spatio-temporal uncertainties that have traditionally been neglected in atmospheric inverse modelling.

International agreements have been implemented to reduce emissions of hydrofluorocarbons (HFCs) to reduce their radiative forcing. Even though reported HFC-23 emissions are at a historical low, observations indicate that emissions have actually increased over recent years to higher levels than previously.

We present Δ14CO2 observations and related greenhouse gas measurements at a background site in Ireland (Mace Head, MHD) and a tall tower site in the east of the UK (Tacolneston, TAC) that is more strongly influenced by fossil fuel sources. These observations have been used to calculate the contribution of fossil fuel sources to the atmospheric CO2 mole fractions; this can be done, as emissions from fossil fuels do not contain 14CO2 and cause a depletion in the observed Δ14CO2 value. The observations are compared to simulated values. Two corrections need to be applied to radiocarbon-derived fossil fuel CO2 (ffCO2): one for pure 14CO2 emissions from nuclear industry sites and one for a disequilibrium in the isotopic signature of older biospheric emissions (heterotrophic respiration) and CO2 in the atmosphere. Measurements at both sites were found to only be marginally affected by 14CO2 emissions from nuclear sites. Over the study period of 2014–2015, the biospheric correction and the correction for nuclear 14CO2 emissions were similar at 0.34 and 0.25 ppm ffCO2 equivalent, respectively. The observed ffCO2 at the TAC tall tower site was not significantly different from simulated values based on the EDGAR 2010 bottom-up inventory. We explored the use of high-frequency CO observations as a tracer of ffCO2 by deriving a constant ratio of CO enhancements to ffCO2 ratio for the mix of UK fossil fuel sources. This ratio was found to be 5.7 ppb ppm−1, close to the value predicted using inventories and the atmospheric model of 5.1 ppb ppm−1. The TAC site, in the east of the UK, was strategically chosen to be some distance from pollution sources so as to allow for the observation of well-integrated air masses. However, this distance from pollution sources and the large measurement uncertainty in 14CO2 lead to a large overall uncertainty in the ffCO2, being around 1.8 ppm compared to typical enhancements of 2 ppm.

As the second most populous country and third fastest growing economy, India has emerged as a global economic power. As such, its emissions of greenhouse and ozone-depleting gases are of global significance. However, unlike neighbouring China, the Indian sub-continent is very poorly monitored by atmospheric measurement networks. India's halocarbon emissions, here defined as chlorofluorocarbons (CFCs), hydrochlorofluorocarbons (HCFCs), hydrofluorocarbons (HFCs) and chlorocarbons, are not well-known. Previous measurements from the region have been obtained at observatories many hundreds of kilometres from source regions, or at high altitudes, limiting their value for the estimation of regional emission rates. Given the projected rapid growth in demand for refrigerants and solvents in India, emission estimates of these halocarbons are urgently needed to provide a benchmark against which future changes can be evaluated. In this study, we report atmospheric-measurement-derived halocarbon emissions from India. With the exception of dichloromethane, these top-down estimates are the first for India's halocarbons. Air samples were collected at low altitude during an aircraft campaign in June and July 2016, and emissions were derived from measurements of these samples using an inverse modelling framework. These results were evaluated to assess India's progress in phasing out ozone-depleting substances under the Montreal Protocol. India's combined CFC emissions are estimated to be 54 (27–86) Tg CO2 eq. yr−1 (5th and 95th confidence intervals are shown in parentheses). HCFC-22 emissions of 7.8 (6.0–9.9) Gg yr−1 are of similar magnitude to emissions of HFC-134a (8.2 (6.1–10.7) Gg yr−1). We estimate India's HFC-23 emissions to be 1.2 (0.9–1.5) Gg yr−1, and our results are consistent with resumed venting of HFC-23 by HCFC-22 manufacturers following the discontinuation of funding for abatement under the Clean Development Mechanism. We report small emissions of HFC-32 and HFC-143a and provide evidence to suggest that HFC-32 emissions were primarily due to fugitive emissions during manufacturing processes. A lack of significant correlation among HFC species and the small emissions derived for HFC-32 and HFC-143a indicate that in 2016, India's use of refrigerant blends R-410A, R-404A and R-507A was limited, despite extensive consumption elsewhere in the world. We also estimate emissions of the regulated chlorocarbons carbon tetrachloride and methyl chloroform from northern and central India to be 2.3 (1.5–3.4) and 0.07 (0.04–0.10) Gg yr−1 respectively. While the Montreal Protocol has been successful in reducing emissions of many ozone-depleting substances, growth in the global emission rates of the unregulated very short-lived substances poses an ongoing threat to the recovery of the ozone layer. Emissions of dichloromethane are found to be 96.5 (77.8–115.6) Gg yr−1, and our estimate suggests a 5-fold increase in emissions since the last estimate derived from atmospheric data in 2008. We estimate perchloroethene emissions from India and chloroform emissions from northern–central India to be 2.9 (2.5–3.3) and 32.2 (28.3–37.1) Gg yr−1 respectively. Given the rapid growth of India's economy and the likely increase in demand for halocarbons such as HFCs, the implementation of long-term atmospheric monitoring in the region is urgently required. Our results provide a benchmark against which future changes to India's halocarbon emissions may be evaluated.

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±0.1 (1σ) Gg yr−1 in the late 1970s to late 1980s, then declined to 0.77±0.03 Gg yr−1 in the mid-1990s to early 2000s, followed by a rise since the early 2000s to 2.20±0.05 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 northwestern Europe and Australia are estimated to be small (≤1 % each). We suggest that emissions from China, India, and Russia are likely related to production of polytetrafluoroethylene (PTFE, “Teflon”) and other fluoropolymers and fluorochemicals 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, at least in South Korea, Japan, Taiwan, and Europe. 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 and though we cannot categorically exclude unknown sources, the start of significant emissions may well be related to the advent of commercial PTFE production in 1947. Process controls or abatement to reduce the c-C4F8 by-product were probably not in place in the early decades, explaining the increase in emissions in the 1960s and 1970s. 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. 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 inferred 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.

We present a method to derive atmospheric-observation-based estimates of carbon dioxide (CO2) fluxes at the national scale, demonstrated using data from a network of surface tall-tower sites across the UK and Ireland over the period 2013–2014. The inversion is carried out using simulations from a Lagrangian chemical transport model and an innovative hierarchical Bayesian Markov chain Monte Carlo (MCMC) framework, which addresses some of the traditional problems faced by inverse modelling studies, such as subjectivity in the specification of model and prior uncertainties. Biospheric fluxes related to gross primary productivity and terrestrial ecosystem respiration are solved separately in the inversion and then combined a posteriori to determine net ecosystem exchange of CO2. Two different models, Data Assimilation Linked Ecosystem Carbon (DALEC) and Joint UK Land Environment Simulator (JULES), provide prior estimates for these fluxes. We carry out separate inversions to assess the impact of these different priors on the posterior flux estimates and evaluate the differences between the prior and posterior estimates in terms of missing model components. The Numerical Atmospheric dispersion Modelling Environment (NAME) is used to relate fluxes to the measurements taken across the regional network. Posterior CO2 estimates from the two inversions agree within estimated uncertainties, despite large differences in the prior fluxes from the different models. With our method, averaging results from 2013 and 2014, we find a total annual net biospheric flux for the UK of 8±79 Tg CO2 yr−1 (DALEC prior) and 64±85 Tg CO2 yr−1 (JULES prior), where negative values represent an uptake of CO2. These biospheric CO2 estimates show that annual UK biospheric sources and sinks are roughly in balance. These annual mean estimates consistently indicate a greater net release of CO2 than the prior estimates, which show much more pronounced uptake in summer months.

Atmospheric inverse modelling has become an increasingly useful tool for evaluating emissions of greenhouse gases including methane, nitrous oxide, and synthetic gases such as hydrofluorocarbons (HFCs). Atmospheric inversions for emissions of CO2 from fossil fuel combustion (ffCO2) are currently being developed. The aim of this paper is to investigate potential errors and uncertainties related to the spatial and temporal prior representation of emissions and modelled atmospheric transport for the inversion of ffCO2 emissions in the US state of California. We perform simulation experiments based on a network of ground-based observations of CO2 concentration and radiocarbon in CO2 (a tracer of ffCO2), combining prior (bottom-up) emission models and transport models currently used in many atmospheric studies. The potential effect of errors in the spatial and temporal distribution of prior emission estimates is investigated in experiments by using perturbed versions of the emission estimates used to create the pseudo-data. The potential effect of transport error was investigated by using three different atmospheric transport models for the prior and pseudo-data simulations. We find that the magnitude of biases in posterior total state emissions arising from errors in the spatial and temporal distribution in prior emissions in these experiments are 1 %–15 % of posterior total state emissions and are generally smaller than the 2σ uncertainty in posterior emissions. Transport error in these experiments introduces biases of −10 % to +6 % into posterior total state emissions. Our results indicate that uncertainties in posterior total state ffCO2 estimates arising from the choice of prior emissions or atmospheric transport model are on the order of 15 % or less for the ground-based network in California we consider. We highlight the need for temporal variations to be included in prior emissions and for continuing efforts to evaluate and improve the representation of atmospheric transport for regional ffCO2 inversions.

We describe the motivation, design, and execution of the Greenhouse gAs Uk and Global Emissions (GAUGE) project. The overarching scientific objective of GAUGE was to use atmospheric data to estimate the magnitude, distribution, and uncertainty of the UK greenhouse gas (GHG, defined here as CO2, CH4, and N2O) budget, 2013–2015. To address this objective, we established a multi-year and interlinked measurement and data analysis programme, building on an established tall-tower GHG measurement network. The calibrated measurement network comprises ground-based, airborne, ship-borne, balloon-borne, and space-borne GHG sensors. Our choice of measurement technologies and measurement locations reflects the heterogeneity of UK GHG sources, which range from small point sources such as landfills to large, diffuse sources such as agriculture. Atmospheric mole fraction data collected at the tall towers and on the ships provide information on sub-continental fluxes, representing the backbone to the GAUGE network. Additional spatial and temporal details of GHG fluxes over East Anglia were inferred from data collected by a regional network. Data collected during aircraft flights were used to study the transport of GHGs on local and regional scales. We purposely integrated new sensor and platform technologies into the GAUGE network, allowing us to lay the foundations of a strengthened UK capability to verify national GHG emissions beyond the project lifetime. For example, current satellites provide sparse and seasonally uneven sampling over the UK mainly because of its geographical size and cloud cover. This situation will improve with new and future satellite instruments, e.g. measurements of CH4 from the TROPOspheric Monitoring Instrument (TROPOMI) aboard Sentinel-5P. We use global, nested, and regional atmospheric transport models and inverse methods to infer geographically resolved CO2 and CH4 fluxes. This multi-model approach allows us to study model spread in a posteriori flux estimates. These models are used to determine the relative importance of different measurements to infer the UK GHG budget. Attributing observed GHG variations to specific sources is a major challenge. Within a UK-wide spatial context we used two approaches: (1) Δ14CO2 and other relevant isotopologues (e.g. δ13CCH4) from collected air samples to quantify the contribution from fossil fuel combustion and other sources, and (2) geographical separation of individual sources, e.g. agriculture, using a high-density measurement network. Neither of these represents a definitive approach, but they will provide invaluable information about GHG source attribution when they are adopted as part of a more comprehensive, long-term national GHG measurement programme. We also conducted a number of case studies, including an instrumented landfill experiment that provided a test bed for new technologies and flux estimation methods. We anticipate that results from the GAUGE project will help inform other countries on how to use atmospheric data to quantify their nationally determined contributions to the Paris Agreement.

We present the organization, instrumentation, datasets, data interpretation, modeling, and accomplishments of the multinational global atmospheric measurement program AGAGE (Advanced Global Atmospheric Gases Experiment). AGAGE is distinguished by its capability to measure globally, at high frequency, and at multiple sites all the important species in the Montreal Protocol and all the important non-carbon-dioxide (non-CO2) gases assessed by the Intergovernmental Panel on Climate Change (CO2 is also measured at several sites). The scientific objectives of AGAGE are important in furthering our understanding of global chemical and climatic phenomena. They are the following: (1) to accurately measure the temporal and spatial distributions of anthropogenic gases that contribute the majority of reactive halogen to the stratosphere and/or are strong infrared absorbers (chlorocarbons, chlorofluorocarbons – CFCs, bromocarbons, hydrochlorofluorocarbons – HCFCs, hydrofluorocarbons – HFCs and polyfluorinated compounds (perfluorocarbons – PFCs), nitrogen trifluoride – NF3, sulfuryl fluoride – SO2F2, and sulfur hexafluoride – SF6) and use these measurements to determine the global rates of their emission and/or destruction (i.e., lifetimes); (2) to accurately measure the global distributions and temporal behaviors and determine the sources and sinks of non-CO2 biogenic–anthropogenic gases important to climate change and/or ozone depletion (methane – CH4, nitrous oxide – N2O, carbon monoxide – CO, molecular hydrogen – H2, methyl chloride – CH3Cl, and methyl bromide – CH3Br); (3) to identify new long-lived greenhouse and ozone-depleting gases (e.g., SO2F2, NF3, heavy PFCs (C4F10, C5F12, C6F14, C7F16, and C8F18) and hydrofluoroolefins (HFOs; e.g., CH2 = CFCF3) have been identified in AGAGE), initiate the real-time monitoring of these new gases, and reconstruct their past histories from AGAGE, air archive, and firn air measurements; (4) to determine the average concentrations and trends of tropospheric hydroxyl radicals (OH) from the rates of destruction of atmospheric trichloroethane (CH3CCl3), HFCs, and HCFCs and estimates of their emissions; (5) to determine from atmospheric observations and estimates of their destruction rates the magnitudes and distributions by region of surface sources and sinks of all measured gases; (6) to provide accurate data on the global accumulation of many of these trace gases that are used to test the synoptic-, regional-, and global-scale circulations predicted by three-dimensional models; and (7) to provide global and regional measurements of methane, carbon monoxide, and molecular hydrogen and estimates of hydroxyl levels to test primary atmospheric oxidation pathways at midlatitudes and the tropics. Network Information and Data Repository: http://agage.mit.edu/data or http://cdiac.ess-dive.lbl.gov/ndps/alegage.html (https://doi.org/10.3334/CDIAC/atg.db1001).

High frequency measurements of trifluoromethane (HFC-23, CHF3), a potent hydrofluorocarbon greenhouse gas, largely emitted to the atmosphere as a by-product of the production of the hydrochlorofluorocarbon HCFC-22 (CHClF2), at five core stations of the Advanced Global Atmospheric Gases Experiment (AGAGE) network, combined with measurements on firn air, old Northern Hemisphere air samples and Cape Grim Air Archive (CGAA) air samples, are used to explore the current and historic changes in the atmospheric abundance of HFC-23. These measurements are used in combination with the AGAGE 2-D atmospheric 12-box model and a Bayesian inversion methodology to determine model atmospheric mole fractions and the history of global HFC-23 emissions. The global modelled annual mole fraction of HFC-23 in the background atmosphere was 28.9 ± 0.6 pmol mol−1 at the end of 2016, representing a 28 % increase from 22.6 ± 0.4 pmol mol−1 in 2009. Over the same time frame, the modelled mole fraction of HCFC-22 increased by 19 % from 199 ± 2 to 237 ± 2 pmol mol−1. However, unlike HFC-23, the annual average HCFC-22 growth rate slowed from 2009 to 2016 at an annual average rate of −0.5 pmol mol−1 yr−2. This slowing atmospheric growth is consistent with HCFC-22 moving from dispersive (high fractional emissions) to feedstock (low fractional emissions) uses, with HFC-23 emissions remaining as a consequence of incomplete mitigation from all HCFC-22 production.Our results demonstrate that, following a minimum in HFC-23 global emissions in 2009 of 9.6 ± 0.6, emissions increased to a maximum in 2014 of 14.5 ± 0.6 Gg yr−1 and then declined to 12.7 ± 0.6 Gg yr−1 (157 Mt CO2 eq. yr−1) in 2016. The 2009 emissions minimum is consistent with estimates based on national reports and is likely a response to the implementation of the Clean Development Mechanism (CDM) to mitigate HFC-23 emissions by incineration in developing (non-Annex 1) countries under the Kyoto Protocol. Our derived cumulative emissions of HFC-23 during 2010–2016 were 89 ± 2 Gg (1.1 ± 0.2 Gt CO2 eq.), which led to an increase in radiative forcing of 1.0 ± 0.1 mW m−2 over the same period. Although the CDM had reduced global HFC-23 emissions, it cannot now offset the higher emissions from increasing HCFC-22 production in non-Annex 1 countries, as the CDM was closed to new entrants in 2009. We also find that the cumulative European HFC-23 emissions from 2010 to 2016 were ∼ 1.3 Gg, corresponding to just 1.5 % of cumulative global HFC-23 emissions over this same period. The majority of the increase in global HFC-23 emissions since 2010 is attributed to a delay in the adoption of mitigation technologies, predominantly in China and East Asia. However, a reduction in emissions is anticipated, when the Kigali 2016 amendment to the Montreal Protocol, requiring HCFC and HFC production facilities to introduce destruction of HFC-23, is fully implemented.

Based on observations of the chlorofluorocarbons CFC-13 (chlorotrifluoromethane), ΣCFC-114 (combined measurement of both isomers of dichlorotetrafluoroethane), and CFC-115 (chloropentafluoroethane) in atmospheric and firn samples, we reconstruct records of their tropospheric histories spanning nearly 8 decades. These compounds were measured in polar firn air samples, in ambient air archived in canisters, and in situ at the AGAGE (Advanced Global Atmospheric Gases Experiment) network and affiliated sites. Global emissions to the atmosphere are derived from these observations using an inversion based on a 12-box atmospheric transport model. For CFC-13, we provide the first comprehensive global analysis. This compound increased monotonically from its first appearance in the atmosphere in the late 1950s to a mean global abundance of 3.18 ppt (dry-air mole fraction in parts per trillion, pmol mol−1) in 2016. Its growth rate has decreased since the mid-1980s but has remained at a surprisingly high mean level of 0.02 ppt yr−1 since 2000, resulting in a continuing growth of CFC-13 in the atmosphere. ΣCFC-114 increased from its appearance in the 1950s to a maximum of 16.6 ppt in the early 2000s and has since slightly declined to 16.3 ppt in 2016. CFC-115 increased monotonically from its first appearance in the 1960s and reached a global mean mole fraction of 8.49 ppt in 2016. Growth rates of all three compounds over the past years are significantly larger than would be expected from zero emissions. Under the assumption of unchanging lifetimes and atmospheric transport patterns, we derive global emissions from our measurements, which have remained unexpectedly high in recent years: mean yearly emissions for the last decade (2007–2016) of CFC-13 are at 0.48 ± 0.15 kt yr−1 (> 15 % of past peak emissions), of ΣCFC-114 at 1.90 ± 0.84 kt yr−1 (∼ 10 % of peak emissions), and of CFC-115 at 0.80 ± 0.50 kt yr−1 (> 5 % of peak emissions). Mean yearly emissions of CFC-115 for 2015–2016 are 1.14 ± 0.50 kt yr−1 and have doubled compared to the 2007–2010 minimum. We find CFC-13 emissions from aluminum smelters but if extrapolated to global emissions, they cannot account for the lingering global emissions determined from the atmospheric observations. We find impurities of CFC-115 in the refrigerant HFC-125 (CHF2CF3) but if extrapolated to global emissions, they can neither account for the lingering global CFC-115 emissions determined from the atmospheric observations nor for their recent increases. We also conduct regional inversions for the years 2012–2016 for the northeastern Asian area using observations from the Korean AGAGE site at Gosan and find significant emissions for ΣCFC-114 and CFC-115, suggesting that a large fraction of their global emissions currently occur in northeastern Asia and more specifically on the Chinese mainland.

High-frequency, in situ global observations of HCFC-22 (CHClF2), HCFC-141b (CH3CCl2F), HCFC-142b (CH3CClF2) and HCFC-124 (CHClFCF3) and their main HFC replacements, HFC-134a (CH2FCF3), HFC-125 (CHF2CF3), HFC-143a (CH3CF3) and HFC-32 (CH2F2), have been used to determine their changing global growth rates and emissions in response to the Montreal Protocol and its recent amendments. Global mean mole fractions of HCFC-22, -141b, and -142b have increased throughout the observation period, reaching 234, 24.3 and 22.4 pmol mol−1, respectively, in 2015. HCFC-124 reached a maximum global mean mole fraction of 1.48 pmol mol−1 in 2007 and has since declined by 23 % to 1.14 pmol mol−1 in 2015. The HFCs all show increasing global mean mole fractions. In 2015 the global mean mole fractions (pmol mol−1) were 83.3 (HFC-134a), 18.4 (HFC-125), 17.7 (HFC-143a) and 10.5 (HFC-32). The 2007 adjustment to the Montreal Protocol required the accelerated phase-out of emissive uses of HCFCs with global production and consumption capped in 2013 to mitigate their environmental impact as both ozone-depleting substances and important greenhouse gases. We find that this change has coincided with a stabilisation, or moderate reduction, in global emissions of the four HCFCs with aggregated global emissions in 2015 of 449 ± 75 Gg yr−1, in CO2 equivalent units (CO2 eq.) 0.76 ± 0.1 Gt yr−1, compared with 483 ± 70 Gg yr−1 (0.82 ± 0.1 Gt yr−1 CO2 eq.) in 2010 (uncertainties are 1σ throughout this paper). About 79 % of the total HCFC atmospheric burden in 2015 is HCFC-22, where global emissions appear to have been relatively similar since 2011, in spite of the 2013 cap on emissive uses. We attribute this to a probable increase in production and consumption of HCFC-22 in Montreal Protocol Article 5 (developing) countries and the continuing release of HCFC-22 from the large banks which dominate HCFC global emissions. Conversely, the four HFCs all show increasing mole fraction growth rates with aggregated global HFC emissions of 327 ± 70 Gg yr−1 (0.65 ± 0.12 Gt yr−1 CO2 eq.) in 2015 compared to 240 ± 50 Gg yr−1 (0.47 ± 0.08 Gt yr−1 CO2 eq.) in 2010. We also note that emissions of HFC-125 and HFC-32 appear to have increased more rapidly averaged over the 5-year period 2011–2015, compared to 2006–2010. As noted by Lunt et al. (2015) this may reflect a change to refrigerant blends, such as R-410A, which contain HFC-32 and -125 as a 50 : 50 blend.

Artificial neural networks are based on mathematical models of biological networks, but it is not clear how similar these two networks are. We have recently demonstrated that we can mechanically manipulate single neurons and create functioning synapses. Here, we build on this discovery and investigate the feasibility and time scales to build an artificial neural network with biological neurons. To achieve this, we characterized the dynamics and forces when pulling functional axonal neurites using a micromanipulation technique with maximum speeds about 300 times faster than the average natural growth rate of 0.0017μm/s. We find that the maximum force required to initiate and extend the neurites is about 1nN. The dynamics of the mechanical extension of the neurite is well described by many elastic springs and viscous dashpots in series. Interestingly, we find that the transport networks, specifically the actin network, lags behind the mechanically pulled structure. These insights could potentially open a new avenue to facilitate and encourage neuronal regrowth not relying on chemical queues. The extracted mechanical parameters and timescales characterize the neurite growth. We predict that it should be possible to use a magnetic trap to wire an artificial network such as a multi-layer perceptron in 17 hours. Once wired, we believe the biological neural network could be trained to process a hand-written digit using artificial neural network concepts applied to biological systems. We show how one could test the stability and robustness of this network by axotomizing (i.e. cutting) specific axons and reconnecting them using mechanical manipulation.

Carbon tetrachloride (CCl4) is an ozone-depleting substance, which is controlled by the Montreal Protocol and for which the atmospheric abundance is decreasing. However, the current observed rate of this decrease is known to be slower than expected based on reported CCl4 emissions and its estimated overall atmospheric lifetime. Here we use a three-dimensional (3-D) chemical transport model to investigate the impact on its predicted decay of uncertainties in the rates at which CCl4 is removed from the atmosphere by photolysis, by ocean uptake and by degradation in soils. The largest sink is atmospheric photolysis (74 % of total), but a reported 10 % uncertainty in its combined photolysis cross section and quantum yield has only a modest impact on the modelled rate of CCl4 decay. This is partly due to the limiting effect of the rate of transport of CCl4 from the main tropospheric reservoir to the stratosphere, where photolytic loss occurs. The model suggests large interannual variability in the magnitude of this stratospheric photolysis sink caused by variations in transport. The impact of uncertainty in the minor soil sink (9 % of total) is also relatively small. In contrast, the model shows that uncertainty in ocean loss (17 % of total) has the largest impact on modelled CCl4 decay due to its sizeable contribution to CCl4 loss and large lifetime uncertainty range (147 to 241 years). With an assumed CCl4 emission rate of 39 Gg year−1, the reference simulation with the best estimate of loss processes still underestimates the observed CCl4 (overestimates the decay) over the past 2 decades but to a smaller extent than previous studies. Changes to the rate of CCl4 loss processes, in line with known uncertainties, could bring the model into agreement with in situ surface and remote-sensing measurements, as could an increase in emissions to around 47 Gg year−1. Further progress in constraining the CCl4 budget is partly limited by systematic biases between observational datasets. For example, surface observations from the National Oceanic and Atmospheric Administration (NOAA) network are larger than from the Advanced Global Atmospheric Gases Experiment (AGAGE) network but have shown a steeper decreasing trend over the past 2 decades. These differences imply a difference in emissions which is significant relative to uncertainties in the magnitudes of the CCl4 sinks.

Atmospheric trace gas inversions often attempt to attribute fluxes to a high-dimensional grid using observations. To make this problem computationally feasible, and to reduce the degree of under-determination, some form of dimension reduction is usually performed. Here, we present an objective method for reducing the spatial dimension of the parameter space in atmospheric trace gas inversions. In addition to solving for a set of unknowns that govern emissions of a trace gas, we set out a framework that considers the number of unknowns to itself be an unknown. We rely on the well-established reversible-jump Markov chain Monte Carlo algorithm to use the data to determine the dimension of the parameter space. This framework provides a single-step process that solves for both the resolution of the inversion grid, as well as the magnitude of fluxes from this grid. Therefore, the uncertainty that surrounds the choice of aggregation is accounted for in the posterior parameter distribution. The posterior distribution of this transdimensional Markov chain provides a naturally smoothed solution, formed from an ensemble of coarser partitions of the spatial domain. We describe the form of the reversible-jump algorithm and how it may be applied to trace gas inversions. We build the system into a hierarchical Bayesian framework in which other unknown factors, such as the magnitude of the model uncertainty, can also be explored. A pseudo-data example is used to show the usefulness of this approach when compared to a subjectively chosen partitioning of a spatial domain. An inversion using real data is also shown to illustrate the scales at which the data allow for methane emissions over north-west Europe to be resolved.

Perfluorocarbons (PFCs) are very potent and long-lived greenhouse gases in the atmosphere, released predominantly during aluminium production and semiconductor manufacture. They have been targeted for emission controls under the United Nations Framework Convention on Climate Change. Here we present the first continuous records of the atmospheric abundance of CF4 (PFC-14), C2F6 (PFC-116) and C3F8 (PFC-218) from 1800 to 2014. The records are derived from high-precision measurements of PFCs in air extracted from polar firn or ice at six sites (DE08, DE08-2, DSSW20K, EDML, NEEM and South Pole) and air archive tanks and atmospheric air sampled from both hemispheres. We take account of the age characteristics of the firn and ice core air samples and demonstrate excellent consistency between the ice core, firn and atmospheric measurements. We present an inversion for global emissions from 1900 to 2014. We also formulate the inversion to directly infer emission factors for PFC emissions due to aluminium production prior to the 1980s. We show that 19th century atmospheric levels, before significant anthropogenic influence, were stable at 34.1 ± 0.3 ppt for CF4 and below detection limits of 0.002 and 0.01 ppt for C2F6 and C3F8, respectively. We find a significant peak in CF4 and C2F6 emissions around 1940, most likely due to the high demand for aluminium during World War II, for example for construction of aircraft, but these emissions were nevertheless much lower than in recent years. The PFC emission factors for aluminium production in the early 20th century were significantly higher than today but have decreased since then due to improvements and better control of the smelting process. Mitigation efforts have led to decreases in emissions from peaks in 1980 (CF4) or early-to-mid-2000s (C2F6 and C3F8) despite the continued increase in global aluminium production; however, these decreases in emissions appear to have recently halted. We see a temporary reduction of around 15 % in CF4 emissions in 2009, presumably associated with the impact of the global financial crisis on aluminium and semiconductor production.

The growth in atmospheric methane (CH4) concentrations over the past 2 decades has shown large variability on a timescale of several years. Prior to 1999 the globally averaged CH4 concentration was increasing at a rate of 6.0 ppb yr−1, but during a stagnation period from 1999 to 2006 this growth rate slowed to 0.6 ppb yr−1. From 2007 to 2009 the growth rate again increased to 4.9 ppb yr−1. These changes in growth rate are usually ascribed to variations in CH4 emissions. We have used a 3-D global chemical transport model, driven by meteorological reanalyses and variations in global mean hydroxyl (OH) concentrations derived from CH3CCl3 observations from two independent networks, to investigate these CH4 growth variations. The model shows that between 1999 and 2006 changes in the CH4 atmospheric loss contributed significantly to the suppression in global CH4 concentrations relative to the pre-1999 trend. The largest factor in this is relatively small variations in global mean OH on a timescale of a few years, with minor contributions of atmospheric transport of CH4 to its sink region and of atmospheric temperature. Although changes in emissions may be important during the stagnation period, these results imply a smaller variation is required to explain the observed CH4 trends. The contribution of OH variations to the renewed CH4 growth after 2007 cannot be determined with data currently available.

High frequency, in situ observations from 11 globally distributed sites for the period 1994–2014 and archived air measurements dating from 1978 onward have been used to determine the global growth rate of 1,1-difluoroethane (HFC-152a, CH3CHF2). These observations have been combined with a range of atmospheric transport models to derive global emission estimates in a top-down approach. HFC-152a is a greenhouse gas with a short atmospheric lifetime of about 1.5 years. Since it does not contain chlorine or bromine, HFC-152a makes no direct contribution to the destruction of stratospheric ozone and is therefore used as a substitute for the ozone depleting chlorofluorocarbons (CFCs) and hydrochlorofluorocarbons (HCFCs). The concentration of HFC-152a has grown substantially since the first direct measurements in 1994, reaching a maximum annual global growth rate of 0.84 ± 0.05 ppt yr−1 in 2006, implying a substantial increase in emissions up to 2006. However, since 2007, the annual rate of growth has slowed to 0.38 ± 0.04 ppt yr−1 in 2010 with a further decline to an annual average rate of growth in 2013–2014 of −0.06 ± 0.05 ppt yr−1. The annual average Northern Hemisphere (NH) mole fraction in 1994 was 1.2 ppt rising to an annual average mole fraction of 10.1 ppt in 2014. Average annual mole fractions in the Southern Hemisphere (SH) in 1998 and 2014 were 0.84 and 4.5 ppt, respectively. We estimate global emissions of HFC-152a have risen from 7.3 ± 5.6 Gg yr−1 in 1994 to a maximum of 54.4 ± 17.1 Gg yr−1 in 2011, declining to 52.5 ± 20.1 Gg yr−1 in 2014 or 7.2 ± 2.8 Tg-CO2 eq yr−1. Analysis of mole fraction enhancements above regional background atmospheric levels suggests substantial emissions from North America, Asia, and Europe. Global HFC emissions (so called “bottom up” emissions) reported by the United Nations Framework Convention on Climate Change (UNFCCC) are based on cumulative national emission data reported to the UNFCCC, which in turn are based on national consumption data. There appears to be a significant underestimate ( > 20 Gg) of “bottom-up” reported emissions of HFC-152a, possibly arising from largely underestimated USA emissions and undeclared Asian emissions.

High-frequency, in situ observations from the Advanced Global Atmospheric Gases Experiment (AGAGE), for the period 2003 to 2012, combined with archive flask measurements dating back to 1977, have been used to capture the rapid growth of HFC-143a (CH3CF3) and HFC-32 (CH2F2) mole fractions and emissions into the atmosphere. Here we report the first in situ global measurements of these two gases. HFC-143a and HFC-32 are the third and sixth most abundant hydrofluorocarbons (HFCs) respectively and they currently make an appreciable contribution to the HFCs in terms of atmospheric radiative forcing (1.7 ± 0.04 and 0.7 ± 0.02 mW m−2 in 2012 respectively). In 2012 the global average mole fraction of HFC-143a was 13.4 ± 0.3 ppt (1σ) in the lower troposphere and its growth rate was 1.4 ± 0.04 ppt yr−1; HFC-32 had a global mean mole fraction of 6.2 ± 0.2 ppt and a growth rate of 1.1 ± 0.04 ppt yr−1 in 2012. The extensive observations presented in this work have been combined with an atmospheric transport model to simulate global atmospheric abundances and derive global emission estimates. It is estimated that 23 ± 3 Gg yr−1 of HFC-143a and 21 ± 11 Gg yr−1 of HFC-32 were emitted globally in 2012, and the emission rates are estimated to be increasing by 7 ± 5% yr−1 for HFC-143a and 14 ± 11% yr−1 for HFC-32.

We present a comprehensive estimate of nitrous oxide (N2O) emissions using observations and models from 1995 to 2008. High-frequency records of tropospheric N2O are available from measurements at Cape Grim, Tasmania; Cape Matatula, American Samoa; Ragged Point, Barbados; Mace Head, Ireland; and at Trinidad Head, California using the Advanced Global Atmospheric Gases Experiment (AGAGE) instrumentation and calibrations. The Global Monitoring Division of the National Oceanic and Atmospheric Administration/Earth System Research Laboratory (NOAA/ESRL) has also collected discrete air samples in flasks and in situ measurements from remote sites across the globe and analyzed them for a suite of species including N2O. In addition to these major networks, we include in situ and aircraft measurements from the National Institute of Environmental Studies (NIES) and flask measurements from the Tohoku University and Commonwealth Scientific and Industrial Research Organization (CSIRO) networks. All measurements show increasing atmospheric mole fractions of N2O, with a varying growth rate of 0.1–0.7% per year, resulting in a 7.4% increase in the background atmospheric mole fraction between 1979 and 2011. Using existing emission inventories as well as bottom-up process modeling results, we first create globally gridded a priori N2O emissions over the 37 years since 1975. We then use the three-dimensional chemical transport model, Model for Ozone and Related Chemical Tracers version 4 (MOZART v4), and a Bayesian inverse method to estimate global as well as regional annual emissions for five source sectors from 13 regions in the world. This is the first time that all of these measurements from multiple networks have been combined to determine emissions. Our inversion indicates that global and regional N2O emissions have an increasing trend between 1995 and 2008. Despite large uncertainties, a significant increase is seen from the Asian agricultural sector in recent years, most likely due to an increase in the use of nitrogenous fertilizers, as has been suggested by previous studies.

We present a hierarchical Bayesian method for atmospheric trace gas inversions. This method is used to estimate emissions of trace gases as well as "hyper-parameters" that characterize the probability density functions (PDFs) of the a priori emissions and model-measurement covariances. By exploring the space of "uncertainties in uncertainties", we show that the hierarchical method results in a more complete estimation of emissions and their uncertainties than traditional Bayesian inversions, which rely heavily on expert judgment. We present an analysis that shows the effect of including hyper-parameters, which are themselves informed by the data, and show that this method can serve to reduce the effect of errors in assumptions made about the a priori emissions and model-measurement uncertainties. We then apply this method to the estimation of sulfur hexafluoride (SF6) emissions over 2012 for the regions surrounding four Advanced Global Atmospheric Gases Experiment (AGAGE) stations. We find that improper accounting of model representation uncertainties, in particular, can lead to the derivation of emissions and associated uncertainties that are unrealistic and show that those derived using the hierarchical method are likely to be more representative of the true uncertainties in the system. We demonstrate through this SF6 case study that this method is less sensitive to outliers in the data and to subjective assumptions about a priori emissions and model-measurement uncertainties than traditional methods.

A modelling experiment has been conceived to assess the impact of transport model errors on methane emissions estimated in an atmospheric inversion system. Synthetic methane observations, obtained from 10 different model outputs from the international TransCom-CH4 model inter-comparison exercise, are combined with a prior scenario of methane emissions and sinks, and integrated into the three-component PYVAR-LMDZ-SACS (PYthon VARiational-Laboratoire de Météorologie Dynamique model with Zooming capability-Simplified Atmospheric Chemistry System) inversion system to produce 10 different methane emission estimates at the global scale for the year 2005. The same methane sinks, emissions and initial conditions have been applied to produce the 10 synthetic observation datasets. The same inversion set-up (statistical errors, prior emissions, inverse procedure) is then applied to derive flux estimates by inverse modelling. Consequently, only differences in the modelling of atmospheric transport may cause differences in the estimated fluxes. In our framework, we show that transport model errors lead to a discrepancy of 27 Tg yr−1 at the global scale, representing 5% of total methane emissions. At continental and annual scales, transport model errors are proportionally larger than at the global scale, with errors ranging from 36 Tg yr−1 in North America to 7 Tg yr−1 in Boreal Eurasia (from 23 to 48%, respectively). At the model grid-scale, the spread of inverse estimates can reach 150% of the prior flux. Therefore, transport model errors contribute significantly to overall uncertainties in emission estimates by inverse modelling, especially when small spatial scales are examined. Sensitivity tests have been carried out to estimate the impact of the measurement network and the advantage of higher horizontal resolution in transport models. The large differences found between methane flux estimates inferred in these different configurations highly question the consistency of transport model errors in current inverse systems. Future inversions should include more accurately prescribed observation covariances matrices in order to limit the impact of transport model errors on estimated methane fluxes.

Since the Montreal Protocol on Substances that Deplete the Ozone Layer and its amendments came into effect, growth rates of the major ozone depleting substances (ODS), particularly CFC-11, -12 and -113 and CH3CCl3, have declined markedly, paving the way for global stratospheric ozone recovery. Emissions have now fallen to relatively low levels, therefore the rate at which this recovery occurs will depend largely on the atmospheric lifetime of these compounds. The first ODS measurements began in the early 1970s along with the first lifetime estimates calculated by considering their atmospheric trends. We now have global mole fraction records spanning multiple decades, prompting this lifetime re-evaluation. Using surface measurements from the Advanced Global Atmospheric Gases Experiment (AGAGE) and the National Oceanic and Atmospheric Administration Global Monitoring Division (NOAA GMD) from 1978 to 2011, we estimated the lifetime of CFC-11, CFC-12, CFC-113 and CH3CCl3 using a multi-species inverse method. A steady-state lifetime of 45 yr for CFC-11, currently recommended in the most recent World Meteorological Organisation (WMO) Scientific Assessments of Ozone Depletion, lies towards the lower uncertainty bound of our estimates, which are 544861 yr (1-sigma uncertainty) when AGAGE data were used and 524561 yr when the NOAA network data were used. Our derived lifetime for CFC-113 is significantly higher than the WMO estimates of 85 yr, being 10999121 (AGAGE) and 10997124 (NOAA). New estimates of the steady-state lifetimes of CFC-12 and CH3CCl3 are consistent with the current WMO recommendations, being 11195132 and 11295136 yr (CFC-12, AGAGE and NOAA respectively) and 5.044.925.20 and 5.044.875.23 yr (CH3CCl3, AGAGE and NOAA respectively).

A modified cumulus convection parametrisation scheme is presented. This scheme computes the mass of air transported upward in a cumulus cell using conservation of moisture and a detailed distribution of convective precipitation provided by a reanalysis dataset. The representation of vertical transport within the scheme includes entrainment and detrainment processes in convective updrafts and downdrafts. Output from the proposed parametrisation scheme is employed in the National Institute for Environmental Studies (NIES) global chemical transport model driven by JRA-25/JCDAS reanalysis. The simulated convective precipitation rate and mass fluxes are compared with observations and reanalysis data. A simulation of the short-lived tracer 222Rn is used to further evaluate the performance of the cumulus convection scheme. Simulated distributions of 222Rn are evaluated against observations at the surface and in the free troposphere, and compared with output from models that participated in the TransCom-CH4 Transport Model Intercomparison. From this comparison, we demonstrate that the proposed convective scheme in general is consistent with observed and modeled results.

HCFC-22 (CHClF2, chlorodifluoromethane) is an ozone-depleting substance (ODS) as well as a significant greenhouse gas (GHG). HCFC-22 has been used widely as a refrigerant fluid in cooling and air-conditioning equipment since the 1960s, and it has also served as a traditional substitute for some chlorofluorocarbons (CFCs) controlled under the Montreal Protocol. A low frequency record on tropospheric HCFC-22 since the late 1970s is available from measurements of the Southern Hemisphere Cape Grim Air Archive (CGAA) and a few Northern Hemisphere air samples (mostly from Trinidad Head) using the Advanced Global Atmospheric Gases Experiment (AGAGE) instrumentation and calibrations. Since the 1990s high-frequency, high-precision, in situ HCFC-22 measurements have been collected at these AGAGE stations. Since 1992, the Global Monitoring Division of the National Oceanic and Atmospheric Administration/Earth System Research Laboratory (NOAA/ESRL) has also collected flasks on a weekly basis from remote sites across the globe and analyzed them for a suite of halocarbons including HCFC-22. Additionally, since 2006 flasks have been collected approximately daily at a number of tower sites across the US and analyzed for halocarbons and other gases at NOAA. All results show an increase in the atmospheric mole fractions of HCFC-22, and recent data show a growth rate of approximately 4% per year, resulting in an increase in the background atmospheric mole fraction by a factor of 1.7 from 1995 to 2009. Using data on HCFC-22 consumption submitted to the United Nations Environment Programme (UNEP), as well as existing bottom-up emission estimates, we first create globally-gridded a priori HCFC-22 emissions over the 15 yr since 1995. We then use the three-dimensional chemical transport model, Model for Ozone and Related Chemical Tracers version 4 (MOZART v4), and a Bayesian inverse method to estimate global as well as regional annual emissions. Our inversion indicates that the global HCFC-22 emissions have an increasing trend between 1995 and 2009. We further find a surge in HCFC-22 emissions between 2005 and 2009 from developing countries in Asia – the largest emitting region including China and India. Globally, substantial emissions continue despite production and consumption being phased out in developed countries currently.

Global emission estimates based on new atmospheric observations are presented for the acylic high molecular weight perfluorocarbons (PFCs): decafluorobutane (C4F10), dodecafluoropentane (C5F12), tetradecafluorohexane (C6F14), hexadecafluoroheptane (C7F16) and octadecafluorooctane (C8F18). Emissions are estimated using a 3-dimensional chemical transport model and an inverse method that includes a growth constraint on emissions. The observations used in the inversion are based on newly measured archived air samples that cover a 39-yr period, from 1973 to 2011, and include 36 Northern Hemispheric and 46 Southern Hemispheric samples. The derived emission estimates show that global emission rates were largest in the 1980s and 1990s for C4F10 and C5F12, and in the 1990s for C6F14, C7F16 and C8F18. After a subsequent decline, emissions have remained relatively stable, within 20%, for the last 5 yr. Bottom-up emission estimates are available from the Emission Database for Global Atmospheric Research version 4.2 (EDGARv4.2) for C4F10, C5F12, C6F14 and C7F16, and inventories of C4F10, C5F12 and C6F14 are reported to the United Nations' Framework Convention on Climate Change (UNFCCC) by Annex 1 countries that have ratified the Kyoto Protocol. The atmospheric measurement-based emission estimates are 20 times larger than EDGARv4.2 for C4F10 and over three orders of magnitude larger for C5F12 (with 2008 EDGARv4.2 estimates for C5F12 at 9.6 kg yr−1, as compared to 67±53 t yr−1 as derived in this study). The derived emission estimates for C6F14 largely agree with the bottom-up estimates from EDGARv4.2. Moreover, the C7F16 emission estimates are comparable to those of EDGARv4.2 at their peak in the 1990s, albeit significant underestimation for the other time periods. There are no bottom-up emission estimates for C8F18, thus the emission rates reported here are the first for C8F18. The reported inventories for C4F10, C5F12 and C6F14 to UNFCCC are five to ten times lower than those estimated in this study. In addition, we present measured infrared absorption spectra for C7F16 and C8F18, and estimate their radiative efficiencies and global warming potentials (GWPs). We find that C8F18's radiative efficiency is similar to trifluoromethyl sulfur pentafluoride's (SF5F3) at 0.57 W m−2 ppb−1, which is the highest radiative efficiency of any measured atmospheric species. Using the 100-yr time horizon GWPs, the total radiative impact of the high molecular weight perfluorocarbons emissions are also estimated; we find the high molecular weight PFCs peak contribution was in 1997 at 24 000 Gg of carbon dioxide (CO2) equivalents and has decreased by a factor of three to 7300 Gg of CO2 equivalents in 2010. This 2010 cumulative emission rate for the high molecular weight PFCs is comparable to: 0.02% of the total CO2 emissions, 0.81% of the total hydrofluorocarbon emissions, or 1.07% of the total chlorofluorocarbon emissions projected for 2010 (Velders et al., 2009). In terms of the total PFC emission budget, including the lower molecular weight PFCs, the high molecular weight PFCs peak contribution was also in 1997 at 15.4% and was 6% of the total PFC emissions in CO2 equivalents in 2009.

Atmospheric observations and trends are presented for the high molecular weight perfluorocarbons (PFCs): decafluorobutane (C4F10), dodecafluoropentane (C5F12), tetradecafluorohexane (C6F14), hexadecafluoroheptane (C7F16) and octadecafluorooctane (C8F18). Their atmospheric histories are based on measurements of 36 Northern Hemisphere and 46 Southern Hemisphere archived air samples collected between 1973 to 2011 using the Advanced Global Atmospheric Gases Experiment (AGAGE) "Medusa" preconcentration gas chromatography-mass spectrometry systems. A new calibration scale was prepared for each PFC, with estimated accuracies of 6.8% for C4F10, 7.8% for C5F12, 4.0% for C6F14, 6.6% for C7F16 and 7.9% for C8F18. Based on our observations the 2011 globally averaged dry air mole fractions of these heavy PFCs are: 0.17 parts-per-trillion (ppt, i.e., parts per 1012) for C4F10, 0.12 ppt for C5F12, 0.27 ppt for C6F14, 0.12 ppt for C7F16 and 0.09 ppt for C8F18. These atmospheric mole fractions combine to contribute to a global average radiative forcing of 0.35 mW m−2, which is 6% of the total anthropogenic PFC radiative forcing (Montzka and Reimann, 2011; Oram et al., 2012). The growth rates of the heavy perfluorocarbons were largest in the late 1990s peaking at 6.2 parts per quadrillion (ppq, i.e., parts per 1015) per year (yr) for C4F10, at 5.0 ppq yr−1 for C5F12 and 16.6 ppq yr−1 for C6F14 and in the early 1990s for C7F16 at 4.7 ppq yr−1 and in the mid 1990s for C8F18 at 4.8 ppq yr−1. The 2011 globally averaged mean atmospheric growth rates of these PFCs are subsequently lower at 2.2 ppq yr−1 for C4F10, 1.4 ppq yr−1 for C5F12, 5.0 ppq yr−1 for C6F14, 3.4 ppq yr−1 for C7F16 and 0.9 ppq yr−1 for C8F18. The more recent slowdown in the growth rates suggests that emissions are declining as compared to the 1980s and 1990s.

We designed a method to simulate atmospheric CO2 concentrations at several continuous observation sites around the globe using surface fluxes at a very high spatial resolution. The simulations presented in this study were performed using the Global Eulerian-Lagrangian Coupled Atmospheric model (GELCA), comprising a Lagrangian particle dispersion model coupled to a global atmospheric tracer transport model with prescribed global surface CO2 flux maps at a 1 × 1 km resolution. The surface fluxes used in the simulations were prepared by assembling the individual components of terrestrial, oceanic and fossil fuel CO2 fluxes. This experimental setup (i.e. a transport model running at a medium resolution, coupled to a high-resolution Lagrangian particle dispersion model together with global surface fluxes at a very high resolution), which was designed to represent high-frequency variations in atmospheric CO2 concentration, has not been reported at a global scale previously. Two sensitivity experiments were performed: (a) using the global transport model without coupling to the Lagrangian dispersion model, and (b) using the coupled model with a reduced resolution of surface fluxes, in order to evaluate the performance of Eulerian-Lagrangian coupling and the role of high-resolution fluxes in simulating high-frequency variations in atmospheric CO2 concentrations. A correlation analysis between observed and simulated atmospheric CO2 concentrations at selected locations revealed that the inclusion of both Eulerian-Lagrangian coupling and high-resolution fluxes improves the high-frequency simulations of the model. The results highlight the potential of a coupled Eulerian-Lagrangian model in simulating high-frequency atmospheric CO2 concentrations at many locations worldwide. The model performs well in representing observations of atmospheric CO2 concentrations at high spatial and temporal resolutions, especially for coastal sites and sites located close to sources of large anthropogenic emissions. While this study focused on simulations of CO2 concentrations, the model could be used for other atmospheric compounds with known estimated emissions.

A chemistry-transport model (CTM) intercomparison experiment (TransCom-CH4) has been designed to investigate the roles of surface emissions, transport and chemical loss in simulating the global methane distribution. Model simulations were conducted using twelve models and four model variants and results were archived for the period of 1990–2007. All but one model transports were driven by reanalysis products from 3 different meteorological agencies. The transport and removal of CH4 in six different emission scenarios were simulated, with net global emissions of 513 ± 9 and 514 ± 14 Tg CH4 yr−1 for the 1990s and 2000s, respectively. Additionally, sulfur hexafluoride (SF6) was simulated to check the interhemispheric transport, radon (222Rn) to check the subgrid scale transport, and methyl chloroform (CH3CCl3) to check the chemical removal by the tropospheric hydroxyl radical (OH). The results are compared to monthly or annual mean time series of CH4, SF6 and CH3CCl3 measurements from 8 selected background sites, and to satellite observations of CH4 in the upper troposphere and stratosphere. Most models adequately capture the vertical gradients in the stratosphere, the average long-term trends, seasonal cycles, interannual variations (IAVs) and interhemispheric (IH) gradients at the surface sites for SF6, CH3CCl3 and CH4. The vertical gradients of all tracers between the surface and the upper troposphere are consistent within the models, revealing vertical transport differences between models. An average IH exchange time of 1.39 ± 0.18 yr is derived from SF6 time series. Sensitivity simulations suggest that the estimated trends in exchange time, over the period of 1996–2007, are caused by a change of SF6 emissions towards the tropics. Using six sets of emission scenarios, we show that the decadal average CH4 growth rate likely reached equilibrium in the early 2000s due to the flattening of anthropogenic emission growth since the late 1990s. Up to 60% of the IAVs in the observed CH4 concentrations can be explained by accounting for the IAVs in emissions, from biomass burning and wetlands, as well as meteorology in the forward models. The modeled CH4 budget is shown to depend strongly on the troposphere-stratosphere exchange rate and thus on the model's vertical grid structure and circulation in the lower stratosphere. The 15-model median CH4 and CH3CCl3 atmospheric lifetimes are estimated to be 9.99 ± 0.08 and 4.61 ± 0.13 yr, respectively, with little IAV due to transport and temperature.

Sulfur hexafluoride (SF6) is a potent greenhouse gas and useful atmospheric tracer. Measurements of SF6 on global and regional scales are necessary to estimate emissions and to verify or examine the performance of atmospheric transport models. Typical precision for common gas chromatographic methods with electron capture detection (GC-ECD) is 1–2%. We have modified a common GC-ECD method to achieve measurement precision of 0.5% or better. Global mean SF6 measurements were used to examine changes in the growth rate of SF6 and corresponding SF6 emissions. Global emissions and mixing ratios from 2000–2008 are consistent with recently published work. More recent observations show a 10% decline in SF6 emissions in 2008–2009, which seems to coincide with a decrease in world economic output. This decline was short-lived, as the global SF6 growth rate has recently increased to near its 2007–2008 maximum value of 0.30±0.03 pmol mol−1 (ppt) yr−1 (95% C.L.).

We present a method for estimating emissions of long-lived trace gases from a sparse global network of high-frequency observatories, using both a global Eulerian chemical transport model and Lagrangian particle dispersion model. Emissions are derived in a single step after determining sensitivities of the observations to initial conditions, the high-resolution emissions field close to observation points, and larger regions further from the measurements. This method has the several advantages over inversions using one type of model alone, in that: high-resolution simulations can be carried out in limited domains close to the measurement sites, with lower resolution being used further from them; the influence of errors due to aggregation of emissions close to the measurement sites can be minimized; assumptions about boundary conditions to the Lagrangian model do not need to be made, since the entire emissions field is estimated; any combination of appropriate models can be used, with no code modification. Because the sensitivity to the entire emissions field is derived, the estimation can be carried out using traditional statistical methods without the need for multiple steps in the inversion. We demonstrate the utility of this approach by determining global SF6 emissions using measurements from the Advanced Global Atmospheric Gases Experiment (AGAGE) between 2007 and 2009. The global total and large-scale patterns of the derived emissions agree well with previous studies, whilst allowing emissions to be determined at higher resolution than has previously been possible, and improving the agreement between the modeled and observed mole fractions at some sites.

We present atmospheric sulfur hexafluoride (SF6) mole fractions and emissions estimates from the 1970s to 2008. Measurements were made of archived air samples starting from 1973 in the Northern Hemisphere and from 1978 in the Southern Hemisphere, using the Advanced Global Atmospheric Gases Experiment (AGAGE) gas chromatographic-mass spectrometric (GC-MS) systems. These measurements were combined with modern high-frequency GC-MS and GC-electron capture detection (ECD) data from AGAGE monitoring sites, to produce a unique 35-year atmospheric record of this potent greenhouse gas. Atmospheric mole fractions were found to have increased by more than an order of magnitude between 1973 and 2008. The 2008 growth rate was the highest recorded, at 0.29 ± 0.02 pmolmol−1 yr−1. A three-dimensional chemical transport model and a minimum variance Bayesian inverse method was used to estimate annual emission rates using the measurements, with a priori estimates from the Emissions Database for Global Atmospheric Research (EDGAR, version 4). Consistent with the mole fraction growth rate maximum, global emissions during 2008 were also the highest in the 1973–2008 period, reaching 7.4 ± 0.6 Gg yr−1 (1-σ uncertainties) and surpassing the previous maximum in 1995. The 2008 values follow an increase in emissions of 48 ± 20% since 2001. A second global inversion which also incorporated National Oceanic and Atmospheric Administration (NOAA) flask measurements and in situ monitoring site data agreed well with the emissions derived using AGAGE measurements alone. By estimating continent-scale emissions using all available AGAGE and NOAA surface measurements covering the period 2004–2008, with no pollution filtering, we find that it is likely that much of the global emissions rise during this five-year period originated primarily from Asian developing countries that do not report detailed, annual emissions to the United Nations Framework Convention on Climate Change (UNFCCC). We also find it likely that SF6 emissions reported to the UNFCCC were underestimated between at least 2004 and 2005.