Editing IPCC:AR6/WGI/Chapter-5 (section)

==== 5.2.2.5 Methane (CH <sub>4</sub> ) Budget ====

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A summary of top-down and bottom-up estimates of CH <sub>4</sub> emissions and sinks for the period 2008–2017 is presented in Figure 5.14 (details in Table 5.2 and the associated text for the emissions). In addition to 483-682 Tg yr <sup>–1</sup> loss of CH <sub>4</sub> in the troposphere by reaction with OH, 1–35 Tg yr <sup>–1</sup> of CH <sub>4</sub> loss is estimated to occur in the lower troposphere due to Cl but are not included in the top-down models as shown in Table 5.2 ( [[#Hossaini--2016|Hossaini et al., 2016]] ; [[#Gromov--2018|Gromov et al., 2018]] ; X. [[#Wang--2019|]] [[#Wang--2019|Wang et al., 2019]] ). The decadal mean CH <sub>44</sub> burden/imbalance increased at the rate of 30, 12, 7 and 21 Tg yr <sup>–1</sup> in the 1980s (1980–1989), 1990s (1990–1999), 2000s (2000–2009) and the most recent decade (2008–2017), respectively ( ''virtually certain'' ), as can be estimated from observed atmospheric growth rate (Cross-Chapter Box 5.2, Figure 1).

Recent analysis using D <sup>14</sup> C-CH <sub>4</sub> in ice samples suggest that CH <sub>4</sub> emissions from fossil fuel exploitation are responsible for 30% of total CH <sub>4</sub> emissions ( [[#Lassey--2007|Lassey et al., 2007]] ; [[#Hmiel--2020|Hmiel et al., 2020]] ), which is largely inconsistent with sectorial budgets where fossil fuel emissions add up to 20% only ( [[#Ciais--2013|Ciais et al., 2013]] ). However, recent model simulations produce fairly consistent d <sup>13</sup> C-CH <sub>4</sub> values and trends, as observed in the atmospheric samples using 20% fossil fuel emissions fraction ( [[#Ghosh--2015|Ghosh et al., 2015]] ; [[#Warwick--2016|Warwick et al., 2016]] ; [[#Fujita--2020|Fujita et al., 2020]] ; [[#Strode--2020|Strode et al., 2020]] ). Further research is needed to clarify the relative roles of CH <sub>4</sub> emissions from fossil fuel exploitation and freshwater components. A key challenge is to accommodate the higher estimated emissions from these two components without a major increase in the sinks, in order to be consistent with the observed changes in the carbon and hydrogen isotopes.

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'''Figure 5.14 |''' '''Global methane (CH''' <sub>4</sub> ''') budget (2008–2017).''' Values and data sources as in Table 5.2 (in TgCH <sub>4</sub> ). The atmospheric stock is calculated from mean CH <sub>4</sub> concentration, multiplying a factor of 2.75 ± 0.015 Tg ppb <sup>–1</sup> , which accounts for the uncertainties in global mean CH <sub>4</sub> ( [[#Chandra--2021|Chandra et al., 2021]] ). Further details on data sources and processing are available in the chapter data table (Table 5.SM.6).

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'''Cross-Chapter Box 5.2 | Drivers of Atmospheric Methane Changes During''' '''1980–2019'''

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'''Contributors:''' Prabir K. Patra (Japan/India), Josep G. Canadell (Australia), Frank J. Dentener (European Union, The Netherlands), Xin Lan (United States of America/China), Vaishali Naik (United States of America)

The atmospheric methane (CH <sub>4</sub> ) growth rate has varied widely over the past three decades, and the causes have been extensively studied since AR5. The mean growth rate decreased from 15 ± 5 ppb yr <sup>–1</sup> in the 1980s to 0.48 ± 3.2 ppb yr <sup>–1</sup> during 2000–2006 (the so-called quasi-equilibrium phase) and returned to an average rate of 7.6 ± 2.7 ppb yr <sup>–1</sup> in the past decade (2010–2019) (based on data in Figure 5.14). Atmospheric CH <sub>4</sub> grew faster (9.3 ± 2.4 ppb yr <sup>–1</sup> ) over the last six years (2014–2019) – a period with prolonged El Niño conditions, which contributed to high CH <sub>4</sub> growth rates consistent with behaviour during previous El Niño events (Figure 5.14b). Because of large uncertainties in both the emissions and sinks of CH <sub>4</sub> , it has been challenging to quantify accurately the methane budget and ascribe reasons for the growth over 1980–2019. In the context of CH <sub>4</sub> emissions mitigation, it is critical to understand if the changes in growth rates are caused by emissions from human activities or by natural processes responding to changing climate. If CH <sub>4</sub> continues to grow at rates similar to those observed over the past decade, it will contribute to decadal scale climate change and hinder the achievement of the long-term temperature goals of the Paris Agreement ( [[IPCC:Wg1:Chapter:Chapter-7#7.3.2.2|Section 7.3.2.2]] ; [[#Nisbet--2019|Nisbet et al., 2019]] ).

Cross-Chapter Box 5.2, Figure 1 shows the decadal CH <sub>4</sub> budget derived from the Global Carbon Project (GCP)-CH <sub>4</sub> synthesis for 1980s, 1990s and 2000s ( [[#Kirschke--2013|Kirschke et al., 2013]] ), and for 2010–2017 ( [[#Saunois--2020|Saunois et al., 2020]] ). The imbalance of the sources and sinks estimated by atmospheric inversions (dark blue bars) can be used to explain the changes in CH <sub>4</sub> concentration increase rates between the decades (Table 5.2).

Since AR5, many studies have discussed the role of different source categories in explaining the increase in CH <sub>4</sub> growth rate since 2007 and a coincident decrease of d <sup>13</sup> C–CH <sub>4</sub> and dD–CH <sub>4</sub> isotopes (Figure 5.13; [[#Rice--2016|Rice et al., 2016]] ). Both <sup>13</sup> C and D are enriched in mass-weighted average source signatures for CH <sub>4</sub> emissions from thermogenic sources (e.g., coal mining, oil and gas industry) and pyrogenic (biomass burning) sources, and depleted in biogenic (e.g., wetlands, rice paddies, enteric fermentation, landfill and waste) sources. Proposed hypotheses for CH <sub>4</sub> growth (2007–2017) are inconclusive and vary from a concurrent decrease in thermogenic and increase in wetland and other biogenic emissions ( [[#Nisbet--2016|Nisbet et al., 2016]] ; [[#Schwietzke--2016|Schwietzke et al., 2016]] ), an increase in emissions from agriculture in the tropics ( [[#Schaefer--2016|Schaefer et al., 2016]] ), a concurrent reduction in pyrogenic emissions and an increase in thermogenic emissions ( [[#Worden--2017|Worden et al., 2017]] ), or an emissions increase from biogenic sources and a slower increase in emissions from thermogenic sources compared to inventory emissions ( [[#Patra--2016|Patra et al., 2016]] ; [[#Thompson--2018|Thompson et al., 2018]] ; [[#Jackson--2020|Jackson et al., 2020]] ; [[#Chandra--2021|Chandra et al., 2021]] ).

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'''Cross-Chapter Box 5.2, Figure 1 |'''

'''Methane sources and sinks for four decades from atmospheric inversions with the budget imbalance''' (source–sink; dark blue bars) (plotted on the left y-axis). Top-down analysis from [[#Kirschke--2013|Kirschke et al. (2013)]] ; [[#Saunois--2020|Saunois et al. (2020)]] . The global CH <sub>4</sub> concentration seen in the black line (plotted on the right y-axis), representing National Oceanic and Atmospheric Administration (NOAA) observed global monthly mean atmospheric CH <sub>4</sub> in dry-air mole fractions for 1983–2019 (Chapter 2, Annex V). Natural sources include emissions from natural wetlands, lakes and rivers, geological sources, wild animals, termites, wildfires, permafrost soils, and oceans. Anthropogenic sources include emissions from enteric fermentation and manure, landfills, waste and wastewater, rice cultivation, coal mining, oil and gas industry, biomass and biofuel burning. The top-down total sink is determined from global mass balance that includes chemical losses due to reactions with hydroxyl (OH), atomic chlorine (Cl), and excited atomic oxygen (O <sup>1</sup> D), and oxidation by bacteria in aerobic soils (Table 5.2). Further details on data sources and processing are available in the chapter data table (Table 5.SM.6).

A few studies emphasize the role of chemical destruction by hydroxyl (OH; the primary sink of methane), in driving changes in the growth of atmospheric methane abundance, in particular after 2006 ( [[#Rigby--2017|Rigby et al., 2017]] ; [[#Turner--2017|Turner et al., 2017]] ). Studies applying three-dimensional atmospheric inversion ( [[#McNorton--2018|McNorton et al., 2018]] ), simple multi-species inversion ( [[#Thompson--2018|Thompson et al., 2018]] ), as well as empirical methods using a variety of observational constraints based on OH chemistry ( [[#Nicely--2018|Nicely et al., 2018]] ; [[#Patra--2021|Patra et al., 2021]] ), do not find trends in OH large enough to explain the methane changes post-2006. On the contrary, global chemistry–climate models based on fundamental principles of atmospheric chemistry and known emissions trends of anthropogenic non-methane short-lived climate forcers simulate an increase in OH over this period ( [[#Zhao--2019|Zhao et al., 2019]] ; [[#Stevenson--2020|Stevenson et al., 2020]] ; see Section 6.2.3). These contrasting lines of evidence suggest that OH changes may have had a small moderating influence on methane growth since 2007 ( ''l'' ''ow confidence'' ).

Cross-Chapter Box 5.2 Figure 2 shows that modelled wetland emissions anomalies for all regions did not exhibit statistically significant trends ( ''high agreement between models, medium evidence'' ). Thus, the inter-decadal difference of total CH <sub>4</sub> emissions derived from inversion models and wetland emissions, arises mainly from anthropogenic activities. The time series of regional emissions suggest that progress towards atmospheric CH <sub>4</sub> quasi-equilibrium was primarily driven by reductions in anthropogenic (fossil fuel exploitation) emissions in Europe, Russia and temperate North America over 1988–2000. In the global totals, emissions equalled loss in the early 2000s. The growth since 2007 is driven by increasing agricultural emissions from East Asia (1997–2017), West Asia (2005–2017), Brazil (1988–2017) and Northern Africa (2005–2017), and fossil fuel exploitations in temperate North America (2010–2017; [[#Lan--2019|Lan et al., 2019]] ; [[#Crippa--2020|Crippa et al., 2020]] ; [[#Höglund-Isaksson--2020|Höglund-Isaksson et al., 2020]] ; [[#Jackson--2020|Jackson et al., 2020]] ; [[#Chandra--2021|Chandra et al., 2021]] ).

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'''Cross-Chapter Box 5.2, Figure 2 |'''

'''Anomalies in global and regional methane (CH''' <sub>4</sub> ''') emissions for 1988–2017''' . The map in the centre shows mean CH <sub>4</sub> emissions for 2010–2016. Multi-model mean (line) and 1-s standard deviations (shaded) for 2000–2017 are shown for 9 surface CH <sub>4</sub> and 10 satellite XCH <sub>4</sub> inversions, and 22 wetland models or model variants that participated in GCP-CH <sub>4</sub> budget assessment ( [[#Saunois--2020|Saunois et al., 2020]] ). The results for the period before 2000 are available from two inversions, one using 19 sites ( [[#Chandra--2021|Chandra et al., 2021]] ; also used for the 2010–2016 mean emissions map) and one for global totals ( [[#Bousquet--2006|Bousquet et al., 2006]] ). The long-term mean values for 2010–2016 (common for all GCP–CH <sub>4</sub> inversions), as indicated within each panel separately, are subtracted from the annual-mean time series for the calculation of anomalies for each region. Further details on data sources and processing are available in the chapter data table (Table 5.SM.6).

There is evidence from emissions inventories at country level and regional scale inverse modelling that CH <sub>4</sub> growth rate variability between 1988 and 2017 is closely linked to anthropogenic activities ( ''medium agreement'' ). Isotopic composition observations and inventory data suggest that concurrent emissions changes from both fossil fuels and agriculture are playing roles in the resumed CH <sub>4</sub> growth since 2007 ( ''high confidence'' ). Shorter-term decadal variability is predominantly driven by the influence of El Niño–Southern Oscillation on emissions from wetlands and biomass burning (Cross-Chapter Box 5.2, Figure 2), and loss due to OH variations ( ''medium confidence'' ), but lacking quantitative contribution from each of the sectors. By synthesizing all available information regionally from a priori (bottom-up) emissions, satellite and surface observations, including isotopic information, and inverse modelling (top-down), the capacity to track and explain changes in, and drivers of, natural and anthropogenic CH <sub>4</sub> regional and global emissions has improved since AR5, but fundamental uncertainties related to OH variations remain unchanged.

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