Methane is an important greenhouse gas, and our understanding of the magnitude and trends of its sources and sinks is incomplete at best (Kirschke et al., 2013; Saunois et al., 2016; Rice et al., 2016; Schaefer et al., 2016; Nisbet et al., 2016; Schwietzke et al., 2016; Worden et al., 2017; Rigby et al., 2017; Turner et al., 2017). Quantifying source and sink attributions, between anthropogenic and natural sources, but also within anthropogenic sources, is key for designing mitigation strategies and estimating their climate impacts (Nisbet et al., 2019). Earth’s degassing is considered a major natural source of methane (CH4) to the atmosphere, as discussed in a wide body of literature (e.g., Lacroix, 1993; Etiope and Klusman, 2002; Judd et al., 2002; Kvenvolden and Rogers, 2005; Etiope et al., 2019). CH4 degassing occurs through five main categories of surface gas manifestations: gas-oil seeps, mud volcanoes, microseepage, submarine seepage, and geothermal and volcanic manifestations. These are defined and described in detail, for example, in Judd et al. (2002), Dimitrov (2003), Etiope and Klusman, (2010), Mazzini and Etiope (2017), Etiope (2015) and Etiope et al. (2019), and references therein.
The global CH4 emissions from these sources have been mainly estimated through bottom-up procedures by various authors (see Table 1), based on process-based modelling, statistical evaluations of experimentally determined emission factors and activity data (number of emission points or emission area) and inventories. Global geo-CH4 emission estimates (including all five geo-CH4 categories) range from 30 to 76 Tg yr–1, with a typical mean around 50 Tg yr–1 (Table 1). As shown in detail below, top-down emission estimates, based on present-day atmospheric data of isotopic (14C and 13C/12C) CH4 composition or ethane (C2H6) emissions (also derived from polar ice cores), are consistent with the order of magnitude of the bottom-up estimates (Etiope et al., 2008; Schwietzke et al., 2016; Nicewonger et al., 2016; Dalsøren et al., 2018). Differently, Petrenko et al. (2017) proposed a substantially lower global estimate, ranging from 0 (zero) to 15.4 Tg yr–1 (95% CI). This range was derived from radiocarbon (14C) measurements in CH4 trapped in ice cores in Antarctica and referring to the atmosphere of 11,000–12,000 years ago, between the Younger Dryas and Preboreal intervals. Assuming that geological emissions today are not higher (or even lower) than in the analysed period, Petrenko et al. (2017) concluded that previous present-day geo-CH4 estimates of ~50 Tg yr–1 are overestimated. As stated in Petrenko et al. (2017), this geo-CH4 downward revision also implies an upward revision of the present-day fossil fuel industry CH4 emission estimates.
The objectives of this paper are: (1) Combine and re-asses the most recent global bottom-up geo-CH4 emission estimates based on recently published global grid maps and updated inventories discussed in Etiope et al. (2019). (2) Summarize top-down emission estimates by combining data from Schwietzke et al. (2016), Nicewonger et al. (2016), Saunois et al. (2016) and Dalsøren et al. (2018), followed by a calculation of the average ethane/methane (C2/C1) ratio for the five geological sources (having specific C2/C1 ratios, as reported in Etiope and Ciccioli, 2009). Based on this overview of geo-CH4 top-down emission estimates using multiple species, datasets, and methods, the discrepancy between these estimates and the range proposed by Petrenko et al. (2017) is addressed. (3) Discuss and quantify in more detail the fossil fuel industry CH4 upward revision proposed by Petrenko et al. (2017). (4) Offer suggestions for further research activities to reconcile the above discrepancies, and to better constrain geological emission estimates.
The objective of the recently published globally gridded dataset of geo-CH4 emissions (Etiope et al., 2019) was to develop the first comprehensive a priori emission grid for atmospheric modelling. The gridding work allowed refining the CH4 emission estimates for mud volcanoes and microseepage, thanks to a better assessment of their activity (global area) and emission factors (Etiope et al., 2019). These new estimates are reported in Table 2. However, these grid maps do not represent the entire global geo-CH4 source because for some categories of geo-CH4 sources, namely onshore gas-oil seeps, submarine seepage and geothermal emissions, the datasets used for the spatial gridding (developed for modelling purposes) are incomplete or do not contain the information necessary for improving all previous estimates.
|Onshore mud volcanoes||6.1||3.9||8.3||Etiope et al. (2019)|
|Onshore gas-oil seep||3.5||3||4||Etiope et al. (2008a)|
|Submarine seepage||7||3||10||Judd (2004); Etiope et al. (2019)|
|Microseepage||24||15||33||Etiope et al. (2019)|
|Geothermal-volcanic manifestations||4.7||2.2||7.3||Etiope (2015)|
The following provides a brief summary of the gridded geo-CH4 estimates (Etiope et al., 2019) as well as the other previous global geo-CH4 total estimates from the literature (Table 1). Note that the approaches used to quantify emission uncertainties vary among the different bottom-up studies given the different spatial scales (global total vs. grid-level uncertainties) as described below. In most cases, however, best estimates of lower and upper bounds were reported, and these ranges are summarized here.
The CH4 emission range of mud volcanoes, 3.9–8.3 (mean 6.1) Tg yr–1, combines uncertainties of the mud volcano areas and emission factors (Etiope et al., 2019). The mud volcano areas were estimated using image (Google Earth) analysis, photos and published literature (uncertainty of 6%) and the emission factors were based on regression analysis between area and seepage flux for 16 mud volcanoes measured in Europe and Asia (uncertainty of about 42%; Etiope et al., 2019).
The emission range of microseepage, 15–33 (mean 24) Tg yr–1, reflects an uncertainty of about 38% estimated through analysis of sensitivity of the microseepage model (based on geospatial and statistical analyses) used to derive the global microseepage emission (Etiope et al., 2019). Briefly, the microseepage emission factors are based on a statistical analysis of a dataset of 1509 flux measurements (acquired by accumulation chamber method) from 19 different petroliferous basins, associated with geological factors (macro-seeps, faults, seismicity) that can influence the microseepage intensity (Etiope et al., 2019); the microseepage area (activity) was estimated using the global area of petroleum fields and macro-seeps (which may occur also outside a petroleum field) and knowing, from the global microseepage dataset, that microseepage (positive CH4 fluxes) occurs in about 57% of the petroleum field area (Etiope et al., 2019). The sensitivity of the model was tested by combining different emission factors (median, geometric mean, upper and lower 95% confidence limit) and activity (microseepage area 20% smaller or higher).
Concerning the global submarine emissions, which only refers to waters shallower than 500 m where emitted methane can reach the atmosphere, a new range is suggested combining data from Etiope et al. (2019) and previous estimates (Kvenvolden et al. 2001; Judd, 2004). Etiope et al. (2019) report a partial dataset (15 areas) of local and regional emission estimates, totally resulting in 1.8–6.0 (mean 3.9) Tg yr–1. They estimate that other 16 areas may release an additional ~1 Tg yr–1. In these areas, gas was observed to reach the sea surface via bubble plumes, but the output to the atmosphere was not provided. This yields a global submarine emission range of ~3–7 Tg yr–1. Previous estimates are those reported by Kvenvolden et al. (2001), where a range of 10–30 Tg yr–1 was proposed (see also Judd, 2004). The partial emission dataset reported in Etiope et al. (2019) includes major submarine seepage areas investigated so far, but may be a conservative (low) estimate. Nevertheless, it suggests that global submarine emissions may not exceed the minimum value of 10 Tg yr–1 by Kvenvolden et al. (2001). Therefore, until further data emerge, the range 3–10 Tg yr–1 is proposed here as a best guess.
Gridding work in Etiope et al. (2019) did not result in new estimates from onshore gas-oil seeps and geothermal manifestations. The most detailed global total emission estimates from these two geo-CH4 categories are still those from previous statistical and process-based modeling (Etiope et al., 2008a; Etiope, 2015; Table 2).
Global CH4 emission estimates from gas-oil seeps, 3–4 (mean 3.5) Tg yr–1, were based on a database of fluxes that were measured directly (typically by accumulation chamber method) from 66 gas seeps in 12 countries, assuming that their flux and size distributions were representative of the global gas-oil seep population (Etiope et al., 2008a). The global emission range reflects an uncertainty of about 15% estimated combining two different extrapolations of emission factors over the global number of seeps (Etiope et al., 2008a). Global CH4 emission estimates from geothermal manifestations, 2.2–7.3 (mean 4.7) Tg yr–1, were derived on the basis of the most updated estimates of global CO2 emissions from volcanic areas (540 Tg yr–1), from non-volcanic areas (300–1,000 Tg yr–1), and a wide dataset on CO2/CH4 compositional ratios in both areas (Etiope 2015 and references therein). The emission range reflects an uncertainty of about 53% derived from the uncertainty of non-volcanic CO2 emissions (Etiope 2015; the uncertainty of volcanic CO2 degassing was not quantified in the original work; Burton et al., 2013).
In summary, considering the updates based on gridded geo-CH4 estimates and literature estimates for the other geo-CH4 categories, the global bottom-up geological CH4 emission is now estimated at ~45 (27–63) Tg yr–1 (Table 2). Note that this estimate uses the best individual estimates presently available for the five geo-CH4 source categories, while the global estimate proposed in Etiope et al. (2019) refers to the global emissions “extrapolated” from gridded maps (Table 1). As explained above, the extrapolation from gridded maps contains incomplete information on oil-gas seeps, submarine and geothermal emissions. Although derived through different approaches the two estimates are however similar.
Top-down geo-CH4 emission estimates can be derived via multiple approaches, based on the present-day fraction of radiocarbon (14C) free CH4 in the atmosphere (Lassey et al., 2007), pre-industrial ethane (C2H6) and CH4 isotopic composition in polar ice cores and box modelling (Nicewonger et al., 2016; Schwietzke et al., 2016; Dalsøren et al., 2018). Similar to the bottom-up approaches described above, the way the reporting of emission uncertainties varies among studies given the different methodologies (e.g., atmospheric forward modelling of set emission scenarios vs. inverse or box modelling with explicit posterior uncertainties). When only lower and upper bounds were reported, then these ranges are summarized here.
Lassey et al. (2007) deduced that 30% ± 2.3% (1 SD) of the global CH4 source for 1986–2000 is 14C-free (and thus fossil), although they considered this “a plausible re-estimate rather than a definitive revision” of previous lower fossil CH4 estimates (on average 20%). Taking into account the average top-down total CH4 source of 560 Tg yr–1 (reported by Saunois et al., 2016 and valid for the period 1986–2000, as indicated by Lassey et al., 2007) yields 168 Tg yr–1 total fossil CH4 emissions, i.e., natural (geological) plus anthropogenic (fossil fuel industry sources including CH4 venting and leaks). Using the range of the fossil fuel industry fraction reported in the average bottom-up and top-down estimates by Saunois et al. (2016), i.e., 101–134 Tg yr–1, yields geological emission range of 34–67 (mean 50.5) Tg yr–1.
The box modelling by Schwietzke et al. (2016), based on CH4 concentration and isotopic data from ice-core records suggest geo-CH4 emissions of 31–71 Tg yr–1 (1 SD, mean 51 Tg yr–1). These estimates consider a new database of present-day 13C/12C signatures for all CH4 sources, and assume that these signatures are also representative of the pre-industrial era. The relatively wide geo-CH4 range is partly due to a wide range of prescribed biomass burning CH4 emissions from present-day estimates. The central value, 51 Tg yr–1, is consistent with the above estimate combining Lassey et al. (2007) and Saunois et al. (2016) data. Note that this wide geo-CH4 uncertainty range is largely due to uncertainties in pre-industrial biomass burning CH4 baseline emission estimates (i.e., long-term averages), but it is unrelated to current-day uncertainties the trend of burning CH4 emissions (Worden et al., 2017).
With the same box model plus 3-D forward modelling, but using present-day atmospheric CH4 and isotopic data, Schwietzke et al. (2016) suggested a total fossil (geological plus fossil fuel industry) CH4 source of 150–200 Tg yr–1. Considering the fossil fuel industry emission estimates of Saunois et al. (2016) above, i.e., 101–134 Tg yr–1, requires geo-CH4 emissions of 16–99 Tg yr–1 (minimum-maximum range). Note that the global total CH4 source budget in Saunois et al. (2016) is ~5% less than in Schwietzke et al. (2016) due to slightly different assumptions in the CH4 sink magnitude. As a result, the above geo-CH4 emission estimate of 16–99 Tg yr–1 may be a slight underestimate.
Based on pre-industrial concentrations of C2H6 in polar ice cores, Nicewonger et al. (2016) estimated total geological C2H6 emissions of 2.2–3.5 Tg yr–1, which is consistent with previous estimates of 2–4 Tg yr–1 proposed by Etiope and Ciccioli (2009). Additional ethane ice core data and modelling suggests that geo-C2H6 emissions could be even higher (5–6 Tg yr–1; Nicewonger et al., 2018). Considering here the conservative range of Nicewonger et al. (2016), we estimate the total geo-CH4 emission estimate using global averages of ethane/methane (C2/C1) ratios from geological sources (reported in Etiope and Ciccioli, 2009). An average, emission-weighted geological C2/C1 ratio can be derived taking into account the emission fraction of the five geological sources identified in Table 2. A C2H6 source of 2.2–3.5 Tg yr–1 (Nicewonger et al., 2016) requires a CH4 source in the range of 29–46 (mean 37.5) Tg yr–1 (Table 3).
|C2/C1 ratioa||C1 emissionb (Tg yr–1)||Em. fraction|
|C2 emission (Tg yr–1)||C1 emission (Tg yr–1)|
|min Nicewonger et al. (2016)||2.2||29|
|max Nicewonger et al. (2016)||3.5||46|
|min Dalsøren et al. (2018)||2||27|
|max Dalsøren et al. (2018)||4||52|
Based on C2H6 observations and simulations with a detailed atmospheric-chemistry transport model, Dalsøren et al. (2018) estimated total geological C2H6 emissions of 2–4 Tg yr–1, similar to Nicewonger et al. (2016) and Etiope and Ciccioli (2009). In this case, using the same C2/C1 ratio estimated above, yields global CH4 emissions in the range of 27–52 (mean 39.5) Tg yr–1 (Table 3).
All geo-CH4 emission estimates are then summarized in Figure 1, which categorizes studies into Petrenko et al. (2017), bottom-up, top-down C2H6-based, and the other top-down studies discussed above. For Petrenko et al. (2017), the 95% confidence interval and range of mean values in Figure 1 are as reported in that study. For bottom-up, we used the minimum and maximum values from Table 2 as described in Section 2. We then assumed a uniform distribution around the minimum and maximum values to calculate 95% confidence intervals and the standard deviation. Note that assuming a uniform distribution is conservative in the sense that the 95% confidence intervals extend closer to the underlying minimum and maximum values compared to, e.g., Gaussian or triangular distributions. In other words, the lack of overlap in 95% confidence intervals between Petrenko et al. (2017) and the bottom-up approach would be even more pronounced if instead a Gaussian or triangular distribution was assumed.
Top-down C2H6-based 95% confidence intervals were calculated from the joint probability distribution of Nicewonger et al. (2016) and Dalsøren et al. (2018) by performing a Monte Carlo simulation (N = 10,000) giving equal weight to each study, and also assuming a uniform distribution using each study’s minimum and maximum values. The colored dots show the 95% confidence intervals of Nicewonger et al. (2016) and Dalsøren et al. (2018) individually. The same approach was used for the top-down “other” category representing Lassey et al. (2007), Saunois et al. (2016), and Schwietzke et al. (2016) as described above. Note that the 95% confidence intervals of Petrenko et al. (2017) only overlap with those of Schwietzke et al. (2016), not with those of the other studies or the three categories. The overlap in the 95% confidence intervals represents the intersection of the extreme ends of both distributions (90% confidence intervals do not overlap; the range is 18–83 Tg yr–1 in Schwietzke et al., 2016). Further, the right side of the 95% confidence interval in Petrenko et al. (2017) is a factor of 3 to 6 lower than all other studies.
For illustration, averaging the mean values of the bottom-up and top-down estimates and using their full range of 95% confidence interval uncertainties in Figure 1 (excluding those in Petrenko et al., 2017) results in a global geo-CH4 emission range of ~28–75 (mean 45) Tg yr–1. In Figure 2, this value is compared with other natural and anthropogenic CH4 sources. Only combining the lowest estimates from different authors, the global geo-CH4 emission would be 18 Tg yr–1 (see Table 6 in Etiope et al., 2019) and compatible with the upper limit of Petrenko et al. (2017).
Present-day total fossil CH4 emissions have been estimated top-down using 13C/12C and 14C data as described above, but these approaches alone do not attribute emissions between the geo-CH4 source and the fossil fuel industry CH4 source. Separate geo-CH4 estimates (bottom-up and top-down, Sections 2 and 3) have been used to then infer the fossil fuel industry CH4 source. As noted in Petrenko et al. (2017), a geo-CH4 downward revision thus necessitates a fossil fuel industry CH4 upward revision to satisfy the present-day 13C/12C and 14C constraints.
Consider a geo-CH4 source of 46 Tg yr–1 (mean of bottom-up and top-down estimates, Section 3), and a total fossil fuel CH4 source of 172 Tg yr–1 (mean of 13C/12C and 14C estimates, Section 3). This implies a fossil fuel industry CH4 source of 126 Tg yr–1 (consistent with the upper end of recently revised bottom-up estimates of Saunois et al., 2016; Section 3). A geo-CH4 downward revision to 0–15 Tg yr–1 (estimate by Petrenko et al., 2017) thus requires a fossil fuel industry CH4 upward revision to 157–172 Tg yr–1, which is 24–35% larger than previous estimates. Note that the percentage range would increase if lower fossil fuel industry CH4 emission estimates were used as a baseline, e.g., the lower bottom-up range in Saunois et al. (2016).
While all bottom-up and top-down estimates, following independent techniques from different authors, have similar ranges, suggesting a global geo-CH4 emission source in the order of 40–50 Tg yr–1, the radiocarbon (14C-CH4) data in ice cores reported by Petrenko et al. (2017) revise these estimates downward, with a range of 0 (zero) to 18.1 Tg yr–1 (<15.4 Tg yr–1, 95% CI) at least for the atmosphere between 11,000 and 12,000 years ago (Younger-Dryas Preboreal transition). Petrenko et al. (2017) assumed that those past geological emissions are not lower than today, claiming therefore that the previous present-day geo-CH4 estimates are too high.
The assumption that geological emissions are constant over the Holocene is not necessarily correct. Earth’s degassing, which is a process mainly driven by gas advection, is basically controlled by gas pressure gradients in the subsurface and permeability of fractured rocks. These factors can vary considerably on short time scales, in relation, for example, to cycles of gas pressure discharges and build-up in reservoirs, seismic activity, mud volcano eruptions, hydraulic pressure of aquifers and neotectonic stresses (e.g., Quigley et al., 1999; Yang et al., 2006; Delisle et al., 2010; Etiope, 2015). Episodes of enhanced mud volcanism, for example, were recognised in the Upper Quaternary (Etiope et al., 2008b). Modern formation of new mud volcanoes and seeps is documented in several countries (Etiope, 2015). Combinations of such temporal variations at regional scale may lead to a significant variation of the global emission.
Similarly, it is not clear whether during the Younger-Dryas Preboreal transition a wider cover of ice and permafrost may have “capped” and lowered a significant portion of seepage in the boreal hemisphere (as a potential explanation for the discrepancy in geo-CH4 estimates).
We identify two major lines of research that can better constrain the geo-CH4 emission estimates: (a) CH4 flux derivation of large active seeps and mud volcanoes based on atmospheric in-situ measurements or remote sensing; (b) improving the definition of global microseepage area.
(a) The existence of mud volcanoes, which alone are estimated to emit ~6 Tg yr–1 (Table 2), provides a unique opportunity for direct measurements that could test the hypothesis of near-zero global geo-CH4 emissions. Twenty–five of the world’s largest mud volcanoes are located in a relatively small region surrounding the Caspian Sea, and they emit an estimated 1.5 Tg yr–1 in aggregate (Etiope et al., 2019). The individual mud volcanoes can be considered point sources with spatial dimensions comparable to oil and gas production and processing facilities. Their emission estimates can thus be empirically verified using “fence-line” downwind measurement methodologies employed during oil and gas methane field measurements over the last decade in the US and internationally (Alvarez et al., 2018). Similarly, the advancement of space-based remote sensing instruments could lead to the flux quantification of the entire region described above. This, however, also requires source attribution of geo-CH4 and other CH4 sources. The 1.5 Tg yr–1 represents only a relatively small fraction of the bottom-up estimated global geo-CH4 source. However, verifying emission estimates from these mud volcanoes represents a check on the overall bottom-up method that is applied similarly for other seepage categories, and simultaneously a check on the near-zero emission hypothesis of the radiocarbon-based geo-CH4 downward revision.
(b) Based on statistical treatment of emission factor and activity (area) data, microseepage is considered the largest geo-CH4 source (10–25 Tg yr–1; Etiope and Klusman, 2010; 24 ± 9 Tg yr–1; Etiope et al., 2019). Its main uncertainty is due to the limited knowledge of the actual global area where microseepage occurs. Ground-based measurements (soil-gas and flux data) and remote sensing surveys (multispectral imagery) demonstrated that every petroleum field investigated so far is characterized by areas of microseepage, especially at the boundary of the field (Klusman et al., 1998; Etiope and Klusman, 2010; Asadzadeh and de Souza Filho, 2017 and references therein). Flux measurements (1509 data from 19 petroleum fields) showed that microseepage occurs in at least half of the petroleum field area (Etiope et al., 2019). A review of remote sensing investigations, preferably with new data, could allow to better assess the statistics of microseepage in petroleum fields and then globally, as the global area of petroleum fields is known (Etiope et al., 2019).
No new measurements were made for this article. All datasets discussed and elaborated in the text are from published scientific literature.
We thank the three anonymous reviewers whose comments helped to substantially improve the paper.
The work was supported by NASA grant NNX17AK20G.
The authors have no competing interests to declare.
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