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

==== 3.3.3.3 Extratropical Jets, Storm Tracks and Blocking ====

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Extratropical jets are wind maxima in the upper troposphere which are often associated with storms, blocking, and weather extremes. Blocking refers to long-lived, stationary high-pressure systems that are often associated with a poleward displacement of the jet, causing cold spells in winter and heatwaves in summer (e.g., [[#Sousa--2018|Sousa et al., 2018]] ). Sections 2.3.1.4.3, 8.3.2.7, and 11.7.2 discuss these features in more detail.

AR5 concluded that models were able to capture the general characteristics of extratropical cyclones and storm tracks, although it also noted that most models underestimated cyclone intensity, that biases in cyclone frequency were linked to biases in sea surface temperatures, and that resolution can play a significant role in the quality of the simulation of storms ( [[#Flato--2013|Flato et al., 2013]] ). Similarly, AR5 found with ''high confidence'' that simulation of blocking was improved with increases in resolution. The AR5 did not specifically assess changes in Southern Hemisphere storm track characteristics or blocking.

Since AR5, new research using CMIP5 and CMIP6 models has confirmed that increasing the model resolution improves the simulation of cyclones and blocking in all seasons albeit with some exceptions and caveats ( [[#Zappa--2013|Zappa et al., 2013]] ; [[#Davini--2017|Davini et al., 2017]] ; [[#Schiemann--2017|Schiemann et al., 2017]] , 2020; [[#Davini--2020|Davini and D’Andrea, 2020]] ; [[#Priestley--2020|Priestley et al., 2020]] ). New research also finds that model performance with respect to the simulation of cyclones and that of blocking events are correlated ( [[#Zappa--2014|Zappa et al., 2014]] ), suggesting biases in both are aspects of the same underlying problems in models (Figure 3.18). In the North Pacific basin the annual mean blocking frequency is now well simulated compared to earlier evaluations, but substantial errors in the blocking frequency remain in the Euro-Atlantic sector (Figure 3.18; [[#Dunn-Sigouin--2013|Dunn-Sigouin and Son, 2013]] ; [[#Davini--2016|Davini and D’Andrea, 2016]] , 2020; [[#Mitchell--2017|Mitchell et al., 2017]] ; [[#Woollings--2018b|Woollings et al., 2018b]] ). While there is a resolution dependence in the size of this bias, even at very high resolution blocking in the Euro-Atlantic sector remains underestimated ( [[#Schiemann--2017|Schiemann et al., 2017]] ), and there is evidence of a compensation of errors as the resolution is increased ( [[#Davini--2017|Davini et al., 2017]] ). [[#Davini--2020|Davini and D’Andrea (2020)]] show that while the simulation of blocking improves with increasing resolution in CMIP3, CMIP5, and CMIP6 models, other factors contribute to biases, particularly to the underestimation of Euro-Atlantic blocking ( [[#Schiemann--2020|Schiemann et al., 2020]] ). The persistence of blocking events, typically underestimated, has not improved from CMIP5 to CMIP6 ( [[#Schiemann--2020|Schiemann et al., 2020]] ). Section 10.3.3.3 discusses the implications of the biases discussed here for regional climate.

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[[File:f46b15e7e781d75ae9cdfbea3c54a0c5 IPCC_AR6_WGI_Figure_3_18.png]]

Figure 3.18 | '''Instantaneous Northern-Hemisphere blocking frequency (% of days) in the extended northern winter season (December–January''' '''–''' '''February–March – DJFM) for the years 197''' '''9–''' '''2000.''' Results are shown for the ERA5 reanalysis (black), CMIP5 (blue) and CMIP6 (red) models. Coloured lines show multi-model means and shaded ranges show corresponding 5–95% ranges constructed with one realization from each model. Figure is adapted from [[#Davini--2020|Davini and D’Andrea (2020)]] , their Figure 12 and following the [[#D’Andrea--1998|D’Andrea et al. (1998)]] definition of blocking. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

For the North Pacific storm track CMIP6 simulations exhibit large remaining underestimations of cyclone frequencies during summer (June to August), which for the low-resolution models have essentially remained unchanged versus CMIP5, and there is only a small resolution dependence of this bias ( [[#Priestley--2020|Priestley et al., 2020]] ). During winter (December to February), both CMIP5 and CMIP6 models tend to place the North Pacific storm track too far equatorward (M. [[#Yang--2018|]] [[#Yang--2018|Yang et al., 2018]] ; [[#Priestley--2020|Priestley et al., 2020]] ), leading to an overestimation of cyclones between 30°N and 40°N in the Pacific and an underestimation to the north of this. Both low- and high-resolution models show this pattern, but low-resolution models generally simulate fewer cyclones throughout the North Pacific ( [[#Priestley--2020|Priestley et al., 2020]] ).

In winter, the North Atlantic storm track remains displaced to the south and east in many models ( [[#Harvey--2020|Harvey et al., 2020]] ), leading to underestimation of cyclone frequencies near the North American coast and overestimation in the eastern North Atlantic. Higher-resolution CMIP6 models perform slightly better in this regard than low-resolution models. In summer (June to August), cyclone frequencies throughout the extratropical North Atlantic, which were substantially underestimated in CMIP5, have improved in CMIP6 high-resolution models. In low-resolution CMIP6 models, the problem is essentially unchanged ( [[#Priestley--2020|Priestley et al., 2020]] ); this is associated with generally underestimated variability of sea level pressure in CMIP models ( [[#Harvey--2020|Harvey et al., 2020]] ).

For the Southern Hemisphere (not considered in AR5), [[#Priestley--2020|Priestley et al. (2020)]] find considerable improvement in the placement of the Southern Ocean storm track during summer (December to February) in CMIP6 models versus CMIP5, consistent with a more realistic annual mean surface wind maximum latitude in CMIP6 than in CMIP5 ( [[#Goyal--2021|Goyal et al., 2021]] ). Relative to CMIP5, both low- and high-resolution CMIP6 models have increased track densities south of about 55°S and decreased track densities between about 40°S and 55°S, in better agreement with observations than CMIP5 models ( [[#Parsons--2016|Parsons et al., 2016]] ; [[#Patterson--2019|Patterson et al., 2019]] ). CMIP5 models and high-resolution CMIP6 models simulate a storm track that is positioned too far equatorward, although the bias is smaller in the high-resolution models. By contrast, the low-resolution CMIP6 models simulate a storm track that is slightly too far poleward on average ( [[#Priestley--2020|Priestley et al., 2020]] ). In winter (June to August), the biases found in CMIP5 are only slightly improved in CMIP6, with models continuing to underestimate the broad maximum cyclone track density in the south-eastern Indian Ocean and overestimate the minimum density in the south-western South Pacific ( [[#Priestley--2020|Priestley et al., 2020]] ).

There is only one contiguous blocking region in the Southern Hemisphere, with the blocking frequency maximizing in the South Pacific and minimizing in the southern Indian Ocean regions ( [[#Parsons--2016|Parsons et al., 2016]] ; [[#Patterson--2019|Patterson et al., 2019]] ). CMIP5 simulations agree relatively well with ERA-Interim in this region regarding the distribution of blocking events ( [[#Parsons--2016|Parsons et al., 2016]] ). Individual models exhibit considerable biases in the blocking frequency; however only in austral summer do [[#Patterson--2019|Patterson et al. (2019)]] find a systematic, multi-model underestimation of the blocking frequency in and around the Tasman Sea. The blocking frequency is anticorrelated with the amplitude of the SAM. Ozone depletion, through stratosphere-troposphere coupling, may have caused an increase in the blocking frequency in the South Atlantic sector ( [[#Dennison--2016|Dennison et al., 2016]] ); this finding requires confirmation using a multi-model approach.

In addition to inadequate resolution, blocking and storm track biases in both hemispheres also result from mean state biases, in particular, biases related to the parameterization of orographic effects and to the misrepresentation of the Gulf Stream SST front ( [[#Anstey--2013|Anstey et al., 2013]] ; [[#Berckmans--2013|Berckmans et al., 2013]] ; [[#Davini--2016|Davini and D’Andrea, 2016]] ; [[#O’Reilly--2016a|O’Reilly et al., 2016a]] ; [[#Pithan--2016|Pithan et al., 2016]] ; [[#Schiemann--2017|Schiemann et al., 2017]] ). Nonetheless overall SST biases have been suggested to have only a weak relevance to blocking ( [[#Davini--2016|Davini and D’Andrea, 2016]] ).

( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4.3|Section 2.3.1.4.3]] assesses that the total number of extratropical cyclones has ''likely'' increased since the 1980s in the Northern Hemisphere ( ''low confidence'' ), but with fewer deep cyclones particularly in summer. This observed reduction in cyclone activity by about 4% per decade in the Northern Hemisphere in summer ( [[#Chang--2016|Chang et al., 2016]] ; [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4.3|Section 2.3.1.4.3]] ) may be associated with human-induced warming. CMIP5 historical simulations generally reproduce a reduction but underestimate its magnitude ( [[#Chang--2016|Chang et al., 2016]] ). Furthermore, feedback mechanisms associated with clouds may be responsible for substantial inter-model spread ( [[#Chang--2016|Chang et al., 2016]] ; [[#Voigt--2016|Voigt and Shaw, 2016]] ). In boreal winter, recent studies have suggested a potential influence of the rapid Arctic warming on observed intensification of Northern Hemisphere storm track activity in the past few decades, while other studies question this possibility (Cross-Chapter Box 10.1).

( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4.3|Section 2.3.1.4.3]] assesses that the extratropical jets and cyclone tracks have ''likely'' shifted poleward in both hemispheres since the 1980s with marked seasonality in trends ( ''medium confidence'' ). For the Southern Hemisphere, studies using CMIP5 and other models imply that both ozone depletion and increasing greenhouse gases have caused substantial atmospheric circulation change since the 1960s when concentrations of ozone-depleting substances started to increase ( [[#Eyring--2013|Eyring et al., 2013]] ; [[#Iglesias-Suarez--2016|Iglesias-Suarez et al., 2016]] ; [[#Karpechko--2018|Karpechko et al., 2018]] ; [[#Son--2018|Son et al., 2018]] ). In particular, ozone depletion, during austral summer, has been linked to a poleward shift of the westerly jet and Southern Hemisphere circulation zones and a southward expansion of the tropics ( [[#Kang--2011|Kang et al., 2011]] ), which is associated with a strengthening trend of the Southern Annular Mode (SAM; [[#3.7.2|Section 3.7.2]] ). This has been well reproduced by climate models with prescribed historical ozone concentration or interactive ozone chemistry ( [[#Gerber--2014|Gerber and Son, 2014]] ; [[#Son--2018|Son et al., 2018]] ; Figure 3.19).

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[[File:adf164c94327a53bfeee634def663a37 IPCC_AR6_WGI_Figure_3_19.png]]

Figure 3.19 | '''Long-term mean (thin black contours) and linear trend (colour) of zonal mean December–January–February zonal winds from 1985 to 2014 in the Southern Hemisphere.''' The figure shows '''(a)''' ERA5 and '''(b)''' the CMIP6 multi-model mean (58 CMIP6 models). The solid contours show positive (westerly) and zero long-term mean zonal wind, and the dashed contours show negative (easterly) long-term mean zonal wind. Only one ensemble member per model is included. Figure is modified from [[#Eyring--2013|Eyring et al. (2013)]] , their Figure 12. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

In summary, there is ''low confidence'' that an observed decrease in the frequency of Northern Hemisphere summertime extratropical cyclones is linked to anthropogenic influence. In the Southern Hemisphere, there is ''high confidence'' that human influence, in the form of ozone depletion, has contributed to the observed poleward shift of the jet in austral summer, while ''confidence'' is ''low'' for human influence on historical blocking activity. The ''low confidence'' statements are due to the limited number of studies available. The shift of the Southern Hemisphere jet is correlated with modulations of the SAM ( [[#3.7.2|Section 3.7.2]] ). There is ''medium confidence'' in model performance regarding the simulation of the extratropical jets, storm track and blocking activity, with increased resolution sometimes corresponding to better performance, but important shortcomings remain, particularly for the Euro-Atlantic sector of the Northern Hemisphere. Nonetheless, synthesizing across Sections 3.3.3.1–3.3.3.3, there is ''high confidence'' that CMIP6 models capture the general characteristics of the tropospheric large-scale circulation.

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