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

==== 4.3.3.1 Northern and Southern Annular Modes ====

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===== 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]] ).

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===== 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'' ).

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