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

=== 4.5.3 Modes of Variability ===

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==== 4.5.3.1 Northern and Southern Annular Modes ====

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===== 4.5.3.1.1 The Northern Annular Mode =====

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The AR5 assessed from CMIP5 simulations that the future boreal wintertime NAM is ''very likely'' to exhibit natural variability and forced trends of similar magnitude to that observed in the historical period and is ''likely'' to become slightly more positive in the future. Considerable uncertainty is related to physical mechanisms to explain the observed and projected changes in the NAM, but NAM trends are clearly closely connected to projected shifts in the mid-latitude jets and storm tracks.

NAM projections from climate models analysed since AR5 reveal broadly similar results to the late 21st century. CMIP6 models show a positive ensemble-mean trend in most seasons and the higher emissions scenarios that is comparable to between-model or between-realization variability (Figure 4.30a). The NAM generally becomes more positive by the end of the century except in boreal summer (JJA) when there is no change in the NAM in these simulations. In boreal winter (DJF) under SSP5-8.5, the central estimate is an increase in the NAM by almost 3 hPa in the long-term compared to 1995–2014. This can be compared to a multi-model mean interannual standard deviation in the winter NAM index of 3.4 hPa during the period 1850–1900. We conclude with ''high confidence'' that in the mid- to long-term, the boreal wintertime surface NAM is more positive under SSP3-7.0 and SSP5-8.5, while under SSP1-1.9 and SSP1-2.6, the NAM does not show any robust change.

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[[File:53baf3a96361d152ad7389c95f52a07d IPCC_AR6_WGI_Figure_4_30.png]]

'''Figure 4.''' '''30 |''' '''CMIP6 Annular Mode index change from 1995–2014 to 2081–2100. (a)''' Northern Annular Mode (NAM) and '''(b)''' Southern Annular Mode (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]] ). The shadings are the 5–95% ranges across the simulations. The numbers near the top are the numbers of model simulations in each SSP ensemble. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

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===== 4.5.3.1.2 The Southern Annular Mode =====

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The AR5 assessed it is ''likely'' that the evolution of the SAM over the 21st century will be primarily determined by the interplay between the effects of ozone recovery and changing GHG concentrations and influence the SAM in opposing ways. Owing to the relative effects of these two drivers, CMIP5 model SAM and Southern Hemisphere circulation projections differed markedly across forcing scenarios and across seasons ( [[#Barnes--2013|Barnes and Polvani, 2013]] ; [[#Barnes--2014|Barnes et al., 2014]] ). CMIP5 models simulated a weak negative SAM trend in austral summer for RCP4.5 by the end of the century (F. [[#Zheng--2013|]] [[#Zheng--2013|Zheng et al., 2013]] ), while for RCP8.5 they simulated a weak positive SAM trend in austral summer (F. [[#Zheng--2013|]] [[#Zheng--2013|Zheng et al., 2013]] ). A substantial fraction of the spread in CMIP5 projections of the end of century SH summer jet shift under RCP8.5 may be attributable to differences in the simulated change in break-up of the stratospheric polar vortex, with models that produce a later break-up date showing a larger summertime poleward jet shift ( [[#Ceppi--2019|Ceppi and]] [[#Shepherd--2019|Shepherd, 2019]] ). For RCP2.6, the effect of ozone recovery on the SAM has been found to dominate over that of GHGs in austral summer ( [[#Eyring--2013|Eyring et al., 2013]] ). In austral winter, the poleward shift of the SH circulation in CMIP5 models, and the associated increase in the SAM index, tends to be larger, on average, in higher forcing scenarios though with substantial inter-model spread ( [[#Barnes--2014|Barnes et al., 2014]] ). New research since the AR5 shows that the previous theory for the apparent relationship across models between the annual mean climatological SH jet position and the amplitude of forced SH jet shift ( [[#Kidston--2010|Kidston and Gerber, 2010]] ) does not hold at seasonal time scales ( [[#Simpson--2016|Simpson and Polvani, 2016]] ).

In most seasons, the SAM becomes more positive by the end of the century relative to 1995–2014 under SSP2-4.5, SSP3-7.0, and SSP5-8.5 (Figure 4.30b). Conversely, under SSP1-1.9 and SSP1-2.6, in most seasons the SAM index does not show a robust change compared to 1995–2014 except in austral summer when it becomes significantly more negative. The greatest change in the SAM occurs in austral winter, where CMIP6 models show an ensemble-mean increase in the SAM index of almost 5 hPa in SSP5-8.5. This can be compared to a multi-model mean interannual standard deviation in the austral winter SAM index of 4.0 hPa during 1850–1900. In conclusion, there is ''high confidence'' that in high emissions scenarios (SSP3-7.0 and SSP5-8.5) the SAM becomes more positive in all seasons, while in the lowest scenario (SSP1-1.9) there is a robust decrease in austral summer.

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==== 4.5.3.2 El Niño–Southern Oscillation ====

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The AR5 assessed that it is ''very likely'' that the El Niño–Southern Oscillation (ENSO) will remain the dominant mode of interannual variability in the future. Moreover, due to increased moisture availability, the associated precipitation variability on regional scales was assessed to ''likely'' intensify. An eastward shift in the patterns of temperature and precipitation variations in the North Pacific and North America related to El Niño and La Niña teleconnections was projected with ''medium confidence'' . The stability of teleconnections to other regional implications including those in Central and South America, the Caribbean, parts of Africa, most of Asia, Australia and most Pacific Islands were assessed to be uncertain ( [[#Christensen--2013|Christensen et al., 2013]] ).

There is no consensus on changes in amplitude of ENSO SST variability across CMIP iterations. The main factors driving the diversity of ENSO SST amplitude change in climate models are internal variability, SST-mean warming pattern, and model systematic biases. First, pronounced low-frequency modulations of ENSO exist even in unforced control simulations due to internal variability, which leads a large uncertainty in quantifying future ENSO changes ( [[#Wittenberg--2009|Wittenberg, 2009]] ; [[#Vega-Westhoff--2017|Vega-Westhoff and Sriver, 2017]] ; [[#Zheng--2018|Zheng et al., 2018]] ). Second, ENSO characteristics depend on the climate mean state of the tropical Pacific; however, ENSO can also influence the mean state through non-linear processes ( [[#Cai--2015|Cai et al., 2015]] ; [[#Timmermann--2018|Timmermann et al., 2018]] ). The response of the tropical Pacific mean state to anthropogenic forcing is characterized by a faster warming on the equator compared to the off-equatorial region, a faster warming of the eastern equatorial Pacific compared to the central tropical Pacific (e.g., El Niño-like mean SST warming, see Section 7.4.4.2), and a weakening of the Walker circulation in most models. Those models with a El Niño-like warming tend to project a strengthening of ENSO SST variability whereas models with a La Niña-like warming tend to project a weakening of variability ( [[#Zheng--2016|Zheng et al., 2016]] ; [[#Kohyama--2017|Kohyama and Hartmann, 2017]] ; J. [[#Wang--2017b|]] [[#Wang--2017|Wang et al., 2017]] b ; [[#Cai--2018a|Cai et al., 2018a]] ; [[#Fredriksen--2020|Fredriksen et al., 2020]] ). Third, how to take model biases into account leads to different ENSO changes. [[#Kim--2014|Kim et al. (2014)]] suggested that a subset of CMIP5 models that simulate linear ENSO stability realistically exhibit a decrease in ENSO amplitude by the second half of the 21st century. However, an increase of ENSO SST variability has been projected when considering biases in ENSO pattern simulation by different models ( [[#Zheng--2016|Zheng et al., 2016]] ; [[#Cai--2018a|Cai et al., 2018a]] ). This highlights the importance of constraining tropical Pacific mean state changes in order to enhance confidence in the projected response of ENSO.

There is also no robust consensus on changes in ENSO diversity. Several studies suggest that an increase in Eastern Pacific (EP)-ENSO events tends to be projected particularly in the models with an El Niño-like warming ( [[#Zheng--2016|Zheng et al., 2016]] ; [[#Cai--2018a|Cai et al., 2018a]] ; [[#Fredriksen--2020|Fredriksen et al., 2020]] ). However, [[#Freund--2020|Freund et al. (2020)]] suggested that models with a El Niño-like mean warming show a tendency toward more Central Pacific (CP) events but fewer EP events compared to models with an La Niña-like warming in both CMIP5 and CMIP6 models.

Even though there is ''low agreement'' in simulated changes in ENSO SST variability, the majority of models project an increase in amplitude of ENSO rainfall variability attributable to the increase in mean SST and moisture in CMIP5 ( [[#Power--2013|Power et al., 2013]] ; [[#Watanabe--2014|Watanabe et al., 2014]] ; [[#Huang--2015|Huang and Xie, 2015]] ) and CMIP6 ( [[#Yun--2021|Yun et al., 2021]] ). It is ''likely'' that extreme El Niño events, accompanied by the eastern equatorial Pacific rainfall exceeding the 5 mm day <sup>–1</sup> rainfall threshold, will increase in intensity ( [[#Cai--2014a|Cai et al., 2014a]] , 2017). However, it has also been suggested that historical model biases over the equatorial Pacific cold tongue in CMIP5 may lead to the greater precipitation mean change and amplification of extreme ENSO-associated rainfall in CMIP5 ( [[#Stevenson--2021|Stevenson et al., 2021]] ).

There is ''limited'' intermodel ''agreement'' on future changes in ENSO teleconnections largely depending on changes in the mean state and changes in ENSO properties ( [[#Yeh--2018|Yeh et al., 2018]] ). Many CMIP5 and CMIP6 models project that the centres of the extratropical teleconnection over North Pacific and North America will shift eastward in association with an eastward shift in tropical convective anomalies ( [[#Yeh--2018|Yeh et al., 2018]] ; [[#Fredriksen--2020|Fredriksen et al., 2020]] ). There is an indication that tropical cyclones will become more frequent during future El Niño events (and less frequent during future La Niña events) by the end of the 21st century ( [[#Chand--2017|Chand et al., 2017]] ), thus contributing to the projected increase in ENSO-associated hydro-climate impacts.

While CMIP6 models show no robust change in ENSO SST amplitude in the mid- and long-term period across all four SSPs, a robust increase in ENSO rainfall amplitude is found particularly in SSP2-4.5, SSP3-7.0, and SSP5-8.5 (Figure 4.10). The changes in ENSO rainfall amplitude in the long-term future (2081–2100) relative to the recent past (1995–2014) are statistically significant at the 95% confidence.

To conclude, the forced change in ENSO SST variability is highly uncertain in CMIP5 and CMIP6 models ( ''medium confidence'' ). However, it is ''very likely'' that ENSO-related rainfall variability will increase significantly regardless of ENSO amplitude changes in the mid- and long-term future. It is ''likely'' that the pattern of ENSO teleconnection over the North Pacific and North America will shift eastward.

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==== 4.5.3.3 Indian Ocean Basin and Dipole Modes ====

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In the mid- to long-term, projected climate mean state changes in the tropical Indian Ocean are expected to resemble a positive IOD state, with faster warming in the west compared to the east ( [[#Cai--2013|Cai et al., 2013]] ; X.-T. [[#Zheng--2013|]] [[#Zheng--2013|Zheng et al., 2013]] ). However, it was argued that this projected mean state change could be due to the large mean state biases in the simulated current climate and potentially not a realistic outcome (G. [[#Li--2016|]] [[#Li--2016|Li et al., 2016]] ). Mean state biases also lead to lack of consensus on projected equatorial Indian Ocean SST variability and equatorial modes of climate variability independent of the IOD ( [[#DiNezio--2020|DiNezio et al., 2020]] ). If mean state change will indeed resemble a positive IOD state, however, this would lead to a reduction in the amplitude difference between positive and negative IOD events, but with no robust change in IOD frequency ( [[#Cai--2013|Cai et al., 2013]] ). For a small subset of CMIP5 models that simulate IOD events best, a slight increase in IOD frequency was found under the CMIP5 RCP4.5 scenario ( [[#Chu--2014|Chu et al., 2014]] ).

However, it was also found that the frequency of extreme positive IOD events, which exhibit the largest climate impacts, might increase by a factor of about three under the CMIP5 RCP8.5 scenario ( [[#Cai--2014b|Cai et al., 2014b]] ). Partially consistent with the above result, a more recent study by [[#Cai--2021|Cai et al. (2021)]] , based on CMIP5 RCP8.5 and CMIP6 SSP5-8.5 simulations, shows a robust increased SST variability of large positive IOD events, but a decreased variability of moderate IOD events. An approximate doubling of these extreme positive IOD events was still found for global warming of 1.5°C warming above pre-industrial levels, without a projected decline thereafter ( [[#Cai--2018b|Cai et al., 2018b]] ). These results depend, however, on the realism of the projected mean state change in the Indian Ocean (G. [[#Li--2016|]] [[#Li--2016|Li et al., 2016]] ).

To conclude, the forced change in IOD in mid- and long-term future remains uncertain due to limited lines of evidence and its dependence on model mean biases. However, there is ''low confidence'' that the frequency of extreme positive IOD events will increase under the high-emissions scenario of SSP5-8.5.

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==== 4.5.3.4 Tropical Atlantic Modes ====

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The AR5 assessed that there is ''low confidence'' in projected changes of the Tropical Atlantic Variability (TAV) because of the general failure of climate models to simulate main aspects of this variability such as the northward displaced ITCZ. The models that best represent the Atlantic meridional mode (AMM) show a weakening for future climate conditions. However, model biases in representation of Altantic Niños strongly limit an assessment of future changes.

Long-term changes in TAVs and associated teleconnections are expected as a result of global warming, but large uncertainties exist due to the models’ systematic underestimation of the connection between PDV and Indo-Pacific SST variations ( [[#Lübbecke--2018|Lübbecke et al., 2018]] ; [[#Cai--2019|Cai et al., 2019]] ). Observational analyses show large discrepancies in SST and trade wind strength ( [[#Servain--2014|Servain et al., 2014]] ; [[#Mohino--2015|Mohino and Losada, 2015]] ). Single-model sensitivity experiments show that Atlantic Niño characteristics at the end of 21st century remain consistent with those of the 20th century, though changes in the climatological SSTs can lead to changes in the associated teleconnections ( [[#Mohino--2015|Mohino and Losada, 2015]] ).

The weakening of the AMOC expected from global warming (see [[#4.3.2.3|Section 4.3.2.3]] ) has been suggested to have an influence on the mean background state of tropical-Atlantic surface conditions, thereby enhancing equatorial Atlantic variability and resulting in a stronger tropical Atlantic–ENSO teleconnection (see [[IPCC:Wg1:Chapter:Chapter-3#3.7.5|Section 3.7.5]] for a detailed discussion; [[#Svendsen--2014|Svendsen et al., 2014]] ). A recent multi-model study, based on CMIP5, concluded that the TAV-Pacific teleconnection will weaken under global warming due to the increased thermal stability of the atmosphere (F. [[#Jia--2019|]] [[#Jia--2019|Jia et al., 2019]] ). However, there is still a clear lack of model studies, and hence no robust evidence on the long-term evolution of TAV and associated teleconnections.

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==== 4.5.3.5 Pacific Decadal Variability ====

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The AR5 assessed that there is ''low'' ''confidence'' in projections of future changes in Pacific decadal variability (PDV) due to the inability of CMIP5 models to represent the connection between PDV and Indo-Pacific SST variations. Because the PDV appears to encompass the combined effects of different dynamical processes operating at different time scales, representation of PDV in climate models remains a challenge ( [[IPCC:Wg1:Chapter:Chapter-3#3.7.6|Section 3.7.6]] ) and its long-term evolution under climate change uncertain.

In addition to uncertainty from the future evolution of the mechanisms that determined the PDV, it is also unclear how the background state in the Pacific Ocean will change due to time-varying radiative forcing, and how this change will interact with variability at interannual and low-frequency time scales ( [[#Fedorov--2020|Fedorov et al., 2020]] ). Recent research suggests that the PDV will have a weaker amplitude and higher frequency with global warming ( [[#Zhang--2016|Zhang and Delworth, 2016]] ; [[#Xu--2017|Xu and Hu, 2017]] ; [[#Geng--2019|Geng et al., 2019]] ). The former appears to be associated with a decrease in SST variability and the meridional gradient over the Kuroshio-Oyashio region, with a reduction in North Pacific wind stress and meandering of the subpolar/subtropical gyre interplay ( [[#Zhang--2016|Zhang and Delworth, 2016]] ). The latter is hypothesized to rely on the enhanced ocean stratification and shallower mixed layers of a warmer climate, which would increase the phase speed of the westward-propagating oceanic waves, hence shortening the decadal to inter-decadal component ( [[#Goodman--1999|Goodman and Marshall, 1999]] ; [[#Zhang--2016|Zhang and Delworth, 2016]] ; [[#Xu--2017|Xu and Hu, 2017]] ). The weakening of the PDV in a warmer climate may reduce the internal variability of global mean surface temperature, to which PDV seems associated ( [[#Zhang--1997|Zhang et al., 1997]] ; [[#Kosaka--2016|Kosaka and Xie, 2016]] ; [[#Geng--2019|Geng et al., 2019]] ). Thus, a weaker and higher frequency PDV could reduce the contribution of internal variability to the GSAT trend and eventually lead to a reduced probability of surface-warming hiatus events.

In summary, based on CMIP5, there is ''medium confidence'' that a weaker and higher frequency PDV is expected under global warming.

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==== 4.5.3.6 Atlantic Multi-decadal Variability ====

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Based on paleoclimate reconstructions and model simulations, AR5 assessed that AMV is ''unlikely'' to change its behaviour in the future. However, AMV fluctuations over the coming decades are ''likely'' to influence regional climate, enhancing or offsetting some of the effects of global warming.

Recent proxy-derived reconstructions of AMV-related signals show persistent multi-decadal variability over the last three centuries ( [[#Kilbourne--2014|Kilbourne et al., 2014]] ; [[#Svendsen--2014|Svendsen et al., 2014]] ; [[#Moore--2017|Moore et al., 2017]] ), up to the last millennium ( [[#Chylek--2011|Chylek et al., 2011]] ; [[#Zhou--2016|Zhou et al., 2016]] ; J. [[#Wang--2017b|]] [[#Wang--2017|Wang et al., 2017]] b ) and beyond ( [[#Knudsen--2011|Knudsen et al., 2011]] ). This implies that in the past AMV properties were little affected by large climatic excursions.

AMV long-term changes under future warming scenarios have so far scarcely been investigated. A study on the CMIP5 multi-model simulations under RCP8.5 scenario by ( [[#Villamayor--2018|Villamayor et al., 2018]] ) found no substantial differences in the simulated SST patterns (and in the related tropical rainfall response) when RCP8.5, historical and piControl simulations are compared. Such results suggest that the AMV is not expected to change under global warming. A more recent single-model large ensemble study ( [[#Hand--2020|Hand et al., 2020]] ) shows a pronounced change in the AMV pattern under global warming linked to a strong reduction of the mean AMOC and its variability. However, since a superposition of multiple processes controls the AMV, as extensively discussed in [[IPCC:Wg1:Chapter:Annex-iv|Annex IV]] (Section AIV.2.7), in [[IPCC:Wg1:Chapter:Chapter-3|Chapter 3]] ( [[IPCC:Wg1:Chapter:Chapter-3#3.7.7|Section 3.7.7]] ), and in [[IPCC:Wg1:Chapter:Chapter-9|Chapter 9]] (Section 9.2.3.1), the length of the RCP8.5 simulations might be not sufficient to properly evaluate the respective weight and interplay of internal components and influences from external forcing on AMV projections.

In conclusion, on the basis of paleoclimate reconstructions and CMIP5 model simulations, there is ''low confidence'' that the AMV is not expected to change in the future.

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