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

=== 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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===== 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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===== 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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===== 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]] ).

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[[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).

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