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

==== 8.5.2.1 Quantification of Water Cycle Internal Variability ====

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Estimating internal variability is an important challenge in the assessment of human-induced changes in the water cycle since its magnitude and range of variability can exceed the anthropogenic signal, at least at the regional scale and for near-term projections or low-emissions scenarios (Sections 4.4.1.4 and 8.4.2.9; Deser et al. , 2012; [[#Shepherd--2014|Shepherd, 2014]] ; Xie et al. , 2015; Sarojini et al. , 2016; [[#Dai--2019|Dai and Bloecker, 2019]] ; Lehner et al. , 2020) . Underestimating internal variability in models may result in the overestimation of anthropogenic climate change because the ‘noise’ in the signal-to-noise ratio is underestimated ( [[#Knutson--2018|Knutson and Zeng, 2018]] ). There is ''medium confidence'' that this underestimation affects global water cycle projections, for instance, in terms of drought persistence and severity in the south-western USA, eastern Australia, southern Africa, the Mediterranean, the southern Amazonian basin and China ( [[#Ault--2014|Ault et al., 2014]] ; [[#Cook--2018|Cook et al., 2018]] ; [[#Gu--2018|Gu et al., 2018]] ). In CMIP6 models, the uncertainty in future projections of 20-year mean precipitation changes attributable to internal variability ranges from 41% in the near term (2021–2040) to 5% in the long term (2081–2100) (Figure 8.23). For decadal-mean precipitation changes, the relative contribution of internal variability is even larger when using large ensembles ( [[#Lehner--2020|Lehner et al., 2020]] ).

Over the 20th century, CMIP5 models show a realistic magnitude of decadal precipitation variability, if not a slight overestimation in some regions ( [[#Knutson--2018|Knutson and Zeng, 2018]] ). However, the relatively short and human-influenced instrumental record limits our ability to quantify the magnitude of internal variability in the water cycle, particularly over long time scales (decadal and beyond). Global extended reanalyses ( [[IPCC:Wg1:Chapter:Chapter-1#1.5.2|Section 1.5.2]] ) have been used to derive long-term variability in the regional water cycle components ( [[#Caillouet--2017|Caillouet et al., 2017]] ), merged with historical meteorological and hydrological local observations ( [[#Bonnet--2017|Bonnet et al., 2017]] ; [[#Devers--2020|Devers et al., 2020]] ). Specific assessment of these types of methodology and related uncertainties is provided in [[IPCC:Wg1:Chapter:Chapter-10|Chapter 10]] (Sections 10.2 and 10.3). Paleoclimate archives (tree rings, corals, ice core, speleothems, lake and ocean sediments) provide extended reconstructions of key water cycle metrics and large-scale circulation features. Some studies have suggested that CMIP5 models underestimate internal variability at decadal and longer time scales, and therefore may be missing important processes in the climate system (Ault et al. , 2012, 2013; Bunde et al. , 2013; Franke et al. , 2013; Cheung et al. , 2017; Hope et al. , 2017; [[#Kravtsov--2017|Kravtsov, 2017]] ; Cassou et al. , 2018) . However, recent assessments using paleoclimate records have found that CMIP5 models are able to reproduce decadal-to-centennial variability, including the severity, persistence and spatial extent of megadroughts (Coats et al., 2015; [[#Stevenson--2015|Stevenson et al., 2015]] ; [[#PAGES%20Hydro2K%20Consortium--2017|PAGES Hydro2K Consortium, 2017]] ), once signal reddening (autocorrelation) in proxy archives is accounted for (Deeet al., 2017; [[#PAGES%20Hydro2K%20Consortium--2017|PAGES Hydro2K Consortium, 2017]] ). Implementation of proxy system models, that is, functions that transform model variables into proxy units, has reduced model–proxy disagreement, although some differences in the magnitude of internal variability remain, particularly at centennial time scales (Deeet al., 2017; [[#Parsons--2017|Parsons et al., 2017]] ). It is unclear whether remaining discrepancies represent limitations of the climate models, or limitations of the proxy system models. Therefore, there is ''medium to high confidence'' (i.e., depending on the region) that climate models do not underestimate water cycle internal variability.

The mechanisms driving internal variability in the water cycle in climate model simulations varies. While models indicate that cool SSTs in the eastern tropical Pacific (La Niña or the cool phase of the PDO) are associated with drought in south-western North America, they also show that atmospheric internal variability may be a more prominent driver (Coats et al., 2015, 2016; [[#Stevenson--2015|Stevenson et al., 2015]] ; [[#Parsons--2018|Parsons et al., 2018]] ). Simulations of the last millennium from CMIP5–PMIP3 reproduce the observed negative correlation between eastern Australian rainfall and the central equatorial Pacific SSTs with varying skill, and also display periods when the ENSO teleconnection weakens substantially for several decades (Brownet al., 2016a). Differences in simulated internal variability have been found to be responsible for the inter-model spread in predicted shifts in subtropical dry zones for a given shift in the Hadley cell (Seviour et al., 2018). CMIP5 models show that both internal variability and anthropogenic forcings are responsible for the drying over the South Atlantic Convergence Zone region, though with large uncertainties ( [[#Zilli--2021|Zilli and Carvalho, 2021]] ). Moreover, the detection of the anthropogenic forcing on the South Atlantic Convergence Zone is strongly dependent on the characterization of model internal variability ( [[#Talento--2012|Talento and Barreiro, 2012]] ).

Beyond the tropics, North Pacific decadal variability (Annex IV.2.6, 2.4.5, 3.7.6) exerts a strong modulation of extratropical ENSO teleconnections, but also influences low-frequency variability of the Walker circulation, which is underestimated by most CMIP5 models (England et al., 2014). Atlantic Multi-decadal Variability (Annex IV.2.7, 2.4.6, 3.7.7) teleconnections show a high model spread among CMIP5 models, both in terms of persistence and spatial coherence (Qasmiet al., 2017), which has potential consequences for the water cycle variability simulated over Europe. For example, internal variability will continue to play an important role in the variability of river flows over France in coming decades ( ''medium confidence'' ) (Giuntoli et al., 2013; [[#Boé--2014|Boé and Habets, 2014]] ; [[#Bonnet--2017|Bonnet et al., 2017]] ).

Ensembles of atmosphere-only simulations driven by observed or reconstructed SST are useful for evaluating the ability of models to capture the circulation and/or precipitation variability observed over the historical period ( [[#Zhou--2016|Zhou et al., 2016]] ; [[#Deng--2018|Deng et al., 2018]] ; [[#Douville--2019|Douville et al., 2019]] ). However, limitations of such AGCM-based attribution methods, that is, related to the lack of air–sea interactions in the response, may lead to erroneous attribution conclusions in some regions for local circulation and mean and extreme precipitation ( [[#Dong--2017|Dong et al., 2017]] ). Other methods to measure the portion of precipitation variability include the partitioning into dynamical as opposed to thermodynamical components ( [[#Saffioti--2016|Saffioti et al., 2016]] ; [[#Fereday--2018|Fereday et al., 2018]] ; [[#Lehner--2018|Lehner et al., 2018]] ), the analysis of variance ( [[#Dong--2018a|Dong et al., 2018a]] ) and direct characterization of stochastic weather-noise ( [[#Short%20Gianotti--2014|Short Gianotti et al., 2014]] ).

Single-model initial condition large ensembles (SMILEs) are a powerful tool for estimating the magnitude of internal variability in historical and future climates ( [[IPCC:Wg1:Chapter:Chapter-1#1.4.4|Section 1.4.4]] ). Using SMILEs, it has been shown, for example, that internal NAO variability imparts substantial uncertainty to future changes in European precipitation (Figure 8.24; [[#Deser--2017|Deser et al., 2017]] ). For the South Asian summer monsoon, internal variability can overshadow the forced monsoon rainfall trend, thereby increasing near-term projection uncertainties (X. [[#Huang--2020|Huang et al., 2020]] a). Specific regional applications of the use of large ensembles are further assessed in Sections 10.3.4.3 and 10.3.4.4.

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[[File:752cba23648b8c84e80e7f016363d6c2 IPCC_AR6_WGI_Figure_8_24.png]]

'''Figure 8.24 |''' '''Impact of the North Atlantic Oscillation (NAO) on''' '''2016–2045''' '''climate trends.''' '''(a)''' Regressions of winter sea level pressure (SLP) and precipitation trends upon the normalized leading principal component (PC) of winter SLP trends in the CESM1 Large Ensemble, multiplied by two to correspond to a two standard deviation anomaly of the PC (as internal climate variability component); '''(b)''' CESM1 ensemble-mean winter SLP and precipitation trends (as forced climate variability component); '''(c)''' b – a (forced minus internal climate variability component); '''(d)''' b + a (forced plus internal climate variability component). Precipitation in colour shading (mm day <sup>–1</sup> per 30 years) and SLP in contours (interval = 1 hPa per 30 years with negative values dashed). Figure adapted from [[#Deser--2017|Deser et al. (2017)]] , https://creativecommons.org/licenses/by/4.0/ ; further details on data sources and processing are available in the chapter data table (Table 8.SM.1).

Since AR5, SMILEs have helped quantify the time of emergence of climate change signals (see Sections 1.4.2.2 and 10.4.3). Results from SMILEs indicate that by 2000–2009 (compared to 1950–1999), simulated anthropogenic shifts in mean annual precipitation already emerged over 36–41% of the globe including high latitudes ( [[#Frankcombe--2018|Frankcombe et al., 2018]] ; [[#Kumar--2018|Kumar and Ganguly, 2018]] ), the eastern subtropical oceans, and the tropics (Zhang andDelworth, 2018). By 2050 (2100), more than 60% (85%) of the globe is projected to show detectable anthropogenic shifts in mean annual precipitation (Zhang andDelworth, 2018). Other SMILE results for the 1950–2100 period ( [[#Kay--2015|Kay et al., 2015]] ; [[#Sigmond--2016|Sigmond and Fyfe, 2016]] ) indicate that internal variability can obscure the detection of the anthropogenic hydroclimatic signal until the middle to late 21st century in many parts of the world for both mean and extreme precipitation ( [[#Martel--2018|Martel et al., 2018]] ; [[#Dai--2019|Dai and Bloecker, 2019]] ). A common finding is that changes in the characteristics of wet extreme events will emerge earlier than changes in average conditions ( [[#Gaetani--2020|Gaetani et al., 2020]] ; [[#Hawkins--2020|Hawkins et al., 2020]] ; [[#Kusunoki--2020|Kusunoki et al., 2020]] ). An assessment of the methods used to estimate time of emergence is presented in [[IPCC:Wg1:Chapter:Chapter-10|Chapter 10]] ( [[IPCC:Wg1:Chapter:Chapter-10#10.3.4.3|Section 10.3.4.3]] ). For specific regional examples of climate change attribution and emergence of anthropogenic signal, see [[IPCC:Wg1:Chapter:Chapter-10#10.4.2|Section 10.4.2]] .

In summary, there is ''medium confidence'' that climate models reproduce the general magnitude and character of internal variability that influences water cycle variables. There is ''high confidence'' that internal variability will continue to be a major source of uncertainty, at least for near-term water cycle projections at the regional scale. There is ''low confidence'' in the region-dependent time of emergence of water cycle changes (see also [[IPCC:Wg1:Chapter:Chapter-10#10.4.3|Section 10.4.3]] ), but there is ''medium confidence'' that changes in wet extreme events will emerge earlier than changes in average conditions.

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