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

=== 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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'''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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'''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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