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

== 5.2 Historical Trends, Variability and Budgets of CO <sub>2</sub> , CH <sub>4</sub> and N <sub>2</sub> O ==

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This section assesses the trends and variability in atmospheric accumulation of the three main greenhouse gases (GHGs) – CO <sub>2</sub> , CH <sub>4</sub> and N <sub>2</sub> O – their ocean and terrestrial sources and sinks as well as their budgets during the Industrial Era (1750–2019). Emphasis is placed on the more recent contemporary period (1959–2019) where understanding is increasingly better constrained by atmospheric, ocean and land observations. The section also assesses our increased understanding of the anthropogenic forcing and processes driving the trends, as well as how variability at the seasonal to decadal scales provide insights on the mechanism governing long-term trends and emerging biogeochemical–climate feedbacks with their regional characteristics.

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=== 5.2.1 CO <sub>2</sub> : Trends, Variability and Budget ===

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==== 5.2.1.1 Anthropogenic CO <sub>2</sub> emissions ====

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There are two anthropogenic sources of carbon dioxide (CO <sub>2</sub> ): fossil emissions and net emissions (including removals) resulting from land-use change and land management (also shown in this chapter as LULUCF: land use, land-use change, and forestry; in previous IPCC reports it has been termed forestry and other land use, FOLU). Fossil CO <sub>2</sub> emissions include the combustion of the fossil fuels coal, oil and gas, covering all sectors of the economy (electricity, transport, industrial, and buildings), fossil carbonates such as in cement manufacturing, and other industrial processes such as the production of chemicals and fertilizers (Figure 5.5a). Fossil CO <sub>2</sub> emissions are estimated by combining economic activity data and emissions factors, with different levels of methodological complexity (tiers) or approaches (e.g., IPCC Guidelines for National Greenhouse Gas Inventories). Several organizations or groups provide estimates of fossil CO <sub>2</sub> emissions, with each dataset having slightly different system boundaries, methods, activity data, and emissions factors ( [[#Andrew--2020|Andrew, 2020]] ). Datasets cover different time periods, which can dictate the datasets and methods that are used for a particular application. The data reported here is from an annually updated data source that combines multiple sources to maximise temporal coverage ( [[#Friedlingstein--2020|Friedlingstein et al., 2020]] ). The uncertainty in global fossil CO <sub>2</sub> emissions is estimated to be ±5% (1 standard deviation).

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[[File:71cb1477ccfff2ba7c5fd4bd04708f42 IPCC_AR6_WGI_Figure_5_5.png]]

'''Figure 5.5 |''' '''Global anthropogenic CO''' <sub>2</sub> '''emissions''' . '''(a)''' Historical trends of anthropogenic CO <sub>2</sub> emissions (fossil fuels and net land-use change, including land management, called LULUCF flux in the main text) for the period 1870 to 2019, with ‘others’ representing flaring, emissions from carbonates during cement manufacture. Data sources: ( [[#Boden--2017|Boden et al., 2017]] ; [[#IEA--2017|IEA, 2017]] ; [[#Andrew--2018|Andrew, 2018]] ; [[#BP--2018|BP, 2018]] ; [[#Le%20Quéré--2018a|Le Quéré et al., 2018a]] ; [[#Friedlingstein--2020|Friedlingstein et al., 2020]] ). '''(b)''' The net land-use change CO <sub>2</sub> flux (PgC yr <sup>–1</sup> ) as estimated by three bookkeeping models and 16 Dynamic Global Vegetation Models (DGVMs) for the global annual carbon budget 2019 ( [[#Friedlingstein--2020|Friedlingstein et al., 2020]] ). The three bookkeeping models are from [[#Hansis--2015|Hansis et al., 2015]] ; [[#Houghton--2017|Houghton and Nassikas, 2017]] ; [[#Gasser--2020|Gasser et al., 2020]] and are all updated to 2019. Their average is used to determine the net land-use change flux in the annual global carbon budget (black line). The DGVM estimates are the result of differencing a simulation with and without land-use changes run under observed historical climate and CO <sub>2</sub> , following the Trendy v9 protocol ( [https://blogs.exeter.ac.uk/trendy/protocol/ https://sites.exeter.ac.uk/trendy/protocol/] ); they are used to provide an uncertainty range to the bookkeeping estimates ( [[#Friedlingstein--2020|Friedlingstein et al., 2020]] ). All estimates are unsmoothed annual data. Estimates differ in process comprehensiveness of the models and in definition of flux components included in the net land use change flux. Further details on data sources and processing are available in the chapter data table (Table 5.SM.6).

Fossil CO <sub>2</sub> emissions have grown continuously since the beginning of the industrial era (Figure 5.5) with short intermissions due to global economic crises or social instability ( [[#Peters--2012|Peters et al., 2012]] ; [[#Friedlingstein--2020|Friedlingstein et al., 2020]] ). In the most recent decade (2010–2019), fossil CO <sub>2</sub> emissions reached an average 9.6 ± 0.5 PgC yr <sup>–1</sup> and were responsible for 86% of all anthropogenic CO <sub>2</sub> emissions. In 2019, fossil CO <sub>2</sub> emissions were estimated to be 9.9 ±0.5 PgC yr <sup>–1</sup> excluding carbonation ( [[#Friedlingstein--2020|Friedlingstein et al., 2020]] ), the highest on record. These estimates exclude the cement carbonation sink of around 0.2 PgC yr <sup>–1</sup> . Fossil CO <sub>2</sub> emissions grew at 0.9% yr <sup>–1</sup> in the 1990s, increasing to 3.0% yr <sup>–1</sup> in the 2000s, and reduced to 1.2% from 2010 to 2019. The slower growth in fossil CO <sub>2</sub> emissions in the last decade is due to a slowdown in growth from coal use. CO <sub>2</sub> emissions from coal use grew at 4.8% yr <sup>–1</sup> in the 2000s, but slowed to 0.4% yr <sup>–1</sup> in the 2010s. CO <sub>2</sub> emissions from oil use grew steadily at 1.1% yr <sup>–1</sup> in both the 2000s and 2010s. CO <sub>2</sub> emissions from gas use grew at 2.5% yr <sup>–1</sup> in the 2000s and 2.4% yr <sup>–1</sup> in 2010s, but has shown signs of accelerated growth of 3% yr <sup>–1</sup> since 2015 ( [[#Peters--2020|]] [[#Peters--2020|Peters et al., 2020]] ). Direct CO <sub>2</sub> emissions from carbonates in cement production are around 4% of total fossil CO <sub>2</sub> emissions, and grew at 5.8% yr <sup>–1</sup> in the 2000s but a slower 2.4% yr <sup>–1</sup> in the 2010s. The uptake of CO <sub>2</sub> in cement infrastructure (carbonation) offsets about one half of the carbonate emissions from current cement production ( [[#Friedlingstein--2020|Friedlingstein et al., 2020]] ). These results are robust across the different fossil CO <sub>2</sub> emissions datasets, despite minor differences in levels and rates, as expected given the reported uncertainties ( [[#Andrew--2020|Andrew, 2020]] ). During 2020, the COVID-19 pandemic led to a rapid, temporary decline in fossil CO <sub>2</sub> emissions, estimated to be around 7% based on a synthesis of four estimates. (Cross-Chapter Box 6.1; [[#Forster--2020|Forster et al., 2020]] ; [[#Friedlingstein--2020|Friedlingstein et al., 2020]] ; [[#Le%20Quéré--2020|Le Quéré et al., 2020]] ; [[#Liu--2020|]] [[#Liu--2020|]] [[#Liu--2020|Liu et al., 2020]] ).

The global net flux from land-use change and land management is composed of carbon fluxes from land-use conversions, land management and changes therein ( [[#Pongratz--2018|Pongratz et al., 2018]] ) and is equivalent to the LULUCF fluxes from the agriculture, forestry and other land use (AFOLU) sector ( [[#Jia--2019|Jia et al., 2019]] ). It consists of gross emissions (loss of biomass and soil carbon in clearing or logging, harvested product decay, emissions from peat drainage and burning, degradation) and gross removals (CO <sub>2</sub> uptake in natural vegetation regrowing after harvesting or agricultural abandonment, afforestation). The LULUCF flux relates to direct human interference with terrestrial vegetation, as opposed to the natural carbon fluxes occurring due to interannual variability or trends in environmental conditions (in particular, climate, CO <sub>2</sub> , and nutrient deposition) ( [[#Houghton--2013|Houghton, 2013]] ).

Progress since AR5 and SRCCL ( [[#IPCC--2019a|IPCC, 2019a]] ) allows more accurate estimates of gross and net fluxes due to the availability of more models, model advancement in terms of inclusiveness of land-use practices, and advanced land-use forcings ( [[#Ciais--2013|Ciais et al., 2013]] ; [[#Klein%20Goldewijk--2017|Klein Goldewijk et al., 2017]] ; [[#Hurtt--2020|Hurtt et al., 2020]] ). In addition, important terminological discrepancies were resolved. First, synergistic effects of land-use change and environmental changes have been identified as a key reason for the large discrepancies between model estimates of the LULUCF flux, explaining up to 50% of differences ( [[#Pongratz--2014|Pongratz et al., 2014]] ; [[#Stocker--2015|Stocker and Joos, 2015]] ; [[#Gasser--2020|Gasser et al., 2020]] ). Another reason for discrepancies relates to natural fluxes being considered as part of the LULUCF flux when occurring on managed land in the United Nations Framework Convention on Climate Change (UNFCCC) national GHG inventories; these fluxes are considered part of the natural terrestrial sink in global vegetation models and excluded in bookkeeping models ( [[#Grassi--2018|Grassi et al., 2018]] ). LULUCF fluxes following national GHG inventories or Food and Agriculture Organization of the United Nations (FAO) datasets, including recent estimates ( [[#Tubiello--2021|Tubiello et al., 2021]] ), are thus excluded from our global assessment, but their comparison against the academic approach is available elsewhere – at the global scale ( [[#Jia--2019|Jia et al., 2019]] ) and European level ( [[#Petrescu--2020|Petrescu et al., 2020]] ).

Land-use-related component fluxes can be verified by the growing databases of global satellite-based biomass observations in combination with information on remotely sensed land cover change. However, they differ from bookkeeping and modelling with Dynamic Global Vegetation Models (DGVMs) in excluding legacy emissions from pre-satellite-era land-use change and land management, and neglecting soil carbon changes, often focusing on gross deforestation, not regrowth ( [[#Jia--2019|Jia et al., 2019]] ).

For the decade 2010–2019, average emissions were estimated at 1.6 ± 0.7 PgC yr <sup>–1</sup> (mean ± standard deviation, 1 sigma; [[#Friedlingstein--2020|Friedlingstein et al., 2020]] ). A ''likely'' general upward trend since 1850 is reversed during the second part of the 20th century (Figure 5.5b). Trends since the 1980s have ''low confidence'' because they differ between estimates, which is related, among other things, to [[#Houghton--2017|Houghton and Nassikas (2017)]] using a different land-use forcing than [[#Hansis--2015|Hansis et al. (2015)]] and the DGVMs. Higher emissions estimates are expected from DGVMs run under transient environmental conditions compared to bookkeeping estimates, because the DGVM estimate includes the loss of additional sink capacity. Because the transient setup requires a reference simulation without land-use change to separate anthropogenic fluxes from natural land fluxes, LULUCF estimates by DGVMs include the sink forests that would have developed in response to environmental changes on areas that in reality have been cleared ( [[#Pongratz--2014|Pongratz et al., 2014]] ). The agricultural areas that replaced these forests have a reduced residence time of carbon, lacking woody material, and thus provide a substantially smaller additional sink over time ( [[#Gitz--2003|Gitz and Ciais, 2003]] ). The loss of additional sink capacity is growing in particular with atmospheric CO <sub>2</sub> and increases DGVM-based LULUCF flux estimates relative to bookkeeping estimates over time (Figure 5.5).

Gross emissions are on average two to three times larger than the net flux from LULUCF, increasing from an average of 3.5 ± 1.2 PgC yr <sup>–1</sup> for the decade of the 1960s to an average of 4.4 ± 1.6 PgC yr <sup>–1</sup> during 2010–2019 ( [[#Friedlingstein--2020|Friedlingstein et al., 2020]] ). Gross removals partly balance these gross emissions to yield the net flux from LULUCF and increase from –2.0 ± 0.7 PgC yr <sup>–1</sup> for the 1960s to –2.9 ± 1.2 PgC yr <sup>–1</sup> during 2010–2019. These large gross fluxes show the relevance of land management, such as harvesting or rotational agriculture, and the large potential to reduce emissions by halting deforestation and degradation.

More evidence on the pre-industrial LULUCF flux has emerged since AR5 in the form of new estimates of cumulative carbon losses until today, and of a better understanding of natural carbon cycle processes over the Holocene ( [[#Ciais--2013|Ciais et al., 2013]] ). Cumulative carbon losses by land-use activities since the start of agriculture and forestry (pre-industrial and industrial era) have been estimated at 116 PgC based on global compilations of carbon stocks for soils ( [[#Sanderman--2017|Sanderman et al., 2017]] ) with about 70 PgC of this occurring prior to 1750, and for vegetation as 447 PgC (inner quartiles of 42 calculations: 375–525 PgC) (Erb et al., 2018). Emissions prior to 1750 can be estimated by subtracting the post-1750 LULUCF flux from Table 5.1 from the combined soil and vegetation losses until today; they would then amount to 328 (161–501) PgC assuming error ranges are independent. A share of 353 (310–395) PgC from prior to 1800 has indirectly been suggested as the difference between net biosphere flux and terrestrial sink estimates, which is compatible with ice-core records due to a low airborne fraction of anthropogenic emissions in pre-industrial times ( [[#Erb--2018|Erb et al., 2018]] ; see also [[#5.1.2.3|Section 5.1.2.3]] ). ''Low confidence'' is assigned to pre-industrial emissions estimates.

Since AR5, evidence emerged that the LULUCF flux might have been underestimated as DGVMs include anthropogenic land cover change, but often ignore land management practices not associated with a change in land cover; land management is more widely captured by bookkeeping models through use of observation-based carbon densities ( [[#Ciais--2013|Ciais et al., 2013]] ; [[#Pongratz--2018|Pongratz et al., 2018]] ). Sensitivity studies show that practices such as wood and crop harvesting increase global net LULUCF emissions ( [[#Arneth--2017|Arneth et al., 2017]] ) and explain about half of the cumulative loss in biomass ( [[#Erb--2018|Erb et al., 2018]] ).

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==== 5.2.1.2 Atmosphere ====

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Atmospheric CO <sub>2</sub> concentration measurements in remote locations began in 1957 at the South Pole Observatory (SPO) and in 1958 at Mauna Loa Observatory (MLO), Hawaii, USA ( [[#Keeling--1960|Keeling, 1960]] ) (Figure 5.6a). Since then, measurements have been extended to multiple locations around the world ( [[#Bacastow--1980|Bacastow et al., 1980]] ; [[#Conway--1994|Conway et al., 1994]] ; [[#Nakazawa--1997|Nakazawa et al., 1997]] ). In addition, high-density global observations of total column CO <sub>2</sub> measurements by dedicated GHG-observing satellites began in 2009 ( [[#Yoshida--2013|Yoshida et al., 2013]] ; [[#O’Dell--2018|O’Dell et al., 2018]] ). Annual mean CO <sub>2</sub> growth rates are observed to be 1.56 ± 0.18 ppm yr <sup>–1</sup> (average and range from 1 standard deviation of annual values) over the 61 years of atmospheric measurements (1959–2019), with the rate of CO <sub>2</sub> accumulation almost tripling from an average of 0.82 ± 0.29 ppm yr <sup>–1</sup> during the decade of 1960–1969 to 2.39 ± 0.37 ppm yr <sup>–1</sup> during the decade of 2010–2019 (Chapter 2). The latter agrees well with that derived for total column (XCO <sub>2</sub> ) measurements by the Greenhouse Gases Observing Satellite (GOSAT; Figure 5.6b). The interannual oscillations in monthly mean CO <sub>2</sub> growth rates (Figure 5.6b) show a close relationship with the El Niño–Southern Oscillation (ENSO) cycle (Figure 5.6b) due to the ENSO-driven changes in terrestrial and ocean CO <sub>2</sub> sources and sinks on the Earth’s surface ( [[#5.2.1.4|Section 5.2.1.4]] ).

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[[File:4bed2863808f5cd02d942ac319456bac IPCC_AR6_WGI_Figure_5_6.png]]

'''Figure 5.6 |''' '''Time series of CO''' <sub>2</sub> '''concentrations and related measurements in ambient air''' . '''(a)''' Concentration time series and MLO-SPO difference, '''(b)''' growth rates, '''(c)''' <sup>14</sup> C and <sup>13</sup> C isotopes, and '''(d)''' O <sub>2</sub> /N <sub>2</sub> ratio. The data for Mauna Loa Observatory (MLO) and South Pole Observatory (SPO) are taken from the Scripps Institution of Oceanography (SIO)/University of California, San Diego ( [[#Keeling--2001|Keeling et al., 2001]] ). The global mean CO <sub>2</sub> are taken from National Oceanic and Atmospheric Administration (NOAA) cooperative network (as in Chapter 2), and Greenhouse Gases Observing Satellite (GOSAT) monthly mean XCO <sub>2</sub> (mixing ratio) time series are taken from National Institute for Environmental Studies ( [[#Yoshida--2013|Yoshida et al., 2013]] ). CO <sub>2</sub> growth rates are calculated as the time derivative of deseasonalized time series ( [[#Nakazawa--1997|Nakazawa et al., 1997]] ). The D(O <sub>2</sub> /N 2 ) are expressed in per meg units (= (FF/M) × 10 <sup>6</sup> , where FF = moles of O <sub>2</sub> consumed by fossil-fuel burning, M = 3.706 × 10 <sup>19</sup> , total number of O <sub>2</sub> molecules in the atmosphere ( [[#Keeling--2014|Keeling and Manning, 2014]] ). The <sup>14</sup> CO <sub>2</sub> time series at Barring Head, Wellington, New Zealand (BHD) is taken from GNS Science and NIWA ( [[#Turnbull--2017|Turnbull et al., 2017]] ). The multivariate ENSO index (MEI) is shown as the shaded background in panel (b); (warmer shade indicates El Niño). Further details on data sources and processing are available in the chapter data table (Table 5.SM.6).

Multiple lines of evidence unequivocally establish the dominant role of human activities in the growth of atmospheric CO <sub>2</sub> . First, the systematic increase in the difference between the MLO and SPO records (Figure 5.6a) is caused primarily by the increase in emissions from fossil fuel combustion in industrialized regions that are situated predominantly in the Northern Hemisphere ( [[#Ciais--2019|Ciais et al., 2019]] ). Second, measurements of the stable carbon isotope in the atmosphere (d <sup>13</sup> C–CO <sub>2</sub> ) are more negative over time because CO <sub>2</sub> from fossil fuels extracted from geological storage is depleted in <sup>13</sup> C (Figure 5.6c; [[#Rubino--2013|Rubino et al., 2013]] ; [[#Keeling--2017|Keeling et al., 2017]] ). Third, measurements of the d(O <sub>2</sub> /N <sub>2</sub> ) ratio show a declining trend because for every molecule of carbon burned, 1.17 to 1.98 molecules of oxygen (O <sub>2</sub> ) is consumed (Figure 5.6d; [[#Ishidoya--2012|Ishidoya et al., 2012]] ; [[#Keeling--2014|Keeling and Manning, 2014]] ). These three lines of evidence confirm unambiguously that the atmospheric increase of CO <sub>2</sub> is due to an oxidative process (i.e., combustion). Fourth, measurements of radiocarbon ( <sup>14</sup> C–CO <sub>2</sub> ) at sites around the world ( [[#Levin--2010|Levin et al., 2010]] ; [[#Graven--2017|Graven et al., 2017]] ; [[#Turnbull--2017|Turnbull et al., 2017]] ) show a continued long-term decrease in the <sup>14</sup> C/ <sup>12</sup> C ratio. Fossil fuels are devoid of <sup>14</sup> C and therefore fossil fuel-derived CO <sub>2</sub> additions decrease the atmospheric <sup>14</sup> C/ <sup>12</sup> C ratio ( [[#Suess--1955|Suess, 1955]] ).

Over the past six decades, the fraction of anthropogenic CO <sub>2</sub> emissions that has accumulated in the atmosphere (referred to as airborne fraction) has remained near constant at approximately 44% (Figure 5.7) ( [[#Ballantyne--2012|Ballantyne et al., 2012]] ; [[#Ciais--2019|Ciais et al., 2019]] ; [[#Gruber--2019b|Gruber et al., 2019b]] ; [[#Friedlingstein--2020|Friedlingstein et al., 2020]] ). This suggests that the land and ocean CO <sub>2</sub> sinks have continued to grow at a rate consistent with the growth rate of anthropogenic CO <sub>2</sub> emissions, albeit with large interannual and sub-decadal variability dominated by the land sinks (Figure 5.7).

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[[File:3ce7222807994e5277656bbc62e28bdc IPCC_AR6_WGI_Figure_5_7.png]]

'''Figure 5.7 |''' '''Airborne fraction and anthropogenic (fossil fuel and land-use change) CO''' <sub>2</sub> '''emissions.''' Data as in [[#5.2.1.1|Section 5.2.1.1]] . The multivariate El Niño–Southern Oscillation (ENSO) index (shaded) and the major volcanic eruptions are marked along the x-axis. Further details on data sources and processing are available in the chapter data table (Table 5.SM.6).

Since AR5, an alternative observable diagnostic to the airborne fraction has been proposed to understand the trends in land and ocean sinks in response to its driving atmospheric CO <sub>2</sub> concentrations ( [[#Raupach--2014|Raupach et al., 2014]] ; [[#Bennedsen--2019|Bennedsen et al., 2019]] ). It is the sink rate that is defined as the combined ocean and land sink flux per unit of atmospheric excess of CO <sub>2</sub> above pre-industrial levels ( [[#Raupach--2014|Raupach et al., 2014]] ). The sink rate has declined over the past six decades, which indicates that the combined ocean and land sinks are not growing as fast as the growth in atmospheric CO <sub>2</sub> ( [[#Raupach--2014|Raupach et al., 2014]] ; [[#Bennedsen--2019|Bennedsen et al., 2019]] ). Possible explanations for the sink rate decline are that the land and/or ocean CO <sub>2</sub> sinks are no longer responding linearly with CO <sub>2</sub> concentrations or that anthropogenic emissions are slower than exponential (Figure 5.7 and Sections 5.2.1.3 and 5.2.1.4; [[#Gloor--2010|Gloor et al., 2010]] ; [[#Raupach--2014|Raupach et al., 2014]] ; [[#Bennedsen--2019|Bennedsen et al., 2019]] ). In addition, both diagnostics are influenced by major climate modes (e.g., ENSO) and volcanic eruptions that contribute to high interannual variability ( [[#Gloor--2010|Gloor et al., 2010]] ; [[#Frölicher--2013|Frölicher et al., 2013]] ; [[#Raupach--2014|Raupach et al., 2014]] ), suggesting high sensitivity to future climate change. Uncertain land-use change fluxes ( [[#5.2.1.2|Section 5.2.1.2]] ) influence the robustness of the trends. Based on the airborne fraction (AF), it is concluded with ''medium confidence'' that both ocean and land CO <sub>2</sub> sinks have grown consistent with the rising of anthropogenic emissions. Further research is needed to understand the drivers of changes in the CO <sub>2</sub> sink rate.

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==== 5.2.1.3 Ocean Carbon Fluxes and Storage ====

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Since AR5 and SROCC, major advances in globally coordinated ocean CO <sub>2</sub> observations (Surface Ocean CO <sub>2</sub> Atlas, SOCAT; and Global Ocean Data Analysis Project, GLODAP), the harmonization of ocean and coastal-observation-based products, atmospheric and oceanic inversion models and forced global ocean biogeochemical models (GOBMs) have increased the level of confidence in the assessment of trends and variability of air–sea fluxes and storage of CO <sub>2</sub> in the ocean during the historical period (1960–2018; see also Supplementary Materials 5.SM.1; [[#Ciais--2013|Ciais et al., 2013]] ; [[#Bakker--2016|Bakker et al., 2016]] ; [[#Landschützer--2016|Landschützer et al., 2016]] , 2020; [[#Bindoff--2019|Bindoff et al., 2019]] ; [[#DeVries--2019|DeVries et al., 2019]] ; [[#Gregor--2019|Gregor et al., 2019]] ; [[#Gruber--2019a|Gruber et al., 2019a]] , b; [[#Tohjima--2019|Tohjima et al., 2019]] ; [[#Friedlingstein--2020|Friedlingstein et al., 2020]] ; [[#Hauck--2020|Hauck et al., 2020]] ; [[#Olsen--2020|Olsen et al., 2020]] ). A major advance since SROCC is that, for the first time, all six published observational product fluxes used in this assessment, are made more comparable using a common ocean and sea ice cover area, integration of climatological coastal fluxes scaled to increasing atmospheric CO <sub>2</sub> and an ensemble mean of ocean fluxes calculated from three re-analysis wind products (Supplementary Materials 5.SM.2; [[#Landschützer--2014|Landschützer]] et al., 2014, 2020; [[#Rödenbeck--2014|Rödenbeck et al., 2014]] ; [[#Zeng--2014|Zeng et al., 2014]] ; [[#Denvil-Sommer--2019|Denvil-Sommer et al., 2019]] ; [[#Gregor--2019|Gregor et al., 2019]] ; [[#Iida--2021|Iida et al., 2021]] ). From a process point of view, the ocean uptake of anthropogenic carbon is a two-step set of abiotic processes that involves the exchange of CO <sub>2</sub> , first across the air–sea boundary into the surface mixed layer, followed by its transport into the ocean interior where it is stored for decades to millennia, depending on the depth of storage ( [[#Gruber--2019b|Gruber et al., 2019b]] ). Two definitions of air–sea fluxes of CO <sub>2</sub> are used in this assessment for both observational products and models: S <sub>ocean</sub> is the global mean ocean CO <sub>2</sub> sink and F <sub>net</sub> denotes the net spatially varying CO <sub>2</sub> fluxes ( [[#Hauck--2020|Hauck et al., 2020]] ). Adjustment of the mean global F <sub>net</sub> for the pre-industrial sea-to-air CO <sub>2</sub> flux associated with land-to-ocean carbon flux term makes F <sub>net</sub> comparable to S <sub>ocean</sub> ( [[#Jacobson--2007|Jacobson et al., 2007]] ; [[#Resplandy--2018|Resplandy et al., 2018]] ; [[#Hauck--2020|Hauck et al., 2020]] ).

There are multiple lines of observational and modelling evidence that support with ''high confidence'' the finding that, in the historical period (1960–2018), air–sea fluxes and storage of anthropogenic CO <sub>2</sub> are largely influenced by atmospheric CO <sub>2</sub> concentrations, physical ocean processes and physicochemical carbonate chemistry, which determines the unique properties of CO <sub>2</sub> in seawater ( [[IPCC:Wg1:Chapter:Chapter-9|Chapter 9]] and Cross-Chapter Box 5.3; [[#Wanninkhof--2014|Wanninkhof, 2014]] ; [[#DeVries--2017|DeVries et al., 2017]] ; [[#McKinley--2017|McKinley et al., 2017]] , 2020, [[#Gruber--2019a|Gruber et al., 2019a]] , b; [[#Hauck--2020|Hauck et al., 2020]] ). Here we assess three different approaches (Figures 5.8a,b and 5.9) that together provide ''high'' ''confidence'' that, during the historical period (1960–2018), the ocean carbon sink (S <sub>ocean</sub> ) and its associated ocean carbon storage have grown in response to global anthropogenic CO <sub>2</sub> emissions ( [[#Gruber--2019a|Gruber et al., 2019a]] ; [[#Hauck--2020|Hauck et al., 2020]] ; [[#McKinley--2020|McKinley et al., 2020]] ).

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===== 5.2.1.3.1 Ocean carbon fluxes and storage: Global multi-decadal trends =====

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In the first assessment approach, the mean global multi-decadal (1960–2019) trends in the ocean sink (S <sub>ocean</sub> ) for CO <sub>2</sub> show a high degree of coherence across the nine GOBMs and six ''p'' CO <sub>2</sub> -based observational product reconstructions (1987–2018) which, despite a temporary slowdown (or ‘hiatus’) in the 1990s, is also quasi-linear over that period (Figure 5.8a; [[#Gregor--2019|Gregor et al., 2019]] ; [[#Hauck--2020|Hauck et al., 2020]] ). This coherence between the GOBMs and observations-based reconstructions (1987–2018; r <sup>2</sup> =0.85) provides ''high confidence'' that the ocean sink (S <sub>ocean</sub> in [[#5.2.1.5|Section 5.2.1.5]] ) evaluated from GOBMs (1960–2019) grew quasi-linearly from 1.0 ± 0.3 PgC yr <sup>–1</sup> to 2.5 ± 0.6 PgC yr <sup>–1</sup> between the decades 1960–1969 and 2010–2019 in response to global CO <sub>2</sub> emissions (Figure 5.8a; Table 5.1; [[#Friedlingstein--2020|Friedlingstein et al., 2020]] ; [[#Hauck--2020|Hauck et al., 2020]] ). The cumulative ocean CO <sub>2</sub> uptake (105 ± 20 PgC) is 23% of total anthropogenic CO <sub>2</sub> emissions (450 ± 50 PgC) for the same period ( [[#Friedlingstein--2020|Friedlingstein et al., 2020]] ). Notwithstanding the ''high confidence'' in the magnitude of the annual to decadal trends for S <sub>ocean,</sub> this assessment is moderated to ''medium'' c ''onfidence'' by the ''low confidence'' in the currently inadequately constrained uncertainties in the pre-industrial land-to-ocean carbon flux, the uncertain magnitude of winter outgassing from the Southern Ocean, and the uncertain effect of the ocean surface cool-skin, the effect of data sparsity, differences between wind products and the uncertain contribution from the changing land–ocean continuum on global and regional fluxes ( [[#Jacobson--2007|Jacobson et al., 2007]] ; [[#Resplandy--2018|Resplandy et al., 2018]] ; [[#Roobaert--2018|Roobaert et al., 2018]] ; [[#Bushinsky--2019|Bushinsky et al., 2019]] ; [[#Hauck--2020|Hauck et al., 2020]] ; [[#Watson--2020|Watson et al., 2020]] ; [[#Gloege--2021|Gloege et al., 2021]] ). However, both GOBMs and ''p'' CO <sub>2</sub> -based observational products independently reveal a slowdown or ‘hiatus’ of the ocean sink in the 1990s, which provides a valuable constraint for model verification and leads to greater confidence in the model outputs (Figure 5.8a; [[#Landschützer--2016|Landschützer et al., 2016]] ; [[#Gregor--2018|Gregor et al., 2018]] ; [[#DeVries--2019|DeVries et al., 2019]] ; [[#Hauck--2020|Hauck et al., 2020]] ). A number of studies point to the role of the Southern Ocean in the global ‘1990s hiatus’ in air–sea CO <sub>2</sub> fluxes, but provide different process-based explanations linking ocean temperature, mixing and meridional overturning circulation (MOC) responses to variability in large-scale climate systems, wind stress and volcanic activity, as well as the sensitivity of the air–sea CO <sub>2</sub> flux to small changes in the atmospheric forcing from anthropogenic CO <sub>2</sub> ( [[#Landschützer--2016|Landschützer et al., 2016]] ; [[#DeVries--2017|DeVries et al., 2017]] ; [[#Bronselaer--2018|Bronselaer et al., 2018]] ; [[#Gregor--2018|Gregor et al., 2018]] ; [[#Gruber--2019a|Gruber et al., 2019a]] ; [[#Keppler--2019|Keppler and Landschützer, 2019]] ; [[#McKinley--2020|McKinley et al., 2020]] ; [[#Nevison--2020|Nevison et al., 2020]] ). Data sparsity in the Southern Ocean could also be a factor amplifying the global decadal perturbation of the 1990s ( [[#Gloege--2021|Gloege et al., 2021]] ). Therefore, while there is ''high confidence'' in the 1990s hiatus of the global ocean sink for anthropogenic CO <sub>2,</sub> and that the Southern Ocean makes an observable contribution to it, there is still ''low confidence'' in the attribution for the processes behind the 1990s hiatus ( [[#5.2.1.3.2|Section 5.2.1.3.2]] ). Observed increases in the amplitude of the seasonal cycle of ocean ''p'' CO <sub>2</sub> and reductions in the mean global buffering capacity provide ''high confidence'' that the growing CO <sub>2</sub> sink is also beginning to drive observable large-scale changes in ocean carbonate chemistry ( [[#Jiang--2019|Jiang et al., 2019]] ). However, there is ''medium confidence'' that these changes which, depending on the emissions scenario, could drive future ocean feedbacks, are still too small to emerge from the historical multi-decadal observed growth rate of S <sub>ocean</sub> (Sections 5.1.2; 5.3.2 and 5.4.2, and Figure 5.8a; SROCC ( [[#5.2.2.3.2|Section 5.2.2.3.2]] ; [[#Bates--2014|Bates et al., 2014]] ; [[#Sutton--2016|Sutton et al., 2016]] ; [[#Fassbender--2017|Fassbender et al., 2017]] ; [[#Landschützer--2018|Landschützer et al., 2018]] ; [[#Jiang--2019|Jiang et al., 2019]] ). A recent model-based study suggests that re-emergence of previously stored anthropogenic CO <sub>2</sub> is changing the buffering capacity of the mixed layer and reducing the ocean sink for anthropogenic CO <sub>2</sub> during the historical period ( [[#Rodgers--2020|Rodgers et al., 2020]] ). This trend is not reflected in observations-based products (Figure 5.8a), so we attribute a ''low confidence'' .

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[[File:b43a260d7811a80998c166fc4239ff8e IPCC_AR6_WGI_Figure_5_8.png]]

'''Figure 5.8 |''' '''Multi-decadal trends for the ocean sink of CO''' <sub>2</sub> '''.''' '''(a)''' The multi-decadal (1960–2019) trends in the annual ocean sink (S <sub>ocean</sub> ) reconstructed from nine Global Ocean Biogeochemical Models (GOBM) forced with atmospheric re-analysis products ( [[#Hauck--2020|Hauck et al., 2020]] ), six observationally based gap-filling products that reconstructed spatial and temporal variability in the ocean CO <sub>2</sub> flux from sparse observations of surface ocean ''p'' CO <sub>2</sub> (Supplementary Materials 5.SM.2). The trends in S <sub>ocean</sub> were calculated from the mean annual GOBM outputs, and the observational products were used to provide confidence in the GOBM assessments (r <sup>2</sup> =0.85). Thick lines represent the multi-model mean. Observationally based products have been corrected for pre-industrial river carbon fluxes (0.62 PgC yr <sup>–1</sup> ) based on the average of estimates from [[#Jacobson--2007|Jacobson et al. (2007)]] and [[#Resplandy--2018|Resplandy et al. (2018)]] . '''(b)''' Mean decadal constraints and their confidence intervals for global ocean sink (S <sub>ocean</sub> ) of anthropogenic CO <sub>2</sub> using multiple independent or quasi-independent lines of evidence or methods for the period 1990–2019 (see Supplementary Materials Tables 5.SM.1 and 5.SM.2 for magnitudes, uncertainties and published sources). Further details on data sources and processing are available in the chapter data table (Table 5.SM.6).

The second assessment approach makes use of six independent methods to constrain the mean decadal ocean sink over the period 1990–2019 (Figure 5.8b). This provides a multi-decadal advance on the 1990–1999 decadal constraint from ( [[#Denman--2007|Denman et al., 2007]] ) that has been widely used as a model constraint for GOBMs used for the global carbon budget ( [[#Hauck--2020|Hauck et al., 2020]] ). The ''medium confidence'' attributed by this assessment of the global multi-decadal trend (Figure 5.8a) is further supported by the broad agreement in magnitude and trend of the decadal mean ocean CO <sub>2</sub> uptake with assessments that also include additional observations-based, independent methods such as ocean CO <sub>2</sub> inversion and atmospheric CO <sub>2</sub> and O <sub>2</sub> /N <sub>2</sub> measurements (Figure 5.8b; Supplementary Materials Tables 5.SM.1 and 5.SM.2).

Here we provide a third comparative assessment approach depicting the spatial coherence of ocean air–sea fluxes and storage rates of CO <sub>2</sub> as well as a quantitative assessment of both fluxes for the same period (1994–2007; Figure 5.9). Observation-based ''p'' CO <sub>2</sub> flux products show that emissions of natural CO <sub>2</sub> occur mostly in the tropics and high-latitude Southern Ocean, and that the uptake and storage of anthropogenic CO <sub>2</sub> occurs predominantly in the mid-latitudes (Chapter 9, Figure 5.9 and Cross-Chapter Box 5.3). Strong ocean CO <sub>2</sub> sink regions are those in the mid-latitudes associated with the cooling of poleward flowing subtropical surface waters as well as equatorward flowing sub-polar surface waters, both of which contribute to the formation of Mode, Intermediate and Deep water masses that transport anthropogenic CO <sub>2</sub> into the ocean interior on time scales of decades to centuries in both hemispheres ( [[IPCC:Wg1:Chapter:Chapter-9#9.2.2.3|Section 9.2.2.3]] and Figure 5.9; [[#DeVries--2014|DeVries, 2014]] ; [[#Gruber--2019b|Gruber et al., 2019b]] ; [[#Wu--2019|Wu et al., 2019]] ). The mean decadal scale magnitude and uncertainties of S <sub>ocean</sub> from net air sea fluxes (F <sub>net</sub> ) were calculated from an ensemble of six observational-based product reconstructions (Figure 5.9a) and the storage rates in the ocean interior derived from multiple ocean interior CO <sub>2</sub> datasets ( [[#Gruber--2019b|Gruber et al., 2019b]] ; Figure 5.9b). The cumulative CO <sub>2</sub> stored in the ocean interior from 1800 to 2007 has been estimated at 140 ±18 PgC ( [[#Gruber--2019b|Gruber et al., 2019b]] ). As reported in SROCC ( [[#5.2.2.3.1|Section 5.2.2.3.1]] ; [[#IPCC--2019b|IPCC, 2019b]] ), the net ocean CO <sub>2</sub> storage between 1994–2007 was 29 ± 4 PgC, which corresponds to a mean storage of 26 ± 5% of anthropogenic CO <sub>2</sub> emissions for that period ( [[#Gruber--2019b|Gruber et al., 2019b]] ). The resulting net annual storage rate of anthropogenic CO <sub>2</sub> , equivalent to S <sub>ocean</sub> for the period mid-1994 to mid-2007 is 2.2 ± 0.3 PgC yr <sup>–1</sup> , which is in very close agreement with the top-down air–sea flux estimate of S <sub>ocean</sub> of 2.1 ± 0.5 PgC yr <sup>–1</sup> from GOBMs and 1.9 ± 0.3PgC yr <sup>–1</sup> from ''p'' CO <sub>2</sub> -based observational products with the steady river carbon flux correction of 0.62 PgC yr <sup>–1</sup> for the same time period ( [[#Gruber--2019b|Gruber et al., 2019b]] ; [[#Hauck--2020|Hauck et al., 2020]] ). This close agreement between these independent ocean CO <sub>2</sub> sink estimates derived from air–sea fluxes and storage rates in the ocean interior support the ''medium confidence'' assessment that the ocean anthropogenic carbon storage rates continue to be determined by the ocean sink (S <sub>ocean</sub> ) in response to growing CO <sub>2</sub> emissions (Figure 5.9; [[#McKinley--2020|McKinley et al., 2020]] ).

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[[File:2e7db8ef46950871e4054833f648d72a IPCC_AR6_WGI_Figure_5_9.png]]

'''Figure 5.9 |''' '''Comparative regional characteristics of the mean decadal (1994–2007) sea-air CO''' <sub>2</sub> '''flux (Fnet) and ocean storage of anthropogenic CO''' <sub>2</sub> '''. (a)''' Regional source–sink characteristics for contemporary ocean air – sea CO <sub>2</sub> fluxes (F <sub>net</sub> ) derived from the ensemble of six observation-based products using Surface Ocean CO <sub>2</sub> ( [[IPCC:Wg1:Chapter:Atlas|Atlas]] (SOCAT)v6 observational dataset ( [[#Landschützer--2014|Landschützer et al., 2014]] ; [[#Rödenbeck--2014|Rödenbeck et al., 2014]] ; [[#Zeng--2014|Zeng et al., 2014]] ; [[#Bakker--2016|Bakker et al., 2016]] ; [[#Denvil-Sommer--2019|Denvil-Sommer et al., 2019]] ; [[#Gregor--2019|Gregor et al., 2019]] ; [[#Iida--2021|Iida et al., 2021]] ). Warm colours depict outgassing fluxes and black contours characterize the super-biomes defined from [[#Fay--2014|Fay and McKinley (2014)]] and adjusted by [[#Gregor--2019|Gregor et al. (2019)]] also used to calculate the variability in regional flux anomalies (Supplementary Materials Figure 5.SM.1); '''(b)''' The regional characteristics of the storage fluxes of CO <sub>2</sub> in the ocean interior for the same period ( [[#Gruber--2019b|Gruber et al., 2019b]] ). The dots reflect ocean areas where the 1-sigma standard deviation of Fnet from the six observational-based product reconstructions is larger than the magnitude of the mean. This reflects source–sink transition areas where the mean Fnet is small and more strongly influenced by spatial and temporal variability across the products. Further details on data sources and processing are available in the chapter data table (Table 5.SM.6).

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<span id="ocean-carbon-fluxes-and-storage-regional-and-global-variability"></span>
===== 5.2.1.3.2 Ocean carbon fluxes and storage: Regional and global variability =====

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The intent of this assessment is to show how global variability can be regionally forced ( [[#Gregor--2019|Gregor et al., 2019]] ; [[#Landschützer--2019|Landschützer et al., 2019]] ; [[#Hauck--2020|Hauck et al., 2020]] ). Since AR5 and SROCC, advances in global ocean CO <sub>2</sub> flux products, GOBMs and atmospheric inversion models have strengthened confidence in the assessment of how ocean regions influence mean global variability and trends of ocean CO <sub>2</sub> air–sea fluxes (F <sub>net</sub> ; see Supplementary Materials Figure 5.SM.1; [[#Ciais--2013|Ciais et al., 2013]] ; [[#Landschützer--2014|Landschützer et al., 2014]] , 2015; [[#Rödenbeck--2014|Rödenbeck et al., 2014]] ; [[#McKinley--2017|McKinley et al., 2017]] ; [[#Bindoff--2019|Bindoff et al., 2019]] ; [[#Gregor--2019|Gregor et al., 2019]] ; [[#Friedlingstein--2020|Friedlingstein et al., 2020]] ; [[#Hauck--2020|Hauck et al., 2020]] ). The coherence in the regional variability of the anomalies in F <sub>net</sub> from three independent lines of evidence support with ''high confidence'' that the non-steady state global interannual-decadal variability of F <sub>net</sub> has clear regional influences ( [[#Gregor--2019|Gregor et al., 2019]] ; [[#Landschützer--2019|Landschützer et al., 2019]] ). The tropical oceans contribute the most to the global mean interannual variability (Supplementary Materials Figure 5.SM.1d). The high latitude oceans, particularly the Southern Ocean, contribute the most to the global-scale decadal variability (Supplementary Materials Figure 5.SM5.1b,c; ( [[#Landschützer--2016|Landschützer et al., 2016]] , 2019; [[#Gregor--2019|Gregor et al., 2019]] ; [[#Gruber--2019a|Gruber et al., 2019a]] ; [[#Hauck--2020|Hauck et al., 2020]] ). The influence of the Southern Ocean on the global mean decadal variability and the 1990s hiatus is supported by the highest regional–global correlation coefficients (Supplementary Materials Figures 5.SM.1a,c). In contrast, the equatorial oceans’ influence on global mean F <sub>net</sub> has a low correlation because, notwithstanding the coherence in interannual variability, it does not show the same global mean trend of strengthening sink in response to growing global emissions (Supplementary Materials Figure 5.SM.1d; [[#Gregor--2019|Gregor et al., 2019]] ). All regions, except the equatorial ocean, contribute to varying extents to the multi-decadal trend of growth in the global ocean sink (Supplementary Materials Figure 5.SM.1). Data sparseness in the high latitudes and the relatively short length of the observational records leads to ''low confidence'' in the attribution of the processes that link regional–global variability to climate ( [[#Landschützer--2019|Landschützer et al., 2019]] ; [[#Gloege--2021|Gloege et al., 2021]] ).

Regional decadal-scale anomalies in the variability of ocean CO <sub>2</sub> storage have also emerged, probably associated with changes in the MOC, which may influence the global variability in F <sub>net</sub> (Chapter 9; [[#DeVries--2017|DeVries et al., 2017]] ). In the interior of the Indian and Pacific sectors of the Southern Ocean, and the North Atlantic, the increase in the CO <sub>2</sub> inventory from 1994 to 2007 was about 20% smaller than expected from the atmospheric CO <sub>2</sub> increase during the same period and the anthropogenic CO <sub>2</sub> inventory in 1994 (Sabine eta al., 2004; [[#Gruber--2019a|Gruber et al., 2019a]] ). There is ''medium confidence'' that the ocean CO <sub>2</sub> inventory strengthened again in the decade 2005–2015 ( [[#DeVries--2017|DeVries et al., 2017]] ). In the North Atlantic, a low rate of anthropogenic CO <sub>2</sub> storage at 1.9 ± 0.4 PgC per decade during the time period of 1989–2003 increased to 4.4 ± 0.9 PgC per decade during 2003–2014. This is associated with changing ventilation patterns driven by the North Atlantic Oscillation ( [[#Woosley--2016|Woosley et al., 2016]] ). In the Pacific sector of the Southern Ocean, the rate of anthropogenic CO <sub>2</sub> storage also increased from 8.8 ± 1.1 (1 σ ) PgC per decade during 1995–2005 to 11.7 ± 1.1 PgC per decade during 2005–2015 ( [[#Carter--2019|Carter et al., 2019]] ). However, in the Subantarctic Mode Water of the Atlantic sector of the Southern Ocean, the storage rate of the anthropogenic CO <sub>2</sub> was rather lower after 2005 than before ( [[IPCC:Wg1:Chapter:Chapter-9#9.2.3.2|Section 9.2.3.2]] ; [[#Tanhua--2017|Tanhua et al., 2017]] ; [[#Bindoff--2019|Bindoff et al., 2019]] ). These changes have been predominantly ascribed to the impact of changes in the MOC on the transport of anthropogenic CO <sub>2</sub> into the ocean interior due to regional climate variability, in addition to the increase in the atmospheric CO <sub>2</sub> concentration ( [[IPCC:Wg1:Chapter:Chapter-9#9.2.3.1|Section 9.2.3.1]] ; [[#Wanninkhof--2010|Wanninkhof et al., 2010]] ; [[#Pérez--2013|Pérez et al., 2013]] ; [[#DeVries--2017|DeVries et al., 2017]] , 2019; [[#Gruber--2019b|Gruber et al., 2019b]] ; [[#McKinley--2020|McKinley et al., 2020]] ). However,the low frequency of carbon observations in the interior of the vast ocean leads to ''medium confidence'' in the assessment of temporal variability in the rate of regional ocean CO <sub>2</sub> storage and its controlling mechanisms.

In summary, multiple lines of observational and modelling evidence provide ''high confidence'' in the finding that the ocean sink for anthropogenic CO <sub>2</sub> has increased quasi-linearly over the past 60 years in response to growing global emissions of anthropogenic CO <sub>2,</sub> with a mean fraction of 23% of total emissions. The ''high confidence'' assessment is moderated to ''medium confidence'' due to a number of ocean CO <sub>2</sub> flux terms yet to be adequately constrained. Observed changes in the variability of ocean ''p'' CO <sub>2</sub> and observed reductions in the mean global buffering capacity provide ''high confidence'' that the growing CO <sub>2</sub> sink is also beginning to drive observable large-scale changes in ocean carbonate chemistry. However, there is ''medium confidence'' that these changes which, depending on the emissions scenario, could drive future ocean feedbacks, are still too small to emerge from the historical multi-decadal observed growth rate of S <sub>ocean</sub> .

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<span id="land-co-2-fluxes-historical-and-contemporary-variability-and-trends"></span>
==== 5.2.1.4 Land CO <sub>2</sub> Fluxes: Historical and Contemporary Variability and Trends ====

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<span id="trend-in-landatmosphere-co-2-exchange"></span>
===== 5.2.1.4.1 Trend in land–atmosphere CO <sub>2</sub> exchange =====

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The global net land CO <sub>2</sub> sink is assessed to have grown over the past six decades ( [[#Sarmiento--2010|Sarmiento et al., 2010]] ; [[#Ballantyne--2017|Ballantyne et al., 2017]] ; [[#Le%20Quéré--2018b|Le Quéré et al., 2018b]] ; [[#Ciais--2019|Ciais et al., 2019]] ; [[#Friedlingstein--2020|Friedlingstein et al., 2020]] ) ( ''high confidence'' ). Estimated as residual from the mass balance budget of fossil fuel CO <sub>2</sub> emissions minus atmospheric CO <sub>2</sub> growth and the ocean CO <sub>2</sub> sink, the global net land CO <sub>2</sub> sink (including both land CO <sub>2</sub> sink and net land-use change emissions) increased from 0.3 ± 0.6 PgC yr <sup>–1</sup> during the 1960s to 1.8 ± 0.8 PgC yr <sup>–1</sup> during the 2010s (Friedlingstein et al., 2020). An increasing global net land CO <sub>2</sub> sink since the 1980s (Figure 5.10) was consistently suggested both by atmospheric inversions (e.g., [[#Peylin--2013|Peylin et al., 2013]] ) and by DGVMs (e.g., [[#Sitch--2015|Sitch et al., 2015]] ; [[#Friedlingstein--2019|Friedlingstein et al., 2019]] ). The Northern Hemisphere contributes more to the net increase in the land CO <sub>2</sub> sink compared to the Southern Hemisphere ( [[#Ciais--2019|Ciais et al., 2019]] ), and boreal and temperate forests probably contribute the most ( [[#Tagesson--2020|Tagesson et al., 2020]] ). Attributing an increased net land CO <sub>2</sub> sink to finer regional scales remains challenging, but inversions of satellite-based column CO <sub>2</sub> products that have emerged since AR5 are a promising tool to further constrain regional land-atmosphere CO <sub>2</sub> exchange ( [[#Ciais--2013|Ciais et al., 2013]] ; [[#Houweling--2015|Houweling et al., 2015]] ; [[#Reuter--2017|Reuter et al., 2017]] ; [[#O’Dell--2018|O’Dell et al., 2018]] ; [[#Palmer--2019|Palmer et al., 2019]] ).

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[[File:24ef3e804f36e2594577172c9a2db0f5 IPCC_AR6_WGI_Figure_5_10.png]]

'''Figure 5.10 |''' '''Trends of the net land CO''' <sub>2</sub> '''sink and related vegetation observations during 1980–2019''' . '''(a)''' Net land CO <sub>2</sub> sink. The residual net land CO <sub>2</sub> sink is estimated from the global CO <sub>2</sub> mass balance (fossil fuel emissions minus atmospheric CO <sub>2</sub> growth rate and ocean CO <sub>2</sub> sink). Inversions indicate the net land CO <sub>2</sub> sink estimated by an ensemble of four atmospheric inversions. Dynamic Global Vegetation Models (DGVMs) indicate the mean net land CO <sub>2</sub> sink estimated by 17 dynamic global vegetation models driven by climate change, rising atmospheric CO <sub>2</sub> , land-use change and nitrogen deposition change (for carbon-nitrogen models). The positive values indicate net CO <sub>2</sub> uptake from the atmosphere. '''(b)''' Normalized difference vegetation index (NDVI). The anomaly of global area-weighted NDVI observed by Advanced Very High Resolution Radiometer (AVHRR) and MODIS satellite sensors. AVHRR data are accessible during 1982–2016 and MODIS data are accessible during 2000–2018. '''(c)''' Near-infrared reflectance of vegetation (NIRv) and contiguous solar-induced chlorophyll fluorescence (CSIF). The standardized anomaly of area-weighted NIRv during 2001–2018 ( [[#Badgley--2017|Badgley et al., 2017]] ) and CSIF during 2000–2018 ( [[#Zhang--2018|Zhang et al., 2018]] ). '''(d)''' Gross primary production (GPP). The GPP from [[#Cheng--2017|Cheng et al. (2017)]] , DGVMs and MODIS GPP product (MOD17A3). GPP from [[#Cheng--2017|Cheng et al. (2017)]] is based on an analytical model driven by climate change, rising atmospheric CO <sub>2</sub> , AVHRR leaf area index datasets and evapotranspiration datasets. GPP from DGVMs is the ensemble mean global GPP estimated by the same 17 DGVMs that provide the net land CO <sub>2</sub> sink estimates. Shaded area indicates 1– σ inter-model spread except for atmospheric inversions, whose ranges were used due to limited number of models. Further details on data sources and processing are available in the chapter data table (Table 5.SM.6).

Carbon uptake by vegetation photosynthesis exerts a first-order control over the net land CO <sub>2</sub> sink. Several lines of evidence show enhanced vegetation photosynthesis over the past decades ( ''medium to high confidence'' ) (Figure 5.10), including increasing satellite-derived vegetation greenness (e.g., see Chapter 2; [[#Mao--2016|Mao et al., 2016]] ; [[#Zhu--2016|Zhu et al., 2016]] ; [[#Jia--2019|Jia et al., 2019]] ) and satellite-derived photosynthesis indicators (e.g., [[#Badgley--2017|Badgley et al., 2017]] ; [[#Zhang--2018|Zhang et al., 2018]] ), change in atmospheric concentration of carbonyl sulphide ( [[#Campbell--2017|Campbell et al., 2017]] ), enhanced seasonal CO <sub>2</sub> amplitude ( [[#Graven--2013|Graven et al., 2013]] ; [[#Forkel--2016|Forkel et al., 2016]] ), observation-driven inference of increasing photosynthesis CO <sub>2</sub> uptake based mostly on enhanced water use efficiency ( [[#Cheng--2017|Cheng et al., 2017]] ), and DGVM simulated increase of photosynthesis CO <sub>2</sub> uptake ( [[#Anav--2015|Anav et al., 2015]] ).

Substantial progress has been made since AR5 on attributing change of the global net land CO <sub>2</sub> sink. Increasing global net land CO <sub>2</sub> sink since the 1980s is mainly driven by the fertilization effect from rising atmospheric CO <sub>2</sub> concentrations ( [[#Schimel--2015|Schimel et al., 2015]] ; [[#Sitch--2015|Sitch et al., 2015]] ; [[#Fernández-Martínez--2019|Fernández-Martínez et al., 2019]] ; [[#O’Sullivan--2019|O’Sullivan et al., 2019]] ; [[#Tagesson--2020|Tagesson et al., 2020]] ; [[#Walker--2021|Walker et al., 2021]] ) ( ''medium confidence'' ). Increasing nitrogen deposition ( [[#de%20Vries--2009|de Vries et al., 2009]] ; [[#Devaraju--2016|Devaraju et al., 2016]] ; [[#Huntzinger--2017|Huntzinger et al., 2017]] ) or the synergy between increasing nitrogen deposition and atmospheric CO <sub>2</sub> concentration ( [[#O’Sullivan--2019|O’Sullivan et al., 2019]] ) could have also contributed to the increasing global net land CO <sub>2</sub> sink. The effects of climate change alone on the global net land CO <sub>2</sub> sink is so divergent that even the signs (directions) of the effects are not the same across DGVMs (e.g., [[#Huntzinger--2017|Huntzinger et al., 2017]] ).

Lower fire emissions of CO <sub>2</sub> and enhanced vegetation carbon uptake due to reduced global burned area have contributed to the increasing global net land CO <sub>2</sub> sink in the recent decade ( [[#Arora--2018|Arora and Melton, 2018]] ; [[#Yin--2020|Yin et al., 2020]] ) ( ''low to medium confidence'' ). Satellite observations reveal a declining trend in global burned area by about 20% over past two decades ( [[#Andela--2017|Andela et al., 2017]] ; [[#Earl--2018|Earl and Simmonds, 2018]] ; [[#Forkel--2019|Forkel et al., 2019]] ), a trend most pronounced in regions like northern Africa ( [[#Forkel--2019|Forkel et al., 2019]] ; [[#Zubkova--2019|Zubkova et al., 2019]] ; [[#Bowman--2020|Bowman et al., 2020]] ) and Mediterranean Europe ( [[#Turco--2016|Turco et al., 2016]] ). However, burned area trends are highly heterogeneous regionally with increasing trends reported in regions like western United States ( [[#Holden--2018|Holden et al., 2018]] ; [[#Abatzoglou--2019|Abatzoglou et al., 2019]] ). Some regions (e.g., Amazon basin and Australia) experienced record-breaking fire events in 2019 and 2020 (e.g., [[#Boer--2020|Boer et al., 2020]] ), whose effects on burned area trends remain to be explored. The burned area trends were primarily attributed to both human-induced climate change and human activities ( [[#Jolly--2015|Jolly et al., 2015]] ; [[#Andela--2017|Andela et al., 2017]] ; [[#Holden--2018|Holden et al., 2018]] ; [[#Turco--2018|Turco et al., 2018]] ; [[#Teckentrup--2019|Teckentrup et al., 2019]] ; [[#Bowman--2020|Bowman et al., 2020]] ), as well as changing frequency of lightning in the boreal region (Veraverbeke et al., 2017). In addition to changes in the burned area, fire dynamics could affect the trend in land-atmosphere CO <sub>2</sub> exchange indirectly through increasing concentration of air pollutants (see Section 6.3.4 for impacts of ozone and aerosol on the carbon cycle; [[#Yue--2018|Yue and Unger, 2018]] ; [[#Lasslop--2019|Lasslop et al., 2019]] ).

Significant uncertainties remain for the land CO <sub>2</sub> sink partition of processes due to challenges in reconciling multiple-scale evidence from experiments to the globe ( [[#Fatichi--2019|Fatichi et al., 2019]] ; [[#Walker--2021|Walker et al., 2021]] ), due to large spatial and inter-model differences in diagnosing dominant driving factors affecting the net land CO <sub>2</sub> sink ( [[#Huntzinger--2017|Huntzinger et al., 2017]] ; [[#Fernández-Martínez--2019|Fernández-Martínez et al., 2019]] ), and due to model deficiency in process representations ( [[#He--2016|He et al., 2016]] ). Nitrogen dynamics, a major gap in DGVMs identified in AR5, have now been incorporated in about half of the DGVMs contributing to the carbon budget of the Global Carbon Project (GCP) (see [[#Le%20Quéré--2018a|Le Quéré et al. (2018a)]] for model characteristics) and a growing number of ESMs ( [[#Arora--2020|Arora et al., 2020]] ). However, as the representations of carbon–nitrogen interactions vary greatly among models, large uncertainties remain on how nitrogen cycling regulates the response of ecosystem carbon uptake to higher atmospheric CO <sub>2</sub> ( [[#Walker--2015|Walker et al., 2015]] ; [[#Wieder--2019|Wieder et al., 2019]] ; [[#Davies-Barnard--2020|Davies-Barnard et al., 2020]] ; [[#Meyerholt--2020|Meyerholt et al., 2020]] ; see [[#5.4.1|Section 5.4.1]] ). Fire modules have been incorporated into 10 of 16 DGVMs contributing to the global carbon budget ( [[#Le%20Quéré--2018a|Le Quéré et al., 2018a]] ), and a growing number of models have representations of human ignitions and fire suppression processes ( [[#Rabin--2017|Rabin et al., 2017]] ; [[#Teckentrup--2019|Teckentrup et al., 2019]] ). There are also growing DGVM developments to include management practices ( [[#Pongratz--2018|Pongratz et al., 2018]] ) and the effects of secondary forest regrowth ( [[#Pugh--2019|Pugh et al., 2019]] ), though models still under-represent intensively managed ecosystems, such as croplands and managed forests ( [[#Guanter--2014|Guanter et al., 2014]] ; [[#Thurner--2017|Thurner et al., 2017]] ). Processes that have not yet played a significant role in the land CO <sub>2</sub> sink of the past decades but can grow in importance, include permafrost (Box 5.1) and peatlands dynamics ( [[#Dargie--2017|Dargie et al., 2017]] ; [[#Gibson--2019|Gibson et al., 2019]] ), have also been incorporated in some DGVMs ( [[#Koven--2015b|Koven et al., 2015b]] ; [[#Burke--2017a|Burke et al., 2017a]] ; [[#Guimberteau--2018|Guimberteau et al., 2018]] ). Growing numbers and varieties of Earth observations are being jointly used to drive and benchmark models, helping to further identify missing key processes or mechanisms that are poorly represented in the current generation of DGVMs (e.g., [[#Collier--2018|Collier et al., 2018]] ).

<div id="5.2.1.4.2" class="h4-container"></div>

<span id="interannual-variability-in-landatmosphere-co-2-exchange"></span>
===== 5.2.1.4.2 Interannual variability in land–atmosphere CO <sub>2</sub> exchange =====

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The AR5 stated that the interannual variability of the atmospheric CO <sub>2</sub> growth rate is dominated by tropical land ecosystems. A set of new satellite measurements applied to assess the variability of the tropical land carbon balance since AR5 ( [[#Ciais--2013|Ciais et al., 2013]] ) confirm this statement, including satellite column CO <sub>2</sub> measurements, estimating the recent anomalous land–atmosphere CO <sub>2</sub> exchange induced by El Niño at continental scale (e.g., J. [[#Liu--2017|]] [[#Liu--2017|Liu et al., 2017]] ; [[#Palmer--2019|Palmer et al., 2019]] ), and L-band vegetation optical depth, estimating tropical above-ground biomass carbon stock changes ( [[#Fan--2019|Fan et al., 2019]] ). In addition, based on ''medium evidence'' and ''medium agreement'' between studies with DGVMs and atmospheric inversions, semi-arid ecosystems over the tropical zones have a larger contribution to interannual variability in global land–atmosphere CO <sub>2</sub> exchange than moist tropical forest ecosystems ( ''low'' to ''medium confidence'' ) ( [[#Poulter--2014|Poulter et al., 2014]] ; [[#Ahlstrom--2015|Ahlstrom et al., 2015]] ; [[#Piao--2020|Piao et al., 2020]] ).

Understanding the mechanisms driving interannual variability in the carbon cycle has the potential to provide insights into whether and to what extent the carbon cycle can affect the climate (carbon–climate feedback), with particular interests over the highly climate-sensitive tropical carbon cycle (e.g., [[#Cox--2013|Cox et al., 2013]] ; [[#Wang--2014|X. Wang et al., 2014]] ; [[#Fang--2017|Fang et al., 2017]] ; [[#Jung--2017|Jung et al., 2017]] ; [[#Humphrey--2018|Humphrey et al., 2018]] ; [[#Malhi--2018|Malhi et al., 2018]] ; see [[#5.4|Section 5.4]] ). Consistent findings from studies with atmospheric inversions, satellite observations and DGVMs (e.g., [[#Malhi--2018|Malhi et al., 2018]] ; [[#Rödenbeck--2018|Rödenbeck et al., 2018]] ) lead to ''high confidence'' that the tropical net land CO <sub>2</sub> sink is reduced under warmer and drier conditions, particularly during El Niño events. Interannual variations in tropical land-atmosphere CO <sub>2</sub> exchange are significantly correlated with anomalies of tropical temperature, water availability and terrestrial water storage (X. [[#Wang--2014|]] [[#Wang--2014|Wang et al., 2014]] ; [[#Jung--2017|Jung et al., 2017]] ; [[#Humphrey--2018|Humphrey et al., 2018]] ; [[#Piao--2020|Piao et al., 2020]] ), whose relative contribution are difficult to separate due to covariations between these climatic factors. At continental scale, the dominant climatic driver of interannual variations of tropical land-atmosphere CO <sub>2</sub> exchange was temperature variations (Figure 5.11; [[#Piao--2020|Piao et al., 2020]] ), which could partly result from the spatial compensation of the water availability effects on land-atmospheric CO <sub>2</sub> exchange ( [[#Jung--2017|Jung et al., 2017]] ).

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[[File:067816dc3276215cf4b949a44f046544 IPCC_AR6_WGI_Figure_5_11.png]]

'''Figure 5.11 |''' '''Interannual variation in detrended anomalies of the net land CO''' <sub>2</sub> '''sink and land surface air temperature during 1980–2019.''' Correlation coefficients between the net land CO <sub>2</sub> sink anomalies and temperature anomalies are show on the right bar plots. The net land CO <sub>2</sub> sink is estimated by four atmospheric inversions (blue) and 15 Dynamic Global Vegetation Models (DGVMs) (green), respectively ( [[#Friedlingstein--2020|Friedlingstein et al., 2020]] ). Solid blue and green lines show model mean detrended anomalies of the net land CO <sub>2</sub> sink. The ensemble mean of DGVMs is bounded by the 1– σ inter-model spread in each large latitude band (North 30°N–90°N, Tropics 30°S–30°N, South 90°S–30°S) and the globe. The ensemble mean of atmospheric inversions is bounded by model spread. For each latitudinal band, the anomalies of the net land CO <sub>2</sub> sink and temperature (orange) were obtained by removing the long-term trend and seasonal cycle. A 12-month running mean was taken to reduce high-frequency noise. The bars in the right panels show correlation coefficients between the net land CO <sub>2</sub> sink anomalies and temperature anomalies for each region. ** indicates P&lt;0.01; * indicates P&lt;0.05. The grey shaded area shows the intensity of El Niño–Southern Oscillation (ENSO) as defined by the Niño 3.4 index. Two volcanic eruptions (El Chichón and Mount Pinatubo) are indicated with light blue dashed lines. Temperature data are from the Climatic Research Unit (CRU), University of East Anglia ( [[#Harris--2014|Harris et al., 2014]] ). Anomalies were calculated following [[#Patra--2005|Patra et al. (2005)]] , but using a 12-month low-pass filter and detrended to obtain interannual variations. Further details on data sources and processing are available in the chapter data table (Table 5.SM.6).

<div id="cross-chapter-box-5.1" class="h2-container box-container"></div>

'''Cross-Chapter Box 5.1 | Interactions Between the Carbon and Water Cycles, Particularly Under Dro''' '''ught 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.

<div id="5.2.1.5" class="h3-container"></div>

<span id="co-2-budget"></span>
==== 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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<span id="methane-ch-4-trends-variability-and-budget"></span>
=== 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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<span id="atmosphere-1"></span>
==== 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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'''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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<span id="n-2-o-trends-variability-and-budget"></span>
=== 5.2.3 N <sub>2</sub> O: Trends, Variability and Budget ===

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In natural ecosystems, nitrous oxide (N <sub>2</sub> O) is primarily produced as a by-product during the remineralization of organic matter via the primary processes of nitrification and denitrification ( [[#Butterbach-Bahl--2013|Butterbach-Bahl et al., 2013]] ; [[#Voss--2013|Voss et al., 2013]] ). The net N <sub>2</sub> O production is highly sensitive to local environmental conditions such as temperature, oxygen concentrations, pH and the concentrations of ammonium and nitrate, among others, causing strong variability of N <sub>2</sub> O emissions in time and space, even at small scales. Changes in the atmospheric abundance of N <sub>2</sub> O result largely from the balance of the net N <sub>2</sub> O sources on land and ocean, and the photochemical destruction of N <sub>2</sub> O in the stratosphere.

Since AR5 (WGI, Section 6.4.3), improved understanding of N <sub>2</sub> O sources allows for a more comprehensive assessement of the global N <sub>2</sub> O budget (Table 5.3). This progress is based on extended atmospheric observations ( [[#Francey--2003|Francey et al., 2003]] ; [[#Elkins--2018|Elkins et al., 2018]] ; [[#Prinn--2018|Prinn et al., 2018]] ), improved atmospheric N <sub>2</sub> O inversions ( [[#Saikawa--2014|Saikawa et al., 2014]] ; [[#Thompson--2019|Thompson et al., 2019]] ), updated and expanded inventories of N <sub>2</sub> O sources ( [[#Winiwarter--2018|Winiwarter et al., 2018]] ; [[#Janssens-Maenhout--2019|Janssens-Maenhout et al., 2019]] ), as well as improved bottom-up estimate of freshwater, ocean and terrestrial sources ( [[#Martinez-Rey--2015|Martinez-Rey et al., 2015]] ; [[#Landolfi--2017|Landolfi et al., 2017]] ; [[#Buitenhuis--2018|Buitenhuis et al., 2018]] ; [[#Lauerwald--2019|Lauerwald et al., 2019]] ; [[#Maavara--2019|Maavara et al., 2019]] ; [[#Tian--2019|Tian et al., 2019]] ).

The human perturbation of the natural nitrogen cycle through the use of synthetic fertilizers and manure, as well as nitrogen deposition resulting from land-based agriculture and fossil fuel burning has been the largest driver of the increase in atmospheric N <sub>2</sub> O of 31.0 ± 0.5 ppb (10%) between 1980 and 2019 ( ''robust evidence'' , ''high agreement'' ) ( [[#Tian--2020|Tian et al., 2020]] ). The long atmospheric lifetime of N <sub>2</sub> O implies that it will take more than a century before atmospheric abundances stabilize after the stabilization of global emissions. The rise of atmospheric N <sub>2</sub> O is of concern, not only because of its contribution to the anthropogenic radiative forcing (Chapter 7) but also because of the importance of N <sub>2</sub> O in stratospheric ozone loss ( [[#Ravishankara--2009|Ravishankara et al., 2009]] ; [[#Fleming--2011|Fleming et al., 2011]] ; W. [[#Wang--2014|]] [[#Wang--2014|Wang et al., 2014]] ).

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<span id="atmosphere-2"></span>
==== 5.2.3.1 Atmosphere ====

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The tropospheric abundance of N <sub>2</sub> O was 332.1 ± 0.4 ppb in 2019 (Figure 5.15), which is 23% higher than pre-industrial levels of 270.1 ± 6.0 ppb ( ''robust evidence, high agreement'' ). Current estimates are based on atmospheric measurements with high accuracy and density ( [[#Francey--2003|Francey et al., 2003]] ; [[#Elkins--2018|Elkins et al., 2018]] ; [[#Prinn--2018|Prinn et al., 2018]] ), and pre-industrial estimates are based on multiple ice-core records [[IPCC:Wg1:Chapter:Chapter-2#2.2.3.2.3|Section 2.2.3.2.3]] ). The average annual tropospheric growth rate was 0.85 ± 0.03 ppb yr <sup>–1</sup> <sub></sub> during the period 1995 to 2019 (Figure 5.15a). The atmospheric growth rate increased by about 20% between the decade 2000–2009 and the most recent decade of 2010–2019 (0.95 ± 0.04 ppb yr <sup>–1</sup> ) ( ''robust evidence, high agreement'' ). The growth rate in 2010–2019 was also higher than during 1970–2000 (0.6–0.8 ppb yr <sup>–1</sup> ; [[#Ishijima--2007|Ishijima et al., 2007]] ) and the 30-year period prior to 2011 (0.73 ± 0.03 ppb yr <sup>–1</sup> ), as reported by AR5. New evidence since AR5 (WGI, Section 6.4.3) confirms that, in the tropics and subtropics, large interannual variations in the atmospheric growth rate are negatively correlated with the multivariate ENSO index (MEI) and associated anomalies in land and ocean fluxes ( [[#Ji--2019|Ji et al., 2019]] ; [[#Thompson--2019|Thompson et al., 2019]] ; S. [[#Yang--2020|]] [[#Yang--2020|Yang et al., 2020]] ) (Figure 5.15a).

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[[File:8269d59e29a66c0307693bea2ae395f8 IPCC_AR6_WGI_Figure_5_15.png]]

'''Figure 5.15 |''' '''Changes in atmospheric nitrous oxide (N''' <sub>2</sub> '''O) and its isotopic composit''' '''ion since 1940''' . '''(a)''' Atmospheric N <sub>2</sub> O abundance (parts per billion, ppb) and growth rat (ppb yr <sup>–1</sup> ); '''(b)''' δ <sup>15</sup> N of atmospheric N <sub>2</sub> O; and '''(c)''' alpha-site <sup>15</sup> N–N <sub>2</sub> O. Estimates are based on direct atmospheric measurements in the Advanced Global Atmospheric Gases Experiment (AGAGE), Commonwealth Scientific and Industrial Research Organisation (CSIRO), and National Oceanic and Atmospheric Administration (NOAA) networks ( [[#Prinn--2000|Prinn et al., 2000]] , 2018; [[#Francey--2003|Francey et al., 2003]] ; [[#Hall--2007|Hall et al., 2007]] ; [[#Elkins--2018|Elkins et al., 2018]] ), archived air samples from Cape Grim, Australia ( [[#Park--2012|Park et al., 2012]] ), and firn air from the North Greenland Ice Core Project (NGRIP) Greenland and H72 Antarctica ( [[#Ishijima--2007|Ishijima et al., 2007]] ), Law Dome Antarctica ( [[#Park--2012|Park et al., 2012]] ), as well as a collection of firn ice samples from Greenland ( [[#Prokopiou--2017|Prokopiou et al., 2017]] , 2018). Shading in (a) is based on the multivariate El Niño–Southern Oscillation (ENSO) index, with red indicating El Niño conditions ( [[#Wolter--1998|Wolter and Timlin, 1998]] ). Further details on data sources and processing are available in the chapter data table (Table 5.SM.6).

As assessed by SRCCL ( [[#IPCC--2019a|IPCC, 2019a]] ), combined firn, ice, air and atmospheric measurements show that the <sup>15</sup> N/ <sup>14</sup> N isotope ratio ( ''robust evidence'' , ''high agreement'' ) and the predominant position of the <sup>15</sup> N atom in atmospheric N <sub>2</sub> O ( ''limited evidence'' , ''low agreement'' ) in N <sub>2</sub> O has changed since 1940 (Figure 5.15b, c) whereas they were relatively constant in the pre-industrial period ( [[#Ishijima--2007|Ishijima et al., 2007]] ; [[#Park--2012|Park et al., 2012]] ; [[#Prokopiou--2017|Prokopiou et al., 2017]] , 2018). The SRCCL concluded that this change indicates a shift in the nitrogen-substrate available for denitrification, and the relative contribution of nitrification to the global N <sub>2</sub> O source ( ''robust evidence'' , ''high agreement'' ), which are associated with increased fertilizer use in agriculture ( [[#Park--2012|Park et al., 2012]] ; [[#Snider--2015|Snider et al., 2015]] ; [[#Prokopiou--2018|Prokopiou et al., 2018]] ).

Since AR5 (WGI, Section 6.4.3), the mean atmospheric lifetime of N <sub>2</sub> O has been revised to 116 ± 9 years ( [[#Prather--2015|Prather et al., 2015]] ). The small negative feedback of the N <sub>2</sub> O lifetime to increasing atmospheric N <sub>2</sub> O results in a slightly lower residence time (109 ± 10 years) of N <sub>2</sub> O perturbations compared with that assessed by AR5 (118–131 years) ( [[#Prather--2015|Prather et al., 2015]] ). The dominant N <sub>2</sub> O loss occurs through photolysis and oxidation by O(1D) radicals in the stratosphere and amounts to approximately 13.1 (12.4–13.6) TgN yr <sup>–1</sup> ( [[#Minschwaner--1993|Minschwaner et al., 1993]] ; [[#Prather--2015|Prather et al., 2015]] ; [[#Tian--2020|Tian et al., 2020]] ).

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<span id="anthropogenic-n-2-o-emissions"></span>
==== 5.2.3.2 Anthropogenic N <sub>2</sub> O Emissions ====

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The AR5 (WGI, Section 6.4.3) and SRCCL ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.3|Section 2.3.3]] ) concluded that agriculture is the largest anthropogenic source of N <sub>2</sub> O emissions. Since SRCCL (2.3.3), a new synthesis of inventory-based and modelling studies shows that the widespread use of synthetic fertilizers and manure on cropland and pasture, manure management and aquaculture resulted in 3.8 (2.5–5.8) TgN yr <sup>–1</sup> (average 2007–2016) ( ''robust evidence, high agreement'' ) (Table 5.3; [[#Winiwarter--2018|Winiwarter et al., 2018]] ; [[#FAO--2019|FAO, 2019]] ; [[#Janssens-Maenhout--2019|Janssens-Maenhout et al., 2019]] ; [[#Tian--2020|Tian et al., 2020]] ). Observations from field-measurements ( [[#Song--2018|Song et al., 2018]] ), inventories ( [[#Wang--2020|Wang et al., 2020]] ) and atmospheric inversions ( [[#Thompson--2019|Thompson et al., 2019]] ) further corroborate the assessment of SRCCL that there is a non-linear relationship between N <sub>2</sub> O emissions and nitrogen input, implying an increasing fraction of fertilizer lost as N <sub>2</sub> O with larger fertilizer excess ( ''medium evidence'' , ''high agreement'' ). Several studies using complementary methods indicate that agricultural N <sub>2</sub> O emissions have increased by more than 45% since the 1980s ( ''high confidence'' ) (Figure 5.16 and Table 5.3; [[#Davidson--2009|Davidson, 2009]] ; [[#Winiwarter--2018|Winiwarter et al., 2018]] ; [[#Janssens-Maenhout--2019|Janssens-Maenhout et al., 2019]] ; [[#Tian--2020|Tian et al., 2020]] ), mainly due to the increased use of nitrogen fertilizer and manure. N <sub>2</sub> O emissions from aquaculture are among the fastest rising contributors of N <sub>2</sub> O emissions, but their overall magnitude is still small in the overall N <sub>2</sub> O budget ( [[#Tian--2020|Tian et al., 2020]] ).

<div id="_idContainer048" class="Basic-Text-Frame"></div>

[[File:6f0779b71e8fa59d513466d8418a459c IPCC_AR6_WGI_Figure_5_16.png]]

'''Figure 5.16 |''' '''Decadal mean nitrous oxide (N''' <sub>2</sub> '''O) emissions for 2007–2016 and its change since 1850 based on process-model projections''' . The total effect, including that from anthropogenic nitrogen additions (atmospheric deposition, manure addition, fertilizer use and land-use), is evaluated against the background flux driven by changes in atmospheric carbon dioxide (CO <sub>2</sub> ) concentration, and climate change. Fluxes are derived from the N <sub>2</sub> O model intercomparison project ensemble of terrestrial biosphere models ( [[#Tian--2019|Tian et al., 2019]] ) and three ocean biogeochemical models ( [[#Landolfi--2017|Landolfi et al., 2017]] ; [[#Battaglia--2018a|Battaglia and Joos, 2018a]] ; [[#Buitenhuis--2018|Buitenhuis et al., 2018]] ). Further details on data sources and processing are available in the chapter data table (Table 5.SM.6).

The principal non-agricultural anthropogenic sources of N <sub>2</sub> O are industry, specifically chemical processing, wastewater, and the combustion of fossil fuels (Table 5.3). Industrial emissions of N <sub>2</sub> O mainly due to nitric and adipic acid production have decreased in North America and Europe since the widespread installation of abatement technologies in the 1990s (Pérez-Ram '''ί''' rez et al., 2003; [[#Lee--2011|Lee et al., 2011]] ; [[#Janssens-Maenhout--2019|Janssens-Maenhout et al., 2019]] ). There is still considerable uncertainty in industrial emissions from other regions of the world with contrasting trends between inventories ( [[#Thompson--2019|Thompson et al., 2019]] ). Globally, industrial emissions and emissions from fossil fuel combustion by stationary sources, such as power plants, as well as smaller emissions from mobile sources (e.g., road transport and aviation) have remained nearly constant between the 1980s and 2007–2016 ( ''medium evidence'' , ''medium agreement'' ) ( [[#Winiwarter--2018|Winiwarter et al., 2018]] ; [[#Janssens-Maenhout--2019|Janssens-Maenhout et al., 2019]] ; [[#Tian--2020|Tian et al., 2020]] ). Wastewater N <sub>2</sub> O emissions, including those from domestic and industrial sources, have increased from 0.2 (0.1–0.3) TgN yr <sup>–1</sup> to 0.35 (0.2–0.5) TgN yr <sup>–1</sup> between the 1980s and 2007–2016 ( [[#Tian--2020|Tian et al., 2020]] ).

Biomass burning from crop residue burning, grassland, savannah and forest fires, as well as biomass burnt in household stoves, releases N <sub>2</sub> O during the combustion of organic matter. Updated inventories since AR5 (WGI, Section 6.4.3) result in a lower range of the decadal mean emissions of 0.6 (0.5–0.8) TgN yr <sup>–1</sup> ( [[#van%20der%20Werf--2017|van der Werf et al., 2017]] ; [[#Tian--2020|Tian et al., 2020]] ). The attribution of grassland, savannah or forest fires to natural or anthropogenic origins is uncertain, preventing a separation of the biomass burning source into natural and anthropogenic.

<div id="5.2.3.3" class="h3-container"></div>

<span id="emissions-from-ocean-inland-water-bodies-and-estuaries"></span>
==== 5.2.3.3 Emissions from Ocean, Inland Water Bodies and Estuaries ====

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Since AR5 (WGI, Section 6.4.3), new estimates of the global ocean N <sub>2</sub> O source derived from ocean biogeochemistry models are 3.4 (2.5–4.3) TgN yr <sup>–1</sup> for the period 2007–2016 (Figure 5.16; [[#Manizza--2012|Manizza et al., 2012]] ; [[#Suntharalingam--2012|Suntharalingam et al., 2012]] ; [[#Martinez-Rey--2015|Martinez-Rey et al., 2015]] ; [[#Landolfi--2017|Landolfi et al., 2017]] ; [[#Buitenhuis--2018|Buitenhuis et al., 2018]] ; [[#Tian--2020|Tian et al., 2020]] ). This is slightly lower than climatological estimates from empirically based methods and surface ocean data syntheses ( [[#Bianchi--2012|Bianchi et al., 2012]] ; S. [[#Yang--2020|]] [[#Yang--2020|Yang et al., 2020]] ). Nitrous oxide processes in coastal upwelling zones continue to be poorly represented in global estimates of marine N <sub>2</sub> O emissions ( [[#Kock--2016|Kock et al., 2016]] ), but may account for an additional 0.2–0.6 TgN yr <sup>–1</sup> of the global ocean source ( [[#Seitzinger--2000|Seitzinger et al., 2000]] ; [[#Nevison--2004|Nevison et al., 2004]] ).

In the oxic ocean (&gt;97% of ocean volume), nitrification is believed to be the primary N <sub>2</sub> O source ( [[#Freing--2012|Freing et al., 2012]] ). In sub-oxic ocean zones ( [[#5.3|Section 5.3]] ), where denitrification prevails, higher N <sub>2</sub> O yields and turnover rates make these regions potentially significant sources of N <sub>2</sub> O ( [[#Arévalo-Martínez--2015|Arévalo-Martínez et al., 2015]] ; [[#Babbin--2015|Babbin et al., 2015]] ; [[#Ji--2015|Ji et al., 2015]] ). The relative proportion of ocean N <sub>2</sub> O from oxygen-minimum zones is highly uncertain ( [[#Zamora--2012|Zamora et al., 2012]] ). Estimates derived from in situ sampling, particularly in the eastern tropical Pacific, suggest significant fluxes from these regions, and potentially account for up to 50% of the global ocean source ( [[#Codispoti--2010|Codispoti, 2010]] ; [[#Arévalo-Martínez--2015|Arévalo-Martínez et al., 2015]] ; [[#Babbin--2015|Babbin et al., 2015]] ). However, recent global-scale analyses estimate lower contributions (4–7%, [[#Battaglia--2018b|Battaglia and Joos, 2018b]] ; [[#Buitenhuis--2018|Buitenhuis et al., 2018]] ). Further investigation is required to reconcile these estimates and provide improved constraints on the N <sub>2</sub> O source from low-oxygen zones.

Atmospheric deposition of anthropogenic N on oceans can stimulate marine productivity and influence ocean emissions of N <sub>2</sub> O. New ocean model analyses since AR5 (WGI, 6.4.3), suggest a relatively modest global potential impact of 0.01–0.32 TgN yr <sup>–1</sup> (pre-industrial to present-day) equivalent to 0.5–3.3% of the global ocean N <sub>2</sub> O source ( [[#Suntharalingam--2012|Suntharalingam et al., 2012]] ; [[#Jickells--2017|Jickells et al., 2017]] ; [[#Landolfi--2017|Landolfi et al., 2017]] ). However, larger proportionate impacts are predicted in nitrogen-limited coastal and inland waters downwind of continental pollution outflow, such as the Northern Indian Ocean ( [[#Jickells--2017|Jickells et al., 2017]] ; [[#Suntharalingam--2019|Suntharalingam et al., 2019]] ).

Inland waters and estuaries are generally sources of N <sub>2</sub> O as a result of nitrification and denitrification of dissolved inorganic nitrogen, however, they can serve as N <sub>2</sub> O sinks in specific conditions ( [[#Webb--2019|Webb et al., 2019]] ). Since AR5 (WGI, 6.4.3), improved emissions factors, including their spatio-temporal scaling, and consideration of transport within the aquatic system allows for better constraint of these emissions ( [[#Murray--2015|Murray et al., 2015]] ; [[#Hu--2016|Hu et al., 2016]] ; [[#Lauerwald--2019|Lauerwald et al., 2019]] ; [[#Maavara--2019|Maavara et al., 2019]] ; [[#Kortelainen--2020|Kortelainen et al., 2020]] ; [[#Yao--2020|Yao et al., 2020]] ). Despite uncertainties because of the side effects of canals and reservoirs on nutrient cycling, these advances permit attribution of a fraction of inland water N <sub>2</sub> O emissions to anthropogenic sources ( [[#Tian--2020|Tian et al., 2020]] ), which contributes to the increased anthropogenic share of the global N <sub>2</sub> O source in this report compared to AR5 ( [[#Ciais--2013|Ciais et al., 2013]] ). As an indirect consequence of agricultural nitrogen use and waste-water treatment, the anthropogenic emissions from inland waters have increased by about a quarter (0.1 TgN yr <sup>–1</sup> ) between the 1980s and 2007–2016 ( [[#Tian--2020|Tian et al., 2020]] ).

<div id="5.2.3.4" class="h3-container"></div>

<span id="emissions-and-sinks-in-non-agricultural-land"></span>
==== 5.2.3.4 Emissions and Sinks in Non-agricultural Land ====

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Soils are the largest natural source of N <sub>2</sub> O, arising primarily from nitrogen processing associated with microbial nitrification and denitrification (Table 5.3; [[#Butterbach-Bahl--2013|Butterbach-Bahl et al., 2013]] ; [[#Snider--2015|Snider et al., 2015]] ). Under some conditions, soils can also act as a net sink of N <sub>2</sub> O, but this effect is small compared to the overall source ( [[#Schlesinger--2013|Schlesinger, 2013]] ). Since AR5 (WGI, Section 6.4.3), improved global process-based models ( [[#Tian--2019|Tian et al., 2019]] ) suggest a present-day source of 6.7 (5.3–8.1) TgN yr <sup>–1</sup> (2007–2016 average), which is consistent with the estimate in AR5. Process-based models and inventory-based methods show that increased N deposition has enhanced terrestrial N <sub>2</sub> O emissions by 0.8 (0.4–1.4 TgN yr <sup>–1</sup> ) relative to approximately pre-industrial times, and by 0.2 (0.1–0.2) TgN yr <sup>–1</sup> between the 1980s and 2007–2016 ( ''limited evidence'' , ''medium agreement'' ) (Figure 5.16; [[#Tian--2019|Tian et al., 2019]] ). This estimate is at the high end of the range reported in AR5 (WGI, Section 6.4.3). Model projections further show that global warming has led to increased soil N <sub>2</sub> O emissions of 0.8 (0.3–1.3) TgN yr <sup>–1</sup> since approximately pre-industrial times, of which about half occurred since the 1980s ( ''limited evidence'' , ''high agreement'' ) ( [[#Tian--2019|Tian et al., 2019]] , 2020).

The SRCCL assessed that deforestation and other forms of land-use change significantly alter terrestrial N <sub>2</sub> O emissions through emission pulses following conversions, generally resulting in long-term reduced emissions in unfertilized ecosystems ( ''medium evidence, high agreement'' ). This conclusion is supported by a recent study demonstrating that the deforestation-pulse effect is offset by the effect of reduced area of mature tropical forests ( [[#Tian--2020|Tian et al., 2020]] ).

Uncertainties remain in process-based models with respect to their ability to capture the complicated responses of terrestrial N <sub>2</sub> O emissions to rain pulses, freeze–thaw cycles and the net consequences of elevated levels of CO <sub>2</sub> accurately ( [[#Tian--2019|Tian et al., 2019]] ). Emerging literature suggests that permafrost thaw may contribute significantly to arctic N <sub>2</sub> O emissions ( [[#Voigt--2020|Voigt et al., 2020]] ), but these processes are not yet adequately represented in models and upscaling to large-scale remains a significant challenge.

<div id="5.2.3.5" class="h3-container"></div>

<span id="n-2-o-budget"></span>
==== 5.2.3.5 N <sub>2</sub> O Budget ====

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The synthesis of bottom-up estimates of N <sub>2</sub> O sources (Sections 5.2.3.2–5.2.3.4 and Figure 5.17) yields a global source of 17.0 (12.2 to 23.5) TgN yr <sup>–1</sup> for the years 2007–2016 (Table 5.3). This estimate is comparable to AR5, but the uncertainty range has been reduced primarily due to improved estimates of ocean and anthropogenic N <sub>2</sub> O sources. Since AR5 (WGI, Section 6.4.3), improved capacity to estimate N <sub>2</sub> O sources from atmospheric N <sub>2</sub> O measurements by inverting models of atmospheric transport provides a new and independent constraint for the global N <sub>2</sub> O budget ( [[#Saikawa--2014|Saikawa et al., 2014]] ; [[#Thompson--2019|Thompson et al., 2019]] ; [[#Tian--2020|Tian et al., 2020]] ). The decadal mean source derived from these inversions is remarkably consistent with the bottom-up global N <sub>2</sub> O budget for the same period, however, the split between land and ocean sources based on atmospheric inversions is less constrained, yielding a smaller land source of 11.3 (10.2 to 13.2) TgN yr <sup>–1</sup> and a larger ocean source of 5.7 (3.4 to 7.2) TgN yr <sup>–1</sup> , respectively, compared to bottom-up estimates.

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[[File:f57d2c2590e8bcd70a228730cd6cefc3 IPCC_AR6_WGI_Figure_5_17.png]]

'''Figure 5.17 |''' '''Global nitrous oxide (N''' <sub>2</sub> '''O) budget (2007–2016).''' Values and data sources as in Table 5.3. The atmospheric stock is calculated from mean N <sub>2</sub> O concentration, multiplying a factor of 4.79 ± 0.05 Tg ppb <sup>–1</sup> ( [[#Prather--2012|Prather et al., 2012]] ). Pool sizes for the other reservoirs are largely unknown. Further details on data sources and processing are available in the chapter data table (Table 5.SM.6).

Supported by multiple studies and extensive observational evidence (Sections 5.2.3.2–5.2.3.4 and Figure 5.17), anthropogenic emissions contributed about 40% (7.3; uncertainty range: 4.2 to 11.4 TgN yr <sup>–1</sup> ) to the total N <sub>2</sub> O source in 2007–2016 ( ''high confidence'' ). This estimate is larger than in AR5 (WGI, 6.4.3) due to a larger estimated effect of nitrogen deposition on soil N <sub>2</sub> O emissions and the explicit consideration of the role of anthropogenic nitrogen in determining inland water and estuary emissions.

Based on bottom-up estimates, anthropogenic emissions from agricultural nitrogen use, industry and other indirect effects have increased by 1.7 (1.0 to 2.7) TgN yr <sup>–1</sup> between the decades 1980–1989 and 2007–2016, and are the primary cause of the increase in the total N <sub>2</sub> O source ( ''high confidence'' ). Atmospheric inversions indicate that changes in surface emissions, rather than in the atmospheric transport or sink of N <sub>2</sub> O, are the cause for the increased atmospheric growth rate of N <sub>2</sub> O ( ''robust evidence, high agreement'' ) ( [[#Thompson--2019|Thompson et al., 2019]] ). However, the increase of 1.6 (1.4 to 1.7) TgN yr <sup>–1</sup> in global emissions between 2000–2005 and 2010–2015 based on atmospheric inversions is somewhat larger than bottom-up estimates over the same period, primarily because of differences in the estimates of land-based emissions.

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'''Table 5.3 |''' '''Global N''' <sub>2</sub> '''O budget (units TgN y''' '''r''' –1 ''') averaged over the 1980s, 1990s, 2000s as well as the recent decade starting in 2007''' . Uncertainties represent the assessed range of source/sink estimates. All numbers are reproduced from [[#Tian--2020|Tian et al. (2020)]] based on a compilation of inventories, bottom-up models, as well as atmospheric inversions. For detailed information on datasets, see Data Table 5.SM.6.

{| class="wikitable"
|-
! colspan="2"|
! AR6 1980–1989

(TgN yr <sup>–1</sup> )

! AR6 1990–1999

(TgN yr <sup>–1</sup> )

! AR6 2000–2009

(TgN yr <sup>–1</sup> )

! AR6 (2007–2016)

(TgN yr <sup>–1</sup> )

! AR5 (2006–2011)

(TgN yr <sup>–1</sup> )

|-
! colspan="7"| '''B''' '''ottom-up Budget'''

|-
| colspan="7"| '''Anthro''' '''pogenic sources'''

|-
|
| Fossil fuel combustion and Industry

| 0.9 (0.8 to 1.1)

| 0.9 (0.9 to 1.0)

| 1.0 (0.8 to 1.0)

| 1.0 (0.8 to 1.1)

| 0.7 (0.2 to 1.8)

|-
|
| Agriculture (incl. aquaculture)

| 2.6 (1.8 to 4.1)

| 3.0 (2.1 to 4.8)

| 3.4 (2.3 to 5.2)

| 3.8 (2.5 to 5.8)

| 4.1 (1.7 to 4.8)

|-
|
| Biomass and biofuel burning

| 0.7 (0.7 to 0.7)

| 0.7 (0.6 to 0.8)

| 0.6 (0.6 to 0.6)

| 0.6 (0.5 to 0.8)

| 0.7 (0.2 to 1.0)

|-
|
| Wastewater

| 0.2 (0.1 to 0.3)

| 0.3 (0.2 to 0.4)

| 0.3 (0.2 to 0.4)

| 0.4 (0.2 to 0.5)

| 0.2 (0.1 to 0.3)

|-
|
| Inland water, estuaries, coastal zones

| 0.4 (0.2 to 0.5)

| 0.4 (0.2 to 0.5)

| 0.4 (0.2 to 0.6)

| 0.5 (0.2 to 0.7)

|
|-
|
| Atmospheric nitrogen deposition on ocean

| 0.1 (0.1 to 0.2)

| 0.1 (0.1 to 0.2)

| 0.1 (0.1 to 0.2)

| 0.1 (0.1 to 0.2)

| 0.2 (0.1 to 0.4)

|-
|
| Atmospheric nitrogen deposition on land

| 0.6 (0.3 to 1.2)

| 0.7 (0.4 to 1.4)

| 0.7 (0.4 to 1.3)

| 0.8 (0.4 to 1.4)

| 0.4 (0.3 to 0.9)

|-
|
| Other indirect effects from CO <sub>2</sub> , climate and land-use change

| 0.1 (–0.4 to 0.7)

| 0.1 (–0.5 to 0.7)

| 0.2 (–0.4 to 0.9)

| 0.2 (–0.6 to 1.1)

|
|-
|
| '''Tota''' '''l anthropogenic'''

| '''5.6 (3.6 to 8.7)'''

| '''6.2 (3.9 to 9.6)'''

| '''6.7 (4.1 to 10.3)'''

| '''7.3 (4.2 to 11.4)'''

| '''6.3 (2.6 to 9.2)'''

|-
| colspan="7"| '''Natural so''' '''urces and sinks'''

|-
|
| Rivers, estuaries, and coastal zones

| 0.3 (0.3 to 0.4)

| 0.3 (0.3 to 0.4)

| 0.3 (0.3 to 0.4)

| 0.3 (0.3 to 0.4)

| 0.6 (0.1 to 2.9)

|-
|
| Open oceans

| 3.6 (3.0 to 4.4)

| 3.5 (2.8 to 4.4)

| 3.5 (2.7 to 4.3)

| 3.4 (2.5 to 4.3)

| 3.8 (1.8 to 9.4)

|-
|
| Soils under natural vegetation

| 5.6 (4.9 to 6.6)

| 5.6 (4.9 to 6.5)

| 5.6 (5.0 to 6.5)

| 5.6 (4.9 to 6.5)

| 6.6 (3.3 to 9.0)

|-
|
| Atmospheric chemistry

| 0.4 (0.2 to 1.2)

| 0.4 (0.2 to 1.2)

| 0.4 (0.2 to 1.2)

| 0.4 (0.2 to 1.2)

| 0.6 (0.3 to 1.2)

|-
|
| Surface sink

| –0.01 (–0.3 to 0)

| –0.01 (–0.3 to 0)

| –0.01 (–0.3 to 0)

| –0.01 (–0.3 to 0)

| –0.01 (–1 to 0)

|-
|
| '''Total natural'''

| '''9.9 (8.5''' – '''12.2)'''

| '''9.8 (8.3–12.1)'''

| '''9.8 (8.2''' – '''12.0)'''

| '''9.7 (8.0''' – '''12.0)'''

| '''11.6 (5.5–23.5)'''

|-
| colspan="2"| '''Total b''' '''ottom-up source'''

| '''15.5 (12.1 to 20.9)'''

| '''15.9 (12.2 to 21.7)'''

| '''16.4 (12.3 to 22.4)'''

| '''17.0 (12.2 to 23.5)'''

| '''17.9 (8.1 to 30.7)'''

|-
| colspan="2"| '''Obser''' '''ved growth rate'''

|
| '''3.7 (3.7 to 3.7)'''

| '''4.5 (4.3 to 4.6)'''

| '''3.6 (3.5 to 3.8)'''

|-
| colspan="2"| '''Inferred str''' '''atospheric sink'''

|
| '''12.9 (12.2-13.5)'''

| '''13.1 (12.4–13.6)'''

| '''14.3 (4.3 to 28.7)'''

|-
| colspan="7"| '''Atmosp''' '''heric inversion'''

|-
|
| Atmospheric loss

|
| 12.1 (11.4 to 13.3)

| 12.4 (11.7 to 13.3)

|
|-
|
| Total source

|
| 15.9 (15.1 to 16.9)

| 16.9 (15.9 to 17.7)

|
|-
|
| Imbalance

|
| 3.6 (2.2 to 5.7)

| 4.2 (2.4 to 6.4)

|
|}

<div id="5.2.4" class="h2-container"></div>

<span id="the-relative-importance-of-co-2-ch-4-and-n-2-o"></span>
=== 5.2.4 The Relative Importance of CO <sub>2</sub> , CH <sub>4</sub> , and N <sub>2</sub> O ===

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The total influence of anthropogenic greenhouse gases (GHGs) on the Earth’s radiative balance is driven by the combined effect of those gases, and the three most important – carbon dioxide (CO <sub>2</sub> ), methane (CH <sub>4</sub> ), nitrous oxide (N <sub>2</sub> O) – were discussed in the previous sections. This section compares the balance of the sources and sinks of these three gases and their regional net flux contributions to the radiative forcing. CO <sub>2</sub> has multiple residence times in the atmosphere – from one year to many thousands of years (Box 6.1 in [[#Ciais--2013|Ciais et al., 2013]] ) – and N <sub>2</sub> O has a mean lifetime of 116 years. They are both long-lived GHGs, while CH <sub>4</sub> has a lifetime of 9.1 years and is considered a short-lived GHG (see [[IPCC:Wg1:Chapter:Chapter-2|Chapter 2]] for lifetime of GHGs, [[IPCC:Wg1:Chapter:Chapter-6|Chapter 6]] for CH <sub>4</sub> chemical lifetime, and [[IPCC:Wg1:Chapter:Chapter-7|Chapter 7]] for effective radiative forcing of all GHGs).

Figure 5.18 shows the contribution to radiative forcing of CO <sub>2</sub> , CH <sub>4</sub> , N <sub>2</sub> O, and the halogenated species since the 1900s to more recent decades. For the period 1960–2019, the relative contribution to the total effective radiative forcing (ERF) was 63% for CO <sub>2</sub> , 11% for CH <sub>4</sub> , 6% for N <sub>2</sub> O, and 17% for the halogenated species (Chapter 7; Figure 5.18). The systematic decline in the relative contribution to ERF for CH <sub>4</sub> since 1850 is caused by a slower increase rate of CH <sub>4</sub> in the recent decades, at 6, 10 and 5 ppb yr <sup>–1</sup> during 1850–2019, 1960–2019 and 2000–2019, respectively, in comparison with the increasing rate of CO <sub>2</sub> (at 0.7, 1.6 and 2.2 ppm yr <sup>–1</sup> , respectively) and N <sub>2</sub> O (at 0.4, 0.7 and 0.9 ppb yr <sup>–1</sup> , respectively; Figure 5.4). Owing to the shorter lifetime of CH <sub>4</sub> , the effect of a reduction in the emissions increase rate on the ERF increase is evident at inter-decadal time scales.

<div id="_idContainer053" class="_idGenObjectStyleOverride-1"></div>

[[File:84c5e7cc6a8405220dc64e9d2535e0c7 IPCC_AR6_WGI_Figure_5_18.png]]

'''Figure 5.18 |''' '''Contributions of carbon dioxide (CO''' <sub>2</sub> '''), methane (CH''' <sub>4</sub> '''), nitrous oxide (N''' <sub>2</sub> '''O) and halogenated species to the total effective radiative forcing (ERF) increases in 2019 since 1850, 1960 and 2000, respectively''' . ERF data are taken from [[IPCC:Wg1:Chapter:Annex-iii|Annex III]] (based on calculations from Chapter 7). Note that the sum of the ERFs exceeds 100% because there are negative ERFs due to aerosols and clouds. Further details on data sources and processing are available in the chapter data table (Table 5.SM.6).

Atmospheric abundance of GHGs is proportional to their emissions-loss budgets in the Earth’s environment. There are multiple metrics to evaluate the relative importance of different GHGs for the global atmospheric radiation budget and the socio-economic impacts ( [[IPCC:Wg1:Chapter:Chapter-7#7.6|Section 7.6]] ). Metrics for weighting emissions are further developed in AR6 WGIII. Figure 5.19 shows the regional emissions of the three main GHGs. For North Asia, Europe, Temperate North America and West Asia, the most dominant GHG source is CO <sub>2</sub> ( ''high confidence'' ) (Figure 5.19) while, for East Asia, South Asia, South East Asia, Tropical South America, Temperate North America and Central Africa, the source is CH <sub>4</sub> (Figure 5.19). The N <sub>2</sub> O emissions are dominant in regions with intense use of nitrogen fertilizers in agriculture. Only boreal North America showed net sinks of CO <sub>2</sub> , while close to flux neutrality is observed for North Asia, Southern Africa, and Australasia. Persistent emissions of CO <sub>2</sub> are observed for Tropical and South America, northern Africa, and South East Asia ( ''medium confidence'' ). The ''medium confidence'' arises from large uncertainties in the estimated non-fossil fuel CO <sub>2</sub> fluxes over these regions due to the lack of high-quality atmospheric measurements.

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[[File:6c194ad7d7408a230161b3930730cfc2 IPCC_AR6_WGI_Figure_5_19.png]]

'''Figure 5.19 |''' '''Regional distributions of net fluxes of carbon dioxide (CO''' <sub>2</sub> '''), methane (CH''' <sub>4</sub> '''), nitrous oxide (N''' <sub>2</sub> '''O) on the Earth’s surface.''' The region divisions, shown as the shaded map, are made based on ecoclimatic characteristics of the land. The fluxes include those from anthropogenic activities and natural causes that result from responses to anthropogenic greenhouse gases and climate change (feedbacks) as in the three budgets shown in Sections 5.2.1.5, 5.2.2.5, and 5.2.3.5. The CH <sub>4</sub> and N <sub>2</sub> O emissions are weighted by arbitrary factors of 50 and 500, respectively, for depiction by common y-axes. Fluxes are shown as the mean of the inverse models as available from Thompson et al. (2019); Friedlingstein et al. (2020); Saunois et al. (2020). Further details on data sources and processing are available in the chapter data Table (Table 5.SM.6).

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