Understanding and quantifying the global methane (CH₄) budget is important for assessing realistic pathways to mitigate climate change. Atmospheric emissions and concentrations of CH₄ continue to increase, making CH₄ the second most important human-influenced greenhouse gas in terms of climate forcing, after carbon dioxide (CO₂). The relative importance of CH₄ compared to CO₂ depends on its shorter atmospheric lifetime, stronger warming potential, and variations in atmospheric growth rate over the past decade, the causes of which are still debated. Two major challenges in reducing uncertainties in the atmospheric growth rate arise from the variety of geographically overlapping CH₄ sources and from the destruction of CH₄ by short-lived hydroxyl radicals (OH). To address these challenges, we have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate new research aimed at improving and regularly updating the global methane budget. Following Saunois et al. (2016), we present here the second version of the living review paper dedicated to the decadal methane budget, integrating results of top-down studies (atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up estimates (including process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations). For the 2008–2017 decade, global methane emissions are estimated by atmospheric inversions (a top-down approach) to be 576 Tg CH₄ yr⁻¹ (range 550–594, corresponding to the minimum and maximum estimates of the model ensemble). Of this total, 359 Tg CH₄ yr⁻¹ or ∼ 60 % is attributed to anthropogenic sources, that is emissions caused by direct human activity (i.e. anthropogenic emissions; range 336–376 Tg CH₄ yr⁻¹ or 50 %–65 %). The mean annual total emission for the new decade (2008–2017) is 29 Tg CH₄ yr⁻¹ larger than our estimate for the previous decade (2000–2009), and 24 Tg CH₄ yr⁻¹ larger than the one reported in the previous budget for 2003–2012 (Saunois et al., 2016). Since 2012, global CH₄ emissions have been tracking the warmest scenarios assessed by the Intergovernmental Panel on Climate Change. Bottom-up methods suggest almost 30 % larger global emissions (737 Tg CH₄ yr⁻¹, range 594–881) than top-down inversion methods. Indeed, bottom-up estimates for natural sources such as natural wetlands, other inland water systems, and geological sources are higher than top-down estimates. The atmospheric constraints on the top-down budget suggest that at least some of these bottom-up emissions are overestimated. The latitudinal distribution of atmospheric observation-based emissions indicates a predominance of tropical emissions (∼ 65 % of the global budget, < 30^∘ N) compared to mid-latitudes (∼ 30 %, 30–60^∘ N) and high northern latitudes (∼ 4 %, 60–90^∘ N). The most important source of uncertainty in the methane budget is attributable to natural emissions, especially those from wetlands and other inland waters. Some of our global source estimates are smaller than those in previously published budgets (Saunois et al., 2016; Kirschke et al., 2013). In particular wetland emissions are about 35 Tg CH₄ yr⁻¹ lower due to improved partition wetlands and other inland waters. Emissions from geological sources and wild animals are also found to be smaller by 7 Tg CH₄ yr⁻¹ by 8 Tg CH₄ yr⁻¹, respectively. However, the overall discrepancy between bottom-up and top-down estimates has been reduced by only 5 % compared to Saunois et al. (2016), due to a higher estimate of emissions from inland waters, highlighting the need for more detailed research on emissions factors. Priorities for improving the methane budget include (i) a global, high-resolution map of water-saturated soils and inundated areas emitting methane based on a robust classification of different types of emitting habitats; (ii) further development of process-based models for inland-water emissions; (iii) intensification of methane observations at local scales (e.g., FLUXNET-CH₄ measurements) and urban-scale monitoring to constrain bottom-up land surface models, and at regional scales (surface networks and satellites) to constrain atmospheric inversions; (iv) improvements of transport models and the representation of photochemical sinks in top-down inversions; and (v) development of a 3D variational inversion system using isotopic and/or co-emitted species such as ethane to improve source partitioning. The data presented here can be downloaded from https://doi.org/10.18160/GCP-CH4-2019 (Saunois et al., 2020) and from the Global Carbon Project.

The global methane (CH₄) budget is becoming an increasingly important component for managing realistic pathways to mitigate climate change. This relevance, due to a shorter atmospheric lifetime and a stronger warming potential than carbon dioxide, is challenged by the still unexplained changes of atmospheric CH₄ over the past decade. Emissions and concentrations of CH₄ are continuing to increase, making CH₄ the second most important human-induced greenhouse gas after carbon dioxide. Two major difficulties in reducing uncertainties come from the large variety of diffusive CH₄ sources that overlap geographically, and from the destruction of CH₄ by the very short-lived hydroxyl radical (OH). To address these difficulties, we have established a consortium of multi-disciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate research on the methane cycle, and producing regular (∼ biennial) updates of the global methane budget. This consortium includes atmospheric physicists and chemists, biogeochemists of surface and marine emissions, and socio-economists who study anthropogenic emissions. Following Kirschke et al. (2013), we propose here the first version of a living review paper that integrates results of top-down studies (exploiting atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up models, inventories and data-driven approaches (including process-based models for estimating land surface emissions and atmospheric chemistry, and inventories for anthropogenic emissions, data-driven extrapolations). For the 2003–2012 decade, global methane emissions are estimated by top-down inversions at 558 Tg CH₄ yr⁻¹, range 540–568. About 60 % of global emissions are anthropogenic (range 50–65 %). Since 2010, the bottom-up global emission inventories have been closer to methane emissions in the most carbon-intensive Representative Concentrations Pathway (RCP8.5) and higher than all other RCP scenarios. Bottom-up approaches suggest larger global emissions (736 Tg CH₄ yr⁻¹, range 596–884) mostly because of larger natural emissions from individual sources such as inland waters, natural wetlands and geological sources. Considering the atmospheric constraints on the top-down budget, it is likely that some of the individual emissions reported by the bottom-up approaches are overestimated, leading to too large global emissions. Latitudinal data from top-down emissions indicate a predominance of tropical emissions (∼ 64 % of the global budget, < 30° N) as compared to mid (∼ 32 %, 30–60° N) and high northern latitudes (∼ 4 %, 60–90° N). Top-down inversions consistently infer lower emissions in China (∼ 58 Tg CH₄ yr⁻¹, range 51–72, −14 %) and higher emissions in Africa (86 Tg CH₄ yr⁻¹, range 73–108, +19 %) than bottom-up values used as prior estimates. Overall, uncertainties for anthropogenic emissions appear smaller than those from natural sources, and the uncertainties on source categories appear larger for top-down inversions than for bottom-up inventories and models. The most important source of uncertainty on the methane budget is attributable to emissions from wetland and other inland waters. We show that the wetland extent could contribute 30–40 % on the estimated range for wetland emissions. Other priorities for improving the methane budget include the following: (i) the development of process-based models for inland-water emissions, (ii) the intensification of methane observations at local scale (flux measurements) to constrain bottom-up land surface models, and at regional scale (surface networks and satellites) to constrain top-down inversions, (iii) improvements in the estimation of atmospheric loss by OH, and (iv) improvements of the transport models integrated in top-down inversions. The data presented here can be downloaded from the Carbon Dioxide Information Analysis Center (http://doi.org/10.3334/CDIAC/GLOBAL_METHANE_BUDGET_2016_V1.1) and the Global Carbon Project.

Atmospheric greenhouse gas (GHG) concentrations are at unprecedented, record-high levels compared to the last 800 000 years. Those elevated GHG concentrations warm the planet and – partially offset by net cooling effects by aerosols – are largely responsible for the observed warming over the past 150 years. An accurate representation of GHG concentrations is hence important to understand and model recent climate change. So far, community efforts to create composite datasets of GHG concentrations with seasonal and latitudinal information have focused on marine boundary layer conditions and recent trends since the 1980s. Here, we provide consolidated datasets of historical atmospheric concentrations (mole fractions) of 43 GHGs to be used in the Climate Model Intercomparison Project – Phase 6 (CMIP6) experiments. The presented datasets are based on AGAGE and NOAA networks, firn and ice core data, and archived air data, and a large set of published studies. In contrast to previous intercomparisons, the new datasets are latitudinally resolved and include seasonality. We focus on the period 1850–2014 for historical CMIP6 runs, but data are also provided for the last 2000 years. We provide consolidated datasets in various spatiotemporal resolutions for carbon dioxide (CO₂), methane (CH₄) and nitrous oxide (N₂O), as well as 40 other GHGs, namely 17 ozone-depleting substances, 11 hydrofluorocarbons (HFCs), 9 perfluorocarbons (PFCs), sulfur hexafluoride (SF₆), nitrogen trifluoride (NF₃) and sulfuryl fluoride (SO₂F₂). In addition, we provide three equivalence species that aggregate concentrations of GHGs other than CO₂, CH₄ and N₂O, weighted by their radiative forcing efficiencies. For the year 1850, which is used for pre-industrial control runs, we estimate annual global-mean surface concentrations of CO₂ at 284.3 ppm, CH₄ at 808.2 ppb and N₂O at 273.0 ppb. The data are available at https://esgf-node.llnl.gov/search/input4mips/ and http://www.climatecollege.unimelb.edu.au/cmip6. While the minimum CMIP6 recommendation is to use the global- and annual-mean time series, modelling groups can also choose our monthly and latitudinally resolved concentrations, which imply a stronger radiative forcing in the Northern Hemisphere winter (due to the latitudinal gradient and seasonality).

The growth in global methane (CH₄) concentration, which had been ongoing since the industrial revolution, stalled around the year 2000 before resuming globally in 2007. We evaluate the role of the hydroxyl radical (OH), the major CH₄ sink, in the recent CH₄ growth. We also examine the influence of systematic uncertainties in OH concentrations on CH₄ emissions inferred from atmospheric observations. We use observations of 1,1,1-trichloroethane (CH₃CCl₃), which is lost primarily through reaction with OH, to estimate OH levels as well as CH₃CC₃ emissions, which have uncertainty that previously limited the accuracy of OH estimates. We find a 64–70% probability that a decline in OH has contributed to the post-2007 methane rise. Our median solution suggests that CH₄ emissions increased relatively steadily during the late 1990s and early 2000s, after which growth was more modest. This solution obviates the need for a sudden statistically significant change in total CH₄ emissions around the year 2007 to explain the atmospheric observations and can explain some of the decline in the atmospheric ¹³CH₄/¹²CH₄ ratio and the recent growth in C₂H₆. Our approach indicates that significant OH-related uncertainties in the CH₄ budget remain, and we find that it is not possible to implicate, with a high degree of confidence, rapid global CH₄ emissions changes as the primary driver of recent trends when our inferred OH trends and these uncertainties are considered.

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).

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.