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

=== 8.4.2 Projected Changes in Large-scale Phenomena and Regional Variability ===

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A weakening of the tropical circulation represents a balance between thermodynamic increases in low level water vapour (about 7% °C <sup>–1</sup> ) and smaller increases in global precipitation (1 – 3% °C <sup>–1</sup> ) that are influenced by rapid adjustments to radiative forcings as well as slow responses to warming ( [[#8.2.2.2|Section 8.2.2.2]] ; Bony et al. , 2013; Chadwick et al. , 2013; Ma et al. , 2018) . Since AR5, additional drivers of tropical circulation weakening have been identified, including mean SST warming and changes in spatial patterns of SST ( [[#He--2015|He and Soden, 2015]] ), and the direct CO <sub>2</sub> radiative effect ( [[#Bony--2013|Bony et al., 2013]] ; [[#He--2015|He and Soden, 2015]] ; [[#Merlis--2015|Merlis, 2015]] ).

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==== 8.4.2.1 ITCZ and Tropical Rain Belts ====

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CMIP5 projections show no consistent shift in the zonal mean position of the ITCZ ( Donohoe et al. , 2013; [[#Donohoe--2017|Donohoe and Voigt, 2017]] ; Byrne et al. , 2018 ). The ITCZ position is strongly connected to cross-equatorial energy transport ( Kang et al. , 2008; [[#Bischoff--2014|Bischoff and Schneider, 2014]] ), which also shows no consistent change in future projections ( [[#Donohoe--2013|Donohoe et al., 2013]] ). Since AR5 it has been reported that most CMIP5 models project a narrowing of the ITCZ in response to surface warming together with intensified ascent in the core region and weakened ascent on the ITCZ edges ( [[#Lau--2015|Lau and Kim, 2015]] ; [[#Byrne--2018|Byrne et al., 2018]] ), implying a narrowing of precipitation regions influenced by the ITCZ. Modelled changes in the width and intensity of the zonal mean ITCZ are strongly anti-correlated, for example, narrowing is associated with increased intensity while broadening with decreased intensity. Such changes are associated with changes in tropical high cloud fraction and outgoing longwave radiation ( [[#Su--2017|Su et al., 2017]] ; [[#Byrne--2018|Byrne et al., 2018]] ).

Regional shifts in tropical convergence zones are much larger than their zonal mean, and associated regional changes in precipitation ( [[#Chadwick--2013|Chadwick et al., 2013]] ; [[#Mamalakis--2021|Mamalakis et al., 2021]] ) are characterized by considerable uncertainties across models ( [[#Kent--2015|Kent et al., 2015]] ; [[#Oueslati--2016|Oueslati et al., 2016]] ). Over the tropical oceans, shifts in rain bands are strongly coupled with changes in SSTs (Xie et al. , 2010; Huang et al. , 2013) . Over tropical land, factors including remote SST increases ( [[#Giannini--2010|Giannini, 2010]] ), the direct CO <sub>2</sub> effect ( [[#Biasutti--2013|Biasutti, 2013]] ) and land–atmosphere interactions ( [[#Chadwick--2017|Chadwick et al., 2017]] ; [[#Kooperman--2018|Kooperman et al., 2018]] ) influence projections. CMIP6 models project a clear northward ITCZ shift over eastern Africa and the Indian Ocean as well as a southward shift over the eastern Pacific and Atlantic oceans, as a result of regionally-contrasting inter-hemispheric energy flows ( [[#Mamalakis--2021|Mamalakis et al., 2021]] ). The northward movement of the ITCZ over Africa has been linked to an intensification of the Saharan heat low associated with greenhouse gas (GHG) warming ( [[#Dong--2015|Dong and Sutton, 2015]] ), causing the tropical rain belt to seasonally migrate farther northward and reside there longer ( [[#Cook--2012|Cook and Vizy, 2012]] ; [[#Dunning--2018|Dunning et al., 2018]] ). In southern Africa, the projected delay in the wet season onset (Dunning et al. , 2018) is also associated with a circulation-based northward shift in the tropical rain band (Lazenby et al., 2018) .

In summary, consistent with the AR5, the overall weakening of the tropical circulation is projected in CMIP5 and CMIP6 simulations with ''high confidence'' . It is ''likely'' that the zonal mean of the ITCZ will narrow and strengthen in the core region with projected surface warming ( ''high confidence'' ). Distinct regional shifts in the ITCZ will be associated with regional changes in precipitation amount and seasonality ( ''medium co'' ''nfidence'' ).

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==== 8.4.2.2 Hadley Circulation and Subtropical Belt ====

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The AR5 found that the Hadley cells are ''likely'' to slow down and expand in response to radiative forcing, but with considerable internal variability. Given the complexities in forcing mechanisms, AR5 assigned ''low confidence'' to near-term changes in the structure of the Hadley circulation. The widening Hadley cells were expected to result in a poleward expansion of subtropical dry zones.

Model simulations since AR5 project a more noticeable and consistent weakening of the Northern Hemisphere (NH) winter Hadley cell than the Southern Hemisphere (SH) winter cell ( [[#Seo--2014|Seo et al., 2014]] ; [[#Zhou--2016|Zhou et al., 2016]] ), related to changes in meridional temperature gradient, static stability, and tropopause height ( [[#Seo--2014|Seo et al., 2014]] ; [[#D’Agostino--2017|D’Agostino et al., 2017]] ). Changes in SST patterns reduces the magnitude of Hadley cell weakening ( [[#Gastineau--2009|Gastineau et al., 2009]] ; [[#Ma--2012|Ma et al., 2012]] ). There is considerable structure in Hadley circulation strength changes with longitude, associated with cloud-circulation interactions ( [[#Su--2014|Su et al., 2014]] ). Subtropical anticyclones are projected to intensify over the north Atlantic and south Pacific but to weaken elsewhere ( [[#He--2017|He et al., 2017]] ).

A consistent poleward expansion of the edges of the Hadley cells is projected ( [[#Nguyen--2015|Nguyen et al., 2015]] ; [[#Grise--2020|Grise and Davis, 2020]] ), particularly in the SH, consistent with observed trends (Figure 8.21 and [[#8.3.2.2|Section 8.3.2.2]] ; Nguyen et al. , 2015) . The main driver of future expansion appears to be greenhouse gas forcing ( [[#Grise--2019|Grise et al., 2019]] ), with uncertainty in magnitude due to internal variability ( [[#Kang--2013|Kang et al., 2013]] ). Proposed mechanisms for poleward expansion include increased dry static stability (Frierson et al. , 2007; Lu et al. , 2007) , increased tropopause height ( [[#Chen--2007|Chen and Held, 2007]] ; Chen et al. , 2008) , stratospheric influences ( [[#Kidston--2015|Kidston et al., 2015]] ) and radiative effects of clouds and water vapour ( [[#Shaw--2016|Shaw and Voigt, 2016]] ; see also [[IPCC:Wg1:Chapter:Chapter-4#4.5.1.5|Section 4.5.1.5]] ). Hadley cell expansion is thought to be associated with the precipitation declines projected in many subtropical regions ( [[#Shaw--2016|Shaw and Voigt, 2016]] ), but more recent work suggests that these reductions are mainly due to the direct radiative effect of CO <sub>2</sub> forcing ( [[#He--2015|He and Soden, 2015]] ), land – sea contrasts in the response to forcing (Shaw and Voigt, 2016; Brogli et al. , 2019) and SST changes ( [[#Sniderman--2019|Sniderman et al., 2019]] ). In semi-arid, winter rainfall-dominated regions (such as the Mediterranean), thermodynamic processes associated with the land – sea thermal contrast and lapse rate changes dominate the projected precipitation decline in summer, whereas circulation changes are of greater importance in winter (Brogli et al. , 2019) . The hydroclimates in these regions are projected to evolve with time due to changing contributions from rapid atmospheric circulation changes and their associated SST responses, as well as slower SST responses to anthropogenic forcing (Zappa et al., 2020) .

In summary, CMIP5 and CMIP6 models project a weakening of the Hadley cells, with ''high confidence'' for the NH in boreal winter and ''low confidence'' for the SH in austral winter. The Hadley cells are projected to expand polewards with global warming, most notably in the SH ( ''high confidence'' ). There is currently ''low confidence'' in the impacts on regional precipitation in subtropical regions.

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==== 8.4.2.3 Walker Circulation ====

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The AR5 determined that the Pacific Walker circulation was ''likely'' to slow down over the 21st century, which would lead to decreased precipitation over the western tropical Pacific and increases over the central and eastern Pacific. Recent studies show consistency with AR5 conclusions but also show an eastward shift over the Pacific, mostly due to a shift towards more ‘El Niño-like’ conditions under global warming ( [[#Bayr--2014|Bayr et al., 2014]] ). Other studies suggest that the weakening of the Walker circulation is related to the response of the western North Pacific monsoon and to changing land–sea temperature contrasts, while a positive ocean–atmosphere feedback amplifies the weakening of both east–west SST gradient and trade winds in the tropical Pacific (Zhang and Li, 2017).

Since AR5, the paradox between the projected weakening and the observed strengthening of the Walker circulation since the 1990s ( [[#8.3.2.2|Section 8.3.2.2]] ) has triggered debate about the drivers of these changes (England et al. , 2014; McGregor et al. , 2014; [[#Kociuba--2015|Kociuba and Power, 2015]] ; Vilasa et al. , 2017; Chung et al. , 2019) . Projected changes in equatorial SST gradients are not entirely consistent with observed trends (Coats and Karnauskas, 2017; [[#Seager--2019a|Seager et al., 2019a]] ), and one CMIP5 model that projects a future strengthening of the Walker circulation is more consistent with observations than other models (Kohyama et al., 2017). Other studies suggest that these differences arise from the dominant influence of internal climate variability to the observed trends ( [[#Chung--2019|Chung et al., 2019]] ), or as a consequence of a systematic cold bias of most CMIP5 models in their Equatorial Pacific cold tongues ( [[#Seager--2019a|Seager et al., 2019a]] ). However, the latter hypothesis is based on a simplified model of tropical Pacific dynamics and is not consistent with the current physical understanding of the tropical circulation response to increasing CO <sub>2</sub> levels ( [[#8.2.2.2|Section 8.2.2.2]] ) or with independent paleoclimate evidence suggesting a weaker Walker circulation under warmer climates ( [[#Tierney--2019|Tierney et al., 2019]] ; [[#McClymont--2020|McClymont et al., 2020]] ). Different time scales of the tropical Pacific responses to global warming have been highlighted by numerical experiments with both comprehensive and simplified models. Results suggest a transient strengthening of the Walker circulation related to Indian Ocean warming (L. [[#Zhang--2018|]] [[#Zhang--2018|]] [[#Zhang--2018|]] [[#Zhang--2018|]] [[#Zhang--2018|Zhang et al., 2018]] ), followed by a slower weakening linked to a strengthened eastern Pacific cold tongue warming emerging after 50 – 100 years ( [[IPCC:Wg1:Chapter:Chapter-7#7.4.4.2.1|Section 7.4.4.2.1]] ; [[#Heede--2020|Heede et al., 2020]] ).

CMIP6 projections provide further evidence of a significant long-term weakening of the Walker circulation (Figure 8.21). For instance, a pronounced weakening of the upper-level tropical easterly jet is projected both over the Indian Ocean and tropical eastern Pacific, where declines are projected to exceed 70% by 2100 in the high-emissions SSP5-8.5 scenario (S. [[#Huang--2020|Huang et al., 2020]] ). CMIP6 models agree on a future decrease of the equatorial zonal temperature gradient ( [[#Fredriksen--2020|Fredriksen et al., 2020]] ), which can lead to weaker trade winds over the tropical Pacific. However, CMIP6 models show a diversity of SST warming patterns in the tropical Pacific ( [[#Freund--2020|Freund et al., 2020]] ), which contributes to uncertainties in the response of both Walker circulation and ENSO to continued warming.

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[[File:1db82c46f3cd13224b03acaa27e2a877 IPCC_AR6_WGI_Figure_8_21.png]]

'''Figure 8.21 |''' '''Schematic depicting large-scale circulation changes and impacts on the regional water cycle.''' The central figures show precipitation minus evaporation (P–E) changes at 3°C or global warming relative to an 1850–1900 base period (mean of 23 CMIP6 SSP5-8.5 simulations). Annual mean changes (large map) include contours (ocean only) depicting control climate P–E = 0 mm day – 1 lines with the solid contour enclosing the tropical rain belt region and dashed lines representing the edges of subtropical regions. Confidence levels assess understanding of how large-scale circulation change affect the regional water.

In summary, there is ''high confidence'' that the Pacific Walker circulation will weaken by the end of the 21st century, and will be associated with decreased precipitation over the western tropical Pacific and increases farther east. Discrepancies between observed and simulated changes in SSTs in the tropics indicate that a temporary strengthening of the Walker Circulation can arise from a transient response to GHG radiative forcing ( ''low confidence'' ) and from internal variability ( ''medium co'' ''nfidence'' ).

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==== 8.4.2.4 Monsoons ====

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In AR5, monsoon precipitation over land was projected to intensify by the end of the 21st century, due to thermodynamic increases in moisture convergence despite weakening of the tropical circulation (see [[#8.2.1.3|Section 8.2.1.3]] ). Following the definition of regional monsoons in [[IPCC:Wg1:Chapter:Annex-v|Annex V]] and Figure 8.11, and the assessment of the observed changes ( [[#8.3.2.4|Section 8.3.2.4]] ), here we provide an assessment of projected changes in regional monsoons. Assessment is provided either in terms of SSP and RCP scenarios and global warming levels available since AR5, or from the newly available CMIP6 projections (Figure 8.22 and Table 8.2). Table 8.2 provides projected changes across the five SSPs used in this Report for precipitation (mm day <sup>–1</sup> ), P–E (mm day <sup>–1</sup> ) and runoff (mm day <sup>–1</sup> ) over the regional monsoons for the mid (2041 – 2060) and long term (2081 – 2100).

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'''Table 8.2 |''' '''Monsoon mean water cycle projections in the mid-term''' ( '''2041–2060''' ''') and long term''' ( '''2081–2100''' ''') relative to present day''' ( '''1995–2014''' '''), showing present-day mean and 90% confidence range across CMIP6 models (historical experiment) and projected mean changes and the 90% confidence range across the same set of models and a range of Shared Socio-economic Pathway scenarios. All statistics are in units of mm day''' –1 '''.''' Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).

{| class="wikitable"
|-
!
! colspan="5"| '''Mid-term: 2041–2061 Minus Reference Period'''

! colspan="5"| '''Long Term: 2081–2100 Minus Reference Period'''

|-
!
! '''1995''' – '''2014 Reference Period'''

! '''SSP1-1.9'''

! '''SSP1-2.6'''

! '''SSP2-4.5'''

! '''SSP3-7.0'''

! '''SSP5-8.5'''

! '''SSP1-1.9'''

! '''SSP1-2.6'''

! '''SSP2-4.5'''

! '''SSP3-7.0'''

! '''SSP5-8.5'''

|-
| colspan="12"| '''South and South East Asian Monsoon (June–July–August–September, JJAS)'''

|-
| Precipitation

| 8.42 [6.66 to 10.14]

| 0.44 [0.08 to 0.74]

| 0.47 [0.1 to 0.96]

| 0.42 [0.03 to 0.81]

| 0.32 [–0.08 to +0.94]

| 0.54 [0.11–1.18]

| 0.46 [0.16 to 0.7]

| 0.52 [0.13 to 1.09]

| 0.66 [0.16 to 1.1]

| 0.94 [0.3 to 1.78]

| 1.46 [0.66 to 2.49]

|-
| Runoff

| 3.75 [1.8 to 5.71]

| 0.23 [0.1 to 0.38]

| 0.29 [0.02 to 0.65]

| 0.29 [–0.0 to +0.66]

| 0.24 [–0.04 to +0.52]

| 0.38 [0.07–0.78]

| 0.19 [–0.02 to +0.35]

| 0.29 [–0.04 to +0.65]

| 0.42 [0.04 to 0.83]

| 0.7 [0.12 to 1.2]

| 1.14 [0.36 to 2.05]

|-
| P–E

| 5.19 [3.68 to 6.5]

| 0.28 [0.03 to 0.52]

| 0.36 [–0.0 to +0.76]

| 0.36 [0.02 to 0.69]

| 0.3 [–0.04 to +0.85]

| 0.45 [0.06–0.95]

| 0.27 [0.06 to 0.38]

| 0.38 [0.11 to 0.76]

| 0.51 [0.02 to 0.83]

| 0.81 [0.24 to 1.56]

| 1.15 [0.45 to 1.84]

|-
| colspan="12"| '''East Asian Monsoon (June–July–August, JJA)'''

|-
| Precipitation

| 5.59 [4.47 to 6.86]

| 0.37 [–0.09 to +0.93]

| 0.37 [–0.09 to +0.87]

| 0.34 [0.05 to 0.76]

| 0.22 [–0.16 to +0.88]

| 0.43 [0.03 to 1.1]

| 0.43 [0.07 to 1.02]

| 0.44 [–0.0 to +1.08]

| 0.51 [0.11 to 1.09]

| 0.59 [0.02 to 1.31]

| 0.84 [0.24 to 1.74]

|-
| Runoff

| 2.24 [1.28 to 3.41]

| 0.11 [–0.16 to +0.4]

| 0.13 [–0.19 to +0.42]

| 0.13 [–0.15 to +0.4]

| 0.15 [–0.29 to +0.76]

| 0.2 [–0.11 to +0.72]

| 0.16 [–0.08 to +0.49]

| 0.16 [–0.13 to +0.58]

| 0.22 [–0.13 to +0.64]

| 0.36 [–0.05 to +0.87]

| 0.51 [0.06 to 1.24]

|-
| P–E

| 2.41 [1.51 to 3.31]

| 0.1 [–0.31 to +0.51]

| 0.13 [–0.2 to +0.48]

| 0.17 [–0.04 to +0.53]

| 0.17 [–0.2 to +0.75]

| 0.23 [–0.09 to +0.86]

| 0.16 [–0.07 to +0.57]

| 0.18 [–0.18 to +0.65]

| 0.24 [–0.1 to +0.76]

| 0.4 [–0.08 to +0.93]

| 0.5 [–0.13 to +1.34]

|-
| colspan="12"| '''North American Monsoon (July–August–September, JAS)'''

|-
| Precipitation

| 3.05 [2.24 to 3.96]

| 0.13 [–0.08 to +0.43]

| 0.07 [–0.27 to +0.32]

| 0.02 [–0.32 to +0.41]

| –0.03 [–0.37 to +0.38]

| –0.03 [–0.43 to +0.52]

| 0.18 [–0.05 to +0.44]

| 0.04 [–0.35 to +0.39]

| –0.1 [–0.51 to +0.37]

| –0.19 [–0.76 to +0.44]

| –0.15 [–0.96 to +0.57]

|-
| Runoff

| 0.46 [0.09 to 0.87]

| 0.03 [–0.04 to +0.12]

| 0.03 [–0.07 to +0.16]

| 0.02 [–0.1 to +0.14]

| –0.0 [–0.1 to +0.14]

| –0.0 [–0.11 to +0.14]

| 0.04 [–0.03 to +0.15]

| –0.0 [–0.19 to +0.15]

| –0.03 [–0.22 to +0.14]

| –0.05 [–0.23 to +0.19]

| –0.06 [–0.29 to +0.23]

|-
| P–E

| 0.78 [–0.1 to +1.45]

| 0.06 [–0.1 to +0.2]

| 0.02 [–0.18 to +0.24]

| 0.0 [–0.22 to +0.23]

| –0.03 [–0.24 to +0.2]

| –0.04 [–0.31 to +0.27]

| 0.09 [–0.06 to +0.31]

| 0.01 [–0.22 to +0.25]

| –0.08 [–0.28 to +0.25]

| –0.17 [–0.68 to +0.25]

| –0.18 [–0.72 to +0.38]

|-
| colspan="12"| '''South American Monsoon (December–January–February, DJF)'''

|-
| Precipitation

| 8.44 [5.98 to 10.22]

| 0.09 [–0.2 to +0.3]

| 0.12 [–0.29 to +0.62]

| 0.09 [–0.47 to +0.62]

| 0.07 [–0.55 to +0.62]

| 0.07 [–0.5 to +0.71]

| 0.02 [–0.32 to +0.36]

| 0.09 [–0.33 to +0.58]

| 0.07 [–0.63 to +0.81]

| 0.05 [–1.17 to +0.82]

| –0.0 [–1.22 to +1.19]

|-
| Runoff

| 2.49 [1.11 to 4.38]

| –0.02 [–0.23 to +0.26]

| –0.01 [–0.43 to +0.53]

| 0.01 [–0.45 to +0.46]

| –0.03 [–0.49 to +0.36]

| –0.03 [–0.56 to +0.53]

| –0.04 [–0.27 to +0.28]

| –0.01 [–0.41 to +0.39]

| –0.01 [–0.58 to +0.55]

| –0.06 [–0.81 to +0.24]

| –0.04 [–0.85 to +0.93]

|-
| P–E

| 4.5 [2.83 to 6.01]

| 0.04 [–0.23 to +0.25]

| 0.08 [–0.26 to +0.47]

| 0.04 [–0.43 to +0.53]

| 0.04 [–0.5 to +0.61]

| 0.02 [–0.45 to +0.58]

| –0.01 [–0.32 to +0.29]

| 0.03 [–0.34 to +0.43]

| –0.02 [–0.63 to +0.62]

| –0.02 [–1.03 to +0.72]

| –0.09 [–1.11 to +0.98]

|-
| colspan="12"| '''Australian and Maritime Continent Monsoon (December–January–February, DJF)'''

|-
| Precipitation

| 8.63 [6.79 to 10.7]

| 0.26 [0.04 to 0.49]

| 0.22 [–0.23 to +0.53]

| 0.28 [–0.2 to +0.79]

| 0.25 [–0.14 to +0.73]

| 0.38 [0.0 to 0.84]

| 0.15 [–0.09 to +0.34]

| 0.24 [–0.36 to +0.74]

| 0.5 [–0.1 to +1.07]

| 0.65 [–0.08 to +1.33]

| 0.9 [0.09 to 1.76]

|-
| Runoff

| 3.82 [1.78 to 7.25]

| 0.2 [–0.01 to +0.48]

| 0.23 [–0.11 to +0.48]

| 0.29 [–0.11 to +0.7]

| 0.24 [–0.13 to +0.56]

| 0.35 [–0.03 to +0.87]

| 0.12 [–0.06 to +0.39]

| 0.29 [–0.08 to +0.88]

| 0.49 [0.09 to 1.25]

| 0.61 [–0.09 to +1.05]

| 0.92 [0.14 to 1.83]

|-
| P–E

| 4.8 [3.19 to 6.63]

| 0.22 [0.03 to 0.47]

| 0.13 [–0.23 to +0.42]

| 0.2 [–0.16 to +0.7]

| 0.2 [–0.14 to +0.62]

| 0.27 [–0.09 to +0.61]

| 0.12 [–0.1 to +0.31]

| 0.16 [–0.31 to +0.54]

| 0.38 [–0.05 to +0.75]

| 0.54 [–0.08 to +1.13]

| 0.69 [0.09 to 1.27]

|-
| colspan="12"| '''West African Monsoon (June–July–August–September, JJAS)'''

|-
| Precipitation

| 5.14 [3.62 to 7.18]

| 0.16 [–0.19 to +0.4]

| 0.14 [–0.22 to +0.56]

| 0.24 [–0.14 to +0.72]

| 0.3 [–0.1 to +0.85]

| 0.38 [–0.12 to +1.24]

| 0.06 [–0.25 to +0.52]

| 0.1 [–0.25 to +0.57]

| 0.25 [–0.32 to +0.91]

| 0.38 [–0.49 to +1.14]

| 0.49 [–0.55 to +1.56]

|-
| Runoff

| 1.43 [0.34 to 2.57]

| 0.06 [–0.07 to +0.22]

| 0.05 [–0.18 to +0.27]

| 0.14 [–0.13 to +0.54]

| 0.2 [–0.05 to +0.7]

| 0.24 [–0.1 to +0.8]

| –0.01 [–0.2 to +0.21]

| 0.03 [–0.25 to +0.35]

| 0.1 [–0.25 to +0.51]

| 0.25 [–0.28 to +0.85]

| 0.3 [–0.33 to +0.93]

|-
| P–E

| 2.41 [1.05 to 4.07]

| 0.08 [–0.2 to +0.35]

| 0.1 [–0.2 to +0.4]

| 0.2 [–0.11 to +0.63]

| 0.23 [–0.11 to +0.74]

| 0.36 [–0.06 to +1.11]

| –0.01 [–0.27 to +0.35]

| 0.07 [–0.2 to +0.44]

| 0.18 [–0.21 to +0.6]

| 0.28 [–0.38 to +0.95]

| 0.46 [–0.44 to +1.4]

|}

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[[File:3be7e22b5339e28a893382bf3347666c IPCC_AR6_WGI_Figure_8_22.png]]

'''Figure 8.22 |''' '''Projected regional monsoons precipitation changes.''' Percentage change in projected seasonal mean precipitation over regional monsoon domains (as defined in Figure 8.11, [[#8.3.2.4|Section 8.3.2.4]] and Annex V) for near term (2021–2040), mid-term (2041–2060), and long term (2081–2100) periods based on 24 CMIP6 models and three SSP scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5). Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).

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<span id="south-and-south-east-asian-monsoon-1"></span>
===== 8.4.2.4.1 South and South East Asian Monsoon =====

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In AR5, South and South East Asian monsoon (SAsiaM) precipitation was projected to increase by the end of the 21st century but with a weakening of the circulation, with ''high agreement'' across the CMIP5 models (Kitoh et al. , 2013; Menon et al. , 2013; Sharmila et al. , 2015; Sooraj et al. , 2015; [[#Kitoh--2017|Kitoh, 2017]] ; Kulkarni et al. , 2020) . Since AR5, most studies have confirmed projected increases in South Asian monsoon precipitation ( ''high confidence'' ), while one high-resolution model (35 km in latitude/longitude) projects monsoon precipitation decreases during the 21st century following the RCP4.5 scenario ( [[#Krishnan--2016|Krishnan et al., 2016]] ).

Over South Asia, the moisture-bearing monsoon low-level jet is projected to shift northward in CMIP3 and CMIP5 models ( [[#Sandeep--2015|Sandeep and Ajayamohan, 2015]] ). Greater warming over the Asian land region compared to the ocean contributes to intensification of the monsoon low-level south-westerly winds and precipitation ( [[#Endo--2018|Endo et al., 2018]] ), even though the combined effect of upper and lower tropospheric warming makes the Asian monsoon circulation response rather complicated. A high resolution model projection, based on the RCP8.5 scenario, indicates that a northward shift of the low-level jet and associated weakening of the large-scale monsoon circulation can induce a large reduction in the genesis of monsoon low pressure systems by the late 21st century ( [[#Sandeep--2018|Sandeep et al., 2018]] ). Experiments with constant forcing indicate that at 1.5°C and 2°C global warming levels, mean precipitation and monsoon extremes are projected to intensify in summer over India and South Asia ( [[#Chevuturi--2018|Chevuturi et al., 2018]] ; D. [[#Lee--2018|]] [[#Lee--2018|Lee et al., 2018]] ) and that a 0.5°C difference would imply a 3% increase of precipitation ( [[#Chevuturi--2018|Chevuturi et al., 2018]] ). CMIP5 models project an increase in short intense active days and decrease in long active days, with no significant change in the number of break spells for India ( [[#Sudeepkumar--2018|Sudeepkumar et al., 2018]] ).

Future monsoon projections from CMIP6 models show an increase of SAsiaM precipitation across all the scenarios and across all the time frames (Figure 8.22) with the maximum increase at the end of the 21st century in SSP5-8.5 (Almazroui et al. , 2020c; Z. Chen et al. , 2020b; Ha et al. , 2020; Wang et al. , 2021) . Table 8.2 confirms that changes in runoff and P–E over SAsiaM region are positive and largest in the higher emissions scenarios considered, as in precipitation. On the other hand, changes in the ensemble mean for all the variables considered in the SSP1-1.9 scenario are negative for both mid and long-term periods (Table 8.2). This is also consistently reflected in the spatial map of future precipitation changes (Figure 8.15). Different near-term projections of the SAsiaM may result given the diversity in the future aerosol emissions pathways and policies for regulating air pollution ( [[#Wilcox--2020|Wilcox et al., 2020]] ). Additionally, near-term projections of SAsiaM precipitation are expected to be constrained by internal variability associated with the PDV (X. [[#Huang--2020|Huang et al., 2020]] a). CMIP6 models also indicate a lengthening of the summer monsoon over India by the end of the 21st century, at least in SSP2-4.5, with considerable inter-model spread in the projected late retreat ( [[#Ha--2020|Ha et al., 2020]] ).

In summary, consistent with AR5, there is ''high confidence'' that SAsiaM precipitation is projected to increase during the 21st century in response to continued global warming across the CMIP6 higher emissions scenarios, mostly in the mid- and long terms.

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===== 8.4.2.4.2 East Asian Monsoon =====

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In AR5, the East Asian monsoon (EAsiaM) was projected to intensify in terms of precipitation, with an earlier onset and longer duration of the summer season. Since AR5, there has been improved understanding of future projected changes in the EAsiaM.

CMIP5 projections indicated a possible intensification of the EAsiaM circulation during the 21st century, in addition to precipitation increase, although there is a lack of consensus on changes in the western North Pacific subtropical high, this is an important feature of the EAsiaM circulation ( [[#Kitoh--2017|Kitoh, 2017]] ). Furthermore, the EAsiaM precipitation enhancements in the CMIP5 projections are prominent over the southern part of the Baiu rainband by the late 21st century, with no significant changes in the Meiyu precipitation over central-eastern China ( [[#Horinouchi--2019|Horinouchi et al., 2019]] ). It was also shown that the Baiu precipitation response in CMIP5 projections is accompanied by a southward retreat of the western North Pacific subtropical high and a southward shift of the East Asian subtropical jet ( [[#Horinouchi--2019|Horinouchi et al., 2019]] ). According to the high-resolution MRI-AGCM global warming experiments, future summer precipitation could potentially increase on the southern side and decrease on the northern side of the present-day Baiu location in response to downward-motion tendencies which can offset the ‘wet-gets-wetter’ effect, but is subject to large model uncertainties ( [[#Ose--2019|Ose, 2019]] ). Future projections of land warming over the Eurasian continent ( [[#Endo--2018|Endo et al., 2018]] ) and intensified land – sea thermal contrast (Z. [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|Wang et al., 2016]] ; [[#Tian--2019|Tian et al., 2019]] ) can potentially intensify the EAsiaM circulation during the 21st century. However, there are large uncertainties in projected water cycle changes over the region ( [[#Endo--2018|Endo et al., 2018]] ), mostly in the near-term because of uncertainties in future aerosol emissions scenarios ( [[#Wilcox--2020|Wilcox et al., 2020]] ), as well as due to the interplay between internal variability and anthropogenic external forcing ( [[#Wang--2021|Wang et al., 2021]] ).

Inter-hemispheric mass exchange can act as a bridge connecting SH circulation with EAsiaM rainfall, however this inter-hemispheric link is projected to weaken in a future warmer climate as seen from a CCSM4 projection using the RCP8.5 scenario ( [[#Yu--2018|Yu et al., 2018]] ). A comparison of 1.5°C and 2°C global warming levels reveals how a 0.5°C difference could result in precipitation enhancement over large areas of East Asia ( D. Lee et al. , 2018; J. Liu et al. , 2018; Chen et al. , 2019 ), with substantial increases in the frequency and intensity of extremes ( [[#Chevuturi--2018|Chevuturi et al., 2018]] ; D. [[#Li--2019|]] [[#Li--2019|Li et al., 2019]] ). Future monsoon projections from the CMIP6 models show increase of EAsiaM precipitation across all the scenarios (Z. [[#Chen--2020|]] [[#Chen--2020|Chen et al., 2020]] b), though with a large model spread mostly on the long-term and in the higher emissions scenarios (Figure 8.22). Considering all the five scenarios used across the report, changes in precipitation, runoff and P–E over the EAsiaM are positive and become larger for highest emissions scenarios and for the long-term mean, except for the mid-term SSP1-1.9 scenario where the changes are close to zero or even negative (Table 8.2). Additionally, CMIP6 models confirm a projected increased length of the EAsiaM season due to early onset and late retreat ( [[#Ha--2020|Ha et al., 2020]] ).

In summary, despite the uncertainties in the monsoon circulation response in CMIP5 and CMIP6 models, there is ''high confidence'' that summer monsoon precipitation over East Asia will increase in the 21st century and ''medium confidence'' that the monsoon season will be longer.

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===== 8.4.2.4.3 West African Monsoon =====

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The AR5 concluded that projections of West African monsoon (WAfriM) rainfall are highly uncertain in CMIP3 and CMIP5 models, but still suggest a small delay and intensification in late wet season rains. Studies published since AR5 are broadly consistent with this assessment. CMIP6 models agree on statistically significant projected increases in rainfall in eastern-central Sahel and a decrease in the west for the end of the 21st century ( [[#Roehrig--2013|Roehrig et al., 2013]] ; [[#Biasutti--2019|Biasutti, 2019]] ; [[#Monerie--2020|Monerie et al., 2020]] ). However, the magnitude of WAfriM projected precipitation depends on the convective parametrization used ( [[#Hill--2017|Hill et al., 2017]] ), and large uncertainties remain in WAfriM projections because of large inter-model spread, particularly over the western Sahel ( [[#Roehrig--2013|Roehrig et al., 2013]] ; [[#Biasutti--2019|Biasutti, 2019]] ; [[#Monerie--2020|Monerie et al., 2020]] ). CMIP6 models show a general increase of WAfriM precipitation across all future scenarios but with a substantial model spread for the SSP5-8.5 scenario (Figure 8.22). This sensitivity arises from the combined and contrasting influences of anthropogenic greenhouse gas and aerosol forcing that affect WAfriM precipitation (particularly over the Sahel) directly and also indirectly through subtropical North Atlantic SST changes ( [[#Giannini--2019|Giannini and Kaplan, 2019]] ) . The large model spread and associated uncertainties in projected precipitation changes is reflected also in runoff and P–E changes (Table 8.2). Regional climate models (RCMs) ensembles (e.g., [[#Klutse--2018|Klutse et al., 2018]] ) agree with CMIP5 projected rainfall trends but some individual models show rainfall declines (e.g., [[#Sylla--2015|Sylla et al., 2015]] ; [[#Akinsanola--2018|Akinsanola et al., 2018]] ), highlighting the existing large uncertainties in RCMs WAfriM rainfall projections.

Changes in seasonality (Box 8.2) are projected with a later monsoon onset ( ''high confidence'' ) over the Sahel and a late cessation ( ''medium confidence'' ), suggesting a delayed wet season as a regional response to global GHG forcing ( [[#Biasutti--2013|Biasutti, 2013]] ; [[#Dunning--2018|Dunning et al., 2018]] ; [[#Akinsanola--2019|Akinsanola and Zhou, 2019]] ). Rainfall distribution is projected to be highly variable with a decrease in the number of rainy days in the western Sahel, consistent with an increase in consecutive dry days and a reduction in the number of growing season days ( [[#Cook--2012|Cook and Vizy, 2012]] ; [[#Diallo--2016|Diallo et al., 2016]] ). A decrease in the frequency but an increase in the intensity of very wet events is projected to be more pronounced over the Sahel than over Guinean coast, and also under higher emissions scenarios (i.e., RCP8.5; [[#Sylla--2015|Sylla et al., 2015]] ; [[#Akinsanola--2018|Akinsanola et al., 2018]] ).

In summary, post-AR5 studies and newly available CMIP6 results indicate projected rainfall increases in the eastern-central WAfriM region but decreases in the west ( ''high confidence'' ), with a delayed wet season ( ''medium confidence'' ). Overall, WAfriM summer precipitation is projected to increase during the 21st century but with larger uncertainty noted under high-emissions scenarios ( ''medium co'' ''nfidence'' ).

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===== 8.4.2.4.4 North American Monsoon =====

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The AR5 concluded that the North American monsoon (NAmerM) will ''likely'' intensify in the future, even though there is ''low agreement'' among models. The AR5 reported ''medium confidence'' that precipitation associated with the NAmerM will arrive later in the annual cycle and persist longer.

Since AR5, analyses of CMIP5 projections suggest little change in the overall amount of NAmerM precipitation in response to rising global surface temperature. However, significant declines are projected in the early monsoon season and increases in the late monsoon season, suggesting a shift in seasonality toward a delayed monsoon onset and demise ( [[#Cook--2013|Cook et al., 2013]] ). It is recognized that CMIP5 models are generally too coarsely-resolved to simulate the Gulf of California and the moisture surges associated with the NAmerM ( [[#Pascale--2017|Pascale et al., 2017]] ). Under different RCPs, CMIP5 models tend to project a reduction in NAmerM precipitation but an increase in extreme precipitation events (Torres-Alavez et al. , 2014; Bukovsky et al. , 2015; Pascale et al. , 2019) . The almost unchanged or slight decrease in NAmerM total precipitation amount under global warming projections is at odds with paleoclimate records that suggest increased monsoon precipitation under past warm conditions ( [[#D’Agostino--2019|D’Agostino et al., 2019]] ; [[#Seth--2019|Seth et al., 2019]] ). However, there is ''low agreement'' on how those changes and the mechanisms that drive them are affected under different RCPs since most simulations are model-dependant ( [[#Cook--2013|Cook and Seager, 2013]] ; [[#Geil--2013|Geil et al., 2013]] ; [[#Pascale--2019|Pascale et al., 2019]] ). Projections from six CMIP6 models show a shortening of the NAmerM under the SSP5-8.5 scenario due to earlier demises ( [[#Moon--2020|Moon and Ha, 2020]] ). In addition, CMIP6 projections show a decrease in NAmerM precipitation under SSP2-4.5 and SSP5-8.5 scenarios by the end of the 21st century with large inter-model spread (Figure 8.22). This result is also supported by the analysis of 31 CMIP6 models under the SSTP5-8.5 scenario for the 2080 – 2099 period ( [[#Almazroui--2021|Almazroui et al., 2021]] ). Non-linearities and uncertainties in the NAmerM projected changes are valid for many water cycle variables, like precipitation, runoff and P–E (Table 8.2).

In summary, there is ''low agreement'' on a projected decrease of NAmerM precipitation, however there is ''high confidence'' in delayed onsets and demises of the summer monsoon.

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===== 8.4.2.4.5 South American Monsoon =====

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The AR5 reported ''medium confidence'' that the South American monsoon (SAmerM) overall precipitation will remain unchanged, and ''medium confidence'' in projections of extreme precipitation. The AR5 also stated ''high confidence'' in the spatial expansion of the SAmerM, resulting from increased temperature and humidity.

Since AR5, some studies indicate that the SAmerM would experience changes in its seasonal cycle, with delayed monsoon onsets under increasing GHG emissions associated to different RCPs (Fu et al. , 2013; Reboita et al. , 2014; Boisier et al. , 2015; Pascale et al. , 2016; Seth et al. , 2019; [[#Sena--2020|Sena and Magnusdottir, 2020]] ) . In contrast, other studies indicate projected earlier onsets and delayed retreats of the SAmerM under the RCP8.5 scenario based on six CMIP5 models ( [[#Jones--2013|Jones and Carvalho, 2013]] ). These differences have been linked to the methodology used to determine monsoon timing, and sensitivity to the monsoon domain considered ( [[#8.3.2.4.5|Section 8.3.2.4.5]] ; [[#Correa--2021|Correa et al., 2021]] ). Recent studies provide further evidence for the projection of delayed SAmerM onsets by the late 21st century ( [[#Sena--2020|Sena and Magnusdottir, 2020]] ). An analysis of six CMIP6 models under the SSP5-8.5 scenario confirm the projections of delayed SAmerM onsets by the end of the 21st century ( [[#Moon--2020|Moon and Ha, 2020]] ). In addition, projected changes in the intensity and length of the SAmerM season have been found to be model-dependent ( [[#Pascale--2019|Pascale et al., 2019]] ). The analysis of CMIP5 projections of total monsoon rainfall indicate mixed signals in the Amazon and SAmerM regions ( [[#Jones--2013|Jones and Carvalho, 2013]] ; [[#Marengo--2014|Marengo et al., 2014]] ), with some studies suggesting increased summer precipitation in the core SAmerM region ( [[#Kitoh--2013|Kitoh et al., 2013]] ; [[#Seth--2013|Seth et al., 2013]] ). Dynamical downscaling of CMIP5 projections under the RCP4.5 and RCP8.5 scenarios with the Eta RCM suggests reductions of austral summer precipitation over the SAmerM region throughout the 21st century ( [[#Chou--2014|Chou et al., 2014]] ). Further analysis using 15 different CMIP6 models for the SSP2-4.5 scenario suggest reductions in total SAmerM rainfall ( [[#Wang--2020|]] [[#Wang--2020|]] [[#Wang--2020|]] [[#Wang--2020|B. Wang et al., 2020]] ). However, other analyses of CMIP6 projections under different SSP scenarios do not report clear changes in the SAmerM precipitation throughout the 21st century (Figure 8.22; Z. [[#Chen--2020|]] [[#Chen--2020|Chen et al., 2020]] b; [[#Jin--2020|Jin et al., 2020]] ). Similar uncertainties for all the SSP scenarios used across the report are found for other water cycle variables, including runoff and P–E (Table 8.2). Furthermore, there is disagreement in projected extreme precipitation in the region, with some CMIP5-based studies suggest reductions ( [[#Marengo--2014|Marengo et al., 2014]] ), while others indicate increases based on CMIP5 and CMIP6 models ( [[#Kitoh--2013|Kitoh et al., 2013]] ; [[#Sena--2020|Sena and Magnusdottir, 2020]] ).

In summary, there is ''high confidence'' that the SAmerM will experience delayed onsets in association with increases in GHG. However, there is ''low agreement'' on the projected changes in terms of total precipitation of the South American summer monsoon season.

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===== 8.4.2.4.6 Australian and Maritime Continent Monsoon =====

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The AR5 concluded that projected changes in Australian and Maritime Continent monsoon (AusMCM) rainfall and seasonality are uncertain in the CMIP5 models, with some projecting increases and others projecting decreases for the range of emissions scenarios. Models that perform better at simulating present day regional climate project little change or an increase in Australian monsoon rainfall ( [[#Jourdain--2013|Jourdain et al., 2013]] ; CSIRO and BoM, 2015; [[#Brown--2016b|Brown et al., 2016b]] ). CMIP6 models project increased AusMCM precipitation in the 21st century but with a more robust signal in SSP2-4.5 and SSP5-8.5 rather than in lower emissions scenarios (Figure 8.22). A reduced range of CMIP6 rainfall projections but continued disagreement on the sign of change is reported over Australia ( [[#Narsey--2020|Narsey et al., 2020]] ).

The northern and eastern parts of the Maritime Continent have projected increases in rainfall in CMIP5 models ( [[#Siew--2014|Siew et al., 2014]] ), while there are projected decreases over Java, Sulawesi and southern parts of Borneo and Sumatra. Rainfall changes are correlated with the extent of warming in the western tropical Pacific in CMIP5 models ( [[#Brown--2016b|Brown et al., 2016b]] ) but inter-model differences are also related to modelled large-scale zonal mean precipitation response in both CMIP5 and CMIP6 model ensembles ( [[#Narsey--2020|Narsey et al., 2020]] ). Decomposition of projected rainfall changes indicates that the largest source of model uncertainty is associated with shifts in the spatial pattern of convection ( [[#Chadwick--2013|Chadwick et al., 2013]] ; [[#Brown--2016b|Brown et al., 2016b]] ). Uncertainties in capturing the spatial and temporal features of the Maritime Continent monsoon depend also on the horizontal resolution of coupled climate models (e.g., [[#Jourdain--2013|Jourdain et al., 2013]] ).

The role of anthropogenic aerosol forcing in future projections of the Australian monsoon has been investigated for CMIP5 models ( [[#Dey--2019a|Dey et al., 2019a]] ); decreases in anthropogenic aerosol concentrations over the 21st century are expected to produce relatively greater warming in the NH than SH, favouring a northward shift of the tropical rain belt (e.g., [[#Rotstayn--2015|Rotstayn et al., 2015]] ).

There are some clear projected changes in the rainfall variability and extremes of the Australian monsoon. Rainfall variability in the Australian monsoon domain increases on time scales from daily to decadal in CMIP5 models ( [[#Brown--2017|Brown et al., 2017]] ), indicating either more intense wet days or more dry days or both. There is also a projected increase in the intensity of extreme rainfall but a reduction in the frequency of heavy rainfall days for the Australian monsoon (Dey et al. , 2019a) . This is consistent with [[#Moise--2020|Moise et al. (2020)]] , who found an increase in Australian monsoon active phase or ‘burst’ rainfall intensity but a reduction in the number of burst days and events.

H. [[#Zhang--2013|]] [[#Zhang--2013|Zhang et al. (2013)]] examined changes in Australian monsoon onset and duration in CMIP3 models and found model agreement on a delay in onset and shortened duration to the north of Australia, but less agreement over the interior of the continent. An updated study of CMIP5 models found similar mean changes with delayed onset and shortened duration, but substantial model disagreement (H. [[#Zhang--2016|]] [[#Zhang--2016|]] [[#Zhang--2016|]] [[#Zhang--2016|Zhang et al., 2016]] ).

In summary, CMIP6 projections show an increase of AusMCM precipitation across all emissions scenarios. There is strong model agreement on an increase in monsoon precipitation over the Maritime Continent while there is ''low agreement'' on the direction of change over northern Australia. There is a projected increase in rainfall variability over northern Australia, with increased intensity of rainfall during the active or ‘burst’ phase ( ''medium con'' ''fidence'' ).

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<span id="tropical-cyclones-1"></span>
==== 8.4.2.5 Tropical Cyclones ====

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Tropical cyclones (TCs) projections are primarily assessed in [[IPCC:Wg1:Chapter:Chapter-11#11.7.1.5|Section 11.7.1.5]] . Here, we extend this analysis by assessing the implications of projected changes in tropical cyclones on the water cycle.

The AR5 concluded that TC rainfall rate was ''likely'' to increase through the 21st century. [[IPCC:Wg1:Chapter:Chapter-11#11.7.1.5|Section 11.7.1.5]] assesses that the average tropical cyclone rain-rate is projected to increase with warming ( ''high confidence'' ), and peak rain rates are projected to increase at greater than the Clausius–Clapeyron scaling rate of 7% °C <sup>–1</sup> warming in some regions due to increased low-level moisture convergence ( ''medium confidence'' ). The increase in TC rainfall rate is explained by increased TC intensity resulting from increasing SSTs, and increased environmental water vapour ( [[#Chauvin--2017|Chauvin et al., 2017]] ; M. [[#Liu--2019|]] [[#Liu--2019|Liu et al., 2019]] ).

Consistent with the observed poleward migration of tropical cyclone activity ( [[#Kossin--2014|Kossin et al., 2014]] ), in the SH a larger proportion of storms are projected to decay south of 25°S at the end of the 21st century but with negligible changes in genesis latitude and storm duration for the Australian region (CSIRO and BoM, 2015; [[#Sharmila--2018|Sharmila and Walsh, 2018]] ) . An analysis of projections for North Pacific islands indicate that the maximum intensity of storms will increase but the number of tropical cyclones will decrease in some places, such as Guam and Kwajalein Atoll in the tropical north-western Pacific, or remain the same in other regions like near Okinawa (Japan) or Oahu (Hawaii) ( [[#Widlansky--2019|Widlansky et al., 2019]] ). TC-induced storm tides affecting landfall in the Pearl River delta over South China are projected to increase by the end of the 21st century (J. [[#Chen--2020|]] [[#Chen--2020|Chen et al., 2020]] b)

In summary, there is ''high confidence'' that heavy precipitation associated with tropical cyclones is projected to increase, in response to well-understood processes related to increased low-level moisture convergence and environmental water vapour.

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==== 8.4.2.6 Stationary Waves ====

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The AR5 did not provide an assessment of stationary wave projections as distinct from other related aspects of circulation, such as blocking, modes of variability, and storm tracks. Here we provide a brief assessment of stationary wave projections from the water cycle perspective, with the related circulation aspects considered separately in the following sections.

Several studies based on CMIP5 projections show changes in NH winter stationary waves that increase precipitation over the west coast of North America and decrease it over the eastern Mediterranean and parts of south-western North America ( Neelin et al. , 2013; Seager et al. , 2014a, b, 2019b; Simpson et al. , 2016; Wills et al. , 2019 ), although the underlying dynamics are not yet fully understood ( [[#Seager--2019b|Seager et al., 2019b]] ; [[#Wills--2019|Wills et al., 2019]] ). For the NH winter global teleconnection pattern, the majority of the models analyzed in ( [[#Sandler--2020|Sandler and Harnik, 2020]] ) project the development of a preferred longitudinal phasing for the pattern, but with strong disagreement among models over the details of the phasing and therefore the associated regional hydrologic impacts.

While the potential role of increasing hydrologic extremes with quasi-resonant stationary waves during NH summer has received considerable attention (see [[#8.3.2.6|Section 8.3.2.6]] ), as yet there is no clear evidence in model projections that this variability will increase ( [[#Teng--2019|Teng and Branstator, 2019]] ). The influence of the Arctic on mid-latitude circulation is assessed in Cross-Chapter Box 10.1, which reports that there is ''low confidence'' in the dominant contribution of Arctic warming compared to other drivers in future projections. Potential changes to the stratospheric polar vortex in CMIP5 models have a substantial influence on tropospheric stationary waves and associated hydrologic impacts in both the NH ( [[#Zappa--2017|Zappa and Shepherd, 2017]] ) and SH ( [[#Mindlin--2020|Mindlin et al., 2020]] ). CMIP5 models have some important limitations in their representation of stationary waves ( [[#Lee--2013|Lee and Black, 2013]] ; [[#Simpson--2016|Simpson et al., 2016]] ; [[#Garfinkel--2020|Garfinkel et al., 2020]] ) and this aspect of CMIP6 models has not yet been comprehensively evaluated.

In summary, future changes in stationary waves may have an important influence on both the mean state and variability of the water cycle. Limitations in model representation, dynamical understanding, and the number of targeted studies on the topic currently constrain the assessment of future changes in stationary waves. Based on current knowledge, there is ''low confidence'' that projected changes in stationary wave activity will contribute to decreases of cold season precipitation over the eastern Mediterranean and increases over the west coast of North America.

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==== 8.4.2.7 Atmospheric Blocking ====

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In AR5, the increased ability of models to simulate blocking and higher agreement on projections led to an assessment with ''medium confidence'' that the frequency of NH and SH blocking will not increase, but future changes in blocking intensity and persistence were deemed uncertain (AR5 Chapter 14, ES and Box 14.2). Blocking influences precipitation (e.g., [[#Trigo--2004|Trigo et al., 2004]] ), flooding (e.g., Yamada et al. , 2016) , drought (e.g., Dong et al. , 2018b) , snow (e.g., [[#García-Herrera--2006|García-Herrera and Barriopedro, 2006]] ) , and glacier melt (e.g., [[#Hanna--2013|Hanna et al., 2013]] ), and so is of broad importance to the water cycle in areas of blocking activity.

Blocking projections are assessed in this Report in [[IPCC:Wg1:Chapter:Chapter-4|Chapter 4]] ( [[IPCC:Wg1:Chapter:Chapter-4#4.5.1.6|Section 4.5.1.6]] ), and model performance in simulating blocking is also discussed in [[IPCC:Wg1:Chapter:Chapter-3|Chapter 3]] ( [[IPCC:Wg1:Chapter:Chapter-3#3.3.3.3|Section 3.3.3.3]] ). CMIP5 projections suggest a complex response in blocking frequencies with an eastward shift in NH winter blocking, mid-latitude decreases in boreal summer except in eastern Europe – western Russia, and SH decreases in the Pacific sector during austral spring and summer. CMIP6 projections (Figure. 4.28) show a notable decrease in blocking activity over Greenland and the North Pacific for the SSP3-7.0 and SSP5-8.5 scenarios. However, the continued large differences among current models as well as the sensitivity to blocking detection methods limits confidence in projected regional changes in blocking (see also [[IPCC:Wg1:Chapter:Chapter-10#10.3.3.3.1|Section 10.3.3.3.1]] ). The influence of blocking on multiple elements of the water cycle means that the uncertainty in blocking projections adds a corresponding layer of uncertainty to water cycle projections.

In summary, and despite recent improvements in the simulation of blocking, there is ''limited evidence'' in model projections of future changes, except for boreal winter over Greenland and the North Pacific where there is high confidence that blocking events are not expected to increase in the SSP3-7.0 and SSP5-8.5 scenarios. As with stationary waves, this adds uncertainty to mid-latitude water cycle projections at the regional scale.

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==== 8.4.2.8 Extratropical Cyclones, Storm Tracks and Atmospheric Rivers ====

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<span id="extratropical-cyclones-and-storm-tracks-1"></span>
===== 8.4.2.8.1 Extratropical cyclones and storm tracks =====

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The AR5 found that extratropical storms were expected to decrease in the Northern Hemisphere (NH), but only by a few percent. Meanwhile, precipitation associated with extratropical storms was projected to increase due to thermodynamic increases in moisture but potentially also due to intensification from increased latent heat release. Latent heating is a strong influence on extratropical storms, so it is plausible that changes in precipitation and associated latent heating could affect extratropical storm intensity and thus precipitation (Z. Zhang et al., 2019) .

There is increased evidence that precipitation associated with individual extratropical storms is projected to increase, following thermodynamic drivers with negligible dynamic change ( [[#Yettella--2017|Yettella and Kay, 2017]] ). Comparisons with reanalyses also support the projected increase in thermodynamic precipitation with little dynamic response for precipitation associated with extratropical storms ( [[#Li--2014|Li et al., 2014]] ). There is ''high confidence'' that projected increases inprecipitation associated with extratropical storms in the NH (Marciano et al. , 2015; Pepler et al. , 2016; Michaelis et al. , 2017; [[#Yettella--2017|Yettella and Kay, 2017]] ; [[#Zhang--2017|Zhang and Colle, 2017]] ; Hawcroft et al. , 2018; Kodama et al. , 2019) . A projected decrease in the number of extratropical cyclones over the NH during the boreal summer in CMIP5 models was reported by [[#Chang--2016|Chang et al. (2016)]] who related this decrease with a decrease in cloudiness and thus accentuating increased maximum temperatures. However, model spread was quite large, especially over North America, thus there is only ''low confidence'' in this seasonal signal.

In AR5, the Southern Hemisphere (SH) storm track was deemed ''likely'' to shift poleward, the North Pacific storm track ''more likely than not'' to shift poleward, while the North Atlantic storm track was ''unlikely'' to display any discernible changes. There was ''low confidence'' in regional storm track changes and the associated surface climate impacts, although a weakening of the Mediterranean storm track was a robust response of the models. Since AR5, the SH mid-latitude storm track is projected to shift poleward and the westerlies are projected to strengthen over Australia (CSIRO and BoM, 2015). Although thermodynamic effects were considered to be the most important factor in overall projections of increased mid-latitude precipitation, the general poleward shift in cyclogenesis and an enhanced latitudinal displacement of individual cyclones may play a role ( [[#Tamarin-Brodsky--2017|Tamarin-Brodsky and Kaspi, 2017]] ).

In AR5, several factors were identified as relevant to the uncertainties in projections of cyclone intensity, frequency, location of storm tracks and precipitation associated with ETCs. These include horizontal resolution, resolution of the stratosphere, and how changes in the Atlantic meridional overturning circulation (AMOC) were simulated. Since AR5, projections of extratropical cyclones and storm tracks have been examined further, largely confirming previous assessments. In particular, extratropical cyclone precipitation scales with the product of cyclone intensity (as measured by near-surface wind speed) and atmospheric moisture content ( [[#Pfahl--2016|Pfahl and Sprenger, 2016]] ) . Booth et al. (2018) showed that the fraction of rainfall generated by the convection scheme in simulated extratropical cyclones is highly model- and resolution-dependent, which may be a source of uncertainty regarding their precipitation response to anthropogenic forcings. Also, increased moisture availability may increase the maximum intensity of individual storms while reducing the overall frequency as poleward energy transport becomes more efficient.

The role of temperature trends in influencing storm tracks has been further investigated, both in terms of upper tropospheric tropical warming ( [[#Zappa--2017|Zappa and Shepherd, 2017]] ) and lower tropospheric Arctic amplification (J. [[#Wang--2017|]] [[#Wang--2017|]] [[#Wang--2017|Wang et al., 2017]] ), including the direct role of Arctic sea ice loss ( [[#Zappa--2018|Zappa et al., 2018]] ), and the competition between their influences (Shaw et al. , 2016) . Physical linkages between Arctic amplification and changes in the mid-latitudes are uncertain, as discussed in [[IPCC:Wg1:Chapter:Chapter-10|Chapter 10]] (Cross-Chapter Box 10.1). The remote and local SST influence has been further examined by [[#Ciasto--2016|Ciasto et al. (2016)]] , who confirmed sensitivity of the storm tracks to the SST trends generated by the models and suggested that the primary greenhouse gas influence on storm track changes was indirect, acting through the greenhouse gas influence on SSTs. The importance of the stratospheric polar vortex in storm track changes has received more attention ( [[#Zappa--2017|Zappa and Shepherd, 2017]] ; Mindlin et al. , 2020) and the anticipated recovery of the ozone layer further complicates the role of the stratosphere ( [[#Shaw--2016|Shaw et al., 2016]] ; [[#Bracegirdle--2020b|Bracegirdle et al., 2020b]] ).

Biases remain in cyclone locations, intensities, cloud features, and precipitation ( [[#Catto--2016|Catto, 2016]] , [[#Chang--2016|Chang et al., 2016]] ). Uncertainties in projected precipitation changes in many mid-latitude regions can be explained to a large degree by uncertainties in projected storm track or ETC changes. Multiple studies ( [[#Chang--2013|Chang et al., 2013]] ; [[#Zappa--2015|Zappa et al., 2015]] ; [[#Chang--2018|Chang, 2018]] ) have shown strong relationships between model-projected precipitation change in many regions and model-projected change in storm track activity near that regions. While front frequency is well represented, frontal precipitation frequency is too high and the intensity is too low ( [[#Catto--2015|Catto et al., 2015]] ). Some of the bias in storm tracks appears to be related to limitations in model realization of blocking ( [[#Zappa--2014|Zappa et al., 2014]] ). The CMIP6 generation of models has improved representation of storm tracks in both hemispheres ( [[#Bracegirdle--2020a|Bracegirdle et al., 2020a]] ; [[#Harvey--2020|Harvey et al., 2020]] ). Simulation of storm tracks and their associated precipitation generally improve with increasing resolution beyond that used in most current climate models ( Jung et al. , 2006; Michaelis et al. , 2017; Barcikowska et al. , 2018 ). In terms of projections, the decreases in cyclone occurrence over the Mediterranean were replicated in a higher resolution model ( [[#Raible--2018|Raible et al., 2018]] ).

The projected changes in storm tracks and the associated mechanisms have several important implications for water cycle projections. P–E changes in the Mediterranean, California and Chile are directly linked to storm track changes (Zappa et al. , 2020) . Where the storm tracks are robustly projected to shift (SH, North Pacific) or weaken (Mediterranean), understanding the physical causes of the related changes in precipitation helps increase confidence in the projections. Understanding the competing influences provides context for why other regions do not exhibit a consistent signal and cautions against regional projections based on individual models. However, model bias and the need for relatively high resolution to reproduce the relevant dynamics is an important overall limit on confidence in current CMIP6 projections.

In summary, there is the ''high confidence'' that precipitation associated with extratropical storms will increase with global warming in most regions. The SH storm track will ''likely'' shift poleward, the North Pacific storm track ''more likely than not'' will shift poleward, and the North Atlantic storm track is ''unlikely'' to have a simple poleward shift/ display any discernible changes. There is ''low confidence'' in regional storm track changes, although a weakening of the Mediterranean storm track is a robust response of the models.

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===== 8.4.2.8.2 Atmospheric rivers =====

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Atmospheric rivers were not assessed in AR5 but are important in the water cycle as they are linked to extreme rainfall, flooding, and changes in terrestrial water storage including melt and ablation of glaciers and snowpack (Sections 8.2.3). In a warming world, there is ''high confidence'' that thermodynamical increases in atmospheric water vapour ensure that atmospheric rivers will become wetter, hence stronger, and longer-lasting ( [[#Payne--2020|Payne et al., 2020]] ). This is clearly observed in several regional ( [[#Ralph--2011|Ralph and Dettinger, 2011]] ; [[#Lavers--2013|Lavers et al., 2013]] ; [[#Gao--2015|Gao et al., 2015]] ; [[#Payne--2015|Payne and Magnusdottir, 2015]] ; [[#Warner--2015|Warner et al., 2015]] ; [[#Hagos--2016|Hagos et al., 2016]] ; [[#Gershunov--2019|Gershunov et al., 2019]] ) and in one global study (V. [[#Espinoza--2018|]] [[#Espinoza--2018|Espinoza et al., 2018]] ) of atmospheric river activity in CMIP5 model projections. [[#Lavers--2015|Lavers et al. (2015)]] indicate that integrated vapour transport under RCP 8.5 and 4.5 could increase, and consequently this thermodynamic response ( [[#O’Gorman--2015|O’Gorman, 2015]] ) could affect mid-latitude regions where orographic precipitation is important ( [[#Gershunov--2019|Gershunov et al., 2019]] ).

Under continued global warming, more intense moisture transport within atmospheric river events is projected to increase the magnitude of heavy precipitation events on the west coast of the USA ( [[#Ralph--2011|Ralph and Dettinger, 2011]] ; [[#Lavers--2015|Lavers et al., 2015]] ; [[#Warner--2017|Warner and Mass, 2017]] ), in Western Europe ( [[#Lavers--2015|Lavers et al., 2015]] ; [[#Ralph--2016|Ralph et al., 2016]] ; [[#Ramos--2016|Ramos et al., 2016]] ), and in East Asia ( ''very likely'' ) ( [[#Kamae--2019|Kamae et al., 2019]] ). All CMIP5 models analysed agreed under a range of scenarios, except over the Iberian Peninsula ( [[#Ramos--2016|Ramos et al., 2016]] ) where there is only ''low confidence'' in projected changes. [[#Kamae--2019|Kamae et al. (2019)]] reported a 1% increase per °C warming in the frequency of atmospheric rivers affecting East Asia, but this is strongly affected by SST changes. Emerging evidence of possible regional changes due to dynamical factors are uncertain ( [[#Lavers--2013|Lavers et al., 2013]] ; [[#Gao--2015|Gao et al., 2015]] ; [[#Payne--2015|Payne and Magnusdottir, 2015]] ). The frequency, magnitude and duration of atmospheric rivers making landfall along the North American west coast are projected to increase ( [[#Gershunov--2019|Gershunov et al., 2019]] ). In contrast, V. [[#Espinoza--2018|]] [[#Espinoza--2018|Espinoza et al. (2018)]] suggest that the number of atmospheric river events is projected to slightly decrease globally.

In semi-arid regions where atmospheric rivers have historically been important and precipitation is mainly confined to the cold season, the contribution of atmospheric rivers to annual total precipitation may be expected to grow disproportionately. For example, in California decreases in precipitation frequency are projected as a result of fewer non-atmospheric river storms, while the projected increase in heavy and extreme precipitation events are almost entirely a result of increased atmospheric river activity ( [[#Gershunov--2019|Gershunov et al., 2019]] ). Interannual variability in precipitation amounts is projected to increase because of the overall decrease in the frequency of storms but a stronger dependence on extremes ( [[#Polade--2014|Polade et al., 2014]] ), particularly due to atmospheric rivers ( [[#Gershunov--2019|Gershunov et al., 2019]] ), especially where interaction with topography are important ( Polade et al. , 2014; Gershunov et al., 2019 ).

In summary, there is ''high confidence'' that the magnitude and duration of atmospheric rivers are projected to increase in future, leading to increased precipitation. This is projected to increase the intensity of heavy precipitation events on the west coast of the USA and in western Europe ( ''high co'' ''nfidence'' ).

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==== 8.4.2.9 Modes of Climate Variability and Regional Teleconnections ====

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Following on from the assessment of projected changes in modes of climate variability (MoVs) and regional teleconnections ( [[IPCC:Wg1:Chapter:Chapter-4#4.5.3|Section 4.5.3]] ), here we assess their consequences for projected water cycle changes.

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===== 8.4.2.9.1 Tropical modes =====

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CMIP6 projections indicate that the amplitude of ENSO (Annex IV.2.3) variability will not substantially change during the 21st century ( ''high confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-4#4.4.3.2|Section 4.4.3.2]] ). However, rainfall variability related to ENSO is projected to increase significantly by the second half of the 21st century, regardless of ENSO amplitude ( [[IPCC:Wg1:Chapter:Chapter-4#4.5.3.2|Section 4.5.3.2]] ). Regional precipitation variability associated with ENSO increases due to increases in atmospheric moisture, regardless of changes in ENSO variability itself ( [[#Pendergrass--2017|Pendergrass et al., 2017]] ). In many regions, the magnitude of the projected changes related to ENSO is small compared with historical interannual variability ( [[#Bonfils--2015|Bonfils et al., 2015]] ; [[#Power--2018|Power and Delage, 2018]] ; [[#Perry--2020|Perry et al., 2020]] ). Uncertainties in precipitation projections related to ENSO depend on internal variability associated with the mode ( [[#8.5.2|Section 8.5.2]] ), hence the need to have relatively large ensembles (about 15 members) to adequately estimate uncertainty (Deser et al. , 2018; N. Maher et al. , 2018; C. Sun et al. , 2018; Zheng et al., 2018) .

Even over regions with statistically significant simulated rainfall teleconnections during the historical period, CMIP5 models do not project clear changes ( [[#Perry--2020|Perry et al., 2020]] ). Nonetheless, CMIP5 models that realistically reproduce Indian summer monsoon rainfall indicate a strengthening of its relationship with ENSO in RCP8.5 projections, though the response is not consistent for different varieties of ENSO events ( [[#Roy--2019|Roy et al., 2019]] ). Inconsistent changes in the ENSO–Indian summer monsoon relationship in response to global warming in CMIP5 and CMIP6 models may be related to statistical issues rather than dynamical changes ( [[#Bódai--2020|Bódai et al., 2020]] ; [[#Haszpra--2020|Haszpra et al., 2020]] ). Over East Africa during the boreal spring and summer, ENSO teleconnections are projected to become stronger in the future ( [[#Endris--2019|Endris et al., 2019]] ). Meteorological drought consequences of each strong El Niño are projected to become more severe in the region ( [[#Rifai--2019|Rifai et al., 2019]] ).

Indian Ocean Dipole (IOD, Annex IV.2.4) and Indian Ocean Basin (IOB, Annex IV.2.4) interactions with ENSO are expected to persist in the future ( [[IPCC:Wg1:Chapter:Chapter-4#4.5.3.3|Section 4.5.3.3]] ) but projected changes in the frequency and intensity of events remain uncertain ( [[#Hui--2018|Hui and Zheng, 2018]] ; [[#Endris--2019|Endris et al., 2019]] ; [[#McKenna--2020|McKenna et al., 2020]] ). Climate extremes such as those associated with the extreme positive IOD event of 2019 are expected to occur more frequently under continued global warming ( [[#Cai--2021|Cai et al., 2021]] ). Projected changes in IOD teleconnections are linked to model performance in representing the IOD and its remote influence in the present climate, apparently dominated by a positive IOD event-like mean state (G. [[#Wang--2017|]] [[#Wang--2017|]] [[#Wang--2017|Wang et al., 2017]] ; [[#Huang--2019|Huang et al., 2019]] ). Interactions between the IOD and the Indian Ocean mean state, via atmosphere–ocean feedbacks, can affect the behaviour of the IOD ( [[#Ng--2018|Ng et al., 2018]] ). In the eastern Horn of Africa, OND rainfall is projected to increase because of IOD-ENSO related SST changes in the Indo-Pacific region and associated Walker circulation changes ( [[#Endris--2019|Endris et al., 2019]] ).

Sensitivity studies generally project increases in Madden Julian Oscillation (MJO, Annex IV.2.8) precipitation amplitude in a warmer climate, with increases of up to 14% °C <sup>–1</sup> of warming ( [[#Arnold--2013|Arnold et al., 2013]] , 2015; [[#Caballero--2013|Caballero and Huber, 2013]] ; [[#Liu--2013|Liu and Allan, 2013]] ; [[#Maloney--2013|Maloney and Xie, 2013]] ; [[#Schubert--2013|Schubert et al., 2013]] ; [[#Subramanian--2014|Subramanian et al., 2014]] ; [[#Carlson--2016|Carlson and Caballero, 2016]] ; [[#Pritchard--2016|Pritchard and Yang, 2016]] ; [[#Adames--2017a|Adames et al., 2017a]] ; [[#Wolding--2017|Wolding et al., 2017]] ; [[#Haertel--2018|Haertel, 2018]] ). However, in CMIP5 models with realistic historical MJO behaviour, the precipitation amplitude over the Indo-Pacific warm pool region changes from – 4% to +8% °C <sup>–1</sup> in the RCP8.5 scenario relative to the end of the 20th century ( [[#Bui--2018|Bui and Maloney, 2018]] ; [[#Maloney--2019|Maloney et al., 2019]] ). When simulated MJO precipitation amplitude increases with warming, the leading factor for such change is the intensification of the lower tropospheric vertical moisture gradient, that supports stronger vertical moisture advection per unit diabatic heating ( Arnold et al. , 2015; Adames et al. , 2017a, b; Wolding et al. , 2017 ). In idealized simulations with constant CO <sub>2</sub> forcing with El Niño-like patterns, the MJO activity penetrates farther east into the central and east Pacific with increased warming ( [[#Subramanian--2014|Subramanian et al., 2014]] ; [[#Adames--2017a|Adames et al., 2017a]] ). Increased MJO convective variability in a warmer climate does not reflect into increased ability of the MJO to force the extratropics ( [[#Wolding--2017|Wolding et al., 2017]] ).

In summary, even though there is ''low confidence'' in how the tropical MoVs will change in the future (Sections 4.3.3.2 and 4.5.3.3), their regional hydrological consequences, in terms of precipitation, are projected to intensify ( ''medium confidence'' ). For example, the ENSO influence on precipitation over the Indo–Pacific sector is projected to strengthen and shift eastward ( ''medium confidence'' ). The MJO is projected to intensify in a warmer climate, with increased associated precipitation ( ''medium co'' ''nfidence'' ).

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===== 8.4.2.9.2 Extratropical modes =====

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CMIP6 projections indicate that the Northern Annular Mode (NAM; Annex IV.2.1) is expected to become more positive in winter throughout the 21st century in the SSP3-7.0 and SSP5-8.5 scenarios ( [[IPCC:Wg1:Chapter:Chapter-4#4.5.1|Section 4.5.1]] ). In the near term, the Southern Annular Mode (SAM, Annex IV.2.2) is projected to become less positive than observed during the end of the 20th century during the austral summer in all SSPs scenarios ( [[IPCC:Wg1:Chapter:Chapter-4#4.3.3.1|Section 4.3.3.1]] ).

In the CMIP5 RCP8.5 scenario, increased amplitude and frequency of the North Atlantic Oscillation (NAO, Annex IV.2) during boreal winter (December–January–February, DJF) is associated with higher precipitation in northern Europe and lower precipitation in southern Europe ( [[#Tsanis--2019|Tsanis and Tapoglou, 2019]] ). However, large-ensemble analyses show how the NAO leads to significant uncertainty in future changes of regional climate ( [[#8.5.2|Section 8.5.2]] ). For example, more than a 85% increase in precipitation is projected over northern Europe, western Russia and much of eastern North America, with similar decreasing resulting in drying over north-western Africa and regions adjacent to the Mediterranean Sea ( [[#Deser--2017|Deser et al., 2017]] ).

In the SH, the positive trend projected for the SAM in the CMIP5 RCP8.5 scenario appears to mitigate the wetting in the mid- to high latitudes and the drying over the subtropics, but with strong seasonal dependence ( [[#Lim--2016|Lim et al., 2016]] ). Regional precipitation changes in South America, South Africa, Southern Australia and New Zealand are not well explained by changes in the SAM, but are related to broad-scale changes in north – south temperature gradients associated with enhanced warming of the tropical upper troposphere and strengthening of the stratospheric polar vortex ( [[#Mindlin--2020|Mindlin et al., 2020]] ).

In summary, projected changes in the intensity, frequency and phase of extratropical MoVs (see also Sections 4.3 and 4.5) may amplify regional changes in precipitation and contribute to an increase in their intra-seasonal and interannual variability ( ''medium confidence'' ). Regionally, there are potentially significant precipitation and atmospheric circulation changes associated with changes in extratropical dynamics ( ''low con'' ''fidence'' ).

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