Editing IPCC:AR6/WGI/Chapter-8 (section)

=== 8.3.2 Observed Variations in Large-scale Phenomena and Regional Variability ===

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Observed changes in large-scale circulation indicators (Cross-Chapter Box 2.2) are assessed in Chapters 2 and 3 (Sections 2.3.1.4 and 3.3.3). In this chaper we focus on the influence of regional scale teleconnection variabililty on the water cycle and the attribution of these circulation changes. While observed changes in modes of variability are assessed in Chapters 2 and 4 (Sections 2.4 and 4.3.3), here focus on hydrological teleconnections of relevance to the water cycle.

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==== 8.3.2.1 Inter-tropical Convergence Zone and Tropical Rain Belts ====

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The AR5 concluded it is ''likely'' that the tropical belt, as delimited by the Hadley circulation, has widened since the 1970s. Observations in the satellite era indicate precipitation increases in the core of the Pacific Inter-tropical Convergence Zone (ITCZ) and decreases on the ITCZ margins ( [[#Gu--2016|Gu et al., 2016]] ; [[#Su--2017|Su et al., 2017]] ). As the satellite period has lengthened, observations have increasingly been used to assess trends in the ITCZ and tropical rain belt. Since AR5, significant narrowing and strengthening of the Pacific ITCZ after 1979 have been identified in atmospheric reanalyses ( [[#Wodzicki--2016|Wodzicki and Rapp, 2016]] ), but no change in the ITCZ location ( [[#Byrne--2018|Byrne et al., 2018]] ). Atmospheric model simulations suggest that with a narrower ITCZ, the subtropical jet becomes baroclinically unstable at a lower latitude and allows mid-latitude eddies to propagate farther equatorward ( [[#Watt-Meyer--2019|Watt-Meyer and Frierson, 2019]] ). Observational analyses also show that the ITCZ narrowing ( [[#Zhou--2020|Zhou et al., 2020]] ) is associated with increased precipitation in the ITCZ core region that is strongly coupled to increasing Outgoing Longwave Radiation (OLR) in the expanding dry zones, particularly over land regions in the subtropics and mid-latitudes ( [[#Lau--2020|Lau and Tao, 2020]] ). In addition, an eastward movement of the South Pacific Convergence Zone (SPCZ) between 1977 and 1999 has been reported, with associated significant precipitation trends in the South Pacific regions ( [[#Salinger--2014|Salinger et al., 2014]] ).

ITCZ trends seen in satellites, precipitation measurements and reanalysis data are further supported by ocean surface-salinity observations. Long-term salinity observations show a freshening in the cores of the Atlantic and Pacific ITCZs and increased salinity on the ITCZ margins ( [[#Durack--2010|Durack and Wijffels, 2010]] ; [[#Durack--2012|Durack et al., 2012]] ; [[#Terray--2012|Terray et al., 2012]] ; [[#Skliris--2014|Skliris et al., 2014]] ). By investigating simultaneous changes in precipitation, temperature and continental aridity in CMIP5 historical simulations, [[#Bonfils--2020|Bonfils et al. (2020)]] found a secondary signal (Figure 8.9, right column) characterized by a robust inter-hemispheric temperature contrast ( [[IPCC:Wg1:Chapter:Chapter-3#3.3.1.1|Section 3.3.1.1]] ), a latitudinal shift in the ITCZ (in accordance with the theory of cross-equatorial energy transport; [[#8.2.2.2|Section 8.2.2.2]] ), and changes in aridity in the Sahel ( [[#8.3.1.6|Section 8.3.1.6]] ). These forced changes are statistically detectable in reanalyses datasets over the 1950 – 2014 period at the 95% confidence level.

Reconstructions in the Sahel ( [[#Carré--2019|Carré et al., 2019]] ) and Belize ( [[#Ridley--2015|Ridley et al., 2015]] ) support the southward displacement of the tropical rain belt since 1850 and the narrowing trend of the tropical rainbelt detected in observations ( [[#Rotstayn--2002|Rotstayn et al., 2002]] ; [[#Hwang--2013|Hwang et al., 2013]] ). Decreasing precipitation trends in the NH during the 1950s to 1980s have been attributed to anthropogenic aerosol emissions from North America and Europe, which peaked during the late1970s and declined thereafter following improved air quality regulations, causing dimming (brightening) through reduced (increased) surface solar radiation (Box 8.1 Figure 1), in agreement with model simulations ( [[#Chiang--2013|Chiang et al., 2013]] ; [[#Hwang--2013|Hwang et al., 2013]] ). This is consistent with energetic constraints where tropical precipitation shifts are anti-correlated with cross-equatorial energy transport (Section 6.3.3, Box 8.1). It also provides a physical mechanism for the severe drought in the Sahel that peaked in the mid-1980s (Sections 8.3.2.4.3 and 10.4.2.1) and the southward shift of the NH tropical edge from the 1950s to the 1980s ( [[#Allen--2014|Allen et al., 2014]] ; [[#Brönnimann--2015|Brönnimann et al., 2015]] ). However, CMIP5 and CMIP6 models still exhibit strong biases in representing the ITCZ, such as the simulation of a double ITCZ ( [[#Oueslati--2015|Oueslati and Bellon, 2015]] ; [[#Adam--2018|Adam et al., 2018]] ; [[#Tian--2020|Tian and Dong, 2020]] ). The impacts of aerosols and volcanic activity on the position of the ITCZ have been investigated but changes are difficult to characterize from observations (Section 6.3.3.2; Friedman et al. , 2013; J.M. Haywood et al. , 2013; [[#Iles--2014|Iles and Hegerl, 2014]] ; Colose et al. , 2016; [[#Chung--2017|Chung and Soden, 2017]] ). Such systematic shifts of the ITCZ can have important regional impacts like changes in precipitation (Figure 8.9).

In summary, there is ''medium confidence'' that the tropical rain belts over the oceans have been narrowing and strengthening in recent decades, leading to increased precipitation in the ITCZ core region ( [[#8.2.2.2|Section 8.2.2.2]] ). Decreasing precipitation trends in the NH during the 1950s – 1980s have been attributed to anthropogenic aerosol emissions from North America and Europe ( ''high co'' ''nfidence'' ).

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==== 8.3.2.2 Hadley Circulation and Subtropical Belt ====

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The AR5 reported ''low confidence'' in trends in the strength of the Hadley circulation (HC) due to uncertainties in reanalyses but ''high confidence'' on the widening of the tropical belt since 1979. In AR6, [[IPCC:Wg1:Chapter:Chapter-2|Chapter 2]] ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4.1|Section 2.3.1.4.1]] ) states that the HC has ''very likely'' widened and strengthened since at least the 1980s, mostly in the NH ( ''medium con'' ''fidence'' ).

The poleward shift of the HC is closely related to migration of the location of tropical cyclone trajectories in bothhemispheres ( [[#Sharmila--2018|Sharmila and Walsh, 2018]] ; [[#Studholme--2018|Studholme and Gulev, 2018]] ), with a ''very likely'' poleward shift over the western North Pacific Oceans since the 1940s ( [[IPCC:Wg1:Chapter:Chapter-11#11.7.1.2|Section 11.7.1.2]] ). Moreover, the Western North Pacific Subtropical High has extended westward since the 1970s, resulting in a monsoon rain band shift over China, with excessive rainfall along the middle and lower reaches of the Yangtze River valley along about 30°N over eastern China. At the same time, the effect of anthropogenic aerosols dominated the response to GHG increases over East Asia, resulting in a weakening of the East Asian summer monsoon and causing a drying trend in north-eastern China ( [[#Hu--2003|Hu, 2003]] ; [[#Yu--2007|Yu and Zhou, 2007]] ; T. [[#Wang--2013|]] [[#Wang--2013|]] [[#Wang--2013|]] [[#Wang--2013|Wang et al., 2013]] ; Z. [[#Li--2016|Li et al., 2016]] b; [[#Lau--2017|Lau and Kim, 2017]] ) and northern parts of South Asia ( [[#8.3.2.4.2|Section 8.3.2.4.2]] ; [[#Preethi--2017|Preethi et al., 2017]] ). During 1977 – 2007, the precipitation variability over the eastern USA increased due to changes in the intensity and position of the western ridge of the North Atlantic Subtropical High ( [[#Li--2011|Li et al., 2011]] ; [[#Diem--2013|Diem, 2013]] ).

In the Southern Hemisphere (SH), the HC expansion has been associated with both the intensification and poleward shift of the subtropical high pressure belt ( [[#Nguyen--2015|Nguyen et al., 2015]] ), with consequences for precipitation amount over Africa, Australia, South America, and subtropical Pacific islands (Cai et al. , 2012; Grose et al. , 2015; Nguyen et al. , 2015; [[#Sharmila--2018|Sharmila and Walsh, 2018]] ; McGree et al. , 2019). The subtropical ridge in Australia has intensified significantly since 1970, with marked declines observed in April to October rainfall across south-eastern and south-western Australia ( [[#Timbal--2013|Timbal and Drosdowsky, 2013]] ).

The local tropical edges of the meridional overturning cells (as diagnosed from the horizontally divergent wind) are more closely associated with hydroclimate variations than the subtropical ridge ( [[#Staten--2019|Staten et al., 2019]] ). Poleward expansion of the tropical belt strongly contributes to precipitation decline in the poleward edge of the subtropics ( [[#Cai--2012|Cai et al., 2012]] ; [[#Scheff--2012|Scheff and Frierson, 2012]] ; [[#Timbal--2013|Timbal and Drosdowsky, 2013]] ; [[#He--2017|He and Soden, 2017]] ; H. [[#Nguyen--2018|]] [[#Nguyen--2018|Nguyen et al., 2018]] ; [[#Tang--2018|Tang et al., 2018]] ), although recent modelling evidence suggests that subtropical precipitation declines are a response to direct CO <sub>2</sub> radiative forcing mainly over ocean, irrespective of the HC expansion ( [[#He--2017|He and Soden, 2017]] ). Both reanalyses datasets and climate model simulations suggest that the HC expansion is not associated with widespread, zonally symmetric subtropical drying over land ( [[#Schmidt--2017|Schmidt and Grise, 2017]] ).

Since AR5, an improved understanding of the key drivers of the recent HC expansion has been achieved, identifying the role of both internal variability and anthropogenic climate change. Part of the recent expansion (1979 – 2005) of the HC has been driven by a swing from warm to cold phase of the Pacific Decadal Variability (PDV; [[#Meehl--2016|Meehl et al., 2016]] ; [[#Grise--2019|Grise et al., 2019]] ). The presence of large multi-decadal variability in 20th-century reanalyses means there is ''limited evidence'' on the human influence on the recent HC strengthening, yet the southward shift of the southern edge and widening of the SH HC appeared as robust features in all reanalysis datasets, and their trends have accelerated during 1979 – 2010 ( [[#D’Agostino--2017|D’Agostino and Lionello, 2017]] ). As assessed in [[IPCC:Wg1:Chapter:Chapter-3#3.3.3.1|Section 3.3.3.1]] , GHG increases and stratospheric ozone depletion have contributed to the expansion of the zonal mean HC in the SH since around 1980, and the expansion of the NH HC has not exceeded the range of internal variability ( ''medium confidence'' ). Moreover, Antarctic ozone depletion can cause a poleward shift in the SH mid-latitude jet and HC (Sections 3.3.3 and 6.3.3.2). Further assessment of the attribution of recently observed changes in the HC extent and intensity is found in [[IPCC:Wg1:Chapter:Chapter-3#3.3.3.1|Section 3.3.3.1]] .

In summary, it is ''very likely'' that the recent HC expansion was associated with poleward shifts of tropical cyclone tracks over the western North Pacific Ocean since the 1940s, and of extratropical storm tracks in the SH since the 1970s. Changes to the HC in the NH may have contributed to subtropical drying and a poleward expansion of aridity during the boreal summer, but there is ''low confidence'' due to ''limited evidence'' . GHG increases and stratospheric ozone depletion have contributed to expansion of the zonal mean HC in the SH since around 1970, while the expansion of the NH HC has not exceeded the range of internal variability ( ''medium co'' ''nfidence'' ).

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==== 8.3.2.3 Walker Circulation ====

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The AR5 concluded that the long-term weakening of the Pacific Walker circulation (WC) from the late 19th century to the 1990s has been largely offset by a recent strengthening ( ''high confidence'' ), though with ''low confidence'' in trends of the WC strength due to reanalysis uncertainties and large natural variability. The observed trends in the WC since 1980 are consistent with a ''very likely'' WC strengthening in the Pacific, similar to a La Niña pattern, with ''medium confidence'' in the magnitude of these changes due to differences between satellite observations and reanalyses.

The causes of the observed strengthening of the WC during 1980 – 2014 are not well understood due to competing influences from individual external forcings and since this strengthening is outside the range of variability simulated in coupled models ( ''medium confidence'' ), as assessed in [[IPCC:Wg1:Chapter:Chapter-3|Chapter 3]] ( [[IPCC:Wg1:Chapter:Chapter-3#3.3.3.1|Section 3.3.3.1]] ). Recent strengthening in the WC has been linked with internal variability ( [[#Chung--2019|Chung et al., 2019]] ), although one study argues that it could be a response forced by GHG that models do not capture because of common sea surface temperature (SST) biases in the equatorial Pacific ( [[#Seager--2019a|Seager et al., 2019a]] ). It could be also related to an interbasin thermostat mechanism whereby the human-induced Indian Ocean warming emerged earlier than in the tropical Pacific (L. [[#Zhang--2018|]] [[#Zhang--2018|]] [[#Zhang--2018|]] [[#Zhang--2018|]] [[#Zhang--2018|Zhang et al., 2018]] ) and induced a transient strengthening of the zonal sea level pressure gradient and easterly trades in the tropical Pacific (L. [[#Zhang--2019|]] [[#Zhang--2019|Zhang et al., 2019]] ).

The weakening of the PWC observed during most of the 20th century is associated with reductions in land rainfall over the Maritime Continent during 1950 – 1999 ( [[#Tokinaga--2012|Tokinaga et al., 2012]] ; [[#Yoden--2017|Yoden et al., 2017]] ). In contrast, the recent strengthening of the WC has been associated with an intensification of extreme flooding ( [[#Barichivich--2018|Barichivich et al., 2018]] ) and an increased frequency of wet days ( [[#Espinoza--2016|J.C. Espinoza et al., 2016]] , 2018) over the north-western Amazon, increased precipitation in South America ( [[#Yim--2017|Yim et al., 2017]] ), reduced precipitation over eastern Africa ( [[#Williams--2011|Williams and Funk, 2011]] ; [[#Lyon--2012|Lyon and Dewitt, 2012]] ), and increased rainfall in southern Africa ( [[#Maidment--2015|Maidment et al., 2015]] ). Internal variability has been shown to have a dominant role in the recent strengthening of the WC ( [[#Chung--2019|Chung et al., 2019]] ).

In summary, there is ''high confiden'' ce that changes in the WC are associated with changes in the water cycle over regions like the Maritime Continent, South America and Africa. It is ''very likely'' that the WC has strengthened in the Pacific since the 1980s, with ''medium confidence'' that this strengthening is within the range of internal variability.

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==== 8.3.2.4 Monsoons ====

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The AR5 reported ''low confidence'' in the attribution of changes in monsoons to human influence, although a detailed attribution assessment of the observed changes in the regional monsoons was not presented.

Large human populations in the monsoon regions of the world heavily depend on freshwater supply for agriculture, water resources, industry, transport and various socio-economic activities. The effects of GHG forcing combined with water vapour feedback (R.J. [[#Allen--2015|]] [[#Allen--2015|Allen et al., 2015]] ; [[#Dong--2015|Dong and Sutton, 2015]] ; [[#Evan--2015|Evan et al., 2015]] ; [[#Dunning--2018|Dunning et al., 2018]] ) and cloud feedbacks ( [[#Stephens--2015|Stephens et al., 2015]] ; [[#Potter--2017|Potter et al., 2017]] ) are fundamental to monsoon precipitation changes in a warming world. Since AR5 there has been improved understanding of precipitation changes associated with regional monsoons. Sections 2.3.1.4.2 and 3.3.3.2 provide an assessment of observed changes and attribution for the global monsoon. Here we provide an assessment of the observed changes in regional monsoons (see [[IPCC:Wg1:Chapter:Annex-v|Annex V]] and Figure 8.11) and underlying causes. In AR6, the definition of regional monsoons slightly differs from AR5 and the rationale for it is provided in [[IPCC:Wg1:Chapter:Annex-v|Annex V]] (see Glossary). Specific examples of regional monsoons are discussed further in [[IPCC:Wg1:Chapter:Chapter-10#10.4.2|Section 10.4.2]] , from the perspective of climate change attribution and in [[IPCC:Wg1:Chapter:Chapter-10#10.6.3|Section 10.6.3]] , from the viewpoint of constructing regional climate messages.

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[[File:ae05328fe795c6cc346a35ff910aecd9 IPCC_AR6_WGI_Figure_8_11.png]]

Figure 8.11 | '''Regional monsoon precipitation changes from observations and model attribution.''' Precipitation changes during 1951–2014 are shown as least-square linear trends in box-whisker plots (first and fourth rows) over the six regional monsoons, for example, North American monsoon (NAmerM, July–August–September, JAS), West African monsoon (WAfriM, June–July–August–September, JJAS), South and South East Asian monsoon (SAsiaM, June–July–August–September, JJAS), East Asian monsoon (EAsiaM, June–July–August), South American monsoon (SAmerM, December–January–February, DJF), Australian and Maritime Continent monsoon (AusMCM, December–January–February, DJF), and over the two land domains, for example, equatorial America (EqAmer, June–July–August, JJA) and South Africa (SAfri, December–January–February, DJF), as identified in the map shown in the middle and as described in Annex V. Precipitation changes are computed from observations and from Detection and Attribution Model Intercomparison Project (DAMIP) CMIP6 experiments over the historical period with all-forcing (ALL), GHG-only forcing (labelled GHG), Aerosol-only (AER) and Natural (NAT) forcings prescribed. Observations are based on the CRU (light green) and GPCC (light blue) datasets and the APHRODITE (light orange) dataset for SAsiaM and EAsiaM. CMIP6 simulations are taken from nine CMIP6 models contributing to DAMIP, with at least three members. Ensembles are weight-averaged for the respective model ensemble size. Observed trends are shown as coloured circles and the simulated trends from the CMIP6 multi-model experiments are shown as box-whisker plots. Precipitation anomaly time-series are shown in the second and third row. The thick black line is the multi-model ensemble-mean precipitation anomaly time-series from the ALL experiment and the grey shading shows the spread across the multi-model ensembles. An 11-year running mean has been applied on the precipitation anomaly time-series prior to calculating the multi-model ensemble mean. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).

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===== 8.3.2.4.1 South and South East Asian Monsoon =====

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The AR5 reported a decreasing trend of global land monsoon precipitation over the last half-century, with primary contributions from the weakened summer monsoon systems in the Northern Hemisphere (NH). Since AR5, several studies have documented long-term variations and changes in the South and South East Asian summer monsoon (SAsiaM) rainfall. The SAsiaM strengthened during past periods of enhanced summer insolation in the NH, such as the early-to-mid Holocene warm period around 9000 to 6000 years before the present (BP) ( [[#Masson-Delmotte--2013|Masson-Delmotte et al., 2013]] ; [[#Mohtadi--2016|Mohtadi et al., 2016]] ; [[#Braconnot--2019|Braconnot et al., 2019]] ) and weakened during cold periods ( ''high confidence'' ), such as the Last Glacial Maximum (LGM) and Younger Dryas (Shakun et al. , 2007; Cheng et al. , 2012; Dutt et al. , 2015; Chandana et al. , 2018; Hong et al. , 2018; E. Zhang et al. , 2018). These long-time scale changes in monsoon intensity are tightly linked to orbital forcing and changes in high-latitude climate (Braconnot et al. , 2008; Battisti et al. , 2014; Araya-Melo et al. , 2015; Rachmayani et al. , 2016; Bosmans et al. , 2018; E. Zhang et al. , 2018). A weakening trend of the SAsiaM during the last 200 years has been documented based on tree ring oxygen isotope chronology from the northern Indian subcontinent ( [[#Xu--2018|Xu et al., 2018]] ) and South East Asia ( [[#Xu--2013|Xu et al., 2013]] ), oxygen isotopes in speleothems from northern India ( [[#Sinha--2015|Sinha et al., 2015]] ), and tree ring width chronologies from the Indian core monsoon region ( [[#Shi--2017|Shi et al., 2017]] ). Nevertheless, the detection of century-long decreases in regional monsoon rainfall is obscured by the presence of multi-decadal time scale precipitation variations ( [[#Turner--2012|Turner and Annamalai, 2012]] ; [[#Knutson--2018|Knutson and Zeng, 2018]] ) which are evident in long-term rain guage records extending back to the early 1800s ( [[#Sontakke--2008|Sontakke et al., 2008]] ) and emerge in long-term climate simulations ( [[#Braconnot--2019|Braconnot et al., 2019]] ).

A significant decline in summer monsoon precipitation is observed over India since the mid-20th century, which is accompanied by a weakening of the large-scale monsoon circulation (Mishra et al. , 2012; Abish et al. , 2013; Krishnan et al. , 2013, 2016; Saha et al. , 2014; Roxy et al. , 2015; Guhathakurta et al. , 2017; Samanta et al. , 2020). This precipitation decline is corroborated by a decreasing trend in the frequency of monsoon depressions that form over Bay of Bengal ( [[#Prajeesh--2013|Prajeesh et al., 2013]] ; [[#Vishnu--2016|Vishnu et al., 2016]] ), an increasing trend in the frequency and duration of monsoon breaks or ‘dry spells’ ( [[#Singh--2014|Singh et al., 2014]] ), significant decreases in soil moisture and increases in drought severity across different parts of India post-1950 (Niranjan Kumar et al. , 2013; Ramarao et al. , 2015, 2019; Krishnan et al. , 2016; Ganeshi et al. , 2020; Mujumdar et al. , 2020). While recent studies have reported an apparent recovery of the Indian summer monsoon over a relatively short period since 2003 ( [[#Jin--2017|Jin and Wang, 2017]] ; [[#Hari--2020|Hari et al., 2020]] ), long-term trends for the period 1951 – 2015 indicate an overall decrease in the regional monsoon precipitation ( [[#Kulkarni--2020|Kulkarni et al., 2020]] ; [[#Ayantika--2021|Ayantika et al., 2021]] ). A case study on the Indian summer monsoon is provided in [[IPCC:Wg1:Chapter:Chapter-10#10.6.3|Section 10.6.3]] .

Evidence from several climate modelling studies indicates that the observed decrease in the regional monsoon precipitation during the second half of the 20th century is dominated by the radiative effects of NH anthropogenic aerosols, with smaller contributions due to volcanic aerosols from the Mount Pinatubo (1991) and El Chichón (1982) eruptions (Bollasina et al. , 2011; Polson et al. , 2014; Sanap et al. , 2015; Krishnan et al. , 2016; Liu et al. , 2016; [[#Lau--2017|Lau and Kim, 2017]] ; Lin et al. , 2018; Takahashi et al. , 2018; Undorf et al. , 2018a, b; Patil et al. , 2019; M. Singh et al. , 2020; see Box 8.1, Figure 1 and Figure 8.11). Land-use changes over South and South East Asia and the rapid warming trend of the equatorial Indian Ocean during the recent few decades also appear to have contributed to the observed decrease in monsoon precipitation ( [[#Roxy--2015|Roxy et al., 2015]] ; [[#Krishnan--2016|Krishnan et al., 2016]] ; [[#Singh--2016|Singh, 2016]] ). Overall, the magnitude of the precipitation response to anthropogenic forcing exhibits large spread across CMIP5 models pointing to the strong internal variability of the regional monsoon ( [[#Saha--2014|Saha et al., 2014]] ; [[#Salzmann--2014|Salzmann et al., 2014]] ; [[#Sinha--2015|Sinha et al., 2015]] ), including variations linked to phase changes of the Pacific Decadal Variability (Section AVI.2.6; X. [[#Huang--2020|Huang et al., 2020]] a), uncertainties in representing aerosol – cloud interactions ( [[#Takahashi--2018|Takahashi et al., 2018]] ), and the effects of local compared with remote aerosol forcing (Bollasina et al. , 2014; Polson et al. , 2014; Undorf et al. , 2018b). CMIP3 and CMIP5 models do not accurately reproduce the observed seasonal cycle of precipitation over the major river basins of South and South East Asia, limiting the attribution of observed regional hydroclimatic changes ( [[#Hasson--2014|Hasson et al., 2014]] , 2016; [[#Biasutti--2019|Biasutti, 2019]] ). While warm rain processes and organized convection are known to dominate the heavy orographic monsoon rainfall over the Western Ghats mountains ( [[#Shige--2017|Shige et al., 2017]] ; [[#Choudhury--2018|Choudhury et al., 2018]] ), in various parts of India ( [[#Konwar--2012|Konwar et al., 2012]] ) and East Asia ( [[IPCC:Wg1:Chapter:Chapter-11#11.7.3.1|Section 11.7.3.1]] ), there are uncertainties in representing the regional physical processes of the monsoon environment, including cloud – aerosol interactions ( [[#Sarangi--2017|Sarangi et al., 2017]] ), land – atmosphere (e.g., Bartonet al., 2020) and ocean – atmosphere coupling ( [[#Annamalai--2017|Annamalai et al., 2017]] ), in state-of-the-art climate models (see also [[#8.5.1|Section 8.5.1]] ).

In summary, there is ''high confidence'' in observational evidence for a weakening of the SAsiaM in the second half of the 20th century. Results from climate models indicate that anthropogenic aerosol forcing has dominated the recent decrease in summer monsoon precipitation, as opposed to the expected intensification due to GHG forcing ( ''high confidence'' ). On paleoclimate time scales, the SAsiaM strengthened in response to enhanced summer warming in the NH during the early-to-mid Holocene, while it weakened during cold intervals ( ''high confidence'' ). These changes are tightly linked to orbital forcing and changes in high-latitude climate ( ''medium co'' ''nfidence'' ).

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===== 8.3.2.4.2 East Asian Monsoon =====

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The AR5 reported ''low confidence'' in the observed weakening of the East Asian monsoon (EAsiaM) since the mid-20th century. Since AR5, there has been improved understanding of changes in the EAsiaM, based on paleoclimatic evidence, instrumental observations and climate modeling simulations. Rainfall reconstructions from the Loess Plateau in China indicate that the northern extent of the monsoon rain belts migrated at least 300 km to the north-west from the LGM to the mid-Holocene ( [[#Yang--2015|Yang et al., 2015]] ). Similarly, Pliocene reconstructions indicate stronger intensity of the EAsiaM with a more northward penetration of the monsoon rain belt (S. [[#Yang--2018|]] [[#Yang--2018|Yang et al., 2018]] a). EAsiaM variability has been related to Atlantic Meridional Overturning Circulation (AMOC) dynamics, especially during the last glacial period, but whether the relationship is negative or positive remains uncertain ( [[#Sun--2012|Sun et al., 2012]] ; [[#Cheung--2018|Cheung et al., 2018]] ; [[#Kang--2018|Kang et al., 2018]] ).

Long-term precipitation observations from China indicate a trend of drying in the north and wetting in the central-eastern China along the Yangtze river valley since the 1950s ( [[#Qian--2014|Qian and Zhou, 2014]] ; [[#Zhou--2017b|Zhou et al., 2017b]] ; [[#Day--2018|Day et al., 2018]] ), with a weakened EAsiaM low-level circulation that penetrates less far into northern China, increased surface pressure over north-east China and southward shift of the jet stream ( [[#Song--2014|Song et al., 2014]] ). The southward shift and enhancement of the jet stream explains the increase of rainfall especially from the Meiyu front ( [[#Day--2018|Day et al., 2018]] ) at the expense of drying over north-east China.

Anthropogenic factors such as GHGs and aerosols had an influence on the EAsiaM changes (Figure 8.11; T. Wang et al. , 2013; Song et al. , 2014; Xie et al. , 2016; [[#Chen--2017|Chen and Sun, 2017]] ; Ma et al. , 2017; L. Zhang et al. , 2017; Day et al. , 2018; Tian et al. , 2018). Increased precipitation in the southern region has been linked to increased moisture flux convergence driven by GHG forcing while changes in anthropogenic aerosols have weakened the EAsiaM and reduced precipitation in the northern regions ( [[#Tian--2018|Tian et al., 2018]] ). Aerosol-induced cooling, associated atmospheric circulation changes and sea surface temperature (SST) feedbacks weaken the EAsiaM and favour the observed dry-north and wet-south pattern of rainfall anomalies (T. Wang et al. , 2013; Song et al. , 2014; L. Zhang et al. , 2017; G. Chen et al. , 2018; X. Chen et al. , 2018; Undorf et al., 2018b).

Internal variability and volcanic eruptions also contributed to the weakened EAsiaM (Hsu et al. , 2014; [[#Qian--2014|Qian and Zhou, 2014]] ; Zhou et al. , 2017a; [[#Knutson--2018|Knutson and Zeng, 2018]] ). Since the late 1970s, the EAsiaM weakening has been also linked to SST changes in the Pacific Ocean with warm conditions in the central-eastern tropical part and cold ones in the north, similar to a positive phase of the Pacific Decadal Variability (PDV; Section AVI.2.6; Z. Li et al. , 2016b; Zhou et al. , 2017a ). In the late 1990s the transition from a positive to a negative PDV has been associated with the recent recovery observed in the EAsiaM strength ( [[#Zhou--2017a|Zhou et al., 2017a]] ). Atlantic Multi-decadal Variability (AMV) also has an influence on the EAsiaM via the global teleconnection pattern propagating from the North Atlantic through the westerly jet ( [[#Zuo--2013|Zuo et al., 2013]] ; [[#Wu--2016a|Wu et al., 2016a]] , b). This North Atlantic influence has contributed to the increase of precipitation over the Huaihe-Huanghe valley since the late 1990s (Y. [[#Li--2017|]] [[#Li--2017|]] [[#Li--2017|]] [[#Li--2017|Li et al., 2017]] ). When PDV and AMV are in opposite phase, the former has a larger influence in driving the southern flooding and northern drought pattern over the region (Q. [[#Yang--2017|]] [[#Yang--2017|Yang et al., 2017]] ).

In summary, there is strong evidence of a stronger EAsiaM and northward migration of the rainbelt during warmer climates based on paleoclimate reconstructions. There is ''high confidence'' that anthropogenic forcing has been influencing historical EAsiaM changes with drying in the north and wetting in the south observed since the 1950s, but there is ''low confidence'' in the magnitude of the anthropogenic influence. The transition towards a positive PDV phase has been one of the main drivers of the EAsiaM weakening since the 1970s ( ''high co'' ''nfidence'' ).

<div id="8.3.2.4.3" class="h4-container"></div>

<span id="west-african-monsoon"></span>
===== 8.3.2.4.3 West African Monsoon =====

<div id="h4-7-siblings" class="h4-siblings"></div>

Since AR5, there has been improved understanding of the West African monsoon (WAfriM) response to natural and anthropogenic forcing. On paleoclimate time scales, enhanced summer insolation in the Northern Hemisphere (NH) intensified the WAfriM precipitation during the early-to-mid Holocene ( ''high confidence'' ), as seen in rainfall proxy records and climate model simulations (Masson-Delmotte et al. , 2013; Mohtadi et al. , 2016; Braconnot et al. , 2019). Despite improvements in model simulations of the present-day monsoons, CMIP5 and CMIP6 models underestimate mid-Holocene changes in the amount and spatial extent of the WAfriM precipitation ( [[IPCC:Wg1:Chapter:Chapter-3#3.3.3.2|Section 3.3.3.2]] ; [[#Brierley--2020|Brierley et al., 2020]] ).

During the recent past, long-term rain gauge observations display substantial variability in the WAfriM precipitation over the 20th century ( [[IPCC:Wg1:Chapter:Chapter-10#10.4.2.1|Section 10.4.2.1]] ). The WAfriM experienced the wettest decade of the 20th century during the 1950s and early 1960s ( ''high confidence'' ), over much of the western and central Sahel region, followed abruptly by the driest years during 1970 – 1989 ( [[#Ali--2009|Ali and Lebel, 2009]] ; [[#Nicholson--2013|Nicholson, 2013]] ; [[#Descroix--2015|Descroix et al., 2015]] ). The percentage deficit in the annual rainfall during 1970–1989, relative to the long-term mean, ranged from 60% in the north of Sahel to 25 – 30% in the south ( [[#Le%20Barbé--2002|Le Barbé et al., 2002]] ; [[#Lebel--2003|Lebel et al., 2003]] ). The long decline in annual rainfall is related to a decrease of rain occurrence over the Sahel ( [[#Le%20Barbé--1997|Le Barbé and Lebel, 1997]] ; [[#Frappart--2009|Frappart et al., 2009]] ; [[#Bodian--2016|Bodian et al., 2016]] ) and the Soudano-Guinean sub-region of West Africa ( [[#Le%20Barbé--2002|Le Barbé et al., 2002]] ), even though the interannual variability pattern is more complex ( [[#Balme--2006|Balme et al., 2006]] ). Decrease of rainfall occurrences resulted from decreases in large convective events in the core of the rainy season ( [[#Bell--2006|Bell et al., 2006]] ), that modulate interannual variability of the WAfriM ( [[#Panthou--2018|Panthou et al., 2018]] ).

Wetter conditions of the WAfriM prevailed later from the mid-to-late 1990s, although the positive trend in precipitation started since the late 1980s (see also [[IPCC:Wg1:Chapter:Chapter-10#10.4.2.1|Section 10.4.2.1]] ) over the Sahel ( ''high confidence'' ) and in the Guinean coastal region ( ''medium confidence'' ), indicating the geographical variation in the wetting recovery (Descroix et al. , 2015; Sanogo et al. , 2015; Bodian et al. , 2016; Nicholson et al. , 2018). While the interannual and decadal variability of annual rainfall is not homogeneous over the entire Sahel, the rainfall recovery was stronger in the east than in the west of the region ( [[IPCC:Wg1:Chapter:Chapter-10#10.4.2.1|Section 10.4.2.1]] ; [[#Nicholson--2018|Nicholson et al., 2018]] ). A shift in the seasonality of the Sahelian rainfall, including delayed cessation has also been reported ( [[IPCC:Wg1:Chapter:Chapter-10#10.4.2.1|Section 10.4.2.1]] ; [[#Nicholson--2013|Nicholson, 2013]] ; [[#Dunning--2018|Dunning et al., 2018]] ).

In the Sahel region, the emergence of this new rainfall regime is reflected in increased number of heavy and extreme events, compared to the 1970s – 1980s, still not exceeding the values registered in the 1950s to 1960s ( [[#Descroix--2013|Descroix et al., 2013]] , 2015; [[#Panthou--2014|Panthou et al., 2014]] , 2018; [[#Sanogo--2015|Sanogo et al., 2015]] ), and in higher interannual variability (W. [[#Zhang--2017|Zhang et al., 2017]] b; [[#Akinsanola--2020|Akinsanola and Zhou, 2020]] ) associated with SST variations in the tropical Atlantic, Pacific and Mediterranean Sea ( [[#Rodríguez-Fonseca--2015|Rodríguez-Fonseca et al., 2015]] ; [[#Diakhaté--2019|Diakhaté et al., 2019]] ). Increased frequency of extreme rainfall events impacts high flow occurrences of the large Sahelian rivers as well as small to meso-scale catchments ( [[#Wilcox--2018|Wilcox et al., 2018]] ). Overall, extreme intense precipitation events are more frequent in the Sahel since the beginning of the 21st century (Giannini et al. , 2013; Panthou et al. , 2014, 2018; Sanogo et al. , 2015; Taylor et al. , 2017). Intensification of mesoscale convective systems associated with extreme rainfall in the WAfriM is favoured by enhancement of meridional temperature gradient by the warming of the Sahara desert ( [[#Taylor--2017|Taylor et al., 2017]] ) at a pace that is two to four times greater than that of the tropical-mean temperature (K.H. [[#Cook--2015|]] [[#Cook--2015|]] [[#Cook--2015|Cook et al., 2015]] ; [[#Vizy--2017|Vizy et al., 2017]] ). Periods of monsoon-breaks and the persistence of low rainfall events are still prominent, particularly after the onset, thus exposing West Africa simultaneously to the potential impacts of dry spells (W. [[#Zhang--2017|Zhang et al., 2017]] b) and also extreme localized rains and floods ( [[#Engel--2017|Engel et al., 2017]] ; [[#Lafore--2017|Lafore et al., 2017]] ). Occurrence of extreme events is compounded by land use and land cover changes leading to increased runoff ( [[#Bamba--2015|Bamba et al., 2015]] ; [[#Descroix--2018|Descroix et al., 2018]] ).

The Sahel drought from the 1970s until the early 1990s was related to anthropogenic emissions of sulphate aerosols in the Atlantic, which led to an inter-hemispheric pattern of SST anomalies and associated regional precipitation changes (Section 6.3.3.2 and Box 8.1). Also the combined effects of anthropogenic aerosols and GHG forcing appear to have contributed to the late twentieth century drying of the Sahel through their effect on SST, by cooling the North Atlantic and warming the tropical oceans ( [[#Giannini--2019|Giannini and Kaplan, 2019]] ; [[#Hirasawa--2020|Hirasawa et al., 2020]] ). Subsequent aerosol removal led to SST warming of the North Atlantic, shifting the ITCZ further northward and strengthening the WAfriM ( [[#Giannini--2019|Giannini and Kaplan, 2019]] ). The recent recovery has been ascribed to prevailing positive SST anomalies in the tropical North Atlantic potentially associated with a positive phase of the Atlantic Multi-decadal Oscillation ( [[#Diatta--2014|Diatta and Fink, 2014]] ; [[#Rodríguez-Fonseca--2015|Rodríguez-Fonseca et al., 2015]] ). The Sahel rainfall recovery has also been attributed to higher levels of GHG in the atmosphere and increases in atmospheric temperature ( [[#Dong--2015|Dong and Sutton, 2015]] ).

In summary, most regions of West Africa experienced a wet period in the mid-20th century followed by a very dry period in the 1970s and 1980s that is attributed to aerosol cooling of the NH ( ''high confidence'' ). Recent estimates provide evidence of a WAfriM recovery from the mid-to-late 1990s, with more intense extreme events partly due to the combined effects of increasing GHG and decreasing anthropogenic aerosols over Europe and North America ( ''high confidence'' ). On paleoclimate time scales, there is ''high confidence'' that the WAfriM strengthened during the early-to-mid Holocene in response to orbitally-forced enhancement of summer warming in the NH.

<div id="8.3.2.4.4" class="h4-container"></div>

<span id="north-american-monsoon"></span>
===== 8.3.2.4.4 North American Monsoon =====

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Since AR5, there have been updates on the observed long-term variations and changes in the North American monsoon (NAmerM). During the Last Glacial Maximum (LGM; 21,000 – 19,000 years ago), the NAmerM was substantially weaker due to cold, dry mid-latitude air associated with the Laurentide Ice Sheet ( T. Bhattacharya et al. , 2017, 2018 ). The NAmerM strengthened until the mid-Holocene period, in response to ice-emsheet retreat and rising summer insolation, but probably did not exceed the strength of the modern system ( ''low confidence'' ), as indicated by model simulations ( [[#Metcalfe--2015|Metcalfe et al., 2015]] ) and paleoclimatic reconstructions ( [[#Bhattacharya--2018|Bhattacharya et al., 2018]] ). Paleoclimatic evidence from proxy datasets and mid-Pliocene (PlioMIP1) simulations suggest a wetter south-western USA during that warmer period (A.M. [[#Haywood--2013|]] [[#Haywood--2013|Haywood et al., 2013]] ; [[#Pound--2014|Pound et al., 2014]] ; [[#Ibarra--2018|Ibarra et al., 2018]] ) but it is not clear whether this is due to increases of precipitation associated with the monsoon or occurring during the winter season.

During 1948 – 2010, trends of boreal summer precipitation amount were significantly positive over New Mexico and the core NAmerM region, but significantly negative over south-western Mexico ( [[#Hoell--2016|Hoell et al., 2016]] ). In addition, diverse datasets like CRU, CHIRPS and GPCP show significant decreases of precipitation in parts of the south-western USA and north-western Mexico, including the NAmerM region ( [[#Cavazos--2020|Cavazos et al., 2020]] ; [[#Ashfaq--2021|Ashfaq et al., 2021]] ). Other studies suggest a strengthening of the NAmerM upper level anticyclone since the mid-1970s, with a more frequent northward location ( [[#Diem--2013|Diem et al., 2013]] ). Between 1910 – 2010, the number of precipitation events increased across the northern Chihuahuan desert, within the NAmerM domain, despite a decrease in their magnitude, and the length of extreme dry and wet periods also increased ( [[#Petrie--2014|Petrie et al., 2014]] ).

An increase in intense rainfall and severe weather events has been observed in several locations, especially in south-western Arizona since 1991, resulting from increases in atmospheric moisture content and instability; a change that has been confirmed by convective-permitting model simulations ( [[#Luong--2017|Luong et al., 2017]] ; [[#Pascale--2019|Pascale et al., 2019]] ). A dense network of 59 rain gauges located in south-eastern Arizona suggests an intensification of monsoon sub-daily rainfall since the mid-1970s ( [[#Demaria--2019|Demaria et al., 2019]] ), as expected by a stronger global warming signature for sub-daily rather than daily or monthly precipitation accumulation ( [[IPCC:Wg1:Chapter:Chapter-11#11.4|Section 11.4]] ). [[IPCC:Wg1:Chapter:Chapter-10#10.4.2.3|Section 10.4.2.3]] provides further details on changes in precipitation in south-western North America. Evidence from multiple reanalyses suggests that increases in NAmerM rainfall have contributed to the increasing trend of global monsoon precipitation ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4.2|Section 2.3.1.4.2]] ; [[#Lin--2014|Lin et al., 2014]] ). In addition, more frequent occurrence of earlier retreats of the NAmerM since 1979 is documented ( [[#Arias--2012|Arias et al., 2012]] , 2015), in association with the positive phase of the Atlantic Multi-decadal Variability (AMV) and a westward expansion of the North Atlantic Subtropical High (W. [[#Li--2011|Li et al., 2011]] , 2012).

Analyses from a 50-km resolution GCM indicate that the NAmerM response to CO <sub>2</sub> is very sensitive to SST biases, showing reductions in summer NAmerM precipitation with increased CO <sub>2</sub> when the SST biases are small ( [[#Pascale--2017|Pascale et al., 2017]] ) in contrast to CMIP5 models ( [[#Cook--2013|Cook and Seager, 2013]] ; [[#Maloney--2014|Maloney et al., 2014]] ; [[#Torres-Alavez--2014|Torres-Alavez et al., 2014]] ; [[#Hoell--2016|Hoell et al., 2016]] ). The NAmerM has been shown to be also sensitive to sulphur dioxide (SO <sub>2</sub> ) emissions ( [[#García-Martínez--2020|García-Martínez et al., 2020]] ).

In summary, both paleoclimate evidence and observations indicate an intensification of the NAmerM in a warmer climate ( ''medium confidence'' ). The intensification recorded since about the 1970s has been partly driven by GHG emissions ( ''medium con'' ''fidence'' ).

<div id="8.3.2.4.5" class="h4-container"></div>

<span id="south-american-monsoon"></span>
===== 8.3.2.4.5 South American Monsoon =====

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Since AR5, there has been improved understanding of changes in the South American monsoon (SAmerM) as evidenced from paleoclimate records, instrumental observations and climate model simulations. However, general circulation models (GCMs) still exhibit difficulties in reproducing SAmerM precipitation amount ( [[#Rojas--2016|Rojas et al., 2016]] ; [[#D’Agostino--2020b|D’Agostino et al., 2020b]] ). Paleoclimate evidence suggests a relatively stronger SAmerM during the 1400–1600 period (Bird et al. , 2011b; Vuille et al. , 2012; Ledru et al. , 2013; Apaéstegui et al. , 2014; Novello et al. , 2016; Wortham et al. , 2017). Last millennium GCM simulations are able to reproduce stronger SAmerM during the 1400–1600 period in comparison with warmer epochs such as the 900–1100 period (Rojas et al., 2016) or the current warming period (Díaz and Vera, 2018). PMIP3/CMIP5 simulations indicate a consistent weaker SAmerM during the mid-Holocene (6000 years ago; see Cross-Chapter Box 2.1) in comparison to current conditions (Bird et al., 2011a; [[#Mollier-Vogel--2013|Mollier-Vogel et al., 2013]] ; [[#Prado--2013a|Prado et al., 2013a]] ; [[#D’Agostino--2020b|D’Agostino et al., 2020b]] ), thus favouring savannah/grassland-like vegetation (Smith and Mayle, 2018), in agreement with climate reconstructions from different proxies (Prado et al., 2013b). Signals of weak and strong SAmerM during mid-Holocene and LGM, respectively, are evident also in high-resolution long-term (i.e., more than about 22,000 years) rainfall reconstructions based on oxygen isotopes in speleothems from Brazil (Novello et al. , 2017; Stríkis et al. , 2018; Campos et al., 2019).

Isotope records from caves in the central Peruvian Andes show that the late Holocene (&lt;3000 years ago) was characterized by multi-decadal and centennial-scale periods of significant decline in intensity of the SAmerM ( [[#Bird--2011a|Bird et al., 2011a]] ; [[#Vuille--2012|Vuille et al., 2012]] ). This could be partly due to a reduction in the zonal SST gradient of the Pacific Ocean, favouring El Niño-like conditions (Kanner et al., 2013). Other studies suggest increased SAmerM precipitation amount during the Late Holocene, in association with the expansion of the tropical forest (Smith and Mayle, 2018). Well-dated equilibrium lines of glaciers during the deglaciation suggest that the AMOC enhances Atlantic moisture sources and precipitation amount increase over the tropical and southern Andes ( [[#Beniston--2018|Beniston et al., 2018]] ).

Observations during 1979 – 2014 suggest that poleward shifts in the South Atlantic Convergence Zone (SACZ) noted in recent decades ( [[#Talento--2018|Talento and Barreiro, 2018]] ; [[#Zilli--2019|Zilli et al., 2019]] ), are associated with precipitation amount decrease along the equatorward margin and increase along the poleward margin of the convergenze zone ( [[#Zilli--2019|Zilli et al., 2019]] ). Several observational studies identified delayed onsets of the SAmerM after 1978 related to longer dry seasons in the southern Amazon (Fu et al. , 2013; Yin et al. , 2014; Arias et al. , 2015; Debortoli et al. , 2015; Arvor et al. , 2017; Giráldez et al. , 2020; Haghtalab et al. , 2020; Correa et al. , 2021). In contrast, other studies indicate a trend toward earlier onsets of the SAmerM ( [[#Jones--2013|Jones and Carvalho, 2013]] ). These discrepancies are explained by the methodology used and the domain considered for the SAmerM, confirming the occurrence of delayed onsets of the SAmerM since 1978 ( [[#Correa--2021|Correa et al., 2021]] ). CMIP5 simulations show trends toward delayed onsets of the SAmerM in association with anthropogenic forcing, although the simulated trends underestimate the observed trends ( [[#Fu--2013|Fu et al., 2013]] ). Total rainfall reductions are observed in the southern Amazon during September – October – November after 1978 ( [[#Fu--2013|Fu et al., 2013]] ; [[#Bonini--2014|Bonini et al., 2014]] ; [[#Debortoli--2015|Debortoli et al., 2015]] , 2016; [[#Espinoza--2019|Espinoza et al., 2019]] ), consistent with reductions in river discharge in the region (Molina-Carpio et al. , 2017; Espinoza et al. , 2019; Heerspink et al., 2020).

Significant increases in precipitation have been observed over south-eastern Brazil during 1902 – 2005 while non-significant decreases have been found over central Brazil (Vera andDíaz, 2015). In Bolivia, increases were observed during 1965 – 1984, while reductions have occurred since then ( [[#Seiler--2013|Seiler et al., 2013]] ). However, the Peruvian Amazon does not reveal significant changes in mean rainfall during 1965–2007 ( [[#Lavado--2013|Lavado et al., 2013]] ; [[#Ronchail--2018|Ronchail et al., 2018]] ). Historical simulations from CMIP5 ensembles adequately capture the observed summer precipitation amount over central and south-eastern Brazil, thereby providing ''high confidence'' in interpreting the observed variability of SAmerM for the period 1960 – 1999 ( [[#Gulizia--2015|Gulizia and Camilloni, 2015]] ; [[#Pascale--2019|Pascale et al., 2019]] ). Also, CMIP5 simulations indicate that the anthropogenic forcing associated with increased GHG emissions is necessary to explain the positive trends in upper-troposphere zonal winds observed over the South American Altiplano ( [[#Vera--2019|Vera et al., 2019]] ). However, the detection of anthropogenically-induced signals for precipitation is still ambiguous in monsoon regions, like the SAmerM ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ).

In summary, there is ''high confidence'' that the SAmerM onset has been delayed since the late 1970s. This is reproduced by CMIP5 simulations that consider anthropogenic forcing. There is also ''high confidence'' that precipitation during the dry-to-wet transition season has been reduced over the southern Amazon. Paleoclimate reconstructions and simulations suggest a weaker SAmerM during warmer epochs such as the Mid-Holocene or the 900–1100 period, and stronger monsoon during colder epochs such as the LGM or the 1400–1600 period ( ''high con'' ''fidence'' ).

<div id="8.3.2.4.6" class="h4-container"></div>

<span id="australian-and-maritime-continent-monsoon"></span>
===== 8.3.2.4.6 Australian and Maritime Continent Monsoon =====

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Since AR5, several studies have examined observed variability and changes in the Australian and Maritime Continent monsoon (AusMCM) using paleoclimate records, instrumental observations and modeling studies ( [[#Denniston--2016|Denniston et al., 2016]] ; [[#Zhang--2016|Zhang and Moise, 2016]] ). Paleoclimate reconstructions and modelling indicate that the Indo–Australian monsoon may vary in or out of phase with the EAsiaM, depending on whether there is a meridional displacement or expansion of the tropical rainfall belt ( [[#Ayliffe--2013|Ayliffe et al., 2013]] ; [[#Denniston--2016|Denniston et al., 2016]] ). For instance, mid-Holocene simulations suggest that the AusMCM weakens and contracts due to a decreased net energy input and a weaker dynamic component ( [[#D’Agostino--2020b|D’Agostino et al., 2020b]] ).

Rainfall increases have been observed over northern Australia since the 1950s, with most of the increases occurring in the north-west ( [[#Dey--2019a|Dey et al., 2019a]] , b; [[#Dai--2021|Dai, 2021]] ) and decreases observed in the north-east (J. [[#Li--2012|]] [[#Li--2012|Li et al., 2012]] ) since the 1970s. There is also a trend towards more intense convective rainfall from thunderstorms over northern Australia ( [[#Dowdy--2020|Dowdy, 2020]] ). There is no consensus on the cause of the observed Australian monsoon rainfall trends, with some studies suggesting changes are due to altered circulation driving increased moisture transport or increased frequency of the wettest synoptic regimes ( [[#Catto--2012|Catto et al., 2012]] ; [[#Clark--2018|Clark et al., 2018]] ). Other studies find that model simulations that include anthopogenic aerosols ( [[#Rotstayn--2012|Rotstayn et al., 2012]] ; [[#Dey--2019a|Dey et al., 2019a]] ) are better able to capture observed Australian monsoon rainfall trends than simulations with natural or GHG forcing only ( [[#Knutson--2018|Knutson and Zeng, 2018]] ).

The Maritime Continent (MC) experiences the influence of both the Asian and the Australian monsoons, with rainfall peaking during boreal winter/austral summer ( [[#Robertson--2011|Robertson et al., 2011]] ). Reductions in land rainfall and marine cloudiness over the MC and weakening of surface moisture flux convergence have been observed in the period 1950 – 1999 (Tokinaga et al., 2012; [[#Yoden--2017|Yoden et al., 2017]] ). These trends are indicative of a slowdown of the Walker Circulation, with positive sea level pressure trends over the MC and negative trends over the central equatorial Pacific (Tokinaga et al., 2012). More recently (1981 – 2014), a trend of increasing annual rainfall over large areas of the MC has been identified (Hassim and Timbal, 2019). Given the large variability in MC rainfall on interannual time scales, the choice of time period may influence the calculated rainfall trend (Hassim and Timbal, 2019).

During 1951 – 2007 daily rainfall extremes did not increase over the MC, in contrast to the rest of South East Asia ( [[IPCC:Wg1:Chapter:Chapter-11#11.4.2|Section 11.4.2]] ; [[#Villafuerte--2015|Villafuerte and Matsumoto, 2015]] ). Rainfall extremes in Indonesia increased in austral summer, as evidenced from station weather observations for the period 1983 – 2012 (Supari et al., 2018).

In summary, notable rainfall increases have been observed in parts of northern Australia since the 1970s, although there is ''low confidence'' in the human contribution to these changes. Rainfall changes have been observed over the MC region but there is ''low confidence'' in the identification of trends because of large variability at interannual time scales.

<div id="8.3.2.5" class="h3-container"></div>

<span id="tropical-cyclones"></span>
==== 8.3.2.5 Tropical Cyclones ====

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The AR5 assessed ''low confidence'' in centennial changes in tropical cyclone (TC) activity globally, and in the attribution of observed changes in TCs to anthropogenic forcing. Since AR5, there has been considerable progress in understanding the observed changes of TCs and an overall improved knowledge of the sensitivity of TCs to both GHG and aerosol forcing ( [[#Knutson--2019|Knutson et al., 2019]] ; [[#Sobel--2019|Sobel et al., 2019]] ).

Although observational data limitations ( [[#Lau--2012|Lau and Zhou, 2012]] ) tend to limit detection of anthropogenic forced increases in TC precipitation ( [[#Knutson--2019|Knutson et al., 2019]] ), there is ''medium confidence'' that anthropogenic forcing has contributed to observed heavy rainfall events over the USA associated with TCs ( [[#Kunkel--2012|Kunkel et al., 2012]] ) and other regions with sufficient data coverage ( [[IPCC:Wg1:Chapter:Chapter-11#11.7.1.2|Section 11.7.1.2]] ; [[#Bindoff--2013|Bindoff et al., 2013]] ). There has been increased frequency of TC heavy rainfall events over several areas in the USA since the late 19th century that is greater than what would be expected solely from changes in US landfall frequency, suggesting the increasing role of TCs have in causing heavy rainfall events ( [[#Kunkel--2010|Kunkel et al., 2010]] ). For example, there is evidence for an anthropogenic contribution to the extreme rainfall of Hurricane Harvey in 2017 ( [[#Emanuel--2017|Emanuel, 2017]] ; [[#Risser--2017|Risser and Wehner, 2017]] ; [[#van%20Oldenborgh--2017|van Oldenborgh et al., 2017]] ; [[#Trenberth--2018|Trenberth et al., 2018]] ; S.-Y.S. [[#Wang--2018|]] [[#Wang--2018|]] [[#Wang--2018|]] [[#Wang--2018|]] [[#Wang--2018|]] [[#Wang--2018|Wang et al., 2018]] ).

While TCs cause extreme local rainfall and flooding, they can be also an important contributor to annual precipitation and regional fresh water resources ( [[#Hristova-Veleva--2020|Hristova-Veleva et al., 2020]] ). Transport of moisture by TCs is an important contributor for precipitation over the coastal areas of East Asia mostly from July through October, with the TC rainfall accounting for nearly 10% to 30% of the total rainfall in the region (L. [[#Guo--2017|]] [[#Guo--2017|Guo et al., 2017]] ). Local TC rainfall totals depend on rain-rate and translation speed (the speed of TC movement along the storm track) with slow TCs such as Hurricane Harvey (2017), providing a clear example of the effect of slow translation speed on local rainfall accumulation, with urbanization exacerbating the storm total rainfall and flooding ( [[IPCC:Wg1:Chapter:Chapter-11#11.7.1|Section 11.7.1]] ; W. [[#Zhang--2018|]] [[#Zhang--2018|]] [[#Zhang--2018|]] [[#Zhang--2018|]] [[#Zhang--2018|Zhang et al., 2018]] ).

In addition to evidence that rain-rates have increased, there is evidence that TC translation speed has slowed globally ( [[#Kossin--2018|Kossin, 2018]] ) thus amplifying thermodynamic intensification of rainfall and may be linked to anthropogenic forcing ( [[#Gutmann--2018|Gutmann et al., 2018]] ). This is ''limited evidence'' however, so there is ''medium'' ''confidence'' of a detectable change in TC translation speed over the US. Since the 1900s, and there is ''low'' ''confidence'' for a global signal because of ''limited agreement'' among models and due to data heterogeneity. However, the slowdown is consistent with theoretical and modelling studies that indicate a general weakening of the tropical circulation with warming that reduces the speed of the TC system ( [[#Chauvin--2017|Chauvin et al., 2017]] ), though there is ''limited'' observational ''evidence'' (Sections 8.2.3.5 and 11.7.1).

In summary, there is ''medium confidence'' of an observed increase in TC precipitation intensity in regions with sufficient data coverage Robust physical understanding ( [[#8.2.3.2|Section 8.2.3.2]] ) and detailed singular event attribution studies provide evidence that tropical cyclone rainfall has increased with a warming climate ( ''high confidence'' , [[IPCC:Wg1:Chapter:Chapter-11#11.7.1.4|Section 11.7.1.4]] ).

<div id="8.3.2.6" class="h3-container"></div>

<span id="stationary-waves"></span>
==== 8.3.2.6 Stationary Waves ====

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Stationary waves are planetary-scale waves that are approximately stable (stationary) in terms of geographic position, as opposed to propogating planetary waves, and are important both as part of the climatological general circulation and seasonal and shorter-term anomalies. They are related to surface features including land – ocean contrasts and major mountain ranges, as well as atmospheric features including the jet stream, storm tracks, and blocking, which are considered separately in the following sections. While zonal mean changes in P–E (precipitation minus evaporation) are dominated by thermodynamic effects ( [[#8.2.2.1|Section 8.2.2.1]] ), changes in stationary waves are of key importance in understanding zonal asymmetries in the water cycle response to global warming ( [[#Wills--2015|Wills and Schneider, 2015]] ; [[#Wills--2019|Wills et al., 2019]] ). The AR5 did not explicitly assess stationary waves, but noted changes in related circulation features such as a ''likely'' poleward shift of the Northern Hemisphere (NH) storm tracks and an increase in frequency and eastward shift in North Atlantic blocking anticyclones, although there was ''low confidence'' in the global assessment of blocking.

Since AR5, several studies have demonstrated a link between stationary wave amplitude and wet and dry extremes in several different regions of the NH ( [[#Liu--2012|Liu et al., 2012]] ; [[#Coumou--2014|Coumou et al., 2014]] ; [[#Screen--2014|Screen and Simmonds, 2014]] ; [[#Yuan--2015|Yuan et al., 2015]] ) with changes in moisture transport playing an important role ( [[#Yuan--2015|Yuan et al., 2015]] ). A ‘resonance mechanism’ has been proposed for an increasing amplitude of stationary waves ( [[#Petoukhov--2013|Petoukhov et al., 2013]] , 2016; [[#Coumou--2014|Coumou et al., 2014]] ; [[#Kornhuber--2017|Kornhuber et al., 2017]] ) and several studies have linked increasing amplitude of stationary waves to Arctic warming ( [[#Francis--2012|Francis and Vavrus, 2012]] , 2015; Liu et al. , 2012; Tang et al. , 2014) as well as to global warming (Mann et al. , 2017). However, other studies have not identified an increase in stationary wave amplitude ( [[#Barnes--2013|Barnes, 2013]] ; Screen and Simmonds, 2013a, b).

There has been considerable work on linkages (teleconnections) between Arctic warming and the mid-latitude circulation (see also Cross-Chapter Box 10.1). The limited amount of research on Southern Hemisphere (SH) stationary waves suggests changes in high-latitude, mid-tropospheric stationary waves which influence Antarctic precipitation ( [[#Turner--2017|Turner et al., 2017]] ) and changes in stratospheric stationary waves that are associated with ozone depletion rather than increases in GHGs (L. [[#Wang--2013|]] [[#Wang--2013|]] [[#Wang--2013|]] [[#Wang--2013|Wang et al., 2013]] ). The observed climatology of NH winter stationary waves is well-represented in the CMIP5 multi-model mean ( [[#Wills--2019|Wills et al., 2019]] ) but individual models have important deficiencies in reproducing stationary wave variability ( [[#Lee--2013|Lee and Black, 2013]] ). In the SH, the observed climatology of stationary waves in CMIP5 models has considerable bias in both phase and amplitude ( [[#Garfinkel--2020|Garfinkel et al., 2020]] ). A comprehensive assessment is not yet available for CMIP6 models.

In summary, there is ''low confidence'' in strengthened winter stationary wave activity over the North Atlantic, associated with increased poleward moisture fluxes east of North America There is ''medium confidence'' in a recent amplification of the NH stationary waves in summer, but no formal attribution to anthropogenic climate change.

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<span id="atmospheric-blocking"></span>
==== 8.3.2.7 Atmospheric Blocking ====

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Atmospheric blocking refers to persistent, semi-stationary weather patterns characterized by a high-pressure (anticyclonic) anomaly that interrupts the westerly flow in the mid-latitudes of both hemispheres. By redirecting the pathways of mid-latitude cyclones, blocking can affect the water cycle and lead to negative precipitation anomalies in the region of the blocking anticyclone and positive anomalies in the surrounding areas ( [[#Sousa--2017|Sousa et al., 2017]] ). In this way, blocking can also be associated with extreme events such as heavy precipitation ( [[#Lenggenhager--2019|Lenggenhager et al., 2019]] ), drought ( [[#Schubert--2014|Schubert et al., 2014]] ) and heatwaves ( [[#Miralles--2014a|Miralles et al., 2014a]] ). The AR5 reported ''low confidence'' in global-scale changes in blocking, due to methodological differences between studies.

Currently no consensus exists on observed trends in blocking during 1979 – 2013. ( [[#Horton--2015|Horton et al., 2015]] ) identified increasing trends in anticyclonic circulation regimes based on geopotential height fields in the mid-troposphere, which may be partly related to the tropospheric warming itself and thus not represent real changes in the statistics of weather ( [[#Horton--2015|Horton et al., 2015]] ; [[#Woollings--2018|Woollings et al., 2018]] ). [[#Hanna--2018|Hanna et al. (2018)]] and ( [[#Davini--2020|Davini and D’Andrea, 2020]] ) reported a significant increase in the frequency of summer blocking over Greenland. A weakening of the zonal wind, eddy kinetic energy and amplitude of Rossby waves in summer in the NH ( [[#Coumou--2015|Coumou et al., 2015]] , [[#Kornhuber--2019|Kornhuber et al., 2019]] ) and an increased ‘waviness’ of the jet stream associated with Arctic warming ( [[#Francis--2015|Francis and Vavrus, 2015]] ; [[#Pfahl--2015|Pfahl et al., 2015]] ; [[#Luo--2019|Luo et al., 2019]] ) have also been identified, which may be linked to increased blocking.

In contrast, it has been shown that observed trends in blocking are sensitive the choice of the blocking index, and that there is a large internal variability that complicates the detection of forced trends ( [[#Barnes--2014|Barnes et al., 2014]] ; [[#Cattiaux--2016|Cattiaux et al., 2016]] ; [[#Woollings--2018|Woollings et al., 2018]] ), compromising the attribution of any observed changes in blocking. Many climate models still underestimate the occurrence of blocking, at least in winter over north-eastern Atlantic and Europe ( [[#Dunn-Sigouin--2013|Dunn-Sigouin and Son, 2013]] ), which leads to caution in the interpretation of their results for these regions. However, over the Pacific Ocean there have been large improvements in the simulation of blocking for the last 20 years ( [[#Davini--2016|Davini and D’Andrea, 2016]] ; Patterson et al. , 2019) . In the SH, increases in blocking frequency have occurred in the South Atlantic in austral summer ( [[#Dennison--2016|Dennison et al., 2016]] ) and in the southern Indian Ocean in austral spring ( [[#Schemm--2018|Schemm, 2018]] ). A reduced blocking frequency has been found over the south-western Pacific in austral spring (Sections 2.3.1.4.3 and 3.4.1.3.3; [[#Schemm--2018|Schemm, 2018]] ).

In summary, no robust trend in atmospheric blocking has been detected in modern reanalyses and in CMIP6 historical simulations ( ''medium confidence'' ). The lack of trend is explained by strong internal variability and/or the competing effects of low-level Arctic amplification and upper-level tropical amplification of the equator-to-pole temperature gradient ( ''medium co'' ''nfidence'' ).

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<span id="extratropical-cyclones-storm-tracks-and-atmospheric-rivers"></span>
==== 8.3.2.8 Extratropical Cyclones, Storm Tracks and Atmospheric Rivers ====

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<span id="extratropical-cyclones-and-storm-tracks"></span>
===== 8.3.2.8.1 Extratropical cyclones and storm tracks =====

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The AR5 indicated ''low confidence'' in long-term changes in the intensity of extratropical cyclones (ETC) over the 20th century derived from centennial reanalyses and storminess proxies based upon sea level pressure. This was confirmed by the SREX assessment that the main Northern Hemisphere (NH) and Southern Hemisphere (SH) extratropical storm tracks ''likely'' experienced a poleward shift during the last 50 years ( [[#Seneviratne--2012|Seneviratne et al., 2012]] ) with ''low confidence'' , and inconsistencies within reanalysis datasets remain.

Since AR5 there has been considerable progress in quantifying storm track activity using multiple reanalysis products and different methodologies (Hodges et al. , 2011; Neu et al. , 2013; Tilinina et al. , 2013; X.L. Wang et al. , 2016). Over the NH increases in the total number of cyclones from 1979 show a large spread of trends across different estimates ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4.3|Section 2.3.1.4.3]] ; [[#Neu--2013|Neu et al., 2013]] ; Z. [[#Li--2016|Li et al., 2016]] a; [[#Grieger--2018|Grieger et al., 2018]] ) resulting in ''low confidence'' in any clear increase of in the total number of cyclones. However, starting from the early 1990s, most reanalyses show increases in the total cyclone number by about 2 – 5% per decade (Figure 8.12). Increasing trends in the total number of cyclones are dominated by the increase in the number of shallow and moderate cyclones (which are more dependent on the datasets and identification methods used) than with decreasing number of deep cyclones since the early 1990s ( [[#Tilinina--2013|Tilinina et al., 2013]] ; [[#Chang--2018|Chang, 2018]] ). In the SH the variability of the total number of cyclones is characterized by strong inter-decadal variability preventing a clear assessment of trends. However, in contrast to the NH,there is a significant increasing trend in the number of deep cyclones (about 10% over 1979 – 2018) in ERA5, ERA-Interim, JRA55 and MERRA, and in the CFSR dataset after 2000 (Figure 8.12; [[#Reboita--2015|Reboita et al., 2015]] ; X.L. [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|Wang et al., 2016]] ).

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[[File:7df44346fb7c505f3a447802068e15be IPCC_AR6_WGI_Figure_8_12.png]]

'''Figure 8.12 |''' '''Annual anomalies (with respect to the reference period''' '''1979–2018''' ''') of the total number of extratropical cyclones (a, b) and of the number of deep cyclones (&lt;980 hPa) (c, d) over the Northern (a, c) and the Southern (b, d) Hemispheres in different reanalyses (shown in colours in the legend).'''
Note different vertical scales for panels (a, b) and (c, d). Thin lines indicate annual anomalies and bold lines indicate five-year running averages. (e, f) The number of reanalyses (out of five) simultaneously indicating statistically significant (90% level) linear trends of the same sign during 1979–2018 for JFM (January–February–March) over the Northern Hemisphere (e) and over the Southern Hemisphere (f). Updated from [[#Tilinina--2013|Tilinina et al. (2013)]] . Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).

Changes in the number of deep storms, which are often associated with heavier precipitiation over the North Atlantic and North Pacific, exhibit strong seasonal differences and decadal variability (Colle et al. , 2015; Chang et al. , 2016; Matthews et al. , 2016; Priestley et al. , 2020a). An increase in the number of summer cyclones over the Atlantic-European sector (Tilinina et al. , 2013) is consistent with the increase in the strength of the strongest fronts over Europe (Schemm et al. , 2018). Chang et al. (2016) reported a decrease in the number of strong summer storms in the latitudinal band 40°N – 75°N over the last decades, however, the assessment of seasonal trends in the Atlantic-European sector is complicated by the choice of region, attribution of tracks to the region selected, and thresholds used to identify trajectories, leading to ''low confidence'' on regional seasonal trends. For the SH, [[#Grieger--2018|Grieger et al. (2018)]] reported a growing number of cyclones over sub-Antarctic region in the austral-summer during 1979 – 2010, while statistically significant trends were absent during the austral winter.

Analysis of storm track activity over longer periods suffers from uncertainties associated with changing data assimilation and observations before and during the satellite era, resulting in in homogeneities and discontinuities in centennial reanalyses (Krueger et al. , 2013; X.L. Wang et al. , 2013, 2016; [[#Chang--2016|Chang and Yau, 2016]] ; Varino et al. , 2019). Feser et al. (2015) reviewed multiple storm track records for the Atlantic-European sector and demonstrated growing storm activity north of 55°N from the 1970s to the mid-1990s with declining trend thereafter, sugesting strong inter-decadal variability in storm track activity. This was also confirmed by [[#Krueger--2019|Krueger et al. (2019)]] from the analysis of geostrophic winds derived from sea level pressure gradients.

Poleward deflection of mostly oceanic winter storm tracks since 1979 was reported in both the North Atlantic and North Pacific ( [[#Tilinina--2013|Tilinina et al., 2013]] ; J. [[#Wang--2017|]] [[#Wang--2017|]] [[#Wang--2017|Wang et al., 2017]] ). This large-scale tendency has regional variations and may be seasonally dependent. [[#Wise--2017|Wise and Dannenberg (2017)]] reported a southward shift in the east Pacific storm track from the 1950s to mid-1980s followed by northward deflection in the later decades. ( [[#King--2019|King et al., 2019]] ) reported an association of Atlantic storm track migrations with SSW events with Central and South European precipitation anomalies. Over centennial time scales, [[#Gan--2014|Gan and Wu (2014)]] reported an intensification of storm tracks in the poleward and downstream regions of the North Pacific and North Atlantic upper troposphere using the NOAA–CIRES–DOE Twentieth Century Reanalysis. Poleward migration of the SH storm tracks ( [[#Grise--2014|Grise et al., 2014]] ; X.L. [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|Wang et al., 2016]] ; [[#Dowdy--2019|Dowdy et al., 2019]] ) was identified during the austral summer and is closely associated with cyclone-associated frontal activity ( [[#Solman--2014|Solman and Orlanski, 2014]] , 2016) and cloud cover ( [[#Bender--2012|Bender et al., 2012]] ; [[#Norris--2016|Norris et al., 2016]] ).

The representation of ETCs in both climate models and reanalyses is resolution-dependent, hence changes must be assessed with caution ( [[IPCC:Wg1:Chapter:Chapter-3#3.3.3.3|Section 3.3.3.3]] ). In particular, CMIP5 models show a systematic underestimation of the intensity of ETCs ( [[#Zappa--2014|Zappa et al., 2014]] ), a feature that is partially related to their relatively coarse resolution or other possible deficiencies such as an excess of dissipation ( [[#Chang--2013|Chang et al., 2013]] ). The best representation of ETCs and their intensity in the North Atlantic are provided by relatively high horizontal resolution CMIP5 models ( [[#Zappa--2014|Zappa et al., 2014]] ). Using a single high-resolution climate model, ( [[#Hawcroft--2016|Hawcroft et al., 2016]] ) showed that precipitation amount associated with ETCs was generally well simulated, though with too much precipitation during the strongest ECTs compared with observed estimations.

In summary, there is ''low confidence'' in recent changes in the total number of extratropical cyclones over both hemispheres. It is ''as likely as not'' that the number of deep cyclones over the NH has decreased after 1979 and it is ''likely'' that the number of deep extratropical cyclones increased over the same period in the SH. It is ''likely'' that extratropical cyclone activity in the SH has intensified during austral summer with no significant changes in austral winter. There is ''medium confidence'' that boreal-winter storm tracks during the last decades experienced poleward shifts over the NH and SH oceans. There is ''low'' ''confidence'' of changes in extratropical cyclone activity prior 1979 due to inhomogeneities in the intrumental records and modern reanalyses.

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<span id="atmospheric-rivers"></span>
===== 8.3.2.8.2 Atmospheric rivers =====

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Atmospheric rivers (ARs) are long, narrow (up to a few hundred kilometres wide), shallow (up to few kilometres deep) and transient corridors of strong horizontal water vapour transport that are typically associated with a low-level jet stream ahead of the cold front of an extratropical cyclone ( [[#Ralph--2018|Ralph et al., 2018]] ). Atmospheric rivers were not assessed in AR5. ARs are associated with atmospheric moisture transport from the tropics to the mid- and high latitudes ( [[#Zhu--1998|Zhu and Newell, 1998]] ), although the drivers of moisture transport relative to the different airstreams within extratropical cyclones remains a subject of current study ( [[#Dacre--2019|Dacre et al., 2019]] ). While much previous research has focused on the west coast of North America, ARs occur throughout extratropical and polar regions (e.g., [[#Guan--2015|Guan and Waliser, 2015]] ) and are often associated with locally-heavy precipitation, including a substantial fraction of all mid-latitude extreme precipitation events (e.g., [[#Waliser--2017|Waliser and Guan, 2017]] ). ARs also affect East Asia strongly during the period from late spring to summer ( [[#Kamae--2017|Kamae et al., 2017]] ). ARs can be related to warming/melt events trough the intrusions of warm and moist air in Antarctica, Greenland and New Zealand ( [[#Bozkurt--2018|Bozkurt et al., 2018]] ; [[#Mattingly--2018|Mattingly et al., 2018]] ; [[#Little--2019|Little et al., 2019]] ), contributing about 45 – 60% of total annual precipitation in subtropical South America ( [[#Viale--2018|Viale et al., 2018]] ). They also '''''transport moisture from South America to the western and central South Atlantic, feeding the ARs that reach the west coast of South Africa''''' ( [[#Ramos--2019|Ramos et al., 2019]] ). However, the estimation of precipitation rate from ARs can have large uncertainties, especially as ARs hit topographically complex coastal regions ( [[#Behrangi--2016|Behrangi et al., 2016]] ), which can cause complexities in quantifying AR-related precipitation.

Analysis of observed trends in the characteristics of ARs has been limited. [[#Gershunov--2017|Gershunov et al. (2017)]] and Sharma and Déry (2019) have shown a rising trend in land-falling AR activity over the west coast of North American since 1948. ( [[#Gonzales--2019|Gonzales et al., 2019]] ) have also documented a seasonally-asymmetric warming of ARs affecting the West Coast of the USA since 1980, which has hydrological implications for the timing and magnitude of regional runoff. Longer-term paleoclimate analysis of ARs is even more limited, although Lora et al. (2017) reported that in the last glacial maximum, AR landfalls over the North American west coast were shifted southward compared to the present conditions.

In summary, it is ''likely'' that there was an increasing trend in the AR activity in the eastern North Pacific since the mid-20th century. However, there is ''low confidence'' in the magnitude of this trend and no formal attribution, although such an increase in activity is consistent with the expected and observed increase in precipitable water associated with human-induced global warming.

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<span id="modes-of-climate-variability-and-regional-teleconnections"></span>
==== 8.3.2.9 Modes of Climate Variability and Regional Teleconnections ====

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Following on from the assessment in Chapters 2 and 3, this section considers changes in modes of variability at seasonal to interannual time scales in terms of their implications on recent water cycle changes. These modes are described in details in Technical Annex IV.

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<span id="tropical-modes"></span>
===== 8.3.2.9.1 Tropical modes =====

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The amplitude of the El Niño–Southern Oscillation (ENSO; Section AIV.2.3) variability has increased since 1950 ( [[IPCC:Wg1:Chapter:Chapter-2#2.4.2|Section 2.4.2]] ) but there is no clear evidence of human influence (Sections 2.4.2 and 3.7.3).

ENSO influences precipitation and evaporation dynamics, river flow and flooding at a global scale (Figure 3.37; [[#Ward--2014|Ward et al., 2014]] , 2016; [[#Martens--2018|Martens et al., 2018]] ). Reconstruction (1804 – 2005) of Thailand’s Chao Praya River peak season streamflow displays a strong correlation with ENSO ( [[#Xu--2019|Xu et al., 2019]] ). Based on water storage estimates from 2002 to 2015, drought conditions over the Yangtze River basin followed La Niña events and flood conditions followed El Niño events (Z. [[#Zhang--2015|]] [[#Zhang--2015|Zhang et al., 2015]] ). Strong correlation between ENSO and terrestrial water storage has been identified mostly in the subtropics but with diverse intensities and time lags depending on the region ( [[#Ni--2018|Ni et al., 2018]] ). The likelihood of increased/decreased flood hazard during ENSO events has a complex spatial pattern with large uncertainties ( [[#Emerton--2017|Emerton et al., 2017]] ).

Tropical SSTs and associated global circulation may increase rainfall in West Africa, as observed in some years during 1950 – 2015, despite the presence of El Niño ( [[#Pomposi--2020|Pomposi et al., 2020]] ). During an El Niño summer, equatorial convective systems and the associated Walker circulation tend to shift eastward, leading to decreases in Indian summer monsoon rainfall ( [[#Li--2015|Li and Ting, 2015]] ; [[#Roy--2019|Roy et al., 2019]] ). This teleconnection is modulated by Indian Ocean Variability ( [[#Terray--2021|Terray et al., 2021]] ), as observed during the extreme positive IOD event in 2019 ( [[#Ratna--2021|Ratna et al., 2021]] ). Since the end of the 19th century, synchronous hydroclimate changes ( ''medium confidence'' ) have been identified over south-eastern Australia and South Africa ( [[#Gergis--2017|Gergis and Henley, 2017]] ) modulated by ENSO, as well as other regional fluctuations like the Botswana High over southern Africa ( [[#Driver--2017|Driver and Reason, 2017]] ). Over southern South America, the ENSO influence on precipitation ( [[#Cai--2020|Cai et al., 2020]] ; [[#Poveda--2020|Poveda et al., 2020]] ) interacts with the influence of SAM ( [[#Pedron--2017|Pedron et al., 2017]] ), exhibiting large multi-decadal variations because of changes in the correlation between the two large-scale modes ( [[#Vera--2018|Vera and Osman, 2018]] ). Other processes underlying ENSO teleconnections of relevance for water cycle changes include water vapour and moisture transports, like over the Middle East ( [[#Sandeep--2018|Sandeep and Ajayamohan, 2018]] ), south-eastern China (S. [[#Yang--2018|]] [[#Yang--2018|Yang et al., 2018]] b), or central Asia (X. [[#Chen--2018|]] [[#Chen--2018|]] [[#Chen--2018|Chen et al., 2018]] ), south-eastern South America (Martin-Gomez et al., 2016; [[#Martín-Gómez--2016|Martín-Gómez and Barreiro, 2016]] ), Australia ( [[#Rathore--2020|Rathore et al., 2020]] ) and southern USA ( [[#Okumura--2017|Okumura et al., 2017]] ).

There is no evidence of a trend in the Indian Ocean Dipole (IOD; Section AIV.2.4) mode and associated anthropogenic forcing (Sections 2.4.3 and 3.7.4). The AR5 concluded that the IOD is ''likely'' to remain active, affecting climate extremes in Australia, Indonesia and East Africa. Since the AR5, IOD teleconnections have been identified extending further to the Middle East ( [[#Chandran--2016|Chandran et al., 2016]] ), to the Yangtze river ( [[#Xiao--2015|Xiao et al., 2015]] ), where in boreal summer and autumn positive IOD events tend to increase the precipitation in the south-eastern and central part of the basin, and to the southern Africa extreme wet seasons ( [[#Hoell--2018|Hoell and Cheng, 2018]] ). During the last millenium, the combined effect of a positive IOD and El Niño conditions have caused severe droughts over Australia ( [[#Abram--2020|Abram et al., 2020]] ). In the satellited period, it is found more effective in inducing significant decrease of rainfall over Indonesia, with the opposite occurring for negative IOD events ( [[#As-syakur--2014|As-syakur et al., 2014]] ; [[#Nur’utami--2016|Nur’utami and Hidayat, 2016]] ; [[#Pan--2018|Pan et al., 2018]] ). Similarly, over the Ganges and Brahmaputra river basins major droughts have been recorded during co-occurring El Niño and positive IOD, while floods occurred during La Niña and negative IOD conditions ( [[#Pervez--2015|Pervez and Henebry, 2015]] ). Over equatorial East Africa the IOD affects the short rain season ( ''medium confidence'' ) exacerbating flooding and inundations independently of ENSO ( [[#Behera--2005|Behera et al., 2005]] ; [[#Conway--2005|Conway et al., 2005]] ; [[#Ummenhofer--2009|Ummenhofer et al., 2009]] ; [[#Hirons--2018|Hirons and Turner, 2018]] ). Extreme conditions, like the 2019 Australian bushfires and African flooding, have been associated with strong positive IOD conditions ( [[#Cai--2021|Cai et al., 2021]] ).

Intraseasonal variability, like the Madden Julian Oscillation (MJO, Section AIV.2.8) and the Boreal Summer Intraseasonal Oscillation (BSISO), are highly relevant to the water cycle ( [[#Maloney--2000|Maloney and Hartmann, 2000]] ; [[#Lee--2013|Lee et al., 2013]] ; [[#Yoshida--2014|Yoshida et al., 2014]] ; [[#Nakano--2015|Nakano et al., 2015]] ). Since AR5, studies on MJO teleconnections within the tropics and from the tropics to higher latitudes have continued ( [[#Guan--2012|Guan et al., 2012]] ; [[#Mundhenk--2018|Mundhenk et al., 2018]] ; [[#Tseng--2019|Tseng et al., 2019]] ; [[#Aberson--2020|Aberson and Kaplan, 2020]] ; [[#Finney--2020b|Finney et al., 2020b]] ; [[#Fowler--2020|Fowler and Pritchard, 2020]] ; [[#Fromang--2020|Fromang and Rivière, 2020]] ).

The strength and frequency of the MJO have increased over the past century ( ''medium confidence'' ) ( [[#Oliver--2012|Oliver and Thompson, 2012]] ; [[#Maloney--2019|Maloney et al., 2019]] ; [[#Cui--2020|Cui et al., 2020]] ) because of global warming ( [[#Arnold--2015|Arnold et al., 2015]] ; [[#Carlson--2016|Carlson and Caballero, 2016]] ; [[#Wolding--2017|Wolding et al., 2017]] ; [[#Maloney--2019|Maloney et al., 2019]] ). A 20th century reconstruction suggests a 13% increase of the MJO amplitude ( [[#Oliver--2012|Oliver and Thompson, 2012]] ), with differences in seasonal variability ( [[#Tao--2015|Tao et al., 2015]] ; Z. [[#Wang--2020|]] [[#Wang--2020|]] [[#Wang--2020|]] [[#Wang--2020|Wang et al., 2020]] ). However, up to half of changes recorded during the second half of the 20th century could be due to internal variability ( [[#Schubert--2013|Schubert et al., 2013]] ). Other observed changes in MJO characteristics include a decrease (by three to four days) in the residence time over the Indian Ocean but an increase (by five to six days) over the Indo-Pacific and Maritime Continent sectors ( [[#Roxy--2019|Roxy et al., 2019]] ).

Consequences of these changes are increased rainfall over South East Asia, northern Australia, south-west Africa and the Amazon, and drying over the west coast of the USA and Equador ( [[#Roxy--2019|Roxy et al., 2019]] ). During the austral summer, air – sea interactions and location of the MJO active phase are important to modulate the strength of the rainfall response in the South Atlantic Convergence Zone ( [[#Shimizu--2016|Shimizu and Ambrizzi, 2016]] ; [[#Alvarez--2017|Alvarez et al., 2017]] ), including its southward shift ( [[#Barreiro--2019|Barreiro et al., 2019]] ). In the austral winter, the intraseasonal variability is mostly influential over regions of the Amazonian basin ( [[#Mayta--2019|Mayta et al., 2019]] ). Some MJO phases are particularly effective in conjuction with tropical cyclones in enhancing westerly moisture fluxes towards East Africa ( [[#Finney--2020b|Finney et al., 2020b]] ).

Simulated changes in MJO precipitation amplitude are extremely sensitive to the pattern of SST warming ( [[#Takahashi--2011|Takahashi et al., 2011]] ; [[#Maloney--2013|Maloney and Xie, 2013]] ; [[#Arnold--2015|Arnold et al., 2015]] ) and ocean – atmosphere coupling ( [[#DeMott--2019|DeMott et al., 2019]] ; [[#Klingaman--2020|Klingaman and Demott, 2020]] ). In agreement with results from previous model generations, most CMIP5 models still underestimate MJO amplitude, and struggle to generate a coherent eastward propagation of precipitation and wind ( [[#Hung--2013|Hung et al., 2013]] ; [[#Jiang--2015|Jiang et al., 2015]] ; [[#Ahn--2017|Ahn et al., 2017]] ), affecting regional surface climate in the tropics and extratropics. In addition, most CMIP5 models simulate an MJO that propagates faster compared with observations, with a poorly represented intra-seasonal precipitation variability ( [[#Ahn--2017|Ahn et al., 2017]] ). Over the Indian Ocean, the propagation speed of convection in some CMIP5 models tends to be slower than observed due to a strong persistence of equatorial precipitation ( [[#Hung--2013|Hung et al., 2013]] ; [[#Jiang--2015|Jiang et al., 2015]] ). Among other processes, improving the moisture-convection coupling, the representation of moist convection, the interaction between lower tropospheric heating and boundary layer convergence, and the topography of the Maritime Continent improve simulations of the MJO ( Ahn et al. , 2017, 2020a; [[#Kim--2017|Kim and Maloney, 2017]] ; [[#Yang--2019|Yang and Wang, 2019]] ; H. Tan et al. , 2020; Y.-M. Yang et al. , 2020 ). In fact, CMIP6 models reproduce the amplitude and propagation of the MJO better than CMIP5 models due to increased horizontal moisture advection over the Maritime Continent ( [[#Ahn--2020b|Ahn et al., 2020b]] ). Despite the diverse theories of MJO evolution and processes that have been developed since its discovery, a better understanding of its dynamics is still needed ( [[#Jiang--2020|Jiang et al., 2020]] ; [[#Zhang--2020|Zhang et al., 2020]] ). Furthermore, metrics based on dynamical processes are needed to assess model simulations of these events ( [[#Stechmann--2017|Stechmann and Hottovy, 2017]] ; [[#Wang--2018|]] [[#Wang--2018|]] [[#Wang--2018|]] [[#Wang--2018|]] [[#Wang--2018|]] [[#Wang--2018|B. Wang et al., 2018]] ) as well as related teleconnections (J. [[#Wang--2020|]] [[#Wang--2020|]] [[#Wang--2020|]] [[#Wang--2020|Wang et al., 2020]] ).

In summary, multiple water cycle changes related to ENSO and IOD teleconnections have been observed across the 20th century ( ''high confidence'' ), mostly dominated by interannual to multi-decadal variations. The MJO amplitude has increased in the second half of the 20th century partly because of anthropogenic global warming ( ''medium confidence'' ) altering regional precipitation signals.

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===== 8.3.2.9.2 Extratropical modes =====

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A positive trend has been observed in the Northern Annular Mode (NAM; Section AIV.2.1) in the second half of the 20th century, which partially reversed since the 1990s ( [[IPCC:Wg1:Chapter:Chapter-2#2.4.5.1|Section 2.4.5.1]] ), but the detection and attribution of these changes remain difficult ( [[IPCC:Wg1:Chapter:Chapter-3#3.7.1|Section 3.7.1]] ). The linkages of the NAM with weather and climate extremes in the northern extratropics are still unclear in models and observations ( [[#Vihma--2014|Vihma, 2014]] ; [[#Overland--2016|Overland et al., 2016]] ; [[#Screen--2018|Screen et al., 2018]] ). However, robust links are identified between precipitation trends and variability in Europe and the phases of the Atlantic component of the NAM, that is, the NAO ( [[#Moore--2013|Moore et al., 2013]] ; [[#Comas-Bru--2014|Comas-Bru and McDermott, 2014]] ). Reduced winter precipitation is well correlated with the NAO over Southern Europe and Mediterranean countries (Kalimeris et al. , 2017; Corona et al. , 2018; [[#Vazifehkhah--2018|Vazifehkhah and Kahya, 2018]] ; Neves et al. , 2019). NAO teleconnections in those regions include influences on groundwater and streamflow ( [[#Zamrane--2016|Zamrane et al., 2016]] ; [[#Massei--2017|Massei et al., 2017]] ; [[#Jemai--2018|Jemai et al., 2018]] ). Remote teleconnections of the NAO have been identified over Northern China, the Yangtze River valley and India ( [[#Jin--2017|Jin and Guan, 2017]] ; [[#Di%20Capua--2020|Di Capua et al., 2020]] ). The summer phase of the NAO is significantly correlated with variations in summer rainfall in East China, with the thermal forcing of the Tibetan Plateau providing a link to this Eurasian teleconnection (Z. [[#Wang--2018|]] [[#Wang--2018|]] [[#Wang--2018|]] [[#Wang--2018|]] [[#Wang--2018|]] [[#Wang--2018|Wang et al., 2018]] ).

In the Southern Hemisphere (SH), an observed positive trend is identified in the strength of the Southern Annular Mode (SAM, Section AIV.2.2) since 1950, especially in austral summer ( ''high confidence'' , [[IPCC:Wg1:Chapter:Chapter-2#2.4.1.2|Section 2.4.1.2]] ). While stratospheric ozone depletion and GHG increases largely contributed to this change, climate models still have trouble simulating the SAM and its response to ozone and GHGs ( [[IPCC:Wg1:Chapter:Chapter-3#3.7.2|Section 3.7.2]] ). Shifts in the south-westerly winds ( [[#Fletcher--2018|Fletcher et al., 2018]] ) and the expansion of the SH Hadley cell ( [[#Kang--2011|Kang and Polvani, 2011]] ; H. [[#Nguyen--2018|]] [[#Nguyen--2018|Nguyen et al., 2018]] ) influence SAM-related rainfall anomalies in in southern South America and southern Australia during the austral spring–summer. Over New Zealand, large-scale SLP and zonal wind patterns associated with SAM phases modulate regional river flow ( [[#Li--2017|Li and McGregor, 2017]] ). The SAM also influences precipitation and water vapour changes over Antarctica via moisture fluxes ( [[#Marshall--2017|Marshall et al., 2017]] ; [[#Oshima--2017|Oshima and Yamazaki, 2017]] ; [[#Grieger--2018|Grieger et al., 2018]] ) but CMIP5 models are limited in their ability to simulate these regional teleconnections ( [[#Marshall--2015|Marshall and Bracegirdle, 2015]] ; [[#Palerme--2017|Palerme et al., 2017]] ). SAM and its interaction with other large-scale modes of climate variability, like ENSO ( [[#Fogt--2011|Fogt et al., 2011]] ) and the Indian Ocean Dipole ( [[#Hoell--2017a|Hoell et al., 2017a]] ), are responsible for fluctuations in southern African rainfall ( [[#Nash--2017|Nash, 2017]] ) and southern South America ( [[#Gergis--2017|Gergis and Henley, 2017]] ). In May, the SAM can trigger a southern Indian Ocean Dipole SSTA favoring more or less precipitation over the Indian sub-continent and adjacent areas ( [[#Dou--2017|Dou et al., 2017]] ), also affecting subsequent summer monsoon in the South China Sea (T. [[#Liu--2018|]] [[#Liu--2018|]] [[#Liu--2018|]] [[#Liu--2018|Liu et al., 2018]] ). Over South America, a positive SAM is associated with dry conditions ( [[#Holz--2017|Holz et al., 2017]] ) due to reduced frontal and orographic precipitation and weakening of moisture convergence. Regions particularly affected include Chile ( [[#Boisier--2018|Boisier et al., 2018]] ) and the rivers of central Patagonia ( [[#Rivera--2018|Rivera et al., 2018]] ).

In summary, while the attribution of 20th century variations of the NAM/NAO is still unclear, there is a strong relationship with precipitation changes over Europe and in the Mediterranean region ( ''high confidence'' ). SAM teleconnections are associated with changes in moisture transport and extend to South America, Australia and Antarctica ( ''high confidence'' ) with documented drying occurring as a result of the ''very likely'' human-induced SAM trend toward its positive phase observed from the 1970s until the 1990s ( [[IPCC:Wg1:Chapter:Chapter-3#3.7.2|Section 3.7.2]] ).

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