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

==== 4.5.1.6 Sea Level Pressure, Large-scale Atmospheric Circulation, Storm Tracks and Blocking ====

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This subsection provides a global overview of long-term changes in atmospheric dynamical features that is complementary to the regional assessment of links to the hydrological cycle in [[IPCC:Wg1:Chapter:Chapter-8|Chapter 8]] (Section 8.4.2), and assessment of the connections to extreme events in [[IPCC:Wg1:Chapter:Chapter-11|Chapter 11]] (Section 11.7.2).

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===== 4.5.1.6.1 Sea level pressure =====

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The AR5 assessed that mean sea level pressure is projected to decrease in high latitudes and to increase in mid-latitudes. Such a pattern is associated with a poleward shift in the storm track and an increase in the annular mode index. This broad pattern is also found in CMIP6 models (Figure 4.25). Under SSP1-2.6, the pattern in sea level pressure change resembles that for SSP3-7.0, but the amplitudes are small compared to internal variability in 20-year means (Figure 4.25). One exception is found in the SH mid-latitudes, where pressure robustly increases in SSP3-7.0 in both austral summer and winter, but shows no robust change in SSP1-2.6. This is ''likely'' attributable to the larger GHG forcing in SSP3-7.0 compared to SSP1-2.6, which contributes to a poleward shift of the SH mid-latitude circulation and becomes relatively more important than the effect of ozone recovery which drives an equatorward shift in the circulation (see [[#4.5.3.1|Section 4.5.3.1]] on the Southern Annular Mode; [[#Barnes--2013|Barnes and Polvani, 2013]] ; [[#Barnes--2014|Barnes et al., 2014]] ; [[#Bracegirdle--2020b|Bracegirdle et al., 2020b]] ). The poleward shift in SH mid-latitude circulation in SSP3-7.0 ''likely'' contributes to the wetting trend at high southern latitudes (Figure 4.25).

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[[File:a1782691bffb170fc1f2303f8eafb95f IPCC_AR6_WGI_Figure_4_25.png]]

'''Figure 4.25 |''' '''Long-term change of seasonal-mean sea level pressure.''' Displayed are projected spatial patterns of multi-model mean change in '''(top)''' December–January–February (DJF) and '''(bottom)''' June–July–August (JJA) mean sea level pressure (hPa) in 2081–2100 relative to 1995–2014, for '''(left)''' SSP1-2.6 and '''(right)''' SSP3-7.0. The number of models used is indicated in the top right of the maps. No overlay indicates regions where the change is robust and ''likely'' emerges from internal variability, that is, where at least 66% of the models show a change greater than the internal-variability threshold ( [[#4.2.6|Section 4.2.6]] ) and at least 80% of the models agree on the sign of change. Diagonal lines indicate regions with no change or no robust significant change, where fewer than 66% of the models show change greater than the internal-variability threshold. Crossed lines indicate areas of conflicting signals where at least 66% of the models show change greater than the internal-variability threshold but fewer than 80% of all models agree on the sign of change. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

As was found in AR5, several regional sea level pressure features stand out from the zonal-mean change. Sea level pressure markedly decreases in north-eastern North America and north-eastern Asia in boreal winter. In boreal summer, sea level pressure robustly decreases in the Mediterranean and the Middle-East, a decrease that has been linked to a large-scale heat low forced by the amplified warming of the region ( [[#Haarsma--2009|Haarsma et al., 2009]] ). It is ''likely'' that sea level pressure will increase across the south-western North America and Central America in boreal summer under SSP3-7.0 due to an intensification of the eastern North Pacific subtropical summer high ( [[#Li--2012|Li et al., 2012]] ) and a weakening of the North American monsoon ( [[#4.5.1.5|Section 4.5.1.5]] ; [[#Pascale--2017|Pascale et al., 2017]] ; [[#Wang--2020|Wang et al., 2020]] ). These changes in circulation are connected to drying across the eastern subtropical Pacific and Central America regions (Figure 4.24).

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===== 4.5.1.6.2 Zonal wind and westerly jets =====

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Storm tracks and mid-latitude westerly jets are dynamically related aspects of mid-latitude circulation. The AR5 assessed that a poleward shift of the SH westerlies and storm track is ''likely'' by the end of the 21st century under RCP8.5 ( ''medium confidence'' ). In contrast, ''low confidence'' was assessed for the storm-track response in the NH.

Under both SSP1-2.6 and SSP3-7.0 there is a strengthening and lifting of the subtropical jets in both hemispheres (Figure 4.26), consistent with the response to large-scale tropospheric warming found in earlier generations of climate models ( [[#Collins--2013|Collins et al., 2013]] ). In the SH, GHG emissions tend to force a poleward shift of the jet, but this is opposed, particularly in austral summer, by the stratospheric ozone hole recovery ( [[#Barnes--2013|Barnes and Polvani, 2013]] ; [[#Barnes--2014|Barnes et al., 2014]] ; [[#Bracegirdle--2020b|Bracegirdle et al., 2020b]] ). Consistent with sea level pressure changes, CMIP6 models project a strengthening and poleward shift of the SH jet in austral summer and winter under SSP3-7.0, but smaller and non-robust changes in SH mid-latitude zonal winds under SSP1-2.6 (Figure 4.26; see also [[#4.5.3.1|Section 4.5.3.1]] ). CMIP6 models show an improved simulation of the SH jet stream latitude ( [[#Bracegirdle--2020a|Bracegirdle et al., 2020a]] ; [[#Curtis--2020|Curtis et al., 2020]] ). This has been linked to a reduction in the projected poleward shift of the SH jet in austral summer compared to the CMIP5 models ( [[#Curtis--2020|Curtis et al., 2020]] ; [[#Goyal--2021|Goyal et al., 2021]] ), although differences in the pattern of SST response may also play a role ( [[#Wood--2020|Wood et al., 2020]] ). In the NH extratropics, the changes in lower-tropospheric zonal-mean zonal winds by the end of the century are generally smaller than in the SH. In boreal winter, there is a weak poleward shift of the NH zonal-mean westerly jet maximum in SSP3-7.0.

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[[File:f54d6787f8e183a379169ed8be4933e4 IPCC_AR6_WGI_Figure_4_26.png]]

'''Figure 4.26 |''' '''Long-term change of zonal-mean, zonal wind.''' Displayed are multi-model mean changes in '''(left)''' boreal winter(December–January–February, DJF) and '''(right)''' austral winter (June–July–August, JJA) zonal mean, zonal wind (m s <sup>–1</sup> ) in 2081–2100 for (top) SSP1-2.6 and (bottom) SSP3-7.0 relative to 1995–2014. The 1995–2014 climatology is shown in contours with spacing 10 m s <sup>–1</sup> . Diagonal lines indicate regions where less than 80% of the models agree on the sign of the change and no overlay where at least 80% of the models agree on the sign of the change. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

CMIP5 and CMIP6 models show a strong seasonal and regional dependence in the response to climate change of NH westerlies ( [[#Barnes--2013|Barnes and Polvani, 2013]] ; [[#Grise--2014b|Grise and Polvani, 2014b]] ; [[#Simpson--2014|Simpson et al., 2014]] ; [[#Zappa--2015|Zappa et al., 2015]] ; [[#Harvey--2020|Harvey et al., 2020]] ; [[#Oudar--2020|Oudar et al., 2020]] ). CMIP5 projections indicate a poleward shift of the westerlies in the North Atlantic in boreal summer, while the North Pacific jet weakens in this season ( [[#Simpson--2014|Simpson et al., 2014]] ; [[#Davini--2020|Davini and D’Andrea, 2020]] ; [[#Harvey--2020|Harvey et al., 2020]] ). There is a poleward shift in the westerlies in both the North Pacific and North Atlantic in Autumn ( [[#Barnes--2013|Barnes and Polvani, 2013]] ; [[#Simpson--2014|Simpson et al., 2014]] ). However, the shift of the westerlies is more uncertain in the other seasons, particularly in the North Atlantic in winter ( [[#Simpson--2014|Simpson et al., 2014]] ; [[#Zappa--2017|Zappa and Shepherd, 2017]] ). Here, the circulation response is not well described as a simple shift, since the North Atlantic jet tends to be squeezed on both its equatorward and poleward flanks, together with an eastward extension into Europe ( [[#Li--2018|Li et al., 2018]] ; [[#Peings--2018|Peings et al., 2018]] ; [[#Simpson--2019a|Simpson et al., 2019a]] ; [[#Harvey--2020|Harvey et al., 2020]] ; [[#Oudar--2020|Oudar et al., 2020]] ). Simulations indicate that most of the changes in winter storminess over the Euro-Atlantic region will occur only after exceeding the 1.5°C warming level ( [[#Barcikowska--2018|Barcikowska et al., 2018]] ).

Progress since AR5 has improved understanding of the climate change aspects that can drive these different, and potentially opposite, responses in the mid-latitude jets and storm tracks. A poleward shift of the jets and storm tracks is expected in response to an increase in the atmospheric stratification and in the upper-tropospheric equator-to-pole meridional temperature gradient, while it is opposed by the decrease in the meridional temperature gradient in the lower troposphere associated with the polar amplification of global warming ( [[#Harvey--2014|Harvey et al., 2014]] ; [[#Shaw--2016|Shaw et al., 2016]] ). Recent analyses have identified additional climate aspects that can drive mid-latitude jet changes, including patterns in sea surface warming ( [[#Mizuta--2014|Mizuta et al., 2014]] ; [[#Langenbrunner--2015|Langenbrunner et al., 2015]] ; [[#Ceppi--2018|Ceppi et al., 2018]] ; [[#Wood--2020|Wood et al., 2020]] ), land–sea warming contrast ( [[#Shaw--2015|Shaw and Voigt, 2015]] ), loss of sea ice ( [[#Deser--2015|Deser et al., 2015]] ; [[#Harvey--2015|Harvey et al., 2015]] ; [[#Screen--2018b|Screen et al., 2018b]] ; [[#Zappa--2018|Zappa et al., 2018]] ), and changes in the strength of the stratospheric polar vortex ( [[#Manzini--2014|Manzini et al., 2014]] ; [[#Grise--2017|Grise and Polvani, 2017]] ; [[#Simpson--2018|Simpson et al., 2018]] ; [[#Ceppi--2019|Ceppi and]] [[#Shepherd--2019|Shepherd, 2019]] ). From an energetics perspective,the uncertainty in the response of the jet streams depends on the response of clouds, their non-spatially uniform radiative feedbacks shaping the meridional profile of warming ( [[#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]] ). Climate models seem to underestimate the forced component of the year-to-year variability in the atmospheric circulation, particularly in the North Atlantic sector ( [[#Scaife--2018|Scaife and Smith, 2018]] ), which suggests some relevant dynamical processes may not be well represented. Whether and how this may affect long-term projections is unknown. In conclusion, due to the influence from competing dynamical drivers and the absence of observational evidence, there is ''medium confidence'' in a projected poleward shift of the NH zonal-mean low-level westerlies in autumn and summer and ''low confidence'' in the other seasons. There is also overall ''low confidence'' in projected regional changes in the NH low-level westerlies, particularly for the North Atlantic basin in boreal winter.

The anthropogenic forced signal in extratropical atmospheric circulation may well be small compared to internal variability ( [[#Deser--2012b|Deser et al., 2012b]] , 2014) and, as assessed in AR5, there is generally '''low agreement''' across models in many aspects of regional atmospheric circulation change particularly in the NH ( [[#Shepherd--2014|Shepherd, 2014]] ). The latter means that, in some regions, a multi-model average perspective of atmospheric circulation change represents a small residual after averaging over large intermodel spread. This is in strong contrast to thermodynamic aspects of climate change, such as surface temperature change, for which model results are generally highly consistent (see, e.g., Figure 4.19). Furthermore, models share systematic biases in some aspects of extratropical atmospheric circulation such as mid-latitude jets, which can have complex implications for understanding forced changes ( [[#Simpson--2016|Simpson and Polvani, 2016]] ). Given these issues, an emerging field of research since AR5 has focused on the development of ‘storylines’ for regional atmospheric circulation change ( [[#Shepherd--2019|Shepherd, 2019]] ). The storyline approach is grounded in the identification of a set of physical predictors of atmospheric circulation change, such as those described above ( [[#Harvey--2014|Harvey et al., 2014]] ; [[#Manzini--2014|Manzini et al., 2014]] ; [[#Shepherd--2018|Shepherd et al., 2018]] ), which act together to determine a specific outcome in the projected atmospheric circulation change. The consequences of multi-model spread in the physical predictors of atmospheric circulation change can be investigated, conditioned on a specified level of global warming (see also [[IPCC:Wg1:Chapter:Chapter-1#1.4.4.2|Section 1.4.4.2]] and Box 10.2; [[#Zappa--2017|Zappa and Shepherd, 2017]] ; [[#Zappa--2019|Zappa, 2019]] ; [[#Mindlin--2020|Mindlin et al., 2020]] ).

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===== 4.5.1.6.3 Storm tracks =====

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As stated in AR5, the number of extratropical cyclones (ETC) composing the storm tracks is projected to weakly decline in future projections, but by no more than a few percent change. The reduction is mostly located on the equatorward flank of the storm tracks, which is associated with the Hadley cell expansion and a poleward shift in the mean genesis latitude of ETCs ( [[#Tamarin-Brodsky--2017|Tamarin-Brodsky and Kaspi, 2017]] ). Furthermore, the poleward propagation of individual ETCs is expected to increase with warming ( [[#Graff--2014|Graff and LaCasce, 2014]] ; [[#Tamarin-Brodsky--2017|Tamarin-Brodsky and Kaspi, 2017]] ), thus contributing to a poleward shift in the mid-latitude transient-eddy kinetic energy. The increased poleward propagation results from the strengthening of the upper tropospheric jet and increased cyclone-associated precipitation ( [[#Tamarin-Brodsky--2017|Tamarin-Brodsky and Kaspi, 2017]] ), which are robust aspects of climate change.

In the NH boreal winter, CMIP6 models show a northward shift of the ETC density in the North Pacific, a tripolar pattern in the North Atlantic, and a weakening of the Mediterranean storm track (Figure 4.27a). CMIP6 models show overall ''low agreement'' on changes in ETC density in the North Atlantic in boreal winter (Figure 4.27a). A poleward shift of the storm track is evident in the SH (Figure 4.27b), particularly in the Indian and Pacific Ocean sectors. CMIP6 models still feature long-standing biases in the representation of storm tracks; for example, the winter storm track into Europe is too zonal, though different measures of storm track activity indicate some improvements compared to the previous generations of models ( [[#Harvey--2020|Harvey et al., 2020]] ; [[#Priestley--2020|Priestley et al., 2020]] ).

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[[File:efca96cede6e9a3da0fcdf9a22d101a5 IPCC_AR6_WGI_Figure_4_27.png]]

'''Figure''' '''4.27 |''' '''Changes in extratropical storm track density.''' Displayed are projected spatial pattern of multi-model mean change of extratropical storm track density in winter (Northern Hemisphere December –January–Februrary, NH DJF, and Southern Hemisphere June–July–August, SH JJA) in 2080–2100 for SSP5-8.5 relative to 1979–2014 based on 13 CMIP6 models. Diagonal lines indicate regions where fewer than 80% of the models agree on the sign of the change and no overlay where at least 80% of the models agree on the sign of change. Units are number density per 5° spherical cap per month. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

Regarding the dynamical intensity of the storm tracks (Section 11.7.2), the number of ETCs associated with intense surface wind speeds and undergoing explosive pressure deepening are projected to strongly decrease in the NH winter ( [[#Seiler--2016|Seiler and Zwiers, 2016]] ; [[#Chang--2018|Chang, 2018]] ). The weakening of surface winds of ETCs in the NH is attributed to the reduced low-level baroclinicity from SST and sea ice changes ( [[#Harvey--2014|Harvey et al., 2014]] ; [[#Seiler--2016|Seiler and Zwiers, 2016]] ; J. [[#Wang--2017a|]] [[#Wang--2017|Wang et al., 2017]] a ). There are, however, regional exceptions such as in the northern North Pacific, where explosive and intense ETCs are projected to increase in association with the poleward shift of the jet and increased upper-level baroclinicity ( [[#Seiler--2016|Seiler and Zwiers, 2016]] ). Eddy kinetic energy and intense cyclone activity are also projected to decrease in the NH summer in association with a weakening of the jet ( [[#Lehmann--2014|Lehmann et al., 2014]] ; [[#Chang--2016|Chang et al., 2016]] ). However, explosive cyclones tend to be too weak in climate models ( [[#Seiler--2016|Seiler and Zwiers, 2016]] ; [[#Priestley--2020|Priestley et al., 2020]] ), though this bias seems to be reduced in high-resolution simulations ( [[#Jiaxiang--2020|Jiaxiang et al., 2020]] ). Furthermore, models may not fully capture the contribution of the future increase in mesoscale latent heating to cyclone intensification ( [[#Li--2014|Li et al., 2014]] ; [[#Pfahl--2015|Pfahl et al., 2015]] ; [[#Willison--2015|Willison et al., 2015]] ; [[#Michaelis--2017|Michaelis et al., 2017]] ). In conclusion, there is only ''medium confidence'' in the projected decrease in the frequency of intense NH ETCs.

In contrast to the Northern Hemisphere, the Southern Hemisphere shows an increase in the frequency of intense ETCs in CMIP5 models ( [[#Chang--2017|Chang, 2017]] ), and there is ''high confidence'' that wind speeds associated with ETCs are expected to intensify in the SH storm track for high emissions scenarios. These changes in intensity are accompanied by an overall southward shift of the SH winter storm track (Figure 4.27b) due to the poleward shift in the upper-level jet and the increase in the meridional SST gradient linked to the slower warming of the Southern Ocean ( [[#Grieger--2014|Grieger et al., 2014]] ).

Regardless of dynamical intensity changes, there is ''high confidence'' that the number of ETCs associated with extreme precipitation is projected to increase with warming, due to the increased moisture-loading capacity of the atmosphere (Section 8.4.2; [[#Yettella--2017|Yettella and Kay, 2017]] ; [[#Hawcroft--2018|Hawcroft et al., 2018]] ).

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===== 4.5.1.6.4 Atmospheric blocking =====

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Blocking is associated with a class of quasi-stationary, high-pressure weather systems in the middle and high latitudes that disrupt the prevailing westerly flow. These events can persist for extended periods, such as a week or longer, and can cause long-lived extreme weather conditions, from heat waves in summer to cold spells in winter (see Section 11.7.2 for a detailed discussion of these features and [[IPCC:Wg1:Chapter:Chapter-3#3.3.3.3%20|Section 3.3.3.3]] for the assessment of blocking biases in models simulations). The AR5 assessed with ''medium confidence'' that the frequency of blocking would not increase under enhanced GHG concentrations, while changes in blocking intensity and persistence remained uncertain.

The CMIP5 projections suggest that the response of blocking frequency to climate change might be quite complex ( [[#Dunn-Sigouin--2013|Dunn-Sigouin et al., 2013]] ; [[#Masato--2013|Masato et al., 2013]] ). An eastward shift of winter blocking activity in the NH is indicated ( [[#Masato--2013|Masato et al., 2013]] ; [[#Kitano--2016|Kitano and Yamada, 2016]] ; [[#Lee--2017|Lee and Ahn, 2017]] ; [[#Matsueda--2017|Matsueda and Endo, 2017]] ) while during boreal summer, blocking frequency tends to decrease in mid-latitudes ( [[#Matsueda--2017|Matsueda and Endo, 2017]] ), with the exception of the eastern Europe–western Russia region ( [[#Masato--2013|Masato et al., 2013]] ). The projected decrease of blocking in boreal summer partially contrasts with the observed increase in Greenland blocking ( [[#Hanna--2018|Hanna et al., 2018]] ; [[#Davini--2020|Davini and D’Andrea, 2020]] ). However, as shown in [[#Woollings--2018|Woollings et al. (2018)]] , the spatial distribution and the magnitude of the suggested changes are sensitive to the blocking detection methods ( [[#Schwierz--2004|Schwierz et al., 2004]] ; [[#Barriopedro--2010|Barriopedro et al., 2010]] ; [[#Davini--2012|Davini et al., 2012]] ). In the SH, blocking frequency is projected to decrease in the Pacific sector during austral spring and summer. However, seasonal and regional changes are not totally consistent across the models ( [[#Parsons--2016|Parsons et al., 2016]] ), and, as assessed in [[IPCC:Wg1:Chapter:Chapter-3#3.3.3.3|Section 3.3.3.3]] , model biases might affect their response.

To better understand the uncertainty in future blocking activity, a process-oriented approach has been proposed that aims to link blocking responses to different features of the global warming pattern. Upper-level tropical warming might be the key factor leading to a reduced blocking, because of the strengthening of zonal winds ( [[#Kennedy--2016|Kennedy et al., 2016]] ). The more controversial influence of near-surface Arctic warming might lead to an increased blocking frequency ( [[#Mori--2014|Mori et al., 2014]] ; [[#Francis--2015|Francis and Vavrus, 2015]] ) (see Chapter 10, Box 10.1).

Figure 4.28 shows a clear decrease in blocking activity over Greenland and North Pacific for SSP7.0 and SSP8.5. Models with the largest decrease in blocking frequency in boreal winter are those showing the smallest frequency bias during the historical period ( [[#Davini--2020|Davini and D’Andrea, 2020]] ). In conclusion, there is ''medium confidence'' that the frequency of atmospheric blocking events over Greenland and the North Pacific will decrease in boreal winter in the SSP3-7.0 and SSP5-8.5 scenarios.

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[[File:bfba819d75ace59a766e614db907a4ec IPCC_AR6_WGI_Figure_4_28.png]]

'''Figure''' '''4.28 |''' '''Projected winter atmospheric blocking frequencies.''' Box plot showing December –March atmospheric blocking frequencies from historical simulations over 1995–2014 and projections over 2081–2100, over '''(a)''' the Central European region (20°W–20°E, 45°N–65°N); '''(b)''' the Greenland region (65°W–20°W, 62.5°N–72.5°N); '''(c)''' the North Pacific region (130°E–150°W, 60°N–75°N). Values show the percentage of blocked days per season following the ( [[#Davini--2012|Davini et al., 2012]] ) index. Median values are the thick black horizontal bar. The lower whiskers extend from the first quartile to the smallest value in the ensemble, and the upper whiskers extend from the third quartile to the largest value. The whiskers are limited to an upper bound that is 1.5 times the interquartile range (the distance between the third and first quartiles). Black dots show outliers from the whiskers. The numbers below each bar report the number of models included. Observationally-based values are obtained as the average of the ERA-Interim Reanalysis, the JRA-55 Reanalysis and the NCEP/NCAR Reanalysis. Adapted from [[#Davini--2020|Davini and D’Andrea (2020)]] . Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

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