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

==== 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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'''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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