What do we know about the global methane budget? Results from four decades of atmospheric CH₄ observations and the way forward

(a) Experimental methods

Blake et al. [7] showed that CH₄ in the surface atmosphere varied spatially and seasonally across the globe. Recognizing the importance of improved understanding of the global CH₄ budget for climate, NOAA began measurements of atmospheric CH₄ from discrete air samples collected in its existing Cooperative Global Air Sampling Network in 1983, expanding available high-quality data both in frequency and spatially [8] to better constrain the global CH₄ budget. Since 1998, δ¹³C_CH4 has been measured in a subset of the same air samples by the Institute of Arctic and Alpine Research (INSTAAR), University of Colorado [9]. Currently, many laboratories around the world monitor atmospheric CH₄ abundance and a few measure δ¹³C_CH4. To help ensure the availability of comparable, high-quality observations for global CH₄ budget studies, these efforts are organized under the umbrella of the World Meteorological Organization (WMO) Global Atmosphere Watch (GAW) program, and data are reported to the World Data Center for Greenhouse Gases hosted by the Japan Meteorological Agency (https://gaw.kishou.go.jp/). Even with this structure in place, there are still ‘ease of use’ issues; not all laboratories report data in a timely fashion, they report data on different standard scales without providing conversion to the WMO GAW CH₄ mole fraction scale, uncertainties are not reported, etc. Isotopic data are further hindered by a lack of CH₄-in-air reference materials and different methods of tracing δ¹³C_CH4 to calcite reference materials [10]. To circumvent these issues, some research programs have collected data and made packages of observations available (see e.g. https://www.esrl.noaa.gov/gmd/ccgg/obspack/). Future effort is required to ensure high-quality measurements can be traced back to common standard scales, because our understanding of the global CH₄ budget relies on precise spatio-temporal differences from these measurements. The data should also be publicly accessible in a timely manner, fully quality controlled and ready for scientific analysis; data users should not have to add uncertainties nor ensure traceability to a common standard scale.

We do not cover column CH₄ abundances retrieved from measurements of radiance by satellite or surface IR sensors, which are not direct, calibrated measurements of CH₄ abundance and have potential biases similar in magnitude to the large-scale signals we are trying to determine. While they are still useful, especially in the tropics, where direct measurements remain sparse, and in regional studies, they will always require calibrated direct measurements of CH₄ for evaluation.

Here, we focus on NOAA CH₄ and INSTAAR δ¹³C_CH4 measurements in air samples collected from the Global Monitoring Laboratory's Cooperative Global Air Sampling Network. The network and sampling procedures were described in Dlugokencky et al. [11]. Briefly, weekly discrete whole-air samples are collected in pairs of borosilicate glass flasks with PTFE O-ring seals using a portable air sampler from a globally distributed network of air sampling sites (https://www.esrl.noaa.gov/gmd/ccgg/flask.html). Air samples were analysed for CH₄ by gas chromatography with flame ionization detection through mid-2019. Since then, samples have been analysed for CH₄ using a cavity ring-down spectrometer (CRDS). Responses of the GC/FID and CRDS were calibrated for CH₄ abundance against a CH₄ standard scale maintained at NOAA ([12]; this scale serves as the WMO CH₄ mole fraction scale, X2004A). These data provide a spatially and temporally consistent set of observations. INSTAAR began analysing a subset of air samples for δ¹³C_CH4 in 1998. CH₄ from the sample is pre-concentrated, separated from other remaining components in a GC, combusted to CO₂ and the CH₄-derived CO₂ analysed by continuous flow isotope ratio mass spectrometry. CH₄ and δ¹³C_CH4 data are available from NOAA's data server: ftp://aftp.cmdl.noaa.gov/data/trace_gases/xxx/flask/, where xxx = ‘ch4’ or ‘ch4c13’.

(b) Documenting CH₄'s global burden

Observations of the spatial and temporal distribution of atmospheric CH₄ abundance provide key constraints on the global CH₄ budget. In 2020, preliminary globally averaged CH₄ determined by smoothing data as described below was 1879.2 ± 0.6 ppb, with an increase of 14.8 ± 0.5 ppb from January 1, 2020 to January 1, 2021 (https://gml.noaa.gov/ccgg/trends_ch4/; downloaded 8 July 2021). This implies an imbalance between emissions and sinks in 2020 of 41.0 Tg CH₄ (based on 1 globally averaged ppb = 2.77 Tg CH₄ [13]). The time series of these observations from 1983.5 through 2019 is shown in figure 1a. Global (and zonal) means are calculated by first smoothing data from a subset of sampling sites that capture well-mixed, background air, extracting values from the smoothed curve fits at synchronized time steps, then smoothing again as a function of latitude to define a matrix of CH₄ abundance as a function of time (48 values per year) and latitude (intervals of sine (latitude) = 0.05). The quality of the data ensures that the variations in growth rate are significant, as indicated by the uncertainties (dashed lines in figure 1b) determined with a combination of resampling of the network (bootstrap; [14]) and Monte Carlo (to assess measurement uncertainty) methods (see electronic supplemenatry material for details).

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(c) Long-term changes

From the start of NOAA's measurements in 1983 through 2006, the CH₄ growth rate was decreasing with significant interannual variability (IAV) superimposed on top of that. Then, globally averaged CH₄ began increasing again in 2007.

For 1983–2006, if we assume no trend in the atmospheric CH₄ lifetime, then the atmospheric CH₄ burden was approaching steady state [15]. Starting from a global mass balance and rearranging to solve for emissions we have:

where E is emissions, [CH₄] is the global annual mean CH₄ burden and d[CH₄]/dt its annual increase, both determined from our surface global means, and τ is the atmospheric CH₄ lifetime. After calculating emissions using τ = 9.1 yr [16] and fitting a straight line to emissions from 1984 through 2006, we find no trend in emissions (electronic supplementary material, figure S1; slope = 0.0 ± 0.3 Tg CH₄ yr⁻¹; uncertainty is 68% C.I.). It may seem surprising that atmospheric CH₄ continues to increase while emissions and lifetime are constant, but this is expected from a chemical system with pseudo-first-order loss approaching steady state,

Accounting for the atmospheric CH₄ abundance at the start of our measurements gives

where subscripts ‘ss’ and ‘0’ are for steady state and initial conditions (start of measurements). Equation (2.3) is fitted to the global means from 1983.5 through 2006, solving for [CH₄]_ss, ([CH₄]_ss - [CH₄]₀), and τ to give a steady-state value of 1794 ppb and a lifetime of 9.2 yr, in agreement with IPCC's Fifth Assessment Report CH₄ budget lifetime of 9.1 yr [16]. A curve calculated using the coefficients of the fit, extrapolated to 2020, is plotted in figure 1a (green), and the difference between it and the deseasonalized trend (green, in ppb) is shown with the growth rate (red) in figure 1b.

Since 2007, when this difference starts increasing dramatically, there has been a significant change in the global CH₄ budget that will be explored later. Our conclusion of no trend in emissions from 1983 to 2006 is true only if there has been no trend in τ_CH4, which is the largest source of uncertainty in this simple global mass balance. For changes in global CH₄ emission rates to be consistent with the observed global means, τ_CH4 would have had to change in a way that still coincidentally fits this steady-state model.

(d) Interannual variability

In addition to the long-term changes in atmospheric CH₄, IAV is also evident in the growth rate and the residuals (figure 1b), and it has inherent information about budget processes. When the process, or processes, responsible for IAV can be identified, it allows us to quantitatively test our understanding. An example is a large increase in CH₄ growth rate in 1991 (red line in figure 1b) and the positive anomaly in the difference between the trend and steady-state fit in 1991 (green line in figure 1b). In this smoothed representation, which is similar to a 12-month running mean, the anomaly peaked at approximately 5 ppb; at weekly time resolution, it was approximately 9 ppb (see electronic supplementary material, figure S2). The subsequent decrease in 1992 growth rate is a result of the trend adjusting back to the long-term behaviour as indicated by the difference between the trend and steady-state fit returning to near-zero (green line in figure 1b).

Dlugokencky et al. [17] attributed this positive anomaly to the impact of the eruption of Mt. Pinatubo on the atmospheric CH₄ sink. SO₂ and ash from the eruption, and subsequent sulfate aerosol produced from the oxidation of SO₂, decreased transmission of UV radiation, which decreased production of O(¹D) and, ultimately, primary production of hydroxyl radical (OH). Qualitatively, this made sense, since anomalies in CO abundance were coincident and consistent in magnitude with the changes in CH₄. Bândă et al. [18] investigated this change with a three-dimensional global chemistry model and found SO₂ and sulfate reduced the tropospheric CH₄ sink from reaction with OH by 17.8 Tg CH₄ from June, 1991 to June, 1993, which only explained approximately 40% of the change in atmospheric CH₄ burden from June, 1991 to May, 1992. Bândă et al. [19] identified additional factors that contributed significantly to changes in atmospheric CH₄ growth rate including: changes in stratospheric O₃, which also affected UV flux; atmospheric temperature, which affected CH₄ emission rates from wetlands and the rate of oxidation of CH₄ (k_OH+CH4 is strongly temperature-dependent, approximately 2% K⁻¹); biomass burning; and changes in rates of emission of other compounds that affect OH concentration (e.g. CO and NMHCs).

(e) Spatial patterns as budget constraints

Because atmospheric mixing is not instantaneous, signals of sources and sinks exist in the atmospheric CH₄ distribution that are useful for quantifying emissions. Fung et al. [13] compared the time-averaged latitude gradient calculated with a chemical transport model (CTM) to NOAA CH₄ observations (electronic supplementary material, figure S3) to rule out specific emission scenarios that were inconsistent with atmospheric observations. They showed, for example, that early estimates of CH₄ emissions from northern wetlands were too large to be consistent with the observed latitude gradient in surface CH₄.

We exploited the gradient further by analysing temporal changes in spatial patterns to identify changes in the distribution of emissions, although this analysis cannot quantitatively determine the magnitude of the emission changes. Following the economic collapse in the former Soviet Union, Dlugokencky et al. [20] showed that a change in inter-polar CH₄ difference (IPD; the difference between zonal averages calculated for 53–90° N and 53–90° S) was consistent with a decrease in CH₄ emissions from the former Soviet Union of approximately 10 Tg between 1991 and 1992. Agreement between observations and a forward simulation with an atmospheric transport model (TM3) using emission estimates from Emissions Database for Global Atmospheric Research (EDGAR) was quite good; both showed a decrease of approximately 12 ppb in annual means calculated for high-northern latitude sites relative to the South Pole between the mid-1980s and the late-1990s (electronic supplementary material, figure S4).

Such analyses are only qualitative, because observed trends in spatial patterns are affected by changes in atmospheric transport, not just emissions [21], so quantitative estimates of emission changes must be assessed with a CTM. However, even without the rigour of a transport model, the observations show that, as of 2019, IPD has not recovered to the levels seen in the late-1980s (see electronic supplementary material, figure S5), suggesting there have not been large increases in Arctic CH₄ emissions from clathrates and permafrost carbon in response to increasing temperature.

(f) Other tracers: δ¹³C_CH4

We get additional information about processes responsible for emitting CH₄ from other tracers. For example, anomalies in CO abundance measured in the same air samples measured for CH₄ were the right magnitude to be consistent with a decreased atmospheric OH sink after the eruption of Mt. Pinatubo. CO is also used as a tracer for biomass burning, where its molar emission ratio with CH₄ is significantly larger than for the sink process. Ethane (C₂H₆) has been used as a tracer for fossil fuel emissions of CH₄. Both are also emitted from other sources, which can complicate their interpretation. For example, Lan et al. [22] analysed long-term observations of CH₄ and a suite of NMHCs from samples collected at a site significantly influenced by oil and gas production. Because emission ratios of C₂H₆/CH₄ from fossil fuel production vary with time, predicting trends in CH₄ enhancements from trends in C₂H₆ (and other NMHCs) enhancements and an assumed constant C₂H₆/CH₄ emission ratio results in significant over-estimates of those trends.

A much better tracer than CO or C₂H₆ to identify processes affecting atmospheric CH₄ is the isotopic composition of CH₄, which is emitted and removed by exactly the same processes as CH₄ itself, though with isotope signatures that vary by source, and fractionation factors that are unique to various sink pathways. Here, we focus on δ¹³C_CH4, which can be used to partition the relative fraction of emissions from broad categories: microbial (mic), fossil (fe; including natural fossil emissions)) and biomass burning (bb). Globally averaged atmospheric δ¹³C_CH4 is equal to the average δ¹³C_CH4 of sources mass-weighted by their emissions (equation (2.4)) and corrected for fractionation by sinks [23] (equation (2.5)).

and, at steady-state,

where the subscript ‘Q’ refers to sources. An isotopic fractionation factor, α, is defined as the ratio of rate coefficients (k¹³C/k¹²C) for the reaction of ¹³CH₄ relative to that for ¹²CH₄ for a specific sink (e.g. reaction with OH). It is related to ε by ε = (α − 1) In equation (2.5), ε is weighted by the relative contributions of each sink process and commonly reported in per mille (‰). Interpretation of the isotope data requires knowledge of the mass-weighted isotopic composition of the sources that emit CH₄ to the atmosphere. An isotope source signature database compiled by Sherwood et al. [24] greatly increased the sample size and reduced the uncertainty of source signatures for a global CH₄ isotope mass balance. Within each source category, a range of δ¹³C_CH4 values has been observed as a result of systematic spatial differences in these source signatures. These differences help constrain the spatial distribution of CH₄ emissions. This database was complemented by other studies on the spatial patterns in δ¹³C_CH4 of sources ([25] for oil, natural gas, coal, biomass burning and ruminants; [26] for wetlands; [27] for geological seeps; and [28] for ruminants), which can help to better leverage the spatial information in observed δ¹³C_CH4._.Adjustment for fractionation by sinks, as with emissions, is weighted by the magnitude of each sink process. Since atmospheric observations of CH₄ isotopes are sensitive to CH₄ sources and sinks with their distinct fingerprints, they are an extremely powerful constraint on the CH₄ budget (see the following section for the δ¹³C_CH4 constraints on global CH₄ budget).

In figure 2, CH₄ abundance (2a) and δ¹³C_CH4 (2b) are plotted. The start of the decline in δ¹³C_CH4 is nearly coincident with the increase in atmospheric CH₄ budget. Unfortunately, INSTAAR measurements of δ¹³C_CH4 started after the large El Niño of 1997/1998, when there were large biomass burning signals, which δ¹³C_CH4 should be very sensitive to. But based on analysis of measurements of CH₄ abundance and δ¹³C_CH4 at Ny Ålesund, Spitzbergen, Morimoto et al. [29] found significant enhancements of CH₄ emissions from both wetlands and biomass burning in 1998, highlighting that the interpretation of the observations can be complicated by multiple processes changing nearly simultaneously. From measurements of air extracted from ice cores, we know that δ¹³C_CH4 was increasing for approximately 200 yr [30] before the recent decline began. This suggests a rather significant change in the global budget of CH₄ sources and sinks that will be explored below, although the timing of the change in emissions is difficult to match directly with the δ¹³C_CH4 observations because of the slow response of δ¹³C_CH4 to changes in CH₄ emissions or average source signatures [31].

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(a) Sink processes

Oxidation of atmospheric CH₄ by OH is the largest term in the global CH₄ budget, and small trends, much less than 1% yr⁻¹, are important. We cannot accurately evaluate magnitudes and trends in global total CH₄ emissions (nor determine which particular sources are changing), without an accurate understanding of the magnitude and trend in [OH]. To date, [OH] and its trend have been assessed indirectly through observations of 1,1,1-trichloroethane (CH₃CCl₃; common name: methyl chloroform, which we abbreviate to MC), an anthropogenic tracer predominantly removed from the atmosphere by reaction with OH. To be effective, the method required accurate estimates of MC emissions. When MC production was phased out by the Montreal Protocol on Substances that Deplete the Ozone Layer, it allowed Montzka et al. [45] to exploit the greatly decreased emissions of MC to estimate [OH] averaged over multiple years based on the rate of decay in the atmospheric burden of MC. However, as the abundance of MC in the atmosphere has decreased closer to its analytical detection limit, this approach has become more sensitive to small remaining emissions. Moreover, while this approach was useful for assessing the lifetimes of CH₄ and MC averaged over some years and their IAV, it was less suitable to assess small trends in [OH].

Recent reports of a decreasing trend in [OH] driving the renewed growth in atmospheric CH₄ since 2007 may be artefacts of the box-modelling methods used ([46] and references therein) and are inconsistent with models of atmospheric chemistry and transport [41]. This scenario is also inconsistent with trends in δ¹³C_CH4 as discussed above. While the reaction rate coefficient for OH + CH₄, including its temperature dependence, has been greatly studied [47], uncertainty in the fractionation of the reaction on atmospheric δ¹³C_CH4 is still substantial, and that uncertainty affects our ability to quantitatively partition emissions among broad source categories. Saueressig et al. [48] reported a significantly smaller KIE for fractionation of ¹³C by OH (1.0039 ± 0.0004) than reported by Cantrell et al. [49] (1.0054 ± 0.0009). Uncertainty in this factor muddles interpretation of measurements of atmospheric δ¹³C_CH4 imposing a 20% uncertainty on emissions from the fossil fuel sector (approx. 30 Tg CH₄ yr⁻¹) [25].

Oxidation of CH₄ initiated by Cl has little impact on atmospheric CH₄ total loss, but strongly fractionates δ¹³C_CH4. Using a CTM (TOMCAT) and sources of Cl from natural and anthropogenic halocarbons and dechlorination of sea-salt aerosol, Hossaini et al. [50] estimated a sink for atmospheric CH₄ by reaction with Cl of 12–13 Tg CH₄ yr⁻¹. Based on analysis of δ¹³C_CO measurements in the extra-tropical Southern Hemisphere, Gromov et al. [51] concluded that the loss of CH₄ by reaction with Cl is no greater than 1% of the total atmospheric CH₄ sink (less than half the estimate of Hossaini et al. [50]), and even 1% makes it difficult to balance the ¹³C(CO) budget. Wang et al. [52] calculated a similar size sink for CH₄ from reaction with Cl, 5.3 Tg CH₄ yr⁻¹. The impact of Cl on the interpretation of δ¹³C_CH4 observations is still clear; Strode et al. [53] compared multiple tropospheric Cl spatial and seasonal distributions and found the choice impacts the north-south gradient and seasonal cycle of δ¹³C_CH4. And Lan et al. [25] found that the magnitude of the Cl sink chosen (none or 13 Tg CH₄ yr⁻¹) impacts the amount of fossil emissions that can be consistent with observations by approximately 20%.

Oxidation of CH₄ in soils is intermediate in magnitude, much smaller than loss initiated by reaction with OH and likely much larger than reaction with Cl. Saunois et al. [35] reported a bottom-up range for soil loss of 12 to 49 Tg CH₄ yr⁻¹ based on a literature review. Fractionation of CH₄ carbon isotopes by soil loss is reported to be approximately −20‰, with a range of measured values of −16 to 27‰ (e.g. [54]). Since the sink mostly occurs in forested soils, which are susceptible to land use, the magnitude [55] and distribution of the sink may be changing. Given that the fluxes of soil loss are very small and occur over large areas of forested and Savannah soils, better estimates of global soil loss will remain challenging.

(b) Wetlands

CH₄ emissions from wetlands are the largest natural source of atmospheric CH₄. Emissions from wetlands vary greatly over small spatial scales and in time; this variability, combined with relatively few flux measurements, makes up-scaling uncertain. Prior estimates of emissions, therefore, rely on process models, but the range of emissions estimated among different wetland process models is quite large, approximately 40% [56]. One limitation is the accurate assessment of time-varying wetland area and inundation extent. While space-based methods seem ideal to determine wetland area, these methods are limited by cloud cover [57]. New methods based on the Cyclone Global Navigation Satellite System (CYGNSS), which operate in a frequency region that allows them to see through clouds, rain and most vegetation, may provide an important improvement [58]. Wetlands remain a limitation for top-down studies of the entire CH₄ budget, because estimates of emissions from all other sources are impacted by inaccurate estimates of the magnitude and distribution of CH₄ emissions from wetlands.

Using the isotopic constraint effectively requires that the source signatures of different wetland ecosystems are accounted for. Recently, Ganesan et al. [26] developed a map of spatially varying δ¹³C_CH4 source signatures for wetlands, which vary by approximately 10‰ between high-northern latitudes and the tropics. The δ¹³C_CH4 of CH₄ emitted from a wetland will depend on factors such as the mechanism of CH₄ production (CO₂ versus acetate reduction), the source of the carbon (the proportion of plants with C3 versus C4 photosynthetic pathways), and oxidation between production and emission to the atmosphere, which depends on the transport route. Further efforts to identify the underlying processes that control wetland isotopic signature can improve the spatial mapping and thus improve the accuracy of attributing wetland emissions spatially.