Editing IPCC:AR6/WGI/Chapter-3 (section)

== 3.6 Human Influence on the Biosphere ==

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=== 3.6.1 Terrestrial Carbon Cycle ===

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The AR5 did not make attribution statements on changes in global carbon sinks. The IPCC Special Report on Climate Change and Land (SRCCL) assessed with ''high confidence'' that global vegetation photosynthetic activity has increased over the last 2–3 decades ( [[#Jia--2019|Jia et al., 2019]] ). That increase was attributed to direct land use and management changes, as well as to CO <sub>2</sub> fertilization, nitrogen deposition, increased diffuse radiation and climate change ( ''high confidence'' ). The AR5 assessed with ''high confidence'' that CMIP5 Earth System Models (ESMs) simulate the global mean land and ocean carbon sinks within the range of observation-based estimates ( [[#Flato--2013|Flato et al., 2013]] ). The IPCC SRCCL, however, noted the remaining shortcomings of carbon cycle schemes in ESMs ( [[#Jia--2019|Jia et al., 2019]] ), which for example do not properly incorporate thermal responses of respiration and photosynthesis, and frequently omit representations of permafrost thaw ( [[#Comyn-Platt--2018|Comyn-Platt et al., 2018]] ), the nitrogen cycle (R.Q. [[#Thomas--2015|]] [[#Thomas--2015|Thomas et al., 2015]] ) and its influence on vegetation dynamics ( [[#Jeffers--2015|Jeffers et al., 2015]] ), the phosphorus cycle ( [[#Fleischer--2019|Fleischer et al., 2019]] ), and accurate implications of carbon store changes for a range of land use and land management options ( [[#Erb--2018|Erb et al., 2018]] ; [[#Harper--2018|Harper et al., 2018]] ) (see Sections 5.2.1.4.1 and 5.4, Figure 5.24 and Table 5.4 for details).

This section considers three main large-scale indicators of climate change relevant to the terrestrial carbon cycle: atmospheric CO <sub>2</sub> concentration, atmosphere-land CO <sub>2</sub> fluxes, and leaf area index. These indicators were chosen because they have been the target of attribution studies. Other indicators, like land use and management, and wildfires, relate to human influence but are discussed in Chapter 5. [[IPCC:Wg1:Chapter:Chapter-7|Chapter 7]] discusses energetic consequences of changes in the terrestrial carbon cycle in Section 7.4.2.5.2. CMIP5 and CMIP6 ESMs are most often run with prescribed observed historical changes in atmospheric CO <sub>2</sub> concentration and diagnose CO <sub>2</sub> emissions consistent with these. Such calculations require that the models simulate realistic changes in the terrestrial carbon cycle over the historical period, as changes to land carbon stores will influence the size of CO <sub>2</sub> emissions consistent with prescribed CO <sub>2</sub> pathways, and associated remaining carbon budgets (Section 5.5). Such testing of existing models is needed while also recognising there are process representations still requiring inclusion.

Since AR5, atmospheric inversion studies have further tested or constrained models, while new datasets have been used to constrain specific parts of the terrestrial carbon cycle such as plant respiration ( [[#Huntingford--2017|Huntingford et al., 2017]] ). Figure 3.31 compares historical emissions-driven CMIP6 simulations of global mean atmospheric CO <sub>2</sub> concentration and net ocean and land carbon fluxes to the assessed CO <sub>2</sub> concentration and fluxes from the Global Carbon Project ( [[#Friedlingstein--2019|Friedlingstein et al., 2019]] ). For 2014, the CMIP6 models simulate a range of CO <sub>2</sub> concentrations centred around the observed value of 397 ppmv, with a range of 381 to 412 ppmv. GSAT anomalies simulated over the historical period are very similar in models that simulate or prescribe changes in atmospheric CO <sub>2</sub> concentrations (Figures 3.31b and 3.4a). Most models simulate realistic temporal evolution of the global net ocean and land carbon fluxes, although model spread is larger over land (Figure 3.31c,d; see also Sections 3.6.2 and 5.4.5.2, and Figure 5.24). Although literature published soon after AR5 highlighted the importance of representing nitrogen limitation on plant growth ( [[#Peng--2015|Peng and Dan, 2015]] ; R.Q. [[#Thomas--2015|]] [[#Thomas--2015|Thomas et al., 2015]] ), more recent studies note that models without nitrogen limitation can still be consistent with the latest estimates of historical carbon cycle changes ( [[#Arora--2020|Arora et al., 2020]] ; [[#Meyerholt--2020|Meyerholt et al., 2020]] ). Uncertainties in the photosynthetic response to atmospheric CO <sub>2</sub> concentrations at global scales, shifts in carbon allocation and turnover, land-use change ( [[#Hoffman--2014|Hoffman et al., 2014]] ; [[#Wieder--2019|Wieder et al., 2019]] ), and water limitation are also important influences on land carbon fluxes.

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[[File:501d0df3133a78097b6b250e7ec347b7 IPCC_AR6_WGI_Figure_3_31.png]]

Figure 3.31 | '''Evaluation of historical emissions-driven CMIP6 simulations for 185''' '''0–''' '''2014.''' Observations (black) are compared to simulations of global mean '''(a)''' atmospheric CO <sub>2</sub> concentration (ppmv), with observations from the National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA ESRL; [[#Dlugokencky--2020|Dlugokencky and Tans, 2020]] ); '''(b)''' surface air temperature anomaly (°C) with respect to the 1850–1900 mean, with observations from HadCRUT4 ( [[#Morice--2012|Morice et al., 2012]] ); '''(c)''' land carbon uptake (PgC yr <sup>–</sup> <sup>1</sup> ), '''(d)''' ocean carbon uptake (PgC yr <sup>–1</sup> ), both with observations from the Global Carbon Project (GCP; [[#Friedlingstein--2019|Friedlingstein et al., 2019]] ) and grey shading indicating the observational uncertainty. Land and ocean carbon uptakes are plotted using a 10-year running mean for better visibility. The ocean uptake is offset to 0 in 1850 to correct for pre-industrial riverine-induced carbon fluxes. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

All models and observational estimates agree that interannual variability in net CO <sub>2</sub> uptake is much larger over land than over the ocean. Studies demonstrate that regional variations in both the trends and the yearly strength of the terrestrial carbon sink are considerable. Land carbon uptake is dominated by the extratropical northern latitudes (see also Section 5.4.5.3 and Figure 5.25; [[#Ciais--2019|Ciais et al., 2019]] ) because the tropics may have become a net source of carbon ( [[#Baccini--2017|Baccini et al., 2017]] ). At local to regional scales, the dominant driver of yearly sink strength variations is water availability, but at continental to global scales, temperature anomalies are the dominant driver (Section 5.2.1.4.2; [[#Jung--2017|Jung et al., 2017]] ). The major role of levels of water stored in the ground in influencing land-atmosphere CO <sub>2</sub> exchange has also been confirmed through simultaneous analysis of satellite gravimetry and atmospheric CO <sub>2</sub> levels ( [[#Humphrey--2018|Humphrey et al., 2018]] ). When considered globally, simulated land and ocean carbon sinks fall within the range of observation-based estimates with ''high confidence'' . But there is also ''high confidence'' that that apparent success arises for the wrong reasons, as models underestimate the Northern Hemisphere carbon sink, as discussed in Section 5.4.5.3.

The seasonal cycle in atmospheric CO <sub>2</sub> , which is driven by the drawdown of carbon by photosynthesis on land during the summer and release by respiration during the winter, has increased in amplitude since the start of systematic monitoring (Figure 3.32; see also ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.4.1|Section 2.3.4.1]] ). This trend, which is larger at higher latitudes of the Northern Hemisphere, was first reported by [[#Keeling--1996|Keeling et al. (1996)]] and has continued. Changes in vegetation productivity have also been observed, as well as longer growing seasons ( [[#Park--2016|Park et al., 2016]] ). However, a slow down of the increasing trend has been noted, linked to a slow down of both vegetation greening and growing-season length increases ( [[#Buermann--2018|Buermann et al., 2018]] ; Z. [[#Li--2018|]] [[#Li--2018|Li et al., 2018]] ; K. [[#Wang--2020|]] [[#Wang--2020|Wang et al., 2020]] ). Figure 3.32 shows that CMIP6 terrestrial carbon cycle models partially capture the increasing amplitude of the seasonal cycle of the land carbon sink, also seen in observational reconstructions. However, the identification of the human influence that contributes most strongly to these changes in the seasonal cycle is debated.

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[[File:594bc4f0d772378d2cb5a8ee0040d8ad IPCC_AR6_WGI_Figure_3_32.png]]

Figure 3.32 | '''Relative change in the amplitude of the seasonal cycle of global land carbon uptake in the historical CMIP6 simulations from 1961–2014.''' Net biosphere production estimates from 19 CMIP6 models (red), the data-led reconstruction JMA-TRANSCOM ( [[#Maki--2010|Maki et al., 2010]] ; dotted) and atmospheric CO <sub>2</sub> seasonal cycle amplitude changes from observations (global as dashed line, Mauna Loa Observatory (MLO) ( [[#Dlugokencky--2020|Dlugokencky et al., 2020]] ) in bold black). Seasonal cycle amplitude is calculated using the curve fit algorithm package from the National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA ESRL). Relative changes are referenced to the 1961–1970 mean and for short time series adjusted to have the same mean as the model ensemble in the last 10 years. Interannual variation was removed with a nine-year Gaussian smoothing. Shaded areas show the one sigma model spread (grey) for the CMIP6 ensemble and the one sigma standard deviation of the smoothing (red) for the CO <sub>2</sub> MLO observations. Inset: average seasonal cycle of ensemble mean net biosphere production and its one sigma model spread for 1961–1970 (orange dashed line, light orange shading) and 2005–2014 (solid green line, green shading). Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

Proposed causes of the trend in the amplitude of the seasonal cycle of CO <sub>2</sub> , and its amplification at higher latitudes, include increases in the summer productivity and/or increases in the magnitude of winter respiration of northern ecosystems ( [[#Barichivich--2013|Barichivich et al., 2013]] ; [[#Graven--2013|Graven et al., 2013]] ; [[#Forkel--2016|Forkel et al., 2016]] ; [[#Wenzel--2016|Wenzel et al., 2016]] ), increases in productivity throughout the Northern Hemisphere by CO <sub>2</sub> fertilization, and increases in the productivity of agricultural crops in northern mid-latitudes ( [[#Gray--2014|Gray et al., 2014]] ; [[#Zeng--2014|Zeng et al., 2014]] ). Recent studies have attempted to quantify the different contributions by comparing atmospheric CO <sub>2</sub> observations with ensembles of land surface model simulations. [[#Piao--2017|Piao et al. (2017)]] found that CO <sub>2</sub> fertilization of photosynthesis is the main driver of the increase in the amplitude of the seasonal cycle of atmospheric CO <sub>2</sub> but noted that climate change drives the latitudinal differences in that increase. North of 40°N, [[#Bastos--2019|Bastos et al. (2019)]] also found CO <sub>2</sub> fertilization to be the most likely driver, with warming at northern high latitudes contributing a decrease in amplitude, in contrast to earlier conclusions ( [[#Graven--2013|Graven et al., 2013]] ; [[#Forkel--2016|Forkel et al., 2016]] ), and agricultural and land use changes making only a small contribution. For temperate regions of the Northern Hemisphere, K. [[#Wang--2020|]] [[#Wang--2020|Wang et al. (2020)]] found that the importance of CO <sub>2</sub> fertilization is decreased by drought stress, but also found only a small contribution from agricultural and land use changes. However, many global models do not include nitrogen fertilization, changes to crop cultivars or irrigation effects, with the latter associated with deficiencies in simulated terrestrial water cycling (H. [[#Yang--2018|]] [[#Yang--2018|Yang et al., 2018]] ). All these factors influence the capability of models to simulate accurately the seasonal cycle in atmosphere-land CO <sub>2</sub> exchanges. Model comparisons to the atmospheric CO <sub>2</sub> concentration record for Barrow, Alaska, suggest that models underestimate current levels of carbon fixation ( [[#Winkler--2019|Winkler et al., 2019]] ) and have deficiencies in their phenological representation of greenness levels, particularly for autumn (Z. [[#Li--2018|]] [[#Li--2018|Li et al., 2018]] ). Based on these studies and noting the uncertainty in the processes ultimately driving changes in atmospheric CO <sub>2</sub> seasonal cycles (Section 5.2.1.4), we assess with ''medium confidence'' that fertilization by anthropogenic increases in atmospheric CO <sub>2</sub> concentrations is the main driver of the increase in the amplitude of the seasonal cycle of atmospheric CO <sub>2</sub> .

Detection and attribution methods have been applied to leaf area index, which represents ‘greenness’ and general photosynthetic productivity (see [[IPCC:Wg1:Chapter:Chapter-2#2.3.4.3|Section 2.3.4.3]] ). Nitrogen deposition and land cover change trends remain small compared to variability, so attributing changes in leaf area index to those processes is difficult. Using three satellite products and ten land models, [[#Zhu--2016|Zhu et al. (2016)]] found increases in leaf area index (greening) over 25–50% of global vegetated areas, and they attributed 70% of this greening to CO <sub>2</sub> fertilization, although they found that land use change can dominate regionally. This is consistent with the attribution study of observed greening of [[#Mao--2016|Mao et al. (2016)]] , and with [[#Mao--2013|Mao et al. (2013)]] who found that CO <sub>2</sub> fertilization was the dominant cause of enhanced vegetation growth, with latitudinal changes in leaf area index explained by the larger land surface warming in the Northern Hemisphere. These conclusions are also consistent with those of [[#Zhu--2017|Zhu et al. (2017)]] , who found a dominant role for CO <sub>2</sub> fertilization in driving leaf area index changes in an attribution study in which land models were first weighted by performance. However, [[#Chen--2019|Chen et al. (2019)]] has challenged these results by showing that greening in India and China was driven by land-use change.

Leaf area index increases attributed to CO <sub>2</sub> fertilization are due to a direct raised physiological response. However, for drylands, CO <sub>2</sub> -induced stomatal closure may act to conserve soil moisture and thereby indirectly drive higher photosynthesis through higher water use efficiency ( [[#Lu--2016|Lu et al., 2016]] ). In models with nitrogen deposition, there is evidence that this simulated effect also influences leaf area index trends, however, because of a lack of literature based on large-scale land simulations including both nutrient limitation and crop intensification, it is not yet possible to make an attribution statement about their individual roles in leaf area index changes.

In summary, Earth system models simulate globally averaged land carbon sinks within the range of observation-based estimates ( ''high confidence'' ), but global-scale agreement masks large regional disagreements. Based on new studies that attribute changes in atmospheric CO <sub>2</sub> seasonal cycle to CO <sub>2</sub> fertilization, albeit counteracted by other factors, combined with the ''medium confidence'' that models represent the processes driving changes in the seasonal cycle, we assess that there is ''medium confidence'' that CO <sub>2</sub> fertilization is the main driver of the increase in the amplitude of the seasonal cycle of atmospheric CO <sub>2</sub> . Based on available literature, CO <sub>2</sub> fertilization has been the main driver of the observed greening trend, but there is only ''low confidence'' in this assessment because of ongoing debate about the relative roles of CO <sub>2</sub> fertilization, high latitude warming, and land management, and the low number of models that represent the whole suite of processes involved.

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=== 3.6.2 Ocean Biogeochemical Variables ===

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Since CMIP5, there has been a general increase in ocean horizontal and vertical grid resolution in ocean model components ( [[#Arora--2020|Arora et al., 2020]] ; [[#Séférian--2020|Séférian et al., 2020]] ). The latter of these developments is particularly significant for projections of ocean stressors as it directly affects the representation of stratification. Updates in the representation of ocean biogeochemical processes between CMIP5 and CMIP6 have typically involved an increase in model complexity. Specific developments have been the more widespread inclusion of micronutrients, such as iron, variable stoichiometric ratios, more detailed representation of lower trophic levels including bacteria and the cycling and sinking of organic matter. CMIP6 biogeochemical model performance is generally an improvement on that of the parent CMIP5 generation of models ( [[#Séférian--2020|Séférian et al., 2020]] ). The global representation of present-day air-sea carbon fluxes and surface chlorophyll concentrations show moderate improvements between CMIP5 and CMIP6. Similar improvements are seen in the representation of subsurface oxygen concentrations in most ocean basins, while the representation of surface macronutrient concentrations in CMIP6 is shown to have improved with respect to silicic acid but declined slightly with respect to nitrate. Model representation of the micronutrient iron has not improved substantially since CMIP5, but many more models are capable of representing iron. In addition, a comparison of the carbon concentration and carbon climate feedbacks shows no significant change between CMIP5 and CMIP6 ( [[#Arora--2020|Arora et al., 2020]] ).

Since AR5, research has also focused on the detection and attribution of regional patterns in ocean biogeochemical change relating to interior deoxygenation, air-sea CO <sub>2</sub> flux, and ocean carbon uptake and associated acidification. Characterization of flux variability requires understanding of the suite of physical and biological processes including transport, heat fluxes, interior ventilation, biological production and gas exchange which can have very different controls on seasonal versus interannual time scales in both the North Pacific ( [[#Ayers--2012|Ayers and Lozier, 2012]] ) and North Atlantic ( [[#Breeden--2016|Breeden and McKinley, 2016]] ). In the Southern Ocean, models have difficulty reproducing the observed seasonal cycle and interannual variability, making attribution particularly challenging ( [[#Lovenduski--2016|Lovenduski et al., 2016]] ; [[#Mongwe--2016|Mongwe et al., 2016]] , 2018).

The AR5 concluded that oxygen concentrations have decreased in the open ocean since 1960 and such decreases can be attributed in part to human influence with ''medium confidence'' . The decrease in ocean oxygen content in the upper 1000 m, between 1970 and 2010, is further confirmed in SROCC ( ''medium confidence'' ), with the oxygen minimum zone expanding in volume (see also Section 5.3.3.2). Observed oxygen declines over the last several decades ( [[#Stendardo--2012|Stendardo and Gruber, 2012]] ; [[#Stramma--2012|Stramma et al., 2012]] ; [[#Schmidtko--2017|Schmidtko et al., 2017]] ) match model estimates in the surface ocean ( [[#Oschlies--2017|Oschlies et al., 2017]] ) but are much larger than model derived estimates in the interior ( [[#Bopp--2013|Bopp et al., 2013]] ; [[#Cocco--2013|Cocco et al., 2013]] ). Some of this difference has been interpreted as due to a lack of representation of coastal eutrophication in these models ( [[#Breitburg--2018|Breitburg et al., 2018]] ), but much of it remains unexplained. This disparity is particularly apparent in the eastern Pacific oxygen minimum zone, where some CMIP5 models showed increasing trends whereas observations show a strong decrease ( [[#Cabré--2015|Cabré et al., 2015]] ). However, proxy reconstructions suggest that over the last century the ocean may have in fact undergone increases in oxygen in the most oxygen poor regions ( [[#Deutsch--2014|Deutsch et al., 2014]] ). As discussed in Section 5.3.1, ocean oxygen went through wide oscillations on multi-centennial time scales through the last deglaciation, with abrupt warming resulting in loss of oxygen in subsurface waters of the North Pacific ( [[#Praetorius--2015|Praetorius et al., 2015]] ). The global upper ocean oxygen inventory is negatively correlated with ocean heat content with a regression coefficient comparable to that found in ocean models ( [[#Ito--2017|Ito et al., 2017]] ). Variability and trends in the observed upper ocean oxygen concentration are mainly driven by the apparent oxygen utilization component with small contributions from oxygen solubility, suggesting that changing ocean circulation, mixing, and/or biochemical processes, rather than thermally induced solubility effects may be the main drivers of observed deoxygenation. The spatial distribution of the ocean deoxygenation in the interior of the ocean as well as over coastal areas is further assessed in Section 5.3.

As one of the most commonly observed surface parameters, the partial pressure of CO <sub>2</sub> has been the topic of considerable detection and attribution work. In North Atlantic subtropical and equatorial biomes, warming has been shown to be a significant and persistent contributor to the observed increase in the partial pressure of CO <sub>2</sub> since the mid‐2000s with long‐term warming leading to a reduction in ocean carbon uptake ( [[#Fay--2013|Fay and McKinley, 2013]] ), and with both the partial pressure of CO <sub>2</sub> and associated carbon uptake demonstrating strong predictability as a function of interannual to decadal climate state (H. [[#Li--2016|]] [[#Li--2016|Li et al., 2016]] ; [[#Li--2018|Li and Ilyina, 2018]] ). In the Southern Ocean however, detection and attribution of surface trends in the partial pressure of CO <sub>2</sub> has proven more elusive and dependent on methodology, with some studies suggesting that Southern Ocean carbon uptake slowed from about 1990 to 2006 and subsequently strengthened from 2007 to 2010 ( [[#Lovenduski--2008|Lovenduski et al., 2008]] ; [[#Fay--2014|Fay et al., 2014]] ; [[#Ritter--2017|Ritter et al., 2017]] ). Other studies have suggested that poor representation of the seasonal cycle in the Southern Ocean may confound the models’ ability to represent changes in the partial pressure of CO <sub>2</sub> in the Southern Ocean ( [[#Nevison--2016|Nevison et al., 2016]] ; [[#Mongwe--2018|Mongwe et al., 2018]] ).

Section 5.2.1.3 assesses that both observational reconstructions based on the partial pressure of CO <sub>2</sub> and ocean biogeochemical models show a quasi-linear increase in the ocean sink of anthropogenic CO <sub>2</sub> from 1.0 ± 0.3 PgC yr <sup>–1</sup> to 2.5 ± 0.6 PgC yr <sup>–1</sup> between 1960–1969 and 2010–2019 in response to global CO <sub>2</sub> emissions ( ''high confidence'' ). During the 1990s, the global net flux of CO <sub>2</sub> into the ocean is estimated to have weakened to 0.8 ± 0.5 PgC yr <sup>–1</sup> while in 2000 and thereafter, it is estimated to have strengthened considerably to rates of 2.0 ± 0.5 PgC yr <sup>–1</sup> , associated with changes in SST, the surface concentration of dissolved inorganic carbon and alkalinity, and decadal variations in atmospheric forcing ( [[#Landschützer--2016|Landschützer et al., 2016]] , see also Section 5.2).

Ocean acidification is one of the most detectible metrics of environmental change and was well covered in AR5, in which it was assessed that the uptake of anthropogenic CO <sub>2</sub> had ''very likely'' resulted in acidification of surface waters ( [[#Bindoff--2013|Bindoff et al., 2013]] ). Since then, observations and simulations of multi-decadal trends in surface carbon chemistry have increased in robustness. The evidence on ocean pH decline had further strengthened in SROCC with good agreement found between CMIP5 models and observations and an assessment that the ocean was continuing to acidify in response to ongoing carbon uptake ( [[#Bindoff--2019|Bindoff et al., 2019]] ). An observed decrease in global surface open ocean pH is assessed in [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.5|Section 2.3.3.5]] to be ''virtually certain'' to have occurred with a rate of 0.003–0.026 per decade for the past 40 years. The ocean acidification has occurred not only in the surface layer but also in the interior of the ocean (Sections 2.3.3.5 and 5.3.3). Rates have been observed to be between −0.015 and −0.020 per decade in mode and intermediate waters of the North Atlantic through the combined effect of increased anthropogenic and remineralized carbon ( [[#Ríos--2015|Ríos et al., 2015]] ) and acidification has been observed down to 3000 m in the deep water formation regions ( [[#Perez--2018|Perez et al., 2018]] ). There has also been considerable improvement in detection and attribution of anthropogenic CO <sub>2</sub> versus eutrophication-based acidification in coastal waters ( [[#Wallace--2014|Wallace et al., 2014]] ).

The increased evidence in recent studies supports an assessment that it is ''virtually certain'' that the uptake of anthropogenic CO <sub>2</sub> was the main driver of the observed acidification of the global surface open ocean. The observed increase in acidification over the North Atlantic subtropical and equatorial regions since 2000 is ''likely'' associated in part with an increase in ocean temperature, a response which corresponds to the expected weakening of the ocean carbon sink with warming. Due to strong internal variability, systematic changes in carbon uptake in response to climate warming have not been observed in most other ocean basins at present. We further assess, consistent with AR5 and SROCC, that deoxygenation in the upper ocean is due in part to anthropogenic forcing, with ''medium'' confidence. There is ''high confidence'' that Earth system models simulate a realistic time evolution of the global mean ocean carbon sink.

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