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

=== 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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'''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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