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

=== 3.3.3 Atmospheric Circulation ===

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==== 3.3.3.1 The Hadley and Walker Circulations ====

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The tropical tropospheric circulation features meridional and zonal overturning circulations, called Hadley and Walker circulations. In the zonal mean, the downwelling branch of the Hadley circulation cell is located in the subtropics and is often used as an indicator of the meridional extent of the tropics. In the equatorial zonal-vertical section, the major rising branch of the Walker circulation is located over the Maritime continent with secondary ascending regions over northern South America and Africa. The zonal component of the surface trade winds over most of the equatorial Pacific and Atlantic is associated with the Walker circulation. This section assesses the zonal-mean Hadley cell extent and the Pacific Walker circulation strength. Regional and water cycle aspects of these circulations are assessed in more detail in Section 8.3.2.

AR5 found ''medium confidence'' that the depletion of stratospheric ozone had contributed to Hadley cell widening in the Southern Hemisphere in austral summer ( [[#Bindoff--2013|Bindoff et al., 2013]] ). It also noted that in contrast to a simulated weakening in response to greenhouse gas forcing, the Walker circulation had actually strengthened since the early 1990s, precluding any detection of human influence.

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===== 3.3.3.1.1 Hadley cell extent =====

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[[#Grise--2019|Grise et al. (2019)]] found that a metric based on surface zonal winds, which are well constrained by surface observations, best compares reanalyses with CMIP5 models. With this method and new reanalysis products, the CMIP5 historical simulations exhibit comparable mean states and variability of the subtropical edge latitude of the Hadley cells to those observed ( [[#Grise--2019|Grise et al., 2019]] ).

( [[IPCC:Wg1:Chapter:Chapter-2|Chapter 2]] assesses that there has ''very likely'' been a widening of the Hadley circulation since the 1980s ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4.1|Section 2.3.1.4.1]] ). The CMIP5 ( [[#Davis--2017|Davis and Birner, 2017]] ; [[#Grise--2018|Grise et al., 2018]] ) and CMIP6 ( [[#Grise--2020|Grise and Davis, 2020]] ) historical simulation ensembles span the observed trends of the zonal-mean Hadley cell edges since the 1980s (Figure 3.16a–c). Studies based on CMIP5 models find a contribution from human influence to the observed widening trend, especially in the Southern Hemisphere ( [[#Gerber--2014|Gerber and Son, 2014]] ; [[#Staten--2018|Staten et al., 2018]] , 2020; [[#Grise--2019|Grise et al., 2019]] ; [[#Jebri--2020|Jebri et al., 2020]] ), which is confirmed based on CMIP6 (Figure 3.16b,c; [[#Grise--2020|Grise and Davis, 2020]] ).

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[[File:a1a79ce037eeab534e19613ebdd8b703 IPCC_AR6_WGI_Figure_3_16.png]]

Figure 3.16 | '''Model evaluation and attribution of changes in Hadley cell extent and Walker circulation strength. (a–c)''' Trends in subtropical edge latitude of the Hadley cells in '''(a)''' the Northern Hemisphere for 1980–2014 annual means and '''(b, c)''' Southern Hemisphere for '''(b)''' 1980–2014 annual means and '''(c)''' 1980/81–1999/2000 December–January–February means. Positive values indicate northward shifts. '''(d–f)''' Trends in the Pacific Walker circulation strength for '''(d)''' 1901–2010, '''(e)''' 1951–2010 and '''(f)''' 1980–2014. Positive values indicate strengthening. Based on CMIP5 historical (extended with RCP4.5), CMIP6 historical, AMIP, pre-industrial control, and single forcing simulations along with HadSLP2 and reanalyses. Pre-industrial control simulations are divided into non-overlapping segments of the same length as the other simulations. White boxes and whiskers represent means, interquartile ranges and 5th and 95th percentiles, calculated after weighting individual members with the inverse of the ensemble size of the same model, so that individual models are equally weighted ( [[#3.2|Section 3.2]] ). The filled boxes represent the 5–95% confidence interval on the multi-model mean trends of the models with at least three ensemble members, with dots indicating the ensemble means of individual models. The edge latitude of the Hadley cell is where the surface zonal wind velocity changes sign from negative to positive, as described in the Appendix of [[#Grise--2018|Grise et al. (2018)]] . The Pacific Walker circulation strength is evaluated as the annual mean difference of sea level pressure between 5°S–5°N, 160°W–80°W and 5°S–5°N, 80°E–160°E. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

In the annual mean, internal variability, including Pacific Decadal Variability (PDV; Annex IV.2.6), contributed to the observed zonal-mean Hadley cell expansion since 1980 comparably with human influence ( [[#Allen--2014|Allen et al., 2014]] ; [[#Allen--2017|Allen and Kovilakam, 2017]] ; [[#Mantsis--2017|Mantsis et al., 2017]] ; [[#Amaya--2018|Amaya et al., 2018]] ; [[#Grise--2018|Grise et al., 2018]] ). Indeed, the ensemble-mean expansion in historical simulations is significantly weaker than in most of the reanalyses shown in Figure 3.16a–c, while the Atmospheric Model Intercomparison Project (AMIP) simulations forced by observed SSTs (Figure 3.16a–c) show stronger trends than historical coupled simulations on average ( [[#Nguyen--2015|Nguyen et al., 2015]] ; [[#Davis--2017|Davis and Birner, 2017]] ; [[#Grise--2018|Grise et al., 2018]] ). The human-induced change has not yet clearly emerged out of the internal variability range in the Northern Hemisphere ( [[#Quan--2018|Quan et al., 2018]] ; [[#Grise--2019|Grise et al., 2019]] ), whereas the trend in the annual-mean Southern Hemisphere edge is outside the 5th–95th percentile range of internal variability in CMIP6 in three out of the four reanalyses (Figure 3.16b). For the Southern Hemisphere summer when the simulated human influence is strongest, the 1981–2000 trend in three out of the four reanalyses falls outside the 5th–95th percentile range of internal variability (Figure 3.16c; L. [[#Tao--2016|]] [[#Tao--2016|Tao et al., 2016]] ; [[#Grise--2018|Grise et al., 2018]] , 2019).

In CMIP5 simulations, greenhouse gas increases and, in austral summer, stratospheric ozone depletion, contribute to the Southern Hemisphere expansion ( [[#Gerber--2014|Gerber and Son, 2014]] ; [[#Nguyen--2015|Nguyen et al., 2015]] ; L. [[#Tao--2016|]] [[#Tao--2016|Tao et al., 2016]] ; Y.H. [[#Kim--2017|]] [[#Kim--2017|Kim et al., 2017]] ), but the ozone influence is not significant in available CMIP6 simulations (Figure 3.16b–c). Since the 2000s, the stabilization or slight recovery of stratospheric ozone ( [[IPCC:Wg1:Chapter:Chapter-2#2.2.5.2|Section 2.2.5.2]] ) is consistent with the smaller observed trends ( [[#Banerjee--2020|Banerjee et al., 2020]] ). While many CMIP5 models under-represent the magnitude of the PDV, implying potential overconfidence on the detection of human influence on the Hadley cell expansion, this is less the case for the CMIP6 models ( [[#3.7.6|Section 3.7.6]] ). However, the mechanism underlying the Hadley cell expansion remains unclear ( [[#Staten--2018|Staten et al., 2018]] , 2020), precluding a process-based validation of the simulated human influence.

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===== 3.3.3.1.2 Walker circulation strength =====

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CMIP5 models reproduce the mean state of the Walker circulation with reasonable fidelity, evidenced by the spatial pattern correlations of equatorial zonal mass stream function between models and observations being larger than 0.88 ( [[#Ma--2016|Ma and Zhou, 2016]] ). CMIP5 historical simulations on average simulate a significant weakening of the Pacific Walker circulation over the 20th century ( [[#DiNezio--2013|DiNezio et al., 2013]] ; [[#Sandeep--2014|Sandeep et al., 2014]] ; [[#Kociuba--2015|Kociuba and Power, 2015]] ), which is also seen in CMIP6 (Figure 3.16d). This weakening is accompanied by a reduction of convective activity over the Maritime Continent and an enhancement over the central equatorial Pacific ( [[#DiNezio--2013|DiNezio et al., 2013]] ; [[#Sandeep--2014|Sandeep et al., 2014]] ; [[#Kociuba--2015|Kociuba and Power, 2015]] ). In the CMIP6 simulations, greenhouse gas forcing induces this weakening (Figure 3.16d), which is consistent with theories based on radiative-convective equilibrium ( [[#Vecchi--2006|Vecchi et al., 2006]] ; [[#Vecchi--2007|Vecchi and Soden, 2007]] ) and thermodynamic air-sea coupling ( [[#Xie--2010|Xie et al., 2010]] ), but inconsistent with a theory highlighting the ocean dynamical effect which suggests a strengthening in response to greenhouse gas increases ( [[#Clement--1996|Clement et al., 1996]] ; [[#Seager--2019|Seager et al., 2019]] ; see also Section 7.4.4.2.1). [[#Seager--2019|Seager et al. (2019)]] attributed this inconsistency to equatorial Pacific SST biases in the models ( [[#3.5.1.2.1|Section 3.5.1.2.1]] ). However, observational and reanalysis datasets disagree on the sign of trends in the Walker Circulation strength over the 1901–2010 period (Figure 3.16d), and [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4.1|Section 2.3.1.4.1]] assesses ''low confidence'' in observed long-term Walker Circulation trends. The observational uncertainty remains high in the trends since the 1950s ( [[#Tokinaga--2012|Tokinaga et al., 2012]] ; [[#L’Heureux--2013|L’Heureux et al., 2013]] ), though both CMIP5 and CMIP6 historical simulations span trends of all but one observational data set (Figure 3.16e). For this period, external influence simulated in CMIP6 is insignificant due to a partial compensation of forced responses to greenhouse gases and aerosols and large internal decadal variability (Figure 3.16e). It is notable that while AMIP simulations on average show strengthening over both the periods, those simulations are forced by one reconstruction of SST, which itself is subject to uncertainty before the 1970s ( [[#Deser--2010|Deser et al., 2010]] ; [[#Tokinaga--2012|Tokinaga et al., 2012]] ).

Observational SST products indicate that the equatorial zonal SST gradient from the western to the eastern equatorial Pacific has strengthened since 1870 (Section 7.4.4.2.1). While CMIP5 historical simulations on average simulate a weakening, large ensemble simulations span the observed strengthening since the 1950s ( [[#Watanabe--2021|Watanabe et al., 2021]] ) suggesting an important contribution from internal variability. [[#Coats--2017|Coats and Karnauskas (2017)]] also find that the anthropogenic influence on the SST gradient is yet to emerge out of internal variability even on centennial time scales.

Trends since the 1980s in in-situ and satellite observations and reanalyses exhibit strengthening of the Pacific Walker circulation and SST gradient ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4.1|Section 2.3.1.4.1]] and Figure 3.16f; L’Heureux et al., 2013; [[#Boisséson--2014|Boisséson et al., 2014]] ; [[#England--2014|England et al., 2014]] ; [[#Kociuba--2015|Kociuba and Power, 2015]] ; [[#Ma--2016|Ma and Zhou, 2016]] ). AMIP simulations reproduce this strengthening (Figure 3.16d; [[#Boisséson--2014|Boisséson et al., 2014]] ; [[#Ma--2016|Ma and Zhou, 2016]] ), indicating a dominant role of SST changes. However, all reanalysis trends lie outside the 5–95% range of simulated CMIP6 historical Walker circulation trends over this period (Figure 3.16f), consistent with CMIP5 results ( [[#England--2014|England et al., 2014]] ; [[#Kociuba--2015|Kociuba and Power, 2015]] ). This may be in part caused by the underestimation of the PDV magnitude especially in CMIP5 models (Section [[#_idTextAnchor002|3.7.6]] ; [[#Kociuba--2015|Kociuba and Power, 2015]] ; [[#Chung--2019|Chung et al., 2019]] ), but also suggests a potential error in simulating the forced changes of the Walker circulation. Specifically, anthropogenic and volcanic aerosol changes over this period may have driven a strengthening ( [[#DiNezio--2013|DiNezio et al., 2013]] ; [[#Takahashi--2016|Takahashi and Watanabe, 2016]] ; [[#Hua--2018|Hua et al., 2018]] ). This aerosol influence may be indirect via Atlantic Multi-decadal Variability (AMV; Annex IV.2.7) through inter-basin teleconnections ( [[#McGregor--2014|McGregor et al., 2014]] ; [[#Chikamoto--2016|Chikamoto et al., 2016]] ; [[#Kucharski--2016|Kucharski et al., 2016]] ; X. [[#Li--2016|]] [[#Li--2016|Li et al., 2016]] a; [[#Ruprich-Robert--2017|Ruprich-Robert et al., 2017]] ), which may be underestimated in models due to SST biases in the equatorial Atlantic ( [[#3.5.1.2.2|Section 3.5.1.2.2]] ; [[#McGregor--2018|McGregor et al., 2018]] ). Note also the large uncertainty in aerosol influence on the Walker circulation ( [[#Kuntz--2016|Kuntz and Schrag, 2016]] ; [[#Hua--2018|Hua et al., 2018]] ; [[#Oudar--2018|Oudar et al., 2018]] ), which is also seen in CMIP6 (Figure 3.16f).

Paleoclimate data from the Pliocene epoch suggest that there was a reduction in the zonal SST gradient in the tropical Pacific under a similar CO <sub>2</sub> concentration as today (Section 7.4.4.2.2 and Cross-Chapter Box 2.4). [[#Tierney--2019|Tierney et al. (2019)]] found that this weaker gradient compared to pre-industrial, which suggests a weaker Walker circulation, is captured by climate models under Pliocene CO <sub>2</sub> levels, in agreement with the CMIP6 response to greenhouse gas forcing (Figure 3.16d), though the magnitude of this effect varies strongly between models ( [[#Corvec--2017|Corvec and Fletcher, 2017]] ).

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===== 3.3.3.1.3 Summary =====

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It is ''likely'' that human influence has contributed to the poleward expansion of the zonal mean Hadley cell in the Southern Hemisphere since the 1980s. This assessment is supported by studies since AR5, which consistently find human influence from greenhouse gas increases on the expansion, with additional influence from ozone depletion in austral summer. For the strong ozone depletion period of 1981–2000, human influence is detectable in the summertime poleward expansion in the Southern Hemisphere ( ''medium confidence'' ). By contrast, there is ''medium confidence'' that the expansion of the zonal mean Hadley cell in the Northern Hemisphere is within the range of internal variability, with contributions from PDV and other internal variability. The causes of the observed strengthening of the Pacific Walker circulation over the 1980–2014 period are not well understood, since the observed strengthening trend is outside the range of variability simulated in the coupled models ( ''medium confidence'' ). Large observational uncertainty, lack of understanding of the mechanism underlying the poleward Hadley cell expansion, and contradicting theories on the greenhouse gas influence and uncertainty in the aerosol influence on the Walker circulation strength, limit confidence in these assessments.

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==== 3.3.3.2 Global Monsoon ====

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Monsoons are seasonal transitions of regimes in atmospheric circulation and precipitation with the annual cycle of solar insolation, in association with redistribution of moist static energy ( [[#Wang--2008|Wang and Ding, 2008]] ; P.X. [[#Wang--2014|]] [[#Wang--2014|Wang et al., 2014]] ; [[#Biasutti--2018|Biasutti et al., 2018]] ). The global monsoon can be defined to encompasses all monsoon systems based on precipitation contrast in the solstice seasons ( [[#Wang--2008|Wang and Ding, 2008]] ; Figure 3.17). All regional monsoons are intimately connected to the global tropical atmospheric overturning by mass ( [[#Trenberth--2000|Trenberth et al., 2000]] ), momentum and energy budgets ( [[#Biasutti--2018|Biasutti et al., 2018]] ; [[#Geen--2020|Geen et al., 2020]] ). Assessments of regional monsoon changes are made in Sections 8.3.2.4, 10.4.2.1 and 10.6.3.

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[[File:fdafa2a0d46675b23309ca966f9b3f3a IPCC_AR6_WGI_Figure_3_17.png]]

Figure 3.17 | '''Model evaluation of global monsoon domain, intensity, and circulation. (a, b)''' Climatological summer-winter range of precipitation rate, scaled by annual mean precipitation rate (shading) and 850 hPa wind velocity (arrows) based on (a) GPCP and ERA5 and (b) a multi-model ensemble mean of CMIP6 historical simulations for 1979–2014. The region enclosed by red lines is the monsoon domain based on the definition by [[#Wang--2008|Wang and Ding (2008)]] . '''(c, d)''' Five-year running mean anomalies of (c) global land monsoon precipitation index defined as the percentage anomaly of the summertime precipitation rate averaged over the monsoon regions over land, relative to its average for 1979–2014 (the period indicated by light grey shading) and (d) the tropical monsoon circulation index defined as the vertical shear of zonal winds between 850 and 200 hPa levels averaged over 0°–20°N, from 120°W eastward to 120°E in Northern Hemisphere summer ( [[#Wang--2013|Wang et al., 2013]] ; m s <sup>–1</sup> ) in CMIP5 historical and RCP4.5 simulations, and CMIP6 historical and AMIP simulations. Summer and winter are defined for individual hemispheres: May to September is defined as Northern Hemisphere summer and Southern Hemisphere winter, and November to March is defined as Northern Hemisphere winter and Summer Hemisphere summer. The numbers of models and simulations are given in the legend. The multi-model ensemble mean and percentiles are calculated after weighting individual ensemble members with the inverse of the ensemble size of the same model, so that individual models are equally weighted irrespective of ensemble size. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

AR5 assessed that CMIP5 models simulated monsoons better than CMIP3 models but that biases remained in domains and intensity ( ''high confidence'' ) ( [[#Flato--2013|Flato et al., 2013]] ). There were no detection and attribution assessment statements on the decreasing trend of global monsoon precipitation over land from the 1950s to the 1980s or the increasing trend of global monsoon precipitation afterwards. In the paleoclimate context, it was determined with ''high confidence'' that orbital forcing produces strong interhemispheric rainfall variability evident in multiple types of proxies ( [[#Masson-Delmotte--2013|Masson-Delmotte et al., 2013]] ).

Paleoclimate proxy evidence shows that the global monsoon has varied with orbital forcing and greenhouse gases ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4.2|Section 2.3.1.4.2]] ; [[#Mohtadi--2016|Mohtadi et al., 2016]] ; [[#Seth--2019|Seth et al., 2019]] ). These large-magnitude intensifications and weakenings in the global monsoon involved in some cases orders-of-magnitude changes in precipitation locally ( [[#Harrison--2014|Harrison et al., 2014]] ; [[#Tierney--2017|Tierney et al., 2017]] ). Paleoclimate modelling and limited data from past climate states with high CO <sub>2</sub> suggest that precipitation intensifies in the monsoon domain under elevated greenhouse gases, providing context for present and future trends ( [[#Passey--2009|Passey et al., 2009]] ; [[#Haywood--2013|Haywood et al., 2013]] ; [[#Zhang--2013b|Zhang et al., 2013b]] ). In model simulations of the mid-Pliocene, when globally averaged temperature was higher than present day, precipitation was larger in West African, South Asian and East Asian monsoons than under pre-industrial conditions, consistent with proxy evidence ( [[#Zhang--2015|Zhang et al., 2015]] ; [[#Sun--2016|Sun et al., 2016]] , 2018; [[#Corvec--2017|Corvec and Fletcher, 2017]] ; X. [[#Li--2018|]] [[#Li--2018|Li et al., 2018]] ). [[#Prescott--2019|Prescott et al. (2019)]] and R. [[#Zhang--2019|]] [[#Zhang--2019|Zhang et al. (2019)]] find an important role for orbital forcing and CO <sub>2</sub> in the mid-Pliocene monsoon expansion and intensification. Models are also able to capture interhemispherically contrasting monsoon changes in the Last Interglacial in response to orbital forcing and greenhouse gases, with wetter West African and Asian monsoons and a drier South American monsoon as seen in proxies ( [[#Govin--2014|Govin et al., 2014]] ; [[#Gierz--2017|Gierz et al., 2017]] ; [[#Pedersen--2017|Pedersen et al., 2017]] ). In overall agreement with proxy evidence, a model with transient forcing simulates wetting and drying respectively of the Southern and Northern Hemisphere monsoons during the last deglaciation, with an important contribution from Atlantic Meridional Overturning Circulation (AMOC) slowdown ( [[#Otto-Bliesner--2014|Otto-Bliesner et al., 2014]] ; [[#Mohtadi--2016|Mohtadi et al., 2016]] ).

During the mid-Holocene, global monsoons were stronger especially in the Northern Hemisphere with an expansion of the West African monsoon domain in response to orbital forcing ( [[#Biasutti--2018|Biasutti et al., 2018]] ; [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4.2|Section 2.3.1.4.2]] ). Simulations of the mid-Holocene with CMIP5 and CMIP6 models qualitatively capture the stronger Northern Hemisphere monsoon ( [[#Jiang--2015|Jiang et al., 2015]] ; [[#Brierley--2020|Brierley et al., 2020]] ), mainly driven by atmospheric circulation changes ( [[#D’Agostino--2019|D’Agostino et al., 2019]] ). However, the models underestimate the monsoon expansion found in proxy reconstructions ( [[#Perez-Sanz--2014|Perez-Sanz et al., 2014]] ; [[#Harrison--2015|Harrison et al., 2015]] ; [[#Tierney--2017|Tierney et al., 2017]] ), which may be linked to mean biases in the monsoon domain ( [[#Brierley--2020|Brierley et al., 2020]] ) and may be improved by imposing vegetation and dust changes ( [[#Pausata--2016|Pausata et al., 2016]] ). The models simulate the weaker Southern Hemisphere monsoon during the mid-Holocene ( [[#D’Agostino--2020|D’Agostino et al., 2020]] ), consistent with proxy evidence ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4.2|Section 2.3.1.4.2]] ). These studies indicate that models can qualitatively reproduce past global monsoon changes seen in proxies, though issues remain in quantitatively reproducing proxy observations. Studies of last millennium simulations show that simulated global monsoon precipitation increases with global mean temperature, while changes in monsoon circulation and hemispheric monsoon precipitation depend on forcing sources ( [[#Liu--2012|Liu et al., 2012]] ; [[#Chai--2018|Chai et al., 2018]] ). Compared to greenhouse gas and solar variations, volcanic forcing is more effective in changing the global monsoon precipitation over the last millennium ( [[#Chai--2018|Chai et al., 2018]] ).

Reproducing monsoons in terms of domain, precipitation amount, and timings of onset and retreat over the historical period also remains difficult. While CMIP5 historical simulations broadly capture global monsoon domains and intensity based on summer and winter precipitation differences, they underestimate the extent and intensity of East Asian and North American monsoons while overestimating them over the tropical western North Pacific ( [[#Lee--2014|Lee and Wang, 2014]] ; M. [[#Yan--2016|]] [[#Yan--2016|]] [[#Yan--2016|Yan et al., 2016]] ). [[#Wang--2020|]] [[#Wang--2020|B. Wang et al. (2020)]] reported that CMIP6 models simulate the global monsoon domain and precipitation better (Figure 3.17a,b), albeit with biases in annual mean precipitation and the timings of onset and withdrawal of the Southern Hemisphere monsoon. Notable inter-model differences were identified in CMIP5, with the multi-model ensemble mean outperforming individual models ( [[#Lee--2014|Lee and Wang, 2014]] ). Common biases were identified across CMIP5 models in moist static energy and upper-tropospheric temperature associated with the South Asian summer monsoon, which may arise from overly smoothed model topography ( [[#Boos--2012|Boos and Hurley, 2012]] ). However, in atmospheric models with increasing resolution approaching 20 km, improvements in monsoon precipitation are not universal across regions and models, and overall improvements are unclear ( [[#Johnson--2016|Johnson et al., 2016]] ; [[#Ogata--2017|Ogata et al., 2017]] ; L. [[#Zhang--2018b|]] [[#Zhang--2018|Zhang et al., 2018]] b ) ''.''

In instrumental records, global summer monsoon precipitation intensity (measured by summer precipitation averaged over the monsoon domain) decreased from the 1950s to 1980s, followed by an increase ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4.2|Section 2.3.1.4.2]] and Figure 3.17c), arising mainly from variations in Northern Hemispheric land monsoons. A CMIP5 multi-model study by Y. [[#Zhang--2018|Zhang et al. (2018)]] found that observed 1951–2004 trends of the global and Northern Hemisphere summer land monsoon precipitation intensity are well captured by historical simulations, and CMIP6 models show similar results for global land summer monsoon precipitation (Figure 3.17c). However, the 1960s peak in the Northern Hemisphere summer monsoon circulation is outside the 5th–95th percentile range of CMIP5 and CMIP6 historical simulations for two out of three reanalyses (Figure 3.17d). Modelling studies show that greenhouse gas increases act to enhance Northern Hemisphere summer monsoon precipitation intensity ( [[#Liu--2012|Liu et al., 2012]] ; [[#Polson--2014|Polson et al., 2014]] ; [[#Chai--2018|Chai et al., 2018]] ; L. [[#Zhang--2018b|]] [[#Zhang--2018|Zhang et al., 2018]] b ). Since the mid-20th century, however, modelling studies show that this effect was overwhelmed by the influence of anthropogenic aerosols in CMIP5 ( [[#Polson--2014|Polson et al., 2014]] ; [[#Guo--2015|Guo et al., 2015]] ; Y. [[#Zhang--2018|Zhang et al., 2018]] ; [[#Giannini--2019|Giannini and Kaplan, 2019]] ) and in CMIP6 (T. [[#Zhou--2020|]] [[#Zhou--2020|Zhou et al., 2020]] ). Weakening of the monsoon circulation and reduction of moisture availability are important in this aerosol influence (T. [[#Zhou--2020|]] [[#Zhou--2020|Zhou et al., 2020]] ). Besides these human influences, the global monsoon is sensitive to internal variability and natural forcing including ENSO and volcanic aerosols on interannual time scales and PDV and AMV on decadal to multi-decadal time scales ( [[#Wang--2013|Wang et al., 2013]] , 2018; F. [[#Liu--2016|]] [[#Liu--2016|Liu et al., 2016]] ; [[#Jiang--2019|Jiang and Zhou, 2019]] ; [[#Zuo--2019|Zuo et al., 2019]] ); though AMV in the 20th century may have been partly driven by aerosols, see [[#3.7.7|Section 3.7.7]] . Indeed, AMIP simulations better reproduce the observed multi-decadal variations of the global monsoon precipitation and circulation (Figure 3.17c,d). Y. [[#Zhang--2018|Zhang et al. (2018)]] find that the multi-model ensemble mean trend of global land monsoon precipitation in historical simulations, dominated by anthropogenic aerosol forcing contributions, emerges out of the 90% range of internally-driven trends in pre-industrial control simulations. However, it should be noted that CMIP5 models tend to under-represent the PDV magnitude ( [[#3.7.6|Section 3.7.6]] ), suggesting potential overconfidence in the detection of the forced signal. An observed enhancement in global summer monsoon precipitation since the 1980s is accompanied by an intensification of the Northern Hemisphere summer monsoon circulation (Figure 3.17c,d). These trends appear to be at the extreme of the range of the CMIP6 historical simulation ensemble but are well captured by AMIP simulations (Figure 3.17c,d). While the precipitation increase is consistent with greenhouse gas forcing, the circulation intensification is opposite to the simulated response to greenhouse gas forcing, and these enhancements have been attributed to PDV and AMV ( [[#Wang--2013|Wang et al., 2013]] ; [[#Kamae--2017|Kamae et al., 2017]] ).

In summary, while greenhouse gas increases acted to enhance the global land monsoon precipitation over the 20th century ( ''medium confidence'' ), consistent with projected future enhancement ( [[IPCC:Wg1:Chapter:Chapter-4#4.5.1.5|Section 4.5.1.5]] ), this tendency was overwhelmed by anthropogenic aerosols from the 1950s to the 1980s, which contributed to weakening of global land summer monsoon precipitation intensity for this period ( ''medium confidence'' ). There is ''medium confidence'' that the intensification of global monsoon precipitation and Northern Hemisphere summer monsoon circulation since the 1980s is dominated by internal variability. These assessments are supported respectively by multi-model detection and attribution studies which find an important role for anthropogenic aerosols in the weakening trend, and studies that identify a role for AMV and PDV in inducing the Northern Hemisphere summer monsoon circulation enhancement since the 1980s. Supported by multi-model simulations that are qualitatively consistent with proxy evidence, there is ''high confidence'' that orbital forcing contributed to higher Northern Hemisphere monsoon precipitation in the mid-Pliocene and mid-Holocene than pre-industrial. While CMIP5 models can capture the domain and precipitation intensity of the global monsoon, biases remain in their regional representations, and they are unsuccessful in quantitatively reproducing changes in paleo reconstructions ( ''high confidence'' ). CMIP6 models reproduce the domain and precipitation intensity of the global monsoon observed over the instrumental period better than CMIP5 models ( ''medium confidence'' ). However, CMIP5 and CMIP6 models fail to fully capture the variations of the Northern Hemisphere summer monsoon circulation (Figure 3.17d), but there is ''low confidence'' in this assessment due to a lack of evidence in the literature.

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==== 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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==== 3.3.3.4 Sudden Stratospheric Warming Activity ====

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Sudden stratospheric warmings (SSWs) are stratospheric weather events associated with anomalously high temperatures at high latitudes persisting from days to weeks. [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4.5|Section 2.3.1.4.5]] discusses the definition and observational aspects of SSWs. SSWs are often associated with anomalous weather conditions, for example, winter cold spells, in the lower atmosphere (e.g., [[#Butler--2015|Butler et al., 2015]] ; [[#Baldwin--2021|Baldwin et al., 2021]] ).

[[#Seviour--2016|Seviour et al. (2016)]] found that stratosphere-resolving CMIP5 models, on average, reproduce the observed frequency of vortex splits (one form of SSWs) but with a wide range of model-specific biases. Models that produce a better mean state of the polar vortex also tend to produce a more realistic SSW frequency ( [[#Seviour--2016|Seviour et al., 2016]] ). The mean sea level pressure anomalies occurring in CMIP5 model simulations when an SSW is underway, however, differ substantially from those in reanalyses ( [[#Seviour--2016|Seviour et al., 2016]] ). Unlike stratosphere-resolving models, models with limited stratospheric resolution, which make up more than half of the CMIP5 ensemble, underestimate the frequency of SSWs ( [[#Osprey--2013|Osprey et al., 2013]] ; J. [[#Kim--2017|]] [[#Kim--2017|Kim et al., 2017]] ). [[#Taguchi--2017|Taguchi (2017)]] found a general underestimation in CMIP5 models of the frequency of ‘major’ SSWs (which are associated with a break-up of the polar vortex), an aspect of an under-representation in those models of dynamical variability in the stratosphere. [[#Wu--2020|Wu and Reichler (2020)]] found that finer vertical resolution in the stratosphere and a model top above the stratopause tend to be associated with a more realistic SSW frequency in CMIP5 and CMIP6 models.

Some studies find an increase in the frequency of SSWs under increasing greenhouse gases (e.g., [[#Schimanke--2013|Schimanke et al., 2013]] ; [[#Young--2013|Young et al., 2013]] ; J. [[#Kim--2017|]] [[#Kim--2017|Kim et al., 2017]] ). However, this behaviour is not robust across ensembles of chemistry-climate models ( [[#Mitchell--2012|Mitchell et al., 2012]] ; [[#Ayarzagüena--2018|Ayarzagüena et al., 2018]] ; [[#Rao--2021|Rao and Garfinkel, 2021]] ). There is an absence of studies specifically focusing on simulated trends in SSWs during recent decades, and the short record and substantial decadal variability yields ''low confidence'' in any observed trends in the occurrence of SSW events in the Northern Hemisphere winter ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4.5|Section 2.3.1.4.5]] ). Such an absence of a trend and large variability would also be consistent with a recent reconstruction of SSWs extending back to 1850, based on sea level pressure observations ( [[#Domeisen--2019|Domeisen, 2019]] ), although this time series has limitations as it is not based on direct observations of SSWs.

In summary, an anthropogenic influence on the frequency or other aspects of SSWs has not yet been robustly detected. There is ''low confidence'' in the ability of models to simulate any such trends over the historical period because of large natural interannual variability and also due to substantial common biases in the simulated mean state affecting the simulated frequency of SSWs.

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