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

== 8.3 How Is the Water Cycle Changing and Why? ==

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This section focuses on the evaluation and attribution of past and recent water cycle changes using observational datasets, theoretical understanding and model simulations. Paleoclimate records and historical observations provide evidence for past water cycle changes caused both by natural variability and human activities ( [[#Haug--2003|Haug et al., 2003]] ; [[#Buckley--2010|Buckley et al., 2010]] ; [[#Pederson--2014|Pederson et al., 2014]] ). Key elements of the observed water cycle changes are assessed in this section, including flux and storage variations across the atmosphere, the continents and to a lesser extent the ocean and cryosphere, as well as related changes in large-scale atmospheric circulation and modes of variability. Particular emphasis is placed on assessing changes across regions and seasons (Box 8.2). Detailed regional assessments are presented in Chapters 10, 11, 12 and Atlas. Further information concerning large-scale observed water cycle changes and their attribution is available in Sections 2.3.1.3 and 3.3.2.

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=== 8.3.1 Observed Water Cycle Changes Based on Multiple Datasets ===

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This section provides a process-based evaluation and a comprehensive assessment of observed water cycle changes by integrating multiple lines of evidence including paleoclimate data, historical datasets, theoretical understanding ( [[#8.2|Section 8.2]] ) and model simulations.

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==== 8.3.1.1 Global Water Cycle Intensity and P–E Over Land and Oceans ====

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The human influence on the global water cycle is often summarized as an intensification ( [[#Huntington--2006|Huntington, 2006]] ; [[#DeAngelis--2015|DeAngelis et al., 2015]] ; W. [[#Zhang--2019|]] [[#Zhang--2019|Zhang et al., 2019]] b) or an overall strengthening which has been observed since at least 1980 ( ''high confidence'' ) (see Chapter 2). There is, however, no unique definition of the global water cycle intensity ( [[#Trenberth--2011|Trenberth, 2011]] ; [[#Ficklin--2019|Ficklin et al., 2019]] ; [[#Sprenger--2019|Sprenger et al., 2019]] ). One simple metric is the global and annual mean amount of precipitation. Although an increase in global precipitation is consistent with physical expectations ( [[#8.2.1|Section 8.2.1]] ), it has not yet been detected and attributed to human activities given large observational uncertainties and low signal-to-noise ratio ( [[IPCC:Wg1:Chapter:Chapter-3#3.3.2.2|Section 3.3.2.2]] ). Other metrics are more suitable to detect and attribute changes in the global water cycle, including the ''likely'' increase in global land precipitation since 1950 ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4|Section 2.3.1.4]] ) which is ''likely'' due to a human influence ( [[IPCC:Wg1:Chapter:Chapter-3#3.3.2.3|Section 3.3.2.3]] ).

The flux of freshwater between the ocean and atmosphere is determined by the difference between precipitation and evaporation (P–E). Evaporation is measured in very few locations across the global ocean, so that directly assessing P–E over the ocean is very challenging and relies on indirect reanalysis estimates ( [[#Robertson--2020|Robertson et al., 2020]] ). The AR5 presented ''robust evidence'' of an amplified oceanic pattern in P–E since the 1960s from both regional and global surface and subsurface salinity measurements and reanalyses. This pattern is consistent with our theoretical understanding of human-induced changes in the water cycle, leading to the conclusion that these changes are ''very likely'' the result of anthropogenic forcings ( [[IPCC:Wg1:Chapter:Chapter-9#9.2.2.2|Section 9.2.2.2]] ).

In contrast, AR5 did not provide a conclusive assessment of observed changes in P–E over land. Continental P–E estimated from reanalyses and data-driven land surface models indicate that interannual variations are linked to ENSO ( [[#Robertson--2014|Robertson et al., 2014]] , 2020). Increasing trends in P–E since 1979 based on land models are not statistically significant. Observations and models show evidence that P–E increases in the wet parts and decreases in the dry parts of tropical circulation systems, which shift in location seasonally and from year to year, with increases in seasonality since 1979 (see Box 8.2; [[#Chou--2013|Chou et al., 2013]] ; [[#Liu--2013|Liu and Allan, 2013]] ; [[#Fu--2014|Fu and Feng, 2014]] ).

In summary, a low signal-to-noise ratio, observational uncertainties and current data assimilation techniques limit the assessment of recent global trends in P–E over both land and ocean. It is ''likely'' that the global land P–E variations observed since the late 1970s were dominated by internal variability, mostly linked to ENSO teleconnections ( ''medium confidence'' ). In contrast, the attribution of changes in sea surface salinity ( [[IPCC:Wg1:Chapter:Chapter-3#3.5.2.2|Section 3.5.2.2]] ) suggests that it is ''extremely likely'' that human influence has contributed to the regional changes in P–E observed over the global ocean since the mid-20th century.

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==== 8.3.1.2 Water Vapour and Its Transport ====

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The AR5 presented evidence of increases in global near-surface and tropospheric specific humidity since the 1970s but with ''medium confidence'' of a slowing of near-surface moistening trends over land associated with reduced relative humidity since the late 1990s. According to AR5, radiosonde, Global Positioning System (GPS) and satellite observations of tropospheric water vapour indicate ''very likely'' increases at near global scales since the 1970s occurring at a rate that is generally consistent with the Clausius–Clapeyron relation (about 7% °C <sup>–1</sup> at low altitudes) and the observed atmospheric warming ( [[#Hartmann--2013|Hartmann et al., 2013]] ).

Since AR5, it is ''very likely'' that increases in global atmospheric water vapour were observed based on in situ, satellite and reanalysis data (with ''medium confidence'' in the magnitude; [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.3|Section 2.3.1.3]] ). Satellite records show increases in upper tropospheric water vapour (constant relative humidity while temperatures have increased) since 1979 ( E.-S. Chung et al. , 2014 ; [[#Blunden--2020|Blunden and Arndt, 2020]] ), to which human influence has ''likely'' contributed ( [[IPCC:Wg1:Chapter:Chapter-3#3.3.2.2|Section 3.3.2.2]] ). Combined satellite and reanalysis estimates and CMIP6 atmosphere-only simulations (1988–2014) show global mean precipitable water vapour increases of 6.7 ± 0.3 % °C <sup>–1</sup> , very close to the Clausius–Clapeyron rate ( [[#Allan--2020|Allan et al., 2020]] ). Satellite-based products show increases close to the Clausius–Clapeyron rate over the ice-free oceans (about 7 to 9 % °C <sup>–1</sup> ; 1998 – 2008), but reanalysis estimates outside this range ( [[#Schröder--2019|Schröder et al., 2019]] ) are an expected consequence of their changing observing systems ( [[#Allan--2014|Allan et al., 2014]] ; [[#Parracho--2018|Parracho et al., 2018]] ). Increases in precipitable water vapour are found over the central and sub-Arctic based on multiple reanalyses with some corroboration from sparse, in situ data ( [[#Vihma--2016|Vihma et al., 2016]] ; [[#Rinke--2019|Rinke et al., 2019]] ; [[#Nygård--2020|Nygård et al., 2020]] ).

Declining near-surface relative humidity over land areas (e.g., the USA, Mediterranean, South Asia, South America and southern Africa) is evident in surface observations ( [[#Willett--2014|Willett et al., 2014]] , 2020; [[#Dunn--2017|Dunn et al., 2017]] ). This is consistent with a faster rate of warming over land than ocean (Sections 2.3.1.3 and 8.2.2.1; [[#Byrne--2018|Byrne and O’Gorman, 2018]] ). CMIP5 simulations underestimate the observed decreases in relative humidity over much of global land during 1979–2015 ( [[#Douville--2017|Douville and Plazzotta, 2017]] ; [[#Dunn--2017|Dunn et al., 2017]] ) even when observed SSTs are prescribed (–0.05 to –0.25% per decade compared with an observed rate of –0.4 to –0.8% per decade). It is not yet clear if this discrepancy is related to internal variability or can be explained by deficiencies in models ( [[#Vannière--2019|Vannière et al., 2019]] ; [[#Douville--2020|Douville et al., 2020]] ) or observations ( [[#Willett--2014|Willett et al., 2014]] ). Over the NH mid-latitude continents, there is ''medium confidence'' that human influence has contributed to a decrease in near-surface relative humidity in summer (Sections 2.3.1.3 and 3.3.2.3).

Water vapour transport (or convergence) estimates from observations have substantial uncertainties even in regions of high quality radiosonde data. Consequently many studies use reanalyses for water transport estimates instead of instrumental observations. For example, increases in low-level (800 – 1000 hPa) moisture convergence into the tropical wet regime with a smaller outflow increase in the mid-troposphere (400 – 800 hPa) with warming was detected in one reanalysis (ERA-Interim; [[#Allan--2014|Allan et al., 2014]] ). Modelling evidence combined with statistical analysis demonstrate consistency between reanalysis moisture convergence and P–E over land ( [[#Robertson--2016|Robertson et al., 2016]] ). Advances in reanalysis representation of atmospheric moisture and winds in addition to new observational isotope analysis have improved the ability to identify the main sources of water vapour for key continental regions and quantify the relative contributions from moisture advection and recycling (Gimeno et al. , 2012; van Der Ent et al. , 2014; Joseph et al., 2016).

Observed changes in moisture transport can also arise from changes in atmospheric circulation as well as thermodynamics. For instance, moisture transport into the Arctic region estimated from reanalyses datasets is consistent with radiosonde data ( [[#Dufour--2016|Dufour et al., 2016]] ) ''',''' with increases since 1979 linked to atmospheric circulation ( [[#Nygård--2020|Nygård et al., 2020]] ). Moisture transport into the Eurasian Arctic was identified to increase by 2.6% per decade during 1948 – 2008 based on a reanalysis estimate (X. [[#Zhang--2013|]] [[#Zhang--2013|Zhang et al., 2013]] ). More intense moist intrusions associated with atmospheric rivers affecting the Arctic and Europe have been documented since 1979, but with a substantial influence from decadal internal variability ( [[#Ummenhofer--2017|Ummenhofer et al., 2017]] ; [[#Mattingly--2018|Mattingly et al., 2018]] ). A recent strengthening of tropical circulation and associated moisture convergence has been identified since around 2000 for the Amazon region (Arias et al. , 2015; Barichivich et al. , 2018; J.C. Espinoza et al. , 2018; X.Y. Wang et al. , 2018). This was also strenghtened by increased moisture transport from the North Atlantic, driving more abundant latent heat release ( [[#Segura--2020|Segura et al., 2020]] ) and leading to an increased frequency of extreme floods in the northern Amazon ( [[#Barichivich--2018|Barichivich et al., 2018]] ; [[#Heerspink--2020|Heerspink et al., 2020]] ). Overall, increased moisture transport has been linked to increased precipitation over wet tropical land areas ( [[#Gimeno--2020|Gimeno et al., 2020]] ) and to more extreme and persistent wet and dry weather events ( [[#Konapala--2020|Konapala et al., 2020]] ) in many regions worldwide.

In summary, there is ''high confidence'' that human-caused global warming has led to an overall increase in water vapour and moisture transport throughout the troposphere, at least since the mid-1990s. In particular, there is ''high confidence'' that moisture transport into the Arctic has increased but only ''medium confidence'' in the attribution of such a trend to a human influence. There is ''medium confidence'' that human influence has contributed to a decrease in near-surface relative humidity over the Northern Hemisphere mid-latitude continents during summer (see also Sections 2.3.1.3 and 3.3.2.3).

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==== 8.3.1.3 Precipitation Amount, Frequency and Intensity ====

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This section assesses observed changes in precipitation at global and regional scales. Note that changes in precipitation seasonality are assessed in Box 8.2 and that changes in regional monsoons are assessed in section 8.3.2.4 where observed changes in both circulation and rainfall are considered. Further assessment of regional changes in precipitation is presented in Chapters 10, 12 and Atlas, while extreme precipitation is presented in Chapter 11.

The AR5 concluded that it is ''likely'' there has been an overall increase in annual mean precipitation amount over mid-latitude land areas in the NH, with ''low confidence'' since 1901, but ''medium confidence'' after 1951. There is further evidence of a faster increase since the 1980s ( ''medium confidence'' ) (Sections 2.3.1.3.4 and 3.3.2.2). Precipitation has increased from 1950 to 2018 over mid-high latitude Eurasia, most of North America, south-eastern South America, and north-western Australia, while it has decreased over most of Africa, eastern Australia, the Mediterranean region, the Middle East, and parts of East Asia, central South America, and the Pacific coasts of Canada, as simulated by the CMIP5 multi-ensemble mean ( [[#Dai--2021|Dai, 2021]] ). Since AR5, there have been updates of several precipitation datasets, including satellite estimates, reanalysis and merged products ( [[#Adler--2017|Adler et al., 2017]] ; [[#Roca--2019|Roca, 2019]] ). However, observational uncertainties remain an issue for assessing regional trends in seasonal or annual mean precipitation amount (Hegerl et al. , 2015; Maidment et al. , 2015; Sarojini et al. , 2016; Beck et al. , 2017) , as well as the convective and stratiform types of precipitation (e.g., [[#Ye--2017|Ye et al., 2017]] ). Precipitation trends at regional scales are dominated by internal variability across much of the world ( [[#Knutson--2018|Knutson and Zeng, 2018]] ). Regional changes in precipitation amounts can also be obscured by contrasting responses to GHG compared with aerosol forcings ( [[#Wu--2013|Wu et al., 2013]] ; [[#Hegerl--2015|Hegerl et al., 2015]] ; [[#Xie--2016|Xie et al., 2016]] ; [[#Zhao--2019|Zhao and Suzuki, 2019]] ; [[#Zhao--2020|Zhao et al., 2020]] ) and changes in precipitation intensity versus frequency ( [[#Shang--2019|Shang et al., 2019]] ).

Global and regional changes in precipitation frequency and intensity have been observed over recent decades. An analysis of 1875 rain gauge records worldwide over the period 1961–2018 indicates that there has been a general increase in the probability of precipitation exceeding 50 mm day <sup>–1</sup> , mostly due to an overall boost in rain intensity ( [[#Benestad--2019|Benestad et al., 2019]] ). Such changes in precipitation intensity and frequency have not been formally attributed to human activities, but are consistent with the heating effect of increasing CO <sub>2</sub> levels on the distribution of daily precipitation rates ( [[#8.2.3.2|Section 8.2.3.2]] ) and with a distinct overall intensification of heavy precipitation events found in both observations and CMIP5 models, though with an underestimated magnitude ( [[#Fischer--2014|Fischer and Knutti, 2014]] ). Beyond amplified precipitation extremes ( [[IPCC:Wg1:Chapter:Chapter-11#11.4.2|Section 11.4.2]] ), CMIP5 models also indicate that anthropogenic forcings have increased temporal variability of annual precipitation amount over land from 1950 to 2005, which is most pronounced in annual mean daily precipitation intensity ( [[#Konapala--2017|Konapala et al., 2017]] ).

Anthropogenic aerosols can alter precipitation intensities both through radiative and microphysical effects (Box 8.1 and [[#8.5.1.1.2|Section 8.5.1.1.2]] ). Precipitation suppression through aerosol microphysical effects has been observed in shallow cloud regimes over South America and the south-eastern Atlantic, associated with local biomass burning ( [[#Andreae--2004|Andreae et al., 2004]] ; [[#Costantino--2010|Costantino and Bréon, 2010]] ), and in industrial regions in Australia ( [[#Rosenfeld--2000|Rosenfeld, 2000]] ; [[#Hewson--2013|Hewson et al., 2013]] ; [[#Heinzeller--2016|Heinzeller et al., 2016]] ). In contrast, precipitation intensification through aerosol microphysical effects in deep convective clouds is seen in many regions such as the Amazon, southern USA, India, and Korea. This is associated with anthropogenic aerosols from cities ( [[#Hewson--2013|Hewson et al., 2013]] ; [[#Fan--2018|Fan et al., 2018]] ; [[#Lee--2018|S.S. Lee et al., 2018]] ; [[#Sarangi--2018|Sarangi et al., 2018]] ).

In the tropics, increases in precipitation amount are observed in convergence zones and decreases in the descending branches of the atmospheric circulation since 1979 ( [[#Chou--2013|Chou et al., 2013]] ; [[#Liu--2013|Liu and Allan, 2013]] ; [[#Gu--2016|Gu et al., 2016]] ; [[#Polson--2016|Polson et al., 2016]] ; [[#Polson--2017|Polson and Hegerl, 2017]] ), consistent with increased moisture transports with warming ( [[#Gimeno--2020|Gimeno et al., 2020]] ). Over tropical land areas, there is substantial variability in the ‘wet convergent regimes get wetter’ and ‘dry divergent regimes get drier’ pattern of trends observed since 1950 that are modulated by decadal changes in ENSO ( [[#Liu--2013|Liu and Allan, 2013]] ; [[#Gu--2018|Gu and Adler, 2018]] ). CMIP6 models indicate an increased contrast between wet and dry regions in the tropics and subtropics (Figure 8.7; [[#Schurer--2020|Schurer et al., 2020]] ). This provides further evidence that rainfall has increased in wet regimes, and slightly decreased in dry regimes over the period 1988 – 2019 (Figure 3.14). This greater contrast is primarily attributable to greenhouse gas forcings, although the observed trends are statistically larger than the model responses ( [[IPCC:Wg1:Chapter:Chapter-3#3.3.2.3|Section 3.3.2.3]] ).

Over the African continent, there are distinct precipitation trends observed in multiple datasets since the 1980s (Figure 8.7; [[#Maidment--2015|Maidment et al., 2015]] ; P. [[#Nguyen--2018|]] [[#Nguyen--2018|Nguyen et al., 2018]] ). Increases in intense convective storms affecting the Sahel have been attributed to increased land – ocean temperature gradients ( [[#Taylor--2017|Taylor et al., 2017]] ), enhanced by intense heating of the Sahara ( [[#Dong--2015|Dong and Sutton, 2015]] ) rather than thermodynamics ( [[#8.2.2|Section 8.2.2]] ). Changes in Sahel rainfall, with reduced precipitation amounts from the 1960s to the 1980s and a subsequent recovery, are assessed in Sections 8.3.2.4.3 and 10.4.2.1. In eastern Africa, decreasing precipitation amount (−2 to −7 % per decade for 1983 – 2010) was reported for the March to May ‘long rains’ season ( [[#Lyon--2012|Lyon and Dewitt, 2012]] ; [[#Viste--2013|Viste et al., 2013]] ; [[#Liebmann--2014|Liebmann et al., 2014]] ; [[#Maidment--2015|Maidment et al., 2015]] ; [[#Rowell--2015|Rowell et al., 2015]] ) and evidence of a recovery since, with internal variability playing a large role in these decadal changes ( [[#Wainwright--2019|Wainwright et al., 2019]] ). In contrast, the second ‘short rains’ season in eastern Africa (October to December) does not exhibit significant precipitation trends ( [[#Rowell--2015|Rowell et al., 2015]] ). Increases in annual southern African rainfall of 6 – 7% per decade during 1983 – 2010 are linked with the Pacific Decadal Oscillation (PDO; [[#Maidment--2015|Maidment et al., 2015]] ).

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[[File:7ceff4aed13183efc0490dbc9fa605ac IPCC_AR6_WGI_Figure_8_7.png]]

'''Figure 8.7 | Linear trends in annual mean precipitation (mm day''' <sup>–1</sup> '''per decade) for''' '''1901–1984''' '''(left) and''' '''1985–2014''' '''(right):''' '''(a, e) observational dataset, and the CMIP6 multi-model ensemble mean historical simulations driven by: (b, f) all radiative forcings; (c, g) GHG-only radiative forcings; (d, h) aerosol-only radiative forcings experiment.''' Colour shades without grey cross correspond to the regions exceeding 10% significant level. Grey crosses correspond to the regions not reaching the 10% statistically significant level. Nine CMIP6-DAMIP models have been used having at least three members. The ensemble mean is weighted per each model on the available and used members. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).

( [[#8.3.1.6|Section 8.3.1.6]] assesses changes in precipitation over the Mediterranean region and its connection with drought and aridity.

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]] , [[#Dey--2019b|b]] ; [[#Dai--2021|Dai, 2021]] ) and decreases observed in the north-east ( [[#Li--2012|]] [[#Li--2012|J. Li et al., 2012]] ) since the 1970s. In contrast, there has been a decline in rainfall over southern Australia related to changes in the intensification and position of the subtropical ridge (CSIRO and BoM, 2015) and anthropogenic effects ( [[#Knutson--2018|Knutson and Zeng, 2018]] ). The drying trend over south-west Australia is most pronounced during May to July, where rainfall has declined by 20% below the 1900–1969 average since 1970 and by about 28% since 2000 (BoM and CSIRO, 2020).

Over South America, there is observational and paleoclimate evidence of declining precipitation amount during the past 50 years over the Altiplano and central Chile, primarily explained by the PDO but with at least 25% of the decline attributed to anthropogenic influence ( [[#Morales--2012|Morales et al., 2012]] ; [[#Neukom--2015|Neukom et al., 2015]] ; [[#Boisier--2016|Boisier et al., 2016]] ; [[#Seager--2019b|Seager et al., 2019b]] ; [[#Garreaud--2020|Garreaud et al., 2020]] ). In contrast, a significant rainfall increase has been detected over the Peruvian–Bolivian Altiplano (from observational data and satellite-based estimations) since the 1980s (Figure 8.7; [[#Imfeld--2020|Imfeld et al., 2020]] ; [[#Segura--2020|Segura et al., 2020]] ). Long-term (1902 – 2005) precipitation data indicate positive trends over south-eastern South America and negative trends over the southern Andes, with at least a partial contribution from anthropogenic forcing ( [[#Gonzalez--2014|Gonzalez et al., 2014]] ; [[#Vera--2015|Vera and Díaz, 2015]] ; [[#Díaz--2017|Díaz and Vera, 2017]] ; [[#Boisier--2018|Boisier et al., 2018]] ; [[#Knutson--2018|Knutson and Zeng, 2018]] ; see further assessment in [[IPCC:Wg1:Chapter:Chapter-10#10.4.2.2|Section 10.4.2.2]] and Atlas.7.2.2). The Peruvian Amazon has exhibited significant rainfall decreases during the dry season since 1980 ( [[#Lavado--2013|Lavado et al., 2013]] ; [[#Ronchail--2018|Ronchail et al., 2018]] ). Increases in wet season rainfall in the northern and central Amazon since the 1980s and decreases during the dry season in the southern Amazon ( [[#Barreiro--2014|Barreiro et al., 2014]] ; [[#Gloor--2015|Gloor et al., 2015]] ; [[#Martín-Gómez--2016|Martín-Gómez and Barreiro, 2016]] ; [[#Espinoza--2018|J.C. Espinoza et al., 2018]] ; [[#Wang--2018|X.Y. Wang et al., 2018]] ; [[#Haghtalab--2020|Haghtalab et al., 2020]] ) are not explained by radiative forcing based on CMIP6 experiments (Figure 8.7) and trends are insignificant over longer periods since 1930 ( [[#Kumar--2013|Kumar et al., 2013]] ) or more recently, since 1973 ( [[#Almeida--2017|Almeida et al., 2017]] ). See ( [[#8.3.2.4.5|Section 8.3.2.4.5]] for monsoon-related changes. For the tropical Andes region, trends in annual precipitation show heterogenous patterns, ranging between –4% per decade and +4% per decade in the northern and southern tropical Andes for a 30-year period at the end of the 20th century, although increases during 1965 – 1984 and decreases since 1984 have been registered in Bolivia ( [[#Carmona--2014|Carmona and Poveda, 2014]] ; [[#Pabón-Caicedo--2020|Pabón-Caicedo et al., 2020]] ).

Over China, annual precipitation totals changed little from 1973 to 2016, but precipitation intensity significantly increased at a rate of 0.12 mm day <sup>–1</sup> per decade, while the number of days with precipitation exceeding 0.1 mm day <sup>–1</sup> significantly decreased at a rate of 0.9 days per decade ( [[#Shang--2019|Shang et al., 2019]] ). There is consistency in trend estimates during 1998 – 2015 over mainland China among satellite-based products and station data, which show increased precipitation amounts in autumn and winter and decreases in summer ( [[#Chen--2018|Chen and Gao, 2018]] ), consistent with a decreased intensity of East Asian monsoon precipitation ( [[#Lin--2014|Lin et al., 2014]] ; [[#Deng--2018|Deng et al., 2018]] ). Further assessment of precipitation changes over the South and South East Asian and the East Asian monsoon regions is presented in [[#8.3.2.4|Section 8.3.2.4]] . An increasing trend in the frequency of heavy rainfall occurrences at the expense of low and moderate rainfall occurrences is found over central India ( [[#Krishnan--2016|Krishnan et al., 2016]] ; [[#Roxy--2017|Roxy et al., 2017]] ) and over eastern China with the latter due to increasing high aerosol levels ( [[#Qian--2009|Qian et al., 2009]] ; [[#Guo--2017|J. Guo et al., 2017]] ; [[#Xu--2017|Xu et al., 2017]] ; [[#Day--2018|Day et al., 2018]] ), consistent with the effects of absorbing aerosol on stability and convective inhibition (Box 8.1).

Observed precipitation records since the early 1900s show increases in precipitation totals over central and north-eastern North America that are attributable to anthropogenic warming but larger in magnitude than found in CMIP5 simulations ( [[#Knutson--2018|Knutson and Zeng, 2018]] ; [[#Guo--2019|Guo et al., 2019]] ). Decreases in precipitation amount over the central and south-western USA and increases over the north-central USA during 1983 – 2015 ( [[#Cui--2017|Cui et al., 2017]] ; P. [[#Nguyen--2018|]] [[#Nguyen--2018|Nguyen et al., 2018]] ), are not clearly associated with forced responses in CMIP6 simulations (Figure 8.7; see also [[IPCC:Wg1:Chapter:Chapter-10#10.4.2.3|Section 10.4.2.3]] ). Over Europe, precipitation trends since 1979 do not show coherence across datasets ( [[#Zolina--2014|Zolina et al., 2014]] ; P. [[#Nguyen--2018|]] [[#Nguyen--2018|Nguyen et al., 2018]] ). Longer records since 1910 show increases for much of Scandinavia, north-western Russia, and parts of north-western Europe/United Kingdom and Iceland ( [[#Knutson--2018|Knutson and Zeng, 2018]] ). Records since 1930 show increases of annual preciptation amount over western Russia (see also Atlas.8.2). Widespread increases in daily precipitation intensity appear clearly over regions with a high density of rain gauges, such as Europe and North America over the 1951 – 2014 period ( [[#Alexander--2016|Alexander, 2016]] ). Observations during 1966 – 2016 over northern Eurasia show increases in the contribution of heavy convective showers to total precipitation by 1 – 2% on average (with local trends of up to 5%) for all seasons except for winter ( [[#Chernokulsky--2019|Chernokulsky et al., 2019]] ). Increases in convective precipitation intensity have been identified, particularly on sub-daily time scales, using a range of modelling and observational data ( [[#Berg--2013|Berg et al., 2013]] ; [[#Kanemaru--2017|Kanemaru et al., 2017]] ; [[#Pfahl--2017|Pfahl et al., 2017]] ).

Snowfall is an important component of precipitation in high-latitude and mountain watersheds. Reanalysis data indicate significant reductions in annual mean potential snowfall areas over NH land by 0.52 million km <sup>2</sup> per decade, with the largest decline over the Alps, with snow water equivalent reductions of about 20 mm per decade ( [[#Tamang--2020|Tamang et al., 2020]] ). In the Tibetan Plateau, region-wide winter snowfall has increased but summer snowfall has decreased during the 1960 – 2014 period ( [[#Deng--2017|Deng et al., 2017]] ). State-of-the-art model simulations indicate reduced mean annual snowfall in the Arctic, despite the strong precipitation increase, mainly in summer and autum, when temperatures are close to the melting point ( [[#Bintanja--2017|Bintanja and Andry, 2017]] ).

In summary, regional changes in precipitation amounts can be obscured by the contrasting responses to GHG and aerosol forcings across much of the 20th century and can thus be dominated by internal variability at decadal to multi-decadal time scales ( ''high confidence'' ). There is, however, a detectable increase in northern high-latitude annual precipitation over land which has been primarily driven by human-induced global warming ( ''high confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-3#3.3.2|Section 3.3.2]] ). Human influence has strengthened the zonal mean precipitation contrast between the wet tropics and dry subtropics since the 1980s ( ''medium confidence'' ), although regional studies suggest a more complex precipitation response to evolving anthropogenic forcings. There is ''high confidence'' that daily mean precipitation intensities have increased since the mid-20th century in a majority of land regions with available observations and it is ''likely'' that such an increase is mainly due to GHG forcing (see [[IPCC:Wg1:Chapter:Chapter-11#11.4|Section 11.4]] ). [[#8.3.2.4|Section 8.3.2.4]] assesses monsoon precipitation changes in detail.

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==== 8.3.1.4 Evapotranspiration ====

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The AR5 assessed that there was ''medium confidence'' that pan evaporation declined in most regions over the last 50 years, yet ''medium confidence'' that evapotranspiration increased from the early 1980s to the late 1990s. Since AR5, these conflicting observations have been attributed to internal variability and by the fact that evapotranspiration is less sensitive to trends in wind speed and is partly controlled by vegetation greening ( [[#Zhang--2015|K. Zhang et al., 2015]] ; [[#Zhang--2016|Y. Zhang et al., 2016]] ; [[#Zeng--2018b|Z. Zeng et al., 2018b]] ). Observation-based estimates show a robust positive trend in global terrestrial evapotranspiration between the early 1980s and the early 2010s ( [[#Miralles--2014b|Miralles et al., 2014b]] ; [[#Zeng--2014|Z. Zeng et al., 2014]] , [[#Zeng--2018b|2018b]] ; [[#Zhang--2015|K. Zhang et al., 2015]] ; [[#Zhang--2016|Y. Zhang et al., 2016]] ). The rate of increase varies among datasets, with an ensemble mean terrestrial average rate of 7.6 ± 1.3 mm yr <sup>–</sup> <sup>1</sup> per decade for 1882–2011 (Z. [[#Zeng--2018|Zeng et al., 2018]] a). In addition, a decreasing trend in pan evaporation plateaued or reversed after the mid-1990s (C.M. [[#Stephens--2018|]] [[#Stephens--2018|Stephens et al., 2018]] ) has been reported as due to a shift from a dominant influence of wind speed to a dominant effect of water vapour pressure deficit, which has increased sharply since the 1990s ( [[#Yuan--2019|Yuan et al., 2019]] ). The absence of a trend in evapotranspiration in the decade following 1998 was shown to be at least partly an episodic phenomenon associated with ENSO variability (Miralles et al. , 2014b; K. Zhang et al. , 2015; Martens et al. , 2018). Thus, there is ''medium confidence'' that the apparent pause in the increase in global evapotranspiration from 1998 to 2008 is mostly due to internal variability. In contrast to AR5, there are now consistent trends in pan evaporation and evapotranspiration at the global scale, given the recent increase in both variables since the mid-1990s ( ''medium confidence'' ). Given the growing number of quantitative studies, there is ''high confidence'' that global terrestrial annual evapotranspiration has increased since the early 1980s.

Since AR5, the predominant contribution of transpiration to the observed trends in terrestrial evapotranspiration has been revisited and confirmed ( [[#Good--2015|Good et al., 2015]] ; [[#Wei--2017|Wei et al., 2017]] ). Using satellite and ecosystem models, [[#Zhu--2016|Zhu et al. (2016)]] found a positive trend in leaf area index during 1982 – 2009, indicating that greening could contribute to the observed positive trend of evapotranspiration, in line with similar studies that focused on the 1981–2012 (Y. Zhang et al. , 2016) and 1982–2013 (K. Zhang et al. , 2015) periods . [[#Zeng--2018|Zeng et al. (2018)]] determined that the 8% global increase in satellite-observed leaf area index between the 1980s and the 2010s may explain an increase in evapotranspiration of 12.0 ± 2.4 mm yr <sup>–1</sup> (about 55 ± 25% of the total observed increase). [[#Forzieri--2020|Forzieri et al. (2020)]] estimated that the recent increase in leaf area index led to 3.66 ± 0.45 W m <sup>–2</sup> in latent heat flux (about 51 ± 6 mm yr <sup>–1</sup> ) and that the sensitivity of energy fluxes to leaf area index increased by about 20% over the 1982–2016 period. Overall, there is ''medium confidence'' that greening has contributed to the global increase in evapotranspiration since the 1980s.

Plant water use efficiency (WUE) is expected to rise with CO <sub>2</sub> levels ( ''high confidence'' ) ( [[#8.2.3.3|Section 8.2.3.3]] and Box 5.2), and can in theory counteract rising evapotranspiration in a warmer atmosphere ( [[#8.2.3.3|Section 8.2.3.3]] ). However, observational studies suggest that this may not be the case in some ecosystems. For example, [[#Frank--2015|Frank et al. (2015)]] found that while the WUE increased in European forests across the 20th century, transpiration also increased due to more plant growth, a lengthened growing season, and increased evaporative demand. Likewise [[#Guerrieri--2019|Guerrieri et al. (2019)]] observed that while WUE and photosynthesis increased in North American forests, stomatal conductance experienced only modest declines that were restricted to moisture-limited forests. Other studies further suggest that in many ecosystems increased WUE will not compensate for increased plant growth, amplifying declines in surface water availability (De Kauwe et al. , 2013; Ukkola et al. , 2016b; A. Singh et al. , 2020) , while drought conditions can also offset the CO <sub>2</sub> fertilization effect and lead to a decline in WUE (N. [[#Liu--2020|]] [[#Liu--2020|]] [[#Liu--2020|Liu et al., 2020]] ). There is ''low confidence'' regarding the impact of plant physiological effects on observed trends in evapotranspiration.

An increasing number of studies have identified signals of attribution in the recent observed trends in evapotranspiration. [[#Douville--2013|Douville et al. (2013)]] found that the post-1960 rise in evapotranspiration in both the mid-latitudes and northern high latitudes was related to anthropogenic radiative forcing. An analysis of CMIP5 simulations suggests that anthropogenic forcing accounts for a large fraction of the global mean evapotranspiration trend from 1982 to 2010 ( [[#Dong--2017|Dong and Dai, 2017]] ) . [[#Padrón--2020|Padrón et al. (2020)]] determined that increases in evapotranspiration were responsible for the majority of the anthropogenic pattern in dry-season water availability that dominates global trends since 1984. These findings are further supported by CMIP6 model results (Figure 8.8) that show that the recent summer increase in evapotranspiration in the northern mid- and high latitudes is due to GHG forcing and decreasing anthropogenic aerosol emissions over Europe.

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[[File:215fc21844454b8df7280e4cb1c97939 IPCC_AR6_WGI_Figure_8_8.png]]

'''Figure 8.8 |''' '''Linear trends in annual mean evapotranspiration (mm day''' <sup>–1</sup> '''per decade) for''' '''1901–1984''' '''(left) and''' '''1985–2014''' '''(right):''' '''(a, e) Land Model Intercomparison Project''' '''(LMIP) and observational dataset, and the CMIP6 multi-model ensemble mean historical simulations driven by (b, f) all radiative forcings, (c, g) GHG-only radiative forcings, (d, h) aerosol-only radiative forcings experiment.''' Colour shade without grey cross correspond to the regions exceeding 10% significant level. Grey crosses correspond to the regions not reaching the 10% statistically significant level. Nine CMIP6-DAMIP models have been used having at least three members. The ensemble mean is weighted per each model on the available and used members. The Global Land Data Assimilation System (GLDAS) was not available over the early 20th century so was replaced by a multi-model off-line reconstruction, LMIP, which is consistent with GLDAS over the recent period but may be less reliable over the early 20th century given larger uncertainties in the atmospheric forcings. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).

In summary, there is ''high confidence'' that terrestrial evapotranspiration has increased since the 1980s. There is ''medium confidence'' that this trend is driven by both increasing atmospheric water demand and vegetation greening, and ''high confidence'' that it can be partly attributed to anthropogenic forcing. There is ''low confidence'' about the extent to which increases in plant water use efficiency have influenced observed changes in evapotranspiration.

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==== 8.3.1.5 Runoff, Streamflow and Flooding ====

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The AR5 reported ''low confidence'' in the assessment of trends in global river discharge during the 20th century. This is because many streamflow observations have been impacted by land use and dam construction, and the largest river basins worldwide differ in many characteristics, including geography and morphology. In regions with seasonal snow storage, AR5 WGII assessed that there is ''robust evidence'' and ''high agreement'' that warming has led to earlier spring discharge maxima and ''robust evidence'' of earlier breakup of Arctic river ice, as well as indications that warming has led to increased winter flows and decreased summer flows where streamflows are lower and that the observed increases in extreme precipitation led to greater probability of flooding at regional scales with ''medium confidence'' . The SROCC found ''robust evidence'' and ''high agreement'' that discharge due to melting glaciers has already reached its maximum point and has begun declining with smaller glaciers, but only ''low confidence'' that anthropogenic climate change has already affected the frequency and magnitude of floods at the global scale.

Significant trends in streamflow and continental runoff were observed in 55 out of 200 large river basins during 1948 – 2012, with an even distribution of increasing and decreasing trends ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.3.6|Section 2.3.1.3.6]] ; [[#Dai--2016|Dai, 2016]] ). A global detection and attribution study shows that the simulation of spatially heterogeneous historical trends in streamflow is consistent with observed trends only if anthropogenic forcings are considered ( [[#Gudmundsson--2019|Gudmundsson et al., 2019]] ). [[IPCC:Wg1:Chapter:Chapter-3#3.3.2.4|Section 3.3.2.4]] assesses with ''medium confidence'' that anthropogenic climate change has altered regional and local streamflows, although a significant trend has not been observed in the global average (Sections 2.3.1.3.6 and 3.3.2.3). Multiple human-induced and natural drivers have been shown to play an important but variable role in observed regional trends of streamflow for several different areas (Fenta et al. , 2017; Ficklin et al. , 2018; Glas et al. , 2019; Vicente-Serrano et al. , 2019) . For instance, decreasing runoff during the dry season has been observed over the Peruvian Amazon since the 1980s ( [[#Lavado--2013|Lavado et al., 2013]] ; [[#Ronchail--2018|Ronchail et al., 2018]] ). Up to 30–50% of the recent multi-decadal decline in streamflow across the Colorado River Basin can be attributed to anthropogenic warming and its impacts on snow and evapotranspiration ( [[#Woodhouse--2016|Woodhouse et al., 2016]] ; [[#McCabe--2017|McCabe et al., 2017]] ; [[#Udall--2017|Udall and Overpeck, 2017]] ; [[#Xiao--2018|Xiao et al., 2018]] ; [[#Milly--2020|Milly and Dunne, 2020]] ). In the Upper Missouri River basin, [[#Martin--2020|Martin et al. (2020)]] found that warming temperatures have contributed to streamflow reductions since at least the late 20th century. Cold regions in the NH have experienced an earlier occurrence of snowmelt floods, an overall increase in water availability and streamflow during winter, and a decrease in water availability and streamflow during the warm season ( [[#Aygün--2019|Aygün et al., 2019]] ).

Some studies have suggested that dam construction and water withdrawals can be the dominant drivers in observed trends in streamflow amount ( [[#Wada--2013|Wada et al., 2013]] ). Regionally, land-use and land cover changes have been identified as important factors for streamflow (H. [[#Chen--2020|]] [[#Chen--2020|Chen et al., 2020]] ). The impact of surface dimming from aerosol emissions on evaporation was identified as a discernible influence in NH streamflows ( [[#Gedney--2014|Gedney et al., 2014]] ). While changes in annual mean streamflow present a complicated picture, recent studies of changes in the timing of streamflow in snow-influenced basins continue to support a prominent influence from warming ( [[#Kang--2016|Kang et al., 2016]] ; [[#Dudley--2017|Dudley et al., 2017]] ; [[#Kam--2018|Kam et al., 2018]] ). Global land runoff variations correlate significantly with ENSO variability ( [[#Miralles--2014b|Miralles et al., 2014b]] ; [[#Schubert--2016|Schubert et al., 2016]] ).

Observed changes in flooding are assessed in [[IPCC:Wg1:Chapter:Chapter-11#11.5.2|Section 11.5.2]] and are summarized as follows. For changes in the magnitude of peak flow, recent studies show strong spatial heterogeneity in the sign, size and significance of trends. For changes in timing of peak flows, recent studies further support observed changes in snowmelt-driven rivers. Observed changes in runoff and flood magnitude cannot be explained by precipitation changes alone given the possible season- and region-dependent decreases in antecedent soil moisture and snowmelt, which can partly offset the increase in precipitation intensity ( [[#Sharma--2018|Sharma et al., 2018]] ), or the expected effect of urbanization and deforestation which can, on the contrary, amplify the runoff response ( [[#Chen--2017|Chen et al., 2017]] ; [[#Abbott--2019|Abbott et al., 2019]] ; [[#Cavalcante--2019|Cavalcante et al., 2019]] ). Simulations of mean and extreme river flows are consistent with the observations only when anthropogenic radiative forcing is considered ( [[#Gudmundsson--2021|Gudmundsson et al., 2021]] ).

In summary, the assessment of observed trends in the magnitude of runoff, streamflow, and flooding remains challenging, due to the spatial heterogeneity of the signal and to multiple drivers. There is, however, ''high confidence'' that the amount and seasonality of peak flows have changed in snowmelt-driven rivers due to warming. There is also ''high confidence'' that land-use change, water management and water withdrawals have altered the amount, seasonality, and variability of river discharge, especially in small and human-dominated catchments.

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<span id="aridity-and-drought"></span>
==== 8.3.1.6 Aridity and Drought ====

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The AR5 reported ''low confidence'' that changes in drought since the mid-20th century could be attributed to human influence, owing to observational uncertainties and difficulties in distinguishing decadal-scale variability from long-term trends. Changes in soil moisture, a metric of aridity, were not assessed thoroughly in AR5. Since AR5, new satellite products, land surface reanalyses, and land surface models have been used to document recent changes in soil moisture at the global scale. The science of detection and attribution has also progressed considerably ( [[#Trenberth--2015|Trenberth et al., 2015]] ; [[#Easterling--2016|Easterling et al., 2016]] ; [[#Stott--2016|Stott et al., 2016]] ). Attribution efforts have further benefited from the increased use of paleoclimate information, which provides an important constraint on natural variability that is insufficiently sampled by short observational record ( [[#Cook--2018|Cook et al., 2018]] ; [[#Kageyama--2018|Kageyama et al., 2018]] ).

Several studies have identified a persistent ‘fingerprint’ of anthropogenic forcing in global trends in aridity spanning the last 120 years. Using a combination of tree ring data, CMIP5 model simulations, and reanalysis products, [[#Marvel--2019|Marvel et al. (2019)]] determined that the dominant trend in aridity since 1900, characterized by drying in North and Central America and the Mediterranean, is detectable and attributable to external forcing from 1900 to 1949. This trend weakens from 1950 to 1975, possibly due to aerosol forcing ( [[#Marvel--2019|Marvel et al., 2019]] ), but then emerges again from 1981 to present, although it is not detectable in the GLEAM nor MERRA-2 soil moisture reanalysis products. Likewise, [[#Bonfils--2020|Bonfils et al. (2020)]] investigated changes in precipitation, temperature and continental aridity in CMIP5 historical simulations and found that the dominant multivariate fingerprint, an amplification of wet–dry latitudinal patterns and progressive continental aridification, was associated with greenhouse gas emissions (Figure 8.9a , d), and the second leading fingerprint was associated with anthropogenic aerosols (Figure 8.9e , h). This study found that the anthropogenic greenhouse gas signal is statistically detectable in reanalyses over the 1950–2014 period (signal-to-noise ratio above 1.96). [[#Gu--2019|Gu et al. (2019)]] found that a global trend in declining soil moisture is detectable in the GLDAS-2 reanalysis product and is attributable to greenhouse gas forcing. [[#Padrón--2020|Padrón et al. (2020)]] reconstructed the global patterns of dry season water availability from 1902–2014, and found it ''extremely likely'' (99% range) that trends in the last three decades of the analysis period could be attributed to anthropogenic forcing, mainly due to increases in evapotranspiration. It is ''very likely'' (&gt;90% range) that anthropogenic forcing has affected global patterns of soil moisture over the 20th century.

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[[File:44c55b7cf362f36b7ea9f26f789671a6 IPCC_AR6_WGI_Figure_8_9.png]]

'''Figure 8.9 |''' '''Spatial expressions (a–c, e–g) of the leading multivariate fingerprints of temperature (°C), precipitation (mm day''' <sup>–1</sup> '''), and aridity (CMI; the Climate Moisture Index) in CMIP5 historical simulations and the corresponding temporal evolution in both CMIP5 and reanalysis products (d, h).'''
The first leading fingerprint is associated with greenhouse gas forcing (a–d) and the second leading fingerprint is associated with aerosol forcing (e–h). CMI is a dimensionless aridity indicator that combines precipitation and atmospheric evaporative demand. Figure after [[#Bonfils--2020|Bonfils et al. (2020)]] . Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).

On a regional scale, the robustness of trend attribution for drought and aridity varies widely. Key trends and their attributions are summarized here, while a complete regional assessment of observed trends in drought and aridity is in [[IPCC:Wg1:Chapter:Chapter-11|Chapter 11]] (Sections 11.6.2, 12.3.2 and 12.4).

Several studies have analyzed CMIP5 and land surface models and detected a significant summer drying trend in the NH across the late 20th century that is attributable to anthropogenic forcings ( [[#Mueller--2016|Mueller and Zhang, 2016]] ; [[#Douville--2017|Douville and Plazzotta, 2017]] ). This trend is mainly driven by dryland areas such as the western USA and west-central Asia, where both reanalysis products and satellite data confirm there has been a persistent decline in soil moisture since 1990 (Y. [[#Liu--2019|]] [[#Liu--2019|Liu et al., 2019]] a). In the western USA, snow deficits have ''very likely'' contributed to recent drying ( [[#Mote--2018|Mote et al., 2018]] ). Spring snow water equivalent across the Sierra Nevada Mountains reached a record low in 2015 ( [[#Margulis--2016|Margulis et al., 2016]] ; [[#Mote--2016|Mote et al., 2016]] ), possibly the lowest of the last five hundred years ( [[#Belmecheri--2016|Belmecheri et al., 2016]] ). Over the longer California drought (2011–2015) anthropogenic warming alone reduced snowpack levels in the Sierras by 25% ( [[#Berg--2017|Berg and Hall, 2017]] ). The north-western USA also experienced snow drought in 2015, despite near-normal levels of total cold season precipitation ( [[#Mote--2016|Mote et al., 2016]] ; [[#Marlier--2017|Marlier et al., 2017]] ). There is ''high confidence'' that anthropogenic warming contributed to these recent snow droughts ( [[#Belmecheri--2016|Belmecheri et al., 2016]] ; [[#Mote--2016|Mote et al., 2016]] ).

In the western USA, anthropogenic warming is amplifying drought and aridity by increasing evaporative demand and water loss to the atmosphere ( [[#Weiss--2009|Weiss et al., 2009]] ; [[#Overpeck--2013|Overpeck, 2013]] ; [[#Cook--2014|Cook et al., 2014]] ; [[#Griffin--2014|Griffin and Anchukaitis, 2014]] ; [[#Williams--2020|Williams et al., 2020]] ). For the California drought between 2012–2014, [[#Griffin--2014|Griffin and Anchukaitis (2014)]] used paleoclimate reconstructions to determine that while rainfall deficits were not unprecedented, record-high temperatures drove an exceptional decline in soil moisture relative to the last millennium. [[#Williams--2015|Williams et al. (2015)]] concluded that anthropogenic warming accounted for 8–27% of these soil moisture deficits. [[#Robeson--2015|Robeson (2015)]] estimated that the California drought was a 1-in-10,000 year event. Tree ring reconstructions indicate that prolonged megadroughts have occurred in the western USA throughout the last 1200 years ( Cook et al. , 2004, 2010; B.I. Cook et al. , 2015 ), forced by internal variability ( [[#Coats--2016|Coats et al., 2016]] ; [[#Cook--2016b|Cook et al., 2016b]] ). However, [[#Williams--2020|Williams et al. (2020)]] determined that 2000–2018 drought across the south-western USA was the second driest 19-year period since 800 CE, and attributed nearly half the magnitude of this event to anthropogenic forcing (see also [[IPCC:Wg1:Chapter:Chapter-10#10.4.2.3|Section 10.4.2.3]] ). Evidence for human signals in drought can also be found in western North American streamflow records, as noted above in [[#8.3.1.5|Section 8.3.1.5]] . There is ''high confidence'' that anthropogenic forcing has contributed to recent droughts and drying trends in western North America.

Large areas of east-central Asia experienced drying in the early 2000s as a result of warmer temperatures, lower humidity, and declining soil moisture ( [[#Wei--2013|Wei and Wang, 2013]] ; Z. Li et al. , 2017; Hessl et al. , 2018). Paleoclimate data from the Mongolian plateau suggest that this recent central Asian drought exceeds the 900-year return interval, but is not unprecedented in the last 2060 years ( [[#Hessl--2018|Hessl et al., 2018]] ). There is ''low confidence'' due to ''limited evidence'' that recent droughts in central Asia can be attributed to anthropogenic forcing.

The Mediterranean region has experienced notable changes in drought and aridity. A number of studies have identified a decline in precipitation since 1960 and attributed this to anthropogenic forcing ( [[#Hoerling--2012|Hoerling et al., 2012]] ; [[#Gudmundsson--2016|Gudmundsson and Seneviratne, 2016]] ; [[#Knutson--2018|Knutson and Zeng, 2018]] ; [[#Seager--2019b|Seager et al., 2019b]] ). [[#Kelley--2015|Kelley et al. (2015)]] showed that climate change caused a three-fold increase in the likelihood of the 2007–2010 meteorological drought in the eastern Mediterranean. However, historical trends in precipitation across the Mediterranean are spatially variable and contain substantial decadal variability, such that an anthropogenic influence may not be detectable in all areas ( [[#Zittis--2018|Zittis, 2018]] ; [[#Vicente-Serrano--2021|Vicente-Serrano et al., 2021]] ). Records of soil moisture provide a clearer signal, indicating that higher temperatures and increased atmospheric demand have played a strong role in driving Mediterranean aridity ( [[#Vicente-Serrano--2014|Vicente-Serrano et al., 2014]] ). Hydrological modeling suggests that the recent decline in soil moisture in the Mediterranean is unprecedented in the last 250 years ( [[#Hanel--2018|Hanel et al., 2018]] ). Paleoclimate evidence extends this view, additionally indicating that dryness in the Mediterranean is approaching an extreme condition compared to the last millennium ( [[#Markonis--2018|Markonis et al., 2018]] ) and that the 15-year drought in the Levant (1998–2012) has an 89% likelihood of being the driest of the last 900 years ( [[#Cook--2016a|Cook et al., 2016a]] ). [[#Marvel--2019|Marvel et al. (2019)]] found that the Mediterranean region contributes strongly to the anthropogenic warming component of the global trend in aridity. There is ''high confidence'' that anthropogenic forcings are causing increased aridity and drought severity in the Mediterranean region.

Both central and north-eastern Africa have experienced a decline in rainfall since about 1980 ( ''high confidence'' ) ( [[#Lyon--2012|Lyon and Dewitt, 2012]] ; [[#Lyon--2014|Lyon, 2014]] ; [[#Hua--2016|Hua et al., 2016]] ; [[#Nicholson--2017|Nicholson, 2017]] ). In Central Africa, the decline has been attributed to atmospheric responses to Indo-Pacific sea surface temperature variability ( [[#Hua--2018|Hua et al., 2018]] ). In north-eastern Africa, droughts have become longer and more intense in recent decades, continuing across rainy seasons ( [[#Hoell--2017b|Hoell et al., 2017b]] ; [[#Nicholson--2017|Nicholson, 2017]] ), and this trend appears to be unusual in the context of the last 1500 years ( [[#Tierney--2015|Tierney et al., 2015]] ). [[#Knutson--2018|Knutson and Zeng (2018)]] attribute decreased annual precipitation over the Sudan to anthropogenic forcing, but other studies argue that the recent trend cannot yet be distinguished from natural variability, at least over parts of this region ( [[#Hoell--2017b|Hoell et al., 2017b]] ; [[#Philip--2018|Philip et al., 2018]] ). There remains ''low confidence'' due to ''limited evidence'' that drying the north-eastern Africa is attributable to human influence. In the Western Cape region of South Africa, human influence increased the likelihood of the severe 2015–2017 drought by a factor of 3–6, depending on the analysis ( [[#Otto--2018|Otto et al., 2018]] ; [[#Pascale--2020|Pascale et al., 2020]] ). Anthropogenic forcing also contributed to the 2018 drought, mainly by increasing evapotranspiration ( [[#Nangombe--2020|Nangombe et al., 2020]] ). While some analysis of instrumental precipitation data in this region detect a slight long-term drying trend consistent with the simulated anthropogenic response ( [[#Seager--2019b|Seager et al., 2019b]] ), there is strong multi-decadal variability in the data ( [[#Wolski--2021|Wolski et al., 2021]] ). However, a study of streamflow in southern Africa detected a significant decline ( [[#Gudmundsson--2019|Gudmundsson et al., 2019]] ; see also [[IPCC:Wg1:Chapter:Chapter-10#10.6.2|Section 10.6.2]] ). There is ''medium confidence'' in the long-term drying trend in this region and its attribution to anthropogenic forcing, and ''medium confidence'' that anthropogenic warming has contributed to recent severe drought events.

Several subtropical, semi-arid regions in the Southern Hemisphere have experienced long-term drying trends in the late 20th century. South-western South America (central Chile) experienced a multi-decadal decline in precipitation and streamflow culminating in a post-2010 megadrought that has been partly attributed to anthropogenic GHG emissions and ozone depletion (Boisier et al. , 2016, 2018; Saurral et al. , 2017; [[#Knutson--2018|Knutson and Zeng, 2018]] ; Seager et al. , 2019b; Garreaud et al. , 2020). There is ''medium confidence'' that drying in central Chile can be attributed to human influence. The tree-ring paleoclimate record demonstrates that the mid-century increase in exteme drought events in southern South America is unusual in the context of the last 600 years, suggesting an emerging influence of anthropogenic forcing ( [[#Morales--2020|Morales et al., 2020]] ).

There has been a 20% decrease in winter (May to July) rainfall in south-western Australia since 1970, with the decline increasing to around 28% since 2000 ( [[#Delworth--2014|Delworth and Zeng, 2014]] ; BoM and CSIRO, 2020). There has also been a significant increase in the average intensity of seasonal droughts in the region since 1911in response to both lower precipitation and increased atmospheric evaporative demand ( [[#Gallant--2013|Gallant et al., 2013]] ). Several studies attribute the precipitation declines in south-western Australia to anthropogenic changes in GHG and ozone ( [[#Delworth--2014|Delworth and Zeng, 2014]] ; [[#Knutson--2018|Knutson and Zeng, 2018]] ; [[#Seager--2019b|Seager et al., 2019b]] ). There is ''high confidence'' that the observed drying in south-western Australia can be attributed to anthropogenic forcing.

In south-eastern Australia, the average length of droughts have increased significantly, lasting between 10 and 69% longer than droughts during the first half of the 20th century ( [[#Gallant--2013|Gallant et al., 2013]] ). Paleoclimate reconstructions indicate a 97.1% probability that the decadal rainfall anomaly recorded during the 1997–2009 Millennium drought in south-eastern Australia was the worst experienced since 1783 ( [[#Gergis--2012|Gergis et al., 2012]] ), and that the spatial extent and duration of cool season (April to September) rainfall anomalies were either very much below average or unprecedented over at least the last 400 years ( [[#Freund--2017|Freund et al., 2017]] ). Other paleoclimate studies suggest that the Millennium drought in eastern Australia was not unusual in the context of natural variability reconstructed over the past millennium (Palmer et al. , 2015; Cook et al. , 2016c; Kiem et al. , 2020). While there is currently ''low confidence'' that recent droughts in eastern Australia can be clearly attributed to human influence ( [[#Cai--2014|Cai et al., 2014]] ; [[#Delworth--2014|Delworth and Zeng, 2014]] ; [[#Rauniyar--2020|Rauniyar and Power, 2020]] ), there is emerging evidence that declines in April to October rainfall in south-eastern Australia since the 1990s would not have been as large without the influence of increasing levels of atmospheric GHGs ( [[#Rauniyar--2020|Rauniyar and Power, 2020]] ).

In summary, it is ''very likely'' that anthropogenic factors have influenced global trends in aridity, mainly through competing changes in evapotranspiration and/or atmospheric evaporative demand due to anthropogenic emissions of GHG and aerosols. There is ''high confidence'' that the frequency and the severity of droughts has increased over the last decades in the Mediterranean, western North America, and south-western Australia and that this can be attributed to anthropogenic warming. There is ''medium confidence'' that recent drying and severe droughts in southern Africa and south-western South America can be attributed to human influence. In some regions of western North America and the Mediterranean, paleoclimate evidence suggests that recent warming has resulted in droughts that are of similar or greater intensity than those reconstructed over the last millennium ( ''medium co'' ''nfidence'' ).

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<span id="freshwater-reservoirs"></span>
==== 8.3.1.7 Freshwater Reservoirs ====

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<span id="glaciers"></span>
===== 8.3.1.7.1 Glaciers =====

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The AR5 and SROCC found, with ''very high confidence,'' a general decline in glaciers due to climate change in recent decades. There is ''very high confidence'' that during the decade 2010–2019 glaciers lost more mass than in any other decade since the beginning of the observational record (Sections 2.3.2.3 and 9.5.1). Human influence is ''very likely'' the main driver of the global, near-universal retreat of glaciers since the 1990s ( [[IPCC:Wg1:Chapter:Chapter-3#3.4.3.1|Section 3.4.3.1]] ). In Table 9.5, the contribution of glaciers to sea level rise for different periods is presented; in 1971 – 2018 glacier mass loss contributed 20.9 [10.0 to 31.7] mm or 22.2% of the sea level rise during that period. The highest mass loss rates are observed in the southern Andes, New Zealand, Alaska, Central Europe and Iceland while the largest mass loss are observed in Alaska, the periphery of Greenland and Arctic Canada ( [[IPCC:Wg1:Chapter:Chapter-9#9.5.1%20|Section 9.5.1]] and Figure 9.20). Predominantly '','' runoff from small glaciers such as in Canada has decreased because of glacier mass loss, while runoff from larger glaciers such as in Alaska has typically increased ( [[#Bolch--2010|Bolch et al., 2010]] ; [[#Thomson--2011|Thomson et al., 2011]] ; [[#Tennant--2012|Tennant et al., 2012]] ; [[#WGMS--2017|WGMS, 2017]] ; [[#Huss--2018|Huss and Hock, 2018]] ). Asia contains the largest concentration of glaciers outside the polar regions where the total glacier mass change is –16.3 ± 3.5 Gt yr <sup>–1</sup> over 2000 – 2016 with considerable intra-regional variability ( [[#Brun--2017|Brun et al., 2017]] ). Mass losses of glaciers in Asia between 2000 and 2018 are – 19.0 ± 2.5 Gt yr <sup>–1</sup> ( [[#Shean--2020|Shean et al., 2020]] ). The most negative changes were found in Nyainqentanglha with −4.0 ± 1.5 Gt yr <sup>–1</sup> , while glaciers in Kunlun, northern Tibetan Plateau, slightly gained mass at 1.4 ± 0.8 Gt yr <sup>–1</sup> .

There is some evidence that an increase of precipitation over high mountains can offset glacier ablation (melt; [[#Farinotti--2020|Farinotti et al., 2020]] ). However, this process has only been described from the Karakoram region in the north-western Himalaya, where it is thought to be partly responsible to the advances of glacier changes in the last two decades, referred to as the ‘Karakoram Anomaly’ ( [[#Farinotti--2020|Farinotti et al., 2020]] ). In the Himalaya, [[#Maurer--2019|Maurer et al. (2019)]] observed faster ice loss during 2000–2016 (7.5 ± 2.3 Gt yr <sup>–1</sup> ) compared to 1975–2000 (–3.9 ± 2.2 Gt yr <sup>–1</sup> ). In the Southern Hemisphere, the rate of glacier mass lost in South America is estimated at 19.4 ± 0.6 Gt yr <sup>–1</sup> based on surface elevation changes over 2000 – 2011, which include the North and South Patagonian Icefields of South America ( [[#Braun--2019|Braun et al., 2019]] ), and at −22.9 ± 5.9 Gt yr <sup>–1</sup> over 2000 – 2018 ( [[#Dussaillant--2019|Dussaillant et al., 2019]] ).

In summary, human-induced global warming has been the primary driver of a global glacier recession since the early 20th century ( ''high confidence'' ). Most glaciers have lost mass more rapidly since the 1960s and in an unprecedented way over the last decade, thereby contributing to increased glacier runoff, especially from larger glaciers until a maximum is reached, which tends to occur later in basins with larger glaciers and higher ice-cover fractions ( ''high co'' ''nfidence'' ).

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<span id="seasonal-snow-cover"></span>
===== 8.3.1.7.2 Seasonal snow cover =====

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The AR5 assessed that Northern Hemisphere (NH) snow cover extent (SCE) has decreased since the late 1960s, especially in spring ( ''very high confidence'' ). This is confirmed by recent studies ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.2|Section 2.3.2.2]] ; [[#Kunkel--2016|Kunkel et al., 2016]] ). AR6 assesses that NH spring snow cover has been decreasing since 1978 ( ''very high confidence'' ) and that this trend extends back to 1950 ( ''high confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-9#9.5.3|Section 9.5.3]] ). Human-caused global warming is the dominant driver of this observed decline ( [[IPCC:Wg1:Chapter:Chapter-3#3.4.2|Section 3.4.2]] ; [[#Estilow--2015|Estilow et al., 2015]] ). Model simulations suggest that surface temperature responses at hemispheric/regional scales explain between 40% and 85% of the SCE trend variability ( [[#Mudryk--2017|Mudryk et al., 2017]] ). A decreasing trend in snowfall has also been detected in the NH (Figure 8.1; [[#Rupp--2013|Rupp et al., 2013]] ). Snowfall as a proportion of precipitation has decreased significantly in recent years ( [[#Berghuijs--2014|Berghuijs et al., 2014]] ). However, a late-20th-century increase in snowfall in West Antarctica observed in ice cores has been linked to a combination of factors including the anthropogenically forced deepening of the Amundsen Sea Low ( [[#Thomas--2015|Thomas et al., 2015]] , 2017).

Observations show a rapid recent decrease of spring SCE in NH, mostly in Eurasia and North America, closely linked to temperature change, for example, March to April SCE is decreasing at 3.4% ± 1.1 % per decade (1979–2005; [[#Brown--2011|Brown and Robinson, 2011]] ; [[#Hernández-Henríquez--2015|Hernández-Henríquez et al., 2015]] ). An overall increasing annual trend of the NH SCE since the late 1980s has been observed, in contrast to decreasing trends over 1960s to 1980s that are dominated by the autumn and winter seasons ( [[#Barry--2020|Barry and Gan, 2020]] ). Such recent positive trends in snow cover extent are however at odds with other surface and satellite datasets and with the negative trends simulated by most CMIP5 and CMIP6 models ( [[#Mudryk--2017|Mudryk et al., 2017]] , 2020). [[#Hernández-Henríquez--2015|Hernández-Henríquez et al. (2015)]] also detected positive trends in October to November SCE in in the NOAA SCE Climate Data Record (NOAA-CDR), which are not replicated in other datasets ( [[IPCC:Wg1:Chapter:Chapter-9#9.5.3|Section 9.5.3]] ). [[#Wu--2018|Wu et al. (2018)]] found slower snowmelt rates over the NH in 1980–2017, with higher ablation rates in locations with deep snow water equivalent (SWE), but due to the reduction of SWE in deep snowpacks, moderate/high ablation rates showed decreasing trends. [[#Santolaria-Otín--2020|Santolaria-Otín and Zolina (2020)]] reported weak but significant decline in SCE in autumn over northern Eurasia and North America during 1979 – 2005, and similarly for spring, except for northern Siberia which showed higher spring SCE. [[#Kapnick--2012|Kapnick and Hall (2012)]] detected significant loss of spring mountain snowpack in western USA in 1950 – 2008. For Canada, extensive decreasing snow depths, SCE and duration were detected since mid-1970s, especially in western Canada during winter and spring (DeBeer et al. , 2016). [[#Berghuijs--2014|Berghuijs et al. (2014)]] show that across the continental USA, catchments with more snowfall than rainfall generally have higher mean streamflow, which will probably decrease with smaller fractions of precipitation falling as snow because of climate warming.

In summary, a decline in the spring NH snow cover extent, snow depth and duration has been observed since the late 1960s and has been attributed to human influence ( ''high confidence'' ). Depending on the region and season, there is ''low-to-medium confidence'' in the main drivers of snow cover changes, although various regions exhibit a shortening of the snow cover season which is consistent with global warming. A more detailed assessment of observed changes in seasonal snow cover is provided in [[IPCC:Wg1:Chapter:Chapter-9#9.5.3|Section 9.5.3]] .

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<span id="wetlands-and-lakes"></span>
===== 8.3.1.7.3 Wetlands and lakes =====

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Wetlands and lakes affect the climate through their impact on carbon and methane budgets ( [[IPCC:Wg1:Chapter:Chapter-5#5.2.2|Section 5.2.2]] ; e.g., [[#Saunois--2016|Saunois et al., 2016]] ; [[#Zhan--2019|Zhan et al., 2019]] ) and on surface heat fluxes, with coupled weather and climate effects(e.g., [[#Zhan--2019|Zhan et al., 2019]] ). Although these features are also affected by human activities and by climate change, AR5 did not specifically report on wetlands and lakes.

Inventories of surface water bodies are not systematically produced at national or regional levels. However, assessments are undertaken at the global scale ( [[#Ramsar%20Convention%20on%20Wetlands--2018|Ramsar Convention on Wetlands, 2018]] ). Merging observations from multiple satellite sensors makes it possible to detect surface water even under vegetation and clouds over about 25 years, but with low spatial resolution ( [[#Prigent--2016|Prigent et al., 2016]] ). Most recent multi-satellite products from visible, infrared, and microwave measurements, estimate a surface water area of about 12 to 14 million km <sup>2</sup> (including permanent and transitory surfaces, e.g., [[#Aires--2018|Aires et al., 2018]] ; [[#Davidson--2018|Davidson et al., 2018]] ), which is much higher than those provided by optical imagery (about 3 million km <sup>2</sup> ). Inventories show a strong decrease in natural surface water of about 0.8% yr <sup>–1</sup> in total from 1970 to the present ( [[#Ramsar%20Convention%20on%20Wetlands--2018|Ramsar Convention on Wetlands, 2018]] ) but the sites are not evenly distributed. Multi-satellite estimates show a strong interannual variability in surface water extent over the period 1992 – 2015 with no clear long-term trend ( [[#Prigent--2020|Prigent et al., 2020]] ).

Human-made water bodies represent approximately 10% of the total continental water surfaces (Figure 8.1; [[#Ramsar%20Convention%20on%20Wetlands--2018|Ramsar Convention on Wetlands, 2018]] ) and consist mainly of reservoirs and rice paddies. High resolution optical imagery over the period 1984 – 2015 ( [[#Donchyts--2016|Donchyts et al., 2016]] ; [[#Pekel--2016|Pekel et al., 2016]] ) shows a net increase of about 0.1 million km <sup>2</sup> in artifical water surfaces, mainly due to the construction of reservoirs. Surfaces of rice paddies are also increasing, especially in South East Asia ( [[#Davidson--2018|Davidson et al., 2018]] ).

In summary, there is ''high confidence'' that the extent of human-made surface water has increased over the 20th and early 21st centuries. In contrast, due to ''low agreement'' in the observational records at the global scale, there is only ''low confidence'' in the observed decline of the natural surface water extent in recent years (see also SRCCL).

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<span id="groundwater"></span>
===== 8.3.1.7.4 Groundwater =====

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As the world’s most widespread store of freshwater (R.G. [[#Taylor--2013|Taylor et al., 2013]] a), groundwater is estimated to supply between a quarter and a third of the world’s annual freshwater withdrawals to meet agricultural, industrial and domestic demands ( [[#Döll--2012|Döll et al., 2012]] ; [[#Wada--2014|Wada et al., 2014]] ; [[#Hanasaki--2018|Hanasaki et al., 2018]] ).

Attribution of changes in groundwater storage, observed locally through piezometry (Figure 8.10; R.G. [[#Taylor--2013|Taylor et al., 2013]] a) or estimated from GRACE satellite measurements ( [[#Rodell--2018|Rodell et al., 2018]] ) at regional scales (&gt;100,000 km <sup>2</sup> ), is often complicated by non-climate influences that include land-use change ( [[#Favreau--2009|Favreau et al., 2009]] ) and human withdrawals ( [[#Bierkens--2019|Bierkens and Wada, 2019]] ).

<div id="_idContainer033" class="Basic-Text-Frame"></div>

[[File:0f716d182560d9bc8edaeede0f45dd62 IPCC_AR6_WGI_Figure_8_10.png]]

'''Figure 8.10 |''' '''Trends in Terrestrial Water Storage (TWS; in centimetres per year, cm y''' '''r''' <sup>–1</sup> ''') obtained on the basis of GRACE observations from April 2002 to March 2016.''' The cause of the trend in each outlined study region is briefly explained and colour-coded by category. The trend map was smoothed with a 150 km radius Gaussian filter for the purpose of visualization. However, all calculations were performed at the native 3° resolution of the data product. Figure from [[#Rodell--2018|Rodell et al. (2018)]] . Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).

Following a global review of groundwater and climate change (R.G. [[#Taylor--2013|Taylor et al., 2013]] a) and AR5 WGII, evidence of an association between heavy or extreme precipitation and groundwater recharge has continued to grow, especially in tropical ( [[#Asoka--2018|Asoka et al., 2018]] ; [[#Cuthbert--2019a|Cuthbert et al., 2019a]] ; [[#Kotchoni--2019|Kotchoni et al., 2019]] ) and subtropical regions ( [[#Meixner--2016|Meixner et al., 2016]] ). Stable-isotope ratios of oxygen and hydrogen at 14 of 15 sites across the tropics trace groundwater recharge to intensive monthly rainfall, commonly exceeding the 70th intensity percentile, approximately ( [[#Jasechko--2015|Jasechko and]] [[#Taylor--2015|Taylor, 2015]] ). Further, heavy rainfall recharging groundwater resources is often influenced by climate variability such as ENSO and PDO (R.G. [[#Taylor--2013|Taylor et al., 2013]] b; [[#Kuss--2014|Kuss and Gurdak, 2014]] ; [[#Asoka--2017|Asoka et al., 2017]] ; [[#Cuthbert--2019b|Cuthbert et al., 2019b]] ; [[#Kolusu--2019|Kolusu et al., 2019]] ; [[#Shamsudduha--2020|Shamsudduha and Taylor, 2020]] ). Additionally, increases in groundwater storage estimated from GRACE for 37 of the world’s large-scale aquifer systems from 2002 to 2016 are generally found to result from episodic recharge associated with extreme (&gt;90th percentile) annual precipitation.

The overall underestimation of precipitation intensities in global climate models ( [[#Wehner--2010|Wehner et al., 2010]] , 2020; [[#Goswami--2017|Goswami and Goswami, 2017]] ) and of their sensitivity to warming temperatures ( [[#Borodina--2017|Borodina et al., 2017]] ) may lead to underestimates of their recharging effect on groundwater ( [[#Mileham--2009|Mileham et al., 2009]] ; [[#Cuthbert--2019b|Cuthbert et al., 2019b]] ). The limited ability of global climate models to represent key controls on regional rainfall variability like ENSO (Technical ( [[IPCC:Wg1:Chapter:Annex-vi|Annex VI]] and [[IPCC:Wg1:Chapter:Chapter-3#3.7.3|Section 3.7.3]] ; R. [[#Chen--2020|]] [[#Chen--2020|Chen et al., 2020]] ) may also underestimate observed recharge from such events that are of particular importance in drylands (R.G. Taylor et al. , 2013b; Cuthbert et al. , 2019b) . Numerical representations of the impact of precipitation intensification on groundwater recharge in large-scale models remain constrained by the challenges of including key recharge pathways that consider preferential flowpaths in soils ( [[#Beven--2018|Beven, 2018]] ) and focused recharge through leakage from surface waters ( [[#Döll--2014|Döll et al., 2014]] ).

Increasing global freshwater withdrawals, primarily associated with the expansion of irrigated agriculture in drylands, have led to global groundwater depletion that has an estimated range of about 100 and about 300 km <sup>3</sup> yr <sup>–1</sup> from hydrological models and volumetric-based calculations ( [[#Bierkens--2019|Bierkens and Wada, 2019]] ). The magnitude of this change is such that its estimated contribution to global sea level rise is in the order of 0.3 to 0.9 mm yr <sup>−1</sup> (Wada et al. , 2010; [[#Konikow--2011|Konikow, 2011]] ; Döll et al. , 2014; Pokhrel et al. , 2015; de Graaf et al. , 2017; Hanasaki et al. , 2018) . Groundwater depletion has been observed regionally in The USA High Plains, California’s Central Valley ( [[#Scanlon--2012|Scanlon et al., 2012]] ), north-west India (Rodell et al. , 2009; Asoka et al. , 2017), Upper Ganges in India ( [[#MacDonald--2016|MacDonald et al., 2016]] ), North China Plain ( [[#Feng--2013|Feng et al., 2013]] ), north-central Middle East region of Tigris–Euphrates–Western Iran ( [[#Voss--2013|Voss et al., 2013]] ), Central Asia ( [[#Hu--2019|Hu et al., 2019]] ), and North Africa ( [[#Bouchaou--2013|Bouchaou et al., 2013]] ). The regional contribution of agricultural irrigation to groundwater depletion was previously highlighted by SRCCL but no formal assessment of observed changes in global or regional groundwater featured in AR5.

Quantification of changes in groundwater storage from GRACE is currently constrained by uncertainty in the estimation of changes in other terrestrial water stores using uncalibrated, global-scale Land Surface Models (Döll et al. , 2014; Scanlon et al. , 2018) and the limited duration of the period of GRACE observations (2002 to 2016). Centennial-scale piezometry in north-west India reveals that recent groundwater depletion traced by GRACE ( [[#Rodell--2009|Rodell et al., 2009]] ; [[#Chen--2014|Chen et al., 2014]] ), follows more than a century of groundwater accumulation through canal leakage ( [[#MacDonald--2016|MacDonald et al., 2016]] ). Further, groundwater depletion is often localized occurring below the footprint (200,000 km <sup>2</sup> ) of GRACE, as has been well demonstrated by detailed modelling studies in the California Central Valley ( [[#Scanlon--2012|Scanlon et al., 2012]] ) and North China Plain ( [[#Cao--2016|Cao et al., 2016]] ).

Climate variability and drought affect groundwater depletion mainly due to amplified groundwater withdrawals. For instance, the depletion rate in Central Valley aquifer in the USA from 2006 to 2010 is estimated to range from 6 to 8 km <sup>3</sup> yr <sup>–1</sup> using GRACE data ( [[#Scanlon--2012|Scanlon et al., 2012]] ). In India, [[#Asoka--2017|Asoka et al. (2017)]] show contrasting trends in groundwater storage in the north (declining at 2 cm yr <sup>–1</sup> ) and south (increasing at 1–2 cm yr <sup>–1</sup> ) that is explained by variations in human withdrawals and precipitation linked to Indian Ocean sea surface temperature variability.

Changes in meltwater regimes from glaciers and seasonal snow packs tend to reduce the seasonal duration and magnitude of recharge ( [[#Tague--2009|Tague and Grant, 2009]] ). Aquifers in mountain valleys show shifts in the timing and magnitude of: (i) peak groundwater levels due to an earlier spring melt; and (ii) low groundwater levels associated with lower baseflow periods ( [[#Allen--2010|Allen et al., 2010]] ; [[#Dierauer--2018|Dierauer et al., 2018]] ; [[#Hayashi--2020|Hayashi, 2020]] ). The effects of receding alpine glaciers on groundwater systems are not well understood but long-term loss of glacier storage is estimated to reduce summer baseflow ( [[#Gremaud--2009|Gremaud et al., 2009]] ). In permafrost regions, coupling between surface water and groundwater systems may be particularly enhanced by warming ( [[#Lamontagne-Hallé--2018|Lamontagne-Hallé et al., 2018]] ; [[#Lemieux--2020|Lemieux et al., 2020]] ). In areas of seasonal or perennial ground frost, increased recharge is expected despite a decrease in absolute snow volume (Okkonen and Kløve, 2011; Walvoord and Kuryl yk, 2016) .

Coastal aquifers are the interface between the oceanic and terrestrial hydrological systems. Global sea level rise (SLR) causes interfaces between freshwater and saline-water to move inland. The extent of seawater intrusion into coastal aquifers depends on a variety of factors including coastal topography, recharge, and groundwater abstraction from coastal aquifers ( [[#Comte--2016|Comte et al., 2016]] ). Modelling results suggest that the impact of SLR on seawater intrusion is negligible compared to that of groundwater abstraction (Ferguson and Gleeson, 2012; [[#Yu--2019|Yu and Michael, 2019]] ) . Coastal aquifers under very low hydraulic gradients, such as the Asian mega-deltas, are theoretically sensitive to SLR but, according to evidence from [[#Akter--2019|Akter et al. (2019)]] in the Ganges-Brahmaputra-Megna basin, may be more severely and widely affected by changes in upstream river discharge. They argue further that saltwater inundation from storm surges will have the greatest localized effects.

In summary, there is ''medium confidence'' that increased precipitation intensities, partly due to human influence, have enhanced groundwater recharge, most notably in the tropics. There is ''high confidence'' that groundwater depletion has occurred since at least the start of the 21st century as a consequence of groundwater withdrawals for irrigation in some of the world’s most productive agricultural areas in drylands (e.g., southern High Plains and California Central Valley in the USA, the North China Plain, north-west India).

<div id="8.3.2" class="h2-container"></div>

<span id="observed-variations-in-large-scale-phenomena-and-regional-variability"></span>
=== 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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<span id="inter-tropical-convergence-zone-and-tropical-rain-belts"></span>
==== 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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<span id="hadley-circulation-and-subtropical-belt"></span>
==== 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'' ).

<div id="8.3.2.3" class="h3-container"></div>

<span id="walker-circulation"></span>
==== 8.3.2.3 Walker Circulation ====

<div id="h3-20-siblings" class="h3-siblings"></div>

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.

<div id="8.3.2.4" class="h3-container"></div>

<span id="monsoons"></span>
==== 8.3.2.4 Monsoons ====

<div id="h3-21-siblings" class="h3-siblings"></div>

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.

<div id="_idContainer035" class="Basic-Text-Frame"></div>

[[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).

<div id="8.3.2.4.1" class="h4-container"></div>

<span id="south-and-south-east-asian-monsoon"></span>
===== 8.3.2.4.1 South and South East Asian Monsoon =====

<div id="h4-5-siblings" class="h4-siblings"></div>

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'' ).

<div id="8.3.2.4.2" class="h4-container"></div>

<span id="east-asian-monsoon"></span>
===== 8.3.2.4.2 East Asian Monsoon =====

<div id="h4-6-siblings" class="h4-siblings"></div>

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]] ).

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<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.

<div id="8.3.2.9" class="h3-container"></div>

<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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<span id="extratropical-modes"></span>
===== 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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