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

== Cross-Chapter Box 5.1 | Interactions Between the Carbon and Water Cycles, Particularly Under Drought Conditions ==

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'''Contributors:''' Josep G. Canadell (Australia), Philippe Ciais (France), Hervé Douville (France), Sabine Fuss (Germany), Robert Jackson (United States of America), Annalea Lohila (Finland), Shilong Piao (China), Sonia I. Seneviratne (Switzerland), Sergio M. Vicente-Serrano (Spain), Sönke Zaehle (Germany)

This box presents an assessment of interactions between the carbon and water cycles that influence the dynamics of the biosphere and its interaction with the climate system. It also highlights carbon–water trade-offs arising from the use of land-based climate change mitigation options. Individual aspects of the interactions between the carbon and water cycles are addressed in separate chapters (Sections 5.2.1, 5.4.1, 8.2.3, 8.3.1, 8.4.1 and 11.6). The influence of wetlands and dams on methane emissions is assessed elsewhere (Sections 5.2.2, 5.4.7 and 8.3.1), as well as the consequences of permafrost thawing ([[IPCC:Wg1:Chapter:Chapter-9#9.5.2|Section 9.5.2]] and Box 5.1) and/or increased flooding (Sections 8.4.1, 11.5 and 12.4) on wetland extent in the northern high latitudes and wet tropics.

'''Does elevated CO <sub>2</sub> alleviate the impacts of drought?'''

Increasing atmospheric CO <sub>2</sub> concentration enhances leaf photosynthesis and drives a partial closure of leaf stomata, leading to higher water-use efficiency (WUE) at the leaf canopy and ecosystem scales ([[#Norby--2011|Norby and Zak, 2011]] ; [[#De%20Kauwe--2013|De Kauwe et al., 2013]] ; [[#Fatichi--2016|Fatichi et al., 2016]] ; [[#Knauer--2017|Knauer et al., 2017]] ; [[#Mastrotheodoros--2017|Mastrotheodoros et al., 2017]]). Since AR5 (Box 6.3), a growing body of evidence from tree-ring and carbon isotopes further confirms an increase of plant water-use efficiency over decadal to centennial time scales, with some evidence for a stronger enhancement of photosynthesis compared to stomatal reductions ([[#Frank--2015|Frank et al., 2015]] ; [[#Guerrieri--2019|Guerrieri et al., 2019]] ; [[#Adams--2020|Adams et al., 2020]]).

Multiple lines of evidence suggest that WUE has increased in near proportionality to atmospheric CO <sub>2</sub> (''high confidence'') at a rate generally consistent with Earth system models (ESMs), despite variation in the WUE response to CO <sub>2</sub> ([[#De%20Kauwe--2013|De Kauwe et al., 2013]] ; [[#Frank--2015|Frank et al., 2015]] ; [[#Keeling--2017|Keeling et al., 2017]] ; [[#Lavergne--2019|Lavergne et al., 2019]] ; [[#Walker--2021|Walker et al., 2021]]). Both field-scale CO <sub>2</sub> enrichment experiments and process models show the effect of physiologically induced water savings, particularly under water-limiting conditions ([[#De%20Kauwe--2013|De Kauwe et al., 2013]] ; [[#Farrior--2015|Farrior et al., 2015]] ; [[#Lu--2016|Lu et al., 2016]] ; [[#Roy--2016|Roy et al., 2016]]). Plants can also benefit from reduced drought stress due to enhanced CO <sub>2</sub> without ecosystem-scale water savings ([[#Jiang--2021|Jiang et al., 2021]]). To some extent, this increased WUE offsets the effects of enhanced vapour pressure deficit (VPD) on plant transpiration ([[#Bobich--2010|Bobich et al., 2010]] ; [[#Creese--2014|Creese et al., 2014]] ; [[#Jiao--2019|Jiao et al., 2019]]), but will have limited effect on ameliorating plant water stress during extreme drought events ([[#Xu--2016|Xu et al., 2016]] ; [[#Menezes-Silva--2019|Menezes-Silva et al., 2019]] ; L. [[#Liu--2020|]] [[#Liu--2020|]] [[#Liu--2020|Liu et al., 2020]]), when leaf stomata are governed primarily by soil moisture ([[#Roy--2016|Roy et al., 2016]]).

Leaf stomata closure can have large effects on land freshwater availability because of reduced plant transpiration, leading in some regions to higher soil moisture and runoff ([[#Roderick--2015|Roderick et al., 2015]] ; [[#Milly--2016|Milly and Dunne, 2016]] ; Y. [[#Yang--2019|Yang et al., 2019]]). However, increased water availability is often not realized because other CO <sub>2</sub> physiological effects that enhance ecosystem evapotranspiration might offset the gains. These effects include plant growth and leaf area expansion ([[#Ainsworth--2005|Ainsworth and Long, 2005]] ; [[#Ukkola--2016|Ukkola et al., 2016]] ; [[#McDermid--2021|McDermid et al., 2021]]), lengthening of the vegetative growing season ([[#Frank--2015|Frank et al., 2015]] ; [[#Lian--2021|Lian et al., 2021]]), and the effects of stomatal closure on near-surface atmosphere that leads to increased air temperature and VPDs ([[#Berg--2016|Berg et al., 2016]] ; [[#Vogel--2018|Vogel et al., 2018]] ; [[#Zhou--2019|Zhou et al., 2019]] ; [[#Grossiord--2020|Grossiord et al., 2020]]).

ESMs show no consensus about the net hydrological response to physiological CO <sub>2</sub> effects. Some studies show water savings as a consequence of the CO <sub>2</sub> effects on leaf stomata closure ([[#Swann--2016|Swann et al., 2016]] ; [[#Lemordant--2018|Lemordant et al., 2018]]), while other studies show that increased leaf area offsets the gains from increased WUE ([[#Mankin--2019|Mankin et al., 2019]]). However, these projections are subject to ESM uncertainties to quantify transpiration ([[#Lian--2021|Lian et al., 2021]]), among them the correct representations of plant hydraulic architecture such as changes in xylem anatomical properties and deep rooting ([[#Nie--2013|Nie et al., 2013]] ; L. [[#Liu--2020|]] [[#Liu--2020|]] [[#Liu--2020|Liu et al., 2020]]).

In conclusion, it is ''very likely'' that elevated CO <sub>2</sub> leads to increased WUE at the leaf level, concurrent with enhanced photosynthesis. Increased CO <sub>2</sub> concentrations alleviate the effects of water deficits on plant productivity (''medium confidence'') but there is ''low confidence'' for its role under extreme drought conditions. There is ''low confidence'' that increased WUE by vegetation will substantially reduce global plant transpiration and diminish the frequency and severity of soil moisture and streamflow deficits associated with the radiative effect of higher CO <sub>2</sub> concentrations.

'''How does drought affect the terrestrial CO <sub>2</sub> sink?'''

Water availability controls the spatial distribution of photosynthesis – gross primary productivity (GPP) – over a larger part of the globe ([[#Beer--2010|Beer et al., 2010]]) and, at local scale, drought decreases GPP more than respiration ([[#Schwalm--2012|Schwalm et al., 2012]]) over most ecosystem types. This makes water availability a major climatic driver of variability in net ecosystem exchange ([[#Jung--2017|Jung et al., 2017]] ; [[#Humphrey--2018|Humphrey et al., 2018]]). In addition to suppressing photosynthesis, field evidence suggests that droughts reduce the land CO <sub>2</sub> sink, also through increasing forest mortality and promoting wildfire ([[#Allen--2015|Allen et al., 2015]] ; [[#Brando--2019|Brando et al., 2019]] ; [[#Abram--2021|Abram et al., 2021]]).

At the global scale, interannual variability in the atmospheric CO <sub>2</sub> growth rate and global-scale terrestrial water storage from satellite show that a lower global net land CO <sub>2</sub> sink is associated with below-average terrestrial water storage ([[#Humphrey--2018|Humphrey et al., 2018]]). Atmospheric inversions based on surface and satellite column CO <sub>2</sub> measurements show significant carbon release during drought events in pan-tropic areas ([[#Phillips--2009|Phillips et al., 2009]] ; [[#Gatti--2014|Gatti et al., 2014]] ; J. [[#Liu--2017|]] [[#Liu--2017|Liu et al., 2017]] ; [[#Palmer--2019|Palmer et al., 2019]]). Regional extreme droughts in the mid-latitudes also decrease GPP and land CO <sub>2</sub> sink ([[#Ciais--2005|Ciais et al., 2005]] ; [[#Wolf--2016|Wolf et al., 2016]] ; W. [[#Peters--2020|]] [[#Peters--2020|Peters et al., 2020]] ; [[#Flach--2021|Flach et al., 2021]]). Droughts are not compensated by equivalent wet anomalies because of the non-linear response of the terrestrial carbon uptake to soil moisture ([[#Green--2019|Green et al., 2019]]).

Uncertainties remain on the magnitude of sensitivity of the land carbon fluxes to droughts. Global studies indicate stronger control of soil moisture to variations in satellite proxies of GPP than VPD ([[#Stocker--2019|Stocker et al., 2019]] ; L. [[#Liu--2020|]] [[#Liu--2020|]] [[#Liu--2020|Liu et al., 2020]]). However, given that VPD increases exponentially with atmospheric warming, some studies suggest that VPD in stomatal regulation will become increasingly more important under a warmer climate ([[#Novick--2016|Novick et al., 2016]] ; [[#Grossiord--2020|Grossiord et al., 2020]]). It is difficult to isolate the relative contributions of warmer temperature, higher VPD and lower soil moisture. This is because land-atmosphere feedbacks cause a simultaneous increase of plant evaporative demand and of root zone water deficit impairing plant root uptake ([[#Berg--2016|Berg et al., 2016]]). These physiological responses can be further compounded by drought legacies ([[#Anderegg--2015|Anderegg et al., 2015]]), changes in structure and population dynamics due to forest mortality (McDowell et al., 2020), disturbances associated with drought (fire, insects damage; [[#Anderegg--2020|Anderegg et al., 2020]]) and possible trade-offs between resistance and resilience (X. [[#Li--2020|]] [[#Li--2020|Li et al., 2020]]). Nonetheless, ESMs suggest that increased drought effects under very high levels of global warming (about 4°C at the end of the 21st century) contribute to the reduced efficiency of the land sink ([[#Green--2019|Green et al., 2019]]).

In conclusion, there is ''high confidence'' that the global net land CO <sub>2</sub> sink is reduced on interannual scale when regional-scale reductions in water availability associated with droughts occur, particularly in tropical regions. There is also ''high confidence'' that the global land sink will become less efficient due to soil moisture limitations and associated drought conditions in some regions for high-emissions scenarios, specially under global warming above 4°C. However, there is ''low confidence'' on how these water cycle feedbacks will play out in lower emissions scenarios (at 2°C global warming or lower) due to uncertainties in regional rainfall changes and the balance between the CO <sub>2</sub> fertilization effect, through WUE, and the radiative impacts of greenhouse gases.

'''What are the limits of carbon dioxide removal from a water cycle perspective?'''

Carbon dioxide removal (CDR) options based on terrestrial carbon sinks will require the appropriation of significant amounts of water at the landscape level. Most mitigation pathways that seek to limit global warming to 1.5°C or less than 2°C require the removal of about 30 to 300 GtC from the atmosphere by 2100 ([[#Rogelj--2018b|Rogelj et al., 2018b]]). Bioenergy with carbon capture and storage (BECCS), and afforestation/reforestation are the dominant CDR options used in climate stabilization scenarios, implying large requirements for land and water ([[#5.6|Section 5.6]] ; [[#Beringer--2011|Beringer et al., 2011]] ; [[#Boysen--2017b|Boysen et al., 2017b]] ; [[#Fajardy--2017|Fajardy and Mac Dowell, 2017]] ; [[#Jans--2018|Jans et al., 2018]] ; [[#Séférian--2018b|Séférian et al., 2018b]] ; [[#Yamagata--2018|Yamagata et al., 2018]] ; [[#Stenzel--2019|Stenzel et al., 2019]]). A review of freshwater requirements for irrigating biomass plantations shows a range between 15 and 1250 km <sup>3</sup> per GtC of biomass harvest. This is equivalent to a water requirement of 99–8250 km <sup>3</sup> for the median BECCS deployment of around 3.3 GtC yr <sup>−1</sup> ([[#Smith--2016|Smith et al., 2016]]) in &lt;2°C-scenarios ([[#Stenzel--2021|Stenzel et al., 2021]]), assuming that biomass is converted to electricity, which is substantially less efficient than converting biomass to heat. These large ranges are the result of different assumptions about the type of biomass and yield improvements, management, and land availability. The use of alternative feedstocks, such as wastes, residues and algae, would lead to smaller water requirements ([[#Smith--2019|Smith et al., 2019]]).

Most of the water consumed in BECCS is used to grow the feedstock, with carbon capture and storage constituting a smaller portion across all crops ([[#Rosa--2020|Rosa et al., 2020]]), with an estimated evaporative loss of 260 km <sup>3</sup> yr <sup>−1</sup> for 3.3 GtC yr <sup>−1</sup> ([[#Smith--2016|Smith et al., 2016]]). The same authors also estimate water use for CDR through afforestation at 1040 km <sup>3</sup> yr <sup>−1</sup> for 3.3 GtC yr <sup>−1</sup> , including interception and transpiration, adjusted for the original land cover’s water use.

The impacts of different CDR options on the water cycle depend crucially on regional climate, prior land cover, and scale of deployment ([[#Trabucco--2008|Trabucco et al., 2008]]). Extensive irrigation for afforestation in drier areas will have larger downstream impacts than in wetter regions, with the difference in water use between the afforested landscapes and its previous vegetation determining the level of potential impacts on evapotranspiration and runoff ([[#Jackson--2005|Jackson et al., 2005]] ; [[#Teuling--2017|Teuling et al., 2017]]). Afforestation and reforestation sometimes enhances precipitation through atmospheric feedbacks such as increased convection, at least in the tropics ([[#Ellison--2017|Ellison et al., 2017]]) and the increase in precipitation can, in some regions, even cancel out the increased evapotranspiration ([[#Li--2018|Li et al., 2018]]).

In conclusion, extensive deployment of BECCS and afforestation/reforestation will require larger amounts of freshwater resources than used by the previous vegetation, altering the water cycle at regional scales (''high confidence''). Consequences of high water consumption on downstream uses, biodiversity, and regional climate depend on prior land cover, background climate conditions, and scale of deployment (''high confidence''). Therefore, a regional approach is required to determine the efficacy and sustainability of CDR projects.
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==== 5.2.1.5 CO <sub>2</sub> Budget ====

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The global CO <sub>2</sub> budget (Figure 5.12) encompasses all natural and anthropogenic CO <sub>2</sub> sources and sinks. Table 5.1 shows the perturbation of the global carbon mass balance between reservoirs since the beginning of the industrial era, circa 1750.

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'''Table 5.1 |''' '''Global anthropogenic CO''' <sub>2</sub> '''budget accumulated since the Industrial Revolution (onset in 1750) and averaged over the 1980s, 1990s, 2000s, and 2010s''' . By convention, a negative ocean or land to atmosphere CO <sub>2</sub> flux is equivalent to a gain of carbon by these reservoirs. The table does not include natural exchanges (e.g., rivers, weathering) between reservoirs. Uncertainties represent the 68% confidence interval ([[#Friedlingstein--2020|Friedlingstein et al., 2020]]).

{| class="wikitable"
|-
!
! 1750–2019

Cumulative

(PgC)

! 1850–2019

Cumulative

(PgC)

! 1980–1989

Mean Annual Growth Rate

(PgC yr <sup>–1</sup>)

! 1990–1999

Mean Annual Growth Rate

(PgC yr <sup>–1</sup>)

! 2000–2009

Mean Annual Growth Rate

(PgC yr <sup>–1</sup>)

! 2010–2019

Mean Annual Growth Rate

(PgC yr <sup>–1</sup>)

|-
| colspan="7"| '''Emissions'''

|-
| Fossil fuel combustion and cement production

| 445 ± 20

| 445 ± 20

| 5.4 ± 0.3

| 6.3 ± 0.3

| 7.7 ± 0.4

| 9.4 ± 0.5

|-
| Net land-use change

| 240 ± 70

| 210 ± 60

| 1.3 ± 0.7

| 1.4 ± 0.7

| 1.4 ± 0.7

| 1.6 ± 0.7

|-
| Total emissions

| 685 ± 75

| 655 ± 65

| 6.7 ± 0.8

| 7.7 ± 0.8

| 9.1 ± 0.8

| 10.9 ± 0.9

|-
| colspan="7"| '''Partition'''

|-
| Atmospheric increase

| 285 ± 5

| 265 ± 5

| 3.4 ± 0.02

| 3.2 ± 0.02

| 4.1 ± 0.02

| 5.1 ± 0.02

|-
| Ocean sink

| 170 ± 20

| 160 ± 20

| 1.7 ± 0.4

| 2.0 ± 0.5

| 2.1 ± 0.5

| 2.5 ± 0.6

|-
| Terrestrial sink

| 230 ± 60

| 210 ± 55

| 2.0 ± 0.7

| 2.6 ± 0.7

| 2.9 ± 0.8

| 3.4 ± 0.9

|-
| '''B''' '''udget imbalance'''

| 0

| 20

| –0.4

| –0.1

| 0

| –0.1

|}

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[[File:021e8d9bec3c516832577661fc51eb23 IPCC_AR6_WGI_Figure_5_12.png]]

'''Figure 5.12 |''' '''Global carbon (CO''' <sub>2</sub> ''') budget (2010–2019)''' . Yellow arrows represent annual carbon fluxes (in PgC yr <sup>–1</sup>) associated with the natural carbon cycle, estimated for the time prior to the industrial era, around 1750. Pink arrows represent anthropogenic fluxes averaged over the period 2010–2019. The rate of carbon accumulation in the atmosphere is equal to net land-use change emissions, including land management (called LULUCF in the main text) plus fossil fuel emissions, minus land and ocean net sinks (plus a small budget imbalance, Table 5.1). Circles with yellow numbers represent pre-industrial carbon stocks in PgC. Circles with pink numbers represent anthropogenic changes to these stocks (cumulative anthropogenic fluxes) since 1750. Anthropogenic net fluxes are reproduced from [[#Friedlingstein--2020|Friedlingstein et al. (2020)]] . The relative change of gross photosynthesis since pre-industrial times is based on 15 DGVMs used in [[#Friedlingstein--2020|Friedlingstein et al. (2020)]] . The corresponding emissions by total respiration and fire are those required to match the net land flux, exclusive of net land-use change emissions which are accounted for separately. The cumulative change of anthropogenic carbon in the terrestrial reservoir is the sum of carbon cumulatively lost by net land-use change emissions, and net carbon accumulated since 1750 in response to environmental drivers (warming, rising CO <sub>2</sub> , nitrogen deposition). The adjusted gross natural ocean–atmosphere CO <sub>2</sub> flux was derived by rescaling the value in Figure 1 of [[#Sarmiento--2002|Sarmiento and Gruber (2002)]] of 70 PgC yr <sup>–1</sup> by the revised estimate of the bomb radiocarbon (<sup>14</sup> C) inventory in the ocean. The original bomb <sup>14</sup> C inventory yielded an average global gas transfer velocity of 22 cm hr <sup>–1</sup> ; the revised estimate is 17cm hr <sup>–1</sup> leading to 17/22*70=54. Dissolved organic carbon reservoir and fluxes from [[#Hansell--2009|Hansell et al. (2009)]] . Dissolved inorganic carbon exchanges between surface and deep ocean, subduction and obduction from [[#Levy--2013|Levy et al. (2013)]] . Export production and flux from ([[#Boyd--2019|Boyd et al., 2019]]). Net primary production (NPP) and remineralization in surface layer of the ocean from [[#Kwiatkowski--2020|Kwiatkowski et al. (2020)]] ; [[#Séférian--2020|Séférian et al. (2020)]] . Deep ocean reservoir from [[#Keppler--2020|Keppler et al. (2020)]] . Anthropogenic carbon reservoir in the ocean is from [[#Gruber--2019b|Gruber et al. (2019b)]] extrapolated to 2015. Fossil fuel reserves are from [[#BGR--2020|BGR (2020)]] ; fossil fuel resources are 11,490 PgC for coal, 6,780 PgC for oil and 365 PgC for natural gas. Permafrost region stores are from [[#Hugelius--2014|Hugelius et al. (2014)]] ; [[#Strauss--2017|Strauss et al. (2017)]] ; [[#Mishra--2021|Mishra et al. (2021)]] (see also Box 5.1) and soil carbon stocks outside of permafrost region from [[#Batjes--2016|Batjes (2016)]] ; [[#Jackson--2017|Jackson et al. (2017)]] . Biomass stocks (range of seven estimates) are from [[#Erb--2018|Erb et al. (2018)]] . Sources for the fluxes of the land–ocean continuum are provided in main text and adjusted within the ranges of the various assessment to balance the budget ([[#5.2.1.5|Section 5.2.1.5]]).

Since AR5 ([[#Ciais--2013|Ciais et al., 2013]]), a number of improvements have led to the more constrained carbon budget presented here. Some new additions include: (i) the use of independent estimates for the residual carbon sink on natural terrestrial ecosystems ([[#Le%20Quéré--2018a|Le Quéré et al., 2018a]]); (ii) improvements in the estimates of emissions from cement production ([[#Andrew--2019|Andrew, 2019]]) and the sink associated with cement carbonation ([[#Cao--2020|Cao et al., 2020]]); (iii) improved and new emissions estimates from forestry and other land use ([[#Hansis--2015|Hansis et al., 2015]] ; [[#Gasser--2020|Gasser et al., 2020]]); (iv) the use of ocean observation-based sink estimates and a revised river flux partition between hemispheres ([[#Friedlingstein--2020|Friedlingstein et al., 2020]]); and (v) the expansion of constraints from atmospheric inversions, based on surface networks and the use of satellite retrievals.

The budget, based on the annual assessment by the GCP ([[#Friedlingstein--2020|Friedlingstein et al., 2020]]), uses independent estimates of all major flux components: fossil fuel and carbonate emissions (E <sub>FOS</sub>), CO <sub>2</sub> fluxes from land use, land-use change, and forestry (E <sub>LULUCF</sub>), the growth rate of CO <sub>2</sub> in the atmosphere (G <sub>atm</sub>), and the ocean (S <sub>ocean</sub>) and natural land (S <sub>land</sub>) CO <sub>2</sub> sinks. An imbalance term (B <sub>Imb</sub>) is required to ensure mass balance of the source and sinks that have been independently estimated: E <sub>FOS</sub> + E <sub>LULUCF</sub> = G <sub>atm</sub> + S <sub>ocean</sub> + S <sub>land.</sub> + B <sub>Imb.</sub> All estimates are reported with 1 standard deviation (±1 σ , 1 sigma) representing a likelihood of 68%.

Over the past decade (2010–2019), 10.9 ± 0.9 PgC yr <sup>–1</sup> were emitted from human activities, which were distributed between three Earth system components: 46% accumulated in the atmosphere (5.1 ± 0.02 PgC yr <sup>–1</sup>), 23% was taken up by the ocean (2.5 ± 0.6 PgC yr <sup>–1</sup>) and 31% was stored by vegetation in terrestrial ecosystems (3.4 ± 0.9 PgC yr <sup>–1</sup>) (Table 5.1). There is a budget imbalance of 0.1 PgCyr <sup>–1</sup> which is within the uncertainties of the other terms. Over the industrial era (1750–2019), the total cumulative CO <sub>2</sub> fossil fuel and industry emissions were 445 ± 20 PgC, and the LULUCF flux (= net land-use change in Figure 5.12) was 240 ± 70 PgC (''medium confidence''). The equivalent total emissions (685 ± 75 PgC) was distributed between the atmosphere (285 ± 5 PgC), oceans (170 ± 20 PgC) and land (230 ± 60 PgC; Table 5.1), with a budget imbalance of 20 PgC. This budget (Table 5.1) does not explicitly account for source/sink dynamics due to carbon cycling in the land–ocean aquatic continuum comprising freshwaters, estuaries, and coastal areas. Natural and anthropogenic transfers of carbon from soils to freshwater systems are significant (2.4–5.1 PgC yr <sup>–1</sup>) ([[#Regnier--2013|Regnier et al., 2013]] ; [[#Drake--2018|Drake et al., 2018]]). Some of the carbon is buried in freshwater bodies (0.15 PgC) ([[#Mendonça--2017|Mendonça et al., 2017]]), and a significant proportion returns to the atmosphere via outgassing from lakes, rivers and estuaries ([[#Raymond--2013|Raymond et al., 2013]] ; [[#Regnier--2013|Regnier et al., 2013]] ; [[#Lauerwald--2015|Lauerwald et al., 2015]]). The net export of carbon from the terrestrial domain to the open oceans is estimated to be 0.80 PgC yr <sup>–1</sup> (''medium confidence)'' , based on the average of ([[#Jacobson--2007|Jacobson et al., 2007]] ; [[#Resplandy--2018|Resplandy et al., 2018]]) and corrected to account for 0.2 PgC buried in ocean floor sediments. These terms are included in Figure 5.12. Inclusion of other smaller fluxes could further constrain the carbon budget ([[#Ito--2019|Ito, 2019]] ; [[#Friedlingstein--2020|Friedlingstein et al., 2020]]).

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=== 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.

[[File:62cf3c90890669d31b22435c186810c7 IPCC_AR6_WGI_Chapter_5_Table_5_2.png]]

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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<span id="anthropogenic-methane-ch-4-emissions"></span>
==== 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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<span id="land-biospheric-emissions-and-sinks"></span>
==== 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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<span id="ocean-and-inland-water-emissions-and-sinks"></span>
==== 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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<span id="methane-ch-4-budget"></span>
==== 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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