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

== 3.5 Human Influence on the Ocean ==

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The global ocean plays an important role in the climate system, as it is responsible for transporting and storing large amounts of heat (Sections 3.5.1 and 9.2.2.1), freshwater (Sections 3.5.2 and 9.2.2.2) and carbon (Sections 3.6.2 and 5.2.1.3) that are exchanged with the atmosphere. Therefore, accurate ocean simulation in climate models is essential for the realistic representation of the climatic response to anthropogenic warming, including rates of warming, sea level rise and carbon uptake, and the representation of coupled modes of climate variability.

Ocean model development has advanced considerably since AR5 ( [[IPCC:Wg1:Chapter:Chapter-1#1.5.3.1|Section 1.5.3.1]] ). Ongoing model developments since AR5 have focused on improving the realism of the simulated ocean in coupled models, with horizontal resolutions increasing to 10–100 km (from about 200 km in CMIP5), and increased vertical resolutions in many modelling systems of 0–1 m for near-surface levels (from the highest resolution of 10 m in CMIP5). These developments are aimed at improving the representation of the diurnal cycle and coupling to the atmosphere (e.g., [[#Bernie--2005|Bernie et al., 2005]] , 2007, 2008). General improvements to simulated ocean fidelity in response to increasing resolution are expected ( [[#Hewitt--2017|Hewitt et al., 2017]] ), and the effects of model resolution on the fidelity of ocean models are discussed in more detail in Sections 9.2.2 and 9.2.4.

In this section we assess the global and basin-scale properties of the simulated ocean, with a focus on evaluation of the realism of simulated ocean properties, and the detection and attribution of human-induced changes in the ocean over the period of observational coverage. Observed changes to ocean temperature ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.1|Section 2.3.3.1]] ), salinity ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.2|Section 2.3.3.2]] ), sea level ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.3|Section 2.3.3.3]] ) and ocean circulation ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.4|Section 2.3.3.4]] ) are reported in Chapter 2. A more process-based assessment of ocean changes, alongside the assessment of variability and changes in ocean properties with spatial scales smaller than ocean basins, is presented in Chapter 9.

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=== 3.5.1 Ocean Temperature ===

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Ocean temperature and ocean heat content are key physical variables considered for climate model evaluation and are primary indicators of a changing ocean climate. This section assesses the performance of climate models in representing the mean state ocean temperature and heat content ( [[#3.5.1.1|Section 3.5.1.1]] ), with a particular focus on the tropical oceans given the importance of air-sea coupling in these areas ( [[#3.5.1.2|Section 3.5.1.2]] ). This is followed by an assessment of detection and attribution studies of changes in ocean temperature and heat content ( [[#3.5.1.3|Section 3.5.1.3]] ). Changes in global surface temperature are assessed in [[#3.3.1.1|Section 3.3.1.1]] .

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==== 3.5.1.1 Sea Surface and Zonal Mean Ocean Temperature Evaluation ====

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In CMIP3 and CMIP5 models, large SST biases were found in the mid- and high latitudes ( [[#Flato--2013|Flato et al., 2013]] ). In CMIP6, the Northern Hemisphere mid-latitude surface temperature biases appear to be marginally improved in the multi-model mean when contrasted to CMIP5 despite large biases remaining in a few models (Figures 3.23a and 3.24). There is a decreased spread of the zonal mean SST error between 50°N and 30°S, relative to CMIP5 (Figure 3.24a). On the other hand, the Southern Ocean’s warm surface temperature bias remains (Figure 3.23a; [[#Beadling--2020|Beadling et al., 2020]] ), and is on average larger in CMIP6 than in CMIP5 models (Figures 3.23a and 3.24). This warm bias is often associated with persistent overlying atmospheric cloud biases ( [[#Hyder--2018|Hyder et al., 2018]] ). Several other large biases also appear to remain largely unresolved in CMIP6, particularly warm biases in excess of 1°C along the equatorial eastern continental boundaries of the tropical Atlantic and Pacific Oceans (Figure 3.23a).

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[[File:75e0df52ad6c606b3aafe157c00f8761 IPCC_AR6_WGI_Figure_3_23.png]]

Figure 3.23 | '''Multi-model mean bias of (a) sea surface temperature and (b) near-surface salinity, defined as the difference between the CMIP6 m''' '''ulti-mo''' '''del mean and the climatology from the World Ocean Atlas 2018.''' The CMIP6 multi-model mean is constructed with one realization of 46 CMIP6 historical experiments for the period 1995–2014 and the climatology from the World Ocean Atlas 2018 is an average over all available years (1955–2017). Uncertainty is represented using the advanced approach: No overlay indicates regions with robust signal, where ≥66% of models show change greater than the variability threshold and ≥80% of all models agree on sign of change; diagonal lines indicate regions with no change or no robust signal, where &lt;66% of models show a change greater than the variability threshold; crossed lines indicate regions with conflicting signal, where ≥66% of models show change greater than the variability threshold and &lt;80% of all models agree on sign of change. For more information on the advanced approach, please refer to Cross-Chapter Box Atlas.1. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

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[[File:37a6a2360493c05b19a9bdb94f3e2487 IPCC_AR6_WGI_Figure_3_24.png]]

'''Figure 3.24 |''' '''Biases in zonal mean and equatorial sea surface temperature (SST) in CMIP5 and CMIP6 models.''' CMIP6 (red), CMIP5 (blue) and HighResMIP (green) multi-model mean '''(a)''' zonally averaged SST bias; '''(b)''' equatorial SST bias; and '''(c)''' equatorial SST compared to observed mean SST (black line) for 1979–1999. The inter-model 5th and 95th percentiles are depicted by the respective shaded range. Model climatologies are derived from the 1979–1999 mean of the historical simulations, using one simulation per model. The Hadley Centre Sea Ice and Sea Surface Temperature version 1 (HadISST) ( [[#Rayner--2003|Rayner et al., 2003]] ) observational climatology for 1979–1999 is used as the reference for the error calculation in (a) and (b); and for observations in (c). Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

Overall, the simulated and observed trends in SST patterns are generally consistent for the historical period ( [[#Olonscheck--2020|Olonscheck et al., 2020]] ). The CMIP6 models generally represent the observed pattern of trends better than the CMIP5 models, and observed trends fall within the range of simulated trends over a larger area for CMIP6 models than for CMIP5 models ( [[#Olonscheck--2020|Olonscheck et al., 2020]] ).

The CMIP5 multi-model mean zonally averaged subsurface ocean temperature showed warm biases between 200 m and 2000 m (mid-depth) over most latitudes, with exceptions in the Southern Ocean (&gt;60°S, 100–2000 m) and upper (0–400 m) Arctic Ocean. Cold biases were simulated near the surface (0–200 m) at most latitudes ( [[#Flato--2013|Flato et al., 2013]] ). CMIP6 biases are broadly consistent with those reported in CMIP5 for the near-surface (&lt;200 m) and mid-depth (200–2000 m) ocean ( [[#Voldoire--2019b|Voldoire et al., 2019b]] ; [[#Beadling--2020|Beadling et al., 2020]] ; [[#Zhu--2020|]] [[#Zhu--2020|Y. Zhu et al., 2020]] ). The warm bias begins between 100 and 400 m depth in all three basins, however, it is most prominent in the Atlantic Ocean, with a maximum magnitude in the equatorial latitudes, as in CMIP5 (Figure 3.25). In the Pacific, the large warm biases are mostly seen in the subtropical regions (30°N–60°N and 30°S–60°S). The cool near surface tropical bias is most prominent in the Pacific Ocean and also present in the Atlantic, with a smaller magnitude (Figure 3.25). Relative to CMIP5, the most prominent difference is an increase to the mid-depth (300–2000 m) warm bias in CMIP6 and a change in sign of the bias from cold to warm for the Southern Ocean mid-depth (&gt;60°S) from CMIP5 to CMIP6 (Figure 3.25). Compared to CMIP3 and CMIP5, there is improved agreement between most CMIP6 models and observations in their representation of the zonal mean temperature of the upper 100 m of the Southern Ocean ( [[#Beadling--2020|Beadling et al., 2020]] ).

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[[File:e5ffa930cefe2c9e979f700839ee286a IPCC_AR6_WGI_Figure_3_25.png]]

'''Figure 3.25 |''' '''CMIP6 potential temperature and salinity biases for the global ocean, Atlantic Ocean, Pacific Ocean and Indian Ocean.''' Shown in colour are the time-mean differences between the CMIP6 historical multi-model climatological mean and observations, zonally averaged for each basin (excluding marginal and regional seas). The observed climatological values are obtained from the World Ocean ( [[IPCC:Wg1:Chapter:Atlas|Atlas]] 2018 (WOA18, 1981–2010; Prepared by the Ocean Climate Laboratory, National Oceanographic Data Center, Silver Spring, MD, USA), and are shown as labelled black contours for each of the basins. The simulated annual mean climatologies for 1981 to 2010 are calculated from available CMIP6 historical simulations, and the WOA18 climatology utilized synthesized observed data from 1981 to 2010. Output from a total of 30 available CMIP6 models is used for the temperature panels (left column) and 28 models for the salinity panels (right column). Potential temperature units are °C and salinity units are the Practical Salinity Scale 1978 [PSS-78]. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

Focusing on the deep ocean (&gt;2000 m), the CMIP6 ensemble mean shows a prominent and consistent warm bias (Figure 3.25), in all basins except the equatorial and northern Pacific, which contrasts to a cold bias seen in CMIP5 ( [[#Flato--2013|Flato et al., 2013]] ). We note that while an updated observational temperature dataset is used in this assessment (WOA09 was used in AR5, while WOA18, 1981–2010 is used in AR6), the deep-ocean warm bias remains and is approaching double the magnitude (about 0.5°C) of the equivalent CMIP5 multi-model mean bias, a feature which is particularly prominent in the Atlantic and southern Indian Oceans. Increased horizontal resolution as well as the choice of the vertical coordinate are reported to partly improve these biases in some models ( [[#Adcroft--2019|Adcroft et al., 2019]] ; [[#Rackow--2019|Rackow et al., 2019]] ; [[#Hewitt--2020|Hewitt et al., 2020]] ).

Since AR5, there has been growing evidence that the representation of mean surface and deeper ocean temperatures in coupled climate models can be improved by increasing the horizontal resolution both in the ocean and the atmosphere (e.g., [[#Small--2014|Small et al., 2014]] ; [[#Hewitt--2016|Hewitt et al., 2016]] ; [[#Iovino--2016|Iovino et al., 2016]] ; [[#Roberts--2019|Roberts et al., 2019]] ). At an ocean resolution of around 1°, which is typical of CMIP6 models, some processes are parameterized rather than explicitly resolved, leading to a compromise in their dynamical representation. An increase in the model resolution allows for processes to be explicitly resolved, and can for example, enhance the simulation of eddies, thus improving simulated vertical eddy transport, and reducing temperature drifts in the deeper ocean ( [[#Griffies--2015|Griffies et al., 2015]] ; [[#von%20Storch--2016|von Storch et al., 2016]] ). For some models, the mean absolute error in ocean temperature below 500 m is smaller in the high resolution version compared to the low resolution version, particularly in eddy-active regions such as the North Atlantic ( [[#Rackow--2019|Rackow et al., 2019]] ). Increasing the horizontal resolution of individual climate models often leads to an overall decrease in the surface temperature biases over regions where they persisted through earlier CMIP generations, such as the central and western equatorial Pacific, as well as the North and tropical Atlantic (Figure 3.3e; [[#Roberts--2019|Roberts et al., 2019]] ; [[#Hewitt--2020|Hewitt et al., 2020]] ). Despite this, as a group the four HighResMIP models included in Figures 3.3e and 3.24 do not on average show smaller SST biases than the CMIP6 multi-model mean, demonstrating the importance of factors other than resolution in contributing to SST biases.

In summary, there is little improvement in the multi-model mean sea surface and zonal mean ocean temperatures from CMIP5 to CMIP6 ( ''medium confidence'' ). Nevertheless, the CMIP6 models show a somewhat more realistic pattern of SST trends ( ''low confidence'' ).

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==== 3.5.1.2 Tropical Sea Surface Temperature Evaluation ====

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===== 3.5.1.2.1 Tropical Pacific Ocean =====

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In CMIP5, mean state biases in the tropical Pacific Ocean including the excessive equatorial cold tongue, erroneous mean thermocline depth and slope along the equator remained but were improved relative to CMIP3 ( [[#Flato--2013|Flato et al., 2013]] ). Misrepresentation of the interaction between the atmosphere and ocean via the Bjerknes feedback and vertical mixing parameterizations, and a bias in winds were among the suggested reasons for the persistent biases ( [[#Li--2014|Li et al., 2014]] ; [[#Zhu--2018|Zhu and Zhang, 2018]] ). Moving to CMIP6, a reduction of the cold bias in the equatorial cold tongue in the central Pacific is found on average in the CMIP6 models (Figure 3.24b; [[#Grose--2020|Grose et al., 2020]] ; [[#Planton--2021|Planton et al., 2021]] ), however, this reduced bias is not statistically significant when considered across the multi-model ensemble ( [[#Planton--2021|Planton et al., 2021]] ). It is also noteworthy that the longitude of the 28°C isotherm is closer to observed in CMIP6 than in CMIP5, with a coincident reduction in the CMIP6 inter-model standard deviation ( [[#Grose--2020|Grose et al., 2020]] ). The latter result implies that there is an improvement in the representation of the tropical Pacific mean state in CMIP6 models. Comparison of biases in individual HighResMIP models with biases in lower resolution versions of the same models indicates that there is no consistent improvement in SST biases in most of the equatorial Pacific with resolution (Figure 3.3e; [[#Bock--2020|Bock et al., 2020]] ).

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===== 3.5.1.2.2 Tropical Atlantic Ocean =====

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Fundamental features such as the mean zonal SST gradient in the tropical Atlantic were not reproduced in CMIP5 models. Studies have proposed that weaker than observed alongshore winds, underestimation of stratocumulus clouds, coarse model resolution, and insufficient oceanic cooling due to a deeper thermocline depth and weak vertical velocities at the base of the mixed layer in the eastern basin, underpinned these tropical Atlantic SST gradient biases ( [[#Hourdin--2015|Hourdin et al., 2015]] ; [[#Richter--2015|Richter, 2015]] ). The SST gradient biases still remain in CMIP6. On average the cold bias in the western part of the basin is reduced while the warm bias in the eastern part has slightly increased (Figure 3.24b,c; [[#Richter--2020|Richter and Tokinaga, 2020]] ). Several CMIP6 models, however, display large reductions in biases of the zonal SST gradient, such that the eastern equatorial Atlantic warm SST bias and associated westerly wind biases are mostly eliminated in these models ( [[#Richter--2020|Richter and Tokinaga, 2020]] ). The high resolution (HighResMIP) CMIP6 models show a better representation of the zonal SST gradient (Figure 3.24b,c), but some lower resolution models also perform well, suggesting that resolution is not the only factor responsible for biases in Tropical Atlantic SST ( [[#Richter--2020|Richter and Tokinaga, 2020]] ).

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===== 3.5.1.2.3 Tropical Indian Ocean =====

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The tropical Indian Ocean mean state is reasonably well simulated both in CMIP5 and CMIP6 (Figure 3.24b,c). However, CMIP5 models show a large spread in the thermocline depth, particularly in the equatorial part of the basin ( [[#Saji--2006|Saji et al., 2006]] ; [[#Fathrio--2017b|Fathrio et al., 2017b]] ), which has been linked to the parameterization of the vertical mixing and the wind structure, leading to a misrepresentation of the ventilation process in some models ( [[#Schott--2009|Schott et al., 2009]] ; [[#Richter--2015|Richter, 2015]] ; [[#Shikha--2018|Shikha and Valsala, 2018]] ). A common problem with the CMIP5 models is therefore a warm bias in the subsurface, mainly at depths around the thermocline, which is also apparent in the CMIP6 models (Figure 3.25g).

In the CMIP6 multi-model mean, the western tropical Indian Ocean shows a slightly larger warm bias compared to CMIP5 (Figure 3.24 b,c), which in part could be related to excessive supply of warm water from the Red Sea ( [[#Grose--2020|Grose et al., 2020]] ; [[#Semmler--2020|Semmler et al., 2020]] ). The HighResMIP models show decreases in SST bias across the Indian Ocean with increasing resolution (Figure 3.3e; [[#Bock--2020|Bock et al., 2020]] ), though as a group the SST biases in the HighResMIP models are no smaller than those of the full CMIP6 ensemble.

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===== 3.5.1.2.4 Summary =====

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In summary, the structure and magnitude of multi-model mean ocean temperature biases have not changed substantially between CMIP5 and CMIP6 ( ''medium confidence'' ). Although biases remain in the latest generation models, the broad consistency between the observed and simulated basin-scale ocean properties suggests that CMIP5 and CMIP6 models are appropriate tools for investigating ocean temperature and ocean heat content responses to forcing. This also provides ''high confidence'' in the utility of CMIP-class models for detection and attribution studies, for both ocean heat content ( [[#3.5.1.3|Section 3.5.1.3]] ) and thermosteric sea level applications ( [[#3.5.3.2|Section 3.5.3.2]] ).

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==== 3.5.1.3 Ocean Heat Content Change Attribution ====

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The ocean plays an important role as the Earth’s primary energy store. The AR5 and SROCC assessed that the ocean accounted for more than 90% of the Earth’s energy change since the 1970s ( [[#Rhein--2013|Rhein et al., 2013]] ; [[#Bindoff--2019|Bindoff et al., 2019]] ). These assessments are consistent with recent studies assessed in Section 7.2 and Cross-Chapter Box 9.1, which find that 91% of the observed change in Earth’s total energy from 1971 to 2018 was stored in the ocean ( [[#von%20Schuckmann--2020|von Schuckmann et al., 2020]] ). The AR5 concluded that anthropogenic forcing has ''very likely'' made a substantial contribution to ocean warming above 700 m, whereas below 700 m, limited measurements restricted the assessment of ocean heat content changes in AR5 and prevented a robust comparison between observations and models ( [[#Bindoff--2013|Bindoff et al., 2013]] ).

With the recent increase in ocean sampling by Argo to 2000 m ( [[#Roemmich--2015|Roemmich et al., 2015]] ; [[#Riser--2016|Riser et al., 2016]] ; [[#von%20Schuckmann--2016|von Schuckmann et al., 2016]] ) and the resulting improvements in estimates of ocean heat content ( [[#Abraham--2013|Abraham et al., 2013]] ; [[#Balmaseda--2013|Balmaseda et al., 2013]] ; [[#Durack--2014b|Durack et al., 2014b]] ; [[#Cheng--2017|Cheng et al., 2017]] ; [[#von%20Schuckmann--2020|von Schuckmann et al., 2020]] ), a more quantitative assessment of the global ocean heat content changes that extends into the intermediate ocean (700–2000 m) over the more recent period (from 2005 to the present; [[#Durack--2018|Durack et al., 2018]] ) can be performed. Observed ocean heat content changes are discussed in [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.1|Section 2.3.3.1]] , where it is reported that it is ''virtually certain'' that the global upper ocean (0–700 m) and ''very likely'' that the global intermediate ocean (700–2000 m) warmed substantially from 1971 to the present. Further, ocean layer warming contributions are reported as 61% (0–700 m), 31% (700–2000 m) and 8% (&gt;2000 m) for the 1971 to 2018 period (Table 2.7). CMIP5 model simulations replicate this partitioning fairly well for the industrial-era (1865 to 2017) throughout the upper (0–700 m, 65%), intermediate (700–2000 m, 20%) and deep (&gt;2000 m, 15%) layers ( [[#Gleckler--2016|Gleckler et al., 2016]] ; [[#Durack--2018|Durack et al., 2018]] ). The corresponding warming percentages for the multi-model mean of a subset of CMIP6 simulations over the 1850–2014 period are 58% for the upper, 21% for the intermediate, and 22% for the deep-ocean layers (Figure 3.26). These results are consistent with SROCC which assessed that it is ''virtually certain'' that both the upper and intermediate ocean warmed from 2004 to 2016, with an increased rate of warming since 1993 ( [[#Bindoff--2019|Bindoff et al., 2019]] ). The spatial distribution of these changes for different ocean depths is assessed in Section 9.2.2.1.

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[[File:dcba862d8c947f6b3c983b6ca47cb154 IPCC_AR6_WGI_Figure_3_26.png]]

Figure 3.26 | '''Global ocean heat content in CMIP6 simulations and observations.''' Time series of observed (black) and simulated (red) global ocean heat content anomalies with respect to 1995–2014 for the full ocean depth '''(left-hand panel)''' ; upper layer: 0–700 m '''(top right-hand panel)''' ; intermediate layer: 700–2000 m '''(middle right-hand panel)''' ; and the abyssal ocean: &gt;2000 m '''(bottom right-hand panel)''' . The best estimate observations (black solid line) for the period of 1971–2018, along with ''very likely'' ranges (black shading) are from [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.1|Section 2.3.3.1]] . For the models (1860–2014), ensemble members from 15 CMIP6 models are used to calculate the multi-model mean values (red solid line) after averaging across simulations for each independent model. The ''very likely'' ranges in the simulations are shown in red shading. Simulation drift has been removed from all CMIP6 historical runs using a contemporaneous portion of the linear fit to each corresponding pre-industrial control run ( [[#Gleckler--2012|Gleckler et al., 2012]] ). Units are zettajoules (ZJ; 10 <sup>21</sup> joule). Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

The multi-model means of both CMIP5 and CMIP6 historical simulations forced with time varying natural and anthropogenic forcing shows robust increases in ocean heat content in the upper (0–700 m) and intermediate (700–2000 m) ocean ( ''high confidence'' ) (Figure 3.26; [[#Cheng--2016|Cheng et al., 2016]] , [[#Cheng--2019|2019]] ; [[#Gleckler--2016|Gleckler et al., 2016]] ; [[#Bilbao--2019|Bilbao et al., 2019]] ; [[#Tokarska--2019|Tokarska et al., 2019]] ). Temporary (&lt;10 years) surface and subsurface cooling during and after large volcanic eruptions is also captured in the upper-ocean, and global mean ocean heat content ( [[#Balmaseda--2013|Balmaseda et al., 2013]] ). The ocean heat content increase is also reflected in the corresponding ocean thermal expansion which is a leading contributor to global mean sea level rise (Sections 3.5.3.2 and 9.2.4, and Box 9.1).

For the period 1971–2014, the rate of ocean heat uptake for the global ocean in the CMIP6 models is about 6.43 [2.08–8.66] ZJ yr <sup>–1</sup> , with the upper, intermediate and deeper layers respectively accounting for 68%, 16% and 16% of the full depth global heat uptake (Figure 3.26). Overall, the simulated ocean heat content changes are consistent with the updated and improved observational analyses, within the ''very likely'' uncertainty range defined for each (see also ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.1|Section 2.3.3.1]] , Table 2.7; [[#Domingues--2008|Domingues et al., 2008]] ; [[#Purkey--2010|Purkey and Johnson, 2010]] ; [[#Levitus--2012|Levitus et al., 2012]] ; [[#Good--2013|Good et al., 2013]] ; [[#Cheng--2017|Cheng et al., 2017]] ; [[#Ishii--2017|Ishii et al., 2017]] ; [[#Zanna--2019|Zanna et al., 2019]] ) as well as with the ocean components of total Earth heating assessed in Section 7.2.2.2, Table 7.1. Nevertheless, large uncertainties remain, particularly in the deeper layers due to the poor temporal and spatial sampling coverage, particularly in the Atlantic, Southern and Indian Oceans ( [[#Garry--2019|Garry et al., 2019]] ). The ''very likely'' ranges of the simulated trends for the full ocean depth and below 2000 m fall within the ''very likely'' range of observed uptake during the last two decades. In the intermediate layer, the multi-model ensemble mean mostly stays above the observed 5th–95th percentile range before the year 2000, and below that range after 2000. For the upper ocean, some individual model realizations show a reduced ocean heat content increase during the 1970s and 1980s, which is then compensated by a greater warming than the observations from the early 1990s. These discrepancies have been linked with a temporary increase in the Southern Ocean deep water formation rate, as well as with the models’ strong aerosol cooling effects and high equilibrium climate sensitivity (see also Section 7.5.6 and Box 7.2; [[#Andrews--2019|Andrews et al., 2019]] , [[#Andrews--2020|2020]] ; [[#Golaz--2019|Golaz et al., 2019]] ; [[#Dunne--2020|Dunne et al., 2020]] ; [[#Winton--2020|Winton et al., 2020]] ).

Nevertheless, simulations show that the rate of ocean heat uptake has doubled in the past few decades, when contrasted to the rate over the complete 20th century (Figure 3.26), with over a third of the accumulated heat stored below 700 m ( [[#Cheng--2016|Cheng et al., 2016]] , [[#Cheng--2019|2019]] ; [[#Gleckler--2016|Gleckler et al., 2016]] ; [[#Durack--2018|Durack et al., 2018]] ). The Southern Ocean shows the strongest ocean heat uptake that penetrates to deeper layers (Section 9.2.3.2), whereas ocean heat content increases in the Pacific and Indian Oceans largely occur in the upper layers ( [[#Bilbao--2019|Bilbao et al., 2019]] ).

Since AR5, the attribution of ocean heat content increases to anthropogenic forcing has been further supported by more detection and attribution studies. These studies have shown that contributions from natural forcing alone cannot explain the observed changes in ocean heat content in either the upper or intermediate ocean layers, and a response to anthropogenic forcing is clearly detectable in ocean heat content ( [[#Gleckler--2016|Gleckler et al., 2016]] ; [[#Bilbao--2019|Bilbao et al., 2019]] ; [[#Tokarska--2019|Tokarska et al., 2019]] ). Moreover, a response to greenhouse gas forcing is detectable independently of the response to other anthropogenic forcings ( [[#Bilbao--2019|Bilbao et al., 2019]] ; [[#Tokarska--2019|Tokarska et al., 2019]] ), which has offset part of the greenhouse gas induced warming. Further evidence is provided by the agreement between observed and simulated changes in global thermal expansion associated with the ocean heat content increase when both natural and anthropogenic forcings are included in the simulations ( [[#3.5.3.2|Section 3.5.3.2]] ), though internal variability plays a larger role in driving basin-scale thermosteric sea level trends ( [[#Bilbao--2015|Bilbao et al., 2015]] ). Over the Southern Ocean, warming is detectable over the late 20th century and is largely attributable to greenhouse gases ( [[#Swart--2018|Swart et al., 2018]] ; [[#Hobbs--2021|Hobbs et al., 2021]] ), while other anthropogenic forcings such as ozone depletion have been shown to mitigate the warming in some of the CMIP5 simulations ( [[#Swart--2018|Swart et al., 2018]] ; [[#Hobbs--2021|Hobbs et al., 2021]] ). The use of the mean temperature above a fixed isotherm rather than fixed depth further strengthens a robust detection of the anthropogenic response in the upper ocean ( [[#Weller--2016|Weller et al., 2016]] ), and better accounting for internal variability in the upper ocean ( [[#Rathore--2020|Rathore et al., 2020]] ), helps explain reported hemispheric asymmetry in ocean heat content change ( [[#Durack--2014b|Durack et al., 2014b]] ).

In summary, there is strong evidence for an improved understanding of the observed global ocean heat content increase. It is ''extremely likely'' that human influence was the main driver of the ocean heat content increase observed since the 1970s, which extends into the deeper ocean ( ''very high confidence'' ). Updated observations, like model simulations, show that warming extends throughout the entire water column ( ''high confidence'' ).

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=== 3.5.2 Ocean Salinity ===

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While ocean assessments have primarily focused on temperature changes, improved observational salinity products since the early 2000s have supported more assessment of long-term ocean salinity change and variability from AR4 ( [[#Bindoff--2007|Bindoff et al., 2007]] ) to AR5 across both models and observations ( [[#Flato--2013|Flato et al., 2013]] ; [[#Rhein--2013|Rhein et al., 2013]] ). The AR5 assessed that it was ''very likely'' that anthropogenic forcings have made a discernible contribution to surface and subsurface ocean salinity changes since the 1960s. The SROCC augmented these insights, noting that observed high latitude freshening and warming have ''very likely'' made the surface ocean less dense with a stratification increase of between 2.18% and 2.42% from 1970 to 2017 ( [[#Bindoff--2019|Bindoff et al., 2019]] ). A recent observational analysis has expanded on these assessments, suggesting a very marked summertime density contrast enhancement across the mixed layer base of 6.2–11.6% per decade, driven by changes in temperature and salinity, which is more than six times larger than previous estimates ( [[#Sallée--2021|Sallée et al., 2021]] ). An idealized ocean modelling study suggests that the enhanced stratification can account for a third of the salinity enhancement signal since 1990 ( [[#Zika--2018|Zika et al., 2018]] ). Thus, there has been an expansion of observed global- and basin-scale salinity change assessment literature since AR5, with many new studies reproducing the key patterns of long-term salinity change reported in AR5 ( [[#Rhein--2013|Rhein et al., 2013]] ), and linking these through modelling studies to coincident changes in evaporation–precipitation patterns at the ocean surface (Sections 2.3.1.3, 3.3.2, 8.2.2.1 and 9.2.2).

Unlike SSTs, simulated sea surface salinity (SSS) does not provide a direct feedback to the atmosphere. However, some recent work has identified indirect radiative feedbacks through sea-salt aerosol interactions ( [[#Ayash--2008|Ayash et al., 2008]] ; [[#Amiri-Farahani--2019|Amiri-Farahani et al., 2019]] ; [[#Wang--2019|]] [[#Wang--2019|Z. Wang et al., 2019]] ) that can act to strengthen tropical cyclones, and increase precipitation ( [[#Balaguru--2012|Balaguru et al., 2012]] , [[#Balaguru--2016|2016]] ; [[#Grodsky--2012|Grodsky et al., 2012]] ; [[#Reul--2014|Reul et al., 2014]] ; [[#Jiang--2019|Jiang et al., 2019]] ). The absence of a direct feedback is one of the primary reasons why salinity simulation is difficult to constrain in ocean modelling systems, and why deviations from the observed near-surface salinity mean state between models and observations are often apparent ( [[#Durack--2012|Durack et al., 2012]] ; [[#Shi--2017|Shi et al., 2017]] ).

<div id="3.5.2.1" class="h3-container"></div>

<span id="sea-surface-and-depth-profile-salinity-evaluation"></span>
==== 3.5.2.1 Sea Surface and Depth-profile Salinity Evaluation ====

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When compared to the routine assessment of simulated SST, simulated SSS has not received the same research attention at global- to basin-scales. For CMIP3, there was reasonable agreement between the basin-scale patterns of salinity, with a comparatively fresher Pacific when contrasted to the salty Atlantic, and basin salinity maxima features aligning well with the corresponding atmospheric evaporation minus precipitation field ( [[#Durack--2012|Durack et al., 2012]] ). Similar features are also reproduced in CMIP5 along with realistic variability in the upper layers, but less variability than observations at 300 m and deeper, especially in the poorly sampled Antarctic region ( [[#Pierce--2012|Pierce et al., 2012]] ). In a regional study, only considering the Indian Ocean, CMIP5 SSS was assessed and it was shown that model biases were primarily linked to biases in the precipitation field, with ocean circulation biases playing a secondary role ( [[#Fathrio--2017a|Fathrio et al., 2017a]] ). The sea surface salinity bias in CMIP6 models is shown in Figure 3.23b.

For the first time in AR5, alongside global zonal mean temperature, global zonal mean salinity bias with depth was assessed for the CMIP5 models. This showed a strong upper ocean (&lt;300 m) negative salinity (fresh) bias of order 0.3 PSS-78, with a tendency toward a positive salinity (salty) bias (&lt;0.25 PSS-78) in the Northern Hemisphere intermediate layers (200–3000 m) ( [[#Flato--2013|Flato et al., 2013]] ). These biases are also present in CMIP6, albeit with slightly smaller magnitudes (Figure 3.25). Here we expand the global zonal mean bias assessment to consider the three independent ocean basins individually, which allows for an assessment as to which basin biases are dominating the global zonal mean. The basin with the most pronounced biases is the Atlantic, with a strong upper ocean (&lt;300 m) fresh bias, of order 0.3 PSS-78 just like the global zonal mean, and a marked subsurface salinity bias that exceeds 0.5 PSS-78 in equatorial waters between 400–1000 m.

The Pacific Ocean shares the strongest similarity to the global bias, with a similar upper ocean (&lt;300 m) fresh bias. Lower magnitude positive salinity biases (about 0.3 PSS-78) are also present in both hemispheres between 200 and 3000 m, and deeper in the Southern Hemisphere (Figure 3.25). The Indian Ocean shows similar features to the Southern Hemisphere Pacific, with a marked upper ocean (&lt;500 m) fresh bias of order 0.3 PSS-78, and a strong near-surface positive bias of order 0.4 PSS-78 associated with the Arabian Sea (Figure 3.25).

For the Southern Ocean in CMIP5, considerable fresh biases exist through the water column, and are most pronounced in the ventilated layers representing the subtropical mode and intermediate water masses ( [[#Sallée--2013|Sallée et al., 2013]] ). A fresh bias in upper and intermediate layers of comparable magnitude is also seen in CMIP6 (Figure 3.25). The structure of the biases in the CMIP6 multi-model mean (which averages across many simulations with differing subsurface geographies and differing Southern Ocean salinity biases ( [[#Beadling--2020|Beadling et al., 2020]] )) is similar to that evident in the CMIP5 multi-model mean, but with slightly smaller magnitudes. The Arctic Ocean also on average exhibits a surface-enhanced fresh bias in the upper ocean (Figure 3.25), which is much larger than its Southern Hemisphere counterpart.

In summary, the structure of the salinity biases in the multi-model mean has not changed substantially between CMIP5 and CMIP6 ( ''medium confidence'' ), though there is ''limited evidence'' that the magnitude of subsurface biases has been reduced. Biases are sufficiently small to provide confidence in the utility of CMIP-class models for detection and attribution of ocean salinity.

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<span id="salinity-change-attribution"></span>
==== 3.5.2.2 Salinity Change Attribution ====

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AR5 concluded that it was ''very likely'' that anthropogenic forcings had made a discernible contribution to surface and subsurface ocean salinity changes since the 1960s ( [[#Bindoff--2013|Bindoff et al., 2013]] ; [[#Rhein--2013|Rhein et al., 2013]] ). It highlighted that the spatial patterns of salinity trends, and the mean fields of salinity and evaporation minus precipitation are all similar, with an enhancement to Atlantic Ocean salinity and freshening in the Pacific and Southern Oceans. Since AR5 all subsequent work on assessing observed and modelled salinity changes has confirmed these results.

Considerable changes to observed broad- or basin-scale ocean near-surface salinity fields have been reported (see [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.2|Section 2.3.3.2]] ), and these have been linked to changes in the evaporation minus precipitation patterns at the ocean surface through model simulations, typically expressing a pattern of change where climatological mean fresh regions become fresher and corresponding salty regions becoming saltier ( [[#Durack--2012|Durack et al., 2012]] , [[#Durack--2013|2013]] ; [[#Zika--2015|Zika et al., 2015]] ; [[#Lago--2016|Lago et al., 2016]] ; [[#Skliris--2016|Skliris et al., 2016]] , [[#Skliris--2018|2018]] ; [[#Cheng--2020|Cheng et al., 2020]] ), also broadly present in the CMIP6 multi-model mean (Figure 3.27). At basin-scales, the depth-integrated effect of mean salinity changes as captured in halosteric sea level for the top 0 to 2000 m has also been assessed based on observational products, and these results mirror near-surface patterns in the CMIP5 and CMIP6 models, with most areas that are becoming fresher at the surface exhibiting increases in halosteric sea level, and areas becoming saltier exhibiting decreases ( [[#Durack--2014a|Durack et al., 2014a]] ; Figure 3.28). Further investigations using observations and models together have tied the long-term patterns of surface and subsurface salinity changes to coincident changes to the evaporation minus precipitation field over the ocean ( [[#Durack--2012|Durack et al., 2012]] , [[#Durack--2013|2013]] ; [[#Durack--2015|Durack, 2015]] ; [[#Levang--2015|Levang and Schmitt, 2015]] ; [[#Zika--2015|Zika et al., 2015]] , [[#Zika--2018|2018]] ; [[#Grist--2016|Grist et al., 2016]] ; [[#Lago--2016|Lago et al., 2016]] ; [[#Cheng--2020|Cheng et al., 2020]] ), however the rate of these changes through time continues to be an active area of active research ( [[#Skliris--2014|Skliris et al., 2014]] ; [[#Zika--2015|Zika et al., 2015]] , 2018; [[#Cheng--2020|Cheng et al., 2020]] ; [[#Sallée--2021|Sallée et al., 2021]] ).

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[[File:d595a2eb3fab5e2e6b766e28c0048d42 IPCC_AR6_WGI_Figure_3_27.png]]

Figure 3.27 | '''Maps of multi-decadal salinity trends for the near-surface''' '''ocean.''' Units are Practical Salinity Scale 1978 [PSS-78] per decade. '''(Top)''' The best estimate ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.2|Section 2.3.3.2]] ) observed trend (1950 – 2019, [[#Durack--2010|Durack and Wijffels, 2010]] ). '''(Bottom)''' Simulated trend from the CMIP6 historical experiment multi-model mean (1950–2014). Black contours show the climatological mean salinity in increments of 0.5 PSS-78 (thick lines 1 PSS-78). Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

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[[File:802053c0a35a550a20c5b3cc04ca9b87 IPCC_AR6_WGI_Figure_3_28.png]]

Figure 3.28 | '''Long-term trends in halosteric and thermosteric sea level in CMIP6 models and observations.''' Units are mm yr <sup>–1</sup> . In The '''right-hand column''' , three observed maps of 0 to 2000 m halosteric sea level trends are shown: '''top (D&amp;W)''' from [[#Durack--2010|Durack and Wijffels (2010)]] , 1950–2019, updated; '''upper-middle (EN4)''' from [[#Good--2013|Good et al. (2013)]] , 1950–2019, updated; and '''lower-middle (Ishii)''' from [[#Ishii--2017|Ishii et al. (2017)]] , 1955–2019, updated. '''Bottom-right:''' the CMIP6 historical multi-model mean (1950–2014). Red and orange colours show a halosteric contraction (enhanced salinity) and blue and green a halosteric expansion (reduced salinity). In The '''left-hand column''' , basin-integrated halosteric '''(top)''' and thermosteric '''(bottom)''' trends for the Atlantic and Pacific, the two largest ocean basins, are shown, where Pacific anomalies are presented on the x-axis and Atlantic on the y-axis. Observational estimates are presented in black, CMIP6 historical (all forcings) simulations are shown in orange squares, with the multi-model mean shown as a dark orange diamond with a black bounding box. CMIP6 hist-nat (historical natural forcings only) simulations are shown in green squares with the multi-model mean as a dark green diamond with a black bounding box. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

Climate change detection and attribution studies have considered salinity, with the first of these assessed in AR5 ( [[#Bindoff--2013|Bindoff et al., 2013]] ). Since that time, the positive detection conclusions ( [[#Stott--2008|Stott et al., 2008]] ; [[#Pierce--2012|Pierce et al., 2012]] ; [[#Terray--2012|Terray et al., 2012]] ) have been supported by a number of more recent and independent assessments which have reproduced the multi-decadal basin-scale patterns of change in observations and models (Figures 3.27 and 3.28; [[#Durack--2014a|Durack et al., 2014a]] ; [[#Durack--2015|Durack, 2015]] ; [[#Levang--2015|Levang and Schmitt, 2015]] ; [[#Skliris--2016|Skliris et al., 2016]] ). Observed depth-integrated basin responses, contrasting the Pacific and Atlantic basins (freshening Pacific and enhanced salinity Atlantic) were also shown to be replicated in most historical (natural and anthropogenically forced) simulations, with this basin contrast absent in CMIP5 and CMIP6 natural-only simulations that exclude anthropogenic forcing ( [[#Durack--2014a|Durack et al., 2014a]] ; Figure 3.28).

While observational sparsity considerably limits quantification of regional changes, a recent study by [[#Friedman--2017|Friedman et al. (2017)]] assessed salinity changes in the Atlantic Ocean from 1896 to 2013 and confirmed the pattern of mid-to-low latitude enhanced salinity and high latitude North Atlantic freshening over this period exists even after accounting for the effects of the NAO and AMO.

Considering the bulk of evidence, it is ''extremely likely'' that human influence has contributed to observed near-surface and subsurface salinity changes across the globe since the mid-20th century. All available multi-decadal assessments have confirmed that the associated pattern of change corresponds to fresh regions becoming fresher and salty regions becoming saltier ( ''high confidence'' ). CMIP5 and CMIP6 models are only able to reproduce these patterns in simulations that include greenhouse gas increases ( ''medium confidence'' ). Changes to the coincident atmospheric water cycle and ocean-atmosphere fluxes (evaporation and precipitation) are the primary drivers of the basin-scale observed salinity changes ( ''high confidence'' ). This result is supported by all available observational assessments, along with a growing number of climate modelling studies targeted at assessing ocean and water cycle changes. The basin-scale changes are consistent across models and intensify on centennial scales from the historical period through to the projections of future climate ( ''high confidence'' ).

<div id="3.5.3" class="h2-container"></div>

<span id="sea-level"></span>
=== 3.5.3 Sea Level ===

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In keeping with the scope of this chapter, this section addresses global and basin-scale sea level changes, whereas regional and local sea level changes are assessed in Section 9.6. In AR5, the observed sea level budget was closed by considering all contributing factors including ocean warming, mass contributions from terrestrial storage, glaciers, and the Antarctic and Greenland ice sheets ( [[#Church--2013b|Church et al., 2013b]] ). The SROCC found that the observed global mean sea level (GMSL) rise is consistent within uncertainties with the sum of the estimated observed contributions for 1993–2015 and 2006–2015.

<div id="3.5.3.1" class="h3-container"></div>

<span id="sea-level-evaluation"></span>
==== 3.5.3.1 Sea Level Evaluation ====

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The current generation of climate models do not fully resolve many of the components required to close the observed sea level budget, such as glaciers, ice sheets and land water storage (see Section 9.6 and Box 9.1). Consequently, most CMIP-based analyses of sea level change have focused on thermosteric sea level changes (i.e., thermal expansion due to warming) and ocean dynamic sea level change, both of which are simulated in the CMIP5-generation of models. The improved agreement between modelled thermal expansion and observed estimates during the historical period led the SROCC to assess a ''high confidence'' level in the simulated thermal expansion using climate models and ''high confidence'' in their ability to project future thermal expansion.

Since CMIP5 models do not include all necessary components of sea level change, this gap has been bridged by using offline models (for glacier melt and ice-sheet surface mass balance) driven by reanalyses and model output. Some studies have used offline mass inputs to account for dynamic ice-sheet and terrestrial contributions. [[#Slangen--2017|Slangen et al. (2017)]] and [[#Meyssignac--2017|Meyssignac et al. (2017)]] suggested including corrections to several contributions to sea level changes including to the Greenland surface mass balance and glacier contributions, based on differences between CMIP5-driven model results and reanalysis-driven results. This helps close the gap between models and observations for the 20th century globally, as well as providing better agreement with tide gauge observations in terms of interannual and multi-decadal variability at the regional scale.

In CMIP6, ice sheets (see Sections 3.4.3.2 and 9.4) are included for the first time in ISMIP6 ( [[#Nowicki--2016|Nowicki et al., 2016]] ). There is also scope for new insights into terrestrial water contributions from land surface (and sub-surface) modelling in the Land Surface, Snow and Soil moisture Model Intercomparison Project (LS3MIP; [[#van%20den%20Hurk--2016|van den Hurk et al., 2016]] ). In parallel, the GlacierMIP project ( [[#Hock--2019a|Hock et al., 2019a]] ; [[#Marzeion--2020|Marzeion et al., 2020]] ; see Sections 3.4.3.1 and 9.5) is also underway, and has provided more quantitative guidance and a comprehensive assessment of the uncertainties and best estimates of the current and future contributions of glaciers to the sea level budget.

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<span id="sea-level-change-attribution"></span>
==== 3.5.3.2 Sea Level Change Attribution ====

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The SROCC concluded with ''high confidence'' that the dominant cause of GMSL rise since 1970 is anthropogenic forcing. Prior to that, AR5 had concluded that it is ''very likely'' that there has been a substantial contribution from anthropogenic forcings to GMSL rise since the 1970s. Since AR5, several studies have identified a human contribution to observed sea level change resulting from a warming climate as manifest in thermosteric sea level change and the contribution from melting glaciers and ice sheets.

For the global mean thermosteric sea level change, [[#Slangen--2014|Slangen et al. (2014)]] showed the importance of anthropogenic forcings (combined greenhouse gas and aerosol forcings) for explaining the magnitude of the observed changes between 1957 and 2005, considering the full depth of the ocean and natural forcings in order to capture the variability (see also Figure 3.29). Over the 1950–2005 period, [[#Marcos--2014|Marcos and Amores (2014)]] found that human influence explains 87% of the 0–700 m global thermosteric sea level rise. Both thermosteric and regional dynamic patterns of sea level change in individual forcing experiments from CMIP5 were considered by [[#Slangen--2015|Slangen et al. (2015)]] who showed that responses to anthropogenic forcings are significantly different from both internal variability and inter-model differences and that although greenhouse gas and anthropogenic aerosol forcings produce opposite GMSL responses, there are differences in the response on regional scales. Based on these studies, we conclude that it is ''very likely'' that anthropogenic forcing was the main driver of the observed global mean thermosteric sea level change since 1970.

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[[File:35bad91d2d74122df9a5fba37539908f IPCC_AR6_WGI_Figure_3_29.png]]

'''Figure 3.29 |''' '''Simulated and observed global mean sea level change due to thermal expansion for CMIP6 models and observations relative to the baseline period 1850–1900.''' Historical simulations are shown in brown, natural only in green, greenhouse gas only in grey, and aerosol only in blue (multi-model means shown as thick lines, and shaded ranges between the 5th and 95th percentile). The best estimate observations (black solid line) for the period of 1971–2018, along with ''very likely'' ranges (black shading) are from [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.1|Section 2.3.3.1]] and are shifted to match the multi-model mean of the historical simulations for the 1995–2014 period. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

In an attribution study of the sea-level contributions of glaciers, [[#Marzeion--2014|Marzeion et al. (2014)]] found that between 1991 and 2010, the anthropogenic fraction of global glacier mass loss was 69 ± 24% (see also [[#3.4.3.1|Section 3.4.3.1]] ). [[#Slangen--2016|Slangen et al. (2016)]] considered all quantifiable components of the GMSL budget and showed that anthropogenically forced changes account for 69 ± 31% of the observed sea level rise over the period 1970 to 2005, whereas natural forcings combined with internal variability have a much smaller effect – only contributing 9 ± 18% of the change over the same period. These studies indicate that about 70% of the combined change in glaciers, ice-sheet surface mass balance and thermal expansion since 1970 can be attributed to anthropogenic forcing, and that this percentage has increased over the course of the 20th century. Detection studies on GMSL change in the 20th century ( [[#Becker--2014|Becker et al., 2014]] ; [[#Dangendorf--2015|Dangendorf et al., (2015)]] found that observed total GMSL change in the 20th century was inconsistent with internal variability. [[#Dangendorf--2015|Dangendorf et al. (2015)]] determined that for 1900 to 2011 at least 45% of GMSL change is human-induced. A study that developed a semi-empirical model to link sea-level change to observed GMST change concluded that at least 41% of the 20th century sea-level rise would not have happened in the absence of the century’s increasing GMST and that there was a 95% probability that by 1970 GMSL was higher than that which would have occurred in the absence of increasing GMST ( [[#Kopp--2016|Kopp et al., 2016]] ). [[#Richter--2020|Richter et al. (2020)]] compared modelled sea level change with the satellite altimeter observations from 1993 to 2015; a period short enough that internal variability can dominate the spatial pattern of change. They found that when GMSL is not removed, model simulated zonally averaged sea level trends are consistent with altimeter observations globally as well as in each ocean basin and much larger than might be expected from internal variability. Using spatial correlation, [[#Fasullo--2018|Fasullo and Nerem (2018)]] showed that the satellite altimeter trend pattern is already detectable.

We note that current detection and attribution studies do not yet include all processes that are important for sea-level change (see Section 9.6). However, based on the body of literature available, we conclude that the main driver of the observed GMSL rise since at least 1971 is ''very likely'' anthropogenic forcing. The assessed period starts in 1971 for consistency with observations assessed in Cross-Chapter Box 9.1.

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<span id="ocean-circulation"></span>
=== 3.5.4 Ocean Circulation ===

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Circulation of the ocean, whether it be wind or density driven, plays a prominent role in the heat and freshwater transport of the Earth system ( [[#Buckley--2016|Buckley and Marshall, 2016]] ). Thus, its accurate representation is crucial for the realistic representation of water mass properties, and replication of observed changes driven by atmosphere-land-ocean coupling. Here, we assess the ability of CMIP models to reproduce the observed large-scale ocean circulation, along with assessment of the detection and attribution of any anthropogenically-driven changes. We also note that the process-based understanding of these circulation changes and circulation changes occurring at smaller scales is assessed in Section 9.2.3.

<div id="3.5.4.1" class="h3-container"></div>

<span id="atlantic-meridional-overturning-circulation-amoc"></span>
==== 3.5.4.1 Atlantic Meridional Overturning Circulation (AMOC) ====

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The Atlantic Meridional Overturning Circulation (AMOC) represents a large-scale flow of warm salty water northward at the surface and a return flow of colder water southward at depth. As such, its mean state plays an important role in transporting heat in the climate system, while its variability can act to redistribute heat (see Sections 2.3.3.4.1 and 9.2.3.1 for more details). Paleo-climatic and model evidence suggest that changes in AMOC strength have played a prominent role in past transitions between warm and cool climatic phases (e.g., [[#Dansgaard--1993|Dansgaard et al., 1993]] ; [[#Ritz--2013|Ritz et al., 2013]] ).

The AR5 concluded that while climate models suggested that an AMOC slowdown would occur in response to anthropogenic forcing, the short direct observational AMOC record precluded it from being used to support this model finding. [[IPCC:Wg1:Chapter:Chapter-2|Chapter 2]] reports with ''high'' ''confidence'' , a weakening of the AMOC was observed in the mid-2000s to the mid-2010s, while again also noting that the observational record was too short to determine whether this is a significant trend or a manifestation of decadal and multi-decadal variability ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.4.1|Section 2.3.3.4.1]] ). Indirect evidence of AMOC weakening since at least the 1950s is also presented, but confidence in this longer-term decrease was ''low'' [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.4.1|Section 2.3.3.4.1]] ).

Despite the additional six years or so of observations since AR5, the evaluation of the AMOC in models continues to be severely hampered by the geographically sparse and temporally short observational record. The longest continuous observational estimates of the AMOC are based on measurements taken at 26°N by the RAPID-MOCHA array ( [[#Smeed--2018|Smeed et al., 2018]] ). Basic evaluation of the AMOC at 26°N shows that the CMIP5 and CMIP6 multi-model mean overturning strength is comparable with RAPID ( [[#Reintges--2017|Reintges et al., 2017]] ; [[#Weijer--2020|Weijer et al., 2020]] ), but the model range is large (12–29 sverdrups (Sv)) for CMIP5 ( [[#Zhang--2013|Zhang and Wang, 2013]] ); and 10–31 Sv for CMIP6 ( [[#Weijer--2020|Weijer et al., 2020]] ) (Figure 3.30a). It is noted that deviations of AMOC strength in CMIP5 models have been related to global-scale sea surface temperature biases ( [[#Wang--2014|]] [[#Wang--2014|C. Wang et al., 2014]] ). Both coupled and ocean-only models also underestimate the depth of the AMOC cell ( [[#Danabasoglu--2014|Danabasoglu et al., 2014]] ; [[#Weijer--2020|Weijer et al., 2020]] ; Figure 3.30a). Paleo-climatic evidence has also raised questions regarding the accuracy of the representation of the strength and depth of the modelled AMOC during past periods ( [[#Otto-Bliesner--2007|Otto-Bliesner et al., 2007]] ; [[#Muglia--2015|Muglia and Schmittner, 2015]] ). Overall, however, both the CMIP5 and CMIP6 model ensembles simulate the general features of the AMOC mean state reasonably well, but there is a large spread in the latitude and depth of the maximum overturning, and the maximum AMOC strength (Figure 3.30a).

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[[File:0f7a5f938dc405d7e735382d89fd7f4b IPCC_AR6_WGI_Figure_3_30.png]]

Figure 3.30 | '''Observed and CMIP6 simulated AMOC mean state, variability and long-term trends. (a)''' AMOC meridional stream function profiles at 26.5°N from the historical CMIP5 (1860–2004) and CMIP6 (1860–2014) simulations compared with the mean maximum overturning depth (horizontal grey line) and magnitude (vertical grey line) from the RAPID observations (2004–2018). The distributions of model ranges of AMOC maximum magnitude and depth are respectively displayed near the x- and y-axis. '''(b)''' Distributions of overlapping eight-year AMOC trends from individual CMIP6 historical simulations (pink box plots) are plotted along with the combined distributions of all available CMIP5 (blue boxplot) and CMIP6 (red boxplot) models. For reference, the observed eight-year trend calculated between 2004 and 2012 is also shown as a horizontal grey line (following [[#Roberts--2014|Roberts et al., 2014]] ). '''(c)''' Distributions of interannual AMOC variability from individual CMIP6 model historical simulations, along with the combined distributions of all available CMIP5 and CMIP6 models. Interannual variability in models and observations is estimated as annual mean (April–March) differences, and the horizontal grey line is the observed value for 2009/2010 minus 2008/2009 (following [[#Roberts--2014|Roberts et al., 2014]] ). '''(d–f)''' Distributions of linear AMOC trends calculated over various time periods (see panel titles) in CMIP6 simulations forced with: greenhouse gas forcing only (GHG), natural forcing only (NAT), anthropogenic aerosol forcing only (AER) and all forcing combined (Historical; HIST). (a–f) Boxes indicate the 25th to 75th percentile range, whiskers indicate 1st and 99th percentiles in (a-c) and 5th and 95th percentile in (d-f), and dots indicate outliers, while the horizontal black line is the multi-model mean trend. In (d–f) the multi-model mean trend is also written above each distribution. The multi-model distributions in (a–c) were produced with one historical ensemble member per model for which the AMOC variable was available (listed), while those in (d–f) were produced with the detection and attribution simulation datasets utilized by [[#Menary--2020|Menary et al. (2020)]] . Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

The short length of the observed time-series (RAPID has measured the AMOC since 2004), sparse observations, observational uncertainties ( [[#Sinha--2018|Sinha et al., 2018]] ), as well as significant observed variability on interannual and longer time scales, makes comparison with modelled AMOC variability challenging. RAPID observations show that the overturning at 26°N was 2.9 Sv weaker in the multi-year average of 2008–2012 relative to 2004–2008 and 2.5 Sv weaker in 2012–2017 relative to 2004–2008 ( [[#Smeed--2014|Smeed et al., 2014]] , [[#Smeed--2018|2018]] ) (see also ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.4.1|Section 2.3.3.4.1]] ). As expected, this weakening was accompanied by a significant reduction in northward heat transport ( [[#Bryden--2020|Bryden et al., 2020]] ). CMIP5 and CMIP6 models produce a forced weakening of the AMOC over the 2012–2017 period relative to 2004–2008, but at 26°N the multi-model mean response is substantially weaker than the observed AMOC decline over the same period. The discrepancy between the modelled multi-model mean (i.e., the forced response) and the RAPID observed AMOC changes has led studies to suggest that the observed weakening over 2004–2017 is largely due to internal variability ( [[#Yan--2018|Yan et al., 2018]] ). However, comparison of observed RAPID AMOC variability with modelled variability also reveals that most CMIP5 models appear to underestimate the interannual and decadal time scale AMOC variability ( [[#Roberts--2014|Roberts et al., 2014]] ; [[#Yan--2018|Yan et al., 2018]] ), and, although the overall variance is larger in CMIP6 than in CMIP5, similar results are found analysing the CMIP6 models (Figure 3.30b,c). It is currently unknown why most models underestimate this AMOC variability, or whether they are underestimating the internal or externally forced components. This underestimation of AMOC variability may also have potential implications for detection and attribution, the relationship between AMOC and AMV (see [[#3.7.7|Section 3.7.7]] ), and near-term predictions. There is also emerging evidence, based on analysis of freshwater transports, that the AMOC in CMIP5-era models is too stable, largely due to systematic biases in ocean salinity (W. [[#Liu--2017|]] [[#Liu--2017|Liu et al., 2017]] ; [[#Mecking--2017|Mecking et al., 2017]] ). Such a systematic bias may potentially be linked with the underestimation of both simulated AMOC internal variability through eddy-mean flow interactions that are poorly represented in standard CMIP-class model resolution ( [[#Leroux--2018|Leroux et al., 2018]] ), and externally forced change.

As reported in [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.4.1|Section 2.3.3.4.1]] , estimates of AMOC since at least 1950, which are generated from observed surface temperatures or sea surface height, suggest the AMOC weakened through the 20th century ( ''low confidence'' ) ( [[#Ezer--2013|Ezer et al., 2013]] ; [[#Caesar--2018|Caesar et al., 2018]] ). Over the same period, the CMIP5 multi-model mean showed no significant net forced response in AMOC ( [[#Cheng--2013|Cheng et al., 2013]] ). However, a significant forced change is simulated in the CMIP6 multi-model mean, where a clear increase of the AMOC is seen over the 1940–1985 period (Figure 3.30e; [[#Menary--2020|Menary et al., 2020]] ). Although there is general agreement that the influence of greenhouse gases acts to a weaken the modelled AMOC ( [[#Delworth--2006|Delworth and Dixon, 2006]] ; [[#Caesar--2018|Caesar et al., 2018]] ), changes in solar, volcanic and anthropogenic aerosol emissions can lead to temporary changes in AMOC on decadal- to multi-decadal time scales ( [[#Delworth--2006|Delworth and Dixon, 2006]] ; [[#Menary--2013|Menary et al., 2013]] ; [[#Menary--2014|Menary and Scaife, 2014]] ; [[#Swingedouw--2017|Swingedouw et al., 2017]] ; [[#Undorf--2018b|Undorf et al., 2018b]] ). As such, the simulated net forced response in AMOC is a balance between the different forcing factors (Section 9.2.3.1; [[#Delworth--2006|Delworth and Dixon, 2006]] ; [[#Menary--2020|Menary et al., 2020]] ). The differing AMOC response of CMIP5 and CMIP6 models during the historical period has been associated with stronger aerosol effective radiative forcing in the CMIP6 models ( [[#Menary--2020|Menary et al., 2020]] ), such that the aerosol-induced AMOC increase during the 1940–1985 period overcomes the greenhouse gas induced decline (Figure 3.30e). However, models simulate a range of anthropogenic aerosol effective radiative forcing and a range of historical AMOC trends in CMIP6 ( [[#Menary--2020|Menary et al., 2020]] ) and there remains considerable uncertainty over the realism of the CMIP6 AMOC response during the 20th century (Figure 3.30d–f) due to disagreement among the differing lines of evidence. For example, ocean reanalysis ( [[#Jackson--2019|Jackson et al., 2019]] ) and forced ocean model simulations ( [[#Robson--2012|Robson et al., 2012]] ; [[#Danabasoglu--2016|Danabasoglu et al., 2016]] ), which show AMOC changes that are broadly consistent with the CMIP6 response, appear to disagree with observational estimates of AMOC over the historical period ( [[#Ezer--2013|Ezer et al., 2013]] ; [[#Caesar--2018|Caesar et al., 2018]] ). It is noted, however, that the relatively short length of the forced ocean simulations and ocean reanalysis precludes a comparable assessment of 20th century trends. Furthermore, despite the similar AMOC evolution seen in forced ocean model simulations and the CMIP6 models, it is unclear whether the same underlying mechanisms are responsible for the changes.

In summary, models do not support robust assessment of the role of anthropogenic forcing in the observed AMOC weakening between the mid-2000s and the mid-2010s, which is assessed to have occurred with ''high confidence'' in [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.4.1|Section 2.3.3.4.1]] , as the changes are outside of the range of modelled AMOC trends (regardless of whether they are forced or internally generated) in most models. Thus, we have ''low confidence'' that anthropogenic forcing has influenced the observed changes in AMOC strength in the post-2004 period. In addition, there remains considerable uncertainty over the realism of the CMIP6 AMOC response during the 20th century due to disagreement among the differing lines of observational and modelled evidence (i.e., historical AMOC estimates, ocean reanalysis, forced ocean simulations and historical CMIP6 simulations). Thus, we have ''low confidence'' that anthropogenic forcing has had a significant influence on changes in AMOC strength during the 1860–2014 period.

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==== 3.5.4.2 Southern Ocean Circulation ====

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The Southern Ocean circulation provides the principal connections between the world’s major ocean basins through the circulation of the Antarctic Circumpolar Current (ACC), while also largely controlling the connection between the deep and upper layers of the global ocean circulation, through its upper and lower overturning cells.

The assessment of observations presented in Sections 2.3.3.4.2 and 9.2.3.2 reports that there is no evidence of an ACC transport change, and it is ''unlikely'' that the mean meridional position of the ACC has moved southward in recent decades (Sections 2.3.3.4.2 and 9.2.3.2). This is despite observations of surface wind displaying an intensification and southward shift ( [[IPCC:Wg1:Chapter:Chapter-2#2.4.1.2|Section 2.4.1.2]] ). There is ''low confidence'' in an observed intensification of upper ocean overturning in the Southern Ocean and there is ''medium confidence'' for a slowdown of the Antarctic Bottom Water circulation and commensurate Antarctic Bottom Water volume decrease since the 1990s (Section 9.2.3.2). Section 9.2.3.2 presents new evidence, since SROCC, which assessed with ''medium confidence'' that the lower cell can episodically increase as a response to climatic anomalies, temporally counteracting the forced tendency for reduced bottom water formation.

The modelled strength of the ACC clearly improved from CMIP3, in which the models tended to underestimate the strength of the ACC, to CMIP5 ( [[#Meijers--2012|Meijers et al., 2012]] ). This improvement in the realism of ACC strength continues from CMIP5 to CMIP6, with the modelled ACC strength converging toward the magnitude of observed estimates of net flow through the Drake Passage ( [[#Beadling--2020|Beadling et al., 2020]] ). There is, however, a small number of models that still display an ACC that is much weaker than that observed, while several models also display much more pronounced ACC decadal variability than that observed ( [[#Beadling--2020|Beadling et al., 2020]] ). The increased realism of the ACC was at least partly related to noted improvements in all metrics of the Southern Ocean’s surface wind stress forcing ( [[#Beadling--2020|Beadling et al., 2020]] ). The most notable wind stress forcing improvements were found in the strength and the latitudinal position of the zonally-averaged westerly wind stress maximum ( [[#Beadling--2020|Beadling et al., 2020]] ; [[#Bracegirdle--2020|Bracegirdle et al., 2020]] ). While the two-cell structure of the overturning circulation appears to be well captured by CMIP5 models ( [[#Sallée--2013|Sallée et al., 2013]] ; [[#Russell--2018|Russell et al., 2018]] ), they tend to underestimate the intensity of the lower cell overturning, and overestimate the intensity of the upper cell overturning ( [[#Sallée--2013|Sallée et al., 2013]] ). As the lower overturning cell is closely related to Antarctic Bottom Water formation and deep convection, both fields also display substantial errors in CMIP5 models ( [[#Heuzé--2013|Heuzé et al., 2013]] , [[#Heuzé--2015|2015]] ). CMIP6 climate models show clear improvements compared to CMIP5 in their representation of Antarctic Bottom Water, which suggests an improved representation of the lower overturning cell ( [[#Heuzé--2021|Heuzé, 2021]] ).

Despite notable improvements of CMIP6 models compared to CMIP5 models, inherent limitations in the representation of important processes at play in the Southern Ocean’s horizontal and vertical circulation remain (Section 9.2.3.2). For instance, Southern Ocean mesoscale eddies are largely parameterized in the current generation of climate models and, despite their small spatial scales, they are a key element for establishing the ACC and upper overturning cell, as well as for their future evolution under changing atmospheric forcing ( [[#Kuhlbrodt--2012|Kuhlbrodt et al., 2012]] ; [[#Downes--2013|Downes and Hogg, 2013]] ; [[#Gent--2016|Gent, 2016]] ; [[#Downes--2018|Downes et al., 2018]] ; [[#Poulsen--2018|Poulsen et al., 2018]] ). The absence of ice-sheet coupling in the CMIP6 model suite is another important limitation, as basal meltwater and calving can influence the circulation, particularly the lower cell of the Southern Ocean ( [[#Bronselaer--2018|Bronselaer et al., 2018]] ; [[#Golledge--2019|Golledge et al., 2019]] ; [[#Lago--2019|Lago and England, 2019]] ; [[#Jeong--2020|Jeong et al., 2020]] ; [[#Moorman--2020|Moorman et al., 2020]] ). We note that early development of global climate models with interactive ice-shelf cavities has begun and is showing potential to be developed ( [[#Jeong--2020|Jeong et al., 2020]] ).

In summary, while there have been improvements across successive CMIP phases (from CMIP3 to CMIP6) in the representation of the Southern Ocean circulation, such that the mean zonal and overturning circulations of the Southern Ocean are now broadly reproduced, substantial observational uncertainty and climate model challenges preclude attribution of Southern Ocean circulation changes ( ''high confidence'' ).

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