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

=== Atlas.7.2 South America ===

<div id="h2-31-siblings" class="h2-siblings"></div>

<div id="Atlas.7.2.1" class="h3-container"></div>

<span id="atlas.7.2.1-key-features-of-the-regional-climate-and-findings-from-previous-ipcc-assessments"></span>
==== Atlas.7.2.1 Key Features of the Regional Climate and Findings From Previous IPCC Assessments ====

<div id="h3-46-siblings" class="h3-siblings"></div>

<div id="Atlas.7.2.1.1" class="h4-container"></div>

<span id="atlas.7.2.1.1-key-features-of-the-regional-climate"></span>
===== Atlas.7.2.1.1 Key Features of the Regional Climate =====

<div id="h4-18-siblings" class="h4-siblings"></div>

Regional synthesis of observed and modelled climate in South America is challenging due to the latitudinal extent of the continent, the Andes Mountains, and local-to-regional climatic features, which are influenced by multiple drivers. The main large-scale drivers include many modes of natural variability (Annex IV.2): the inter-decadal modes, Atlantic Multi-decadal Variability (AMV) and Pacific Decadal Variability (PDV); the interannual-to-annual modes, El Niño–Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), the Southern Annular Mode (SAM) and the North Atlantic Oscillation (NAO); seasonal variability driven by the meridional migration of the Inter-tropical Convergence Zone (ITCZ) and the timing and intensity of the South American Monsoon System (SAmerM, [[IPCC:Wg1:Chapter:Chapter-8#8.3.2.4.5|Section 8.3.2.4.5]] ), the Madden–Julian Oscillation sub-seasonal mode of natural variability (MJO) and the behaviour at finer scales of the tropical easterly waves.

The regional assessment in this section emphasizes the seven new South American reference regions (Figure Atlas.22; [[#Iturbide--2020|Iturbide et al., 2020]] ) that have a largely consistent climate and response to climate change, and can be used for analysis and impact studies ( [[#Solman--2008|Solman et al., 2008]] ; [[#Neukom--2010|Neukom et al., 2010]] ; [[#Barros--2015|Barros et al., 2015]] ; [[#Nobre--2016|Nobre et al., 2016]] ). At the sub-regional scale, several phenomena drive climate variability. Brazil’s north-east (North-Eastern South America; NES) is the most densely populated dryland globally and recurrently affected by climatic extremes. The climate variability, particularly the precipitation, is marked by strong interannual variability related to ENSO, the ITCZ, and the North Tropical Atlantic Ocean SSTs ( [[#Marengo--2018a|Marengo et al., 2018a]] ). Northern (NSA) and North-Western South America (NWS) are part of the Amazonia region. Its most recognizable features are the high rainfall, high humidity and high temperatures that prevail in the region. Rainfall variability in these regions results from the interplay between regional atmospheric circulation, the SST variations in both the Pacific and Atlantic oceans, among other regional-to-local interactions ( [[#Marengo--2016|Marengo and Espinoza, 2016]] ; [[#Espinoza--2020|Espinoza et al., 2020]] ). The South American Monsoon (SAM) region has distinct wet (summer) and dry (winter) periods. Key drivers include the South Atlantic Convergence Zone ( [[#Marengo--2012|Marengo et al., 2012]] ), the Bolivian High, the 40- to 60-day intra-seasonal oscillation, and the forcing of the high Andes Mountains to the west ( [[#Almeida--2017|Almeida et al., 2017]] ). The geographic position of South-Western South America (SWS) results in very specific climatic characteristics since SWS contains subtropical climates as well as sub-Antarctic and Antarctic climates. The climate of SWS is driven by seasonal changes in the position of subtropical high-pressure air masses in the South Atlantic and South Pacific oceans, the Southern Annular Mode, the dynamics of the cold Humboldt ocean current, and icy cold fronts and mid-latitude westerlies ( [[#Valdés-Pineda--2016|Valdés-Pineda et al., 2016]] ). The densely populated, highly productive sub-region of South-Eastern South America (SES) has cool winters and hot summers typical of the temperate zone, and climatic conditions are strongly tied to ENSO, whose influence is moderated by local air-sea thermodynamics in the South Atlantic ( [[#Barreiro--2010|Barreiro, 2010]] ). Lastly, the climate of the southern tip of South America (SSA) is cold and dry, and is influenced by the Southern Annular Mode, and the interaction between the wetter Pacific winds and the Andean Cordillera ( [[#Aceituno--1988|Aceituno, 1988]] ; [[#Silvestri--2009|Silvestri and Vera, 2009]] ).

<div id="Atlas.7.2.1.2" class="h4-container"></div>

<span id="atlas.7.2.1.2-findings-from-previous-ipcc-assessments"></span>
===== Atlas.7.2.1.2 Findings From Previous IPCC Assessments =====

<div id="h4-19-siblings" class="h4-siblings"></div>

According to AR5 WGII Chapter 27 ( [[#Magrin--2014|Magrin et al., 2014]] ), during the last decades of the 20th century, observational studies identified significant trends in precipitation and temperature in South America ( ''high confidence'' ). Increasing trends in annual rainfall in South-Eastern South America contrast with decreasing trends in central southern Chile and some regions of Brazil. Warming has been detected throughout South America (near 0.7°C–1°C in the 40 years since the mid-1970s), except for a cooling off the Chilean coast of about –1°C over the same period.

The AR5 WGI ( [[#Flato--2013|Flato et al., 2013]] ) noted that climate simulations from CMIP3 and CMIP5 models were able to represent well the main climatological features, such as seasonal mean and annual cycle ( ''high confidence'' ), although some biases remained over the Andes, the Amazonian basin and for the South America Monsoon. On the other hand, climate models from CMIP5 showed better results when compared to CMIP3.

The SR1.5 ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ) assessed that a further increase of 0.5°C or 1°C is likely to have detectable effects on mean temperature and precipitation in South America, particularly in tropical regions (NWS, NAS, SAM and NES), as well as in SES, given that changes in mean temperatures and precipitation have already been attributed in the last decades for global warming of less than 1°C.

<div id="Atlas.7.2.2" class="h3-container"></div>

<span id="atlas.7.2.2-assessment-andsynthesis-of-observations-trends-and-attribution"></span>
==== Atlas.7.2.2 Assessment andSynthesis of Observations, Trends and Attribution ====

<div id="h3-47-siblings" class="h3-siblings"></div>

Studies on climatic trends in South America indicate that mean temperature and extremely warm maximum and minimum temperatures have shown an increasing trend ( ''high confidence'' ), particularly for a large region in Northern South America and the south-western Andes (NSA, SAM, NES, SWS and the north of SES; [[#Skansi--2013|Skansi et al., 2013]] ; [[#de%20Barros%20Soares--2017|de Barros Soares et al., 2017]] ). Also, the trend of the difference between the annual mean of the daily maximum temperature and the annual mean of the daily minimum temperature was positive – up to 1°C per decade – over the extratropics with the maximum temperature generally increasing faster than the minimum temperature, while a negative trend of up to –0.5°C per decade was observed over the tropics.

Regionally, analyses of temperatures point to an increased warming trend ( ''high confidence'' ) over Amazonia over the last 40 years, which reached approximately 0.6°C–0.7°C (Figure Atlas.11 and the Interactive Atlas) and with stronger warming during the dry season and over the south-east. The analyses also showed that 2016 was the warmest year since at least 1950 ( [[#Marengo--2018b|Marengo et al., 2018b]] ). Andean temperatures showed significant warming trends, especially at inland and higher-elevation sites, while trends are non-significant or negative at coastal sites ( ''high confidence'' ) ( [[#Vuille--2015|Vuille et al., 2015]] ; [[#Burger--2018|Burger et al., 2018]] ; [[#Vicente-Serrano--2018|Vicente-Serrano et al., 2018]] ; [[#Pabón-Caicedo--2020|Pabón-Caicedo et al., 2020]] ). Over central Chile, positive trends are largely restricted to austral spring, summer and autumn seasons for mean, maximum and minimum temperatures ( [[#Burger--2018|Burger et al., 2018]] ; [[#Vicente-Serrano--2018|Vicente-Serrano et al., 2018]] ). Over Peru, trends of maximum air temperature were mainly amplified during the austral summer, but trends of cold-season minimum air temperature showed an opposite pattern, with the strongest warming being recorded in the austral winter ( [[#Vicente-Serrano--2018|Vicente-Serrano et al., 2018]] ).

In general, the spatial patterns of observed trends in temperature are more consistent than for precipitation across the whole of South America ( ''medium confidence'' ) (Interactive Atlas; [[#de%20Barros%20Soares--2017|de Barros Soares et al., 2017]] ). In south-east Brazil there is a region of highly significant decrease of rainfall in both wet and dry seasons recorded in the period 1979–2011 (Interactive Atlas; [[#Rao--2016|Rao et al., 2016]] ). The most consistent evidence of positive rainfall trend occurs in the southern part of the La Plata basin ( ''high confidence'' ) (southern Brazil, Uruguay, and north-eastern Argentina; [[#de%20Barros%20Soares--2017|de Barros Soares et al., 2017]] ). By contrast, there is ''high confidence'' that annual rainfall has decreased over north-east Brazil during the last decades ( [[#Carvalho--2020|Carvalho et al., 2020]] ). Contrary to temperature changes, trends in annual precipitation exhibit different signs across sectors in the Andes. For instance, annual precipitation trends in the north tropical (north of 8°S) and south tropical (8°S–27°S) Andes do not show a homogeneous pattern. Over the subtropical Andes, central Chile shows a robust signal of declining precipitation since 1970 ( ''high confidence'' ) ( [[#Pabón-Caicedo--2020|Pabón-Caicedo et al., 2020]] ).

Observational studies show that the dry-season length over southern Amazonia has increased significantly since 1979 ( ''high confidence'' ) ( [[#Fu--2013|Fu et al., 2013]] ; [[#Alves--2016|Alves, 2016]] ). In the Peruvian Amazon-Andes basin, there is no trend in mean rainfall during the period 1965–2007 ( [[#Lavado%20Casimiro--2012|Lavado Casimiro et al., 2012]] ) though statistically significant decreases in total annual rainfall in the central and southern Peruvian Andes from 1966 to 2010 were found ( [[#Heidinger--2018|Heidinger et al., 2018]] ). Despite that, recent analyses of Amazon hydrological and precipitation data suggest an intensification of the hydrological cycle over the past few decades ( [[#Gloor--2015|Gloor et al., 2015]] ). In general, these changes are attributed mainly to decadal climate fluctuations ( ''high confidence'' ), ENSO, the Atlantic SST north–south gradient, feedbacks between fire and land-use change mainly across southern south-eastern Amazon, and changes in the frequency of organized deep convection ( [[#Fernandes--2015|Fernandes et al., 2015]] ; [[#Sánchez--2015|Sánchez et al., 2015]] ; [[#Tan--2015|Tan et al., 2015]] ).

Since AR5, there has been limited attribution literature in the South America. Recent publications based on observational and modelling evidence assessed that anthropogenic forcing in CMIP5 models explains the overall warming ( ''high confidence'' ) over the entire South American continent, including the increase in the frequency of extreme temperature events ( [[#Hannart--2015|Hannart et al., 2015]] ). It has a detectable influence in explaining positive and negative precipitation trends observed in regions such as SES and the southern Andes ( [[#Vera--2015|Vera and Díaz, 2015]] ; [[#de%20Barros%20Soares--2017|de Barros Soares et al., 2017]] ; [[#Boisier--2018|Boisier et al., 2018]] ; [[#de%20Abreu--2019|de Abreu et al., 2019]] ). Despite that, there is ''limited evidence'' that human-induced greenhouse gas emissions had an influence on the 2014/2015 water shortage in south-east Brazil ( [[#Otto--2015|Otto et al., 2015]] ). Extreme event attribution on sub-continental scales is assessed in [[IPCC:Wg1:Chapter:Chapter-11|Chapter 11]] and continental-scale attribution in Chapter 3.

In summary, analyses of historical temperature time series point strongly to an increased warming trend ( ''high confidence'' ) across many South American regions, except for a cooling off the Chilean coast. Annual rainfall has increased over South-Eastern South America and decreased in most tropical land regions, particularly in central Chile ( ''high confidence'' ). The number and strength of extreme events, such as extreme temperatures, droughts and floods, have already increased ( ''medium confidence'' ) (Table 11.7).

It is noted that the major barrier to the study of climate change in many regions of South America is still the absence or insufficiency of long time series of observational data ( [[#Carvalho--2020|Carvalho, 2020]] ; [[#Condom--2020|Condom et al., 2020]] ). Most national datasets were created in the 1970s and 1980s, preventing a more comprehensive long-term trend analysis. To fulfil the users’ demand for climatological and meteorological data products covering the whole region, several interpolation techniques have been used with reanalysis and gridded gauge-analysis products to add the necessary spatial detail to the climate analyses over land and for climate variability and trend studies, but these are subject to uncertainties ( [[#Skansi--2013|Skansi et al., 2013]] ; [[#Rozante--2020|Rozante et al., 2020]] ).

<div id="Atlas.7.2.3" class="h3-container"></div>

<span id="atlas.7.2.3-assessment-of-model-performance"></span>
==== Atlas.7.2.3 Assessment of Model Performance ====

<div id="h3-48-siblings" class="h3-siblings"></div>

Since AR5 the number of publications on climate model performance and their projections in South America has increased, particularly for regional climate modelling studies ( [[#Giorgi--2009|Giorgi et al., 2009]] ; [[#Boulanger--2016|Boulanger et al., 2016]] ; [[#Ambrizzi--2019|Ambrizzi et al., 2019]] ) and the understanding of their strengths and weaknesses ( ''high confidence'' ).

Most global and regional climate models can simulate reasonably well the current climatological features of South America, such as seasonal mean and annual cycles. However, significant biases persist mainly at regional scales ( ''high confidence'' ) ( [[#Blázquez--2013b|Blázquez and Nuñez, 2013b]] ; [[#Gulizia--2013|Gulizia et al., 2013]] ; [[#Joetzjer--2013|Joetzjer et al., 2013]] ; [[#Jones--2013|Jones and Carvalho, 2013]] ; [[#Torres--2013|Torres and Marengo, 2013]] ; [[#Gulizia--2015|Gulizia and Camilloni, 2015]] ; [[#Zazulie--2017|Zazulie et al., 2017]] ; [[#Abadi--2018|Abadi et al., 2018]] ; [[#Barros--2018|Barros and Doyle, 2018]] ; [[#Solman--2019|Solman and Blázquez, 2019]] ; [[#Fan--2020|Fan et al., 2020]] ; [[#Rivera--2020|Rivera and Arnould, 2020]] ; [[#Teichmann--2021|Teichmann et al., 2021]] ). During the dry season, precipitation is underestimated in most models over Amazonia ( ''medium evidence, high agreement'' ) ( [[#Torres--2013|Torres and Marengo, 2013]] ; [[#Yin--2013|Yin et al., 2013]] ; [[#Solman--2019|Solman and Blázquez, 2019]] ). Over regions with complex orography, such as the tropical Andes of NWS, CMIP5 models tend to underestimate precipitation which is associated with the misrepresentation of the Pacific ITCZ and local low-level jets ( [[#Sierra--2015|Sierra et al., 2015]] , 2018), whereas over the subtropical central Andes in SWS, the models are found to overestimate both mean temperature and precipitation values ( ''limited evidence'' , ''high agreement'' ) ( [[#Zazulie--2017|Zazulie et al., 2017]] ; [[#Rivera--2020|Rivera and Arnould, 2020]] ; [[#Díaz--2021|Díaz et al., 2021]] ). Most models show a dry bias over SES ( [[#Díaz--2017|Díaz and Vera, 2017]] ; [[#Barros--2018|Barros and Doyle, 2018]] ; [[#Solman--2019|Solman and Blázquez, 2019]] ; [[#Díaz--2021|Díaz et al., 2021]] ) associated with an underestimation of the northern flow that brings water vapour into the region ( ''medium confidence'' ) ( [[#Gulizia--2013|Gulizia et al., 2013]] ; [[#Zazulie--2017|Zazulie et al., 2017]] ; [[#Barros--2018|Barros and Doyle, 2018]] ). The biases in seasonal precipitation, annual precipitation and climate extremes over several regions of South America were reduced, including the Amazon, central South America, Bolivia, eastern Argentina and Uruguay, in the CMIP5 models when compared to those of CMIP3 ( ''medium confidence'' ) ( [[#Joetzjer--2013|Joetzjer et al., 2013]] ; [[#Gulizia--2015|Gulizia and Camilloni, 2015]] ; [[#Díaz--2017|Díaz and Vera, 2017]] ). The evidence is still insufficient to determine whether CMIP6 biases are reduced when compared with CMIP5 simulations regarding precipitation and its variability in South America. The temperature and precipitation patterns of anomalies associated with ENSO in tropical South America (NWS, NSA and NES) are better captured by GCMs in tropical South America (NWS, NSA and NES) than in extratropical South America (SES), particularly during austral summer and autumn ( ''limited evidence'' , ''high agreement'' ) ( [[#Tedeschi--2016|Tedeschi and Collins, 2016]] ; [[#Perry--2020|Perry et al., 2020]] ).

Based on regional simulations, studies showed that some RCMs improve the quality of the simulated climate when compared with the driving GCM ( ''medium evidence'' , ''high agreement'' ) ( [[#Llopart--2014|Llopart et al., 2014]] ; [[#Sánchez--2015|Sánchez et al., 2015]] ; [[#Falco--2019|Falco et al., 2019]] ; [[#Solman--2019|Solman and Blázquez, 2019]] ; [[#Ciarlo%60--2021|Ciarlo` et al., 2021]] ; [[#Teichmann--2021|Teichmann et al., 2021]] ). Regional climate model (RCM) simulations over South America can reproduce the main features of temperature and precipitation in terms of both spatial distributions ( [[#Solman--2013|Solman et al., 2013]] ; [[#Falco--2019|Falco et al., 2019]] ) and seasonal cycles over the different climate regimes, including the main SAmerM features ( ''high confidence'' ) ( [[#Jacob--2012|Jacob et al., 2012]] ; [[#Solman--2013|Solman, 2013]] ; [[#Llopart--2014|Llopart et al., 2014]] ; [[#Reboita--2014|Reboita et al., 2014]] ; [[#de%20Jesus--2016|de Jesus et al., 2016]] ; [[#Lyra--2018|Lyra et al., 2018]] ; [[#Bozkurt--2019|Bozkurt et al., 2019]] ; [[#Ashfaq--2021|Ashfaq et al., 2021]] ). However, RCMs showed systematic biases such as precipitation overestimations and temperature underestimations along the Andes throughout the year ( ''high confidence'' ), although these biases may be artificially amplified by the lack of a dense observational station network ( [[#Jacob--2012|Jacob et al., 2012]] ; [[#Solman--2013|Solman et al., 2013]] ; [[#Bozkurt--2019|Bozkurt et al., 2019]] ; [[#Falco--2019|Falco et al., 2019]] ). RCMs tended to show dry biases over the Amazon and the northern part of the continent (SAM, NSA) during DJF and during the maximum precipitation associated with the ITCZ over NSA during JJA ( ''medium evidence'' , ''high agreement'' ) ( [[#Solman--2013|Solman et al., 2013]] ; [[#Falco--2019|Falco et al., 2019]] ). Temperature overestimation and precipitation underestimation over La Plata basin (in SES) are also RCM common biases, with the warm bias amplified for austral summer and the dry bias amplified for the rainy season ( ''high confidence'' ) ( [[#Solman--2013|Solman et al., 2013]] ; [[#Reboita--2014|Reboita et al., 2014]] ; [[#Solman--2016|Solman, 2016]] ; [[#Falco--2019|Falco et al., 2019]] ). Despite their relevance, RCM simulations at very high resolution (less than 10 km) are still few in South America ( ''high confidence'' ) and are mainly designed for specific regions or purposes ( [[#Lyra--2018|Lyra et al., 2018]] ; [[#Bozkurt--2019|Bozkurt et al., 2019]] ; [[#Bettolli--2021|Bettolli et al., 2021]] ).

The evaluation of statistical downscaling models (ESD) in representing regional climate features in South America has increased since AR5, however there are still few ESD studies over the different sub-regions. Precipitation simulations based on ESD models are able to reproduce mean precipitation over tropical and subtropical South American regions, especially over maximum precipitation areas in western Colombia, south-eastern Peru, central Bolivia, Chile and the La Plata basin ( ''medium confidence'' ) ( [[#Souvignet--2010|Souvignet et al., 2010]] ; [[#Mendes--2014|Mendes et al., 2014]] ; [[#Palomino-Lemus--2015|Palomino-Lemus et al., 2015]] , 2017, 2018; [[#Soares%20dos%20Santos--2016|Soares dos Santos et al., 2016]] ; [[#Troin--2016|Troin et al., 2016]] ; [[#Borges--2017|Borges et al., 2017]] ; [[#Bettolli--2018|Bettolli and Penalba, 2018]] ; [[#Araya-Osses--2020|Araya-Osses et al., 2020]] ; [[#Bettolli--2021|Bettolli et al., 2021]] ). Temperature simulations are fewer but show added value to GCM simulations ( ''medium evidence'' , ''high agreement'' ) ( [[#Souvignet--2010|Souvignet et al., 2010]] ; [[#Borges--2017|Borges et al., 2017]] ; [[#Bettolli--2018|Bettolli and Penalba, 2018]] ; [[#Araya-Osses--2020|Araya-Osses et al., 2020]] ).

Overall, climate modelling has made some progress in the past decade but there is no model that performs well in simulating all aspects of the present climate over South America ( ''high confidence'' ). The performance of the models varies according to the region, time scale and variables analysed ( [[#Abadi--2018|Abadi et al., 2018]] ). There is also a fairly narrow spread in the representation of temperature and precipitation over South America by the CMIP5 GCMs and also the RCMs, with biases that can be associated with the parametrizations and schemes of surface, boundary layer, microphysics and radiation used by the models. Finally, observational reference datasets, such as reanalysis products, used in the calibration and validation of climate models can also be quite uncertain and may explain part of the apparent biases present in climate models ( ''high confidence'' ).

<div id="Atlas.7.2.4" class="h3-container"></div>

<span id="atlas.7.2.4-assessment-and-synthesis-of-projections"></span>
==== Atlas.7.2.4 Assessment and Synthesis of Projections ====

<div id="h3-49-siblings" class="h3-siblings"></div>

It is ''very likely'' that annual mean temperature will increase over South America, with a wide range of projected changes of 1.0°C–6.0°C by the end of the 21st century (from RCP2.6/SSP1-2.6 to RCP8.5/SSP5-8.5 emissions, Figure Atlas.22). Overall, GCMs project higher temperature change than RCMs in austral summer and winter over all sub-regions and in winter mainly over the central part of the continent (Interactive Atlas; [[#Coppola--2014|Coppola et al., 2014]] ; [[#Llopart--2021|Llopart et al., 2021]] ; [[#Teichmann--2021|Teichmann et al., 2021]] ). The largest warmings over the South American continent are projected for the Amazon basin (SAM and NSA) and the central Andes range (southern SAM, northern SWS and south-eastern NWS; Figure Atlas.22), especially during the dry and dry-to-wet transition seasons (austral winter and spring) ( ''high confidence'' ) ( [[#Blázquez--2013a|Blázquez and Nuñez, 2013a]] ; [[#Coppola--2014|Coppola et al., 2014]] ; [[#Pabón-Caicedo--2020|Pabón-Caicedo et al., 2020]] ; [[#Teichmann--2021|Teichmann et al., 2021]] ).

Using warming levels (Figure Atlas.22), the temperature is projected to increase at or above the level of global warming in all regions apart from SSA with additional warming (compared to a 1995–2014 baseline) of over 4°C for the 4°C warming level in NSA and SAM. Changes for other warming levels, sub-regions and emissions pathways are shown in Figure Atlas.22 and can be explored with the Interactive Atlas.

In general, models show a wide regional range in the direction and the magnitude of mean precipitation change in many South American regions, with large significant increases and decreases (Figure Atlas.22 and the Interactive Atlas). In the medium and long term, under the high-emissions scenario, the CMIP5 multi-model ensemble projected an increase in precipitation (generally greater than 10%) in SES and NWS, and a decrease (less than 10%) in NSA across seasons ( ''high confidence'' , ''robust evidence'' ) ( [[#Solman--2013|Solman, 2013]] ; [[#Chou--2014|Chou et al., 2014]] ; [[#Coppola--2014|Coppola et al., 2014]] ; [[#Llopart--2014|Llopart et al., 2014]] , 2021; [[#Reboita--2014|Reboita et al., 2014]] , 2021; [[#Sánchez--2015|Sánchez et al., 2015]] ; [[#Menéndez--2016|Menéndez et al., 2016]] ; [[#Ruscica--2016|Ruscica et al., 2016]] ; [[#Bozkurt--2018a|Bozkurt et al., 2018a]] ; [[#Zaninelli--2019|Zaninelli et al., 2019]] ). Also, in parts of SWS, annual precipitation is projected to decrease (up to 30%) by the late 21st century ( [[#Souvignet--2010|Souvignet et al., 2010]] ; [[#Palomino-Lemus--2017|Palomino-Lemus et al., 2017]] , 2018; [[#Bozkurt--2018a|Bozkurt et al., 2018a]] ). Under high RCPs, the CMIP5 ensemble projects that all Brazilian regions will experience more rainfall variability in the future, so drier dry periods and wetter wet periods on daily, weekly, monthly and seasonal time scales, despite the future changes in mean rainfall being currently uncertain ( ''medium confidence'' ) ( [[#Alves--2021|Alves et al., 2021]] ). Regarding the SAmerM, it is ''very likely'' that the monsoon will experience changes in its life cycle by the end of the 21st century for both RCP4.5 and RCP8.5 emissions and, in particular, delayed onset. However there is ''low agreement'' on the projected changes in terms of extreme and total precipitation of the monsoon season in South America ( [[#Llopart--2014|Llopart et al., 2014]] ; [[#Ashfaq--2021|Ashfaq et al., 2021]] ). Changes in the SAmerM are assessed in [[IPCC:Wg1:Chapter:Chapter-8#8.3.2.4.5|Section 8.3.2.4.5]] .

Projected changes in seasonal precipitation and their uncertainties generally agree with the annual changes, particularly for the decreases in SWS (Figure Atlas.22). DJF precipitation changes in NSA and SAM are largely uncertain, with weak agreements in the projections, particularly for CMIP5 and CMIP6 ensembles, which project almost no change, and decreasing precipitation for NSA and a narrow range from slight increases to no change respectively for SAM.

<div id="Atlas.7.2.5" class="h3-container"></div>

<span id="atlas.7.2.5-summary"></span>
==== Atlas.7.2.5 Summary ====

<div id="h3-50-siblings" class="h3-siblings"></div>

In summary, it is ''virtually certain'' that the climate of South America has warmed. Studies on climate trends in South America indicate that mean temperature and maximum and minimum temperatures have increased over the last 40 years. Long-term observed precipitation trends show an increase over South-Eastern South America and decreases in most tropical land regions ( ''high confidence'' ).

Evaluation of global and regional climate model simulations have increased over South America in the past decade and shown improved performance. However, the results reveal that no model performs well in simulating all aspects of the present climate ( ''very likely'' ) . On the other hand, there is still a lack of high-quality and high-resolution observational data that may explain part of the important biases present in climate models ( ''high confidence'' ).

Climate model projections show a general increase in annual mean surface temperature over the coming century for all emissions scenarios (RCPs and SSPs) ( ''high confidence'' ), consistent with the observed warming, and with all regions except SSA warming faster than the global average. Unlike temperature, annual precipitation has patterns of decrease in North-Eastern South America (NES) and South-Western South America (SWS), and increase in Southern South America (SES) and North-Western South America (NWS) ( ''high confidence'' ), with small changes projected under a low-emissions scenario. However, there is ''low confidence'' in the magnitude because of the large spread among models, both GCMs and RCMs.

<div id="Atlas.8" class="h1-container"></div>

<span id="atlas.8-europe"></span>