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

=== 5.2.2 Methane (CH <sub>4</sub> ): Trends, Variability and Budget ===

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Methane is a much more powerful greenhouse gas than CO <sub>2</sub> (Chapter 7) and participates in tropospheric chemistry (Chapter 6). The CH <sub>4</sub> variability in the atmosphere is mainly the result of the net balance between the sources and sinks on the Earth’s surface and chemical losses in the atmosphere. Atmospheric transport evens out the regional CH <sub>4</sub> differences between different parts of the Earth’s atmosphere. The steady-state lifetime is estimated to be 9.1 ± 0.9 years (Section 6.3.1 and Table 6.2). About 90% of the loss of atmospheric CH <sub>4</sub> occurs in the troposphere by reaction with hydroxyl radical (OH), 5% by bacterial soil oxidation, and the rest 5% by chemical reactions with OH, excited state oxygen (O <sup>1</sup> D), and atomic chlorine (Cl) in the stratosphere ( [[#Saunois--2020|Saunois et al., 2020]] ). Methane has large emissions from natural and anthropogenic origins, but a clear demarcation of their nature is difficult because of the use and conversions of the natural ecosystem for human activities. The largest natural sources are from wetlands, freshwater and geological process, while the largest anthropogenic emissions are from enteric fermentation and manure treatment, landfills and waste treatment, rice cultivation and fossil fuel exploitation (Table 5.2). In the past two centuries, CH <sub>4</sub> emissions have nearly doubled, predominantly human driven since 1900, and persistently exceeded the losses ( ''virtually certain'' ), thereby increasing the atmospheric abundance as evidenced from the ice core and firn air measurements ( [[#Ferretti--2005|Ferretti et al., 2005]] ; [[#Ghosh--2015|Ghosh et al., 2015]] ).

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'''Table 5.2 | Global CH4 budget.''' Sources and sinks of CH4 for the two most recent decades for wich data is available, from bottom-up and top-down estimations (in Tg CH4 yr–1). The data are updated from Saunois et al. (2020), for the bottom-up anthropogenic emissions (FAO, 2019; US EPA, 2019; Crippa et al., 2020; Höglund-Isaksson et al., 2020), top-down geological emissions (Schwietzke et al., 2016; Petrenko et al., 2017; Hmiel et al., 2020), and top-down sinks from seven selected inverse models. The means (min-max) with outliers removed from the range and the means are given. Outliers defined as &gt; 75th percentile + 3 × the interquartile range or &lt; 25th percentile – 3 × the interquartile range. The top-down budget imbalances are calculated for each model separately and averaged. Note also the round-off error for the sources and sinks, which sometimes leads to last digit mismatch in the sums. For detailed information on datasets, see further details on data table 5.SM.6.

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This section discusses both bottom-up and top-down estimates of emissions and sinks. Bottom-up estimates are based on empirical upscaling of point measurements, emissions inventories and dynamical model simulations, while top-down estimates refer to those constrained by atmospheric measurements and chemistry-transport models in inversion systems. Since AR5, a larger suite of atmospheric inversions using both in situ and remote sensing measurement have led to better understanding of the regional CH <sub>4</sub> sources (Cross-Chapter Box 5.2). New ice core measurements of <sup>14</sup> C-CH <sub>4</sub> are used for estimating the geological sources of CH <sub>4</sub> (Table 5.2). Compared to the SRCCL ( [[#IPCC--2019a|IPCC, 2019a]] ; [[#Jia--2019|Jia et al., 2019]] ), we provide a whole atmospheric sources-sinks budget consisting of all emissions and losses.

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==== 5.2.2.1 Atmosphere ====

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Since the start of direct measurements of CH <sub>4</sub> in the atmosphere in the 1970s (Figure 5.13), the highest growth rate was observed from 1977 to 1986 at 18 ± 4 ppb yr <sup>–1</sup> (multi-year mean and 1 standard deviation) ( [[#Rice--2016|Rice et al., 2016]] ). This rapid CH <sub>4</sub> growth followed the green revolution with increased crop production and a fast rate of industrialization that caused rapid increases in CH <sub>4</sub> emissions from ruminant animals, rice cultivation, landfills, oil and gas industry and coal mining ( [[#Ferretti--2005|Ferretti et al., 2005]] ; [[#Ghosh--2015|Ghosh et al., 2015]] ; [[#Crippa--2020|Crippa et al., 2020]] ). Due to increases in oil prices in the early 1980s, emissions from gas flaring declined significantly ( [[#Stern--1996|Stern and Kaufmann, 1996]] ). This explains the first reduction in CH <sub>4</sub> growth rates from 1985 to 1990 ( [[#Steele--1992|Steele et al., 1992]] ; [[#Chandra--2021|Chandra et al., 2021]] ). Further emissions reductions occurred following the Mt Pinatubo eruption in 1991 that triggered a reduction in CH <sub>4</sub> growth rate through a decrease in wetland emissions driven by lower surface temperatures due to the light scattering by aerosols ( [[#Bândă--2016|Bândă et al., 2016]] ; [[#Chandra--2021|Chandra et al., 2021]] ). In the late 1990s through to 2006 there was a temporary pause in the CH <sub>4</sub> growth rate, with higher confidence on its causes than in AR5: emissions from the oil and gas sectors declined by about 10 Tg yr <sup>–1</sup> through the 1990s, and atmospheric CH <sub>4</sub> loss steadily increased ( [[#Dlugokencky--2003|Dlugokencky et al., 2003]] ; [[#Simpson--2012|Simpson et al., 2012]] ; [[#Crippa--2020|Crippa et al., 2020]] ; [[#Höglund-Isaksson--2020|Höglund-Isaksson et al., 2020]] ; [[#Chandra--2021|Chandra et al., 2021]] ). The methane growth rate began to increase again at 7 ± 3 ppb yr <sup>–1</sup> during 2007–2016, the causes of which are highly debated since AR5 ( [[#Rigby--2008|Rigby et al., 2008]] ; [[#Dlugokencky--2011|Dlugokencky et al., 2011]] ; [[#Dalsøren--2016|Dalsøren et al., 2016]] ; [[#Nisbet--2016|Nisbet et al., 2016]] ; [[#Patra--2016|Patra et al., 2016]] ; [[#Schaefer--2016|Schaefer et al., 2016]] ; [[#Schwietzke--2016|Schwietzke et al., 2016]] ; [[#Turner--2017|Turner et al., 2017]] ; [[#Worden--2017|Worden et al., 2017]] ; [[#He--2020|He et al., 2020]] ); studies disagree on the relative contribution of thermogenic, pyrogenic and biogenic emission processes and variability in tropospheric OH concentration. The renewed CH <sub>4</sub> increase is accompanied by a reversal of d <sup>13</sup> C trend to more negative values post 2007; opposite to what occurred in the 200 years prior ( [[#Ferretti--2005|Ferretti et al., 2005]] ; [[#Ghosh--2015|Ghosh et al., 2015]] ; [[#Schaefer--2016|Schaefer et al., 2016]] ; [[#Schwietzke--2016|Schwietzke et al., 2016]] ; [[#Nisbet--2019|Nisbet et al., 2019]] ), suggesting an increasing contribution from animal farming, landfills and waste, and a slower increase in emissions from fossil fuel exploitation since the early 2000s ( [[#Patra--2016|Patra et al., 2016]] ; [[#Jackson--2020|Jackson et al., 2020]] ; [[#Chandra--2021|Chandra et al., 2021]] ). Atmospheric concentrations of CH <sub>4</sub> reached 1866.3 ppb in 2019 (Figure 5.14). A comprehensive assessment of the CH <sub>4</sub> growth rates over the past four decades is presented in Cross-Chapter Box 5.2.

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[[File:8633e13d2dceadf54aefa89175d5574e IPCC_AR6_WGI_Figure_5_13.png]]

'''Figure 5.13 |''' '''Time series of CH''' <sub>4</sub> '''concentrations, growth rates and isotopic composition. (a)''' CH <sub>4</sub> concentrations; '''(b)''' CH <sub>4</sub> growth rates; '''(c)''' d <sup>13</sup> -CH <sub>4</sub> . Data from selected site networks operated by the National Oceanic and Atmospheric Administration (NOAA; [[#Dlugokencky--2003|Dlugokencky et al., 2003]] ), Advanced Global Atmospheric Gases Experiment (AGAGE; [[#Prinn--2018|Prinn et al., 2018]] ) and Portland Airport (PDX, Portland State University; [[#Rice--2016|Rice et al., 2016]] ). To maintain clarity, data from many other measurement networks are not included here, and all measurements are shown in the World Metereological Organization X2004ACH <sub>4</sub> global calibration standard. Global mean values of XCH <sub>4</sub> (total-column), retrieved from radiation spectra measured by the Greenhouse Gases Observing Satellite (GOSAT) are shown in panels (a) and (b). Cape Grim Observatory (CGO; 41°S, 145°E) and Trinidad Head (THD; 41°N, 124°W) data are taken from the AGAGE network. NOAA global and northern hemispheric (NH) means for d <sup>13</sup> C are calculated from 10 and 6 sites, respectively. The PDX data adjusted to NH (period: 1977–2000) are merged with THD (period: 2001–2019) for CH <sub>4</sub> concentration and growth rate analysis, and PDX and NOAA NH means of d <sup>13</sup> C data are used for joint interpretation of long-term trends analysis. The multivariate El Niño–Southern Oscillation (ENSO) index (MEI) is shown in panel (b). Further details on data sources and processing are available in the chapter data table (Table 5.SM.6).

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==== 5.2.2.2 Anthropogenic Methane (CH <sub>4</sub> ) Emissions ====

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The positive gradient between CH <sub>4</sub> at Cape Grim, Australia (41°S) and Trinidad Head, USA (41°N), and the bigger difference between Trinidad Head and global mean CH <sub>4</sub> compared to that between global mean CH <sub>4</sub> and Cape Grim, strongly suggest that the Northern Hemisphere is the dominant origin of anthropogenic CH <sub>4</sub> emissions (Figure 5.13). The loss rate of CH <sub>4</sub> in troposphere does not produce a large positive north–south hemispheric gradient in CH <sub>4</sub> due to parity in hemispheric mean OH concentration ( [[#Patra--2014|Patra et al., 2014]] ), or in the case of greater OH concentrations in the northern rather than the Southern Hemisphere as simulated by the chemistry-climate models ( [[#Naik--2013|Naik et al., 2013]] ). Coal mining contributed about 35% of the total CH <sub>4</sub> emissions from all fossil fuel-related sources. Top-down estimates of fossil fuel emissions (106 Tg yr <sup>–1</sup> ) are smaller than bottom-up estimates (115 Tg yr <sup>–1</sup> ) during 2008–2017 (Table 5.2). Inventory-based estimates suggest that CH <sub>4</sub> emissions from coal mining increased by 17 Tg yr <sup>–1</sup> between the periods 2002–2006 and 2008–2012, with a dominant contribution from China ( [[#Peng--2016|Peng et al., 2016]] ; [[#Crippa--2020|Crippa et al., 2020]] ; [[#Höglund-Isaksson--2020|Höglund-Isaksson et al., 2020]] ). Inventory-based estimates suggest that CH <sub>4</sub> emissions from coal mining increased by 17 Tg yr <sup>–1</sup> between the periods 2002–2006 and 2008–2012, with a dominant contribution from China ( [[#Peng--2016|Peng et al., 2016]] ; [[#Crippa--2020|Crippa et al., 2020]] ; [[#Höglund-Isaksson--2020|Höglund-Isaksson et al., 2020]] ). Recent country statistics and detailed inventory-based estimates show that CH <sub>4</sub> emissions from coal mining in China declined between 2012 and 2016 ( [[#Sheng--2019|Sheng et al., 2019]] ; [[#Gao--2020|Gao et al., 2020]] ), while atmospheric-based estimates suggest a continuation of CH <sub>4</sub> emissions growth but at a slower rate to the year 2015 ( [[#Miller--2019|Miller et al., 2019]] ) and 2016 ( [[#Chandra--2021|Chandra et al., 2021]] ). Emissions from oil and gas extraction and use decreased in the 1980s and 1990s, but increased in the 2000s and 2010s ( [[#Dlugokencky--1994|Dlugokencky et al., 1994]] ; [[#Stern--1996|Stern and Kaufmann, 1996]] ; [[#Howarth--2019|Howarth, 2019]] ; [[#Crippa--2020|Crippa et al., 2020]] ). The attribution to multiple CH <sub>4</sub> sources using spatially aggregated atmospheric d <sup>13</sup> C data remained underdetermined to infer the global total emissions from the fossil fuel industry, biomass burning and agriculture ( [[#Rice--2016|Rice et al., 2016]] ; [[#Schaefer--2016|Schaefer et al., 2016]] ; [[#Schwietzke--2016|Schwietzke et al., 2016]] ; [[#Worden--2017|Worden et al., 2017]] ; [[#Thompson--2018|Thompson et al., 2018]] ).

In the agriculture and waste sectors (Table 5.2), livestock production has the largest emissions source (109 Tg yr <sup>–1</sup> in 2008–2017) dominated by enteric fermentation by about 90%. Methane is formed during the storage of manure, when anoxic conditions are developed ( [[#Hristov--2013|Hristov et al., 2013]] ). Emissions from enteric fermentation and manure have increased gradually from about 87 Tg yr <sup>–1</sup> in 1990–1999 to 109 Tg yr <sup>–1</sup> in 2008–2017 mainly due to the increase in global total animal numbers. Methane production in livestock rumens (cattle, goats, sheep, water buffalo) are affected by the type, amount and quality of feeds, energy consumption, animal size, health and growth rate, meat and milk production rate, and temperature ( [[#Broucek--2014|Broucek, 2014]] ; S.R.O. [[#Williams--2020|]] [[#Williams--2020|Williams et al., 2020]] ; SRCCL [[#5.4.3|Section 5.4.3]] ). Waste management and landfills produced 64 Tg yr <sup>–1</sup> in 2008–2017, with global emissions increasing steadily since the 1970s and, despite significant declines in the USA, western Europe and Japan ( [[#Crippa--2020|Crippa et al., 2020]] ; [[#Höglund-Isaksson--2020|Höglund-Isaksson et al., 2020]] ).

Emissions from rice cultivation decreased from about 45 Tg yr <sup>–1</sup> in the 1980s to about 29 Tg yr <sup>–1</sup> in the decade 2000–2009, but increased again slightly to 31 Tg yr <sup>–1</sup> during 2008–2017, based on inventories data. However, ecosystem models showed a gradual increase with time due to climate change ( ''limited evidence, low agreement'' ) ( [[#Crippa--2020|Crippa et al., 2020]] ; [[#Höglund-Isaksson--2020|Höglund-Isaksson et al., 2020]] ; [[#Ito--2020|Ito, 2020]] ).

Biomass burning and biofuel consumption (including natural and anthropogenic processes) caused at least 30 Tg yr <sup>–1</sup> emissions during 2008–2017 and constituted up to about 5% of global anthropogenic CH <sub>4</sub> emissions. Methane emissions from open biomass burning decreased during the past two decades mainly due to reduction of burning in savanna, grassland and shrubland ( [[#van%20der%20Werf--2017|van der Werf et al., 2017]] ; [[#Worden--2017|Worden et al., 2017]] ). There is recent evidence from the tropics that fire occurrence is non-linearly related to precipitation, implying that severe droughts will increase CH <sub>4</sub> emissions from fires, particularly from the degraded peatlands ( [[#Field--2016|Field et al., 2016]] ).

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==== 5.2.2.3 Land Biospheric Emissions and Sinks ====

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Freshwater wetlands are the single largest global natural source of CH <sub>4</sub> in the atmosphere, accounting for about 26% of the total CH <sub>4</sub> source ( ''robust evidence, medium agreement'' ). Progress has been made since AR5 ( [[#Ciais--2013|Ciais et al., 2013]] ) in better constraining freshwater lake and river emissions and reducing double counting with wetland emissions. Bottom-up and top-down estimates for 2008–2017 are 149 and 180 Tg yr <sup>–1</sup> , respectively, with a top-down uncertainty range of 159–199 Tg yr <sup>–1</sup> (Table 5.2). The large uncertainties stem from challenges in mapping wetland area and temporal dynamics to landscape estimates, and in scaling methane production, transport and consumption processes that are measured with small chambers or flux towers ( [[#Pham-Duc--2017|Pham-Duc et al., 2017]] ). Both the top-down and bottom-up estimates presented in Table 5.2 indicate little increase in wetland CH <sub>4</sub> emissions during the last three decades, with the new estimates being slightly smaller than in AR5 due to updated wetland maps and ecosystem model simulations ( [[#Melton--2013|Melton et al., 2013]] ; [[#Poulter--2017|Poulter et al., 2017]] ). Wetland emissions show strong interannual variability due to the changes in inundated land area, air temperature and microbial activity ( [[#Bridgham--2013|Bridgham et al., 2013]] ). Present terrestrial ecosystem model simulated CH <sub>4</sub> emissions variability does not produce strong correlation with the El Niño–Southern Oscillation (ENSO) cycle (Cross-Chapter Box 5.2, Figure 2), although observation evidence is emerging for lower CH <sub>4</sub> emissions during El Niños and greater emissions during La Niña ( [[#Pandey--2017|Pandey et al., 2017]] ).

Trees in upland and wetland forests contribute to CH <sub>4</sub> emissions by abiotic production in the canopy, by the methanogenesis taking place in the stem, and by conducting CH <sub>4</sub> from soil into the atmosphere ( [[#Covey--2019|Covey and Megonigal, 2019]] ). There is emerging evidence of the important role of trees in transporting and conducting CH <sub>4</sub> from soils into the atmosphere, especially in tropics ( [[#Pangala--2017|Pangala et al., 2017]] ), whereas direct production of CH <sub>44</sub> by vegetation only has a minor contribution ( ''limited evidence, high agreement'' ) ( [[#Bruhn--2012|Bruhn et al., 2012]] ; [[#Covey--2019|Covey and Megonigal, 2019]] ). The contribution of trees in transporting CH <sub>4</sub> may further widen the gap between the bottom-up and top-down estimates in the global budget, particularly needing a re-assessment of emissions in the tropics and in forested wetlands of temperate and boreal regions ( [[#Pangala--2017|Pangala et al., 2017]] ; [[#Jeffrey--2019|Jeffrey et al., 2019]] ; [[#Welch--2019|Welch et al., 2019]] ; [[#Sjögersten--2020|Sjögersten et al., 2020]] ).

Microbial methane uptake by soil comprises up to 5% (30 Tg yr <sup>–1</sup> ) of the total CH <sub>4</sub> sink in 2008–2017 (Table 5.2). There is evidence from experimental and modelling studies of increasing soil microbial uptake due to increasing temperature ( [[#Yu--2017|Yu et al., 2017]] ), although evidence also exists for decreasing CH <sub>4</sub> consumption, possibly linked to precipitation changes ( [[#Ni--2018|Ni and Groffman, 2018]] ). The estimate of global methane loss by microbial oxidation in upland soils has been lowered marginally by 4 Tg yr <sup>–1</sup> , compared to 34 Tg yr <sup>–1</sup> in AR5, for the period 2000–2009. Termites, an infraorder of insects (Isoptera) found in almost all land masses, emitted about 9 Tg yr <sup>–1</sup> of CH <sub>4</sub> in 2000–2009. Increased emissions from insects and other anthropods are projected ( [[#Brune--2018|Brune, 2018]] ).

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==== 5.2.2.4 Ocean and Inland Water Emissions and Sinks ====

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In AR5, the ocean CH <sub>4</sub> emissions were reported together with geological emissions, summing up to 54 (33–75) Tg yr <sup>–1</sup> . Coastal oceans, fjords and mud volcanos are major sources of CH <sub>4</sub> in the marine environment, but CH <sub>4</sub> flux measurements are sparse. [[#Saunois--2020|Saunois et al. (2020)]] estimate that the oceanic budget, including biogenic, geological and hydrate emissions from coastal and open ocean, is 6 (range 4–10) Tg yr <sup>–1</sup> for the 2000s, which is in good agreement with an air–sea flux measurement-based estimate of 6–12 Tg yr <sup>–1</sup> ( [[#Weber--2019|Weber et al., 2019]] ). When estuaries are included, the total oceanic budget is 9–22 Tg yr <sup>–1</sup> , with a mean value of 13 Tg yr <sup>–1</sup> . A recent synthesis suggests that CH <sub>4</sub> emissions from shallow coastal ecosystems, particularly from mangroves, can be as high as 5–6 Tg yr <sup>–1</sup> ( [[#Al-Haj--2020|Al-Haj and Fulweiler, 2020]] ). The reservoir emissions, including coastal wetlands and tidal flats, contribute up to 13 Tg yr <sup>–1</sup> ( [[#Borges--2011|Borges and Abril, 2011]] ; [[#Deemer--2016|Deemer et al., 2016]] ). Methane seepage from the Arctic shelf, possibly triggered by the loss of geological storage due to warming and thawing of permafrost and hydrate decomposition, has a wide estimated range of 0.0–17 Tg yr <sup>–1</sup> ( [[#Shakhova--2010|Shakhova et al., 2010]] , 2014, 2017; [[#Berchet--2016|Berchet et al., 2016]] ); advanced eddy covariance measurements put the best estimate at about 3 Tg yr <sup>–1</sup> from the East Siberian Arctic shelf ( [[#Thornton--2020|Thornton et al., 2020]] ). The current flux is expected to be a mix of pre-industrial and climate change-driven fluxes, CH <sub>4</sub> seepage is anticipated to increase in a warmer world ( [[#Dean--2018|Dean et al., 2018]] ).

All geological sources around the world, including the coastal oceans and fjords, are estimated to emit CH <sub>4</sub> in the range of 35–76 Tg yr <sup>–1</sup> ( [[#Etiope--2019|Etiope et al., 2019]] ). There is evidence that the ventilation of geological CH <sub>4</sub> is ''likely'' to be smaller than 15 Tg yr <sup>–1</sup> ( [[#Petrenko--2017|Petrenko et al., 2017]] ; [[#Hmiel--2020|Hmiel et al., 2020]] ). A lower geological CH <sub>4</sub> ventilation will reduce the gap between bottom-up and top-down estimates (Table 5.2), but widen the gap in the ratio of fossil fuel-derived sources to the biogenic sources for matching the D <sup>14</sup> C-CH <sub>4</sub> observations.

Inland water (lakes, rivers, streams, ponds, estuaries) emissions are proportionally the largest source of uncertainty in the CH <sub>4</sub> budget. Since AR5 ( [[#Ciais--2013|Ciais et al., 2013]] ), the inland water CH <sub>4</sub> source has been revised from 8–73 Tg yr <sup>–1</sup> (1980s) to 117–212 Tg yr <sup>–1</sup> (2000s) with the availability of more observational data and improved areal estimates ( [[#Bastviken--2011|Bastviken et al., 2011]] ; [[#Deemer--2016|Deemer et al., 2016]] ; [[#Stanley--2016|Stanley et al., 2016]] ; [[#DelSontro--2018|DelSontro et al., 2018]] ; [[#Saunois--2020|Saunois et al., 2020]] ). However, it is difficult to estimate bottom-up CH <sub>4</sub> emissions, due to the large spatial and temporal variation in lake and river CH <sub>4</sub> fluxes ( [[#Wik--2016|Wik et al., 2016]] ; [[#Crawford--2017|Crawford et al., 2017]] ; [[#Natchimuthu--2017|Natchimuthu et al., 2017]] ), uncertainties in their global area ( [[#Allen--2018|Allen and Pavelsky, 2018]] ), a relatively small number of observations, and varying measurement methods – for example, those neglecting ebullition, varying upscaling methods, and lack of appropriate processes ( [[#Sanches--2019|Sanches et al., 2019]] ; [[#Engram--2020|Engram et al., 2020]] ; L. [[#Zhang--2020|]] [[#Zhang--2020|]] [[#Zhang--2020|Zhang et al., 2020]] ). Accordingly, there is no clear accounting of inland waters in top-down budgets, which is the main reason for the large gap in bottom-up and top-down estimates of ‘other sources’ in the CH <sub>4</sub> budget (Table 5.2). Despite recent progress in separating wetlands from inland waters, there is double-counting in the bottom-up estimates of their emissions ( [[#Thornton--2016a|Thornton et al., 2016a]] ). Although there is evidence that regional human activities and global warming both increase inland water CH <sub>4</sub> emissions ( [[#Beaulieu--2019|Beaulieu et al., 2019]] ), the increase in the decadal emissions since AR5 ( [[#Ciais--2013|Ciais et al., 2013]] ) rather reflect improvements in the estimate ( ''medium confidence'' ), due to updates in the datasets and new upscaling approaches ( [[#Saunois--2020|Saunois et al., 2020]] ).

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==== 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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[[File:d44325b722c1f64bd18ae35b7649b712 IPCC_AR6_WGI_Figure_5_14.png]]

'''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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[[File:207a64c729e09a7b3dc27844b35bcd98 IPCC_AR6_WGI_CCBox_5_2_Figure_1.png]]

'''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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[[File:ffb928ecae70a1c4030ba22700f29ce0 IPCC_AR6_WGI_CCBox_5_2_Figure_2.png]]

'''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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