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

==== 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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===== 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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