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

== 8.2 Why Should We Expect Water Cycle Changes? ==

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It is well understood that global precipitation and evaporation changes are determined by Earth’s energy balance ( [[#8.2.1|Section 8.2.1]] ). At regional scales smaller than about 4000 km, water cycle changes become dominated by the transport of moisture ( [[#Dagan--2019a|Dagan et al., 2019a]] ; [[#Jakob--2019|Jakob et al., 2019]] ; [[#Dagan--2020|Dagan and Stier, 2020]] ), which depend on both thermodynamic and dynamical processes ( [[#8.2.2|Section 8.2.2]] ). The constraints of energy budgets at global scales and moisture budgets at regional scales cause key water cycle characteristics such as precipitation intensity, duration and intermittence to alter as the climate warms ( [[#Pendergrass--2014b|Pendergrass and Hartmann, 2014b]] ; [[#Döll--2018|Döll et al., 2018]] ). Future water availability is also determined by changes in evaporation, which is driven by a general increase in the atmospheric evaporative demand ( [[#Scheff--2014|Scheff and Frierson, 2014]] ) and modulated by vegetation controls on evaporative losses ( [[#Milly--2016|Milly and Dunne, 2016]] ; Lemordant et al. , 2018; Vicente-Serrano et al. , 2020) . At regional scales, water cycle changes result from the interplay between multiple potential drivers (CO <sub>2</sub> , aerosols, land use change and human water use; [[#8.2.3|Section 8.2.3]] ). This section assesses advances in physical understanding of global to regional drivers of water cycle changes.

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=== 8.2.1 Global Water Cycle Constraints ===

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The Clausius–Clapeyron equation determines that low-altitude specific humidity increases by about 7% °C <sup>–1</sup> of warming, assuming that relative humidity remains constant, which is approximately true at a global scale but not necessarily valid regionally. It is ''very likely'' that near surface specific humidity has increased since the 1970s ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1|Section 2.3.1]] ) and total atmospheric water vapour content (precipitable water) is ''very likely'' to increase at close to a thermodynamic rate on average globally with continued warming. Different radiative forcing mechanisms lead to some variation in the global mean thermodynamic response by altering the relative humidity distribution: the rate of global precipitable water increase with global surface temperature ranges <sup>[[#footnote-000|2]]</sup> from 6.4 ± 1.5% °C <sup>–1</sup> for sulphate aerosol-induced changes to 9.8 ± 3.3% °C <sup>–1</sup> for black carbon-induced changes based on idealized modelling ( [[#Hodnebrog--2019b|Hodnebrog et al., 2019b]] ). Specific humidity increases at a lower rate over land due to decreasing relative humidity ( [[#Collins--2013|Collins et al., 2013]] ) as corroborated by observations and simple models ( [[#Byrne--2018|Byrne and O’Gorman, 2018]] ). Prevalent increases in atmospheric water vapour drive powerful amplifying feedbacks ( [[IPCC:Wg1:Chapter:Chapter-7#7.4.2.2|Section 7.4.2.2]] ), intensify atmospheric moisture transport and heavy precipitation events ( [[#8.2.3.2|Section 8.2.3.2]] ), and alter the surface and atmospheric energy balance, thereby influencing global evaporation and precipitation changes (Figure 8.3).

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[[File:1537bbc4440ef91944d125cb5825462b IPCC_AR6_WGI_Figure_8_3.png]]

'''Figure 8.3 |''' '''Schematic representation of fast and slow responses of the atmospheric energy balance and global precipitation to radiative forcing.''' ( '''‘Baseline’''' panel) The atmospheric energy budget: '''(1)''' responds instantaneously to radiative forcings; '''(2)''' leading to rapid atmospheric adjustments; and '''(3)''' slower semi‐rapid adjustments involving the land surface and vegetation that further modify atmospheric circulation patterns. '''(4)''' This slow precipitation response to global mean surface air temperature is quantified as '''(a)''' the hydrological sensitivity, η , and the total precipitation response, including initial rapid adjustments, is termed the apparent hydrological sensitivity, η <sub>a</sub> . '''(b–d)''' The slow precipitation response over land and ocean develops over time. Large, filled arrows (in panels from ‘baseline’ to 4) depict fluxes or circulation change while small arrows (1–4) denote increases ( ↑ ) or decreases ( ↓ ) in variables (P is precipitation; L is atmospheric longwave radiative cooling; S is solar radiation absorption by the atmosphere; H is sensible heat flux; E is surface evaporative heat flux; and T is temperature). Adapted from [[#Allan--2020|Allan et al. (2020)]] with statistics taken from Figures 7.2 and Figure 8.1.

While thermodynamics exert a strong control on water vapour changes, global mean precipitation and evaporation are constrained by the balance of energy fluxes in the atmosphere and at the surface (Figure 8.3). Global mean precipitation increases of 1 – 3% per 1 °C of warming, as estimated in AR5 (Collins et al., 2013), are explained as a combination of rapid (or fast) atmospheric adjustments and slow temperature-driven responses (Figure 8.3, panels 1 – 4) to radiative forcings (Andrewset al., 2010; [[#Bala--2010|Bala et al., 2010]] ; [[#Cao--2012|Cao et al., 2012]] ). Fast atmospheric adjustments are caused by near-instantaneous (hours to days) changes in the atmospheric energy budget (Figure 8.3, panels 1–3) and atmospheric properties (e.g., temperature, clouds and water vapour) in direct response to the radiative effects of a forcing agent (Sherwoodet al., 2015). A further relatively fast (days to months) adjustment of the climate system involves interactions with vegetation and land surface temperature (Figure 8.3, panel 3), which respond more rapidly than ocean temperature to a radiative forcing ( [[#Cao--2012|Cao et al., 2012]] ; [[#Dong--2014|Dong et al., 2014]] ). The slower temperature-dependent precipitation response is driven by the increased atmospheric radiative cooling rate of a warming atmosphere. Warming drives increases in precipitation intensity while frequency is dominated by rapid atmospheric adjustments to the radiative forcing based on ''abrupt 4×CO'' 2 CMIP6 simulations ( [[#Douville--2021|Douville and John, 2021]] ). Since AR5, many new studies applying the dual rapid adjustment and slow response framework show that global precipitation responses to different forcing agents are physically well understood (Fläschner et al. , 2016; MacIntosh et al. , 2016; Samset et al. , 2016; Myhre et al. , 2018a) . Further confidence in the coupled processes involved are provided by simple models representing the energy budget and thermodynamic constraints that limit global mean evaporation to around 1.5% °C <sup>–1</sup> ( [[#Siler--2019|Siler et al., 2019]] ). This strengthens the physical link between energy budget and thermodynamic drivers of the global water cycle ( [[#8.2.2.1|Section 8.2.2.1]] ).

'''Hydrological sensitivity''' ( η ''')''' is defined as the linear change in global mean precipitation with global surface air temperature (GSAT) once rapid adjustments of the hydrological cycle to radiative forcings have occurred (Figure 8.3a). There is robust understanding and ''high agreement'' across idealized CO <sub>2</sub> forcing CMIP5 and CMIP6 experiments ( [[#Fläschner--2016|Fläschner et al., 2016]] ; [[#Samset--2018b|Samset et al., 2018b]] ; [[#Pendergrass--2020b|Pendergrass, 2020b]] ) that η = 2.1 – 3.1% °C <sup>–1</sup> (Figure 8.4). The magnitude of η depends primarily on atmospheric net radiative cooling which is controlled by thermal deepening of the troposphere ( [[#Jeevanjee--2018|Jeevanjee and Romps, 2018]] ) and limited by surface evaporation and consequent atmospheric latent heat release and warming ( [[#Webb--2018|Webb et al., 2018]] ). Climate feedbacks (e.g., temperature lapse rate and clouds) that vary across models (Sections 7.4 and 3.8.2) also modulate the magnitude of η (O’Gorman et al. , 2012; Fläschner et al. , 2016; T.B. Richardson et al. , 2018a) . Uncertainty in η across CMIP5 models relating to deficiencies in representing low‐altitude cloud feedbacks ( [[#Watanabe--2018|Watanabe et al., 2018]] ) and absorption of shortwave radiation by atmospheric water vapour ( [[#DeAngelis--2015|DeAngelis et al., 2015]] ) do not apply well to CMIP6 simulations, the latter improvement explained by more accurate radiative transfer modelling ( [[#Pendergrass--2020b|Pendergrass, 2020b]] ).

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[[File:459d5d090df716b9c18561b10fd8ec52 IPCC_AR6_WGI_Figure_8_4.png]]

'''Figure 8.4 |''' '''Estimate''' ( '''5–95''' '''% range) of the increase in precipitation and its extremes with global mean surface warming.''' Global time-averaged precipitation changes '''(left)''' are based on responses to increasing CO <sub>2</sub> (apparent hydrological sensitivity, η <sub>a</sub> ) and the temperature-dependent component (hydrological sensitivity, η ), both of which are based on GCM experiments; the land (L) and ocean (O) components ( [[#Fläschner--2016|Fläschner et al., 2016]] ; [[#Richardson--2018|T.B. Richardson et al., 2018]] a; [[#Samset--2018a|Samset et al., 2018a]] ; [[#Pendergrass--2020b|Pendergrass, 2020b]] ; [[#Rehfeld--2020|Rehfeld et al., 2020]] ) and observational estimates (GPCP/HadCRUTv4.6) use trends (1988–2014) as a proxy for η <sub>a</sub> and interannual variability as a proxy for η , with 90% confidence range accounting for statistical uncertainty only ( [[#Adler--2017|Adler et al., 2017]] ; [[#Allan--2020|Allan et al., 2020]] ). For extreme precipitation, assessment is for 24 hour, 99.9th percentile or annual maximum extremes from GCMs ( [[#Fischer--2015|Fischer and Knutti, 2015]] ; [[#Pendergrass--2015|Pendergrass et al., 2015]] ; [[#Borodina--2017|Borodina et al., 2017]] ; [[#Pfahl--2017|Pfahl et al., 2017]] ; [[#Sillmann--2017|Sillmann et al., 2017]] ), regional climate models (RCMs) ( [[#Bao--2017|Bao et al., 2017]] ), an observationally-constrained tropical estimate ( [[#O’Gorman--2012|O’Gorman, 2012]] ) and estimates from observed changes ( [[#Westra--2013|Westra et al., 2013]] ; [[#Donat--2016|Donat et al., 2016]] ; [[#Borodina--2017|Borodina et al., 2017]] ; [[#Zeder--2020|Zeder and Fischer, 2020]] ; [[#Sun--2021|Sun et al., 2021]] ). For hourly and sub-hourly extremes observed changes ( [[#Barbero--2017|Barbero et al., 2017]] ; [[#Guerreiro--2018|Guerreiro et al., 2018]] ) and high-resolution models, including RCM and cloud-resolving models (CRMs) are assessed ( [[#Ban--2015|Ban et al., 2015]] ; [[#Prein--2017|Prein et al., 2017]] ; [[#Haerter--2018|Haerter and Schlemmer, 2018]] ; [[#Hodnebrog--2019a|Hodnebrog et al., 2019a]] ; [[#Lenderink--2019|Lenderink et al., 2019]] ). Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).

Observed estimates of hydrological sensitivity ( η = 3.2 ± 0.8% °C <sup>–1</sup> ) based on interannual variability (Allan et al.,2020) or responses to El Niño–Southern Oscillation (ENSO) of 9% °C <sup>–1</sup> ( [[#Adler--2017|Adler et al., 2017]] ) are not suitable to assess the magnitude of η '''(Figure 8.4)''' . This is because these relationships depend on amplifying feedbacks associated with ENSO-related cloud changes (G.L. [[#Stephens--2018|]] [[#Stephens--2018|Stephens et al., 2018]] ) that may not be relevant for longer term climate change. However, there is ''robust evidence'' and ''high agreement'' across observations, modelling and supporting physics that precipitation increases at a lower % °C <sup>–1</sup> rate than water vapour content in the global mean ( [[#Held--2006|Held and Soden, 2006]] ; [[#Collins--2013|Collins et al., 2013]] ; [[#Allan--2020|Allan et al., 2020]] ), implying an increased residence time of atmospheric water vapour (Hodnebrog et al., 2019b; [[#Dijk--2020|Dijk et al., 2020]] ). Increasing globalprecipitation, evaporation and moisture fluxes with warming thereby drive an intensification but not acceleration of the global water cycle (Sections 8.3.1.1 and 8.4.1.1).

The overall global mean rate of precipitation change per 1 °C of GSAT increase, '''apparent hydrological sensitivity''' ( η a ''')''' , is reduced compared to hydrological sensitivity by the direct influence of radiative forcing agents on the atmospheric energy balance. Rapid atmospheric adjustments that alter precipitation are primarily caused by GHGs and absorbing aerosols, with ''high agreement'' and ''medium evidence'' across idealized simulations (Fläschner et al.,2016; [[#Samset--2016|Samset et al., 2016]] ). A range of rapid precipitation adjustments to CO <sub>2</sub> between models are also attributed to vegetation responses leading to a re-partitioning of surface latent and sensible heat fluxes (DeAngelis et al.,2016). Values obtained from six CMIP5 models simulating the Last Glacial Maximum (LGM; 21,000–19,000 years ago) and pre-industrial period ( η a ''='' 1.6 – 3.0% <sup></sup> °C <sup>–1</sup> ) are larger than for each corresponding ''abrupt 4×CO'' 2 experiment ( η a ''='' 1.3–2.6% °C <sup>–1</sup> ) due to differences in the mix of forcings, vegetation and land surface changes and a higher thermodynamic % °C <sup>–1</sup> evaporation scaling in the colder state (Figure 8.4, [[#8.4.1.1|Section 8.4.1.1]] ; G. [[#Li--2013|]] [[#Li--2013|Li et al., 2013]] ). Updated estimates across comparable experiments from 22 CMIP5/CMIP6 models ( [[#Rehfeld--2020|Rehfeld et al., 2020]] ) display a consistent range ( η a ''='' 1.7 ± 0.6% °C <sup>–1</sup> ). Confirming η a in observations (Figure 8.4) is difficult due to measurement uncertainty, varying rapid adjustments to radiative forcing and unforced variability ( [[#Dai--2019|Dai and Bloecker, 2019]] ; [[#Allan--2020|Allan et al., 2020]] ).

Climate drivers that instantaneously affect the surface much more than the atmospheric energy budget (such as solar forcing and sulphate aerosol) produce only a small rapid adjustment of the global water cycle and therefore larger η a than drivers that immediately modulate the atmospheric energy budget such as GHGs and absorbing aerosol ( [[#Salzmann--2016|Salzmann, 2016]] ; Samset et al. , 2016; Lin et al. , 2018; F. Liu et al. , 2018) . Thus, global precipitation appears more sensitive to radiative forcing from sulphate aerosols (2.8 ± 0.7% °C <sup>–1</sup> ; η a '''≈''' η ) than GHGs (1.4 ± 0.5% °C <sup>–1</sup> ; η a '''&lt;''' η ) while the response to black carbon aerosol can be negative ( – 3.5 ± 5.0% °C <sup>–1</sup> ; η a '''&lt;&lt;''' η ) due to strong atmospheric solar absorption ( [[#Samset--2016|Samset et al., 2016]] ). Therefore, artificially reducing surface-absorbed sunlight through solar radiation modification strategies to mitigate GHG warming will not mitigate precipitation changes (see Sections 4.6.3.3, 6.4.7 and 8.6.3). Aerosol-induced precipitation changes depend upon the type of aerosol species and their spatial distribution. Global mean precipitation increases after complete removal of present-day anthropogenic aerosol emissions (see also [[IPCC:Wg1:Chapter:Chapter-4#4.4.4|Section 4.4.4]] ) in four different climate models ( η a = 1.6 – 5.5% °C <sup>–1</sup> ) are mainly attributed to sulphate aerosol as opposed to other aerosol species ( [[#Samset--2018b|Samset et al., 2018b]] ). Idealized modelling studies show that sulphate aerosol increases over Europe produce a larger global precipitation response than an equivalent increase in aerosol burden or radiative forcing overAsia, explained by differences in cloud climatology and cloud-aerosol interaction ( [[#Kasoar--2018|Kasoar et al., 2018]] ; L. [[#Liu--2018|]] [[#Liu--2018|]] [[#Liu--2018|]] [[#Liu--2018|Liu et al., 2018]] ). The vertical profiles of black carbon and ozone further influence the magnitude of the rapid global precipitation response, yet are difficult to observe and simulate ( [[#Allen--2014|Allen and Landuyt, 2014]] ; MacIntosh et al., 2016; [[#Stjern--2017|Stjern et al., 2017]] ; [[#Sand--2020|Sand et al., 2020]] ).

Hydrological sensitivity is generally lower over land but with a large uncertainty range ( η = – 0.1 to 3.0% °C <sup>–1</sup> ) relative to the oceans ( η = 2.3 to 3.3% °C <sup>–1</sup> ) based on multi-model 4 × CO <sub>2</sub> CMIP6simulations ( [[#Pendergrass--2020b|Pendergrass, 2020b]] ), broadly consistent with comparable CMIP5 experiments (T.B. Richardson et al. , 2018a; Samset et al. , 2018a) . Suppressed hydrological sensitivity over land (Figures 8.3d and 8.4) is associated with greater warming compared with the oceans, which alters atmospheric circulation and precipitation patterns ( [[#Saint-Lu--2020|Saint-Lu et al., 2020]] ). Also, since oceans supply much of the moisture to fuel precipitation over land, the slower ocean warming rate means there is insufficient moisture supplied to maintain continental relative humidity levels ( [[#Byrne--2018|Byrne and O’Gorman, 2018]] ), which can inhibit convection (J. [[#Chen--2020|]] [[#Chen--2020|Chen et al., 2020]] a). Land surface feedbacks involving soil-vegetation-atmosphere coupling further drive continental drying ( [[#Berg--2016|Berg et al., 2016]] ; [[#Kumar--2016|Kumar et al., 2016]] ; [[#Chandan--2020|Chandan and Peltier, 2020]] ). The suppressed hydrological sensitivity is counteracted by rapid precipitation responses in most GHG-forced simulations, explained by increases in surface downward longwave radiation due to CO <sub>2</sub> increases that rapidly warm the land, destabilize the troposphere and strengthen vertical motion in the short term ( [[#Chadwick--2014|Chadwick et al., 2014]] ; T.B. Richardson et al., 2016, 2018a). There is medium understanding of how land–sea warming contrast governs rapid precipitation responses based on idealized modelling that shows similar spatial patterns of precipitation response to radiative forcing from GHGs, solar forcing and absorbing aerosols ( [[#Xie--2013|Xie et al., 2013]] ; [[#Samset--2016|Samset et al., 2016]] ; [[#Kasoar--2018|Kasoar et al., 2018]] ). Rapid precipitation adjustments to CO <sub>2</sub> have been counteracted by cooling from anthropogenic aerosol increases over land (Box 8.1) but this compensation is expected to diminish as aerosol forcing declines ( [[#Richardson--2018|T.B. Richardson et al., 2018]] a). Thefast and slow precipitation responses over global land combine on average during transient climate change (Figure 8.3d). This explains a consistent land and ocean mean precipitation increase in projections (Table 4.3) but this is determined by a complex and model-dependent evolution of continental water cycle changes over space and time.

Increases in global precipitation over time, as the climate warms, are partly offset by the overall cooling effects of anthropogenic aerosol and by rapid atmospheric adjustments to increases in GHGs and absorbing aerosol. This explains why multi-decadal trends in global precipitation responses in the satellite era ( [[#Adler--2017|Adler et al., 2017]] ; [[#Allan--2020|Allan et al., 2020]] ) are small and difficult to interpret given observational uncertainty, internal variability and volcanic forcings. The delayed warming effect of rising CO <sub>2</sub> concentration, combined with declining aerosol cooling, are expected to increase the importance of the slow temperature-related effects on the energy budget relative to the more rapid direct radiative forcing effects as transient climate change progresses ( [[#Shine--2015|Shine et al., 2015]] ; [[#Salzmann--2016|Salzmann, 2016]] ; [[#Myhre--2018b|Myhre et al., 2018b]] ).

In summary, there is ''high confidence'' that global mean evaporation and precipitation increase with global warming, but the estimated rate is model-dependent ( ''very likely'' range of 1 – 3 % °C <sup>–1</sup> ). The global increase in precipitation is determined by a robust response to global surface temperature only ( ''very likely'' 2–3% °C <sup>–1</sup> ) that is partly offset by fast atmospheric adjustments to the vertical profile of atmospheric heating by GHGs and aerosols. Global precipitation increases due to GHGs are offset by the well-understood overall surface radiative cooling effect by aerosols ( ''high confidence'' ). Over land, the average warming-related increase in precipitation is expected to be smaller than over the ocean due to increasing land – ocean thermal contrast and surface feedbacks, but the overall precipitation increase over land is generally reinforced by fast atmospheric responses to GHGs that strengthens convergence of winds ( ''medium confidence'' ). Global mean precipitation and evaporation increase at a lower rate than atmospheric moisture per 1°C of global warming ( ''high confidence'' ), leading to longer water vapour lifetime in the atmosphere and driving changes in precipitation intensity, duration and frequency and an overall intensification but not acceleration of the global water cycle.

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=== 8.2.2 Constraints on the Regional Water Cycle ===

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==== 8.2.2.1 Thermodynamic Constraints on Atmospheric Moisture Fluxes ====

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A warming climate drives increases in atmospheric moisture and horizontal moisture transport from the divergent to the convergent portions of the atmospheric circulation (including storm systems, the tropical rain belt and monsoons) that on average amplifies existing precipitation minus evaporation (P–E) patterns ( [[#Held--2006|Held and Soden, 2006]] ). Increased latent heat transports in high latitudes also contribute to polar amplification of warming ( [[IPCC:Wg1:Chapter:Chapter-7#7.4.4.1|Section 7.4.4.1]] ). Although convergent parts of the atmospheric circulation are expected to become wetter (in terms of increasing P–E) and net evaporative regions drier (increasing E–P) these regions are not geographically and seasonally fixed and their location and timing are expected to alter ( [[#8.2.2.2|Section 8.2.2.2]] ). Atmospheric and ocean circulation changes overall decrease the amplification of P–E and salinity patterns. Paleoclimate evidence confirms that during the LGM zonal mean changes were roughly in agreement with thermodynamic expectations (G. [[#Li--2013|]] [[#Li--2013|Li et al., 2013]] ). However regional changes can be dominated by dynamics, including responses to the large Northern Hemisphere (NH) ice sheets (DiNezio and Tierney, 2013; T. [[#Bhattacharya--2017|]] [[#Bhattacharya--2017|Bhattacharya et al., 2017]] ; [[#Scheff--2017|Scheff et al., 2017]] ; [[#D’Agostino--2019|D’Agostino et al., 2019]] ; [[#Lowry--2019|Lowry and Morrill, 2019]] ) such that altered P–E patterns are not well described by thermodynamic drivers (Osteret al., 2015; [[#Lora--2018|Lora, 2018]] ; [[#Morrill--2018|Morrill et al., 2018]] ).

There is ''robust evidence'' and ''high agreement'' across thermodynamics, detailed modelling and observations that amplification of P–E patterns occurs over the oceans (Figure 8.5a) with an associated ‘fresh gets fresher, salty gets saltier’ signature in ocean salinity (Sections 2.3.3.2 and 3.5.2). This amplification is moderated by proportionally larger increases in subtropical ocean evaporation and weakening of the tropical circulation ( [[#8.2.2.2|Section 8.2.2.2]] ), an expectation supported by observations (Skliriset al., 2016) and process understanding (Yang andRoderick, 2019). Thermodynamics explain a smaller low latitude evaporation increase (1% °C <sup>–1</sup> ) than in high latitudes (5% °C <sup>–1</sup> ) with changes in surface radiation, boundary layer adjustments and ocean heat uptake playing a secondary role, based on idealized modelling ( [[#Siler--2019|Siler et al., 2019]] ). Increased evaporation from warmer oceans and lakes is exacerbated by the loss of surface ice in some regions (Bintanja and Selten, 2014; [[#Laîné--2014|Laîné et al., 2014]] ; W. [[#Wang--2018|]] [[#Wang--2018|]] [[#Wang--2018|]] [[#Wang--2018|]] [[#Wang--2018|]] [[#Wang--2018|Wang et al., 2018]] ; [[#Sharma--2019|Sharma et al., 2019]] ; [[#Woolway--2020|Woolway et al., 2020]] ). This can generate a more local moisture source for precipitation, for example in north-west Greenland during non-summer months since the 1980s ( [[#Nusbaumer--2019|Nusbaumer et al., 2019]] ), though moisture transport changes can counteract this effect ( [[#Nygård--2020|Nygård et al., 2020]] ). Ocean stratification due to heating of the upper layers through radiative forcing has been identified as a mechanism that further amplifies surface salinity patterns beyond the responses driven by water cycle changes alone (Zika et al., 2018) .

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'''Figure 8.5 |''' '''Zonally-averaged annual mean changes in precipitation minus evaporation (P–E) over (a) ocean and (b) land between the historical''' ( '''1995–2014''' ''') and''' '''SSP2-4.5''' ( '''2081–2100''' ''') CMIP6 simulations (blue lines, an average of the CanESM5 and MRI-ESM2-0 models).''' Dashed lines show estimated P–E changes using a simple thermodynamic scaling ( [[#Held--2006|Held and Soden, 2006]] ); dotted lines show estimates using an extended scaling ( [[#Byrne--2016|Byrne and O’Gorman, 2016]] ). All curves have been smoothed in latitude using a three grid-point moving-average filter. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).

Since AR5, numerous studies have confirmed that changes in P–E with warming over land cannot be interpreted simply as a ‘wet regions get wetter, dry regions get drier’ response ( [[#Chadwick--2013|Chadwick et al., 2013]] ; [[#Greve--2014|Greve et al., 2014]] ; [[#Roderick--2014|Roderick et al., 2014]] ; [[#Byrne--2015|Byrne and]] [[#O’Gorman--2015|O’Gorman, 2015]] ; [[#Scheff--2015|Scheff and Frierson, 2015]] ). Firstly, P–E is a simplistic diagnostic of the water cycle that inadequately describes ‘dryness’ or aridity ( [[#Fu--2014|Fu and Feng, 2014]] ; [[#Roderick--2014|Roderick et al., 2014]] ; [[#Greve--2015|Greve and Seneviratne, 2015]] ; [[#Scheff--2015|Scheff and Frierson, 2015]] ; [[#Greve--2019|Greve et al., 2019]] ; [[#Vicente-Serrano--2020|Vicente-Serrano et al., 2020]] ). Secondly, terrestrial P–E is generally positive and balanced by surface runoff and percolation into subsurface soils and aquifers (Figure 8.1). As a result, the simple thermodynamic scaling (Figure 8.5b) predicts that P–E over land will become more positive (wetter) with warming ( [[#Greve--2014|Greve et al., 2014]] ; [[#Roderick--2014|Roderick et al., 2014]] ; [[#Byrne--2015|Byrne and]] [[#O’Gorman--2015|O’Gorman, 2015]] ). This is not necessarily true, however, in the dry seasons and regions where terrestrial water is lost to the atmosphere and exported ( [[#Sheffield--2013|Sheffield et al., 2013]] ; [[#Kumar--2015|Kumar et al., 2015]] ; [[#Keune--2019|Keune and Miralles, 2019]] ). Thirdly, regional P–E patterns over land are affected by changes in atmospheric circulation, oceanic moisture supply and land surface feedbacks. As the land warms more than oceans, spatial gradients in temperature and relative humidity influence moisture supply and reduce P–E over some land regions, such as southern Chile and Argentina around 30°S – 50°S as captured by an extended thermodynamic scaling (Figure 8.5b). Drying of soils can be amplified by vegetation responses ( [[#Berg--2016|Berg et al., 2016]] ; [[#Byrne--2016|Byrne and O’Gorman, 2016]] ; [[#Lambert--2017|Lambert et al., 2017]] ) but limited by atmospheric circulation feedbacks ( [[#Zhou--2021|Zhou et al., 2021]] ). Changes in soil moisture and rainfall intensity (Sections 8.2.3.2 and 8.2.3.3) can alter the partitioning of precipitation between evaporation and runoff, further complicating terrestrial P–E responses ( [[#Short%20Gianotti--2020|Short Gianotti et al., 2020]] ).

The strong physical basis for regionally and seasonally dependent responses of P–E and the expectation for an increasing contrast between wet and dry seasons and weather regimes is supported by ''high agreement'' across multiple observational and CMIP5/CMIP6 modelling studies ( [[#Liu--2013|Liu and Allan, 2013]] ; [[#Kumar--2015|Kumar et al., 2015]] ; [[#Polson--2017|Polson and Hegerl, 2017]] ; [[#Ficklin--2019|Ficklin et al., 2019]] ; [[#Deng--2020|Deng et al., 2020]] ; [[#Schurer--2020|Schurer et al., 2020]] ). Increased moisture transports into storm systems, monsoons and high latitudes increase the intensity of wet events ( [[#8.2.3.2|Section 8.2.3.2]] ), while stronger atmospheric evaporative demand with warming ( [[#Scheff--2014|Scheff and Frierson, 2014]] ; [[#Vicente-Serrano--2018|Vicente-Serrano et al., 2018]] ; [[#Cook--2019|Cook et al., 2019]] ) is an important mechanism for intensifying dry events ( [[#8.2.3.3|Section 8.2.3.3]] ) and decreasing soil moisture over many subtropical land regions. However, aridification is modulated regionally by poleward migration of the subtropical dry zones and an increasing land – ocean temperature contrast that drives declining relative humidity ( [[#8.2.2.2|Section 8.2.2.2]] ).

To summarize, increased moisture transport from evaporative oceans to high precipitation regions of the atmospheric circulation will drive amplified P–E and salinity patterns over the ocean ( ''high confidence'' ) while more complex regional changes are expected over land. Greater warming over land than ocean alters atmospheric circulation patterns and on average reduces continental near-surface relative humidity which along with vegetation feedbacks can contribute to regional decreases in precipitation ( ''high confidence'' ). Based on an improved understanding of thermodynamic drivers since AR5 and multiple lines of evidence, there is ''high confidence'' that very wet or dry seasons and weather patterns will intensify in a warming climate such that wet spells become wetter and dry spells drier.

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==== 8.2.2.2 Large-scale Responses in Atmospheric Circulation Patterns ====

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Responses of the large-scale atmospheric circulation to a warming climate are not as well understood as thermodynamic drivers ( [[#Shepherd--2014|Shepherd, 2014]] ). The AR5 identified robust features including a weakening and broadening of tropical circulation with poleward movement of tropical dry zones and mid-latitude jets ( [[#Collins--2013|Collins et al., 2013]] ). These can dominate regional water cycle changes, affecting the availability of freshwater and the occurrence of climate extremes. Atmospheric circulation changes generally dominate the spatial pattern of rapid precipitation adjustments ( [[#8.2.1|Section 8.2.1]] ) to different forcing agents in the tropics ( [[#Bony--2013|Bony et al., 2013]] ; [[#He--2015|He and Soden, 2015]] ; [[#Richardson--2016|T.B. Richardson et al., 2016]] , 2018a; [[#Tian--2017|Tian et al., 2017]] ; X. [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|Li et al., 2018]] ). Radiative forcing with heterogeneous spatial patterns such as ozone and aerosols (including cloud interactions; Section 6.4.1 and Box 8.1) drive substantial responses in regional atmospheric circulation through uneven heating and cooling effects(L. [[#Liu--2018|]] [[#Liu--2018|]] [[#Liu--2018|]] [[#Liu--2018|Liu et al., 2018]] ; [[#Dagan--2019b|Dagan et al., 2019b]] ; [[#Wilcox--2019|Wilcox et al., 2019]] ). Changes in atmospheric circulation are also driven by slower, evolving patterns of warming and associated changes in temperature and moisture gradients ( [[#Bony--2013|Bony et al., 2013]] ; [[#Samset--2016|Samset et al., 2016]] , 2018a; [[#Ceppi--2018|Ceppi et al., 2018]] ; [[#Ma--2018|Ma et al., 2018]] ). There is strong evidence that large regional water cycle changes arise from the atmospheric circulation response to radiative forcings and associated SST pattern evolution but ''low agreement'' in the sign and magnitude ( [[#Chadwick--2016b|Chadwick et al., 2016b]] ). The role of prolonged weather regimes in determining wet and dry extremes is also better understood since AR5 ( [[#Kingston--2015|Kingston and McMecking, 2015]] ; [[#Schubert--2016|Schubert et al., 2016]] ; D. [[#Richardson--2018|Richardson et al., 2018]] ; [[#Barlow--2019|Barlow et al., 2019]] ). Advances in knowledge of expected large-scale dynamical responses of the water cycle are further assessed in this section (see also Figure 8.21).

Long-term weakening of the tropical atmospheric overturning circulation is expected as climate warms in response to elevated CO <sub>2</sub> ( [[#Collins--2013|Collins et al., 2013]] ). A weaker circulation is required to reconcile global mean low-level water vapour increases (around 7% °C <sup>–1</sup> ) with the smaller global precipitation responses of about 1–3% °C <sup>–1</sup> ( [[#8.2.1|Section 8.2.1]] ). The slowdown can occur in both the Hadley and Walker circulations, but occurs preferentially in the Walker circulation in most climate models (Vecchi and Soden, 2007) but this response has been questioned on the basis of model bias in east Pacific SST ( [[#Seager--2019a|Seager et al., 2019a]] ). Weakening is expected to drive P–E decreases over the western Pacific and increases over the eastern Pacific. However, the driving mechanisms for Walker circulation weakening differ to those involved in determining ENSO variability, so it is too simplistic to interpret changes as an El Niño pattern of regional hydrological cycle extremes ( [[#Sohn--2019|Sohn et al., 2019]] ). Internal variability is also capable of temporarily strengthening the Walker circulation ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4.1|Section 2.3.1.4.1]] ; [[#L’Heureux--2013|L’Heureux et al., 2013]] ; [[#Chung--2019|Chung et al., 2019]] ) while regional responses depend on the pattern of warming (Sandeep et al., 2014).

Model simulations show a stronger Pacific Walker circulation during the LGM in response to a cooler climate (consistent with an expected weakening in a warmer climate), but a weaker Indian Ocean east – west circulation in response to the exposure of the Sunda and Sahul shelves due to lowered sea level (DiNezio et al., 2011). The latter effect is detectable in proxies for hydroclimate, as well as salinity and sea surface temperature (DiNezio and Tierney, 2013; [[#DiNezio--2018|DiNezio et al., 2018]] ). More relevant to future warming is the mid-Pliocene period (3 million years ago), the last time the Earth experienced CO <sub>2</sub> levels comparable to present (see Cross-Chapter Box 2.4). Sea surface temperature (SST) reconstructions show a weakening of the Pacific zonal gradient and a pattern of warmth consistent with a weaker Walker cycle response (Corvec and Fletcher, 2017; [[#Tierney--2019|Tierney et al., 2019]] ; [[#McClymont--2020|McClymont et al., 2020]] ). Although the Pliocene SST pattern and wet subtropics contrast with present conditions ( [[#Burls--2017|Burls and Fedorov, 2017]] ), the paleoclimate record strengthens evidence that a warmer climate is associated with a weaker Walker circulation (Cross-Chapter Box 2.4; [[IPCC:Wg1:Chapter:Chapter-3#3.3.3|Section 3.3.3]] ).

Since AR5, weakening of the tropical circulation has been explained as a rapid response to increasing CO <sub>2</sub> concentrations and slower response to warming and evolving SST patterns ( [[#He--2017|He and Soden, 2017]] ; [[#Xia--2017|Xia and Huang, 2017]] ; [[#Shaw--2018|Shaw and Tan, 2018]] ; [[#Chemke--2020|Chemke and Polvani, 2020]] ). Large-scale tropical circulation weakens by 3 – 4% in a rapid response to a quadrupling of CO <sub>2</sub> concentrations ( [[#Plesca--2018|Plesca et al., 2018]] ), which suppresses tropospheric radiative cooling, particularly in subtropical ocean subsidence regions ( [[#Bony--2013|Bony et al., 2013]] ; [[#Merlis--2015|Merlis, 2015]] ; [[#Richardson--2016|Richardson et al., 2016]] ). The resulting increased atmospheric stability explains the rapid weakening of the Walker circulation ( [[#Wills--2017|Wills et al., 2017]] ) and Northern Hemisphere Hadley Cell ( [[#Chemke--2020|Chemke and Polvani, 2020]] ). Subsequent surface warming contributes up to a 12% slowing of circulation for a uniform 4°C SST increase, driven by thermodynamic decreases in temperature lapse rate ( [[#Plesca--2018|Plesca et al., 2018]] ).

The regional Inter-tropical Convergence Zone (ITCZ) position, width and strength determine the location and seasonality of the tropical rain belt. Since AR5, multiple studies have linked cross-equatorial energy transport to the mean ITCZ position ( [[#Donohoe--2013|Donohoe et al., 2013]] ; [[#Frierson--2013|Frierson et al., 2013]] ; [[#Bischoff--2014|Bischoff and Schneider, 2014]] ; [[#Boos--2016|Boos and Korty, 2016]] ; [[#Loeb--2016|Loeb et al., 2016]] ; [[#Adam--2018|Adam et al., 2018]] ; [[#Biasutti--2019|Biasutti and Voigt, 2019]] ). Multi-model studies agree that aerosol cooling in the NH led to a southward shift in the ITCZ and tropical precipitation after the 1950s up to the 1980s that is linked with the 1980s Sahel drought (Box 8.1; [[#8.3.2.4|Section 8.3.2.4]] and 10.4.2.1). In particular, aerosol-cloud interaction was identified as a potentially important driver of this shift ( [[#Chung--2017|Chung and Soden, 2017]] ) but this is uncertain since observations suggest that models may overestimate (Malavelle et al. , 2017; Toll et al. , 2017) or underestimate (Rosenfeld et al. , 2019) the aerosol cloud-mediated cooling effects. In addition, greenhouse gas forcing has been invoked in explaining much of the increase in Sahel precipitation since the 1980s through enhanced meridional temperature gradient, with only a secondary role for aerosol ( [[#Dong--2015|Dong and Sutton, 2015]] ).

Understanding of how ITCZ width and strength respond to a warming climate has improved since AR5 ( [[#Byrne--2016|Byrne and Schneider, 2016]] ; [[#Harrop--2016|Harrop and Hartmann, 2016]] ; [[#Popp--2017|Popp and Silvers, 2017]] ; [[#Dixit--2018|Dixit et al., 2018]] ; [[#Zhou--2020|Zhou et al., 2020]] ). Studies suggest that convection gets stronger and more focused within the core of the ITCZ ( [[#Lau--2015|Lau and Kim, 2015]] ; [[#Byrne--2018|Byrne et al., 2018]] ). This leads to drying on the equatorward edges of the ITCZ and a moistening tendency in the ITCZ core ( [[#Byrne--2016|Byrne and Schneider, 2016]] ). Feedbacks involving clouds have been identified as an important mechanism leading to tightening and strengthening of the ITCZ ( [[#Popp--2017|Popp and Silvers, 2017]] ; [[#Su--2017|Su et al., 2017]] , 2019, 2020; [[#Talib--2018|Talib et al., 2018]] ). Stronger ascent in the core amplifies the ‘wet get wetter’ response while reduced moisture inflow near the ITCZ edges reduces this response below the 7% °C <sup>–1</sup> Thermodynamic increase in moisture transport. Thus, there is a range of evidence and ''medium agreement'' for strengthening and contraction of the ITCZ with warming that sharpens contrasts between wet and dry regimes. However, understanding of how the regional ITCZ location responds in a warming climate is not robust ( [[#8.4.2.1|Section 8.4.2.1]] ) with ''limited evidence'' of distinct regional responses to GHG forcing including a northward shift over eastern Africa and the Indian Ocean and a southward shift in the eastern Pacific and Atlantic oceans ( [[#Mamalakis--2021|Mamalakis et al., 2021]] ). Paleoclimate evidence highlights the distinct regional ITCZ responses to hemispheric asymmetry in volcanic and orbital forcing ( [[#McGee--2014|McGee et al., 2014]] ; [[#Boos--2016|Boos and Korty, 2016]] ; [[#Colose--2016|Colose et al., 2016]] ; [[#Denniston--2016|Denniston et al., 2016]] ; [[#PAGES%20Hydro2K%20Consortium--2017|PAGES Hydro2K Consortium, 2017]] ; [[#Singarayer--2017|Singarayer et al., 2017]] ; [[#Atwood--2020|Atwood et al., 2020]] ) and rapid (&gt;1° latitude over decades) shifts in the ITCZ and regional monsoons in response to AMOC collapse cannot be ruled out (Sections 8.6.1.1 and 5.1.3).

Monsoons are key components of the tropical overturning circulation that can be understood as a balance between net energy input (e.g., radiative and turbulent fluxes) and the export of moist static energy. This is determined by contrasting surface heat capacity between ocean and land and modified through changes in atmospheric dynamics, tropical tropospheric stability and land surface properties ( [[#Jalihal--2019|Jalihal et al., 2019]] ). Thermodynamic increases in moisture transport are expected to increase monsoon strength and area (Christensen et al., 2013). Since AR5, evidence continues to demonstrate that monsoon circulation is sensitive to spatially varying radiative forcing by anthropogenic aerosols (Hwang et al. , 2013; R.J. Allen et al. , 2015; Z. Li et al. , 2016b) and GHGs (Dong andSutton, 2015). Changes in SST patterns also play a role ( [[#Guo--2016|Guo et al., 2016]] ; W. [[#Zhou--2019|]] [[#Zhou--2019|]] [[#Zhou--2019|Zhou et al., 2019]] ; [[#Cao--2020|Cao et al., 2020]] ) by altering cross-equatorial energy transports and land–ocean temperature contrasts. This evidence continues to support a thermodynamic strengthening of monsoon precipitation that is partly offset by slowing of the tropical circulation but with ''weak evidence'' and ''low agreement'' for regional aspects of circulation changes. Disagreement between paleoclimate and modern observations, physical theory and numerical simulations of global monsoons have been partly reconciled ( [[IPCC:Wg1:Chapter:Chapter-3#3.3.3.2|Section 3.3.3.2]] ) through improved understanding of regional processes ( Harrison et al. , 2015; R. Bhattacharya et al. , 2017; Bhattacharya et al. , 2018; Biasutti et al. , 2018; D’Agostino et al. , 2019; Jalihal et al. , 2019; Seth et al. , 2019 ), although interpreting past changes in the context of future projections requires careful account of differing forcings and feedbacks (D’Agostinoet al., 2019). Assessment of past changes and future projections in regional monsoons are provided in Sections 2.3.1.4.2, 8.3.2.4 and 8.4.2.4.

Since AR5, understanding of poleward expansion of the Hadley Cells has improved ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4.1|Section 2.3.1.4.1]] ) but its role in subtropical drying is limited to the zonal mean and dominated by ocean regions ( [[#Byrne--2015|Byrne and]] [[#O’Gorman--2015|O’Gorman, 2015]] ; [[#Grise--2016|Grise and Polvani, 2016]] ; [[#He--2017|He and Soden, 2017]] ; [[#Schmidt--2017|Schmidt and Grise, 2017]] ; [[#Siler--2018|Siler et al., 2018]] ; [[#Chemke--2019|Chemke and Polvani, 2019]] ; [[#Grise--2020|Grise and Davis, 2020]] ). Over subtropical land, evolving SST patterns and land–ocean warming contrasts, that are partly explained by rapid responses to CO <sub>2</sub> increases, can dominate aspects of the atmospheric circulation response (Byrne and O’Gorman, 2015; [[#He--2015|He and Soden, 2015]] ; [[#Chadwick--2017|Chadwick et al., 2017]] ; H. [[#Yang--2020|]] [[#Yang--2020|Yang et al., 2020]] ) and resultant regional water cycle changes, particularly for projected drying in semi-arid, winter-rainfall dominated subtropical climates (Deitch et al. , 2017; Brogli et al. , 2019; Seager et al. , 2019b; Zappa et al. , 2020) . Poleward expansion of the tropical belt is expected to drive a corresponding shift in mid-latitude storm tracks, but the controlling mechanisms differ between hemispheres. Southern Hemisphere expansion is driven by GHG forcing and amplified by stratospheric ozone depletion, while weaker Northern Hemisphere expansion in response to GHG forcing is modulated by tropospheric ozone and aerosol forcing, particularly black carbon (Davis et al. , 2016; Grise et al. , 2019; Watt-Meyer et al. , 2019; Zhao et al. , 2020) . However, internal variability is found to dominate observed responses in the NH, precluding attribution to radiative forcing ( [[#D’Agostino--2020a|D’Agostino et al., 2020a]] ). Paleoclimate evidence of poleward expansion and weakening of westerly winds in both hemispheres in the warmer Pliocene is linked to reduced equator-to-pole thermal gradients and ice volume ( [[#Abell--2021|Abell et al., 2021]] ).

The influence of amplified Arctic warming on mid-latitude regional water cycles is not well understood based on simple physical grounds due to the large number of competing physical processes (Cross-Chapter Box 10.1). The thermal gradient between polar and lower latitude regions decreases at lowlevels due to Arctic warming amplification. However, at higher altitudes, the corresponding thermal gradient increases with warming due to cooling of the Arctic stratosphere and this is consistent with a strengthening of the winter jet stream in both hemispheres, yet there is ''low agreement'' on the precise mechanisms (Vallis et al., 2015; [[#Vihma--2016|Vihma et al., 2016]] ). Changes in the strength of the polar stratospheric vortex can also alter the mid-latitude circulation in winter, but responses are not consistent across models ( [[#Oudar--2020a|Oudar et al., 2020a]] ). Nevertheless, thermodynamic strengthening of moisture convergence into weather systems and polar regions is robust ( [[#8.2.2.1|Section 8.2.2.1]] ) and remains valid despite weak understanding of atmospheric circulation change.

In summary, there is ''high confidence'' that altered atmospheric wind patterns in response to radiative forcing and evolving surface temperature patterns will affect the regional water cycle in most regions. Mean tropical circulation is expected to slow with global warming ( ''high confidence'' ) but temporary multi-decadal strengthening is possible due to internal variability ( ''medium confidence'' ). Slowing of the tropical circulation reduces the meridional P–E gradient over the Pacific and can partly offset thermodynamic amplification of P–E patterns and strengthening of monsoons ( ''high confidence'' ) but regional characteristics of tropical rain belt changes are not well understood. There is ''medium confidence'' in processes driving strengthening and tightening of the ITCZ that increase the contrasts between wet and dry tropical weather regimes and seasons. There is ''high confidence'' in understanding of how radiative forcing and global warming drive a poleward expansion of the subtropics and mid-latitude storm tracks but only ''low confidence'' in how poleward expansion influences drying of subtropical and mid-latitude climates. There is ''low confidence'' in understanding how Arctic warming amplification affects mid-latitude regional water cycles but ''high confidence'' that thermodynamic strengthening of precipitation within weather systems and in monsoons and polar regions is robust to large-scale circulation changes.

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<span id="local-scale-physical-processes-affecting-the-water-cycle"></span>
=== 8.2.3 Local-scale Physical Processes Affecting the Water Cycle ===

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Processes operating at local scales are capable of substantially modifying the regional water cycle. This section assesses the development in understanding of processes affecting the atmosphere, surface and subsurface, including cryosphere and biosphere interactions and the direct impacts of human activities.

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<span id="hydrological-processes-related-to-ice-and-snow"></span>
==== 8.2.3.1 Hydrological Processes Related to Ice and Snow ====

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Declining ice-sheet mass, glacier extent and Northern Hemisphere (NH) sea ice, snow cover and permafrost ( [[#Collins--2013|Collins et al., 2013]] ; [[#Vaughan--2013|Vaughan et al., 2013]] ) is an expected consequence of a warming climate (Sections 2.3.2, 3.4, 4.3.2.1 and 9.3 – 9.5). A decline in mountain snow cover and increased snow and glacier melt will alter the amount and timing of seasonal runoff in mountain regions (Sections 3.4.2, 3.4.3 and 9.5). Earlier and more extensive winter and spring snowmelt (X. [[#Zeng--2018|Zeng et al., 2018]] ) can reduce summer and autumn runoff in snow-dominated river basins of mid–high latitudes of the NH (Rhoades et al., 2018; [[#Blöschl--2019|Blöschl et al., 2019]] ). Since AR5, an earlier but less rapid snowmelt has been explained by reduced winter snowfall and less intense solar radiation earlier in the season (Musselman et al. , 2017; Wu et al. , 2018; Grogan et al. , 2020). Reduced snow cover also increases energy available for evaporation, which can dominate declining river discharge based on modelling of the Colorado River ( [[#Milly--2020|Milly and Dunne, 2020]] ). An increase in the fraction of precipitation falling as rain compared with snow can lead to declines in both streamflow and groundwater storage in regions where snowmelt is the primary source of recharge ( [[#Earman--2011|Earman and Dettinger, 2011]] ; [[#Berghuijs--2014|Berghuijs et al., 2014]] ). Such regions include western South America and western North America, semi-arid regions which rely on snowmelt from high mountain chains ( [[#Ragettli--2016|Ragettli et al., 2016]] ; [[#Milly--2020|Milly and Dunne, 2020]] ). Rain-on-snow melt events reduce at lower altitudes due to declining snow cover but increase at higher altitudes where snow tends to be replaced by rain based on observations and modelling (Musselman et al., 2018; [[#Pall--2019|Pall et al., 2019]] ), thereby altering seasonal and regional characteristics of flooding ( [[IPCC:Wg1:Chapter:Chapter-11#11.5|Section 11.5]] ).

Seasonal melt water from high mountain glaciers in Asia (see Cross-Chapter Box 10.4) supply the basic needs of 221 ± 97 million people (Pritchard, 2019; [[#Immerzeel--2020|Immerzeel et al., 2020]] ). Glacier-melt in response to warming can initially lead to increased runoff volumes, especially in peak summer flows, but they will eventually decline as most glaciers continue to shrink. SROCC concluded there is ''high confidence'' that the peak runoff has already been passed for some smaller glaciers ( [[#Hock--2019a|Hock et al., 2019a]] ). Increased precipitation and glacier-melt can also contribute to rising lake levels and flood hazards in regions such as the inner Tibetan Plateau, Patagonia, Peru, Alaska and Greenland (Lei et al. , 2017; Shugar et al. , 2020; Stuart-Smith et al. , 2020) . Since AR5, evidence from multiple locations (New Zealand, Greenland, Antarctica) shows that intrusions of warm, moist air are important in controlling glacier mass balance, the likelihood of extreme ablation or snowfall events depending on air temperature (Gorodetskaya et al. , 2014; Mackintosh et al. , 2017; Mattingly et al. , 2018; Little et al. , 2019; Oltmanns et al. , 2019; Wille et al. , 2019; Adusumilli et al. , 2021) . Sensible heating from warm air and increased longwave radiation from atmospheric moisture and low clouds drive melt events (Stuecker et al., 2018).

Reductions in snow, freshwater ice and permafrost affect terrestrial hydrology. Permafrost degradation reduces soil ice and alters the extent of thermokarst lake coverage ( [[IPCC:Wg1:Chapter:Chapter-9#9.5.2|Section 9.5.2]] ; M. [[#Meredith--2019|]] [[#Meredith--2019|Meredith et al., 2019]] ). A lag between current climate change and permafrost degradation is expected, given the slow response rates in frozen ground and the fact that snow cover insulates soil from sensible heat exchanges with the air above (Hoegh-Guldberg et al. , 2018; García-García et al. , 2019; Soong et al. , 2020) . Post‐wildfire areas are also linked with permafrost degradation in the Arctic based on satellite observations ( [[#Yanagiya--2020|Yanagiya and Furuya, 2020]] ). An increase in spring rainfall can increase heat advection by infiltration, exacerbating permafrost thaw and leading to increased methane emissions ( [[IPCC:Wg1:Chapter:Chapter-5#5.4.7|Section 5.4.7]] ; [[#Neumann--2019|Neumann et al., 2019]] ). Increased heat transport by Arctic rivers can also contribute to earlier sea ice melt ( [[#Park--2020|Park et al., 2020]] ).

In summary, it is ''virtually certain'' that warming will cause a loss of frozen water stores, except in areas where temperatures remain below 0°C for most of the year. There is ''high confidence'' that warming and reduced snow volume drives an earlier snowmelt, leading to seasonally dependent changes in streamflow. There is ''medium confidence'' that weaker sunlight earlier in the season can reduce the rate of snowmelt. Melting of snowpack or glaciers can increase streamflow in high-latitude and high-altitude catchments until frozen water reserves are depleted ( ''high confidence'' ). There is ''high confidence'' that warm, moist airflows and associated precipitation dominate glacier mass balance in some regions (New Zealand, Greenland, Antarctica).

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<span id="processes-determining-heavy-precipitation-and-flooding"></span>
==== 8.2.3.2 Processes Determining Heavy Precipitation and Flooding ====

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Evidence that heavy precipitation events (from sub-daily up to seasonal time scales) intensify as the planet warms has strengthened since AR5 ( [[IPCC:Wg1:Chapter:Chapter-11#11.4|Section 11.4]] , Box 11.1 and Cross-Chapter Box 3.2) based on improved physical understanding, extensive modelling and increasing observational corroboration ( [[#O’Gorman--2015|O’Gorman, 2015]] ; [[#Fischer--2016|Fischer and Knutti, 2016]] ; [[#Neelin--2017|Neelin et al., 2017]] ). There is ''robust evidence'' , with ''medium agreement'' across a range of modelling and observational studies, of thermodynamic intensification of wet seasons ( [[#Chou--2013|Chou et al., 2013]] ; [[#Liu--2013|Liu and Allan, 2013]] ; [[#Dunning--2018|Dunning et al., 2018]] ; [[#Lan--2019|Lan et al., 2019]] ; [[#Zhang--2019|Zhang and Fueglistaler, 2019]] ). Extreme daily precipitation is expected to increase at close to the 7% °C <sup>–1</sup> increase in the near-surface atmospheric moisture-holding capacity determined by the Clausius–Clapeyron equation ( [[IPCC:Wg1:Chapter:Chapter-11#11.4|Section 11.4]] , Figure 8.4), with ''limited evidence'' that higher rates apply for shorter duration precipitation events ( [[#Formayer--2017|Formayer and Fritz, 2017]] ; Lenderink et al. , 2017; Ali et al. , 2018; Guerreiro et al. , 2018; Burdanowitz et al. , 2019; W. Zhang et al. , 2019a) . However, observed estimates sample multiple synoptic weather states, mixing thermodynamic and dynamic factors, so are not directly relatable to climate change responses ( [[#Bao--2017|Bao et al., 2017]] ; [[#Drobinski--2018|Drobinski et al., 2018]] ). The contrasting spatial scales sampled by the observations and models (from global to cloud resolving) explain the large range of daily and sub-daily precipitation scaling with temperature assessed in Figure 8.4.

Since AR5, advances in understanding the expected changes in intense rainfall at the sub-daily time scale ( [[IPCC:Wg1:Chapter:Chapter-11#11.4|Section 11.4]] , Figure 8.4) are provided by idealized or high resolution model experiments and observations ( [[#Westra--2014|Westra et al., 2014]] ; [[#Fowler--2021|Fowler et al., 2021]] ). There is ''robust evidence'' from simplified calculations, convection resolving models and observations that thermodynamics drives an increase in convective available potential energy (CAPE) with warming and therefore the intensity of convective storms ( [[#Singh--2013|Singh and O’Gorman, 2013]] ; [[#Romps--2016|Romps, 2016]] ; [[#Barbero--2019|Barbero et al., 2019]] ). Also, declining relative humidity over land (Sections 2.3.1.3.2 and 8.2.2.1) increases lifting condensation level, thereby delaying but intensifying convective systems ( [[#Louf--2019|Louf et al., 2019]] ; J. [[#Chen--2020|]] [[#Chen--2020|Chen et al., 2020]] a). Larger systems are linked with increasing tropopause height ( [[#Lenderink--2017|Lenderink et al., 2017]] ) that can also amplify storm precipitation ( [[#Prein--2017|Prein et al., 2017]] ). However, the heaviest rainfall is not necessarily associated with the most intense (deepest) storms based on satellite data ( [[#Hamada--2015|Hamada et al., 2015]] ; [[#Hamada--2018|Hamada and Takayabu, 2018]] ). Precipitation intensification can exceed thermodynamic expectations where and when additional latent heating invigorates individual storms ( [[IPCC:Wg1:Chapter:Chapter-11#11.4.1|Section 11.4.1]] ) as implied by ''medium agreement'' across modelling and observational studies (Berg et al. , 2013; Molnar et al. , 2015; Scoccimarro et al. , 2015; Prein et al. , 2017; [[#Zhou--2017|Zhou and Wang, 2017]] ; Nie et al. , 2018; Kendon et al. , 2019; Z. Zhang et al. , 2019) . This intensification depends on time of day, based on convection-permitting simulations (E.P. [[#Meredith--2019|]] [[#Meredith--2019|Meredith et al., 2019]] ).

Intensification of sub-daily rainfall is inhibited in regions and seasons where available moisture is limited ( [[#Prein--2017|Prein et al., 2017]] ). However, a fixed threshold temperature above which precipitation is limited by moisture availability is not supported by modelling evidence ( [[#Neelin--2017|Neelin et al., 2017]] ; [[#Prein--2017|Prein et al., 2017]] ). Enhanced latent heating within storms can also suppress convection at larger scales due to atmospheric stabilization as demonstrated with high resolution, idealized and large ensemble modelling studies (Loriaux et al. , 2017; Chan et al. , 2018; Nie et al. , 2018; Tandon et al. , 2018; Kendon et al. , 2019) . Stability is also increased by the direct radiative heating effect of higher CO <sub>2</sub> concentrations ( [[#Baker--2018|Baker et al., 2018]] ) and influenced by aerosol effects on the atmospheric energy budget and cloud development (Box 8.1). Since AR5, modelling evidence shows increases in convective precipitation extremes are limited by droplet/ice fall speeds (Singh and [[#O’Gorman--2014|O’Gorman, 2014]] ; [[#Sandvik--2018|Sandvik et al., 2018]] ) but these processes are only crudely represented ( [[#Tapiador--2019a|Tapiador et al., 2019a]] ). Idealized regional and coupled global models combined with ''limited'' observational ''evidence'' shows that instantaneous precipitation extremes are sensitive to microphysical processes, while daily extremes are determined more by the degree of convective aggregation ( [[#Bao--2019|Bao and Sherwood, 2019]] ; [[#Pendergrass--2020a|Pendergrass, 2020a]] ).

Dynamical changes modify and can dominate thermodynamic drivers of local rainfall and flood hazard change (Box 11.1). For example, increased land – ocean temperature gradients ( [[#8.2.2.2|Section 8.2.2.2]] ) explain more intense rain from convective systems over the Sahel based on satellite data since the 1980s ( [[#Taylor--2017|Taylor et al., 2017]] ) and dynamical feedbacks can invigorate active to break phase transition over India ( [[#Karmakar--2017|Karmakar et al., 2017]] ; [[#Roxy--2017|Roxy et al., 2017]] ). Satellite data shows long-lived, organized mesoscale convective systems contribute disproportionally to extreme tropical precipitation ( [[#Roca--2020|Roca and Fiolleau, 2020]] ). Since AR5, the spatial variability in soil moisture has been linked with the timing and location of convective rainfall by altering the partitioning between latent and sensible heating. This was demonstrated for the Sahel, Europe and India in observations (C.M. [[#Taylor--2013|Taylor et al., 2013]] ; [[#Taylor--2015|Taylor, 2015]] ; [[#Petrova--2018|Petrova et al., 2018]] ; [[#Barton--2020|Barton et al., 2020]] ; [[#Klein--2020|Klein and Taylor, 2020]] ) but depends on the moisture-convergence regime ( [[#Welty--2020|Welty et al., 2020]] ). Only high-resolution convection-permitting models can capture the sub-grid scale mechanisms for convective initiation ( C.M. Taylor et al. , 2013; H. Moon et al. , 2019 ). There is ''medium evidence'' that greater tropical cyclone rainfall totals can be caused by dynamical feedbacks ( [[#Chauvin--2017|Chauvin et al., 2017]] ) and slower propagation speed as tropical circulation weakens ( [[#Kossin--2018|Kossin, 2018]] ). These processes amplify the thermodynamic intensification of rainfall ( [[IPCC:Wg1:Chapter:Chapter-11#11.7.1.2|Section 11.7.1.2]] ), yet observational support is weak ( [[#Chan--2019|Chan, 2019]] ; [[#Lanzante--2019|Lanzante, 2019]] ; I.J. Moon et al. , 2019; Knutson et al. , 2020) . Slower decay following landfall, explained by larger stores of heat and moisture at higher SSTs, can also amplify rainfall amount based on observations and modelling ( [[#Li--2020|Li and Chakraborty, 2020]] ). Rainfall intensity from the outer rain bands of tropical cyclones is also increased by aerosol – cloud interactions (Box 8.1).

The amount and intensity of rainfall within extratropical storms is expected to increase with atmospheric moisture. This is particularly evident for atmospheric rivers (see Glossary) and research since AR5 has confirmed their link with flooding and terrestrial water storage ( [[#Froidevaux--2016|Froidevaux and Martius, 2016]] ; Paltan et al. , 2017; [[#Waliser--2017|Waliser and Guan, 2017]] ; Adusumilli et al. , 2019; Ionita et al. , 2020; Payne et al. , 2020) . There is ''robust evidence'' based on simple physics and detailed modelling that extratropical cyclone rainfall, including atmospheric river events, will intensify through increased atmospheric moisture flux ( [[#Lavers--2013|Lavers et al., 2013]] ; [[#Ramos--2016|Ramos et al., 2016]] ; [[#Yettella--2017|Yettella and Kay, 2017]] ; V. [[#Espinoza--2018|]] [[#Espinoza--2018|Espinoza et al., 2018]] ; [[#Algarra--2020|Algarra et al., 2020]] ; [[#Xu--2020|Xu et al., 2020]] ; [[#Zavadoff--2020|Zavadoff and Kirtman, 2020]] ; [[#Zhao--2020|Zhao, 2020]] ), although changes in dynamical aspects will modify responses regionally ( [[#8.4.2.8|Section 8.4.2.8]] ). For example, stronger latitudinal temperature gradients in the high-latitude upper troposphere drive increased extratropical storm speed around 30°N – 70°N based on CMIP5 simulations ( [[#Dwyer--2017|Dwyer and O’Gorman, 2017]] ), causing reduced precipitation accumulation.

The response of flood hazard to changing rainfall characteristics depends on time and space scale and the nature of the land surface ( [[IPCC:Wg1:Chapter:Chapter-11#11.5.1|Section 11.5.1]] and FAQ 8.2). Sustained and heavy rainfall can lead to widespread flooding and landslides while intensification of short-duration intense rainfall can increase the severity and frequency of flash flooding ( [[#Marengo--2013|Marengo et al., 2013]] ; [[#Chan--2016|Chan et al., 2016]] ; [[#Gariano--2016|Gariano and Guzzetti, 2016]] ; [[#Sandvik--2018|Sandvik et al., 2018]] ). Flooding events in many tropical regions (e.g., north-western South America, southern Africa and Australasia) are associated with ENSO variability ( [[#Emerton--2017|Emerton et al., 2017]] ; [[#Takahashi--2019|Takahashi and Martínez, 2019]] ; [[#Pabón-Caicedo--2020|Pabón-Caicedo et al., 2020]] ) and amplified by thermodynamic increases in water vapour. Flood hazard from heavy rainfall is modulated by snowmelt ( [[#8.2.3.1|Section 8.2.3.1]] ), vegetation characteristics ( [[#Page--2020|Page et al., 2020]] ; [[#Murphy--2021|Murphy et al., 2021]] ) and direct human intervention (Sections 8.2.3.4 and FAQ 8.2) but also can be compounded by sea level rise (Sections 4.3.2.2 and 9.6.4) in coastal and delta regions ( [[#Bevacqua--2019|Bevacqua et al., 2019]] ; [[#Ganguli--2019|Ganguli and Merz, 2019]] ; [[#Eilander--2020|Eilander et al., 2020]] ). Antecedent soil moisture conditions are an important modulator of flooding ( [[IPCC:Wg1:Chapter:Chapter-11#11.5.1|Section 11.5.1]] ) but become less important for smaller catchments and for more severe floods ( [[#Wasko--2019|Wasko and Nathan, 2019]] ). Depleted soil moisture after more intense dry seasons ( [[#8.2.2.1|Section 8.2.2.1]] ) can allow greater uptake of wet season rainfall before soils saturate. Since AR5, evidence confirms that more intense rainfall increases the proportion of runoff and reservoir recharge relative to infiltration into the soil ( [[#Eekhout--2018|Eekhout et al., 2018]] ; [[#Yin--2018|Yin et al., 2018]] ). More intense but less frequent storms ( [[#Kendon--2019|Kendon et al., 2019]] ) favour focused groundwater recharge through leakage from surface waters (R.G. [[#Taylor--2013|Taylor et al., 2013]] a; [[#Cuthbert--2019a|Cuthbert et al., 2019a]] ) and runoff and flash flooding where the percolation capacity of the soil is exceeded ( [[#Yin--2018|Yin et al., 2018]] ).

Increased severity of flooding on larger, more slowly-responding rivers is expected as precipitation accumulations increase during persistent wet events over a season. This can occur where atmospheric blocking patterns repeatedly steer extratropical cyclones across large river catchments, as identified for NH mid-latitudes and Asia ( [[#Takahashi--2015|Takahashi et al., 2015]] ; [[#Pfleiderer--2018|Pfleiderer et al., 2018]] ; [[#Zhou--2018|Zhou et al., 2018]] ; [[#Blöschl--2019|Blöschl et al., 2019]] ; [[#Lenggenhager--2019|Lenggenhager et al., 2019]] ; [[#Nikumbh--2019|Nikumbh et al., 2019]] ; [[#Zanardo--2019|Zanardo et al., 2019]] ), although groundwater flooding and antecedent conditions including soil moisture and snowmelt also play a role ( [[#Muchan--2015|Muchan et al., 2015]] ; [[#Berghuijs--2019|Berghuijs et al., 2019]] ). Increased atmospheric moisture amplifies the severity of these events when they occur in a warmer climate, yet drivers of change in the occurrence of blocking patterns, stationary waves and jet stream position are not well understood ( [[#8.2.2.2|Section 8.2.2.2]] and Cross-Chapter Box 10.1).

In summary, there is ''very high confidence'' that heavy precipitation events will become more intense in a warming climate. There is ''high confidence'' that increased moisture and its convergence within extratropical and tropical cyclones and storms will increase rainfall totals during wet events at close to the 7% °C <sup>–1</sup> Thermodynamic response, with ''low confidence'' of higher rates for sub-daily intensities. There is ''medium confidence'' that more intense but less frequent rainfall increases the proportion of rainfall leading to surface runoff and focused groundwater recharge from temporary water bodies. There is ''low confidence'' in how the frequency of flooding will change regionally as it is strongly dependent on catchment characteristics, antecedent conditions and how atmospheric circulation systems respond to climate change, which is less certain than thermodynamic drivers ( [[IPCC:Wg1:Chapter:Chapter-11#11.5|Section 11.5]] ). However, there is ''high confidence'' that increases in precipitation intensity and amount during very wet events (from sub-daily up to seasonal time scales) will intensify severe flooding when these extremes occur.

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<span id="drivers-of-aridity-and-drought"></span>
==== 8.2.3.3 Drivers of Aridity and Drought ====

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Regional changes in aridity – broadly defined as a deficit of moisture – are expected to occur in response to anthropogenic forcings as a consequence of shifting precipitation patterns, warmer temperatures, changes in cloudiness (affecting solar radiation), declining snowpack, changes in winds and humidity, and vegetation cover (Figure 8.6). Evapotranspiration (see Annex VII: Glossary) is a key component of aridity, and is composed of two main processes: evaporation from soil, water and vegetation surfaces; and transpiration, the exchange of moisture between plants and atmosphere through plant stomata. On a global level, warmer temperatures increase evaporative demand in the atmosphere, and thus (assuming sufficient soil moisture is available) increase moisture loss from evapotranspiration ( ''high confidence'' ) (Dai et al., 2018; [[#Vicente-Serrano--2020|Vicente-Serrano et al., 2020]] ). On a regional level, aridity is further modulated by seasonal rainfall patterns, runoff, water storage, and interactions with vegetation.

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[[File:885a41fb1cdd23ce5c31b01f04499625 IPCC_AR6_WGI_Figure_8_6.png]]

'''Figure 8.6 |''' '''Climatic drivers of drought, effects on water availability, and impacts.''' Plus and minus signs denote the direction of change that drivers have on factors such as snowpack, evapotranspiration, soil moisture, and water storage. The three main types of drought are listed, along with some possible environmental and socio-economic impacts of drought (bottom).

Vegetation is a crucial interface between subsurface water storage (in soil moisture and groundwater) and the atmosphere. Plants alter evapotranspiration and the surface energy balance, and thus can have a large influence on regional aridity ( [[#Lemordant--2018|Lemordant et al., 2018]] ). SRCCL concluded there is ''high confidence'' that higher atmospheric CO <sub>2</sub> increases the ratio of plant CO <sub>2</sub> uptake to water loss (water-use efficiency; WUE) through the combined enhancement of photosynthesis and stomatal regulation ( Section 5.4.1; DeKauwe et al. , 2013; C.D. Jones et al. , 2013; Deryng et al. , 2016; Swann et al. , 2016; Cheng et al. , 2017; Knauer et al. , 2017; Peters et al. , 2018; Guerrieri et al. , 2019) . Modelling studies suggest that increasing WUE can partly counteract water losses from increased evaporative demand in a warmer atmosphere, potentially mitigating aridification (Milly and Dunne, 2016; Bonfils et al. , 2017; Cook et al. , 2018; Y. Yang et al. , 2018) . However, observational studies suggest that this effect may be counter-balanced by the increase in plant growth in response to elevated CO <sub>2</sub> , which results in increased water consumption (De Kauwe et al. , 2013; Donohue et al. , 2013; Ukkola et al. , 2016b; Yang et al. , 2016; Guerrieri et al. , 2019; Mankin et al. , 2019; A. Singh et al. , 2020) . In semi-arid regions, increased plant water consumption can reduce streamflow and exacerbate aridification (Ukkola et al. , 2016b; Mankin et al. , 2019; A. Singh et al. , 2020) . Thus, there is ''low confidence'' that increased WUE in plants can counterbalance increased evaporative demand (Cross-Chapter Box 5.1).

A drought is a period of abnormally dry weather that persists for long enough to cause a serious hydrological imbalance (Glossary; Wilhite and Glantz, 1985; [[#Wilhite--2000|Wilhite, 2000]] ; [[#Cook--2018|Cook et al., 2018]] ). Most droughts begin as persistent precipitation deficits (‘meteorological drought’) that propagate over time into deficits in soil moisture, streamflow, and water storage (Figure 8.6), leading to a reduction in water supply (‘hydrological drought’). Increased atmospheric evaporative demand increases plant water stress, leading to ‘agricultural and ecological drought’ (Williams et al. , 2013; C.D. Allen et al. , 2015; Anderegg et al. , 2016; McDowell et al. , 2016; Grossiord et al. , 2020) . Evaporative demand affects plants in two ways. It increases evapotranspiration, depleting soil moisture and stressing plants through lack of water ( [[#Teuling--2013|Teuling et al., 2013]] ; [[#Sperry--2016|Sperry et al., 2016]] ), and also directly affects plant physiology, causing a decline in hydraulic conductance and carbon metabolism, leading to mortality (Figure 8.6; [[#Breshears--2013|Breshears et al., 2013]] ; [[#Hartmann--2015|Hartmann, 2015]] ; [[#McDowell--2015|McDowell and Allen, 2015]] ; [[#Fontes--2018|Fontes et al., 2018]] ). While droughts are traditionally viewed as ‘slow moving’ disasters that typically take months or years to develop, rapidly evolving and often unpredictable ''flash droughts'' can also occur ( [[#Otkin--2016|Otkin et al., 2016]] , 2018). ''Flash droughts'' can develop within a few weeks, causing substantial disruption to agriculture and water resources ( [[#Pendergrass--2020|Pendergrass et al., 2020]] ). Conversely, droughts that persist for a long time (usually a decade or more) are called ''megadroughts'' . Droughts span a large range of spatial and temporal scales, arise through a variety of climate system dynamics (e.g., internal atmospheric variability, ocean teleconnections), and can be amplified or alleviated by a variety of physical and biological processes. As such, droughts occupy a unique space within the framework of extreme climate and weather events, possessing no singular definition.

While the role of precipitation in droughts is obvious, other climatic drivers are also important, such as temperature, radiation, wind, and humidity (Figure 8.6). These factors have a strong influence on atmospheric evaporative demand, which affects evapotranspiration and soil moisture (Figure 8.6). In snow-dominated regions, high temperatures increase the fraction of precipitation falling as rain instead of snow and advance the timing of spring snowmelt ( ''high confidence'' ) (Vincent et al. , 2015; Mote et al. , 2016, 2018; [[#Berg--2017|Berg and Hall, 2017]] ; Solander et al. , 2018) . This can result in lower than normal snowpack levels (a ‘snow drought’), and thus reduced streamflow, even if total precipitation is at or above normal for the cold season ( [[#Harpold--2017|Harpold et al., 2017]] ). Plants also affect the severity of droughts by modulating evapotranspiration (Figure 8.6). As discussed above, the effect of elevated CO <sub>2</sub> on plants has the potential to both increase and reduce water loss through evapotranspiration via enhanced WUE and plant growth, respectively (Figure 8.6), but there is ''low confidence'' in whether one process dominates over another at the global scale.

Drought severity also depends on human activities and decision-making (AghaKouchak et al. , 2015; Van Loon et al. , 2016; Pendergrass et al. , 2020) . Societies have developed a variety of strategies to manipulate the water cycle to increase resiliency in the face of water scarcity, including irrigation, creation of artificial reservoirs, and groundwater pumping. While potentially buffering water resource capacity, in some cases these interventions may unexpectedly increase vulnerability ( ''medium confidence'' ). For example, while increased irrigation efficiency may ensure more water is available to crops, the corresponding reduction in runoff and subsurface recharge may exacerbate hydrologic drought ( [[#Grafton--2018|Grafton et al., 2018]] ). Furthermore, while building dams and increasing surface reservoir capacity can boost water resources, they may actually increase drought vulnerability if demands rise to take advantage of the increased supply or if over-reliance on these surface reservoirs is encouraged (Di Baldassarre et al., 2018). Interactions between adaptation, vulnerability, and drought impacts are discussed further in WGII (Chapters 2 and 4).

In summary, there is ''high confidence'' that a warming climate drives an increase in atmospheric evaporative demand, decreasing available soil moisture. There is ''high confidence'' that higher atmospheric CO <sub>2</sub> increases plant water-use efficiency, but ''low confidence'' that this physiological effect can counterbalance water losses. Since drought can be defined in a number of ways, there are potentially different responses under a warming climate depending on drought type. Beyond a lack of precipitation, changes in evapotranspiration are critical components of drought, because these can lead to soil moisture declines ( ''high confidence'' ). Under very dry soil conditions, evapotranspiration becomes restricted and plants experience water stress in response to increased atmospheric demand ( ''medium confidence'' ). Human activities and decision-making have a critical impact on drought severity ( ''high co'' ''nfidence'' ).

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<span id="direct-anthropogenic-influence-on-the-regional-water-cycle"></span>
==== 8.2.3.4 Direct Anthropogenic Influence on the Regional Water Cycle ====

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Human activities influence the regional water cycle directly through modifying and exploiting stores and flows from rivers, lakes and groundwater and by altering land cover characteristics. These actions alter surface energy and water balances through changes in permeability, surface albedo, evapotranspiration, surface roughness and leaf area. Direct redistribution of water by human activities for domestic, agricultural and industrial use of about 24,000 km <sup>3</sup> yr <sup>–1</sup> (Figure 8.1) is equivalent to half the global river discharge or double the global groundwater recharge each year ( [[#Abbott--2019|Abbott et al., 2019]] ). Since AR5, both modelling studies and observations have demonstrated that land use change can drive local and remote responses in precipitation and river flow by altering the surface energy balance, moisture advection and recycling, land – sea thermal contrast and associated wind patterns (Alter et al. , 2015; Wey et al. , 2015; De Vrese et al. , 2016; Pei et al. , 2016; Wang-Erlandsson et al. , 2018; Vicente-Serrano et al. , 2019) . There is ''robust evidence'' that a warming climate combined with direct human demand for groundwater will deplete groundwater resources in already dry regions ( [[#Wada--2014|Wada and Bierkens, 2014]] ; [[#D’Odorico--2018|D’Odorico et al., 2018]] ; [[#Jia--2019|Jia et al., 2019]] ).

The SRCCL presented evidence that extraction of water from the ground or river systems and intensive irrigation increases evaporation and atmospheric water vapour locally ( [[#Jia--2019|Jia et al., 2019]] ; [[#Mishra--2020|Mishra et al., 2020]] ). Irrigation can explain declining groundwater storage in some regions, including north-western India and North America ( [[#Asoka--2017|Asoka et al., 2017]] ; G. [[#Ferguson--2018|]] [[#Ferguson--2018|Ferguson et al., 2018]] ). Simulations spanning 1960–2010 indicate that approximately 30% of the present human water consumption is supplied from non-sustainable water resources ( [[#Wada--2014|Wada and Bierkens, 2014]] ). However, there is only ''limited evidence'' that groundwater extraction is lowering streamflow ( [[#Mukherjee--2018|Mukherjee et al., 2018]] ; [[#de%20Graaf--2019|de Graaf et al., 2019]] ). Model experiments show that irrigation can either aggravate or alleviate climate‐induced changes of surface or subsurface water (Lenget al., 2015). Widespread extraction of water from rivers can reduce flows and decrease the level and area of inland seas and lakes (Wurtsbaugh et al. , 2017; Torres-Batlló et al. , 2020; X. Wang et al. , 2020) . Between 1985 and 2015, about 139,000 km <sup>2</sup> of inland water areas have become land, while creation of dams has converted about 95,000 km <sup>2</sup> of land to water, particularly in the Amazon and Tibetan Plateau (Donchyts et al., 2016). Direct management of river flow is comparable in magnitude to climate change effects for snow-fed rivers at a continental scale based on a global analysis and a study of 96 Canadian catchments ( [[#Tan--2015|Tan and Gan, 2015]] ; [[#Arheimer--2017|Arheimer et al., 2017]] ).

The SRCCL assessed with ''medium confidence'' that mean and extreme precipitation is increased over and downwind of urban areas ( [[#Jia--2019|Jia et al., 2019]] ). There is ''medium confidence'' that altered thermodynamic and aerodynamic properties of the land surface from urbanization affects evaporation and increases precipitation over or downwind of cities (Box 10.3) due to altered stability and turbulence (Han et al. , 2014; Pathirana et al. , 2014; Jiang et al. , 2016; D’Odorico et al. , 2018; Sarangi et al. , 2018; Boyaj et al. , 2020) . However, reduced biogenic aerosol, but increased anthropogenic aerosol emissions modify cloud microphysics and precipitation processes ( Box 8.1; Schmidand Niyogi, 2017; D’Odorico et al. , 2018; Fan et al. , 2020; Zheng et al. , 2020) . Urbanization also decreases permeability of the surface, leading to increased surface runoff ( [[#Chen--2017|Chen et al., 2017]] ; [[#Jia--2019|Jia et al., 2019]] ). Large-scale infrastructure, such as the construction and operation of dikes, weirs, and hydropower plants, also alters surface energy and moisture fluxes, potentially influencing the regional water cycle. ''Limited'' modelling ''evidence'' suggests that large-scale solar and wind farms can increase precipitation locally (over the Sahel and North America) when dynamic vegetation responses are represented (Y. [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|Li et al., 2018]] ; [[#Pryor--2020|Pryor et al., 2020]] ), with remote effects also possible ( [[#Lu--2021|Lu et al., 2021]] ).

Changes in land use from forest to agriculture can exert profound regional effects on the water cycle (FAQ 8.1) by modifying the surface energy balance and moisture recycling (Krishnan et al. , 2016; Paul et al. , 2016; Llopart et al. , 2018; Singh et al. , 2019) . There is ''medium evidence'' from modelling and observations over the Amazon and East Africa that deforestation drives increased streamflow (Dos Santos et al., 2018; [[#Guzha--2018|Guzha et al., 2018]] ; [[#Levy--2018|Levy et al., 2018]] ) but ''limited evidence'' that increases in global runoff due to deforestation are counterbalanced by decreases resulting from irrigation (Hoegh-Guldberg et al., 2018). Total Amazon deforestation drives reductions in precipitation but with a large 90% confidence range ( – 38 to +5 %) based on 44 primarily pre-AR5 climate model simulations (Spracklen and Garcia-Carreras, 2015) with smaller reductions ( – 2.3 to – 1.3 %) attributed to observed Amazon deforestation up to 2010. Climate model development has reduced this uncertainty range but has not altered the median change ( [[#Lejeune--2015|Lejeune et al., 2015]] ). Large-scale global deforestation (20 million km <sup>2</sup> ) simulated by 9 CMIP6 models confirms a large range in precipitation amount reduction of – 37 ± 54 mm yr <sup>–1</sup> over the deforested regions ( [[#Boysen--2020|Boysen et al., 2020]] ). However, small-scale deforestation can increase precipitation locally (Lawrence and Vandecar, 2015). A 50–60% deforestation rate corresponded to a wet season delay of about one week and greater chance of dry spells of eight days or longer based on correlation analysis of rain gauge and land-use data for South America (Leite-Filhoet al., 2019). Forest and grassland fires can also modify hydrological response at the watershed scale (Havel et al., 2018). Afforestation or reforestation aimed at removing CO <sub>2</sub> from the atmosphere can also alter the water cycle at the regional scale ( [[#8.4|Section 8.4.3]] and Cross-Chapter Box 5.1).

In summary, there is ''high confidence'' that land-use change and water extraction for irrigation drive local, regional and remote responses in the water cycle. Large-scale deforestation is ''likely'' to decrease precipitation over the deforested regions but there is ''low confidence'' in the effects of limited deforestation. There is ''medium confidence'' that deforestation drives increased streamflow relative to the responses caused by climate change. Urbanization can increase local precipitation ( ''medium confidence'' ) and resulting runoff intensity ( ''high confidence'' ). A warming climate combined with direct human demand for water is expected to deplete groundwater resources in dry regions ( ''high co'' ''nfidence'' ).

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'''Box 8.1 | Role of Anthropogenic Aerosols in Water Cyc''' '''le Changes'''

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Aerosols affect precipitation in two major pathways, by altering the shortwave and longwave radiation and influencing cloud microphysical properties.

'''Aerosol radiative effects on precipitation'''
Aerosols scatter and absorb solar radiation which reduces the energy available for surface evaporation and subsequent precipitation. In addition, cooling is incurred by the radiation that is reflected back to space directly by the aerosols and indirectly by the aerosol effect on cloud brightening. Northern Hemisphere (NH) station data indicate decreasing precipitation trends during the 1950s to the 1980s, which have since partially recovered ( [[#Wild--2012|Wild, 2012]] ; [[#Bonfils--2020|Bonfils et al., 2020]] ). These changes are attributable with ''high confidence'' to anthropogenic aerosol emissions from North America and Europe causing dimming through reduced surface solar radiation. This peaked during the late-1970s and partially recovered thereafter following improved air quality regulations (Section 6.2.1; Box 8.1, Figure 1).

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[[File:e222ac727181417ec565ebd134ba5d39 IPCC_AR6_WGI_Box_8_1_Figure_1.png]]

'''Box 8.1, Figure 1 |'''

'''Northern Hemisphere surface downward radiation anomalies (W m''' <sup>–2</sup> '''; a) and precipitation anomalies (mm day''' <sup>–1</sup> '''; b) for''' '''1951–2014''' '''for summer season (May–September) monsoon region (Polson et al. , 2014)''' '''from CMIP6 DAMIP experiments.''' Observed solar radiation anomalies are from GEBA global data from 1961–2014 ( [[#Wild--2017|Wild et al., 2017]] ) and observed precipitation anomalies are from GPCC and CRU. CMIP6 multi-model mean anomalies are from all-forcings (ALL), greenhouse gas forcing (GHG) and anthropogenic aerosol forcing (AER) experiments. Anomalies are with respect to 1961–1990 and smoothed with a 11-year running mean. Red shading shows the ensemble spread of ALL forcing experiment (5–95% range). Models are masked to the GPCC data set. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).

Dimming over the NH causes a relative cooling, compared to the Southern Hemisphere (SH), which induces a southward shift of the northern edge of the tropical rain belt ( [[IPCC:Wg1:Chapter:Chapter-3#3.3.2.3|Section 3.3.2.3]] ; [[#Allen--2014|Allen et al., 2014]] ; [[#Brönnimann--2015|Brönnimann et al., 2015]] ). CMIP5 simulations show that most of the cooling is caused by the aerosol cloud-mediated effect ( [[#Chung--2017|Chung and Soden, 2017]] ). Dimming also weakens monsoon flow and precipitation, offsetting or even overcoming the expected precipitation increase due to increased GHGs ( [[#Ayantika--2021|Ayantika et al., 2021]] ). The oceanic response to a weakened monsoon cross-equatorial flow can further weaken the South Asian monsoon through an amplifying feedback loop ( [[#Swapna--2012|Swapna et al., 2012]] ; [[#Krishnan--2016|Krishnan et al., 2016]] ; [[#Patil--2019|Patil et al., 2019]] ). These processes partially explain ( ''medium confidence'' ) the southward shift of the NH tropical edge of the tropical rain belt from the 1950s to the 1980s ( [[#Allen--2014|Allen et al., 2014]] ; [[#Brönnimann--2015|Brönnimann et al., 2015]] ) and the severe drought in the Sahel that peaked in the mid-1980s ( [[#Rotstayn--2002|Rotstayn et al., 2002]] ; [[#Undorf--2018b|Undorf et al., 2018b]] ). These processes also explain ( ''high confidence'' ) the observed decrease of South East Asian monsoon precipitation during the second half of the 20th century (Figure 8.7; [[#Bollasina--2011|Bollasina et al., 2011]] ; [[#Sanap--2015|Sanap et al., 2015]] ; [[#Krishnan--2016|Krishnan et al., 2016]] ; [[#Lau--2017|Lau and Kim, 2017]] ; [[#Lin--2018|Lin et al., 2018]] ; [[#Undorf--2018b|Undorf et al., 2018b]] ).

Absorption of solar radiation by anthropogenic aerosols such as black carbon warms the lower troposphere and increases moist static energy, but also results in larger convection inhibition that suppresses light rainfall (Box 8.1, Figure 2; Y. [[#Wang--2013|]] [[#Wang--2013|]] [[#Wang--2013|]] [[#Wang--2013|Wang et al., 2013]] ). Release of aerosol-induced instability, often triggered by topographical barriers, produces intense rainfall, flooding ( [[#Fan--2015|Fan et al., 2015]] ; [[#Lee--2016|Lee et al., 2016]] ) and severe convective storms ( ''medium confidence'' ) ( [[#Saide--2015|Saide et al., 2015]] ). In particular, aerosols induce intense convection at the Himalaya foothills during the pre-monsoon season, which generates a regional convergence there ( ''medium confidence'' ). This mechanism is termed the ‘elevated heat pump hypothesis’ ( [[#Lau--2006|Lau and Kim, 2006]] ; [[#D’Errico--2015|D’Errico et al., 2015]] ).

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[[File:02db3d5a52af3c474c907c7a475bf2f3 IPCC_AR6_WGI_Box_8_1_Figure_2.png]]

'''Box 8.1, Figure 2 |'''

'''Schematic depiction of the atmospheric effects of light-absorbing aerosols on convection and cloud formation: (a) without and (b) with the presence of absorbing aerosols in the planetary boundary layer.''' The dashed and solid blue lines correspond to the vertical temperature profiles in the absence and presence of the absorbing aerosol layer, respectively, and the solid and dashed red lines denote the dry and moist adiabats, respectively. Absorbing aerosols result in an increasing temperature in the atmosphere but a reduced temperature at the surface. The reduced surface temperature and the increased temperature aloft led to a larger negative energy associated with convective inhibition (–) and a higher convection condensation level (CCL) under the polluted conditions. On the other hand, the absorbing aerosol layer induces a larger convective available potential energy (+) above CCL, facilitating more intensive vertical development of clouds, if lifting is sufficient to overcome the larger convective inhibition. Figure from Y. [[#Wang--2013|Wang et al. (2013)]] .

'''Aerosol cloud microphysical effects'''

Cloud droplets nucleate on pre-existing aerosol particles which act as cloud condensation nuclei (CCN). Anthropogenic aerosols add CCN, compared to a pristine background, and produce clouds with more numerous and smaller droplets, slower to coalesce into raindrops and to freeze into ice hydrometeors at temperatures below 0°C. Adding CCN suppresses light rainfall from shallow and short-lived clouds, but it is compensated by heavier rainfall from deep clouds. Adding aerosols to clouds in extremely clean air invigorates them by more efficient vapour condensation on the added drop surfaces ( [[#Koren--2014|Koren et al., 2014]] ; [[#Fan--2018|Fan et al., 2018]] ). Clouds forming in more polluted air masses (hence with more numerous and smaller drops) need to grow deeper to initiate rain ( [[#Freud--2012|Freud and Rosenfeld, 2012]] ; [[#Konwar--2012|Konwar et al., 2012]] ; [[#Campos%20Braga--2017|Campos Braga et al., 2017]] ). This leads to larger amount of cloud water evaporating aloft while cooling and moistening the air there at the expense of the lower levels, which leads to convective invigoration ( [[#Dagan--2017|Dagan et al., 2017]] ; [[#Chua--2020|Chua and Ming, 2020]] ), followed by convergence, air mass destabilization and added rainfall in an amplifying feedback loop

Box 8.1

( [[#Abbott--2021|Abbott and Cronin, 2021]] ). In addition, delaying rain initiation until greater altitudes are reached transports more cloud water above the 0°C altitude and leads to additional release of latent heat of freezing and/or vapour deposition, which in combination with the added latent heat of condensation enhances the cloud updrafts ( [[#Fan--2018|Fan et al., 2018]] ). The stronger updrafts invigorate mixed-phase precipitation and the resultant hail and cloud electrification (Rosenfeldet al., 2008; [[#Thornton--2017|Thornton et al., 2017]] ). This includes the outer convective rainbands of tropical cyclones. There is ''medium confidence'' that air pollution enhances flood hazard associated with the outer rain bands at the expense of the inner rain bands ( [[#Wang--2014|Wang et al., 2014]] ; [[#Zhao--2018|]] [[#Zhao--2018|]] [[#Zhao--2018|C. Zhao et al., 2018]] ; [[#Souri--2020|Souri et al., 2020]] ).

The aerosol effect on invigoration and rainfall from deep convective clouds peaks at moderate levels (aerosol optical depth of 0.2 to 0.3), but reverses into suppression with more aerosols (H. [[#Liu--2019|]] [[#Liu--2019|Liu et al., 2019]] ). More generally, the microphysical aerosol-related processes often compensate or buffer each other ( [[#Stevens--2009|Stevens and Feingold, 2009]] ). For example, suppressed rain by slowing drop coalescence enhances mixed-phase precipitation. Therefore, despite the potentially large aerosol influence on the precipitation forming processes, the net outcome of aerosol microphysical effects on precipitation amount has generally ''low confidence'' , especially when evaluated with respect to the background of high natural variability in precipitation ( [[#Tao--2012|Tao et al., 2012]] ).

Ice nucleating particle (INP) initiate ice precipitation from persistent supercooled water clouds that have cloud droplets too small for efficient warm rain, or expedite mixed-phase precipitation in short-lived supercooled rain clouds ( [[#Creamean--2013|Creamean et al., 2013]] ). Most INPs are desert and soil dust particles, rather than air pollution aerosols ( [[#DeMott--2010|DeMott et al., 2010]] ). Biogenic particles from terrestrial and marine origin are more rare, but important at temperatures above about – 15°C ( [[#Murray--2012|Murray et al., 2012]] ; [[#DeMott--2016|DeMott et al., 2016]] ). Dust particles from long-range transport across the Pacific were found to enhance snow-forming processes over the Sierra Nevada in California ( [[#Creamean--2013|Creamean et al., 2013]] ; [[#Fan--2014|Fan et al., 2014]] ). The impact of INPs was demonstrated by glaciogenic cloud seeding experiments, which enhanced orographic supercooled clouds with ''medium confidence'' of success ( [[#French--2018|French et al., 2018]] ; [[#Rauber--2019|Rauber et al., 2019]] ; [[#Friedrich--2020|Friedrich et al., 2020]] ). There are still major gaps in understanding the effects of INPs mainly on deep convective clouds ( [[#Kanji--2017|Kanji et al., 2017]] ; [[#Stanford--2017|Stanford et al., 2017]] ; [[#Korolev--2020|Korolev et al., 2020]] ).

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