Editing IPCC:AR6/WGI/Chapter-4 (section)

== 4.3 Projected Changes in Global Climate Indices in the 21st Century ==

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This section assesses the latest simulations of representative indicators of global climate change presented as time series and tabulated values over the 21st century and across the main realms of the global climate system. In the atmospheric realm ( [[#4.3.1|Section 4.3.1]] ), we assess simulations of GSAT (Figure 4.2a) and global land precipitation (Figure 4.2b). Across the cryospheric, oceanic, and biospheric realms ( [[#4.3.2|Section 4.3.2]] ), we assess simulations of Arctic SIA (Figure 4.2c), GMSL (Figure 4.2d), the AMOC, ocean and land carbon uptake, and pH. In ( [[#4.3.3|Section 4.3.3]] we assess simulations of several indices of climate variability, namely, the indices of the NAM, SAM, and ENSO. Finally, [[#4.3.4|Section 4.3.4]] assesses future GSAT change based on the CMIP6 ensemble in combination with other lines of evidence. An assessment of projected changes in related global extreme indices can be found in Chapter 11.

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[[File:0c7a2b1600e6473bc774ed3ee1199174 IPCC_AR6_WGI_Figure_4_2.png]]

'''Figure''' '''4.2 |''' '''Selected indicators of global climatechange from CMIP6 historical and scenario simulations. (a)''' Global surface air temperature changes relative to the 1995–2014 average (left axis) and relative to the 1850–1900 average (right axis; offset by 0.82°C, which is the multi-model mean and close to observed best estimate, Cross-Chapter Box 2.1, Table 1). '''(b)''' Global land precipitation changes relative to the 1995–2014 average. '''(c)''' September Arctic sea ice area. '''(d)''' Global mean sea level (GMSL) change relative to the 1995–2014 average. (a), (b) and (d) are annual averages, (c) are September averages. In (a–c), the curves show averages over the CMIP6 simulations, the shadings around the SSP1-2.6 and SSP3-7.0 curves show 5–95% ranges, and the numbers near the top show the number of model simulations used. Results are derived from concentration-driven simulations. In (d), the barystatic contribution to GMSL (i.e., the contribution from land-ice melt) has been added offline to the CMIP6 simulated contributions from thermal expansion (thermosteric). The shadings around the SSP1-2.6 and SSP3-7.0 curves show 5–95% ranges. The dashed curve is the ''low confidence'' and low likelihood outcome at the high end of SSP5-8.5 and reflects deep uncertainties arising from potential ice-sheet and ice-cliff instabilities. This curve at year 2100 indicates 1.7 m of GMSL rise relative to 1995–2014. More information on the calculation of GMSL is available in Chapter 9, and further regional details are provided in the Atlas. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

From the CMIP6 multi-model ensemble we consider historical simulations with observed external forcings to 2014 and extensions to 2100 based on the five high-priority scenarios. We use the first realization (‘r1’) contributed by each modelling group. In tabular form, we show ensemble-mean changes and uncertainties for the near-term (2021–2040), mid-term (2041–2060), and the long-term (2081–2100), relative to present-day (1995–2014) and the approximation to pre-industrial (1850–1900). Changes in precipitation over land near 1.5°C, 2.0°C, 3.0°C, and 4.0°C of global warming relative to 1850–1900 are also assessed.

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=== 4.3.1 Atmosphere ===

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==== 4.3.1.1 Surface Air Temperature ====

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The AR5 assessed from CMIP5 simulations and other lines of evidence that GSAT will continue to rise over the 21st century if greenhouse gas (GHG) concentrations continue increasing ( [[#Collins--2013|Collins et al., 2013]] ). The AR5 concluded that GSAT for 2081–2100, relative to 1986–2005 will ''likely'' be in the 5–95% range of 0.3°C–1.7°C under RCP2.6 and 2.6°C–4.8°C under RCP8.5. The corresponding ranges for the intermediate emissions scenarios with emissions peaking around 2040 (RCP4.5) and 2060 (RCP6.0) are 1.1°C–2.6°C and 1.4°C–3.1°C, respectively. The AR5 further assessed that GSAT averaged over the period 2081–2100 are projected to ''likely'' exceed 1.5°C above 1850–1900 for RCP4.5, RCP6.0 and RCP8.5 ( ''high confidence'' ) and are ''likely'' to exceed 2°C above 1850–1900 for RCP6.0 and RCP8.5 ( ''high confidence'' ). Global surface temperature changes above 2°C under RCP2.6 were deemed ''unlikely'' ( ''medium confidence'' ).

Here, for continuity’s sake, we assess the CMIP6 simulations of GSAT in a fashion similar to the AR5 assessment of the CMIP5 simulations. From these, we compute anomalies relative to 1995–2014 and display the evolution of ensemble means and 5–95% ranges (Figure 4.2). We also use the ensemble mean GSAT difference between 1850–1900 and 1995–2014, 0.82°C, to provide an estimate of the changes since 1850–1900 (Figure 4.2, right axis). Finally, we tabulate the ensemble mean changes between 1995–2014 and 2021–2040, 2041–2060, and 2081–2100 respectively (Figure 4.2).

The CMIP6 models show a 5–95% range of GSAT change for 2081–2100, relative to 1995–2014, of 0.6°C–2.0°C under SSP1-2.6 where CO <sub>2</sub> concentrations peak between 2040 and 2060 (see Table 4.2). The corresponding range under the highest overall emissions scenario (SSP5-8.5) is 2.7°C–5.7°C. The ranges for the intermediate and high emissions scenarios (SSP2-4.5 and SSP3-7.0), where CO <sub>2</sub> concentrations increase to 2100, but less rapidly than SSP5-8.5, are 1.4°C–3.0°C and 2.2°C–4.7°C, respectively. The range for the lowest emissions scenario (SSP1-1.9) is 0.2°C–1.3°C.

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'''Table''' '''4.2 |''' '''CMIP6 annual mean surface air temperature anomalies (°C).''' Displayed are multi-model averages and, in parentheses, the 5–95% ranges, for selected time periods, regions, and SSPs. The numbers of models used are indicated in Figure 4.2.

{| class="wikitable"
|-
| '''Time Period and Region'''

| '''SSP1-1.9 (°C)'''

| '''SSP1-2.6 (°C)'''

| '''SSP2-4.5 (°C)'''

| '''SSP3-7.0 (°C)'''

| '''SSP5-8.5 (°C)'''

|-
| '''Global: 2021–2040'''

Relative to 1995–2014

Relative to 1850–1900

| 0.7 (0.3, 1.1)

1.5 (1.1, 2.2)

| 0.7 (0.4, 1.1)

1.6 (1.1, 2.2)

| 0.7 (0.4, 1.2)

1.6 (1.0, 2.3)

| 0.7 (0.5, 1.2)

1.6 (1.0, 2.4)

| 0.8 (0.5, 1.3)

1.7 (1.2, 2.4)

|-
| '''Global: 2041–2060'''

Relative to 1995–2014

Relative to 1850–1900

| 0.8 (0.3, 1.5)

1.7 (1.1, 2.4)

| 1.0 (0.6, 1.6)

1.9 (1.2, 2.7)

| 1.3 (0.8, 1.9)

2.1 (1.5, 3.0)

| 1.4 (0.9, 2.3)

2.3 (1.6, 3.2)

| 1.7 (1.2, 2.5)

2.6 (1.8, 3.4)

|-
| '''Global: 2081–2100'''

Relative to 1995–2014

Relative to 1850–1900

| 0.7 (0.2, 1.5)

1.5 (1.0, 2.2)

| 1.2 (0.6, 2.0)

2.0 (1.3, 2.8)

| 2.0 (1.4, 3.0)

2.9 (2.1, 4.0)

| 3.1 (2.2, 4.7)

3.9 (2.8, 5.5)

| 4.0 (2.7, 5.7)

4.8 (3.6, 6.5)

|-
| Land: 2081–2100

Relative to 1995–2014

| 0.9 (0.3, 2.0)

| 1.5 (0.8, 2.6)

| 2.7 (1.7, 4.0)

| 4.1 (3.0, 6.2)

| 5.3 (3.5, 7.6)

|-
| Ocean: 2081–2100

Relative to 1995–2014

| 0.6 (0.1, 1.2)

| 1.0 (0.5, 1.8)

| 1.8 (1.2, 2.7)

| 2.7 (1.8, 4.0)

| 3.4 (2.3, 4.9)

|-
| Tropics: 2081–2100

Relative to 1995–2014

| 0.5 (0.1, 1.1)

| 1.0 (0.5, 1.6)

| 1.8 (1.2, 2.5)

| 2.7 (2.0, 4.0)

| 3.5 (2.4, 4.9)

|-
| Arctic: 2081–2100

Relative to 1995–2014

| 2.4 (0.5, 6.6)

| 3.3 (0.4, 7.5)

| 5.4 (2.8, 10.0)

| 7.7 (4.5, 13.4)

| 10.0 (6.2, 15.2)

|-
| Antarctic: 2081–2100

Relative to 1995–2014

| 0.5 (0.0, 1.1)

| 1.1 (0.1, 2.9)

| 1.9 (0.6, 3.2)

| 2.8 (1.3, 4.5)

| 3.6 (1.7, 5.6)

|}

In summary, the CMIP6 models show a general tendency toward larger long-term globally averaged surface warming than did the CMIP5 models, for nominally comparable scenarios ( ''very high confidence'' ). In SSP1-2.6 and SSP2-4.5, the 5–95% ranges have remained similar to the ranges in RCP2.6 and RCP4.5, respectively, but the distributions have shifted upward by about 0.3°C ( ''high confidence'' ). For SSP5-8.5 compared to RCP8.5, the 5% bound of the distribution has hardly changed, but the 95% bound and the range have increased by about 20% and 40%, respectively ( ''high confidence'' ). About half of the warming increase has occurred because of more models with higher climate sensitivity in CMIP6, compared to CMIP5; the other half of the warming increase arises from higher effective radiative forcing in nominally comparable scenarios ( ''medium confidence,'' see [[#4.6.2|Section 4.6.2]] ).

With regards to global warming levels (GWLs) of 1.5°C, 2.0°C and 3.0°C, we note that there is unanimity across all of the CMIP6 model simulations that GSAT change relative to 1850–1900 will rise above: (i) 1.5°C following SSP2-4.5, SSP3-7.0, or SSP5-8.5 (on average around 2030); (ii) 2.0°C following either SSP3-7.0 or SSP5-8.5 (on average around 2043); and (iii) 3.0°C following SSP5-8.5 (on average around 2062). Under SSP1-1.9, 55% and 36% of the model simulations rise above 1.5°C and 2.0°C, respectively, while for SSP1-2.6 those percentages increase to 87% and 58%, respectively. Here, the time of GSAT exceedance is determined as the first year at which 21-year running averages of GSAT exceed the given GWL. In ( [[#4.3.4|Section 4.3.4]] , these values are reassessed using CMIP6 ensemble in combination with other lines of evidence.

CMIP6 models project increases in area-weighted land, ocean, tropical (30°S–30°N), Arctic (67.7°N–90°N), and Antarctic (90°S–55°S) surface air temperature (Table 4.2). Consistent with AR5, and earlier assessments, CMIP6 models project that annual average surface air temperature will warm about 50% more over land than over the ocean, and that the Arctic will warm about more than 2.5 times the global average ( [[#4.5.1|Section 4.5.1]] ). For 2081–2100, relative to 1995–2014, the CMIP6 models show 5–95% ranges of warming over land of 0.3°C–2.0°C and 3.5°C–7.6°C following SSP1-1.9 and SSP5-8.5, respectively. The corresponding ranges for Arctic surface air temperature change are 0.5°C–6.6°C and 6.2°C–15.2°C, respectively.

The concentration-driven simulations presented above use a prescribed CO <sub>2</sub> pathway calculated by the MAGICC7.0 model using the CMIP6 emissions ( [[#Meinshausen--2020|Meinshausen et al., 2020]] ). This is compared here with the CO <sub>2</sub> concentration simulated by CMIP6 ESMs in response to the SSP5-8.5 emissions (Figure 4.3). The 1995–2014 mean simulated CO <sub>2</sub> level is 375 ppm, very similar to the prescribed 378 ppm, but the ESM 5–95% range is 357–391 ppm. By the end of the 21st century (2081–2100), the ESM mean is 953 ppm – below the prescribed CO <sub>2</sub> pathway (1004 ppm), but with a large 5–95% range of 848–1045 ppm, which spans the prescribed concentration level. This result differs from CMIP5, which showed that ESMs typically simulated CO <sub>2</sub> concentrations higher than the prescribed concentration-driven RCP pathways. Reduced spread in CMIP6 carbon cycle feedbacks compared to CMIP5 has been postulated to be due to the inclusion of nitrogen cycle processes in about half of CMIP6 ESMs ( [[#Arora--2020|Arora et al., 2020]] ). This means that the CMIP6 spread in GSAT response to CO <sub>2</sub> emissions is dominated by climate sensitivity differences between ESMs more than by carbon cycle differences ( ''high confidence'' ) ( [[#Jones--2020|Jones and Friedlingstein, 2020]] ; [[#Williams--2020|Williams et al., 2020]] ).

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[[File:4150ef8436b9e8c198170bcd4080d80f IPCC_AR6_WGI_Figure_4_3.png]]

'''Figure''' '''4.3 |''' '''Comparison ofconcentration-driven and emissions-driven simulation. (a)''' Atmospheric CO <sub>2</sub> concentration; '''(b)''' global surface air temperature from models which performed SSP5-8.5 scenario simulations in both emissions-driven (blue) and concentration-driven (red) configurations. For concentration driven simulations, CO <sub>2</sub> concentration is prescribed, and follows the red line in panel (a) in all models. For emissions-driven simulations, CO <sub>2</sub> concentration is simulated and can therefore differ for each model, blue lines in panel (a). Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

Simulated GSAT over 1995–2014, relative to 1850–1900 period, warms by very similar amounts in the two sets of simulations: 0.82°C (0.45–1.31) in emissions-driven compared with 0.75°C (0.53–1.09) in concentration-driven simulations. By the end of the 21st century, warming in emissions-driven simulations is very similar: 4.58°C (3.53–6.70), reflecting the slightly lower CO <sub>2</sub> concentration simulated by the ESMs compared with warming under the prescribed CO <sub>2</sub> pathway of 4.69°C (3.70–6.77). This difference in model-mean response is more than an order of magnitude smaller than the 5–95% spread across model projections. The spread in CO <sub>2</sub> concentration, compared with the prescribed default concentration, leads to a very small increase by about 0.1°C in the spread of GSAT projections, but it is not possible to tell if this is a direct consequence of the simulation configuration or internal variability of the model simulations. These differences due to experimental configuration would be smaller still under scenarios with lower CO <sub>2</sub> levels, and so we assess that results from concentration-driven and emissions-driven configurations do not affect the assessment of GSAT projections ( ''high confidence'' ).

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==== 4.3.1.2 Precipitation ====

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The AR5 assessed from CMIP5 projections that global mean precipitation over the 21st century will increase by more than 0.05 mm day <sup>–1</sup> (about 2% of global precipitation) and 0.15 mm day <sup>–1</sup> (about 5% of global precipitation) under the RCP2.6 and RCP8.5 scenarios, respectively ( [[#Collins--2013|Collins et al., 2013]] ). These changes are generally in line with those from the CMIP6 simulations following SSP1-2.6 and SSP5-8.5 (Table 4.3).

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'''Table''' '''4.3 |''' '''CMIP6 precipitation anomalies (%) relative to averages over 1995–2014 for selected future periods, regions and SSPs.''' Displayed are the multi-model averages across the individual models and, in parentheses, the 5 '''–''' 95% ranges. Also shown are land precipitation anomalies at the time when global increase in GSAT relative to 1850–1900 exceeds 1.5°C, 2.0°C, 3.0°C, and 4.0°C, and the percentage of simulations for which such exceedances are true (to the right of the parentheses). Here, the time of GSAT exceedance is determined as the first year at which 21-year running averages of GSAT exceed the given threshold. Land precipitation percent anomalies are then computed as 21-year averages about the year of the first GSAT crossing. The numbers of models used are indicated in Figure 4.4.

{| class="wikitable"
|-
| colspan="2"| '''Time Period and Region'''

| '''SSP1-1.9 (%)'''

| '''SSP1-2.6 (%)'''

| '''SSP2-4.5 (%)'''

| '''SSP3-7.0 (%)'''

| '''SSP5-8.5 (%)'''

|-
| rowspan="3"| '''Land'''

| 2021–2040

| 2.4 (0.7, 4.1)

| 2.0 (–0.6, 3.6)

| 1.5 (–0.4, 3.6)

| 1.2 (–1.0, 3.4)

| 1.7 (–0.1, 4.1)

|-
| 2041–2060

| 2.7 (0.6, 5.0)

| 2.8 (–0.4, 5.2)

| 2.7 (0.3, 5.2)

| 2.5 (–0.8, 5.1)

| 3.7 (–0.1, 6.9)

|-
| 2081–2100

| 2.4 (–0.2, 4.7)

| 3.3 (0.0, 6.6)

| 4.6 (1.5, 8.3)

| 5.8 (0.5, 9.6)

| 8.3 (0.9, 12.9)

|-
| '''Global'''

| 2081–2100

| 2.0 (0.4, 4.2)

| 2.9 (1.0, 5.2)

| 4.0 (2.3, 6.7)

| 4.7 (2.3, 8.2)

| 6.5 (3.4, 10.9)

|-
| '''Ocean'''

| 2081–2100

| 1.9 (0.6, 4.1)

| 2.8 (1.1, 5.4)

| 3.8 (2.0, 6.8)

| 4.4 (2.1, 7.9)

| 6.0 (2.9, 10.5)

|-
| rowspan="4"| '''Land'''

| ∆T &gt; 1.5°C

| 2.0 (0.6, 4.4) 55

| 1.7 (–2.0, 6.9) 87

| 1.7 (–2.9, 6.2) 100

| 1.5 (–3.9, 6.6) 100

| 1.5 (–3.5, 6.4) 100

|-
| ∆T &gt; 2.0°C

| 3.8 (2.4, 5.8) 36

| 2.2 (–2.0, 4.6) 58

| 2.8 (–2.2, 8.1) 97

| 2.4 (–4.4, 7.7) 100

| 2.8 (–2.8, 8.3) 100

|-
| ∆T &gt; 3.0°C

| – (–, –) 0

| – (–, –) 0

| 4.9 (1.5, 9.6) 54

| 4.3 (–4.4, 11.5) 97

| 4.9 (–2.6, 11.0) 100

|-
| ∆T &gt; 4.0°C

| – (–, –) 0

| – (–, –) 0

| 4.2 (1.3, 6.3) 9

| 5.1 (–2.5, 11.1) 57

| 6.4 (–3.4, 15.0) 85

|}

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[[File:cb61dbfb90e415dbe28bdb48631401f0 IPCC_AR6_WGI_Figure_4_4.png]]

'''Figure''' '''4.4 |''' '''CMIP6 annual mean precipitation changes (%) from historical and scenario simulations. (a)''' Northern Hemisphere extratropics (30°N–90°N). '''(b)''' North Atlantic subtropics (5°N–30°N, 80°W–0°). Changes are relative to 1995–2014 averages. Displayed are multi-model averages and, in parentheses, 5–95% ranges. The numbers inside each panel are the number of model simulations. Results are derived from concentration-driven simulations. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

Unlike AR5, our focus here is on land rather than global precipitation because land precipitation has greater societal relevance. These are displayed as percent changes relative to 1995–2014 (Figure 4.2b). Based on these results, we conclude that global land precipitation is larger during the period 2081–2100 than during the period 1995–2014, under all scenarios considered here ( ''high confidence'' ) (Table 4.3). Global land precipitation for 2081–2100, relative to 1995–2014, shows a 5–95% range of –0.2 to +4.7% under SSP1-1.9 and 0.9–12.9% under SSP5-8.5, respectively. The corresponding ranges under the other emissions scenarios are 0.0–6.6% (SSP1-2.6), 1.5–8.3% (SSP2-4.5), and 0.5–9.6% (SSP3-7.0). A detailed assessment of hydrological sensitivity, or change in precipitation per degree warming, can be found in [[IPCC:Wg1:Chapter:Chapter-8|Chapter 8]] (Section 8.2.1).

For scenarios where unanimity across all of the model simulations that GSAT change relative to 1850–1900 rises above 1.5°C (SSP2-4.5, SSP3-7.0, or SSP5-8.5), the ensemble-mean change in global land precipitation from 1850–1900 until the time of exceedance is on average about 1.6%. For scenarios with unanimous global warming above 2.0°C (SSP3-7.0, or SSP5-8.5) and 3.0°C (SSP5-8.5), the ensemble-mean increase in global land precipitation for those models that do exceed 2.0°C and 3.0°C is on average about 2.6% and 4.9%, respectively. On average under SSP1-1.9 and SSP1-2.6, the global land precipitation change for simulations where global warming exceeds 1.5°C and 2.0°C will be about 1.9% and 3.0%, respectively.

Relative to 1995–2014, and across all of the scenarios considered here, CMIP6 models show greater increases in precipitation over land than either globally or over the ocean (Table 4.3; ''high confidence'' ). Over the Northern Hemisphere (NH) extratropics, the 5–95% changes in precipitation over land between 1995–2014 and 2021–2040, 2041–2060, and 2081–2100, following SSP5-8.5, are 0.6–4.9%, 1.5–8.8%, and 4.7–17.2%, respectively (Figure 4.4). At the other end of scenario spectrum, SSP1-1.9, the corresponding changes are 0.6–5.4%, 0.6–7.3%, and 0.2–7.7%, respectively. By contrast, over the North Atlantic subtropics, precipitation decreases by about 10% following SSP3-7.0 and SSP5-8. There is no change in subtropical precipitation in the North Atlantic following SSP1-1.9, SSP1-2.6, or SSP2-4.5 ( ''high confidence'' ); thereby highlighting the potential limitations of pattern scaling for regional hydrological changes (Section 8.5.3). The reasons for the opposing changes in these two regions are assessed in Chapter 8.

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=== 4.3.2 Cryosphere,Ocean and Biosphere ===

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==== 4.3.2.1 Arctic Sea Ice ====

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The AR5 assessed from CMIP5 simulations that there will be year-round reductions of Arctic sea ice coverage by the end of this century ( [[#Collins--2013|Collins et al., 2013]] ). These range from 43% under RCP2.6 and 94% under RCP8.5 in September, and from 8% under RCP2.6 and 34% under RCP8.5 in March ( ''medium confidence'' ). Based on a five-member selection of CMIP5 models, AR5 further assessed that for RCP8.5, Arctic sea ice coverage in September will drop below 1 million km <sup>2</sup> and be practically ice free at some point between 2040 and 2060. The SROCC further assessed that the probability of an ice-free Arctic in September for stabilized global warming of 1.5°C and 2.0°C is approximately 1% and 10–35%, respectively ( [[#IPCC--2019|IPCC, 2019]] ).

With regards to the model selection in AR5, model evaluation studies have since identified shortcomings of the CMIP5 models to match the observed distribution of sea ice thickness in the Arctic ( [[#Stroeve--2014|Stroeve et al., 2014]] ; [[#Shu--2015|Shu et al., 2015]] ) and the observed evolution of albedo on seasonal scales ( [[#Koenigk--2014|Koenigk et al., 2014]] ). It was also found that many models’ deviation from observed sea ice cover climatology cannot be explained by internal variability, whereas the models’ deviation from observed sea ice cover trend (over the satellite period) can often be explained by internal variability ( [[#Olonscheck--2017|Olonscheck and Notz, 2017]] ). This hinders a selection of models according to their simulated trends, which additionally has been shown to only have a weak effect on the magnitude of simulated future trends (Stroeve and [[#Notz--2015|Notz, 2015]] ).

Based on results from the CMIP6 models, we conclude that on average the Arctic will become practically ice-free in September by the end of the 21st century under SSP2-4.5, SSP3-7.0, and SSP5-8.5 ( ''high confidence'' ) (Figure 4.2c and Table 4.4). Also, in the CMIP6 models, Arctic SIA in March decreases in the future, but to a much lesser degree, in percentage terms, than in September ( ''high confidence'' ) (Table 4.4). A more detailed assessment of projected Arctic and also Antarctic sea ice change can be obtained in [[IPCC:Wg1:Chapter:Chapter-9|Chapter 9]] (Section 9.3.1).

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'''Table 4.4''' '''|''' '''CMIP6 Arctic sea ice area for selected months, time periods, and across five SSPs.''' Displayed are the multi-model averages across the individual models and, in parentheses, the 5 '''–''' 95% ranges. The number of models used in these calculations are shown in Figure 4.2c.

{| class="wikitable"
|-
| colspan="2"| Month and Time Period

| '''SSP1-1.9 (10''' <sup>6</sup> '''km''' <sup>2</sup> ''')'''

| '''SSP1-2.6 (10''' <sup>6</sup> '''km''' <sup>2</sup> ''')'''

| '''SSP2-4.5 (10''' <sup>6</sup> '''km''' <sup>2</sup> ''')'''

| '''SSP3-7.0 (10''' <sup>6</sup> '''km''' <sup>2</sup> ''')'''

| '''SSP5-8.5 (10''' <sup>6</sup> '''km''' <sup>2</sup> ''')'''

|-
| rowspan="3"| '''September'''

| 2021–2040

| 2.6 (1.1, 6.5)

| 2.7 (0.6, 6.4)

| 2.8 (0.7, 6.4)

| 3.1 (1.1, 6.4)

| 2.5 (0.4, 5.8)

|-
| 2041–2060

| 2.2 (0.3, 6.5)

| 2.0 (0.2, 6.1)

| 1.7 (0.1, 5.6)

| 1.7 (0.1, 5.7)

| 1.2 (0.0, 5.2)

|-
| 2081–2100

| 2.4 (0.2, 6.2)

| 1.7 (0.0, 6.0)

| 0.8 (0.0, 4.6)

| 0.5 (0.0, 3.3)

| 0.3 (0.0, 2.2)

|-
| rowspan="3"| '''March'''

| 2021–2040

| 14.0 (11.4, 18.7)

| 14.9 (11.9, 25.8)

| 14.9 (11.9, 23.5)

| 15.0 (11.7, 27.3)

| 14.9 (11.9, 24.7)

|-
| 2041–2060

| 13.8 (10.9, 18.3)

| 14.5 (10.9, 25.7)

| 14.3 (11.1, 23.3)

| 14.2 (10.5, 27.1)

| 13.9 (10.2, 24.5)

|-
| 2081–2100

| 13.7 (10.9, 18.5)

| 14.2 (10.6, 25.7)

| 13.1 (9.5, 22.2)

| 11.8 (5.4, 25.5)

| 9.7 (3.1, 21.6)

|}

Studies focusing on the relationship of sea ice extent and changes in external drivers have consistently found a much-reduced likelihood of a practically ice-free Arctic Ocean during summer for global warming of 1.5°C than for 2.0°C ( [[#Screen--2017|Screen and Williamson, 2017]] ; [[#Jahn--2018|Jahn, 2018]] ; [[#Niederdrenk--2018|Niederdrenk and Notz, 2018]] ; [[#Notz--2018|Notz and Stroeve, 2018]] ; [[#Sigmond--2018|Sigmond et al., 2018]] ; [[#Olson--2019|Olson et al., 2019]] ). This is shown here in a large initial-condition ensemble of observationally constrained model simulations where GSAT are stabilized at 1.5°C, 2.0°C and 3.0°C warming relative to 1850–1900 in the RCP8.5 scenario (Figure 4.5). Temperature stabilization is achieved by switching off all the anthropogenic emissions around the time that GSAT first reaches the stabilization thresholds. Simulations have been observationally constrained to correct for a model bias in simulated historical September sea ice extent. In these simulations, Arctic sea ice coverage in September is simulated, on average, to drop below 1 million km <sup>2</sup> around 2040, consistent with the AR5 set of assessed models ( [[#Sigmond--2018|Sigmond et al., 2018]] ). The individual model simulations, for which there are twenty for each stabilized temperature level, show that the probability of the Arctic becoming practically ice free at the end of the 21st century is significantly higher for 2°C warming than for 1.5°C warming above 1850–1900 levels ( ''high confidence'' ).

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[[File:47131a59275ac70c2dcc3d9dfc87e61a IPCC_AR6_WGI_Figure_4_5.png]]

'''Figure 4.5''' '''|''' '''Arctic sea ice extent in September in a large initial-condition ensemble of observationally-constrained simulations of an Earth system model (CanESM2).''' The black and red curves are averages over twenty simulations following historical forcings to 2015 and RCP8.5 extensions to 2100. The other curves are averages of over 20 simulations each after global surface air temperature has been stabilized at the indicated degree of global mean warming relative to 1850–1900. The bars to the right are the minimum to maximum ranges over 2081–2100 ( [[#Sigmond--2018|Sigmond et al., 2018]] ). The horizontal dashed line indicates a practically sea ice-free Arctic. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

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<span id="global-mean-sea-level"></span>
==== 4.3.2.2 Global Mean Sea Level ====

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The AR5 assessed from CMIP5 process-based simulations that the rate of GMSL rise during the 21st century will ''very likely'' exceed the rate observed during 1971–2010 for all RCP scenarios due to increases in ocean warming and loss of mass from glaciers and ice sheets ( [[#Church--2013|Church et al., 2013]] ). Further, AR5 concluded that for the period 2081–2100, compared to 1986–2005, GMSL rise is ''likely'' ( ''medium confidence'' ) to be in the 5–95% range of projections from process-based models, which give 0.26–0.55 m for RCP2.6, 0.32–0.63 m for RCP4.5, 0.33–0.63 m for RCP6.0, and 0.45–0.82 m for RCP8.5. For RCP8.5, the rise by 2100 is 0.52–0.98 m with a rate during 2081–2100 of 8–16 mm yr <sup>–1</sup> .

There have been substantial modelling advances since AR5, with most sea level projections corresponding to one of three categories: (i) central-range projections, combining scenario-conditional probability distributions for the different contributions to estimate a central range under different scenarios; (ii) probabilistic projections, which explicitly consider outcomes for a wide range of likelihoods, including low-likelihood, high-impact outcomes; and (iii) semi-empirical projections, based on statistical relationships between past GMSL changes and climate variables, which now calibrate individual contributions and are consistent with physical-model based estimates (Section 9.6.3).

Based on the assessment of the latest modelling information (Figure 4.2d and Section 9.6.3), we conclude that under the SSP3-7.0, the ''likely'' range of GMSL change averaged over 2081–2100 relative to 1995–2014 is 0.46–0.74 m. Under SSP1-2.6, the ''likely'' range over the long-term is 0.30–0.54 m. Further, in SSP2-4.5, SSP3-7.0, and SSP5-8.5, the rise in GMSL is projected to accelerate over the 21st century. A detailed assessment of the processes contributing to these projected rises and accelerations in GMSL, together with a comparison to AR5 and SROCC, can be found in [[IPCC:Wg1:Chapter:Chapter-9|Chapter 9]] (Section 9.6.3). Projected changes in the thermosteric component of GMSL beyond 2300 are assessed in [[#4.7.1|Section 4.7.1]] .

In summary, it is ''virtually certain'' that under any one of the assessed SSPs, there will be continued rise in GMSL through the 21st century.

<div id="4.3.2.3" class="h3-container"></div>

<span id="atlantic-meridional-overturning-circulation"></span>
==== 4.3.2.3 Atlantic Meridional Overturning Circulation ====

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The AR5 assessed from CMIP5 simulations that the Atlantic Meridional Overturning Circulation (AMOC) will ''very likely'' weaken over the 21st century, and the projected weakening of the AMOC is consistent with CMIP5 projections of an increase of high-latitude temperature and high-latitude precipitation, with both effects causing the surface waters at high latitudes to become less dense and therefore more stable ( [[#Collins--2013|Collins et al., 2013]] ).

Based on CMIP6 models, we find that over the 21st century, AMOC strength, relative to 1995–2014, shows a multi-model mean decrease in each of the SSP scenarios but with a large spread across the individual simulations (Figure 4.6). We also note that the magnitude of the ensemble-mean strength decrease is approximately scenario independent up to about 2060 ( [[#Weijer--2020|Weijer et al., 2020]] ). A more detailed assessment of these projected AMOC changes, and the mechanisms involved, can be found in [[IPCC:Wg1:Chapter:Chapter-9|Chapter 9]] (Section 9.2.3).

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[[File:bac164475cd281d483ffa55cbe94c78f IPCC_AR6_WGI_Figure_4_6.png]]

'''Figure 4.6 |''' '''CMIP6 annual mean Atlantic Meridional Overturning Circulation (AMOC) strength change in historical and scenario simulations.''' Changes are relative to averages from 1995–2014. The curves show ensemble averages and the shadings the 5–95% ranges across the SSP1-2.6 and SSP3-7.0 ensembles. The circles to the right of the panel show the anomalies averaged from 2081–2100 for each of the available model simulations. The numbers inside the panel are the number of model simulations. Here, the strength of the AMOC is computed as the maximum value of annual mean ocean meridional overturning mass stream function in the Atlantic at 26°N. Results are from concentration-driven simulations. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

In summary, we assess from the CMIP6 models that AMOC weakening over the 21st century is ''very likely'' ; the rate of weakening is approximately independent of the emissions scenario ( ''high confidence'' ).

Based on a large initial condition ensemble of simulations with a CMIP5 model (CanESM2) with emissions scenarios leading to stabilization of global warming of 1.5°C, 2.0°C, or 3.0°C relative to 1850–1900, AMOC continues to decline for 5–10 years after GSAT is effectively stabilized at the given GWL ( [[#Sigmond--2020|Sigmond et al., 2020]] ). This is followed by a recovery of AMOC strength for about the next 150 years to a level that is approximately independent of the considered stabilization scenario. These results are replicated in simulations in a CMIP6 model (CanESM5) with emissions cessation after diagnosed CO <sub>2</sub> emissions reach 750 Gt, 1000 Gt, or 1500 Gt. These emissions levels lead to global warming stabilization at 1.5°C, 2.0°C, or 3.0°C relative to 1850–1900. In summary, in these model simulations the AMOC recovers over several centuries after the cessation of CO <sub>2</sub> emissions ( ''medium confidence'' ).

<div id="4.3.2.4" class="h3-container"></div>

<span id="ocean-and-land-carbon-uptake"></span>
==== 4.3.2.4 Ocean and Land Carbon Uptake ====

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The AR5 concluded with ''very high confidence'' that ocean carbon uptake of anthropogenic CO <sub>2</sub> will continue under all RCPs through the 21st century, with higher uptake corresponding to higher concentration pathways. The future evolution of the land carbon uptake was assessed to be much more uncertain than for ocean carbon uptake, with a majority of CMIP5 models projecting a continued cumulative carbon uptake.

Based on results from the CMIP6 models, we conclude that the flux of carbon from the atmosphere into the ocean increases continually through most of 21st century in the two highest emissions and decreases continually under the other emissions scenarios (Figure 4.7a). The flux of carbon from the atmosphere to land shows a similar 21st century behaviour across the scenarios but with much higher year-to-year variation than ocean carbon flux (Figure 4.7b). A more in-depth assessment and discussion of the mechanism involved can be found in [[IPCC:Wg1:Chapter:Chapter-5|Chapter 5]] (Section 5.4.5).

<div id="_idContainer028" class="Basic-Text-Frame"></div>

[[File:09581ff5c63fc03bfc618b5f774488cc IPCC_AR6_WGI_Figure_4_7.png]]

'''Figure''' '''4.7 |''' '''CMIP6 carbon uptake in historical and scenario simulations. (a)''' Atmosphere to ocean carbon flux (PgC yr <sup>–1</sup> ). '''(b)''' Atmosphere to land carbon flux (PgC yr <sup>–1</sup> ). The curves show ensemble averages and the shadings show the 5–95% ranges across the SSP1-2.6 and SSP3-7.0 ensembles. The numbers inside each panel are the number of model simulations. The land uptake is taken as Net Biome Productivity (NBP) and so includes any modelled net land-use change emissions. Results are from concentration-driven simulations. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

In summary, we assess that the cumulative uptake of carbon by the ocean and by land will increase through the 21st century irrespective of the considered emissions scenarios except SSP1-1.9 ( ''very high confidence'' ).

<div id="4.3.2.5" class="h3-container"></div>

<span id="surface-ocean-ph"></span>
==== 4.3.2.5 Surface Ocean pH ====

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The AR5 assessed from CMIP5 simulations that it is ''virtually certain'' that increasing storage of carbon by the ocean under all four RCPs through to 2100 will increase ocean acidification in the future ( [[#Ciais--2013|Ciais et al., 2013]] ). Specifically, AR5 reported that CMIP5 models project increased ocean acidification globally to 2100 under all RCPs, and that the corresponding model mean and model spread in the decrease in surface ocean pH from 1986–2005 to 2081–2100 would be 0.065 (0.06–0.07) for RCP2.6, 0.145 (0.14–0.15) for RCP4.5, 0.203 (0.20–0.21) for RCP6.0 and 0.31 (0.30–0.32) for RCP8.5.

Based on results from the CMIP6 models we conclude that, except for the lower-emissions scenarios SSP1-1.9 and SSP1-2.6, ocean surface pH decreases monotonically through the 21st century ( ''high confidence'' ) (Figure 4.8).

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[[File:c3df335bb75a1ec547422e0caa92442f IPCC_AR6_WGI_Figure_4_8.png]]

'''Figure''' '''4.8 |''' '''Global average surface ocean pH.''' The shadings around the SSP1-2.6 and SSP5-7.0 curves are the 5–95% ranges across those ensembles. The numbers inside each panel are the number of model simulations. Results are from concentration-driven simulations. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

<div id="4.3.3" class="h2-container"></div>

<span id="modes-of-variability"></span>
=== 4.3.3 Modes of Variability ===

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<div id="4.3.3.1" class="h3-container"></div>

<span id="northern-and-southern-annular-modes"></span>
==== 4.3.3.1 Northern and Southern Annular Modes ====

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<div id="4.3.3.1.1" class="h4-container"></div>

<span id="northern-annular-mode"></span>
===== 4.3.3.1.1 Northern Annular Mode =====

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The Northern Annular Mode (NAM) is the leading mode of variability in the NH extratropical atmosphere (Section AIV.2.1). Throughout this chapter, we use a simple fixed latitude-based NAM index defined as the difference in SLP between 35°N and 65°N (Section AIV.2.1; [[#Li--2003|Li and Wang, 2003]] ). The NAM index computed from the latitudinal gradient in SLP is strongly correlated with variations in the latitudinal position and strength of the mid-latitude westerly jets, and with the spatial distribution of Arctic sea ice ( [[#Caian--2018|Caian et al., 2018]] ). Projected changes in the position and strength of the mid-latitude westerly jets, storm tracks, and atmospheric blocking in both hemispheres are assessed in [[#4.5.1.6|Section 4.5.1.6]] . The AR5 referred to the NAM, and its synonym the Arctic Oscillation (AO), through its regional counterpart, the North Atlantic Oscillation (NAO). Here, we use the term NAM to refer also to the AO and NAO (Section AIV.2.1), accepting that the AO and NAO are not identical entities.

We first summarize the assessment of past NAM changes and their attribution from Chapters 2 and 3 to put into context the future projections described here. Strong positive trends for the NAM/NAO indices were observed since 1960, which have weakened since the 1990s ( ''high confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-2#2.4.1.1|Section 2.4.1.1]] ). The NAO variability in the instrumental record was ''likely'' not unusual in the millennial and multi-centennial context ( [[IPCC:Wg1:Chapter:Chapter-2#2.4.1.1|Section 2.4.1.1]] ). Climate models simulate the gross features of the NAM with reasonable fidelity, including its interannual variability, but models tend to systematically underestimate the amount of multi-decadal variability of the NAM and jet stream compared to observations ( [[IPCC:Wg1:Chapter:Chapter-3#3.7.1|Section 3.7.1]] ; J. [[#Wang--2017b|]] [[#Wang--2017|Wang et al., 2017]] b ; [[#Bracegirdle--2018|Bracegirdle et al., 2018]] ; [[#Simpson--2018|Simpson et al., 2018]] ), with the caveat of the observational record being relatively short to characterize decadal variability ( [[#Chiodo--2019|Chiodo et al., 2019]] ). A realistic simulation of the stratosphere and SST variability in the tropics and northern extratropics are important for a model to realistically capture the observed NAM variability. Despite some evidence from climate model studies that anthropogenic forcings influence the NAM, there is '''limited evidence''' for a significant role for anthropogenic forcings in driving the observed multi-decadal variations of the NAM over the instrumental period ( [[IPCC:Wg1:Chapter:Chapter-3#3.7.1|Section 3.7.1]] ).

The AR5 assessed from CMIP5 simulations that the future boreal wintertime NAM is ''very likely'' to exhibit large natural variations and trends of similar magnitude to that observed in the past and is ''likely'' to become slightly more positive in the future ( [[#Collins--2013|Collins et al., 2013]] ). Based on CMIP6 model results displayed in Figure 4.9a, we conclude that the boreal wintertime surface NAM is more positive by the end of the 21st century under SSP3-7.0 and SSP5-8.5 ( ''high confidence'' ). For these high emissions scenarios, the 5–95% range of NAM index anomalies averaged from 2081–2100 are 0.3–3.8 hPa and 0.32–5.2 hPa, respectively. On the other hand, under neither of the lowest emissions scenarios, SSP1-1.9 and SSP1-2.6, does the NAM show a robust change, by the end of the 21st century ( ''high confidence'' ).

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[[File:12cece94d043af70989e07e96bb53676 IPCC_AR6_WGI_Figure_4_9.png]]

'''Figure 4.9''' '''|''' '''CMIP6 simulations of boreal winter (December–January–February, DJF) Annular Mode indices. (a)''' NAM and '''(b)''' SAM. The NAM is defined as the difference in zonal mean SLP at 35°N and 65°N ( [[#Li--2003|Li and Wang, 2003]] ) and the SAM as the difference in zonal mean SLP at 40°S and 65°S ( [[#Gong--1999|Gong and Wang, 1999]] ). All anomalies are relative to averages from 1995–2014. The curves show multi-model ensemble averages over the CMIP6 r1 simulations. The shadings around the SSP1-2.6 and SSP3-7.0 curves denote the 5–95% ranges of the ensembles. The numbers inside each panel are the number of model simulations. The results are for concentration-driven simulations. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

Significant progress has been made since AR5 in understanding the physical mechanisms responsible for changes in the NAM, although uncertainties remain. It is now clear from the literature that the NAM response, and the closely-related response of the mid-latitude storm tracks, to anthropogenic forcing in CMIP5-era climate models is determined by a ‘tug-of-war’ between two opposing processes ( [[#Harvey--2014|Harvey et al., 2014]] ; [[#Shaw--2016|Shaw et al., 2016]] ; [[#Screen--2018a|Screen et al., 2018a]] ): (i) Arctic amplification (Sections 4.5.1.1 and 7.4.4.1), which decreases the low-level meridional temperature gradient, reduces baroclinicity on the poleward flank of the eddy-driven jet, and shifts the storm tracks equatorward and leading to a ''negative'' NAM (see Box 10.1; [[#Harvey--2015|Harvey et al., 2015]] ; [[#Hoskins--2015|Hoskins and Woollings, 2015]] ; [[#Peings--2017|Peings et al., 2017]] ; [[#Screen--2018a|Screen et al., 2018a]] ); and (ii) enhanced warming in the tropical upper-troposphere, due to GHG increases and associated water vapour and lapse rate feedbacks, which increases the upper-level meridional temperature gradient and causes a poleward shift of the storm tracks and a ''positive'' NAM ( [[#Harvey--2014|Harvey et al., 2014]] ; [[#Vallis--2015|Vallis et al., 2015]] ; [[#Shaw--2019|Shaw, 2019]] ). The large diversity in projected NAM changes in CMIP5 multi-model ensemble ( [[#Gillett--2013|Gillett and Fyfe, 2013]] ) appears to be at least partly explained by the relative importance of these two mechanisms in particular models ( [[#Harvey--2014|Harvey et al., 2014]] , 2015; [[#Vallis--2015|Vallis et al., 2015]] ; [[#McCusker--2017|McCusker et al., 2017]] ; [[#Oudar--2017|Oudar et al., 2017]] ). Models that produce larger Arctic amplification also tend to produce larger equatorward shifts of the mid-latitude jets and associated negative NAM responses ( [[#Barnes--2015|Barnes and Polvani, 2015]] ; [[#Harvey--2015|Harvey et al., 2015]] ; [[#Zappa--2017|Zappa and Shepherd, 2017]] ; [[#McKenna--2018|McKenna et al., 2018]] ; [[#Screen--2018a|Screen et al., 2018a]] ; [[#Zappa--2018|Zappa et al., 2018]] ).

Another area of progress is new understanding the role of cloud radiative effects in shaping the mid-latitude circulation response to anthropogenic forcing. Through their non-uniform distribution of radiative heating, cloud changes can modify meridional temperature gradients and alter mid-latitude circulation and the annular modes in both hemispheres ( [[#Ceppi--2014|Ceppi et al., 2014]] ; [[#Voigt--2015|Voigt and Shaw, 2015]] , 2016; [[#Ceppi--2016|Ceppi and Hartmann, 2016]] ; [[#Ceppi--2017|Ceppi and Shepherd, 2017]] ; [[#Lipat--2018|Lipat et al., 2018]] ; [[#Albern--2019|Albern et al., 2019]] ; [[#Voigt--2019|Voigt et al., 2019]] ). In addition to the effects of changing upper and lower tropospheric temperature gradients on the NAM, progress has been made since AR5 in understanding the effect of simulated changes in the strength of the stratospheric polar vortex on winter NAM projections ( [[#Manzini--2014|Manzini et al., 2014]] ; [[#Zappa--2017|Zappa and Shepherd, 2017]] ; [[#Simpson--2018|Simpson et al., 2018]] ).

<div id="4.3.3.1.2" class="h4-container"></div>

<span id="southern-annular-mode"></span>
===== 4.3.3.1.2 Southern Annular Mode =====

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The Southern Annular Mode (SAM) is the leading mode of large-scale extratropical atmospheric variability in the Southern Hemisphere and influences most of the southern extratropics (Annex IV, Section AIV.2.2). In its positive phase, the SAM characterizes anomalously low pressure over the polar cap and high pressure in southern mid-latitudes ( [[#Marshall--2003|Marshall, 2003]] ). While there are some zonal asymmetries to the structure of the SAM (Section AIV.2.2), it is more symmetric than its NH counterpart ( [[#Fyfe--1999|Fyfe et al., 1999]] ). Throughout this chapter, we use a simple fixed latitude-based SAM index defined as the difference in zonal mean SLP between 40°S and 65°S ( [[#Gong--1999|Gong and Wang, 1999]] ; see Section AIV.2.2 for discussion of other SAM indices). Although the SAM is often used as a proxy for the location of the mid-latitude westerly wind belt, trends in the SAM can reflect a combination of changes in jet position, width, and strength. The changes in the Southern Hemisphere circulation associated with the SAM influence surface wind stress ( [[#Wang--2014|Wang et al., 2014]] ) and hence affect the Southern Ocean.

Over the instrumental period, there has been a robust positive trend in the SAM index, particularly since 1970 ( ''high confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-2#2.4.1.2|Section 2.4.1.2]] ). There is ''medium confidence'' that the recent trend in the SAM is unprecedented in the past several centuries ( [[IPCC:Wg1:Chapter:Chapter-2#2.4.1.2|Section 2.4.1.2]] ). There is ''high confidence'' that stratospheric ozone depletion and GHG increases have contributed to the positive SAM trend during the late 20th century, with ozone depletion dominating in austral summer, following the peak of the Antarctic ozone hole in September –October, and GHG increases dominating in other seasons ( [[IPCC:Wg1:Chapter:Chapter-3#3.7.2|Section 3.7.2]] ). To capture the effects of stratospheric ozone changes on the SAM, climate models must include a realistic representation of ozone variations ( [[IPCC:Wg1:Chapter:Chapter-3#3.7.2|Section 3.7.2]] ). In models that do not explicitly represent stratospheric ozone chemistry, which includes the majority of the CMIP6 model ensemble, an ozone dataset is prescribed. To properly capture the effects of ozone depletion and recovery on the stratosphere and surface climate, the prescribed ozone dataset must realistically capture observed stratospheric ozone trends with sufficiently high temporal resolution ( [[#Neely--2014|Neely et al., 2014]] ; [[#Young--2014|Young et al., 2014]] ). The CMIP6 experiment protocol recommended the use of a prescribed 4-D monthly mean ozone concentration field for models without stratospheric chemistry ( [[#Eyring--2016|Eyring et al., 2016]] ).

The AR5 assessed that the positive trend in the austral summer/autumn SAM observed since 1970 (see [[IPCC:Wg1:Chapter:Chapter-2#2.4.1.2|Section 2.4.1.2]] ) is ''likely'' to weaken considerably as stratospheric ozone recovers through the mid-21st century, while in other seasons the SAM changes depend on the emissions scenario, with a larger increase in SAM for higher emissions scenarios. In CMIP6 models, the austral summer SAM is more positive by the end of the 21st century under SSP3-7.0 and SSP5-8.5 (Figure 4.9b). On the other hand, under SSP1-1.9 and SSP1-2.6, the SAM is projected to be less positive, especially under SSP1-1.9 where the 5–95% ranges of anomalies relative to 1995–2014 are –3.1 to 0.0 hPa averaged from 2081–2100. In summary, under the highest emissions scenarios in the CMIP6 models, the SAM in the austral summer becomes more positive through the 21st century ( ''high confidence'' ).

<div id="4.3.3.2" class="h3-container"></div>

<span id="el-niñosouthern-oscillation"></span>
==== 4.3.3.2 El Niño–Southern Oscillation ====

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The El Niño–Southern Oscillation (ENSO) is the most dominant mode of variability on interannual time scales and also the dominant source of seasonal climate predictability ( [[#box-11.3|Box 11.3]] and Annex IV, Section AIV.2.3; [[#Timmermann--2018|Timmermann et al., 2018]] ). The AR5 assessed from CMIP5 simulations that ENSO variability will ''very likely'' remain the dominant mode of interannual climate variability in the future, and that associated ENSO precipitation variability on regional scales is ''likely'' to intensify ( [[#Christensen--2013|Christensen et al., 2013]] ). However, they assessed there was ''low confidence'' in projected changes in ENSO variability in the 21st century due to a strong component of internal variability.

Among a range of indices proposed for representing ENSO, we use the most prominent one, the Niño 3.4 index, defined as the average equatorial SST or precipitation across the central equatorial Pacific (5°S–5°N, 170°W–120°W; Section AIV.2.3). Here, we consider the evolution of the amplitude of Niño 3.4 index for SST and precipitation over the 21st century as projected by CMIP6 models. Analysis of CMIP6 models shows there is no robust model consensus on the forced changes in the amplitude of ENSO SST variability even under the high-emissions scenarios SSP3-7.0 and SSP5-8.5, but a significant increasing trend in the amplitude of ENSO precipitation variability is projected across the 21st century in the four SSPs (Figure 4.10). This is broadly consistent with results from CMIP5 models ( [[#Christensen--2013|Christensen et al., 2013]] ; [[#Power--2013|Power et al., 2013]] ; [[#Cai--2015|Cai et al., 2015]] ; [[#Chen--2017|Chen et al., 2017]] ; [[#Wengel--2018|Wengel et al., 2018]] ), recent studies with CMIP6 models ( [[#Brown--2020|Brown et al., 2020]] ; [[#Fredriksen--2020|Fredriksen et al., 2020]] ; Freund et al., 2020; [[#Yun--2021|Yun et al., 2021]] ), and large initial-condition ensemble experiments ( [[#Maher--2018|Maher et al., 2018]] ; [[#Zheng--2018|Zheng et al., 2018]] ; [[#Haszpra--2020|Haszpra et al., 2020]] ).

<div id="_idContainer034" class="Basic-Text-Frame"></div>

[[File:992f415ee80cdda28cdc960542811e0c IPCC_AR6_WGI_Figure_4_10.png]]

'''Figure 4.10 |''' '''Changes in amplitude of ENSO Variability.''' Variability of '''(a)''' SST and '''(b)''' precipitation anomalies averaged over Niño 3.4 region for 1950–2014 from CMIP6 historical simulations and for 2015–2100 from four SSPs. Thick lines stand for multi-model mean and shading is the 5–95% range across CMIP6 models for historical simulation (grey), SSP1-2.6 (blue) and SSP3-7.0 (pink), respectively. The amplitude of ENSO SST and rainfall variability is defined as the standard deviation of the detrended Niño 3.4-area averaged SST and rainfall index, respectively, over 30-year running windows. The standard deviation in every single model is normalized by each model’s present-day standard deviation averaged from 1995 to 2014. The number of available models is listed in parentheses. This figure is adopted from [[#Yun--2021|Yun et al. (2021)]] . Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

It is therefore ''very likely'' that the amplitude of ENSO rainfall variability will intensify in response to global warming over the 21st century although there is no robust consensus from CMIP6 climate models for a systematic change in amplitude of ENSO SST variability even in the high-emissions scenarios of SSP3-7.0 and SSP5-8.5.

<div id="4.3.4" class="h2-container"></div>

<span id="synthesis-assessment-of-projected-change-in-global-surface-air-temperature"></span>
=== 4.3.4 Synthesis Assessment of Projected Change in Global Surface Air Temperature ===

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GSAT change is assessed using multiple lines of evidence including the CMIP6 projection simulations out to year 2100. The assessment combines CMIP6 projections driven by SSP scenarios with observational constraints on simulated past warming ( [[#box-4.1|Box 4.1]] and Figure 4.11a,b; [[#Brunner--2020|Brunner et al., 2020]] ; [[#Liang--2020|Liang et al., 2020]] ; [[#Nijsse--2020|Nijsse et al., 2020]] ; [[#Tokarska--2020|Tokarska et al., 2020]] ; [[#Ribes--2021|Ribes et al., 2021]] ), as well as the AR6-updated assessment of ECS and TCR in Section 7.5. The approaches of ( [[#Liang--2020|Liang et al., 2020]] ; [[#Tokarska--2020|Tokarska et al., 2020]] ; [[#Ribes--2021|Ribes et al., 2021]] ) have first been extended to all 20-year averaging periods between 2000 and 2100. For each 20-year period, the 5%, 50%, and 95% percentile GSAT values of these three constrained CMIP6 results are averaged percentile by percentile (Figure 4.11c). Then, an emulator based on a two-layer energy balance model (e.g., [[#Held--2010|Held et al., 2010]] ) is driven by the Chapter 7-derived ERF. The emulator parameters are chosen such that the central estimate, lower bound of the ''very likely'' range, and upper bound of the ''very likely'' range of climate feedback parameter and ocean heat uptake coefficient take the values that map onto the corresponding combination of ECS (3°C, 2°C and 5°C, respectively) and TCR (1.8°C, 1.2°C and 2.4°C, respectively) of Section 7.5 (see [[#box-4.1|Box 4.1]] ). As a final step, the constrained-CMIP6 and the emulator-based 5%, 50%, and 95% percentile GSAT values are averaged percentile by percentile (Figure 4.11c,d and Table 4.5). Constrained CMIP6 results and the ECS- and TCR-based emulator thus contribute one-half each to the GSAT assessment. Because the emulator results and ( [[#Ribes--2021|Ribes et al., 2021]] ) represent the forced response only, and averaging over the other two individual estimates ( [[#Liang--2020|Liang et al., 2020]] ; [[#Tokarska--2020|Tokarska et al., 2020]] ) further reduces the contribution from internal variability, the assessed GSAT time series are assumed to represent purely the forced response.

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[[File:5abe3e9913b6dadb1d9a6a4dcb0b2702 IPCC_AR6_WGI_Figure_4_11.png]]

'''Figure 4.1''' '''1 |''' '''Multiple lines of evidence for global surface air temperature (GSAT) changes for the long-term period, 2081–2100, relative to the average over 1995–2014, for all five priority scenarios.''' The unconstrained CMIP6 5–95% ranges (coloured bars) in '''(a)''' differ slightly because different authors used different subsamples of the CMIP6 archive. The constrained CMIP6 5–95% ranges (coloured bars) in '''(b)''' are smaller than the unconstrained ranges in (a) and differ because of different samples from the CMIP6 archive and because different observations and methods are used. In '''(c)''' , the average of the ranges in (b) is formed (grey bars). Green bars in (c) show the emulator ranges, defined such that the best estimate, lower bound of the ''very likely'' range, and upper bound of the ''very likely'' range of climate feedback parameter and ocean heat uptake coefficient take the values that map onto the corresponding values of ECS and TCR of Section 7.5 (see [[#box-4.1|Box 4.1]] ). The time series in '''(d)''' are constructed by taking the average of the constrained CMIP6 ranges and the emulator ranges. The y-axes on the right-hand side are shifted upward by 0.85°C, the central estimate of the observed warming for 1995–2014, relative to 1850–1900 (Cross-Chapter Box 2.3, Table 1). Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

Averaged over the period 2081–2100, GSAT is ''very'' ''likely'' to be higher than in the recent past (1995–2014) by 0.3°C–0.9°C in the low-emissions scenario SSP1-1.9 and by 2.6°C–4.7°C in the high-emission scenario SSP5-8.5. For the scenarios SSP1-2.6, SSP2-4.5, and SSP3-7.0, the corresponding ''very'' ''likely'' ranges are 0.6°C–1.4°C, 1.3°C–2.5°C, and 2.0°C–3.8°C, respectively (Figure 4.11 and Table 4.5). Because the different approaches for estimating long-term GSAT change produce consistent results (Figure 4.11), there is ''high confidence'' in this assessment. These ranges of the long-term projected GSAT change generally correspond to AR5 ranges for related scenarios but the likelihood is increased to ''very likely'' ranges, in contrast to the ''likely'' ranges in AR5. Over the mid-term period 2041–2060, the ''very likely'' GSAT ranges of SSP1-1.9 and SSP5-8.5 are almost completely distinct ( ''high confidence'' ) (Table 4.5; see also [[#4.3.1|Section 4.3.1]] ).

CMIP6 models project a wider range of GSAT change than the assessed range ( ''high confidence'' ) ( [[#4.3.1|Section 4.3.1]] ). The CMIP6 models with a higher climate sensitivity simulate warming rates higher than assessed ''very likely'' here ( [[#4.3.1|Section 4.3.1]] ); these rates are ''very'' ''unlikely'' but not impossible to occur and hence cannot be excluded. The implications of these ''very'' ''unlikely'' warming rates for patterns of surface temperature and precipitation change are assessed in [[#4.8|Section 4.8]] .

For the near term, initialized decadal forecasts constitute another line of evidence over the period 2019–2028 ( [[#box-4.1|Box 4.1]] ). The forecasts are consistent with the assessed GSAT ''very likely'' range (Box 4.1, Figure 1), strengthening the confidence in the near-term assessment.

The assessed ranges of GSAT change can be converted to change relative to mean GSAT over the period 1850–1900 for a consistent comparison with AR5 ( [[#IPCC--2013|IPCC, 2013]] ) and SR1.5 ( [[#IPCC--2018a|IPCC, 2018a]] ). GSAT was warmer in 1995–2014 (recent past) than 1850–1900 by 0.85 [0.67 to 0.98] °C. GSAT diagnosed for 1986–2005 (AR5 recent past) relative to 1850–1900 is 0.08°C higher than was diagnosed in AR5, due to methodological and dataset updates (Cross-Chapter Box 2.3, Table 1).

The uncertainty in GSAT relative to 1850–1900 includes the ''very likely'' ranges of assessed GSAT change relative to 1995–2014 (depending on scenario and period, between 0.5°C and 2.4°C; Figure 4.11d and Table 4.5), the uncertainty in historical GSAT change from the mean over 1850–1900 to 1995–2014 (about 0.3°C; Cross-Chapter Box 2.3), and the estimate of internal variability in 20-year GSAT averages (5–95% range about 0.15°C, Box 4.1; [[#Maher--2019|Maher et al., 2019]] ). These uncertainties are assumed to be independent and are added in quadrature, meaning that the total uncertainty is only slightly larger than the dominating contribution by the GSAT change relative to 1995–2014 (Table 4.5). The addition is done by numerically sampling a normal distribution fitted to the 5%, 50% and 95% percentiles of the internal variability, as well as sampling skew-normal distributions (e.g., [[#O’Hagan--1976|O’Hagan and Leonard, 1976]] ) fitted to the 5%, 50% and 95% percentiles of both historical warming and GSAT relative to 1995–2014. The result is a joint probability distribution of GSAT change and 20-year period.

Averaged over the period 2081–2100, GSAT is ''very'' ''likely'' to be higher than in the period 1850–1900 by 1.0°C–1.8°C in the low-emissions scenarios SSP1-1.9 and by 3.3°C–5.7°C in the high-emissions scenario SSP5-8.5. For the scenarios SSP1-2.6, SSP2-4.5, and SSP3-7.0, the corresponding ''very'' ''likely'' ranges are 1.3°C–2.4°C, 2.1°C–3.5°C, and 2.8°C–4.6°C, respectively (Table 4.5).

Time series of assessed GSAT change are now used to assess the time when certain thresholds of GSAT increases are crossed (Table 4.5). The threshold-crossing time is defined as the midpoint of the first 20-year period during which the average GSAT exceeds the threshold. During the near term (2021–2040), a 1.5°C increase in the 20-year average of GSAT, relative to the average over the period 1850–1900, is ''very likely'' to occur in scenario SSP5-8.5, ''likely'' to occur in scenarios SSP2-4.5 and SSP3-7.0, and ''more likely than not'' to occur in scenarios SSP1-1.9 and SSP1-2.6. In all scenarios assessed here except SSP5-8.5, the central estimate of crossing the 1.5°C threshold lies in the early 2030s, in the early part of the ''likely'' range (2030 '''–''' 2052) assessed in SR1.5, which assumed continuation of the then-current warming rate. Roughly half of this difference arises from a larger historical warming diagnosed in AR6, while the other half arises because for central estimates of climate sensitivity, most scenarios show stronger warming over the near term than was estimated as ‘current’ in SR1.5 ( ''medium confidence'' ). The SR1.5 estimate with a median of 0.2°C per decade has been confirmed in AR6 ( [[IPCC:Wg1:Chapter:Chapter-3#3.3.1|Section 3.3.1]] ); by contrast, the assessed GSAT change shows central-estimate rates over the period 2010 to 2035 that range from 0.21°C per decade under SSP1-1.9 to 0.30°C per decade under SSP5-8.5. When considering scenarios similar to SSP1-1.9 instead of linear extrapolation, the SR1.5 estimate of when 1.5°C global warming is crossed is close to the central estimate reported here (SR1.5, Table 2.SM. 12). If ECS and TCR lie near the lower end of the assessed ''very likely'' range, crossing the 1.5°C warming threshold is avoided in scenarios SSP1-1.9 and SSP1-2.6 ( ''medium confidence'' ). It is ''more likely than not'' that under SSP1-1.9, GSAT relative to 1850–1900 will remain below 1.6°C throughout the 21st century, implying a potential temporary overshoot above 1.5°C of no more than 0.1°C. All statements about crossing the 1.5°C threshold assume that no major volcanic eruption occurs during the near term.

A warming level of 2°C in GSAT, relative to the period 1850–1900, is ''very likely'' to be crossed in the mid-term period 2041–2060 under SSP5-8.5, ''likely'' to be crossed in the mid-term period under SSP3-7.0, and ''more likely than not'' to be crossed during the mid-term period under SSP2-4.5. During the entire 21st century, a warming level of 2°C in GSAT, relative to the period 1850–1900, will be crossed under SSP5-8.5 and SSP3-7.0, will ''extremely likely'' be crossed under SSP2-4.5, will ''unlikely'' be crossed under SSP1-2.6, and will ''extremely unlikely'' be crossed under SSP1-1.9.

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'''Table 4.''' '''5 |''' '''Assessment results for 20-year averaged GSAT change, based on multiple lines of evidence.''' Thechange is displayed in °C relative to the 1995–2014 and 1850–1900 reference periods for selected time periods (near term 2021–2040, mid-term 2041–2060, and long term 2081–2100), and as the time when certain temperature thresholds are crossed, relative to the period 1850–1900. The recent reference period 1995–2014 was higher in GSAT than the period 1850–1900 by 0.85 [0.67 to 0.98] °C, (Cross-Chapter Box 2.3). The entries give both the central estimate and, in parentheses, the ''very likely'' (5–95%) range. An entry of ‘n.c.’ means that the global warming threshold is ‘not crossed’ during the period 2021–2100.

{| class="wikitable"
|-
| '''Time Period'''

| '''SSP1-1.9 (°C)'''

| '''SSP1-2.6 (°C)'''

| '''SSP2-4.5 (°C)'''

| '''SSP3-7.0 (°C)'''

| '''SSP5-8.5 (°C)'''

|-
| '''Near Term: 2021–2040'''

Relative to 1995–2014

Relative to 1850–1900

| 0.6 [0.4 to 0.9]

1.5 [1.2 to 1.7]

| 0.6 [0.4 to 0.9]

1.5 [1.2 to 1.8]

| 0.7 [0.4 to 0.9]

1.5 [1.2 to 1.8]

| 0.7 [0.4 to 0.9]

1.5 [1.2 to 1.8]

| 0.8 [0.5 to 1.0]

1.6 [1.3 to 1.9]

|-
| '''Mid-term: 2041–2060'''

Relative to 1995–2014

Relative to 1850–1900

| 0.7 [0.4 to 1.1]

1.6 [1.2 to 2.0]

| 0.9 [0.5 to 1.3]

1.7 [1.3 to 2.2]

| 1.1 [0.8 to 1.6]

2.0 [1.6 to 2.5]

| 1.3 [0.9 to 1.7]

2.1 [1.7 to 2.6]

| 1.5 [1.1 to 2.1]

2.4 [1.9 to 3.0]

|-
| '''Long Term: 2081–2100'''

Relative to 1995–2014 Relative to 1850–1900

| 0.6 [0.2 to 1.0]

1.4 [1.0 to 1.8]

| 0.9 [0.5 to 1.5]

1.8 [1.3 to 2.4]

| 1.8 [1.2 to 2.6]

2.7 [2.1 to 3.5]

| 2.8 [2.0 to 3.7]

3.6 [2.8 to 4.6]

| 3.5 [2.4 to 4.8]

4.4 [3.3 to 5.7]

|-
| 1.5°C

Relative to 1850–1900

| 2025–2044

[2013–2032 to n.c.]

| 2023–2042

[2012–2031 to n.c.]

| 2021–2040

[2012–2031 to 2037–2056]

| 2021–2040

[2013–2032 to 2033–2052]

| 2018–2037

[2011–2030 to 2029–2048]

|-
| 2°C

Relative to 1850–1900

| n.c.

[n.c. to n.c.]

| n.c.

[2031–2050 to n.c.]

| 2043–2062

[2028–2047 to 2075–2094]

| 2037–2056

[2026–2045 to 2053–2072]

| 2032–2051

[2023–2042 to 2044–2063]

|-
| 3°C

Relative to 1850–1900

| n.c.

[n.c. to n.c.]

| n.c.

[n.c. to n.c.]

| n.c.

[2061–2080 to n.c.]

| 2066–2085

[2050–2069 to n.c.]

| 2055–2074

[2042–2061 to 2074–2093]

|-
| 4°C

Relative to 1850–1900

| n.c.

[n.c. to n.c.]

| n.c.

[n.c. to n.c.]

| n.c.

[n.c. to n.c.]

| n.c.

[2070–2089 to n.c.]

| 2075–2094

[2058–2077 to n.c.]

|}

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