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

===== 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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