Editing IPCC:AR6/WGI/Chapter-10 (section)

=== 10.4.2 Regional Climate Change Attribution Examples ===

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This section focuses on three illustrative examples that span different regions, time scales, and attribution methods, without aiming at being comprehensive. These examples illustrate attribution statements that are based upon multiple lines of evidence, combining multiple observational datasets, different generations and types of models, process understanding and assessment of various sources of uncertainty. Detection and attribution assessments for all AR6 regions and specific variables can be found in the Atlas.

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<span id="the-sahel-and-west-african-monsoon-drought-and-recovery"></span>
==== 10.4.2.1 The Sahel and West African Monsoon Drought and Recovery ====

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The Sahel, fed by the West African monsoon, has experienced severe decadal rainfall variations (Figure 10.11a). Abundant rainfall in the 1950s–1960s was followed by a large negative trend (Figure 10.11b) until at least the 1980s, over which annual rainfall fell by 20–30% ( [[#Hulme--2001|Hulme, 2001]] ). The subsequent partial recovery ( [[#Wang--2021|]] [[#Wang--2021|B. Wang et al., 2021]] ) is more uncertain: rain-gauge studies suggest a return to long-term positive anomalies in the western Sahel in the early 2000s ( [[#Panthou--2018|Panthou et al., 2018]] ), while CHIRPS merged satellite/gauge data show a wetter western Sahel since 1981 ( [[#Bichet--2018a|Bichet and Diedhiou, 2018a]] , b). The recovery has been more significant over the central rather than the western Sahel ( [[#Lebel--2009|Lebel and Ali, 2009]] ; [[#Maidment--2015|Maidment et al., 2015]] ; Sanogo et al., 2015) and a multiple-gauge record supports a greater recovery to the eastern side ( [[#Nicholson--2018|Nicholson et al., 2018]] ). In this attribution example, drivers of the long-term drought and subsequent partial recovery are discussed, including anthropogenic GHG and aerosol emissions, and sea surface temperature (SST) variations that, in part, relate to internal variability. The reader is also referred to assessment in [[IPCC:Wg1:Chapter:Chapter-8#8.3.2.4|Section 8.3.2.4]] . We define the Sahel within 10°N–20°N across to 30°E, consistent with the eastern boundary used in Chapter 8, and the rainy season as spanning June to September.

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[[File:1db32c7ae496c02335d5444aa0a60a8c IPCC_AR6_WGI_Figure_10_11.png]]

'''Figure 10.11''' '''|''' '''Attribution of historic precipitation change in the Sahelian West African monsoon during June to September. (a)''' Time series of CRU TS precipitation anomalies (mm day <sup>–1</sup> , baseline 1955–1984) in the Sahel box (10°N–20°N, 20°W–30°E) indicated in panel '''(b)''' applying the same low-pass filter as that used in Figure 10.10. The two periods used for difference diagnostics are shown in grey columns. (b) Precipitation change (mm day <sup>–1</sup> ) in CRU TS data for 1980–1990 minus 1950–1960 periods. '''(c)''' Precipitation difference (mm day <sup>–1</sup> ) between 1.5× and 0.2× historical aerosol emissions scaling factors averaged over 1955–1984 and five ensemble members of HadGEM3 experiments after [[#Shonk--2020|Shonk et al. (2020)]] . '''(d)''' Sahel precipitation anomaly time series (mm day <sup>–1</sup> , baseline 1955–1984) in Coupled Model Intercomparison Project Phase 6 (CMIP6) for 49 historical simulations with all forcings (red), and thirteen for each of greenhouse gas-only forcing (light blue) and aerosol-only forcing (grey), with a thirteen-point weighted running mean applied (a variant on the binomial filter with weights [1-6-19-42-71-96-106-96-71-42-19-6-1]). The CMIP6 subsample of all forcings matching the individual forcing simulations is also shown (pink). '''(e)''' Precipitation linear trend (% per decade) for (left) decline (1955–1984) and (right) recovery periods (1985–2014) for ensemble means and individual CMIP6 historical experiments (including single-forcing) as in panel (d) plus 34 CMIP5 models (dark blue). Box-and-whisker plots show the trend distribution of the three coupled and the d4PDF atmosphere-only single-model initial-condition large ensembles (SMILEs) used throughout (Chapter 10 and follow the methodology used in Figure 10.6. The two black crosses represent observational estimates from GPCC and CRU TS. Trends are estimated using ordinary least-squares regression. Further details on data sources and processing are available in the chapter data table (Table 10.SM.11).

The role of SST forcing in the rainfall decline is assessed first. Competing mechanisms from equatorial Atlantic SSTs and inter-hemispheric SST gradients regulate decadal variability in the Sahel ( [[#Nicholson--2013|Nicholson, 2013]] ), alternatively explained by tropical warming leading to Sahel drought, while North Atlantic warming promotes increased rainfall ( [[#Rodríguez-Fonseca--2015|Rodríguez-Fonseca et al., 2015]] ). The SST influence has been formalized in an AMV framework ( [[#Giannini--2013|Giannini et al., 2013]] ; [[#Martin--2014|Martin and Thorncroft, 2014]] ; [[#Martin--2014|Martin et al., 2014]] ; [[#Park--2015|Park et al., 2015]] ), suggesting that relative North Atlantic SST warming increases the Northern Hemisphere differential warming, enhancing Sahel rainfall. The AMV influence is supported by CMIP5 initialized decadal hindcasts ( [[#Gaetani--2013|Gaetani and Mohino, 2013]] ; [[#Mohino--2016|Mohino et al., 2016]] ; [[#Sheen--2017|Sheen et al., 2017]] ), which outperform empirical predictions based on persistence. Some caution is needed since the full magnitude of internal variability is not captured in most CMIP5 models, as poor resolution prevents reproduction of AMV teleconnection responses ( [[#Vellinga--2016|Vellinga et al., 2016]] ), and the magnitude of AMV-related SST variation may be underestimated in CMIP5 ( [[IPCC:Wg1:Chapter:Chapter-3#3.7.7|Section 3.7.7]] , which also assesses that the AMV may be partially forced). The influence of PDV has been studied to a lesser extent, with the PDV positive phase having a negative impact on Sahel rainfall in combined observational/CMIP5 analysis ( [[#Villamayor--2015|Villamayor and Mohino, 2015]] ). The closer match between the observed rainfall declining trend and those in an atmosphere-only SMILE, in which SSTs are matched to observations, compared to three coupled SMILEs in which they are not, suggests that the underlying ocean surface might be essential in driving the decline (Figure 10.11e).

In terms of anthropogenic emissions, regional aerosol emissions from Europe, and to a lesser extent from Asia, have been shown in a global model to weaken Sahel precipitation either through a weakened Saharan heat low or via the Walker circulation ( [[#Dong--2014|Dong et al., 2014]] ). Greenhouse gases (GHGs) and anthropogenic aerosol can be considered together to control ITCZ position based on temperature asymmetry at the hemispheric scale. GHGs increase Sahel precipitation, while aerosol reduces it (in coupled slab-ocean model experiments by [[#Ackerley--2011|Ackerley et al. (2011)]] following [[#Biasutti--2006|Biasutti and Giannini (2006)]] ). This effect is stronger when models account for aerosol–cloud interactions ( [[#Allen--2015|Allen et al., 2015]] ). Perturbed physics GCM ensembles suggests that aerosol emissions were the main driver of observed drying over 1950–1980 ( [[#Ackerley--2011|Ackerley et al., 2011]] ), supported by CMIP5 single-forcing experiments ( [[#Polson--2014|Polson et al., 2014]] ). A coherent drying signal in CMIP5 over the extended 1901–2010 period has also been found, although smaller than the observed trend ( [[#Knutson--2018|Knutson and Zeng, 2018]] ). By applying aerosol scaling factors to the historical period in order to sample the uncertainty in CMIP5 aerosol radiative forcing, [[#Shonk--2020|Shonk et al. (2020)]] found differences of 0.5 mm day <sup>–1</sup> for Gulf of Guinea rainfall between strong and weak aerosol experiments as illustrated in Figure 10.11c, although the drying appears further south than observed due to model bias.

For the partial recovery in West African monsoon and Sahel rainfall since the late 1980s, a detection study using three reanalyses ( [[#Cook--2015|Cook and Vizy, 2015]] ) shows a connection to increasing Saharan temperatures at a rate two to four times greater than the tropical mean, also confirmed by multiple observational and satellite-based data ( [[#Zhou--2016|Zhou and Wang, 2016]] ; [[#Vizy--2017|Vizy and Cook, 2017]] ) and the review of [[#Cook--2019|Cook and Vizy (2019)]] . Reanalyses are also noted to significantly underestimate the Saharan warming ( [[#Zhou--2016|Zhou and Wang, 2016]] ). Saharan warming causes a stronger thermal low and more intense monsoon flow, providing more moisture to the central and eastern Sahel, supported by CMIP5 models ( [[#Lavaysse--2016|Lavaysse et al., 2016]] ), although not all models capture the observed rainfall–heat–low relationship. Sahel rainfall is also incorrectly located in prototype versions of a few CMIP6 models, related to tropospheric temperature biases ( [[#Martin--2017|Martin et al., 2017]] ). Amplified Saharan warming has increased the wind shear, leading to a tripling of extreme storms since 1982, which may partially explain the recovery ( [[#Taylor--2017|Taylor et al., 2017]] ). Instead, observations, multiple models and SST-sensitivity experiments with AGCMs have suggested that stronger Mediterranean Sea evaporation enhances low-level moisture convergence to the Sahel, increasing rainfall ( [[#Park--2016|Park et al., 2016]] ). Meanwhile, an AGCM study suggested that GHGs alone (in the absence of SST warming) could cause Sahel rainfall recovery, with an additional role for anthropogenic aerosol ( [[#Dong--2015|Dong and Sutton, 2015]] ); recent changes in North Atlantic SSTs, although substantial, did not exert a significant impact on the recovery. Large spread in the recovery in a five-member AGCM ensemble suggests that atmospheric internal variability cannot be discounted ( [[#Roehrig--2013|Roehrig et al., 2013]] ).

Consistent timing of the southward ITCZ shift during the decline period in CMIP3 and CMIP5 historical simulations supports the role of external forcing, chiefly anthropogenic aerosol ( [[#Hwang--2013|Hwang et al., 2013]] ). The evolution of the observed decline and recovery is largely followed by the CMIP5 multi-model mean, further supporting the role of external drivers ( [[#Giannini--2019|Giannini and Kaplan, 2019]] ). Updated results from CMIP6 for historical simulations with all and single forcings are represented in Figure 10.11d,e showing smaller trends than those observed. [[#Giannini--2019|Giannini and Kaplan (2019)]] attempted to unify the driving mechanisms for decline and recovery based on singular-value decomposition of observed and modelled SSTs. Since the 1950s, tropical warming arising from GHGs and North Atlantic cooling from aerosol led to regional stabilization, suppressing Sahel rainfall. The subsequent reduction in aerosol emissions then led to North Atlantic warming and recovery of Sahel rainfall. Such mechanisms continue into the near-term future in idealized and modified RCP experiments, with scenarios featuring more aggressive reductions in aerosol emissions, or including aerosol–cloud interactions, favouring a greater northward shift of rainfall ( [[#Allen--2015|Allen, 2015]] ; [[#Westervelt--2017|Westervelt et al., 2017]] , 2018; [[#Scannell--2019|Scannell et al., 2019]] ). There is paleoclimate evidence of changes to Sahel rainfall in the past, in particular with enhancement of the West African monsoon during the mid-Holocene. However, the mechanisms governing such a change have been shown to be largely dynamical in nature ( [[#D’Agostino--2019|D’Agostino et al., 2019]] ), suggesting that the mid-Holocene cannot be used to inform the credibility of changes due to greenhouse warming.

There is ''very high confidence'' ( ''robust evidence'' and ''high agreement'' ) that patterns of 20th-century ocean and land surface temperature variability have caused the Sahel drought and subsequent recovery by adjusting meridional gradients. There is ''high confidence'' ( ''robust evidence'' and ''medium agreement'' ) that the changing temperature gradients that perturb the West African monsoon and Sahel rainfall are themselves driven by anthropogenic emissions: warming by GHG emissions was initially restricted to the tropics but suppressed in the North Atlantic due to nearby emissions of sulphate aerosols, leading to a reduction in rainfall. The North Atlantic subsequently warmed following the reduction of aerosol emissions, leading to rainfall recovery.

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<span id="the-south-eastern-south-america-summer-wetting"></span>
==== 10.4.2.2 The South-Eastern South America Summer Wetting ====

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A positive trend in summer (December to February) precipitation has been detected in multiple observational sources in south-eastern South America since the beginning of the 20th century ( [[#Gonzalez--2013|Gonzalez et al., 2013]] ; [[#Vera--2015|Vera and Díaz, 2015]] ; [[#Wu--2016|Wu et al., 2016]] ; H. [[#Zhang--2016|]] [[#Zhang--2016|]] [[#Zhang--2016|Zhang et al., 2016]] ; [[#Díaz--2017|Díaz and Vera, 2017]] ; [[#Saurral--2017|Saurral et al., 2017]] ). Sedimentary records from the Mar Chiquita lake indicate that the last quarter of the 20th century was wetter than any period during the last 200 years ( [[#Piovano--2004|Piovano et al., 2004]] ). In this attribution example the drivers contributing to the positive trend for the period 1951–2014 are discussed (Figure 10.12a). Precipitation anomalies of Climatic Research Unit Time Series (CRU TS) as well as for the two members of a SMILE with the most negative and positive trends for 1951–2014 are displayed in Figure 10.12b. The trend for 1951–2014 using CRU TS and GPCC is illustrated in Figure 10.12c, and for the region defined by the black quadrilateral, it amounts to 2.8 (CRU TS) – 3.5 (GPCC) mm per month and decade (see black crosses in Figure 10.12d) while the mean summer monthly precipitation for the same period is 104 (CRU TS) –109 (GPCC) mm. The trend is also detectable in daily and monthly extremes ( [[#Re--2009|Re and Barros, 2009]] ; [[#Marengo--2010|Marengo et al., 2010]] ; [[#Penalba--2010|Penalba and Robledo, 2010]] ; [[#Doyle--2012|Doyle et al., 2012]] ; Donat et al., 2013; [[#Lorenz--2016|Lorenz et al., 2016]] ).

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[[File:9df6b6493db188e1f655e2700e811c40 IPCC_AR6_WGI_Figure_10_12.png]]

'''Figure 10.1''' '''2 |''' '''South-Eastern South America positive mean precipitation trend and its drivers during 1951–2014. (a)''' Mechanisms that have been suggested to contribute to South-Eastern South America summer wetting. '''(b)''' Time series of austral summer (December to February) precipitation anomalies (%, baseline 1995–2014) over the South-Eastern South American region (26.25°S–38.75°S, 56.25°W–66.25°W), black quadrilateral in the first map of panel '''(c)''' . Black, brown and green lines show low-pass filtered time series for CRU TS), and the members with driest and wettest trends of the MPI-ESM single-model initial-condition large ensemble (SMILE; between 1951–2014), respectively. The filter is the same as the one used in Figure 10.10. (c) Mean austral summer precipitation spatial linear 1951–2014 trends (mm per month and decade) from CRU TS and GPCC. Trends are estimated using ordinary least squares regression. '''(d)''' Distribution of precipitation 1951–2014 trends over South-Eastern South America from GPCC and CRU TS (black crosses), CMIP6 all-forcing historical (red circles) and MIROC6, CSIRO-Mk3-6-0, MPI-ESM and d4PDF SMILEs (grey box-and-whisker plots). Grey squares refer to ensemble mean trends of their respective SMILE and the red circle refers to the CMIP6 multi-model mean. Box-and-whisker plots follow the methodology used in Figure 10.6. Further details on data sources and processing are available in the chapter data table (Table 10.SM.11).

The influence of SST anomalies on south-eastern South America precipitation have been studied extensively on interannual to multi-decadal time scales ( [[#Paegle--2002|Paegle and Mo, 2002]] ). The positive phase of El Niño–Southern Oscillation (ENSO; Annex IV.2.3) is related to stronger mean and extreme rainfall over south-eastern South America ( [[#Ropelewski--1987|Ropelewski and Halpert, 1987]] ; [[#Grimm--2009|Grimm and Tedeschi, 2009]] ; [[#Robledo--2016|Robledo et al., 2016]] ). The ENSO influence may be modulated by the PDV ( [[#Kayano--2007|Kayano and Andreoli, 2007]] ; [[#Fernandes--2018|Fernandes and Rodrigues, 2018]] ) and the AMV ( [[#Kayano--2014|Kayano and Capistrano, 2014]] ). PDV and AMV also influence the south-eastern South American climate independently of ENSO ( [[#Barreiro--2014|Barreiro et al., 2014]] ; [[#Grimm--2015|Grimm and Saboia, 2015]] ; [[#Robledo--2020|Robledo et al., 2020]] ). While Pacific SSTs dominate the overall influence of oceanic variability in the region, the Atlantic variability seems to dominate on multi-decadal time scales and has been proposed as a driver for the long-term positive trend ( [[#Seager--2010|Seager et al., 2010]] ; [[#Barreiro--2014|Barreiro et al., 2014]] ). Based on experiments designed to test how south-eastern South America precipitation is modulated by tropical Atlantic SSTs, [[#Seager--2010|Seager et al. (2010)]] showed that cold anomalies in the tropical Atlantic favour wetter conditions by inducing an upper-tropospheric flow towards the equator, which, via advection of vorticity, leads to ascending motion over south-eastern South America (Figure 10.12a). [[#Monerie--2019|Monerie et al. (2019)]] supported this argument showing a negative relationship between south-eastern South America precipitation and the AMV index ( [[#Huang--2015|Huang et al., 2015]] ) using an AGCM coupled to an ocean mixed-layer model with nudged SSTs.

The positive trend of precipitation has also been attributed to anthropogenic GHGemissions and stratospheric ozone depletion. CMIP5 models only show a positive trend when including anthropogenic forcings ( [[#Vera--2015|Vera and Díaz, 2015]] ). These results were supported by [[#Knutson--2018|Knutson and Zeng (2018)]] based on univariate detection/attribution analysis of annual mean trends for the 1901–2010 and 1951–2010 periods. However, the main features of summer mean precipitation and variability of South America are still not well-represented in all CMIP5 and CMIP6 models ( [[#Gulizia--2015|Gulizia and Camilloni, 2015]] ; [[#Díaz--2017|Díaz and Vera, 2017]] ; [[#Díaz--2021|Díaz et al., 2021]] ). This motivates the construction of ensembles that exclude the worst performing models ( [[#10.3.3.4|Section 10.3.3.4]] ). The construction of ensembles of CMIP5 historical simulations with realistic representation of precipitation anomalies with opposite sign over south-eastern South America and eastern Brazil showed that the trend since the 1950s could be related to changes in precipitation characteristics only when simulations included anthropogenic forcings ( [[#Díaz--2017|Díaz and Vera, 2017]] ). GHG emissions have been related to increased precipitation in south-eastern South America through three different mechanisms (Figure 10.12a). First, GHG warming induces a non-zonally uniform pattern of SST warming that includes a warming pattern over the Indian and Pacific oceans that excites wave responses over South America ( [[#Junquas--2013|Junquas et al., 2013]] ). Zonally uniform SST patterns of warming alone lead to precipitation signals opposite to those observed in an AGCM ( [[#Junquas--2013|Junquas et al., 2013]] ). Second, GHG radiative forcing drives an expansion of the Hadley cell so that its descending branch moves poleward from the region, generating anomalous ascending motion and precipitation (H. [[#Zhang--2016|]] [[#Zhang--2016|]] [[#Zhang--2016|Zhang et al., 2016]] ; [[#Saurral--2019|Saurral et al., 2019]] ). The third mechanism by which increased GHG can contribute to increased precipitation in the region is through a delay of the stratospheric polar vortex breakdown. As depicted in Figure 10.12a, both stratospheric ozone depletion and increased GHGs have contributed to the later breakdown of the polar vortex in recent decades ( [[#McLandress--2010|McLandress et al., 2010]] ; [[#Wu--2017|Wu and Polvani, 2017]] ; [[#Ceppi--2019|Ceppi and]] [[#Shepherd--2019|Shepherd, 2019]] ). [[#Mindlin--2020|Mindlin et al. (2020)]] developed future atmospheric circulation storylines ( [[#10.3.4.2|Section 10.3.4.2]] , Box 10.2) for Southern Hemisphere mid-latitudes with the CMIP5 models and found that for south-eastern South America summer precipitation, increases are related to the late-spring breakdown of the stratospheric polar vortex. The connecting mechanism is through a lagged southward shift of the jet stream ( [[#Saggioro--2019|Saggioro and]] [[#Shepherd--2019|Shepherd, 2019]] ), which enhances cyclonic activity over the region ( [[#Wu--2017|Wu and Polvani, 2017]] ).

A common feature among the above discussed studies is that even if global models simulate positive trends when forced with GHG and/or stratospheric ozone, these trends are in general smaller than those observed (e.g., CMIP6 trends in red open circles in Figure 10.12d). [[#Díaz--2021|Díaz et al. (2021)]] showed that to capture the observed trend a multi-model ensemble of SMILEs is needed. Out of the 12 large ensembles examined (with ensemble size varying in the 16–100 range), only seven simulated the observed trend within their range. This could partly be explained by model biases in mean precipitation and its interannual variability. In the sub-ensemble of six models that reproduce reasonably well the observed spatial patterns of mean precipitation and interannual variability, the ensemble mean spread is lower, and the forced response, taken as the multi-model ensemble mean, is slightly more positive than that of the six poorly performing models. The signal-to-noise ratio, estimated as the ratio of the forced response to the spread due to internal variability, is also slightly higher for the best-performing models, suggesting that selecting the best-performing models may have an influence on both attribution of the observed trend and emergence of the forced response in future ( [[#10.4.3|Section 10.4.3]] ).

There is ''high confidence'' that South-Eastern South America summer precipitation has increased since the beginning of the 20th century. Since AR5, science has advanced in the identification of the drivers of the precipitation increase in South-Eastern South America since 1950, including GHG through various mechanisms, stratospheric ozone depletion and Pacific and Atlantic variability. There is ''high confidence'' that anthropogenic forcing has contributed to the South-Eastern South America summer precipitation increase since 1950, but ''very low confidence'' on the relative contribution of each driver to the precipitation increase.

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==== 10.4.2.3 The South-western North America Drought ====

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Persistent hydroclimatic drought in south-western North America remains a much-studied event. Drought is a regular feature of the south-western North America’s climate regime, as can be seen in both the modern record, and through paleoclimate reconstructions ( [[#Cook--2010|Cook et al., 2010]] ; [[#Woodhouse--2010|Woodhouse et al., 2010]] ; [[#Williams--2020|Williams et al., 2020]] ), as well as in future climate model projections ( [[#Cook--2015a|Cook et al., 2015a]] ). Since the early 1980s, which were relatively wet in terms of precipitation and streamflow, the region has experienced major multi-year droughts such as the turn-of-the-century drought that lasted from 1999 to 2005, and the most recent and extreme 2012–2014 drought that in certain locations is perhaps unprecedented in the last millennium ( [[IPCC:Wg1:Chapter:Chapter-8#8.3.1.6|Section 8.3.1.6]] ; [[#Griffin--2014|Griffin and Anchukaitis, 2014]] ; [[#Robeson--2015|Robeson, 2015]] ). Shorter dry spells also happened between these multi-year droughts making 1980 to present a period with an exceptionally steep trend from wet to dry (Figure 10.13a), leading to strong declines in Rio Grande and Colorado river flows ( [[#Lehner--2017b|Lehner et al., 2017b]] ; [[#Udall--2017|Udall and Overpeck, 2017]] ). While robust attribution of this trend is complicated by the large natural variability in this region, the 20th century warming has been suggested to increase the chances for hydrological drought periods by lowering runoff efficiency ( [[#Woodhouse--2016|Woodhouse et al., 2016]] ; [[#Lehner--2017b|Lehner et al., 2017b]] ; [[#Woodhouse--2018|Woodhouse and Pederson, 2018]] ) and affecting evapotranspiration ( [[#Williams--2020|Williams et al., 2020]] ). There is some evidence suggesting that the Last Glacial Maximum, a period of low atmospheric CO <sub>2</sub> , about 21 ka ago, has a thermodynamically-driven zonal mean precipitation response similar to that of the current state with relatively high CO <sub>2</sub> levels when compared with the pre-industrial period. Pluvial conditions at that time and a reduction in precipitation from the Last Glacial Maximum to the pre-industrial period are consistent with drying trends for the region in models with GHG concentrations exceeding pre-industrial levels. However, the dominant large-scale drivers responsible for the precipitation changes observed during these two transitions are markedly different: mainly ice-sheet retreat and increasing insolation on one hand, increasing GHGs on the other hand. This suggests that the Last Glacial Maximum correspondence is fortuitous which strongly limits its use to capture future hydrological cycle changes ( [[IPCC:Wg1:Chapter:Chapter-8#8.3.2.4.4|Section 8.3.2.4.4]] ; [[#Morrill--2018|Morrill et al., 2018]] ; [[#Lowry--2019|Lowry and Morrill, 2019]] ). Furthermore, the conclusion of the Last Glacial Maximum drying versus wetting seems to strongly depend on the physical property of interest, hydrologic or vegetation indicators ( [[#Scheff--2017|Scheff et al., 2017]] ). Droughts are characterized by deficits in total soil moisture content that can be caused by a combination of decreasing precipitation and warming temperature, which promotes greater evapotranspiration. Regional-scale attribution of the prevalence of south-western North America drought since 1980 then mostly focuses on the attribution of change in these two variables.

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[[File:724be120d5dc061c3a1e9a1bf1604e0e IPCC_AR6_WGI_Figure_10_13.png]]

'''Figure 10.13''' '''|''' '''Attribution of the south-western North America precipitation decline during the 1983–2014 period. (a)''' Water year (October to September) precipitation spatial linear trend (in percent per decade) over North America from 1983 to 2014. Trends are estimated using ordinary least squares. Top row: observed trends from CRU TS, REGEN, GPCC, and the Global Precipitation Climatology Project (GPCP). Middle row: driest, mean and wettest trends (relative to the region enclosed in the black quadrilateral, bottom row) from the 100 members of the MPI-ESM coupled SMILE. Bottom row: driest, mean and wettest trends relative to the above region from the 100 members of the d4PDF atmosphere-only SMILE. '''(b)''' Time series of water year precipitation anomalies (%, baseline 1971–2000) over the above south-western North America region for CRU TS (grey bar charts). Black, brown and green lines show low-pass filtered time series for CRU TS, driest and wettest members of the d4PDF SMILE, respectively. The filter is the same as the one used in Figure 10.10. '''(c)''' Distribution of south-western region-averaged water-year precipitation 1983–2014 trends (in percent per decade) for observations (CRU TS, REGEN, GPCC and GPCP, black crosses), CMIP6 all-forcing historical simulations (red circles), the MIROC6, CSIRO-Mk3-6-0, MPI-ESM and d4PDF SMILEs (grey box-and-whisker plots). Grey squares refer to ensemble mean trends of their respective SMILE and the red circle refers to the CMIP6 multi-model mean. Box-and-whisker plots follow the methodology used in Figure 10.6. Further details on data sources and processing are available in the chapter data table (Table 10.SM.11).

The observed south-western North America drying fits the narrative of what might happen in response to increasing GHG concentrations due to a poleward expansion of the subtropics, that is conducive to drying trends over subtropical to mid-latitude regions ( [[#Hu--2013b|Hu et al., 2013b]] ; [[#Birner--2014|Birner et al., 2014]] ; [[#Lucas--2014|Lucas et al., 2014]] ). However, several studies based on modern reanalyses and CMIP5 models have recently shown that the current contribution of GHGs to Northern Hemisphere tropical expansion is much smaller than in the Southern Hemisphere and will remain difficult to detect due to large internal variability, even by the end of the 21st century ( [[IPCC:Wg1:Chapter:Chapter-3#3.3.3.1|Section 3.3.3.1]] ; [[#Garfinkel--2015|Garfinkel et al., 2015]] ; [[#Allen--2017|Allen and Kovilakam, 2017]] ; [[#Grise--2018|Grise et al., 2018]] , 2019). In addition, the widening of the Northern Hemisphere tropical belt exhibits strong seasonality and zonal asymmetry, particularly in autumn and the North Atlantic ( [[#Amaya--2018|Amaya et al., 2018]] ; [[#Grise--2018|Grise et al., 2018]] ). Therefore, it seems that the recent Northern Hemisphere tropical expansion results from the interplay of internal and forced modes of tropical width variations and that the forced response has not robustly emerged from internal variability (Sections 3.3.3.1 and 10.4.3).

A second possible causal factor is the role for ocean-forced or internal atmospheric circulation change. Analysis of observed and CMIP5-simulated precipitation indicates that the drought prevalence since 1980 is linked to natural, internal variability in the climate system ( [[#Knutson--2018|Knutson and Zeng, 2018]] ). Based on observations and ensembles of SST-driven atmospheric simulations, [[#Seager--2014|Seager and Hoerling (2014)]] suggested that robust tropical Pacific and tropical North Atlantic forcing drove an important fraction of annual mean precipitation and soil moisture changes and that early 21st century multi-year droughts could be attributed to natural decadal swings in tropical Pacific and North Atlantic SSTs. A cold state of the tropical Pacific would lead by well-established atmospheric teleconnections to anomalous high pressure across the North Pacific and southern North America, favouring a weaker jet stream and a diversion of the Pacific storm track away from the south-west ( [[#Delworth--2015|Delworth et al., 2015]] ; [[#Seager--2017|Seager and Ting, 2017]] ). The multi-year drought of 2012–2016 has been linked to the multi-year persistence of anomalously high atmospheric pressure over the north-eastern Pacific Ocean, which deflected the Pacific storm track northward and suppressed regional precipitation during California’s rainy season ( [[#Swain--2017|Swain et al., 2017]] ). Going into more detail, [[#Prein--2016a|Prein et al. (2016a)]] used an assessment of changing occurrence of weather regimes to judge that changes in the frequency of certain regimes during 1979–2014 have led to a decline in precipitation by about 25%, chiefly related to the prevalence of anticyclonic circulation patterns in the north-east Pacific. Finally, the moderate model performance in representing Pacific SST decadal variability and its remote influence ( [[IPCC:Wg1:Chapter:Chapter-3#3.7.6|Section 3.7.6]] ) as well as its change under warming may affect attribution results of observed and future precipitation changes ( [[#Seager--2019|Seager et al., 2019]] ).

It has also been suggested that the ocean-controlled influence is limited and internal atmospheric variability has to be invoked to fully explain the observed history of drought on decadal time scales ( [[#Seager--2014|Seager and Hoerling, 2014]] ; [[#Seager--2017|Seager and Ting, 2017]] ). From roughly 1980 to the present, the regional climate signals show an interesting mix between forced and internal variability. [[#Lehner--2018|Lehner et al. (2018)]] used a dynamical adjustment method and large ensembles of coupled and SST-forced atmospheric experiments to suggest that the observed south-western North America rainfall decline mainly results from the effects of atmospheric internal variability, which is in part driven by a PDV-related phase shift in Pacific SST around 2000 (Figure 10.13b,c). Based upon four SMILEs (three using a GCM and another one an AGCM constrained by observed SSTs) and a CMIP6 multi-model suite constrained by observed external forcings, Figure 10.13 shows, in agreement with [[#Lehner--2018|Lehner et al. (2018)]] , that observed SSTs with their associated atmospheric response are the main drivers of the south-western North America precipitation decrease during the 1983–2014 period. Once aspects of the internal variability are removed by dynamical adjustment, the observed precipitation change signal and simulated anthropogenically-forced components look more similar ( [[#Lehner--2018|Lehner et al., 2018]] ).

Importantly, as the AR6 assessment views the PDV as being mostly driven by internal variability ( [[IPCC:Wg1:Chapter:Chapter-3#3.7.6|Section 3.7.6]] ), the lines of evidence cited above suggest that the contribution of natural and anthropogenic forcings to the precipitation decline has a small amplitude. Unlike the precipitation deficit, the accompanying south-western North America warming is driven primarily by anthropogenic forcing from GHGs rather than atmospheric circulation variability and may help to enhance the drought through increased evapotranspiration ( [[#Knutson--2013|Knutson et al., 2013]] ; [[#Diffenbaugh--2015|Diffenbaugh et al., 2015]] ; [[#Williams--2015|Williams et al., 2015]] , [[#Williams--2020|Williams et al., 2020]] ; [[#Lehner--2018|Lehner et al., 2018]] , [[#Lehner--2020|2020]] ).

To conclude, there is ''high confidence'' ( ''robust evidence'' and ''medium agreement'' ) that most (&gt;50%) of the anomalous atmospheric circulation that caused the south-western North America negative precipitation trend can be attributed to teleconnections arising from tropical Pacific SST variations related to PDV. There is ''high confidence'' ( ''robust evidence'' and ''medium agreement'' ) that anthropogenic forcing has made a substantial contribution (about 50%) to the south-western North America warming since 1980.

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==== 10.4.2.4 Assessment Summary ====

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The robustness of regional-scale attribution differs strongly between temperature and precipitation changes. While the influence of anthropogenic forcing on regional temperature long-term change has been detected and attributed in almost all land regions, a robust detection and attribution of human influence on regional precipitation change has not yet fully occurred for many land regions ( [[#10.4.3|Section 10.4.3]] ). Although the contribution of anthropogenic forcing to long-term regional precipitation change has been detected in some regions, a robust quantification of the contributions of different drivers remains elusive. The delayed emergence of the anthropogenic precipitation fingerprint with respect to temperature is likely due to the opposing sign of the fast and slow land precipitation forced responses and time-dependent SST change patterns (Sections 8.2.1 and [[#10.4.3|Section 10.4.3]] ), stronger internal variability ( [[#10.3.4.3|Section 10.3.4.3]] ) as well as larger observational uncertainty ( [[#10.2|Section 10.2]] ) and impact of model biases. The contribution of internal variability to the observed changes can also be very sensitive to the period length and level of spatial aggregation for the region under scrutiny ( [[IPCC:Wg1:Chapter:Chapter-4#4.4.1|Section 4.4.1]] and Cross-Chapter Box 3.1; [[#Kumar--2016|Kumar et al., 2016]] ). Finally, even in the case of temperature changes at multi-decadal time scale, internal variability can still be a substantial driver of regional changes due to cancellation between different external forcings ( [[#Nath--2018|Nath et al., 2018]] ).

To conclude, it is ''virtually certain'' ( ''robust evidence'' and ''high agreement'' ) that anthropogenic forcing has been a major driver of temperature change since 1950 in many sub-continental regions of the world. There is ''high confidence'' ( ''robust evidence'' and ''medium agreement'' ) that anthropogenic forcing has contributed to multi-decadal mean precipitation changes in several regions, for example western Africa, south-east South America, south-western Australia, northern central Eurasia, and South and East Asia. However, at regional scale, the role of internal variability is stronger while uncertainties in observations, models and external forcing are all larger than at the global scale, precluding a robust assessment of the magnitude of the relative contributions of greenhouse gases, including stratospheric ozone, and different aerosol species.

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