Editing IPCC:AR6/WGII/Chapter-12 (section)

== 12.3 Hazards, Exposure, Vulnerabilities and Impacts ==

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=== 12.3.1 Central America Sub-region ===

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==== 12.3.1.1 Hazards ====

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Since the mid-20th century, extreme warm temperatures have increased and extreme cold temperatures have decreased in the region ( ''medium confidence'' ). The magnitude and frequency of extreme precipitation events have increased, but droughts have mixed signals ( ''low confidence'' ) (WGI AR6 Table 11.13, Table 11.14, Table 11.15, [[#Seneviratne--2021|Seneviratne et al., 2021]] ). Spatially variable trends have been detected for the MSD timing, the amount of rainy-season precipitation, the number of consecutive and total dry days and extreme wet events at the local scale since the 1980s. At the regional scale, a positive trend in the duration, but not the magnitude, of the MSD was found ( [[#Anderson--2019|Anderson et al., 2019]] ).

Significant increases in tropical cyclone (TC) intensification rates in the Atlantic basin, highly unusual compared to model-based estimates of internal climate variations, have been observed ( [[#Bhatia--2019|Bhatia et al., 2019]] ). TCs contributed approximately 10% of the annual precipitation ( [[#Khouakhi--2017|Khouakhi et al., 2017]] ). During the TC season more TC-driven events of extreme sea level exceed a 10-year return period ( [[#Muis--2019|Muis et al., 2019]] ).

Massive heatwave events and increase in the frequency of warm extremes are projected at the end of the 21st century ( ''high confidence'' ). When comparing 2.0°C with 1.5°C of warming, the longest annual warm wave is projected to increase more than 60 d ( [[#Taylor--2018|Taylor et al., 2018]] ).

General decrease in the magnitude of heavy precipitation extremes ( [[#Chou--2014|Chou et al., 2014]] ; [[#Giorgi--2014|Giorgi et al., 2014]] ) (in 1.5°C projection) but increase in the frequency of extreme precipitation (R50mm) ( [[#Imbach--2018|Imbach et al., 2018]] ) are projected for both 2°C and 4°C global warming level (GWL). Strong declines in mean daily rainfall are projected for July in Belize ( [[#Stennett-Brown--2017|Stennett-Brown et al., 2017]] ; WGI AR6 Table 11.14, [[#Seneviratne--2021|Seneviratne et al., 2021]] ) and decreased rainfall through the year for all capital cities except Panama City ( ''medium confidence: limited evidence, high agreement'' ) ( [[#Pinzón--2017|Pinzón et al., 2017]] ).

The main climate impact drivers like extreme heat, drought, relative SLR, coastal flooding, erosion, marine heatwaves, ocean aridity ( ''high confidence'' ) and aridity, drought and wildfires will increase by mid-century ( ''medium confidence'' ) (Figure 12.6, WGI AR6 Table 12.6, [[#Ranasinghe--2021|Ranasinghe et al., 2021]] ).

The rainy season in CA will likely experience more pronounced MSD by the end of this century, with a signal for reduced minimum precipitation by mid-century for the June July August (JJA) and September October November (SON) quarters, and a broader second peak is projected, consistent with the future south displacement of the Intertropical Convergence Zone (ITCZ) ( ''high confidence'' ) ( [[#Fuentes-Franco--2015|Fuentes-Franco et al., 2015]] ; [[#Hidalgo--2017|Hidalgo et al., 2017]] ; [[#Maurer--2017|Maurer et al., 2017]] ; [[#Imbach--2018|Imbach et al., 2018]] ; [[#Naumann--2018|Naumann et al., 2018]] ; [[#Ribalaygua--2018|Ribalaygua et al., 2018]] ; [[#Corrales-Suastegui--2020|Corrales-Suastegui et al., 2020]] ).

Climate projections indicate a decrease in frequency of TCs in CA accompanied by an increased frequency of intense cyclones (WGI AR6 [[#12.4|Section 12.4.4.3]] , [[#Ranasinghe--2021|Ranasinghe et al., 2021]] ).

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==== 12.3.1.2 Exposure ====

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Of the 47 million Central Americans in 2015, 40% lived in rural areas, with Belize being the least urbanised (54% rural) and Costa Rica the most (21% rural) ( [[#CELADE--2019|CELADE, 2019]] ); 10.5 million lived in the Dry Corridor region, an area recently exposed to severe droughts that have resulted in 3.5 million people in need of humanitarian assistance ( [[#FAO--2016a|FAO, 2016a]] ). Except in Belize and Panama, the majority of the countries’ populations—ranging from 56% in Honduras to 95% in El Salvador—were exposed to two or more risks derived from natural extreme events, affecting between 57% and 96% of the GDP of the countries ( [[#UNISDR%20and%20CEPREDENAC--2014|UNISDR and CEPREDENAC, 2014]] ). CA is one of the regions most exposed to climatic phenomena; with long coastlines and lowland areas, the region is repeatedly affected by drought, intense rains, cyclones and ENSO events ( ''high confidence'' ) ( [[#ECLAC--2015|ECLAC et al., 2015]] ).

Large urban centres are located on mountains or away from the shore, with the notable exceptions of Panama City, Belmopan and Managua, capital cities housing around 3 million people. Urban development in the capital cities and suburbs has almost tripled in the last 40 years, reaching population densities as high as 11,000 inhabitants/km 2 in Guatemala City and Tegucigalpa, with the spread of poor neighbourhoods in steep ravines and other marginal high-risk areas ( [[#Programa%20Estado%20de%20la%20Nación%20–%20Estado%20de%20la%20Región--2016|Programa Estado de la Nación – Estado de la Región, 2016]] ).

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==== 12.3.1.3 Vulnerability ====

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Climate change is exacerbating socioeconomic vulnerability in CA, a region with high levels of socioeconomic, ethnic and gender inequality, high rates of child and maternal mortality and morbidity, high levels of malnutrition and inadequate access to food and drinking water ( [[#ECLAC--2015|ECLAC et al., 2015]] ). Disasters from adverse natural events exacerbate CA’s economic vulnerability, accounting for substantial human and economic losses ( [[#UNISDR%20and%20CEPREDENAC--2014|UNISDR and CEPREDENAC, 2014]] ). Vulnerability in most sectors is considered high or very high ( ''high confidence'' ) (Figure 12.7).

Approximately 40% of the CA population live in poverty. Guatemala (62%), Honduras (60%), Nicaragua (46%) and Belize (42%, 2009) had the highest poverty rates in CSA in 2018 ( [[#ECLAC--2019b|ECLAC, 2019b]] ; [[#BCIE--2020|BCIE, 2020]] ). Rural poverty rates are higher—82% in Honduras and 77% in Guatemala in 2014—as is poverty among Indigenous Peoples, up to 79% in Guatemala. Rural poor are the most sensitive to climate extremes as their main economic activity is based on agriculture in vulnerable terrains (NU [[#CEPAL--2018|CEPAL, 2018]] ). In 2014, all CA countries, except for El Salvador (excluding Belize), had higher GINI coefficients (more inequality) than the average for Latin America (0.473), which in itself is the most unequal region in the world ( [[#ECLAC--2019b|ECLAC, 2019b]] ); in 2018 the situation remained similar, with El Salvador showing the lowest GINI coefficient (40) and the remaining countries showing values higher than the Latin American average ( [[#BCIE--2020|BCIE, 2020]] ).

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==== 12.3.1.4 Impacts ====

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The countries in the region are consistently ranked highest in the world by risk of being impacted by extreme events ( ''high confidence'' ). The economic costs of climate-change impacts in 2010 were estimated as being from 2.9% of GDP for Guatemala to 7.7% for Belize ( [[#ECLAC--2015|ECLAC et al., 2015]] ). For the period 1992–2011, Honduras, Nicaragua and Guatemala were among the 10 most impacted countries in the world by extreme weather events ( [[#UNISDR%20and%20CEPREDENAC--2014|UNISDR and CEPREDENAC, 2014]] ). The number of these events has increased 3% annually in the last 30 years ( [[#Bárcena--2020a|Bárcena et al., 2020a]] ).

Human and economic losses, changing water availability and increasing food insecurity are the most studied impacts of climate change in CA (Figure 12.9) ( [[#Harvey--2018|Harvey et al., 2018]] ; [[#Hoegh-Guldberg--2019|Hoegh-Guldberg et al., 2019]] ). Hydro-meteorological events, such as storm surges and TCs, are the most frequent extreme events and have the highest impact ( ''high confidence'' ) ( [[#Reyer--2017|Reyer et al., 2017]] ). From 2005 to 2014, the cumulative impacts were over 3410 people dead, hundreds of thousands displaced and damages estimated around USD 5.8 billion ( [[#Ishizawa--2016|Ishizawa and Miranda, 2016]] ). One standard deviation in the intensity of a hurricane windstorm leads to a decrease in both the growth of total GDP per capita (0.9% to 1.6%) and total income and labour income by 3%, whereas it increases moderate and extreme poverty by 1.5% in CA ( [[#Ishizawa--2016|Ishizawa and Miranda, 2016]] ).

Food insecurity is a serious impact of climate change in a region where 10% of the GDP depends on agriculture, livestock and fisheries ( ''very high confidence'' ) ( [[#ECLAC--2015|ECLAC et al., 2015]] ; [[#CEPAL--2018|CEPAL et al., 2018]] ; [[#Harvey--2018|Harvey et al., 2018]] ; [[#BCIE--2020|BCIE, 2020]] ). Crop losses largely result from highly variable rainfall and seasonal droughts, which have increased significantly in recent decades (Table 12.3) ( [[#CEPAL%20and%20CAC-SICA--2020|CEPAL and CAC-SICA, 2020]] ), particularly the observed changes in the MSD that reduces rainfall at the onset of the rainy season (May–June) ( [[#Anderson--2019|Anderson et al., 2019]] ). Small and subsistence farmers experience the highest impact because they practice rainfed agriculture ( [[#Imbach--2017|Imbach et al., 2017]] ), along with poor neighbourhoods, which face socioeconomic and physical barriers for adapting to climate change ( [[#Kongsager--2017|Kongsager, 2017]] ). In 2015, precipitation diminished between 50% and 70% of its historic average, causing a loss of up to 80% of beans and 60% of maize, leaving 2.5 million people food insecure, 1.6 million of whom were in the Dry Corridor of CA ( [[#ECLAC--2015|ECLAC et al., 2015]] ; [[#FAO--2016a|FAO, 2016a]] ). In 2019, the region entered its fifth consecutive drought year, with 1.4 million people in need of food aid. Seasonal-scale droughts are projected to lengthen by 12–30%, intensify by 17–42% and increase in frequency by 21–42% in RCP4.5 and RCP8.5 scenarios by the end of the century ( [[#Depsky--2021|Depsky and Pons, 2021]] ).

Studies have shown that the incidence of some vector-borne and zoonotic diseases in CA is correlated to climatic variables, particularly temperature and rainfall ( ''high confidence'' ) (Figure 12.4; Table 12.1). In Honduras, rainfall and relative humidity were positively correlated with the occurrence of haemorrhagic dengue cases ( [[#Zambrano--2012|Zambrano et al., 2012]] ). In Costa Rica, temperature and rainfall were correlated to cattle rabies outbreaks and mortality during 1985–2016 ( [[#Hutter--2018|Hutter et al., 2018]] ); incidence of leishmaniasis showed cycles of 3 years related to temperature changes ( [[#Chaves--2006|Chaves and Pascual, 2006]] ); and snakebites were more likely to occur at high temperatures and were significantly reduced after the rainy season for the period 2005–2013 ( [[#Chaves--2015|Chaves et al., 2015]] ). In Panama, rainfall was associated with an increased number of malaria cases among the Gunas, an Indigenous People with high vulnerability living in poverty conditions on small islands affected by SLR ( [[#Hurtado--2018|Hurtado et al., 2018]] ). These correlations point to a possible change in disease incidence with climate change; evidence of that change is yet to be reported in the literature because longitudinal studies are lacking in the region.

Heat stress is another health concern in this already warm and humid part of the world ( ''high confidence'' ) (Table 12.2); it is an increasing occupational health hazard with potential impacts on kidney disease ( [[#Sheffield--2013|Sheffield et al., 2013]] ; [[#Dally--2018|Dally et al., 2018]] ; [[#Johnson--2019|Johnson et al., 2019]] ). SLR exacerbating wave-driven flooding is expected to impact infrastructure and freshwater availability in small islands and atolls off the coast of Belize ( [[#Storlazzi--2018|Storlazzi et al., 2018]] ). Observed and expected impacts in the coastal and ocean ecosystems of the sub-region are described in Figure 12.9.

Decreasing water availability is another impact of climate change ( ''high confidence'' ). Under a climate-change scenario of 3.5°C warming and a 30% reduction in rainfall, a reduction in the production and export of crops and livestock is projected, affecting the wages and decreasing the GDP of Guatemala by 1.2%, thereby increasing food insecurity ( [[#Vargas--2018b|Vargas et al., 2018b]] ). By 2100, water availability per capita is projected to decrease 82% and 90% on average for the region under B2 (low emissions) and A2 (high emissions) scenarios respectively (Figure 12.3) ( [[#CEPAL--2010|CEPAL, 2010]] ).

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[[File:408541c0d0dc7dd04b0db72c155d851d IPCC_AR6_WGII_Figure_12_003.png]]

'''Figure 12.3 |''' '''Reduction of water availability per capita projected to 2100 without climate change (baseline scenario) and with two climate-change scenarios ( [[#CEPAL--2010|CEPAL, 2010]] ).'''

Impacts on rural livelihoods, particularly for small and medium-sized farmers and Indigenous Peoples in mountains, include an overall reduction in production, yield (Table 12.4), suitable farming area and water availability ( ''high confidence'' ) ( [[#Walshe--2016|Walshe and Argumedo, 2016]] ; [[#Bouroncle--2017|Bouroncle et al., 2017]] ; [[#Hannah--2017|Hannah et al., 2017]] ; [[#Imbach--2017|Imbach et al., 2017]] ; [[#Harvey--2018|Harvey et al., 2018]] ; [[#Batzín--2019|Batzín, 2019]] ; [[#Donatti--2019|Donatti et al., 2019]] ). Bean production in El Salvador, Nicaragua, Honduras and Guatemala, is projected to decrease, using the Decision Support for Agro-Technology Transfer (DSSAT) under the A2 scenario, by 19% for 2050, whereas maize production, depending on the water retention capacity of soils, will drop between 4% and 21% by 2050 ( [[#CEPAL--2018|CEPAL et al., 2018]] ). In Guatemala, the yield of rainfed maize is expected to decrease by 16% by 2050 under RCP8.5 using the Global Gridded Crop Model Intercomparison GGCMI; yields for rainfed sugarcane are expected to drop by 44% and irrigated sugarcane by 36% under the same modelling conditions ( [[#Castellanos--2018|Castellanos et al., 2018]] ). Rice production is expected to decrease by 23% under scenario A2 by 2050 ( [[#CEPAL%20and%20CAC/SICA--2013|CEPAL and CAC/SICA, 2013]] ).

The extent and quality of suitable areas for basic grains are expected to contract ( ''high confidence'' ). The suitable area for maize will experience a 35% reduction of cultivated area expected by 2100 under the A2 scenario. The area suitable for beans is expected to shrink by 2050. Projections show that suitable areas with excellent capacity under current conditions will decrease by 14%, mainly in Panama (41%) Costa Rica (21%) and El Salvador (20%). The Species Distribution Model, using the IPSL GCM, projects that the suitable zones for cacao and coffee will shrink between 25% and 75% under RCP6.0 ( [[#Fernandez-Manjarrés--2018|Fernandez-Manjarrés, 2018]] ; [[#Fernández%20Kolb--2019|Fernández Kolb et al., 2019]] ). Warmer and dryer lower areas will become unsuitable for coffee and will drive its production to higher land ( [[#Läderach--2013|Läderach et al., 2013]] ; [[#Bunn--2015|Bunn et al., 2015]] ). Under the A2 climate-change scenario, areas with excellent capacity for Arabica coffee will decrease by 12% in CA; coffee yield will decrease in suitable zones whereby the extent of high yield (&gt;0.8 T ha −1 ) zones is projected to shrink from 34% to 12%, whereas low-yield (&lt;0.3 T ha −1 ) zones will expand from 14% to 36% by 2100 under the A2 scenario ( [[#CEPAL%20and%20CAC/SICA--2014|CEPAL and CAC/SICA, 2014]] ).

Mesoamerica, a biodiversity hotspot spanning across CA and southern Mexico, is a global priority for terrestrial biodiversity conservation, and it is projected to be negatively impacted by climate change, especially through the contraction of distribution of native species as the area becomes increasingly dryer ( ''high confidence'' ) (Section [https://www.ipcc.ch/chapter/12#CCP1.2.2 CCP1.2.2] ) ( [[#Feeley--2013|Feeley et al., 2013]] ; [[#Manes--2021|Manes et al., 2021]] ). A significant reduction in net primary productivity in tropical forests is expected under both RCP4.5 and RCP8.5 as a result of temperature increase, precipitation reduction and droughts ( [[#Lyra--2017|Lyra et al., 2017]] ; [[#Castro--2018|Castro et al., 2018]] ; [[#Stan--2020|Stan et al., 2020]] ). Aridity index models show that the dry, sub-humid vegetation of the dry corridor will expand to neighbouring areas and replace the humid forests in the Pacific lowlands and the northern parts of Guatemala by 2050 under RCP4.5 and RCP8.5 scenarios ( [[#Pons--2018|Pons et al., 2018]] ; [[#CEPAL%20and%20CAC-SICA--2020|CEPAL and CAC-SICA, 2020]] ). A warming of 3°C would shrink the tropical rainforest and replace it with savannah grassland. Wetlands are also expected to be highly affected by climate change in the region ( [[#Hoegh-Guldberg--2019|Hoegh-Guldberg et al., 2019]] ).

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=== 12.3.2 Northwestern South America Sub-region ===

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==== 12.3.2.1 Hazards ====

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Significant increases in the intensity and frequency of hot extremes and significant decreases in the intensity and frequency of cold extremes ( [[#Dereczynski--2020|Dereczynski et al., 2020]] ; [[#Dunn--2020|Dunn et al., 2020]] ) were ''likely'' [[#footnote-000|2]] observed (Figure 12.6; WGI AR6 Table 11.13) ( [[#Seneviratne--2021|Seneviratne et al., 2021]] ).

Insufficient data coverage and trends in available data are generally not significant for heavy precipitation ( ''low confidence'' ) ( [[#Dereczynski--2020|Dereczynski et al., 2020]] ; [[#Dunn--2020|Dunn et al., 2020]] ; [[#Sun--2021|Sun et al., 2021]] ) (Figure 12.6; WGI AR6 Table 11.14) ( [[#Seneviratne--2021|Seneviratne et al., 2021]] ).

ENSO is the dominant phenomenon affecting weather conditions in all of CSA and along the Pacific Coast of NWS, causing heavy rains, storms, floods, landslides, heat and cold waves and extreme SLR ( [[#Ashok--2007|Ashok et al., 2007]] ; [[#Reguero--2015|Reguero et al., 2015]] ; [[#Wang--2017b|Wang et al., 2017b]] ; [[#Muis--2018|Muis et al., 2018]] ; [[#Rodríguez-Morata--2018|Rodríguez-Morata et al., 2018]] ; [[#Rodríguez-Morata--2019|Rodríguez-Morata et al., 2019]] ; [[#Cai--2020|Cai et al., 2020]] ). There is ''medium confidence'' that extreme ENSO will increase long after 1.5°C warming stabilisation according to CMIP5 ( [[#Cai--2015|Cai et al., 2015]] , 2018; [[#Wang--2017b|Wang et al., 2017b]] ). It is ''very likely'' that ENSO rainfall variability, used for defining extreme El Niño and La Niña, will increase significantly, regardless of amplitude changes in ENSO sea surface temperature (SST) variability, by the second half of the 21st century in scenarios SSP2-4.5, SSP3-7.0 and SSP5-8.5 (WGI AR6 Chapter 4) (Lee et al., 2021).

Warming and drier conditions are projected through the reduction of total annual precipitation, extreme precipitation and consecutive wet days and an increase in consecutive dry days ( [[#Chou--2014|Chou et al., 2014]] ). Heatwaves will increase in frequency and severity in places close to the equator like Colombia ( [[#Guo--2018|Guo et al., 2018]] ; [[#Feron--2019|Feron et al., 2019]] ), with a decrease but strong wetting in coastal areas, pluvial and river flood and mean wind increase ( [[#Mora--2014|Mora et al., 2014]] ). Models project a ''very likely'' 2°C GWL increase in the intensity and frequency of hot extremes and decrease in the intensity and frequency of cold extremes. Nevertheless, models project inconsistent changes in the region for extreme precipitation ( ''low confidence'' ) (Figure 12.6; WGI AR6 Table 12.14) ( [[#Ranasinghe--2021|Ranasinghe et al., 2021]] ). The main climate impact drivers in the region, like extreme heat, mean precipitation and coastal and oceanic drivers, will increase and snow, ice and permafrost will decrease with ''high confidence'' (WGI AR6 Table 12.6) ( [[#Ranasinghe--2021|Ranasinghe et al., 2021]] ).

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==== 12.3.2.2 Exposure ====

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There is ''high confidence'' that coastal lowlands are exposed to SLR in the form of coastal flooding and erosion, subsidence and saltwater intrusion ( [[#Hoyos--2013|Hoyos et al., 2013]] ). Those hazards can affect settlements, ports, industries and other infrastructures. Mangrove and aquaculture areas are among the most exposed systems ( [[#Gorman--2018|Gorman, 2018]] ). The Eastern Tropical Pacific, particularly Sector Niño 3.4, will see the worst increase in SST, affecting industrial and small-scale fisheries ( ''very high confidence'' ) ( [[#Castrejón--2015|Castrejón and Defeo, 2015]] ; [[#Reguero--2015|Reguero et al., 2015]] ; [[#Eddy--2019|Eddy et al., 2019]] ; [[#Bertrand--2020|Bertrand et al., 2020]] ; [[#Castrejón--2020|Castrejón and Charles, 2020]] ; [[#Escobar-Camacho--2021|Escobar-Camacho et al., 2021]] ).

Settlements and agriculture on different scales and hydroelectric infrastructures, especially near big rivers or in plains, are exposed to floods. Exposure and vulnerabilities to precipitation, overflows and related landslides are increasing ( [[#Briones-Estébanez--2017|Briones-Estébanez and Ebecken, 2017]] ).

The Andean piedmont (500–1200 metres above sea level [MASL]) ecosystems and crops and elevation ranges above the treeline are more exposed to thermal anomalies ( ''very high confidence'' ) ( [[#Urrutia--2009|Urrutia and Vuille, 2009]] ; [[#Vuille--2015|Vuille et al., 2015]] ; [[#Aguilar-Lome--2019|Aguilar-Lome et al., 2019]] ; [[#Pabón-Caicedo--2020|Pabón-Caicedo et al., 2020]] ). Temperature rise, combined with precipitation and floods, leaves people more exposed to epidemics ( ''very high confidence'' ) ( [[#Stewart-Ibarra--2013|Stewart-Ibarra and Lowe, 2013]] ; [[#Sippy--2019|Sippy et al., 2019]] ; [[#Petrova--2020|Petrova et al., 2020]] ). A more significant exposure is related to lower socioeconomic conditions, poor health and marginalisation ( [[#Oliver-Smith--2014|Oliver-Smith, 2014]] ).

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==== 12.3.2.3 Vulnerability ====

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Local economies reliant on limited and specialised resources, highly dependent on ecosystem services such as water and soil fertility, such as alpaca and llama herders or small-scale fishers, are among the more vulnerable ( ''very high confidence'' ) ( [[#Hollowed--2013|Hollowed et al., 2013]] ; [[#Postigo--2013|Postigo, 2013]] ; [[#Glynn--2017|Glynn et al., 2017]] ; [[#Duchicela--2019|Duchicela et al., 2019]] ), along with the agricultural sector in the face of extreme events ( [[#Coayla--2020|Coayla and Culqui, 2020]] ). Their vulnerabilities increase as a result of unequal chains of value, incomplete transfers of technology and other socioeconomic and environmental drivers ( ''high confidence'' ) ( [[#Ariza-Montobbio--2020|Ariza-Montobbio and Cuvi, 2020]] ; [[#Gutierrez--2020|Gutierrez et al., 2020]] ).

Informal housing and settlements, usually located in areas exposed to the highest level of risk, exacerbates vulnerability ( ''very high confidence'' ) ( [[#Miranda%20Sara--2014|Miranda Sara and Baud, 2014]] ; [[#Cuvi--2015|Cuvi, 2015]] ; [[#Miranda%20Sara--2016|Miranda Sara et al., 2016]] ). The absence of proper drainage systems in urban areas increases this vulnerability, especially to floods. Most cities and infrastructure are considered highly vulnerable to climate change ( ''high confidence'' ) (Figure 12.7).

Regions dependent on glacier runoff are particularly vulnerable ( [[#Jiménez%20Cisneros--2014|Jiménez Cisneros et al., 2014]] ; [[#Mark--2017|Mark et al., 2017]] ; [[#Polk--2017|Polk et al., 2017]] ). Also biodiversity and water-dependent activities where seasonality and rainfall patterns are changing and where other non-climatic sources of change, such as land use, affect the capacity of ecosystems to provide hydrological services ( ''very high confidence'' ) ( [[#Cerrón--2019|Cerrón et al., 2019]] ; [[#Molina--2020|Molina et al., 2020]] ). The countries in this sub-region (Colombia, Ecuador and Peru) are among the most vulnerable in terms of well-being and health (Figure 12.7; [[#Nagy--2018|Nagy et al., 2018]] ).

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==== 12.3.2.4 Impacts ====

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An increase in the frequency of climate-related disasters has been reported ( ''high confidence'' ) ( [[#Huggel--2015a|Huggel et al., 2015a]] ; [[#Stäubli--2018|Stäubli et al., 2018]] ) (WGI AR6 Chapter 12) ( [[#Ranasinghe--2021|Ranasinghe et al., 2021]] ). Scale studies indicate an increase of flood risk during the 21st century, consistent with more frequent floods, with the risk being worse in higher emission scenarios ( ''high confidence'' ) ( [[#Arnell--2013|Arnell and Gosling, 2013]] ; [[#Hirabayashi--2013|Hirabayashi et al., 2013]] ; [[#Alfieri--2017|Alfieri et al., 2017]] ; WGI AR6 Chapter 12, [[#Ranasinghe--2021|Ranasinghe et al., 2021]] ). Those living on riverbanks and in slums built on steep slopes are among the most affected by floods of all kinds ( ''high confidence'' ) ( [[#Emmer--2016|Emmer et al., 2016]] ; [[#Emmer--2017|Emmer, 2017]] ). There is still uncertainty in relation to future drought intensity and frequency ( [[#Pabón-Caicedo--2020|Pabón-Caicedo et al., 2020]] ).

Increased SST, coupled with stronger ENSO events, will affect marine life and fisheries by loss of productive habitat, disruption of nutrient structure, productivity and alteration of species migration patterns, leading to changes in fishing rates, which will impact coastal livelihoods ( ''high confidence'' ) ( [[#Bayer--2014|Bayer et al., 2014]] ; [[#Cai--2015|Cai et al., 2015]] ; [[#Ding--2017|Ding et al., 2017]] ; Mariano [[#Gutiérrez--2017|Gutiérrez et al., 2017]] ; [[#Bertrand--2020|Bertrand et al., 2020]] ). Figure 12.8 shows other observed sensitivities in several ecosystems and in such places as the Galapagos and Malpelo islands and coastal economic exclusion zones (EEZs).

ENSO events coupled with climate change lead to warmer ocean temperatures, heavy rains, floods and heavy river discharges, which will continue to impact several activities, including small-scale fishery infrastructure ( ''very high confidence'' ). In Peru alone, wet extremes are estimated to be at least 1.5 times more likely to happen compared to pre-industrial times. The extremely wet ENSO event of 2017 resulted in 6–9 billion USD in monetary losses in that country, 1.7 million inhabitants affected and crops, roads, bridges, homes, schools and health service facilities damaged or destroyed. Distinct types of ENSO events can have differentiated impacts ( [[#French--2017|French and Mechler, 2017]] ; [[#Christidis--2019|Christidis et al., 2019]] ; [[#Takahashi--2019|Takahashi and Martínez, 2019]] ; [[#Bertrand--2020|Bertrand et al., 2020]] ; [[#Coayla--2020|Coayla and Culqui, 2020]] ).

Irrigation, potable water, health and education infrastructures, as well as roads, bridges, cities and residential constructions, are frequently damaged or destroyed by extreme precipitation events, which also impact sediment transport, river erosion and annual discharge ( ''very high confidence'' ) ( [[#Martínez--2017|Martínez et al., 2017]] ; [[#Morera--2017|Morera et al., 2017]] ; [[#Isla--2018|Isla, 2018]] ; [[#Rosales-Rueda--2018|Rosales-Rueda, 2018]] ; [[#Salazar--2018|Salazar et al., 2018]] ; [[#Puente-Sotomayor--2021|Puente-Sotomayor et al., 2021]] ). The increasing variability of precipitation has compromised rainfed agriculture and power generation, particularly in the dry season ( ''high confidence'' ) ( [[#Bradley--2006|Bradley et al., 2006]] ; [[#Bury--2013|Bury et al., 2013]] ; [[#Buytaert--2017|Buytaert et al., 2017]] ; [[#Carey--2017|Carey et al., 2017]] ; [[#Vuille--2018|Vuille et al., 2018]] ; [[#Orlove--2019|Orlove et al., 2019]] ). For the Amazon–Andes transition zone, the impacts of hydrological variability and transport of sediments have been noticed in riparian agriculture and biodiversity ( ''high confidence'' ) ( [[#Maeda--2015|Maeda et al., 2015]] ; [[#Espinoza--2016|Espinoza et al., 2016]] ; [[#Vauchel--2017|Vauchel et al., 2017]] ; [[#Ronchail--2018|Ronchail et al., 2018]] ; [[#Ayes%20Rivera--2019|Ayes Rivera et al., 2019]] ; [[#Armijos--2020|Armijos et al., 2020]] ; [[#Figueroa--2020|Figueroa et al., 2020]] ; [[#Pabón-Caicedo--2020|Pabón-Caicedo et al., 2020]] ). Changes in seasonality and rain patterns are affecting coffee producers ( [[#Lambert--2020|Lambert and Eise, 2020]] ).

Increases in vector-borne diseases can be related to increases in rainfall and minimum temperatures during ENSO events ( [[#Stewart-Ibarra--2013|Stewart-Ibarra and Lowe, 2013]] ) and the expansion of the diseases’ altitudinal distribution ( ''high confidence'' ) ( [[#Lowe--2017|Lowe et al., 2017]] ; [[#Lippi--2019|Lippi et al., 2019]] ; [[#Portilla%20Cabrera--2020|Portilla Cabrera and Selvaraj, 2020]] ). ENSO events have been related to such diseases as dengue and leptospirosis ( [[#Quintero-Herrera--2015|Quintero-Herrera et al., 2015]] ; [[#Sánchez--2017|Sánchez et al., 2017]] ; [[#Arias-Monsalve--2019|Arias-Monsalve and Builes-Jaramillo, 2019]] ); they can also lead to an increased incidence of chikungunya (Sections 7.2.2.1 and 7.3.1.3). Precipitation, relative humidity and temperature have influenced dengue incidence in recent years ( [[#Mattar--2013|Mattar et al., 2013]] ) (Table 12.1). Dengue cases are predicted to increase in the 1.5°C and the 3.7°C warming scenarios by 2050 and 2100, with increases ranging from 28,900 to 88,800 in Peru, 34,600 to 110,000 in Ecuador, and 97,400 to 317,000 in Colombia, although these scenarios do not consider the potential effects of vaccines or socioeconomic trajectories ( [[#Colón-González--2018|Colón-González et al., 2018]] ). Other studies found that ''Aedes aegypti'' (arbovirus vector) will shift into higher elevations, increasing the populations at risk (Figure 12.5) ( [[#Lippi--2019|Lippi et al., 2019]] ). Climate change will contribute to increased malaria vectorial capacity ( ''high confidence'' ) ( [[IPCC:Wg2:Chapter:Chapter-7#7.2.2|Section 7.2.2.1]] ) ( [[#Laporta--2015|Laporta et al., 2015]] ). Increases in minimum temperature were associated with historical malaria transmission when taking into consideration disease control interventions and climate factors ( [[#Fletcher--2020|Fletcher et al., 2020]] ). Figure 12.4 shows mixed changes in the number of months suitable for malaria transmission, with low-lying areas in coastal regions becoming more suitable. Zoonotic tick-borne diseases and the epidemiology of tuberculosis are also influenced ( [[#Garcia-Solorzano--2019|Garcia-Solorzano et al., 2019]] ; [[#Rodriguez-Morales--2019|Rodriguez-Morales et al., 2019]] ).

<div id="_idContainer010" class="Figure"></div>

[[File:e1ad2ef05a6338c971f7a248296bd08a IPCC_AR6_WGII_Figure_12_004.png]]

'''Figure 12.4 |''' '''Change in average number of months in a given year suitable for malaria transmission by''' '''Plasmodium falciparum''' ''', from 1950–1959 to 2010–2019.''' The threshold-based model used incorporates precipitation accumulation, average temperature and relative humidity ( [[#Grover-Kopec--2006|Grover-Kopec et al., 2006]] ; [[#Romanello--2021|Romanello et al., 2021]] ).

<div id="_idContainer015" class="Figure"></div>

[[File:caa24597389a053e20ffda3909aa6e1e IPCC_AR6_WGII_Figure_12_005.png]]

'''Figure 12.5 |''' '''Predicted thermal suitability for transmission of dengue by''' '''Aedes aegypti''' '''mosquitoes, mapped as the number of months of the year suitable under baseline or current conditions (2015), and in 2030, 2050 and 2080 under RCP4.''' '''5 and RCP8.5.''' Adapted from [[#Ryan--2019|Ryan et al. (2019)]] . See SM12.8 for additional data on population at risk for dengue and Zika in the sub-regions and methodological details.

Accelerated warming is reducing tropical glaciers. Glacier volume loss and permafrost thawing will continue in all scenarios ( ''high confidence'' ) ( [[#Ranasinghe--2021|Ranasinghe et al., 2021]] ). On average, the tropical Andes have lost about 30% and more of their area since the 1980s ( [[#Basantes-Serrano--2016|Basantes-Serrano et al., 2016]] ; [[#Mark--2017|Mark et al., 2017]] ; [[#Thompson--2017|Thompson et al., 2017]] ; [[#Rabatel--2018|Rabatel et al., 2018]] ; [[#Vuille--2018|Vuille et al., 2018]] ; [[#Reinthaler--2019a|Reinthaler et al., 2019a]] ; [[#Seehaus--2019|Seehaus et al., 2019]] ; [[#Masiokas--2020|Masiokas et al., 2020]] ). In a low-emissions scenario, by the end of the 21st century, Peru will lose about 50% of its present glacier surface, while in a high-emission scenario there will remain very small areas of only about 3–5% on the highest peaks ( [[#Schauwecker--2017|Schauwecker et al., 2017]] ).

Changing glaciers, snow and permafrost (Figure 12.13), in synergy with land use change, have implications for the occurrence, frequency and magnitude of derived floods and landslides ( ''high confidence'' ) ( [[#Huggel--2007|Huggel et al., 2007]] ; [[#Iribarren%20Anacona--2015|Iribarren Anacona et al., 2015]] ; [[#Emmer--2017|Emmer, 2017]] ; [[#Mark--2017|Mark et al., 2017]] ), as well as for landscape transformation through lake formation or drying and for alterations in hydrological dynamics, with impacts on water for human consumption, agriculture, industry, hydroelectric generation, carbon sequestration and biodiversity ( ''high confidence'' ) ( [[#Michelutti--2015|Michelutti et al., 2015]] ; [[#Carrivick--2016|Carrivick and Tweed, 2016]] ; [[#Kronenberg--2016|Kronenberg et al., 2016]] ; [[#Emmer--2017|Emmer, 2017]] ; [[#Mark--2017|Mark et al., 2017]] ; [[#Milner--2017|Milner et al., 2017]] ; [[#Polk--2017|Polk et al., 2017]] ; [[#Reyer--2017|Reyer et al., 2017]] ; [[#Young--2017|Young et al., 2017]] ; [[#Vuille--2018|Vuille et al., 2018]] ; [[#Cuesta--2019|Cuesta et al., 2019]] ; [[#Drenkhan--2019|Drenkhan et al., 2019]] ; [[#Hock--2019|Hock et al., 2019]] ; [[#Motschmann--2020a|Motschmann et al., 2020a]] ).

Water flow has decreased in several basins, such as the Shullcas River in the Cordillera Huaytapallana in Peru, and is expected to decrease in the near future in places such as the Cordillera Blanca in Peru ( ''very high confidence'' ) ( [[#Baraer--2012|Baraer et al., 2012]] ; [[#Vuille--2018|Vuille et al., 2018]] ; [[#Somers--2019|Somers et al., 2019]] ; [[#Molina--2020|Molina et al., 2020]] ). Disruptions in water flows will significantly degrade or eliminate high-elevation wetlands ( ''high confidence'' ) ( [[#Bury--2013|Bury et al., 2013]] ; [[#Dangles--2017|Dangles et al., 2017]] ; [[#Mark--2017|Mark et al., 2017]] ; [[#Polk--2017|Polk et al., 2017]] ; [[#Cuesta--2019|Cuesta et al., 2019]] ). Impacts on wetlands are affecting the wild vicuña and the domesticated alpaca ( [[#Duchicela--2019|Duchicela et al., 2019]] ). New lakes represent a source of future hazards and water scarcity, as well as opportunities to serve as water reservoirs ( [[#Colonia--2017|Colonia et al., 2017]] ; [[#Drenkhan--2019|Drenkhan et al., 2019]] ). The timing and extent of peak water due to glacier shrinkage is spatially highly variable and has passed for a large number of tropical Andes glaciers ( [[#Hock--2019|Hock et al., 2019]] ). Cities dependent on glacier melt have experienced high variability in domestic water supply ( [[#Chevallier--2011|Chevallier et al., 2011]] ; [[#Soruco--2015|Soruco et al., 2015]] ; [[#Mark--2017|Mark et al., 2017]] ), as shown in Case Study 2.7.3, but an increase in demand may also have an effect ( [[#Buytaert--2012|Buytaert and De Bièvre, 2012]] ). Water provision is related to socioeconomic issues ( [[#Drenkhan--2015|Drenkhan et al., 2015]] ). Glacier retreat impacts Andean pastoralists ( ''high confidence'' ), as shown in Case Study 2.6.5.4.

NWS houses several global priority areas for biodiversity conservation, including the Tropical Andes and Tumbes-Chocó-Magdalena terrestrial biodiversity hotspots (Section [https://www.ipcc.ch/chapter/12#CCP1.2.2 CCP1.2.2] ; [[#Manes--2021|Manes et al., 2021]] ). Biodiversity in the Tropical Andes and Tumbes-Chocó-Magdalena is projected to suffer negative impacts ( ''medium confidence: medium evidence, high agreement'' ) (Figure 12.9). Invasive plant species might benefit from climate change in these hotspots ( [[#Wang--2017a|Wang et al., 2017a]] ). Species distribution is changing upslope due to increasing air temperature, leading to range contraction and local extinctions of highland species, whereas lowland species are experiencing range contractions at the rear end and expansions in the front end, including vectors of diseases ( ''high confidence'' ) ( [[#Crespo-Pérez--2015|Crespo-Pérez et al., 2015]] ; [[#Duque--2015|Duque et al., 2015]] ; [[#Morueta-Holme--2015|Morueta-Holme et al., 2015]] ; [[#Moret--2016|Moret et al., 2016]] ; [[#Aguirre--2017|Aguirre et al., 2017]] ; [[#Cuesta--2017a|Cuesta et al., 2017a]] ; [[#Seimon--2017|Seimon et al., 2017]] ; [[#Fadrique--2018|Fadrique et al., 2018]] ; [[#Tito--2018|Tito et al., 2018]] ; [[#Zimmer--2018|Zimmer et al., 2018]] ; [[#Cauvy-Fraunié--2019|Cauvy-Fraunié and Dangles, 2019]] ; [[#Cuesta--2019|Cuesta et al., 2019]] ; [[#Moret--2020|Moret et al., 2020]] ; [[#Rosero--2021|Rosero et al., 2021]] ). Vegetation in summits of the northern Andes is particularly vulnerable because of a high abundance of endemic species with narrow thermal niches and lowland dispersal capacity in comparison to the central Andes ( [[#Cuesta--2020|Cuesta et al., 2020]] ).

The upper limit of alpine vegetation (paramo) shifted upslope 500 m in the Chimborazo ( [[#Morueta-Holme--2015|Morueta-Holme et al., 2015]] ), yet the upper forest limit (the ecotone between forest and alpine vegetation) is migrating at slower rates or not at all ( [[#Harsch--2009|Harsch et al., 2009]] ; [[#Rehm--2015b|Rehm and Feeley, 2015b]] ), so it is expected to be a major barrier to migration to several montane species, leading to population reductions and biodiversity losses ( [[#Lutz--2013|Lutz et al., 2013]] ; [[#Rehm--2015a|Rehm and Feeley, 2015a]] ). Shifts in tree species distribution may result in decreased above-ground carbon stocks and productivity in tropical mountain forests ( ''high confidence'' ) ( [[#Feeley--2011|Feeley et al., 2011]] ; [[#Duque--2015|Duque et al., 2015]] ; [[#Fadrique--2018|Fadrique et al., 2018]] ; [[#Duque--2021|Duque et al., 2021]] ), a biomass loss that will only be partially offset through increased recruitment and growth of lowland species migrating upslope. Water scarcity can enhance tree mortality and decrease above-ground carbon stocks ( [[#Álvarez-Dávila--2017|Álvarez-Dávila et al., 2017]] ; [[#McDowell--2020|McDowell et al., 2020]] ). The agricultural frontier of crops, such as potatoes or maize, is moving upwards ( ''high confidence'' ), following the freezing level height upward displacement ( [[#Morueta-Holme--2015|Morueta-Holme et al., 2015]] ; [[#Skarbø--2016|Skarbø and VanderMolen, 2016]] ; [[#Schauwecker--2017|Schauwecker et al., 2017]] ; [[#Vuille--2018|Vuille et al., 2018]] ). Modelling exercises agree with the observed impacts in species, ecosystem processes, crop impacts and related pests and diseases ( ''high confidence'' ) ( [[#Cernusak--2013|Cernusak et al., 2013]] ; [[#Tovar--2013|Tovar et al., 2013]] ; [[#Ramirez-Villegas--2014|Ramirez-Villegas et al., 2014]] ; [[#Ovalle-Rivera--2015|Ovalle-Rivera et al., 2015]] ; [[#van%20der%20Sleen--2015|van der Sleen et al., 2015]] ; [[#Lowe--2017|Lowe et al., 2017]] ). Agricultural options are changing as a result of intra-seasonal temperature variation ( [[#Ponce--2020|Ponce, 2020]] ). Changes in the timing and amount of precipitation are also impacting agriculture (Table 12.4) ( [[#Heikkinen--2017|Heikkinen, 2017]] ; [[#Altea--2020|Altea, 2020]] ).

Species distribution is changing in dry lowland forests, where deforestation is the more intense driver and climate change is intensely acting ( [[#Aguirre--2017|Aguirre et al., 2017]] ; [[#Manchego--2017|Manchego et al., 2017]] ). Extinctions in amphibians have been related to temperature rises acting in synergy with diseases ( [[#Catenazzi--2014|Catenazzi et al., 2014]] ). The fungus ''Batrachochytrium dendrobatidis'' successfully accompanied and caused disease in high-elevation Andean frogs as they expanded their ranges to 5200–5400 m ( [[#Seimon--2017|Seimon et al., 2017]] ). Several groups of freshwater species of the tropical Andes represent 35% of threatened freshwater species in the world ( [[#Gardner--2018|Gardner and Finlayson, 2018]] ). Potential impacts of species turnover in key areas for biodiversity conservation have been identified ( [[#Cuesta--2017b|Cuesta et al., 2017b]] ).

Climate-change-related hazards could foster rural poverty, and its impacts have led to the modification of agriculture calendars and irrigation adjustments ( [[#Postigo--2014|Postigo, 2014]] ). Livestock populations are diminishing due to rising temperatures, changing water flows and shrinkage of pastures, particularly cattle and pig production ( [[#Bayer--2014|Bayer et al., 2014]] ; [[#Tapasco--2015|Tapasco et al., 2015]] ; [[#Bergmann--2021|Bergmann et al., 2021]] ). In some cases farmers respond to extreme temperatures by increasing use of land and crop intensity ( [[#Aragón--2021|Aragón et al., 2021]] ). Climate change has prompted and will continue to prompt internal and international migrations ( [[#Løken--2019|Løken, 2019]] ; [[#Bergmann--2021|Bergmann et al., 2021]] ). A change in fire regimes and fire risk is expected in highland ecosystems, although it is difficult to determine the influence of human activities and climate change influence on fire patterns ( [[#Oliveras--2014|Oliveras et al., 2014]] , 2018; [[#Armenteras--2020|Armenteras et al., 2020]] ).

<div id="12.3.3" class="h2-container"></div>

<span id="northern-south-america-sub-region"></span>
=== 12.3.3 Northern South America Sub-region ===

<div id="h2-5-siblings" class="h2-siblings"></div>

<div id="12.3.3.1" class="h3-container"></div>

<span id="hazards-2"></span>
==== 12.3.3.1 Hazards ====

<div id="h3-9-siblings" class="h3-siblings"></div>

A significant increase in the intensity and frequency of warm extremes and length of heatwaves and a decrease in the frequency of cold extremes (Skansi et al., 2013) were ''likely'' observed (Figure 12.6) (WGI AR6 Table 11.13) ( [[#Donat--2013|Donat et al., 2013]] ; [[#Almeida--2017|Almeida et al., 2017]] ; [[#Seneviratne--2021|Seneviratne et al., 2021]] ). Precipitation showed increasing trends in annual and wet season totals over the eastern part and decreasing trends in the dry season ( [[#Almeida--2017|Almeida et al., 2017]] ). An increase in the frequency of anomalous severe floods ( [[#Gloor--2015|Gloor et al., 2015]] ) was observed, but insufficient data coverage for extreme precipitation and trends in the available data result in ''low confidence'' ( [[#Avila-Diaz--2020|Avila-Diaz et al., 2020]] ; [[#Dereczynski--2020|Dereczynski et al., 2020]] ; [[#Dunn--2020|Dunn et al., 2020]] ; [[#Sun--2021|Sun et al., 2021]] ) (WGI AR6 Table 11.14) ( [[#Seneviratne--2021|Seneviratne et al., 2021]] ). Droughts presented mixed trends between sub-regions, but evidence indicates an increasing length of dry periods ( ''low confidence'' ) (WGI AR6 Tables 11.15 and 12.3) (Skansi et al., 2013; [[#Marengo--2016|Marengo and Espinoza, 2016]] ; [[#Spinoni--2019|Spinoni et al., 2019]] ; [[#Avila-Diaz--2020|Avila-Diaz et al., 2020]] ; [[#Dereczynski--2020|Dereczynski et al., 2020]] ; [[#Dunn--2020|Dunn et al., 2020]] ; [[#Seneviratne--2021|Seneviratne et al., 2021]] ; [[#Ranasinghe--2021|Ranasinghe et al., 2021]] ).

An overall increase in temperature by the end of the century is projected for all seasons, from 2°C to 6°C depending on the scenario ( [[#Chou--2014|Chou et al., 2014]] ). Projections also suggest increases in the intensity and frequency of hot extremes and decreases in the intensity and frequency of cold extremes ( ''very likely'' for a 2°C GWL) (WGI AR6 Table 11.13) ( [[#López-Franca--2016|López-Franca et al., 2016]] ; [[#Seneviratne--2021|Seneviratne et al., 2021]] ). In the entire region, extreme maximum temperature estimates under the RCP4.5 scenario are projected to increase. Major tropical cities are expected to be strongly affected by heatwaves and daily record temperatures ( [[#Feron--2019|Feron et al., 2019]] ).

A decrease in precipitation over the tropical region but regional changes, such as increases in rainfall amounts in western NSA of up to 40 mm, are expected by mid-century under RCP8.5 ( [[#Teichmann--2013|Teichmann et al., 2013]] ; [[#Sánchez--2015|Sánchez et al., 2015]] ). Changes in the dry season in the central part of South America (SA) due to the late onset and late retreat of monsoon, decreases in precipitation over the Amazon and central Brazil are expected ( [[#Coppola--2014|Coppola et al., 2014]] ; [[#Giorgi--2014|Giorgi et al., 2014]] ; [[#Llopart--2014|Llopart et al., 2014]] ). Further, an increase in the frequency and geographic extent of meteorological drought in the eastern Amazon and the opposite in the west are expected with ''medium confidence'' ( [[#Duffy--2015|Duffy et al., 2015]] ). A decrease in the total annual precipitation but an increase in heavy precipitation ( [[#Seiler--2013|Seiler et al., 2013]] ; [[#Chou--2014|Chou et al., 2014]] ) are projected for a 2°C GWL (Figure 12.6; WGI AR6 Table 11.15) ( [[#Seneviratne--2021|Seneviratne et al., 2021]] ).

Mean precipitation will decrease, and heavy precipitation, aridity and drought are projected to increase with ''medium confidence'' , whereas mean temperature, extreme heat, fire weather and coastal and oceanic climate impact drivers will all increase with ''high confidence'' (WGI AR6 Table 12.6 and Figure 12.8) ( [[#Sun--2019|Sun et al., 2019]] ; [[#Ranasinghe--2021|Ranasinghe et al., 2021]] ).

<div id="12.3.3.2" class="h3-container"></div>

<span id="exposure-2"></span>
==== 12.3.3.2 Exposure ====

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In NSA the percentage of the national population living in low elevation coastal zones (LECZs) and exposed to SLR is 68% for Suriname, 56% for Guyana and 6% for Venezuela ( [[#Nagy--2019|Nagy et al., 2019]] ). In these countries, the exposure of populations, land areas and built capital to coastal floods is projected to continue and increase ( [[#Neumann--2015|Neumann et al., 2015]] ; [[#Reguero--2015|Reguero et al., 2015]] ).

In the Amazon basin, approximately 80% of the population is concentrated in cities due to migrations in search of improvements in education, job opportunities, health and goods and services ( [[#Eloy--2015|Eloy et al., 2015]] ; [[#Pinho--2015|Pinho et al., 2015]] ). These populations settle in areas prone to flooding combined with various levels of sanitation due to limited economic access to areas of lower risk ( [[#Pinho--2015|Pinho et al., 2015]] ; [[#Mansur--2016|Mansur et al., 2016]] ; Andrade and Szlafsztein, 2018; [[#Parry--2018|Parry et al., 2018]] ). In these areas, poor urban planning and high population densities increase exposure levels ( [[#Mansur--2016|Mansur et al., 2016]] ). In this context, 41% of the total population of urban centres of the Amazon delta and estuary (ADE) are exposed to flooding ( [[#Mansur--2016|Mansur et al., 2016]] ), while in Santarem, population and infrastructure are highly exposed to floods and flash floods (Andrade and Szlafsztein, 2018).

Exposure of the Brazilian Amazon to severe to extreme drought has increased from 8% in 2004/2005 to 16% in 2009/2010 and 16% in 2015/2016 ( [[#Anderson--2018b|Anderson et al., 2018b]] ); a similar trend is reported in other regions (Table 12.3). During the extreme drought of 2015/2016 in the Amazonian forests, 10% or more of the area showed negative anomalies of the minimum cumulative water deficit ( [[#Anderson--2018b|Anderson et al., 2018b]] ). This extreme drought also caused an increase in the occurrence and spread of fires in the basin ( ''medium confidence: medium evidence, high agreement'' ) ( [[#Aragão--2018|Aragão et al., 2018]] ; [[#Lima--2018|Lima et al., 2018]] ; [[#Silva%20Junior--2019|Silva Junior et al., 2019]] ; [[#Bilbao--2020|Bilbao et al., 2020]] ). Exposure to anomalous fires in ecosystems such as savannahs, which are more fire-prone, increases the exposure and vulnerability of adjacent forest ecosystems not adapted to fire, such as seasonally flooded forests ( [[#Bilbao--2020|Bilbao et al., 2020]] ; [[#Flores--2021|Flores and Holmgren, 2021]] ).

<div id="12.3.3.3" class="h3-container"></div>

<span id="vulnerability-2"></span>
==== 12.3.3.3 Vulnerability ====

<div id="h3-11-siblings" class="h3-siblings"></div>

NSA is one of the most vulnerable sub-regions in the region, after CA, as evidenced by its very high vulnerability in four of the six sectors assessed (Figure 12.7). The LECZ of Venezuela, Guyana and Suriname are highly vulnerable to climate change due to SLR ( ''high confidence'' ) ( [[#CAF--2014|CAF, 2014]] ; [[#Mycoo--2014|Mycoo, 2014]] ; [[#Reguero--2015|Reguero et al., 2015]] ; [[#Villamizar--2017|Villamizar et al., 2017]] ; [[#Nagy--2019|Nagy et al., 2019]] ). In Guyana, the combined effect of increased rainfall intensity and SLR has caused flooding over the past two decades, increasing the vulnerability of the agricultural sector ( [[#Tomby--2019|Tomby and Zhang, 2019]] ).

The unprecedented extreme events of floods (2009, 2012 and 2014) and drought (2010) in the Amazon basin led to increased societal vulnerability ( ''medium confidence'' : ''medium evidence, high agreement'' ) ( [[#Mansur--2016|Mansur et al., 2016]] ; [[#Debortoli--2017|Debortoli et al., 2017]] ; [[#Marengo--2018|Marengo et al., 2018]] ; [[#Menezes--2018|Menezes et al., 2018]] ). The disruption of the region’s natural hydrology dynamics as a consequence of extreme events increases the sensitivity of the food and transport systems of the Indigenous Peoples and rural resource-dependent communities ( [[#Pinho--2015|Pinho et al., 2015]] ).

Migration by Indigenous Peoples and rural resource-dependent communities to cities has increased due to urbanisation, development of extractive activities, agroindustry and infrastructure. Upon migrating, they are forced to abandon their livelihoods in order to acquire temporary jobs and to live in poverty and exclusion conditions on the periphery of the city ( [[#Cardoso--2018|Cardoso et al., 2018]] ). Between 60% and 90% of the population in the urban centres of ADE live in conditions of moderate to high degree of vulnerability ( [[#Mansur--2016|Mansur et al., 2016]] ) (Figure 12.7). Amazon populations located in remote urban centres with limited or non-existent roads are more vulnerable to extreme events in relation to more connected urban centres ( [[#Parry--2018|Parry et al., 2018]] ). These highly risky circumstances reduce the adaptive capacity of these populations ( [[#Cardoso--2018|Cardoso et al., 2018]] ). Nevertheless, the dynamics of the adaptive capacity of Indigenous Peoples and rural resource-dependent communities is a complex issue. There is a robust and growing body of literature showing that resource-dependent communities located in remote areas address climate anomalies by reducing the vulnerability of socioecological systems through IKLK ( ''high confidence'' ) ( [[#Mistry--2016|Mistry et al., 2016]] ; [[#Vogt--2016|Vogt et al., 2016]] ; [[#Bilbao--2019|Bilbao et al., 2019]] , 2020; [[#Camico--2021|Camico et al., 2021]] ).

Amazonian forests constitute one of the major carbon (C) sinks on Earth ( [[#Pan--2011|Pan et al., 2011]] ), playing a pivotal role in the climate system and regional balance of C and water ( [[#Marengo--2018|Marengo et al., 2018]] ; [[#Molina--2019|Molina et al., 2019]] ). Deforestation, temperature increase and any factor affecting forest ecosystem dynamics will have an impact on atmospheric CO 2 concentrations and, hence, on the global climate ( [[#Ruiz-Vásquez--2020|Ruiz-Vásquez et al., 2020]] ; [[#Sullivan--2020|Sullivan et al., 2020]] ).There is robust scientific evidence of the high vulnerability of Amazon rainforests to increasing temperature and repeated extreme drought events ( ''high confidence'' ) (Figure 12.7) ( [[#Brienen--2015|Brienen et al., 2015]] ; [[#Olivares--2015|Olivares et al., 2015]] ; [[#Feldpausch--2016|Feldpausch et al., 2016]] ; [[#Zhao--2017|Zhao et al., 2017]] ; [[#Anderson--2018b|Anderson et al., 2018b]] ; [[#Anjos--2018|Anjos and De Toledo, 2018]] ; [[#Yang--2018|Yang et al., 2018]] ; [[#Barkhordarian--2019|Barkhordarian et al., 2019]] ; [[#Sampaio--2019|Sampaio et al., 2019]] ; [[#Rammig--2020|Rammig, 2020]] ; [[#Sullivan--2020|Sullivan et al., 2020]] ).

<div id="12.3.3.4" class="h3-container"></div>

<span id="impacts-2"></span>
==== 12.3.3.4 Impacts ====

<div id="h3-12-siblings" class="h3-siblings"></div>

Suriname has experienced coastal erosion and flooding, which causes damage to infrastructure, agriculture and ecosystems, while Georgetown has suffered a significant number of floods ( [[#CAF--2014|CAF, 2014]] ). In Guyana, coastal flooding has negatively impacted agricultural activity ( [[#Tomby--2018|Tomby and Zhang, 2018]] ) (Figure 12.9). Sugarcane production has been one of the most impacted cash crops. The impact on sugar production has affected Guyana’s sugar industry ( [[#Tomby--2019|Tomby and Zhang, 2019]] ). Among the main impacts observed in the sugar industry are an increase in production costs, greater use of pesticides and fertilizers and a reduction in worker income ( [[#Tomby--2018|Tomby and Zhang, 2018]] ).

Indigenous Peoples and resource-dependent rural communities in the Amazon have been impacted over the last decade by extreme drought and flood events in various dimensions of their livelihoods ( [[#Pinho--2015|Pinho et al., 2015]] ). Food security has been strongly impacted since it is based on fishing and small-scale agriculture, two sectors highly vulnerable to climate change. During extreme events, fishing decreases due to limited access to fishing grounds ( ''medium confidence'' : ''low evidence, high agreement'' ) (Figure 12.9) ( [[#Pinho--2015|Pinho et al., 2015]] ; [[#Camacho%20Guerreiro--2016|Camacho Guerreiro et al., 2016]] . Overfishing, deforestation and dam construction are a threat to fishing in the sub-region ( [[#Lopes--2019|Lopes et al., 2019]] ) and therefore contribute to exacerbating the impacts of climate change. Small-scale agriculture practices (e.g., floodplain agriculture and slash and burn) are highly coupled with natural hydrological cycles and therefore severely affected by extreme events (Figure 12.9) ( [[#Cochran--2016|Cochran et al., 2016]] ). Livelihoods are also impacted by disruptions in land and river transport, restrictions in drinking water access, increased incidence of forest fires and disease outbreaks ( ''medium confidence'' : ''medium evidence, high agreement'' ) (Figure 12.9) ( [[#Marengo--2013|Marengo et al., 2013]] ; [[#Pinho--2015|Pinho et al., 2015]] ; [[#Marengo--2016|Marengo and Espinoza, 2016]] ; [[#Marengo--2018|Marengo et al., 2018]] ). In addition, flood events have caused losses of homes and disruption of public and commercial services (Figure 12.9) ( [[#Parry--2018|Parry et al., 2018]] ).

Several vector-driven diseases such as malaria and leishmaniasis are endemic in the Amazon region; however, socio-environmental changes are altering their natural dynamics ( [[#Confalonieri--2014b|Confalonieri et al., 2014b]] ). An important relationship between the outbreak of infectious diseases and changes in climatic events (e.g., droughts, floods, heat waves, ENSO) or environmental events (e.g., deforestation, dam construction and habitat fragmentation) has been found to exist for the Brazilian Amazon ( ''medium confidence'' : ''medium evidence, high agreement'' ) ( [[#Pan--2014|Pan et al., 2014]] ; [[#Filho--2016|Filho et al., 2016]] ; [[#Nava--2017|Nava et al., 2017]] ; [[#Ellwanger--2020|Ellwanger et al., 2020]] ). These impacts are more severe in poor populations with limited access to health services ( [[#Pan--2014|Pan et al., 2014]] ; [[#WHO%20and%20UNFCCC--2020|WHO and UNFCCC, 2020]] ). In the case of Venezuela, the impact of climate change on the epidemiology of malaria has been studied, showing significant influence on transmission in the Amazonia area of the country (Figure 12.4) ( [[#Laguna--2017|Laguna et al., 2017]] ). Other studies from Venezuela have documented the role of ENSO in dengue outbreaks ( [[#Vincenti-Gonzalez--2018|Vincenti-Gonzalez et al., 2018]] ). Table 12.1 shows the changes observed in reproduction potential for dengue in the different sub-regions due to changes in rainfall and temperature. Forest fires pose a major threat to public health in the region because they relate to an increase in hospital admissions due to respiratory problems, mainly among children and the elderly (Figure 12.5). The amount of air pollutants detected is sometimes higher than that observed in large urban areas, especially during dry seasons when biomass burning increases ( [[#Aragão--2016|Aragão et al., 2016]] ; [[#de%20Oliveira%20Alves--2017|de Oliveira Alves et al., 2017]] ; [[#Paralovo--2019|Paralovo et al., 2019]] ).

'''Table 12.1 |''' Environmental suitability for transmission of dengue by ''Aedes aegypti'' as modelled by the influence of temperature and rainfall on vectorial capacity and vector abundance; this is overlaid on human population density data to estimate the reproduction potential for these diseases (R 0 , expected number of secondary infections resulting from one infected person). The southwestern South America (SWS) and southern South America (SSA) sub-regions are not presented because the vector is not abundant in these areas and the estimated R 0 is lower than 0.01. Data were derived from Romanello et al. (2021).

{| class="wikitable"
|-
! '''Sub-region'''

! '''Average R''' 0 '''1950–1954'''

! '''Average R''' 0 '''2016–2020'''

! '''Absolute change in R''' 0 '''from 1950–1954 to 2016–2020'''

! '''Percentage change in R''' 0 '''from 1950–1954 to 2016–2021'''

|-
| Central America (CA)

| 3.00

| 3.53

| 0.53

| 18%

|-
| Northwestern South America (NWS)

| 1.85

| 2.40

| 0.55

| 30%

|-
| Northern South America (NSA)

| 1.31

| 2.05

| 0.74

| 56%

|-
| South America Monsoon (SAM)

| 0.93

| 1.67

| 0.74

| 80%

|-
| Northeastern South America (NES)

| 2.11

| 2.47

| 0.36

| 17%

|-
| Southeastern South America (SES)

| 0.64

| 0.81

| 0.17

| 26%

|}

Climate-change impacts have also been observed in the oceans, coastal ecosystems (coral reefs and mangroves), EEZs and saltmarshes in NSA; further impacts are expected in coral reefs, estuaries, mangroves and EEZs in the sub-region (Figure 12.9). Species in freshwater ecoregions (e.g., the Orinoco and Amazon Rivers and their flooded forests) are predicted to suffer a decrease in range and climatic suitability ( ''medium confidence: low evidence, high agreement'' ) (Section [https://www.ipcc.ch/chapter/12#CCP1.2.3 CCP1.2.3] ; [[#Manes--2021|Manes et al., 2021]] ). A significant decrease in climate refugia (90%) for multiple vertebrate and plant species in the region has been projected for a 4°C scenario, with considerable benefits of mitigation and reducing risks to 40% for a 2°C scenario ( [[#Warren--2018|Warren et al., 2018]] ).

Droughts in 2009/2010 and 2015/2016 increased tree mortality rate in Amazon forests ( [[#Doughty--2015|Doughty et al., 2015]] ; [[#Feldpausch--2016|Feldpausch et al., 2016]] ; [[#Anderson--2018b|Anderson et al., 2018b]] ), while productivity showed no consistent change; some authors reported a drop in productivity ( [[#Feldpausch--2016|Feldpausch et al., 2016]] ), while others found no significant changes ( [[#Brienen--2015|Brienen et al., 2015]] ; [[#Doughty--2015|Doughty et al., 2015]] ). Nevertheless, the combined effect of increasing tree mortality with variations in growth results in a long-term decrease in C stocks in forest biomass, compromising the role of these forests as a C sink ( ''high confidence'' ) ( [[#Brienen--2015|Brienen et al., 2015]] ; [[#Rammig--2020|Rammig, 2020]] ; [[#Sullivan--2020|Sullivan et al., 2020]] ) (Figure 12.9). Under the RCP8.5 scenario for 2070, drought will increase the conversion of rainforest to savannah ( ''medium confidence'' : ''medium evidence, high agreement'' ) ( [[#Anadón--2014|Anadón et al., 2014]] ; [[#Olivares--2015|Olivares et al., 2015]] ; [[#Sampaio--2019|Sampaio et al., 2019]] ). The transformation of rainforest into savannah will bring forth biodiversity loss and alterations in ecosystem functions and services ( ''medium confidence'' : ''medium evidence, high agreement'' ) ( [[#Anadón--2014|Anadón et al., 2014]] ; [[#Olivares--2015|Olivares et al., 2015]] ; [[#Sampaio--2019|Sampaio et al., 2019]] ). In the Amazon basin, the synergistic effects of deforestation, fire, expansion of the agricultural frontier, infrastructure development, extractive activities, climate change and extreme events may exacerbate the risk of savannisation ( ''medium confidence'' : ''medium evidence, high agreement'' ) ( [[#Nobre--2016b|Nobre et al., 2016b]] ; [[#Bebbington--2019|Bebbington et al., 2019]] ; [[#Sampaio--2019|Sampaio et al., 2019]] ; [[#Rammig--2020|Rammig, 2020]] ).

<div id="12.3.4" class="h2-container"></div>

<span id="south-america-monsoon-sub-region"></span>
=== 12.3.4 South America Monsoon Sub-region ===

<div id="h2-6-siblings" class="h2-siblings"></div>

<div id="12.3.4.1" class="h3-container"></div>

<span id="hazards-3"></span>
==== 12.3.4.1 Hazards ====

<div id="h3-13-siblings" class="h3-siblings"></div>

Temperature extremes have ''likely'' increased in the intensity and frequency of hot extremes and decreased in the intensity and frequency of cold extremes ( [[#Donat--2013|Donat et al., 2013]] ; [[#Bitencourt--2016|Bitencourt et al., 2016]] ) (WGI AR6 Table 11.13, [[#Seneviratne--2021|Seneviratne et al., 2021]] ). In a vast transition zone between the Amazon and the Cerrado Biomes within the region, analysis of seasonal precipitation trends suggested that almost 90% of the observational sites showed a reduction in the length of the rainy season in the region ( [[#Debortoli--2015|Debortoli et al., 2015]] ), in the period 1971–2014 ( [[#Marengo--2018|Marengo et al., 2018]] ), confirming the growth in length of the dry season. Changes in the hydrological and precipitation regimes, characterised by a reduction in rainfall in southern Amazonia, in contrast to an increase in northwestern Amazonia, and overall increases in extreme precipitation and in the frequency of consecutive dry days have been reported by several authors ( [[#Fu--2013|Fu et al., 2013]] ; [[#Almeida--2017|Almeida et al., 2017]] ; [[#Marengo--2018|Marengo et al., 2018]] ; [[#Espinoza--2019a|Espinoza et al., 2019a]] ) with ''low confidence'' (WGI AR6 Table 11.14) ( [[#Seneviratne--2021|Seneviratne et al., 2021]] ) due to insufficient data coverage and trends in available data generally not significant.

The Amazon has been identified as one of the areas of persistent and emergent regional climate-change hotspots in response to various representative concentration pathways ( [[#Diffenbaugh--2012|Diffenbaugh and Giorgi, 2012]] ). In Bolivia, CMIP3/5 models projected an increase in temperature (2.5°C–5.9°C), with seasonal and regional differences. In the lowlands, both ensembles agreed on less rainfall (–19%) during drier months (June–August and September–November), with significant changes in interannual rainfall variability, but disagreed on changes during wetter months (January–March) ( [[#Seiler--2013|Seiler et al., 2013]] ). As a consequence of higher temperatures and reduced rainfall, an increased water deficit would be expected in the Brazilian Pantanal ( [[#Marengo--2016|Marengo et al., 2016]] ; [[#Bergier--2018|Bergier et al., 2018]] ; [[#Llopart--2020|Llopart et al., 2020]] ) with ''high confidence'' . The largest increases in warmer days and nights, and aridity, drought and significant increases in fire occurrence are calculated over the Amazon area ( [[#Huang--2016|Huang et al., 2016]] ). Over the entire region, by mid-century (RCP4.5) there is ''medium confidence'' of increases in river and pluvial floods, aridity and mean wind speed, and extreme heat, fire weather and drought are projected to increase with ''high confidence'' (WGI AR6 Table 12.6; [[#Ranasinghe--2021|Ranasinghe et al., 2021]] ).

<div id="12.3.4.2" class="h3-container"></div>

<span id="exposure-3"></span>
==== 12.3.4.2 Exposure ====

<div id="h3-14-siblings" class="h3-siblings"></div>

A large expansion in cropland area (soybean, corn and sugarcane) was observed in the past two decades in SAM, in response to increased local and global demand for biofuels and agricultural commodities ( ''high confidence'' ) ( [[#Lapola--2014|Lapola et al., 2014]] ; [[#Cohn--2016|Cohn et al., 2016]] ). Feedbacks to the climate system resulting from such land use changes are intricate. The clear-cutting of Amazon forest and Cerrado savannah in the region led to a local warming due to an increase in the energy balance and evapotranspiration ( [[#Malhado--2010|Malhado et al., 2010]] ); in contrast, the replacement of pasture by agriculture have led to a local cooling effect, due to changes in the surface albedo ( ''medium confidence: medium evidence, medium agreement'' ). Deforestation of the Amazon for pastures and soybean have decreased evapotranspiration during drought months and caused a localised lengthening of the dry season in northwestern SAM by 6.5 (± 2.5) d since 1979 ( ''medium confidence: medium evidence, medium agreement'' ) ( [[#Fu--2013|Fu et al., 2013]] ).

It is not surprising, therefore, that while SAM is the region in CSA that experienced the highest temperature increase in the last century, it is where most of the fire spots in the sub-continent are located, owing also to the prevalent use of fires in pasturelands ( ''medium confidence: medium evidence, high agreement'' ) ( [[#Bowman--2009|Bowman et al., 2009]] ). Recently, da Silva Junior et al. (2020) reported 6,708,350 and 6,188,606 fire foci in Cerrado and Amazonia, between 1999 and 2018, corresponding to 80% of the total observed in Brazil. The occurrence of extreme droughts has affected the carbon and water cycles in large areas of the Amazon rainforest ( ''high confidence'' ) ( [[#Lapola--2014|Lapola et al., 2014]] ; [[#Agudelo--2019|Agudelo et al., 2019]] ), in particular in its southern and eastern portions, where deforestation rates are higher. The loss of carbon in the Amazon region considering the combined effect of land use change in the southern portion of the region bordering Cerrado and Pantanal and global carbon emission scenarios can be as high as 38% at 4°C of warming, but limited to 8% if the Paris Agreement limit of 1.5°C is achieved ( ''medium confidence, medium evidence, high agreement'' ) ( [[#Burton--2021|Burton et al., 2021]] ), driving the region to be a net carbon source to the atmosphere ( [[#Gatti--2021|Gatti et al., 2021]] ). A recent extreme drought was estimated to affect the photosynthetic capacity of 400,000 km 2 of the forest ( [[#Anderson--2018b|Anderson et al., 2018b]] ); nevertheless, there are considerable uncertainties regarding the effects of CO 2 fertilisation in tropical forests and ecosystems ( ''medium confidence: medium evidence, high agreement'' ) ( [[#Sampaio--2021|Sampaio et al., 2021]] ). Extreme drought events increase forest vulnerability to fire, directly affecting the biodiversity and the forest structure and its plant species distribution ( ''high agreement'' ) ( [[#Brando--2014|Brando et al., 2014]] ). Production sectors are also exposed. SAM is pointed out as a region where agricultural production will be especially impacted by climate change, affecting the production of annual crops, fruits and livestock ( ''medium confidence: medium evidence, high agreement'' ) ( [[#Lapola--2014|Lapola et al., 2014]] ; [[#Zilli--2020|Zilli et al., 2020]] ).

<div id="12.3.4.3" class="h3-container"></div>

<span id="vulnerability-3"></span>
==== 12.3.4.3 Vulnerability ====

<div id="h3-15-siblings" class="h3-siblings"></div>

The largest expanses of remaining vegetation in the Cerrado biome are located in SAM, but the region has a small number of protected areas (only 7.5% of Cerrado vegetation occurs inside protected areas), which will leave fauna and flora with little room for moving across the landscape in the face of climate change. Protected areas—Indigenous lands included—have significantly reduced forest clear-cutting in the Amazon deforestation arc (most of which is inside SAM) ( ''high confidence'' ) ( [[#Nolte--2013|Nolte et al., 2013]] ). However nearly 100 protected areas in the Amazon, Cerrado and Pantanal biomes inside SAM have been identified as highly or moderately vulnerable to future climate change and demand serious adaptation interventions ( ''medium confidence: medium evidence, high agreement'' ) ( [[#Feeley--2016|Feeley and Silman, 2016]] ; [[#Lapola--2019b|Lapola et al., 2019b]] ). Yet the maintenance of these protected areas or even the halting of deforestation may do little to impede a large-scale persistent ecosystem shift to an alternative state (crossing a tipping point) of the Amazon rainforest or even more subtle changes caused by climate change in the region ( ''medium confidence: medium evidence, high agreement'' ) ( [[#Aguiar--2016a|Aguiar et al., 2016a]] ; [[#Boers--2017|Boers et al., 2017]] ; [[#Lapola--2018|Lapola et al., 2018]] ; [[#Lovejoy--2018|Lovejoy and Nobre, 2018]] ).

The agriculture in the region is highly dependent on the climate ( ''high confidence'' ), responsible for three-fourths of the variability in agricultural yields in the region (Table 12.4). Irrigation is an important strategy for agricultural production in part of the region, but it accounts for no more than 8% of the total agricultural area in SA and 7% in CA ( [[#OECD%20and%20FAO--2019|OECD and FAO, 2019]] ). This practice faces potential impacts from reductions in surface water availability in future climate scenarios ( [[#Ribeiro%20Neto--2016|Ribeiro Neto et al., 2016]] ; [[#Zilli--2020|Zilli et al., 2020]] ), enhanced by non-climate drivers such as land use changes ( ''medium confidence: medium evidence, high agreement'' ) ( [[#Spera--2020|Spera et al., 2020]] ). The remaining fluctuation in yields relates to issues of infrastructure, market, economy, policy and social aspects. Good infrastructure, transport logistics, quality of roads and storage strongly influence the vulnerability of the agricultural sector (Figure 12.7).

The combined effects of extreme climate events and ecosystem fragmentation, for example, by deforestation or fire, lead to changes in forest structure, with the death of taller trees and reduction in diversity of plant species, loss of productivity and carbon storage ( ''high agreement'' ) ( [[#Brando--2014|Brando et al., 2014]] ; [[#Reis--2018|Reis et al., 2018]] ). The rise of a large-scale soybean agroindustry in the early 2000s led to a faster increase in human development indicators in some regions, tightly linked to the agricultural production chain ( ''high confidence'' ) ( [[#Richards--2015|Richards et al., 2015]] ). Such a development also came at considerable cost to the environment (e.g., Neill et al. 2013) and the regional climate, even though a moratorium implemented in 2006 on new soy plantations on deforested areas reduced deforestation by a factor of five ( ''high confidence'' ) ( [[#Macedo--2012|Macedo et al., 2012]] ; [[#Kastens--2017|Kastens et al., 2017]] ). The same sort of supply chain interventions along with incentive-based public policies applied to the beef supply chain could minimise the need for agricultural expansion in the SAM deforestation frontier ( ''medium confidence: medium evidence, high agreement'' ) ( [[#Nepstad--2014|Nepstad et al., 2014]] ; [[#Pompeu--2021|Pompeu et al., 2021]] ).

SAM has a low population density, and the majority of the population is located in cities. The populations of some of these cities are reported as being highly vulnerable considering the enormous social inequalities embedded in these cities ( ''high confidence'' ) ( [[#Filho--2016|Filho et al., 2016]] ). Inequalities and uneven access to infrastructure, housing and healthcare increase populations’ vulnerability to atmospheric pollution and drier conditions ( ''high confidence'' ) (Rodrigues et al., 2019; [[#IPAM--2020|IPAM, 2020]] ; [[#Machado-Silva--2020|Machado-Silva et al., 2020]] ).

<div id="12.3.4.4" class="h3-container"></div>

<span id="impacts-3"></span>
==== 12.3.4.4 Impacts ====

<div id="h3-16-siblings" class="h3-siblings"></div>

The Amazon and the Cerrado are among the largest and unique phytogeographical domains in SA. The Brazilian Cerrado is one of the most diverse savannah in the world, with more than 12,600 plant species, with 35% being endemic ( ''high confidence'' ) ( [[#Forzza--2012|Forzza et al., 2012]] ). Historic land cover change and concurrent climate change in the region strongly impacted the biodiversity and led to the extinction of 657 plant species for the Cerrado, which is more than four times the global recorded plant extinctions ( ''high confidence'' ) ( [[#Strassburg--2017|Strassburg et al., 2017]] ; [[#Green--2019|Green et al., 2019]] ). The effects of climate change, expressed by drought and heatwaves, lead to plant stress, compromising growth and increasing mortality ( [[#Yu--2019|Yu et al., 2019]] ). The fauna dependent on dew water was strongly impacted by the 1.6°C temperature rise that occurred from 1961 to 2019 ( ''medium confidence: medium evidence, medium agreement'' ) ( [[#Hofmann--2021|Hofmann et al., 2021]] ). Modelling outcomes project impacts in forest ecosystems in the region, with persistent warming and significant moisture reduction ( [[#Anjos--2021|Anjos et al., 2021]] ), leading to a potential change in the ecosystem structure and distribution in the region ( ''medium confidence: medium evidence, medium agreement'' ) ( [[#Government%20of%20Brazil--2020|Government of Brazil, 2020]] ).

The observed impact on plant species in SAM is projected to worsen in a warmer world ( [[#Warszawski--2013|Warszawski et al., 2013]] ). An increasing dominance of drought-affiliated genera of tree species has been reported in the southern part of the Amazon rainforest in the last 30 years ( ''medium confidence: medium evidence, medium agreement'' ) ( [[#Esquivel-Muelbert--2019|Esquivel-Muelbert et al., 2019]] ). Due to the tight relationship between drought and fire occurrence, an increase of 39% to 95% of burned area is modelled to impact the Cerrado region under RCP4.5 and RCP8.5, while under RCP2.6, a 22% overshoot in temperature is estimated to impact the area in 2050 decreasing to 11% overshoot by 2100 ( [[#Silva--2019d|Silva et al., 2019d]] ), leading to a high impact on agricultural production ( ''high confidence'' ).

SAM hosts the headwaters of important South American rivers, such as the Paraguay, Madeira, Tocantins-Araguaia and Xingu. The impact from climate change is expressed differently in several sub-regions. Extreme floods in the southern Amazon and Bolivian Amazon floodplains were described and related to the exceptionally warm sub-tropical South Atlantic ocean ( ''high confidence'' ) ( [[#Espinoza--2014|Espinoza et al., 2014]] ), causing high economic impacts (losses in crop and livestock production and infrastructure) and number of fatalities ( ''very high confidence)'' ( [[#Ovando--2016|Ovando et al., 2016]] ) ''.'' In contrast, declines in stream flow, particularly in the dry season, expressed by the ratio of runoff to rainfall, are observed for the southern part of the Amazon basin ( ''high evidence'' ) ( [[#Molina-Carpio--2017|Molina-Carpio et al., 2017]] ; [[#Espinoza--2019b|Espinoza et al., 2019b]] ; [[#Heerspink--2020|Heerspink et al., 2020]] ). Observed precipitation reduction in the Cerrado region impacted main water supply reservoirs for important cities in the Brazilian central region, leading to a water crisis in 2016/2017 ( [[#Government%20of%20Brazil--2020|Government of Brazil, 2020]] ) and affecting hydropower energy generation ( [[#Ribeiro%20Neto--2016|Ribeiro Neto et al., 2016]] ). Modelling studies project decreases in the river discharge rate on the order of 27% for the Tapajós basin and 53% for the Tocantins-Araguaia basin for the end of the century, which may affect freshwater biodiversity, navigation and generation of hydroelectric power ( ''medium confidence: medium evidence, high agreement'' ) ( [[#Marcovitch--2010|Marcovitch et al., 2010]] ; [[#Mohor--2015|Mohor et al., 2015]] ). This region also holds one of the largest floodplains in the world, the Pantanal. The climatic connection of Pantanal regions to the Amazon, and the influence of deforestation in local precipitation ( [[#Marengo--2018|Marengo et al., 2018]] ) has implications for conservation of ecosystem services and water security in Pantanal ( ''high confidence'' ) ( [[#Bergier--2018|Bergier et al., 2018]] ). Impacts of extreme drought, with increasing numbers of dry days and the peak of fire foci, were recently reported ( ''robust evidence'' ) ( [[#Lázaro--2020|Lázaro et al., 2020]] ; [[#Garcia--2021|Garcia et al., 2021]] ). The projected impacts of climate change will lead to profound changes in the annual flood dynamics for Pantanal wetlands, altering ecosystem functioning and severely affecting biodiversity ( ''high confidence'' ) ( [[#Thielen--2020|Thielen et al., 2020]] ; [[#Marengo--2021|Marengo et al., 2021]] ).

Soybean and corn yields in the Cerrado region will suffer one of the strongest negative impacts under the estimates of the RCP4.5 and RCP8.5 scenarios and will require high levels of investment in adaptation should they continue to be cultivated in the same areas as currently ( ''high confidence'' ) ( [[#Oliveira--2013|Oliveira et al., 2013]] ; [[#Camilo--2018|Camilo et al., 2018]] ). Changes in precipitation patterns are related to reductions in agricultural productivity and revenues in the southern portion of the Amazon region ( ''medium confidence: medium evidence, high agreement'' ) ( [[#Costa--2019|Costa et al., 2019]] ; [[#Leite-Filho--2021|Leite-Filho et al., 2021]] ). Thus, the future socioeconomic vigour of the region will be, to a large extent, connected to an unlikely stability of the regional climate and eventual fluctuations of global markets potentially affecting the agricultural supply chain ( ''high confidence'' ) ( [[#Nepstad--2014|Nepstad et al., 2014]] ).

Observations from recent past droughts in SAM indicate how the incidence of respiratory diseases may worsen under a drier and warmer climate. Northwest SAM had an approximately 54% increase in the incidence of respiratory diseases associated with forest fires during the 2005 drought compared to a no-drought 10-year mean ( ''high confidence'' ) ( [[#Ignotti--2010|Ignotti et al., 2010]] ; [[#Pereira--2011|Pereira et al., 2011]] ; [[#Smith--2014|Smith et al., 2014]] ). It is estimated that more than 10 million people are exposed to forest fires in the deforestation arc, a region comprising several Brazilian states in the southern and western parts of the Amazon rainforest, with several impacts on human health including potential exacerbation of the COVID-19 crisis in Amazonia ( ''medium confidence: medium evidence, high agreement'' ) ( [[#de%20Oliveira--2020|de Oliveira et al., 2020]] ) (Table 12.5). Increases in hospital admissions, asthma, DNA damage and lung cell death due to the inhalation of fine particulate matter represent an increase in public health system costs ( ''high confidence'' ) ( [[#Ignotti--2010|Ignotti et al., 2010]] ; Silva et al., 2013; [[#de%20Oliveira%20Alves--2017|de Oliveira Alves et al., 2017]] ; [[#Machin--2019|Machin et al., 2019]] ). The patchy landscape created by forest clearing contributes to a rising risk of zoonotic disease emergence by increasing interactions between wildlife, livestock and humans ( ''medium confidence: low evidence, medium agreement'' ) ( [[#Dobson--2020|Dobson et al., 2020]] ; [[#Tollefson--2020|Tollefson, 2020]] ). Recent studies also suggest an influence of climate change in zoonotic diseases, such as ''Orthohantavirus'' and ''Chapare'' viral infections, rodent-borne diseases, in some areas of Bolivia ( [[#Escalera-Antezana--2020a|Escalera-Antezana et al., 2020a]] ; [[#Escalera-Antezana--2020b|Escalera-Antezana et al., 2020b]] ). Extreme fluctuations in Amazon River levels were associated with a significant increase in the incidence of diarrhoea, leptospirosis and dermatitis ( [[#de%20Souza%20Hacon--2019|de Souza Hacon et al., 2019]] ; [[#Government%20of%20Brazil--2020|Government of Brazil, 2020]] ). According to a comprehensive characterisation of future heatwaves and alternative RCPs scenarios, Brazilian urban areas in the SAM region are projected to face increasing related mortality from 400% to 500% in the period 2031–2080 compared to the period 1971–2020, under the highest emission scenario and high-variant population scenario ( ''medium confidence: low evidence, medium agreement'' ) ( [[#Guo--2018|Guo et al., 2018]] ). Table 12.2 shows the increase in days of exposure to heatwaves already observed in the region.

The high risk of floods (high-frequency and costly damage) is centred in the Brazilian states of Acre, Rondônia, Southern Amazonas and Pará (Andrade et al., 2017). Global-scale studies indicate an increase of flood risk for the SAM region during the 21st century (consistent with floods that are more frequent) ( ''high confidence'' ) ( [[#Hirabayashi--2013|Hirabayashi et al., 2013]] ; [[#Arnell--2016|Arnell et al., 2016]] ; [[#Alfieri--2017|Alfieri et al., 2017]] ). Higher emission scenarios result in substantially higher flood risks than low emission scenarios ( [[#Alfieri--2017|Alfieri et al., 2017]] ).

'''Table 12.2 |''' Average change in mean number of days exposed to heatwaves (defined as a period of at least 2 d where both the daily minimum and maximum temperatures are above the 95th percentile for their respective climatologies) in the over-65 population in 2016–2020 relative to 1986–2005. Temperature data taken from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 data set; calculations were derived from Romanello et al. (2021).

{| class="wikitable"
|-
! '''Country'''

! '''Number of additional days of heatwave exposure in 2016–2020 relative to 1986–2005'''

|-
| Argentina

| 4.9

|-
| Belize

| 8.8

|-
| Bolivia

| 2.2

|-
| Brazil

| 3.1

|-
| Chile

| 3.3

|-
| Colombia

| 9.3

|-
| Costa Rica

| 0.8

|-
| Ecuador

| 7.6

|-
| El Salvador

| 2.2

|-
| Guatemala

| 8.4

|-
| Guyana

| 8.2

|-
| Honduras

| 11.2

|-
| Nicaragua

| 2.2

|-
| Panama

| 2.6

|-
| Paraguay

| 2.6

|-
| Peru

| 3.6

|-
| Suriname

| 15.2

|-
| Uruguay

| 2.7

|-
| Venezuela

| 8.5

|}

<div id="12.3.5" class="h2-container"></div>

<span id="northeastern-south-america-sub-region"></span>
=== 12.3.5 Northeastern South America Sub-region ===

<div id="h2-7-siblings" class="h2-siblings"></div>

<div id="12.3.5.1" class="h3-container"></div>

<span id="hazards-4"></span>
==== 12.3.5.1 Hazards ====

<div id="h3-17-siblings" class="h3-siblings"></div>

The region has ''likely'' experienced an increase in temperature, with significant increases in the intensity and frequency of hot extremes and significant decreases in the intensity and frequency of cold extremes ( [[#Donat--2013|Donat et al., 2013]] ) (WGI AR6 Table 11.13, [[#Seneviratne--2021|Seneviratne et al., 2021]] ).

A decrease in the frequency and magnitude of extreme precipitation was observed, but with ''low confidence'' , due to insufficient data coverage and trends in available data being generally insignificant. An increase in drought duration was observed with ''high confidence'' but ''medium confidence'' with respect to the increase of drought intensity (WGI AR6 Table 11.14, [[#Seneviratne--2021|Seneviratne et al., 2021]] ). Table 12.3 shows the estimates of changes in land area per sub-region affected by drought events; NES sub-region presented the highest changes in CSA.

'''Table 12.3 |''' Change in percentage of land area affected by extreme drought in 2010–2019, in relation to 1950–1959 using Standardised Precipitation-Evapotranspiration Index (SPEI); extreme drought is defined as SPEI ≤ −1.6 ( [[#Federal%20Office%20of%20Meteorology%20and%20Climatology%20MeteoSwiss--2021|Federal Office of Meteorology and Climatology MeteoSwiss, 2021]] ). Data were derived from Romanello et al. (2021).

{| class="wikitable"
|-
!
! colspan="3"| '''Average change in percentage of land area in drought in 2010–2019 with respect to 1950–1959'''

|-
! '''Sub-region'''

! '''At least 1 month in drought'''

! '''At least 3 months in drought'''

! '''At least 6 months in drought'''

|-
| Central America (CA)

| 38.8%

| 17.6%

| 6.1%

|-
| Northwestern South America (NWS)

| 51.8%

| 25.3%

| 7.0%

|-
| Northern South America (NSA)

| 52.5%

| 18.3%

| 2.5%

|-
| South America Monsoon (SAM)

| 48.0%

| 34.4%

| 12.2%

|-
| Northeastern South America (NES)

| 64.5%

| 38.4%

| 12.0%

|-
| Southeastern SouthAmerica (SES)

| 16.4%

| 6.7%

| 0.4%

|-
| Southwestern South America (SWS)

| 20.5%

| 13.9%

| 7.5%

|-
| Southern South America (SSA)

| −23.5%

| −8.8%

| —

|}

The projected warming for the extreme annual maximum temperatures (TXx) over NES is +2°C for the 1.5°C scenario and about +2.5°C for the 2°C scenario ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ). An increased number of tropical nights with minimum temperatures exceeding the 20°C threshold is projected ( [[#Orlowsky--2012|Orlowsky and Seneviratne, 2012]] ). In general, extreme heat will increase and cold spells decrease with ''high confidence'' . A decrease in total precipitation is projected with ''high confidence'' , with an increase in heavy precipitation events and an increase in dryness ( ''medium confidence'' ). Increases in drought severity due to the combination of increased temperatures, less rainfall and lower atmospheric humidity (5 to 15% relative humidity reduction) create water deficits, which are projected for the entire region after 2041 (3–4 mm d −1 reduction), particularly over western NES and over the semiarid region ( [[#Marengo--2015|Marengo and Bernasconi, 2015]] ; [[#Marengo--2017|Marengo et al., 2017]] ). Fire will significantly increase ( ''high confidence'' ) (Figure 12.6).

<div id="_idContainer019" class="Figure"></div>

[[File:39389c4e6fe4c88471d70d7987ace51f IPCC_AR6_WGII_Figure_12_006.png]]

'''Figure 12.6 |''' '''Observed trends (WGI AR6 Tables 11.''' '''13, 11.14, 11.15)''' ( [[#Seneviratne--2021|Seneviratne et al., 2021]] ) and summary of confidence in direction of projected change in climatic impact drivers, representing their aggregate characteristic changes for mid-century for RCP4.5, SSP3-44 4.5 and SRES A1B scenarios, or above within each AR6 region, approximately corresponding (for CIDs that are independent of SLR) to global warming levels between 2°C and 2.4°C (WGI AR6 Table 12.6) ( [[#Ranasinghe--2021|Ranasinghe et al., 2021]] ).

<div id="12.3.5.2" class="h3-container"></div>

<span id="exposure-4"></span>
==== 12.3.5.2 Exposure ====

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NES is home to about 60 million people (estimate for 2019 fromIBGE [2020]]), with &gt;70% living in urban areas (data for 2010 from IBGE [2020]; Silva et al. [2017]) and high poverty levels (&gt;50%, data for 2003 fromIBGE [2020]]). People are exposed to intense drought and famine ( ''high confidence'' ), and about 94% of the region has moderate to high susceptibility to desertification ( [[#Marengo--2015|Marengo and Bernasconi, 2015]] ; [[#Spinoni--2015|Spinoni et al., 2015]] ; [[#Vieira--2015|Vieira et al., 2015]] ; [[#Mariano--2018|Mariano et al., 2018]] ; [[#Tomasella--2018|Tomasella et al., 2018]] ; [[#Marengo--2020c|Marengo et al., 2020c]] ). The most severe dry spell of 2012–2013 affected about 9 million people, who were exposed to water, food and energy scarcity ( [[#Marengo--2015|Marengo and Bernasconi, 2015]] ).

People, infrastructure and economic activities are exposed to SLR in the 3800 km of coastline ( ''medium confidence'' ). The high concentration of cities on the coast is a concern ( [[#Martins--2017|Martins et al., 2017]] ), with all state capital cities but one on the coast, totalling almost 12 million vulnerable people (estimate for 2019 fromIBGE [2020]]). The ports of São Luís, Recife and Salvador are important exporters of Brazilian commodities, and the beaches in the sub-region are an international touristic destination, generating considerable revenues (Pegas et al., 2015; [[#Ribeiro--2017|Ribeiro et al., 2017]] ).

Natural systems in NES are also exposed to climate change. In terrestrial ecosystems, 913,000 km 2 of NES’ dry forest Caatinga vegetation ( [[#Silva--2017|Silva et al., 2017]] ) is exposed to predicted increases in dryness. Despite what has been previously suggested, the Caatinga has high biodiversity and endemism ( [[#Silva--2017|Silva et al., 2017]] ), which vulnerable to habitat reduction due to climate change and agricultural expansion ( [[#Silva--2019b|Silva et al., 2019b]] ). Fifty-two percent of the freshwater fish (203 species) are endemic ( [[#Lima--2017|Lima et al., 2017]] ) and are exposed to predicted reduction in river flow due to climate change ( [[#Marengo--2017|Marengo et al., 2017]] ; [[#de%20Jong--2018|de Jong et al., 2018]] ). The coastal waters contain a separate marine ecoregion due to its uniqueness ( [[#Spalding--2007|Spalding et al., 2007]] ). The region is responsible for 99% of Brazilian shrimp production, which is exposed to SLR and increases in ocean temperature and acidification ( [[#Gasalla--2017|Gasalla et al., 2017]] ). Most coral reefs in the Southern Atlantic Ocean are along NES’s coast ( [[#Leão--2016|Leão et al., 2016]] ), increasing its conservation and touristic value. The 685 km 2 of coral reefs along NES’s coast (likely an underestimate [Moura et al. 2013; UNEP-WCMC et al. 2018]) are exposed to increased sea temperatures.

<div id="12.3.5.3" class="h3-container"></div>

<span id="vulnerability-4"></span>
==== 12.3.5.3 Vulnerability ====

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NES is the world’s most densely populated semiarid land and its population is highly vulnerable to droughts ( ''high confidence'' ), which have well-documented impacts on water and food security, human health and well-being in the region (e.g., Confalonieri et al. 2014a; Marengo et al. 2017; Bedran-Martins et al. 2018) (Figure 12.7). The region’s relatively low economic development and poor social and health indicators increase vulnerability, especially that of poor farmers and traditional communities ( [[#Confalonieri--2014a|Confalonieri et al., 2014a]] ; [[#Bech%20Gaivizzo--2019|Bech Gaivizzo et al., 2019]] ). In state capital cities, about 45% of the population live in poverty (data for 2003 fromIBGE [2020]]), often in slums with already deficient water supply and sewage systems and poor access to health and education. Climate change will increase pressures on water availability, threatening water, energy and food security ( [[#Marengo--2017|Marengo et al., 2017]] ).

Natural systems in NES are also vulnerable (Figure 12.7). The Caatinga vegetation is particularly sensitive to variations in water availability and climate change ( [[#Seddon--2016|Seddon et al., 2016]] ; [[#Rito--2017|Rito et al., 2017]] ; [[#Dantas--2020|Dantas et al., 2020]] ). It has already lost about 50% of its original vegetation cover ( [[#Souza--2020|Souza et al., 2020]] ), with only about 2% of the remaining vegetation within fully protected areas ( [[#CNUC%20and%20MMA--2020|CNUC and MMA, 2020]] ). Caatinga’s high vulnerability to climate change is further increased by the extensive conversion of native vegetation ( ''high confidence'' ) ( [[#Rito--2017|Rito et al., 2017]] ; [[#Silva--2019b|Silva et al., 2019b]] , c).

Studies with terrestrial animals show that habitat loss increases the vulnerability of species to climate change ( ''high confidence'' ) ( [[#de%20Oliveira--2012|de Oliveira et al., 2012]] ; [[#Arnan--2018|Arnan et al., 2018]] ; [[#da%20Silva--2018b|da Silva et al., 2018b]] ). NES’s coral reefs have shown some resilience to bleaching, but vulnerability is intensified by the synergy between chronic heat stress caused by increased SST ( [[#Teixeira--2019|Teixeira et al., 2019]] ) and other well-documented stressors, such as coastal runoff, urban development, marine tourism, overexploitation of reef organisms and oil extraction ( ''high confidence'' ) (Figure 12.8) ( [[#Leão--2016|Leão et al., 2016]] ).

<div id="_idContainer023" class="Figure"></div>

[[File:e9b1271e9a6b2cdf853179771da21520 IPCC_AR6_WGII_Figure_12_008.png]]

'''Figure 12.8 |''' '''Climate and non-climate sensitivity drivers of ocean, coastal ecosystems and EEZs of Central and South America.'''

<div id="12.3.5.4" class="h3-container"></div>

<span id="impacts-4"></span>
==== 12.3.5.4 Impacts ====

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The impacts of intense drought have been reported in NES since 1780, with severe losses in agricultural production, livestock death, increase in agricultural prices and human death (Figure 12.9) ( [[#Marengo--2017|Marengo et al., 2017]] , 2020c; [[#Martins--2019|Martins et al., 2019]] ; [[#Government%20of%20Brazil--2020|Government of Brazil, 2020]] ; Silva et al., 2020). The rural population already suffers from natural water scarcity in the countryside. The drought in 2012 was responsible for reducing up to 99% of the corn production in Pernambuco state ( [[#Government%20of%20Brazil--2020|Government of Brazil, 2020]] ). A predicted increase in drought, coupled with inadequate soil management practices by small farmers and agribusiness, increases the region’s susceptibility to desertification ( [[#Spinoni--2015|Spinoni et al., 2015]] ; [[#Vieira--2015|Vieira et al., 2015]] ; [[#Mariano--2018|Mariano et al., 2018]] ; [[#Tomasella--2018|Tomasella et al., 2018]] ; [[#Marengo--2020c|Marengo et al., 2020c]] ). In NES, 70,000 km 2 have reached a point at which agriculture is no longer possible ( [[#Government%20of%20Brazil--2020|Government of Brazil, 2020]] ). Intense droughts have triggered migration to urban centres within and outside NES ( [[#Confalonieri--2014a|Confalonieri et al., 2014a]] ; [[#Government%20of%20Brazil--2020|Government of Brazil, 2020]] ). More than 10 million people have been impacted by the drought of 2012/2014 in the region, which was responsible for water shortage and contamination, increasing death by diarrhoea ( [[#Marengo--2015|Marengo and Bernasconi, 2015]] ; [[#Government%20of%20Brazil--2020|Government of Brazil, 2020]] ).

<div id="_idContainer026" class="Figure"></div>

[[File:0d0865798d21fccc50b02317e6b719c9 IPCC_AR6_WGII_Figure_12_009.png]]

'''Figure 12.9 |''' '''Observed and projected impacts for sub-regions of CSA.''' Impacts are distinguished for main sectors and for their corresponding systems (or components). Observed impacts relate to the last several decades. Projected impacts represent a synthesis across several emission and warming scenarios, indicative of a time period from the middle to end of the 21st century. For each system (e.g., coral reefs) climate-change impacts are identified as being low, medium or high. The references underlying this assessment can be found in SM12.4.1.

There is growing evidence of the impacts of climate change on human health in NES, mostly linked to food and water insecurity caused by recurrent long droughts (e.g., gastroenteritis and hepatitis) ( ''high confidence'' ) (Figure 12.9) ( [[#Sena--2014|Sena et al., 2014]] ; [[#de%20Souza%20Hacon--2019|de Souza Hacon et al., 2019]] ; [[#Marengo--2019|Marengo et al., 2019]] ; [[#Government%20of%20Brazil--2020|Government of Brazil, 2020]] ; [[#Salvador--2020|Salvador et al., 2020]] ). From 2071 to 2099, thermal conditions in NES might improve for vectors of dengue, chikungunya and Zika ( [[#de%20Souza%20Hacon--2019|de Souza Hacon et al., 2019]] ). Additionally, a high risk of mortality associated with climatic stress in the period of 2071–2099 is expected in the São Francisco river basin ( [[#de%20Oliveira--2019|de Oliveira et al., 2019]] ; [[#de%20Souza%20Hacon--2019|de Souza Hacon et al., 2019]] ).

Recent studies predict strong negative impacts of climate change on NES’s agriculture ( ''high confidence'' ) (Ferreira Filho and Moraes, 2015; [[#Nabout--2016|Nabout et al., 2016]] ; [[#Gateau-Rey--2018|Gateau-Rey et al., 2018]] ) (Figure 12.9; Table 12.4). NES concentrates the bulk of the predicted loss of regional gross domestic product (GDP) associated with agriculture in Brazil (Ferreira Filho and Moraes, 2015; [[#Forcella--2015|Forcella et al., 2015]] ). Although agriculture makes a modest contribution to the region’s economy, its drop could have a severe impact on the poorest rural household by shrinking the agricultural labour market and increasing food prices (Ferreira Filho and Moraes, 2015; [[#Government%20of%20Brazil--2020|Government of Brazil, 2020]] ). The expected increase in dryness is also predicted to impact the region’s hydroelectric power generation ( [[#Marengo--2017|Marengo et al., 2017]] ; [[#de%20Jong--2018|de Jong et al., 2018]] ). SLR has also been reported to impact coastal cities such as Salvador, destroying urban constructions ( [[#Government%20of%20Brazil--2020|Government of Brazil, 2020]] ). SLR, increased ocean temperature and acidification may also negatively impact NES’s shrimp aquaculture production (Figure 12.8) ( [[#Gasalla--2017|Gasalla et al., 2017]] ). Along with climate change, overfishing has driven exploited marine fish species to collapse ( [[#Verba--2020|Verba et al., 2020]] ).

Biodiversity in NES is severely threatened by climate change in terrestrial ( ''medium confidence: medium evidence, high agreement'' ) and freshwater ( ''low confidence: low evidence, high agreement'' ) ecosystems (Figure 12.9). There are few studies projecting the likely impact of climate change on NES’s biodiversity, especially its endemic freshwater fish. Recent studies have already reported the reduction in several endemic plant species affecting pollination and seed dispersal ( [[#Bech%20Gaivizzo--2019|Bech Gaivizzo et al., 2019]] ; [[#Cavalcante--2019|Cavalcante and Duarte, 2019]] ; [[#Silva--2019b|Silva et al., 2019b]] ). Studies with terrestrial animals predict that most groups will be negatively impacted by climate change ( [[#de%20Oliveira--2012|de Oliveira et al., 2012]] ; [[#Arnan--2018|Arnan et al., 2018]] ; [[#da%20Silva--2018b|da Silva et al., 2018b]] ; [[#Montero--2018|Montero et al., 2018]] ). Changes in the abundance of coral reef community and extreme reduction in coral cover have been observed in NES ( [[#de%20Moraes--2019|de Moraes et al., 2019]] ; [[#Duarte--2020|Duarte et al., 2020]] ). A number of observed coral bleaching events associated with an abnormal increase in sea temperatures have occurred in NES ( [[#Krug--2013|Krug et al., 2013]] ; [[#Leão--2016|Leão et al., 2016]] ; [[#de%20Oliveira%20Soares--2019|de Oliveira Soares et al., 2019]] ) (Figure 12.8), but thus far mortality has remained low and corals have been able return to normal values or remain stable following sea water temperature rise ( ''medium confidence: medium evidence, high agreement'' ) ( [[#Leão--2016|Leão et al., 2016]] ). Mangroves in the region have shown increased mortality, but they have also expanded their range inland (Figure 12.6) ( [[#Godoy--2015|Godoy and Lacerda, 2015]] ; [[#Cohen--2018|Cohen et al., 2018]] ). Future projections include mangrove landward expansion and lower migration rates by 2100 ( [[#Cohen--2018|Cohen et al., 2018]] ).

<div id="12.3.6" class="h2-container"></div>

<span id="southeastern-south-america-sub-region"></span>
=== 12.3.6 Southeastern South America Sub-region ===

<div id="h2-8-siblings" class="h2-siblings"></div>

<div id="12.3.6.1" class="h3-container"></div>

<span id="hazards-5"></span>
==== 12.3.6.1 Hazards ====

<div id="h3-21-siblings" class="h3-siblings"></div>

An increase in the intensity and frequency of hot extremes and decrease in the intensity and frequency of cold extremes were observed with ''high confidence'' ( [[#Rusticucci--2017|Rusticucci et al., 2017]] ; [[#Wu--2017|Wu and Polvani, 2017]] ) (WGI AR6 Table 11.13) ( [[#Seneviratne--2021|Seneviratne et al., 2021]] ). There is ''low confidence'' that the decrease in hot extremes over SES is related to an increase in extreme precipitation ( [[#Wu--2017|Wu and Polvani, 2017]] ).

Over SES most stations have registered an increase in annual rainfall, largely attributable to changes in the warm season; this is one of few sub-regions where a robust positive trend in precipitation and significant intensification of heavy precipitation have been detected since the early 20th century ( ''high confidence'' ) but with ''medium confidence'' in a reduction of hydrological droughts ( [[#Vera--2015|Vera and Díaz, 2015]] ; [[#Saurral--2017|Saurral et al., 2017]] ; [[#Lovino--2018|Lovino et al., 2018]] ; [[#Avila-Diaz--2020|Avila-Diaz et al., 2020]] ; [[#Carvalho--2020|Carvalho, 2020]] ; [[#Dereczynski--2020|Dereczynski et al., 2020]] ; [[#Dunn--2020|Dunn et al., 2020]] ; [[#Marengo--2020a|Marengo et al., 2020a]] ; [[#Olmo--2020|Olmo et al., 2020]] ) (WGI AR6 Table 11.14) ( [[#Seneviratne--2021|Seneviratne et al., 2021]] ). A higher observed frequency of extratropical cyclones in the region has been detected ( [[#Parise--2009|Parise et al., 2009]] ; [[#Reboita--2018|Reboita et al., 2018]] ) with three cyclogenetic foci: south-southeastern Brazil, extreme south of Brazil and Uruguay, and southeastern Argentina.

In Montevideo, mean sea levels have increased over the past 20 years, reaching 11 cm from 1902 to 2016, and a recent accelerating trend has been observed ( [[#Gutiérrez--2016b|Gutiérrez et al., 2016b]] ). A value of water level rise and its acceleration for Buenos Aires was calculated from a record of annual mean water levels obtained from hourly levels (1905–2003). Annual mean water level showed a trend of +1.7 ± 0.05 mm yr −1 and an acceleration of +0.019 ± 0.005 mm yr −2 ( [[#D’Onofrio--2008|D’Onofrio et al., 2008]] ).

Increasing trends in mean air temperature and extreme heat and decreasing cold spells are projected ( ''high confidence'' ) (WGI AR6 Table 12.6) ( [[#Ranasinghe--2021|Ranasinghe et al., 2021]] ). The increase in the frequency of warm nights is larger than that projected for warm days, consistent with observed past changes that have been related to changes in cloud cover that affect daytime temperatures differently than nighttime temperatures ( [[#López-Franca--2016|López-Franca et al., 2016]] ; [[#Menéndez--2016|Menéndez et al., 2016]] ; [[#Feron--2019|Feron et al., 2019]] ).

Increases in mean precipitation ( ''high confidence'' ), pluvial floods and river floods are projected ( ''medium confidence'' ) ( [[#Nunes--2018|Nunes et al., 2018]] ) (WGI AR6 Table 12.6) ( [[#Ranasinghe--2021|Ranasinghe et al., 2021]] ). Droughts in the River Plate basin will be more frequent in the medium term (2011–2040) and the distant future (2071–2100) (with respect to the 1979–2008 period), but also shorter and more severe, for the more extreme emission scenario (RCP8.5) ( ''low confidence'' ) ( [[#Carril--2016|Carril et al., 2016]] ) ''.''

Negative trends in the annual number of cyclone events in the long term of 3.6% to 6.5% (2070–2098) are projected and showed an increase of 3% to 11% (2080–2100 for the A1B scenario) ( [[#Grieger--2014|Grieger et al., 2014]] ; [[#Reboita--2018|Reboita et al., 2018]] ). All coastal and oceanic climate impact drivers (relative sea level, coastal flood and erosion, marine heatwaves and ocean aridity) are expected to increase by mid-century in the RCP8.5 scenario ( ''high confidence'' ) (WGI AR6 Table 12.6) ( [[#Ranasinghe--2021|Ranasinghe et al., 2021]] ).

<div id="12.3.6.2" class="h3-container"></div>

<span id="exposure-5"></span>
==== 12.3.6.2 Exposure ====

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Higher temperatures and SLR, changes in rainfall patterns, and an increased frequency and intensity of extreme weather events could generate risks to the energy and infrastructure sectors and to the mining and metals industry. In the River Plate basin, urban floods have become more frequent, causing infrastructure damage and sometimes substantial mortality ( ''high confidence'' ) ( [[#Barros--2015|Barros et al., 2015]] ; [[#Zambrano--2017|Zambrano et al., 2017]] ; [[#Nagy--2019|Nagy et al., 2019]] ; [[#Mettler-Grove--2020|Mettler-Grove, 2020]] ; [[#Morales-Yokobori--2021|Morales-Yokobori, 2021]] ; [[#Oyedotun--2021|Oyedotun and Ally, 2021]] ). A large increase in landslides and flash floods is also predicted for the Brazilian portion of SES, where they are responsible for the majority of deaths related to disasters in the country ( ''high confidence'' ) ( [[#Debortoli--2017|Debortoli et al., 2017]] ; [[#Haque--2019|Haque et al., 2019]] ; [[#Saito--2019|Saito et al., 2019]] ; [[#Marengo--2020d|Marengo et al., 2020d]] ; [[#da%20Fonseca%20Aguiar--2021|da Fonseca Aguiar and Cataldi, 2021]] ). Due to uncontrolled urban growth, 21.5 million people living in the large Brazilian cities of São Paulo, Rio de Janeiro and Belo Horizonte (estimate for 2019 fromIBGE [2020]]) are expected to be exposed to water scarcity, despite widespread water availability in the region ( ''medium evidence, medium agreement'' ) ( [[#Marengo--2017|Marengo et al., 2017]] , [[#Marengo--2020b|Marengo et al., 2020b]] ; Lima and Magaña Rueda, 2018).

The expected increase in temperature will also expose the populations in large cities to extreme heat. Urban heat islands are already a reality in large cities in the region, such as Buenos Aires ( ''high confidence'' ) ( [[#Wong--2013|Wong et al., 2013]] ; [[#Sarricolea--2019|Sarricolea and Meseguer-Ruiz, 2019]] ; [[#Wu--2019|Wu et al., 2019]] ; [[#Mettler-Grove--2020|Mettler-Grove, 2020]] ), Rio de Janeiro ( ''high confidence'' ) ( [[#Ceccherini--2016|Ceccherini et al., 2016]] ; [[#Neiva--2017|Neiva et al., 2017]] ; [[#Geirinhas--2018|Geirinhas et al., 2018]] ; Peres et al., 2018; [[#Sarricolea--2019|Sarricolea and Meseguer-Ruiz, 2019]] ; [[#Wu--2019|Wu et al., 2019]] ; [[#de%20Farias--2021|de Farias et al., 2021]] ) and São Paulo ( ''high confidence'' ) ( [[#Mishra--2015|Mishra et al., 2015]] ; [[#Barros--2016|Barros and Lombardo, 2016]] ; [[#Ceccherini--2016|Ceccherini et al., 2016]] ; [[#Vemado--2016|Vemado and Pereira Filho, 2016]] ; [[#de%20Azevedo--2018|de Azevedo et al., 2018]] ; Lima and Magaña Rueda, 2018; [[#Ferreira--2019|Ferreira and Duarte, 2019]] ; [[#Lapola--2019a|Lapola et al., 2019a]] ; [[#Sarricolea--2019|Sarricolea and Meseguer-Ruiz, 2019]] ; [[#Wu--2019|Wu et al., 2019]] ), with reported impact on human health in the latter ( ''medium confidence: medium evidence, medium agreement'' ) (e.g., Araujo et al. 2015; Son et al. 2016; Diniz et al. 2020). These cities alone represent 22 million people exposed to increased heat (estimate for 2019 fromIBGE [2020]] and from INDEC [2010]).

The sub-region presents a high frequency of occurrence of intense severe convection events ( [[#12.3.6.1|Section 12.3.6.1]] ). Because of this situation, strong winds from the south or southeast and high water levels affect the whole Argentine coast, as well as the River Plate shores, Uruguay and southern Brazil ( [[#Isla--2009|Isla and Schnack, 2009]] ). The coast of the River Plate is subject to flooding when there are strong winds from the southeast (sudestadas). As sea level rises as a result of global climate change, storm surge floods will become more frequent in this densely populated area, particularly in low-lying areas ( ''high confidence'' ) (Figure 12.8) ( [[#D’Onofrio--2008|D’Onofrio et al., 2008]] ; [[#Nagy--2014a|Nagy et al., 2014a]] ; [[#Santamaria-Aguilar--2017|Santamaria-Aguilar et al., 2017]] ; [[#Nagy--2019|Nagy et al., 2019]] impacts and adaptation in Central and South America coastal areas; [[#Cerón--2021|Cerón et al., 2021]] ).

The region’s natural systems are also exposed to climate change. The SES region is home to two important biodiversity hotspots, with high levels of species endemism: the Cerrado and the Atlantic Forest, where about 72% of Brazil’s threatened species can be found ( [[#PBMC--2014|PBMC, 2014]] ).

<div id="12.3.6.3" class="h3-container"></div>

<span id="vulnerability-5"></span>
==== 12.3.6.3 Vulnerability ====

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The River Plate basin and the city of Buenos Aires are highly vulnerable to recurring floods, and the increasing number of newcomers to the area reduce the collective cultural adaptation developed by older neighbours ( ''high confidence'' ) ( [[#Barros--2006|Barros, 2006]] ; [[#Nagy--2019|Nagy et al., 2019]] ; [[#Mettler-Grove--2020|Mettler-Grove, 2020]] ; [[#Morales-Yokobori--2021|Morales-Yokobori, 2021]] ; [[#Oyedotun--2021|Oyedotun and Ally, 2021]] ). Extreme events, including storm surges and coastal inundation/flooding, cause injuries and economic/environmental losses on the urbanised coastline of Southern Brazil (States of São Paulo and Santa Catarina) ( ''high confidence'' ) ( [[#Muehe--2010|Muehe, 2010]] ; [[#Khalid--2020|Khalid et al., 2020]] ; [[#Ohz--2020|Ohz et al., 2020]] ; [[#de%20Souza--2021|de Souza and Ramos da Silva, 2021]] ; [[#Quadrado--2021|Quadrado et al., 2021]] ; [[#Silva%20de%20Souza--2021|Silva de Souza et al., 2021]] ).

Cities like Rio de Janeiro and São Paulo are overpopulated, where most people live in poor conditions of inadequate housing and sanitation, such as slums, with little and no trees and high temperatures. These people have low access to sanitation, public health and residential cooling and are vulnerable to the effects of heat islands on human comfort and health (Figure 12.7). These include cardiopulmonary, vector-borne diseases and even death ( ''medium confidence: medium evidence, medium agreement'' ) ( [[#Araujo--2015|Araujo et al., 2015]] ; [[#Mishra--2015|Mishra et al., 2015]] ; [[#Geirinhas--2018|Geirinhas et al., 2018]] ; Peres et al., 2018). Heat stress is known to worsen cardiovascular, diabetic and respiratory conditions ( [[#Lapola--2019a|Lapola et al., 2019a]] ). In connection with the heat island effect, these people are also vulnerable to injuries and casualties due to increased thunderstorms, causing economic losses and other social problems ( [[#Vemado--2016|Vemado and Pereira Filho, 2016]] ).

<div id="12.3.6.4" class="h3-container"></div>

<span id="impacts-5"></span>
==== 12.3.6.4 Impacts ====

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Despite the observed increase in rainfall in the region, between 2014 and 2016 Brazil endured a water crisis that affected the population and economy of major capital cities in the SES region ( [[#Blunden--2014|Blunden and Arndt, 2014]] ; [[#Nobre--2016a|Nobre et al., 2016a]] ). Extremely long dry spells have become more frequent in southeastern Brazil, affecting 40 million people and the economies in cities such as Rio de Janeiro, São Paulo and Belo Horizonte, which are the industrial centres of the country ( ''medium confidence: medium evidence, medium agreement'' ) ( [[#PBMC--2014|PBMC, 2014]] ; [[#Nobre--2016a|Nobre et al., 2016a]] ; [[#Cunningham--2017|Cunningham et al., 2017]] ; [[#Marengo--2017|Marengo et al., 2017]] , 2020b; Lima and Magaña Rueda, 2018). They have also impacted agriculture, affecting food supply and rural livelihoods, especially in Minas Gerais ( [[#Nehren--2019|Nehren et al., 2019]] ). Agricultural prices increased by 30% in some cases, and harvest yields of sugar cane, coffee and fruits suffered a reduction of 15–40% in the region. The number of fires increased by 150%, and energy prices increased by 20–25%, as most electricity comes from hydroelectric power ( [[#Nobre--2016a|Nobre et al., 2016a]] ). In Argentina, projected changes in the hydrology of Andean rivers associated with glacier retreat are predicted to have negative impacts on the region’s fruit production ( ''low evidence, medium agreemen'' t) ( [[#Barros--2015|Barros et al., 2015]] ).

Heat islands affect ecosystems by increasing the energy consumption for cooling, the concentration of pollutants and the incidence of fires ( ''high confidence'' ) ( [[#Wong--2013|Wong et al., 2013]] ; [[#Akbari--2016|Akbari and Kolokotsa, 2016]] ; [[#Singh--2020b|Singh et al., 2020b]] ; [[#Ulpiani--2021|Ulpiani, 2021]] ). It also affects human health, as well increasing the incidence of respiratory and cardiovascular diseases ( ''medium confidence: medium evidence, medium agreement'' ) ( [[#Araujo--2015|Araujo et al., 2015]] ; [[#Barros--2016|Barros and Lombardo, 2016]] ; [[#de%20Azevedo--2018|de Azevedo et al., 2018]] ; [[#Geirinhas--2018|Geirinhas et al., 2018]] ).

Warming temperatures have been implicated in the emergence of dengue in temperate latitudes, increasing populations of ''Aedes aegypti'' ( ''high confidence'' ) ( [[#Natiello--2008|Natiello et al., 2008]] ; [[#Robert--2019|Robert et al., 2019]] , 2020; [[#Estallo--2020|Estallo et al., 2020]] ; [[#López--2021|López et al., 2021]] ) (Table 12.1), and field studies have demonstrated the role of local climate in vector activity ( [[#Benitez--2021|Benitez et al., 2021]] ). Figure 12.5 shows the modelled transmission suitability for dengue for two climate-change scenarios. Future increases in the number of months suitable for transmission of dengue will be highest in SES (see SM12.8 for additional information). There is additional evidence of the spread of arbovirus into southern temperate latitudes ( [[#Basso--2017|Basso et al., 2017]] ); however, a longer historical time series is needed to understand climate–disease interactions, given the relatively recent emergence of arborvirus in this region.

SLR impacts the port complex in Santa Catarina, which during the last 6 years has interrupted its activities 76 times due to strong winds or big waves, with estimated losses varying between USD 25,000 and 50,000 for each 24 idle hours ( [[#Ohz--2020|Ohz et al., 2020]] ). Historically, extratropical cyclones associated with frontal systems cause storm surges in the city of Santos. Although there are no fatality records, these events cause several socioeconomic losses, especially in vulnerable regions, including the Port of Santos, the largest port in Latin America (São Paulo). In an 88-year time span (1928–2016), the frequency of storm surge events was three times greater in the last 17 years (2000–2016) than in the previous period of 71 years (1928–1999) (Souza et al., 2019).

There are many projected impacts of climate change on natural systems. The impacts of SLR are habitat destruction and the invasion of exotic species, which affect biodiversity and the provision of ecosystem services (Figure 12.8) ( [[#Nagy--2019|Nagy et al., 2019]] ).

SES is a global priority for terrestrial biodiversity conservation and is home to two important biodiversity hotspots—the Atlantic Forest and Cerrado—which are among the world’s most studied biodiversity hotspots in connection with climate-change impact on biodiversity, especially for terrestrial vertebrates (Section [https://www.ipcc.ch/chapter/12#CCP1.2.2 CCP1.2.2] ; [[#Manes--2021|Manes et al., 2021]] ). An increasing number of studies show that the Atlantic Forest and Cerrado are at risk of biodiversity loss, largely due to projected reductions of species’ geographic distributions in many different taxa (e.g., Loyola et al. 2012, 2014; Ferro et al. 2014; Hoffmann et al. 2015; Martins et al. 2015; Aguiar et al. 2016b; Vale et al. 2018; Borges et al. 2019; Braz et al. 2019; Vale et al. 2021). Cerrado savannahs are projected to be the hotspot most negatively impacted by climate change within SA, mostly though range contraction of plant species ( ''very high confidence'' ), while the Atlantic Forest is projected to be highly impacted especially though the contraction of the distribution of endemic species ( ''very likely'' ) (Section [https://www.ipcc.ch/chapter/12#CCP1.2.2 CCP1.2.2] ; Figure 12.10) ( [[#Manes--2021|Manes et al., 2021]] ). Reductions in species’ distribution are also projected in the River Plate basin for sub-tropical amphibians ( [[#Schivo--2019|Schivo et al., 2019]] ) and the river tiger ( ''Salminus brasiliensis'' ), a keystone fish of economic value ( [[#Ruaro--2019|Ruaro et al., 2019]] ). Farming of mussels and oysters in the region is predicted to be negatively impacted by climate change, particularly SLR, and ocean warming and acidification ( [[#Gasalla--2017|Gasalla et al., 2017]] ). Some more localised habitats are also at risk of losing area due to climate change, such as the meadows of northwestern Patagonia ( [[#Crego--2014|Crego et al., 2014]] ) and mangroves of southern Brazil ( [[#Godoy--2015|Godoy and Lacerda, 2015]] ). Predicted changes in global climate along with agricultural expansion will strongly affect South American wetlands, which comprise around 20% of the continent and bring many benefits, such as biodiversity conservation and water availability ( [[#Junk--2013|Junk, 2013]] ).

<div id="12.3.7" class="h2-container"></div>

<span id="southwestern-south-america-sub-region"></span>
=== 12.3.7 Southwestern South America Sub-region ===

<div id="h2-9-siblings" class="h2-siblings"></div>

<div id="12.3.7.1" class="h3-container"></div>

<span id="hazards-6"></span>
==== 12.3.7.1 Hazards ====

<div id="h3-25-siblings" class="h3-siblings"></div>

Significant increases in the intensity and frequency of hot extremes and significant decreases in the intensity and frequency of cold extremes have ''likely'' been observed for the region (Skansi et al., 2013; [[#Ceccherini--2016|Ceccherini et al., 2016]] ; [[#Meseguer-Ruiz--2018|Meseguer-Ruiz et al., 2018]] ; [[#Vicente-Serrano--2018|Vicente-Serrano et al., 2018]] ; [[#Dereczynski--2020|Dereczynski et al., 2020]] ; [[#Dunn--2020|Dunn et al., 2020]] ; [[#Olmo--2020|Olmo et al., 2020]] ) (WGI AR6 Table 11.13) ( [[#Seneviratne--2021|Seneviratne et al., 2021]] ). In particular, a significant increment in the duration and frequency of heatwaves mainly in central Chile from 1961 to 2016 has been observed ( [[#Piticar--2018|Piticar, 2018]] ).

A robust drying trend for Chile (30°S–48°S) has been recorded ( ''medium confidence'' ) ( [[#Saurral--2017|Saurral et al., 2017]] ; [[#Boisier--2018|Boisier et al., 2018]] ) ''.'' However, inconsistent trends over the region in the magnitude of precipitation extremes with both decreases and increases ( [[#Chou--2014|Chou et al., 2014]] ; [[#Giorgi--2014|Giorgi et al., 2014]] ; [[#Heidinger--2018|Heidinger et al., 2018]] ; [[#Meseguer-Ruiz--2018|Meseguer-Ruiz et al., 2018]] ) (WGI AR6 Table 11.14) ( [[#Seneviratne--2021|Seneviratne et al., 2021]] ) have been observed ( ''low confidence'' ). The glacier equilibrium line altitude has presented an overall increase over central Chilean Andes ( [[#Barria--2019|Barria et al., 2019]] ).

For central Chile, a significant increase (5% to 20% in the last 60 years) in wave heights in the sea has been observed ( [[#Martínez--2018|Martínez et al., 2018]] ). From 1982 to 2016, sea levels at central Chile have increased 5 mm yr −1 , where El Niño events of 1982–1983 and 1997–1998 caused an extreme increase of 15 to 20 cm in the mean sea level ( [[#Campos-Caba--2016|Campos-Caba, 2016]] ; [[#Martínez--2018|Martínez et al., 2018]] ).

From 1946 to 2017, the number of fires and areas burned have increased significantly in Chile ( ''high confidence'' ) ( [[#González--2011|González et al., 2011]] ; [[#Jolly--2015|Jolly et al., 2015]] ; [[#Úbeda--2016|Úbeda and Sarricolea, 2016]] ; [[#de%20la%20Barrera--2018|de la Barrera et al., 2018]] ; [[#Urrutia-Jalabert--2018|Urrutia-Jalabert et al., 2018]] ). Fires are attributed to changes in temperature regimes ( [[#González--2011|González et al., 2011]] ; [[#de%20la%20Barrera--2018|de la Barrera et al., 2018]] ; [[#Gómez-González--2018|Gómez-González et al., 2018]] ) and precipitation regimes ( ''medium confidence'' ) ( [[#Gómez-González--2018|Gómez-González et al., 2018]] ; [[#Urrutia-Jalabert--2018|Urrutia-Jalabert et al., 2018]] ).

The glaciers of the southern Andes (including the SWS and SSA regions) show the highest glacier mass loss rates worldwide ( ''high confidence'' ) contributing to SLR ( [[#Jacob--2012|Jacob et al., 2012]] ; [[#Gardner--2013|Gardner et al., 2013]] ; [[#Dussaillant--2018|Dussaillant et al., 2018]] ; [[#Braun--2019|Braun et al., 2019]] ; [[#Zemp--2019|Zemp et al., 2019]] ). Since 1985, the glacier area loss in the sub-region is in a range of 20 up to 60% ( [[#Braun--2019|Braun et al., 2019]] ; [[#Reinthaler--2019b|Reinthaler et al., 2019b]] ).

Four sets of downscaling simulations based on the Eta Regional Climate Model forced by two global climate models ( [[#Chou--2014|Chou et al., 2014]] ) projected warmer conditions (more than 1°C) for the entire sub-region by 2050 under the RCP4.5 scenario ( ''medium confidence'' ). Extremely warm December–January–February days as well as the number of heatwaves per season are expected to increase by 5–10 times in northern Chile ( [[#Feron--2019|Feron et al., 2019]] ), ''likely'' increasing in the intensity and frequency of hot extremes over the entire region (WGI AR6 Table 11.13) ( [[#Seneviratne--2021|Seneviratne et al., 2021]] ). Drier conditions ( ''medium confidence'' ), by means of a decrease in total annual and extreme precipitation, are expected to increase for southern Chile, but inconsistent changes are expected in the sub-region ( ''low confidence'' ) ( [[#Chou--2014|Chou et al., 2014]] ) (WGI AR6 Table 11.14) ( [[#Seneviratne--2021|Seneviratne et al., 2021]] ) with ''high confidence'' upon an increase in fire weather and a decrease in permafrost and snow extent (WGI AR6 Table 12.6, [[#Ranasinghe--2021|Ranasinghe et al., 2021]] ).

Regional sea-level change for the region predicted by 2100 shows that total mean SLR along the coast will lie between 34 and 52 cm for the RCP4.5 scenario and between 46 and 74 cm for the RCP8.5 scenario with ''high confidence'' ( [[#Albrecht--2016|Albrecht and Shaffer, 2016]] ; WGI AR6 Table 12.6, [[#Ranasinghe--2021|Ranasinghe et al., 2021]] ).

<div id="12.3.7.2" class="h3-container"></div>

<span id="exposure-6"></span>
==== 12.3.7.2 Exposure ====

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There is ''high confidence'' that age and socioeconomic status are key factors determining health exposure and quality of life in SWS, where low-income areas show an insufficient number of public spaces to provide acceptable environmental quality in comparison with the high-income areas ( [[#Romero-Lankao--2013|Romero-Lankao et al., 2013]] ; [[#Fernández--2016|Fernández and Wu, 2016]] ; [[#Paz--2016|Paz et al., 2016]] ; [[#Hystad--2019|Hystad et al., 2019]] ; [[#Smith--2019|Smith and Henríquez, 2019]] ; [[#Jaime--2020|Jaime et al., 2020]] ; [[#Pino-Cortés--2020|Pino-Cortés et al., 2020]] ).

Profound social inequalities, urban expansion and inadequate city planning (e.g., drainage network) increase exposure to flooding events and landslides ( ''high confidence'' ) ( [[#Müller--2014|Müller and Höfer, 2014]] ; [[#Rojas--2017|Rojas et al., 2017]] ; [[#Lara--2018|Lara et al., 2018]] ), heat hazards such as heatwaves ( ''high confidence'' ) ( [[#Welz--2014|Welz et al., 2014]] ; [[#Qin--2015|Qin et al., 2015]] ; [[#Inostroza--2016|Inostroza et al., 2016]] ; [[#Welz--2016|Welz and Krellenberg, 2016]] ; [[#Krellenberg--2017|Krellenberg and Welz, 2017]] ) and the loss and fragmentation of green infrastructure (GI) ( [[#Hernández-Moreno--2018|Hernández-Moreno and Reyes-Paecke, 2018]] ). SWS cities show the highest levels of air pollution of CSA ( ''medium confidence: medium evidence'' , ''high agreement'' ) ( [[#Pino--2015|Pino et al., 2015]] ; [[#Huneeus--2020|Huneeus et al., 2020]] ; [[#González-Rojas--2021|González-Rojas et al., 2021]] ), where state air quality alerts have limited effect on protective health behaviours, since public perceptions about air pollution vary widely among the population ( [[#Boso--2019|Boso et al., 2019]] ). In particular, human communities living in coastal cities show a negative safety perception about the performance of the infrastructure and coastal defences to flood events ( ''low confidence'' ) ( [[#González--2017|González and Holtmann-Ahumada, 2017]] ; [[#Igualt--2019|Igualt et al., 2019]] ).

Although climate change is critically important for the current and future status of mining activity in SWS ( [[#Odell--2018|Odell et al., 2018]] ), and SWS areas subjected to mining activities are highly exposed to water risk ( [[#Northey--2017|Northey et al., 2017]] ), to date there is ''low evidence'' of climate change impacting mining activities ( [[#Corzo--2018|Corzo and Gamboa, 2018]] ; [[#Odell--2018|Odell et al., 2018]] ).

<div id="12.3.7.3" class="h3-container"></div>

<span id="vulnerability-6"></span>
==== 12.3.7.3 Vulnerability ====

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Rapid changes in temperature and precipitation regimes make terrestrial ecosystems highly vulnerable to climate change ( ''high confidence'' ) ( [[#Salas--2016|Salas et al., 2016]] ; [[#Fuentes-Castillo--2020|Fuentes-Castillo et al., 2020]] ) (Figure 12.7). Terrestrial ecosystems dominated by exotic species (e.g., pine) with lower landscape heterogeneity and degraded soils and that are close to settlements and roads are highly vulnerable to wildfires in comparison to forests dominated by native trees ( ''high confidence'' ) ( [[#Altamirano--2013|Altamirano et al., 2013]] ; [[#Castillo-Soto--2013|Castillo-Soto et al., 2013]] ; [[#Cóbar-Carranza--2014|Cóbar-Carranza et al., 2014]] ; [[#Salas--2016|Salas et al., 2016]] ; [[#Bañales-Seguel--2018|Bañales-Seguel et al., 2018]] ; [[#Gómez-González--2018|Gómez-González et al., 2018]] ; [[#Sarricolea--2020|Sarricolea et al., 2020]] ). Changes in land use, artificial forestation, deforestation, agricultural abandonment and urbanisation have provoked a permanent degradation of old-growth forests, putting at risk the biodiversity, recreation and ecotourism ( ''medium confidence: medium evidence, high agreement'' ) ( [[#Rojas--2013|Rojas et al., 2013]] ; [[#Nahuelhual--2014|Nahuelhual et al., 2014]] ). Marine coastal ecosystems such as dunes, sandy beaches and wetlands show high deterioration, decreasing their ability to mitigate extreme events ( ''medium confidence: low evidence'' , ''high agreement'' ) ( [[#González--2017|González and Holtmann-Ahumada, 2017]] ; [[#Ministerio%20de%20Medio%20Ambiente%20de%20Chile--2019|Ministerio de Medio Ambiente de Chile, 2019]] ).

The water sector shows a very high vulnerability ( ''high confidence'' ) (Figure 12.7) mainly due to weak water governance focused on market aspects (e.g., inter-sectoral water transactions, setting rates, granting concessions, waiving the water right) ( ''high confidence'' ) ( [[#Hurlbert--2013|Hurlbert and Diaz, 2013]] ; [[#Valdés-Pineda--2014|Valdés-Pineda et al., 2014]] ; [[#Barría--2019|Barría et al., 2019]] ; [[#Hurlbert--2019|Hurlbert and Gupta, 2019]] ; [[#Muñoz--2020a|Muñoz et al., 2020a]] ; [[#Urquiza--2020b|Urquiza and Billi, 2020b]] ). Potable water and adequate sanitation are available in SWS; however, water availability in Chile is unevenly distributed in rural communities ( ''high confidence'' ) ( [[#Valdés-Pineda--2014|Valdés-Pineda et al., 2014]] ; [[#Nelson-Nuñez--2019|Nelson-Nuñez et al., 2019]] ). Spatial differences in water availability are enhanced by strong population growth, economic development, mining activities and the high dependence of agriculture on irrigation ( ''high confidence'' ) ( [[#Stathatou--2016|Stathatou et al., 2016]] ; [[#Northey--2017|Northey et al., 2017]] ; [[#Fercovic--2019|Fercovic et al., 2019]] ). Droughts in SWS are a major threat to water security ( ''high confidence'' ) ( [[#Aitken--2016|Aitken et al., 2016]] ; [[#Núñez--2017|Núñez et al., 2017]] ) as river streamflows are highly dependent on the interannual to decadal climate conditions, snow melting processes and rainfall events ( [[#Boisier--2016|Boisier et al., 2016]] ) and impacted by land uses and changes in irrigated agriculture ( ''medium confidence: medium evidence, high agreement'' ) ( [[#Vicuña--2013|Vicuña et al., 2013]] ; [[#Fuentes--2021|Fuentes et al., 2021]] ).

Energy and water needs of large-scale mining activities make this socioeconomic sector particularly vulnerable to climate change; additionally, the relative lack of power of resource-poor communities living in areas where such mining makes claims on water and energy resources renders these communities even more vulnerable ( [[#Odell--2018|Odell et al., 2018]] ). Given new conditions generated by changes in a growing demand and climate change, mining companies will need to increase resilience to extreme events; additionally, the declining concentrations of minerals of interest in raw materials require greater energy input for extraction and processing and new methods to avoid associated emissions are required ( [[#Hodgkinson--2018|Hodgkinson and Smith, 2018]] ).

Urban and agriculture sectors are vulnerable to climate change ( ''medium confidence: medium evidence, high agreement'' ) (Figure 12.7), increasing problems and demand for water ( ''high confidence'' ) ( [[#Monsalves-Gavilán--2013|Monsalves-Gavilán et al., 2013]] ; [[#Meza--2014|Meza et al., 2014]] ; [[#Fercovic--2019|Fercovic et al., 2019]] ). Important health problems (e.g., pathogenic infections, changes in vector-borne diseases, heat-related mortality, lower neurobehavioural performance) have been associated with agriculture, mining and thermal power production activities in SWS ( ''high confidence'' ) ( [[#Muñoz-Zanzi--2014|Muñoz-Zanzi et al., 2014]] ; [[#Valdés-Pineda--2014|Valdés-Pineda et al., 2014]] ; [[#Pino--2015|Pino et al., 2015]] ; [[#Cortés--2016|Cortés, 2016]] ; [[#Berasaluce--2019|Berasaluce et al., 2019]] ; [[#Muñoz--2019a|Muñoz et al., 2019a]] ; [[#Ramírez-Santana--2020|Ramírez-Santana et al., 2020]] ).

Large-scale agricultural growth has increased vulnerability to climate change by disfavouring traditional agriculture, the homogenisation of the biophysical landscape and the replacement of traditional crops and native forests with exotic species like pines and eucalyptus ( ''high confidence'' ) ( [[#Torres--2015|Torres et al., 2015]] ), where farmers’ perceptions of climate change are highly dependent on educational level and access to meteorological information ( ''low confidence'' ) ( [[#Roco--2015|Roco et al., 2015]] ). Agricultural systems owned by Indigenous Peoples (i.e., Mapuche, Quechua and Aymara farmers) seem to pose a lower level of vulnerability to drought and higher response capacity than non-Indigenous farmers thanks to the use of the traditional knowledge of specific management techniques and the tendency to conserve species or varieties of crops tolerant to water scarcity ( ''low confidence'' ) ( [[#Montalba--2015|Montalba et al., 2015]] ; [[#Saylor--2017|Saylor et al., 2017]] ; [[#Meldrum--2018|Meldrum et al., 2018]] ). Fishery- and aquaculture-related livelihoods are vulnerable to climate and non-climate drivers ( ''medium confidence: medium evidence, high agreement'' ), such as sea surface warming and precipitation reduction ( [[#Handisyde--2017|Handisyde et al., 2017]] ; [[#Soto--2019|Soto et al., 2019]] ; [[#González--2021|González et al., 2021]] ), changes in upwelling intensity ( ''low confidence'' ) ( [[#Oyarzún--2019|Oyarzún and Brierley, 2019]] ; [[#Ramajo--2020|Ramajo et al., 2020]] ), eutrophication and harmful algal bloom (HAB) events ( [[#Almanza--2019|Almanza et al., 2019]] ), a lack of observational elements and data management ( [[#Garçon--2019|Garçon et al., 2019]] ) and events such as earthquakes and tsunamis ( [[#Marín--2019|Marín, 2019]] ).

Chile has experienced accelerated economic growth, which has reduced poverty; however, important geographical, economic and educational inequalities remain ( [[#Repetto--2016|Repetto, 2016]] ). The Chilean healthcare system has become more equitable and responsive to the population’s needs (e.g., the Bono AUGE healthcare reform programme); however, the high relative inequalities in terms of income ( [[#OECD--2018|OECD, 2018]] ), education level and rural–urban factors are determinants of quality of care, health system barriers and differential access to healthcare ( ''high confidence'' ) ( [[#Frenz--2014|Frenz et al., 2014]] ). Exposure and vulnerability to psychosocial risks in SWS show significant inequalities in times of disasters such as earthquakes according to socioeconomic, geographic and gender factors ( ''high confidence'' ) ( [[#Labra--2002|Labra, 2002]] ; [[#Vitriol--2014|Vitriol et al., 2014]] ; [[#Quijada--2018|Quijada et al., 2018]] ), which are increased by the absence of local planning and drills and the lack of coordination ( [[#Vitriol--2014|Vitriol et al., 2014]] ). Indigenous Peoples have the highest levels of vulnerability in Chile in terms of income, basic needs and access to services to climate change ( ''low confidence'' ) ( [[#Parraguez-Vergara--2016|Parraguez-Vergara et al., 2016]] ).

<div id="12.3.7.4" class="h3-container"></div>

<span id="impacts-6"></span>
==== 12.3.7.4 Impacts ====

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Increasing temperatures in SWS have impacted temperate forests ( ''high confidence'' ) ( [[#Peña--2014|Peña et al., 2014]] ; [[#Urrutia-Jalabert--2015|Urrutia-Jalabert et al., 2015]] ; [[#Camarero--2017|Camarero and Fajardo, 2017]] ; [[#Fontúrbel--2018|Fontúrbel et al., 2018]] ; [[#Venegas-González--2018b|Venegas-González et al., 2018b]] ; [[#Peña-Guerrero--2020|Peña-Guerrero et al., 2020]] ). Increasing temperatures and decreasing precipitation have increased the impacts of wildfires on terrestrial ecosystems ( ''high confidence'' ) ( [[#Boisier--2016|Boisier et al., 2016]] ; [[#Díaz-Hormazábal--2016|Díaz-Hormazábal and González, 2016]] ; [[#Martinez-Harms--2017|Martinez-Harms et al., 2017]] ; [[#de%20la%20Barrera--2018|de la Barrera et al., 2018]] ; [[#Gómez-González--2018|Gómez-González et al., 2018]] ; [[#Urrutia--2018|Urrutia et al., 2018]] ; [[#Bowman--2019|Bowman et al., 2019]] ), creating conditions for future landslides and floods ( [[#de%20la%20Barrera--2018|de la Barrera et al., 2018]] ).

Future projections show important changes in the productivity, structure and biogeochemical cycles of SWS temperate and rainforests ( ''medium confidence: medium evidence, high agreement'' ) ( [[#Gutiérrez--2014|Gutiérrez et al., 2014]] ; [[#Correa-Araneda--2020|Correa-Araneda et al., 2020]] ) and their fauna ( ''low confidence'' ) ( [[#Glade--2016|Glade et al., 2016]] ; [[#Bourke--2018|Bourke et al., 2018]] ). The Chilean Winter Rainfall-Valdivian Forests are a biodiversity hotspot ( [[#Manes--2021|Manes et al., 2021]] ) (Section [https://www.ipcc.ch/chapter/12#CCP1.2.2 CCP1.2.2] ) projected to suffer habitat change, with loss of vegetation cover in the future due to climate change ( ''medium confidence: medium evidence, high agreement'' ) ( [[#Jantz--2015|Jantz et al., 2015]] ; [[#Mantyka-Pringle--2015|Mantyka-Pringle et al., 2015]] ). Species are projected to suffer changes in their distribution, including a decrease in climatic refugia for vertebrates ( ''low confidence'' ) ( [[#Cuyckens--2015|Cuyckens et al., 2015]] ; [[#Warren--2018|Warren et al., 2018]] ).

Increasing temperatures have enlarged the number and areal extent of glacier lakes in the central Andes, northern Patagonia and southern Patagonia ( ''high confidence'' ) ( [[#Wilson--2018|Wilson et al., 2018]] ), while decreased rainfall and rapid glacier melting have provoked changes in the environmental, biogeochemical and biological properties of central-southern and Andes Chilean lakes ( ''low confidence'' ) ( [[#Pizarro--2016|Pizarro et al., 2016]] ).

Increasing glacier lake outburst floods (GLOFs), ice and rock avalanches, debris flows and lahars from ice-capped volcanoes have been observed in SWS ( [[#Iribarren%20Anacona--2015|Iribarren Anacona et al., 2015]] ; [[#Jacquet--2017|Jacquet et al., 2017]] ; [[#Reinthaler--2019b|Reinthaler et al., 2019b]] ). There is ''low evidence'' on the effects of warming and degrading permafrost on slope instability and landslides in these regions ( [[#Iribarren%20Anacona--2015|Iribarren Anacona et al., 2015]] ).

Increasing temperatures, decreasing precipitation regimes and an unprecedented long-term drought have decreased the annual average river streamflows that supply SWS megacities such as Santiago ( ''high confidence'' ) ( [[#Meza--2014|Meza et al., 2014]] ; [[#Muñoz--2020a|Muñoz et al., 2020a]] ), with important and negative effects on water quality ( [[#Bocchiola--2018|Bocchiola et al., 2018]] ; [[#Yevenes--2018|Yevenes et al., 2018]] ), threatening irrigated agriculture activities ( ''medium confidence: medium evidence, high agreement'' ) ( [[#Yevenes--2018|Yevenes et al., 2018]] ; [[#Oertel--2020|Oertel et al., 2020]] ; [[#Peña-Guerrero--2020|Peña-Guerrero et al., 2020]] ). Large reductions in the availability of groundwater in the SWS region ( [[#Meza--2014|Meza et al., 2014]] ) and a sustained decrease in the mean annual flows ( [[#Ragettli--2016|Ragettli et al., 2016]] ; [[#Bocchiola--2018|Bocchiola et al., 2018]] ), especially during the snowmelt season (Vargas et al., 2013), have been observed in SWS. Drought has affected wetlands ( ''low confidence'' ) ( [[#Zhao--2016|Zhao et al., 2016]] ; [[#Domic--2018|Domic et al., 2018]] ) and desert ecosystems ( ''medium confidence: medium evidence, high agreement)'' ( [[#Acosta-Jamett--2016|Acosta-Jamett et al., 2016]] ; [[#Neilson--2017|Neilson et al., 2017]] ; [[#Díaz--2019|Díaz et al., 2019]] ).

There is ''low evidence'' on shoreline retreat attributed to climate change ( [[#Martínez--2018|Martínez et al., 2018]] ; [[#Ministerio%20de%20Medio%20Ambiente%20de%20Chile--2019|Ministerio de Medio Ambiente de Chile, 2019]] ), although increasing wind intensity along the central Chilean coast has caused serious damage in coastal infrastructure and buildings ( [[#Winckler--2017|Winckler et al., 2017]] ) and changes in seawater properties and processes ( ''low confidence'' ) ( [[#Schneider--2017|Schneider et al., 2017]] ; [[#Aguirre--2018|Aguirre et al., 2018]] ). Ocean and coastal ecosystems in SWS are sensitive to upwelling intensity, which affects the abundance, diversity, physiology and survivorship of coastal species ( ''high confidence'' ) ( [[#Anabalón--2016|Anabalón et al., 2016]] ; [[#Jacob--2018|Jacob et al., 2018]] ; [[#Ramajo--2020|Ramajo et al., 2020]] ) (Figure 12.8). Increasing radiation and temperatures and reduced precipitation, in conjunction with increased nutrient load, have increased HAB events, producing massive fauna mortalities ( ''high confidence'' ) ( [[#León-Muñoz--2018|León-Muñoz et al., 2018]] ; [[#IPCC--2019b|IPCC, 2019b]] , SPM A8.2 and B8.3; [[#Quiñones--2019|Quiñones et al., 2019]] ; [[#Soto--2019|Soto et al., 2019]] ; [[#Armijo--2020|Armijo et al., 2020]] ). Multiple resources subjected to fisheries and aquaculture are highly vulnerable to storms, alluvial disasters, ocean warming, ocean acidification, increasing ENSO extreme events and lower oxygen availability ( ''high confidence'' ) (Figure 12.8; [[#García-Reyes--2015|García-Reyes et al., 2015]] ; [[#Silva--2015|Silva et al., 2015]] ; [[#Duarte--2016|Duarte et al., 2016]] , 2018; [[#Lagos--2016|Lagos et al., 2016]] ; [[#Navarro--2016|Navarro et al., 2016]] ; [[#Lardies--2017|Lardies et al., 2017]] ; [[#IPCC--2019b|IPCC, 2019b]] ; [[#Mellado--2019|Mellado et al., 2019]] ; [[#Ramajo--2019|Ramajo et al., 2019]] ; [[#Silva--2019a|Silva et al., 2019a]] ; [[#Bertrand--2020|Bertrand et al., 2020]] ). Ocean and coastal ecosystems, especially EEZs, will be highly impacted by climate change in the near and long term ( ''high confidence'' ) (Figure 12.8; Table SM12.3; [[#Silva--2015|Silva et al., 2015]] ; [[#Silva--2019a|Silva et al., 2019a]] ).

Changes in temperature and droughts have impacted crops significantly ( ''medium confidence: medium evidence, high agreement'' ) ( [[#Ray--2015|Ray et al., 2015]] ; [[#Zambrano--2016|Zambrano et al., 2016]] ; [[#Lesjak--2017|Lesjak and Calderini, 2017]] ; [[#Ferrero--2018|Ferrero et al., 2018]] ; [[#Piticar--2018|Piticar, 2018]] ; [[#Haddad--2019|Haddad et al., 2019]] ; [[#Zúñiga--2021|Zúñiga et al., 2021]] ). Table 12.4 shows the changes in crop growth duration, which affects yields. Higher negative numbers then indicate yield reduction for the crop. Increasing temperatures and decreasing precipitation are expected to impact the agriculture sector (i.e., fruits crops and forests) across the entire sub-region, with the largest impacts in the northern and central zone ( ''high confidence'' ) ( [[#Mera--2015|Mera et al., 2015]] ; [[#Zhang--2015|Zhang et al., 2015]] ; [[#Silva--2016|Silva et al., 2016]] ; [[#Lizana--2017|Lizana et al., 2017]] ; [[#Reyer--2017|Reyer et al., 2017]] ; [[#Toro-Mujica--2017|Toro-Mujica et al., 2017]] ; [[#Beyá-Marshall--2018|Beyá-Marshall et al., 2018]] ; [[#Lobos--2018|Lobos et al., 2018]] ; [[#O’Leary--2018|O’Leary et al., 2018]] ; [[#Aggarwal--2019|Aggarwal et al., 2019]] ; [[#Ávila-Valdés--2020|Ávila-Valdés et al., 2020]] ; [[#Fernandez--2020|Fernandez et al., 2020]] ; [[#Melo--2021|Melo and Foster, 2021]] ). Observed impacts and future projections warn that increasing temperatures and decreasing precipitation will largely impact water demand by agricultural sectors ( ''high confidence'' ) ( [[#Novoa--2019|Novoa et al., 2019]] ; [[#Peña-Guerrero--2020|Peña-Guerrero et al., 2020]] ; [[#Webb--2020|Webb et al., 2020]] ). Extreme climate events have caused Indigenous Peoples (e.g., Mapuche, Uru and Aymara) to experience water scarcity, a reduction in agricultural production and a displacement of their traditional knowledge and practices ( ''medium confidence: low evidence, high agreement'' ) ( [[#Parraguez-Vergara--2016|Parraguez-Vergara et al., 2016]] ; [[#Meldrum--2018|Meldrum et al., 2018]] ; [[#Perreault--2020|Perreault, 2020]] ).

SWS cities have been largely impacted by wildfires, water scarcity and landslides affecting highways and local roads, as well as potable water supply ( [[#Sepúlveda--2015|Sepúlveda et al., 2015]] ; [[#Araya-Muñoz--2016|Araya-]] [[#Muñoz--2016|Muñoz et al., 2016]] ). Increasing temperature and heat extreme events in cities have increased the demand for water, damage to urban infrastructure ( [[#Monsalves-Gavilán--2013|Monsalves-Gavilán et al., 2013]] ) and accelerated ageing and death of trees ( ''high confidence'' ) ( [[#Moser-Reischl--2019|Moser-Reischl et al., 2019]] ). Increasing temperature will modify energy demand in cities in northern and central Chile ( [[#Rouault--2019|Rouault et al., 2019]] ).

Increasing temperature, heat extreme events and air pollution in SWS have significantly impacted population health (cardiac complications, heat stroke and respiratory diseases) ( ''high confidence'' ) (Table 12.2; Leiva G et al., 2013; [[#Monsalves-Gavilán--2013|Monsalves-Gavilán et al., 2013]] ; [[#Pino--2015|Pino et al., 2015]] ; [[#Herrera--2016|Herrera et al., 2016]] ; [[#Henríquez--2017|Henríquez and Urrea, 2017]] ; [[#Ugarte-Avilés--2017|Ugarte-Avilés et al., 2017]] ; [[#de%20la%20Barrera--2018|de la Barrera et al., 2018]] ; [[#Johns--2018|Johns et al., 2018]] ; [[#Bowman--2019|Bowman et al., 2019]] ; [[#González--2019|González et al., 2019]] ; Matus C and Oyarzún G, 2019; [[#Sánchez--2019|Sánchez et al., 2019]] ; [[#Terrazas--2019|Terrazas et al., 2019]] ; [[#Cakmak--2021|Cakmak et al., 2021]] ; [[#Zenteno--2021|Zenteno et al., 2021]] ). There is ''low confidence'' regarding areal changes in Chagas disease ( [[#Tapia-Garay--2018|Tapia-Garay et al., 2018]] ; [[#Garrido--2019|Garrido et al., 2019]] ) and transmission rates in the future ( [[#Ayala--2019|Ayala et al., 2019]] ).

'''Table 12.4 |''' Average percentage change in crop growth duration for the period 2015–2019. Crop growth duration refers to the time taken in a year for crops to accumulate the reference period (1981–2010) average growing season accumulated temperature total (ATT). As temperatures rise, the ATT is reached earlier (higher negative changes), the crop matures too quickly, and thus yields are lower. “No data” means no data are available for the growth of that crop in the specified region. NP means that the crop is not present in significant areas in that region. Data were derived from Romanello et al. (2021).

{| class="wikitable"
|-
! '''Region'''

! '''Winter wheat'''

! '''Spring wheat'''

! '''Rice'''

! '''Maize'''

! '''Soybean'''

|-
| Central America (CA)

| −4.8%

| No data

| −1.9%

| −5.0%

| −4.7%

|-
| Northwestern South America (NWS)

| −3.8%

| −5.2%

| −5.2%

| −5.6%

| −3.1%

|-
| Northern South America (NSA)

| NP

| NP

| −0.7%

| −3.1%

| 0.0%

|-
| South America Monsoon (SAM)

| −5.3%

| −0.7%

| −1.4%

| −2.9%

| −1.5%

|-
| Northeastern South America (NES)

| −1.0%

| −1.3%

| −0.7%

| −3.5%

| −2.6%

|-
| Southeastern South America (SES)

| −2.3%

| −3.5%

| −2.3%

| −2.4%

| −2.7%

|-
| Southwestern South America (SWS)

| −2.3%

| −5.2%

| −10.0%

| −5.2%

| No data

|-
| Southern South America (SSA)

| −0.8%

| −6.5%

| No data

| −1.6%

| No data

|}

<div id="12.3.8" class="h2-container"></div>

<span id="southern-south-america-sub-region"></span>
=== 12.3.8 Southern South America Sub-region ===

<div id="h2-10-siblings" class="h2-siblings"></div>

<div id="12.3.8.1" class="h3-container"></div>

<span id="hazards-7"></span>
==== 12.3.8.1 Hazards ====

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There were inconsistent trends and insufficient data coverage on extreme temperatures and precipitation ( ''low confidence'' ), but an increase in the frequency of meteorological droughts was observed with ''medium confidence'' ( [[#Dereczynski--2020|Dereczynski et al., 2020]] ; [[#Dunn--2020|Dunn et al., 2020]] ; WGI AR6 Tables 11.13, 11.14, 11.15, [[#Seneviratne--2021|Seneviratne et al., 2021]] ; WGI AR6 Table 12.3, [[#Ranasinghe--2021|Ranasinghe et al., 2021]] ). An increase in precipitation in Trelew, no change for Comodoro Rivadavia, both stations located in eastern Patagonia, and negative trends in austral summer rainfall in the southern Andes were observed ( [[#Vera--2015|Vera and Díaz, 2015]] ; [[#Saurral--2017|Saurral et al., 2017]] ). Chile’s wildfires in Patagonia (fire frequency and intensity) have grown at an alarming rate ( [[#Úbeda--2016|Úbeda and Sarricolea, 2016]] ). Decreasing rainfall patterns in Punta Arenas is closely associated with variability at interannual to inter-decadal time scales of the main forcing system of climate in Patagonia. Snow cover extension (SCE) and snow cover duration decreased by an average of approximately 13 ± 2% and 43 ± 20 d respectively from 2000 to 2016, due to warming rather than drying ( [[#Rasmussen--2007|Rasmussen et al., 2007]] ). In particular, analysis of spatial patterns of SCE indicates a slightly greater reduction on the eastern side (approximately 14 ± 2%) of the Andes Cordillera compared to the western side (approximately 12 ± 3%). According to the longest time series of glacier mass balance data in the Southern Hemisphere, the Echaurren Norte glacier lost 65% of its original area in the period 1955–2015 and disaggregated into two ice bodies in the late 1990s ( [[#Malmros--2018|Malmros et al., 2018]] ; [[#Pérez--2018|Pérez et al., 2018]] ).

Mean temperatures in the SSA sub-region are projected to continue to rise up to +2.5°C by 2080 with respect to the present climate ( [[#Kreps--2012|Kreps et al., 2012]] ). A rise in temperature means that an isotherm of 0°C will move up mountains, leaving less surface for accumulation of snow ( [[#Barros--2015|Barros et al., 2015]] ).

An increase in the intensity and frequency of hot extremes and a decrease in the intensity and frequency of cold extremes are projected to be ''likely'' (WGI AR6 Table 11.13, [[#Seneviratne--2021|Seneviratne et al., 2021]] ); CMIP6 models project an increase in the intensity and frequency of heavy precipitation ( ''medium confidence'' ) ''.''

It is expected that an increase in the intensity of heavy precipitation, droughts and fire weather will intensify through the 21st century in SSA, but mean wind will decrease ( ''medium confidence'' ) ( [[#Kitoh--2011|Kitoh et al., 2011]] ; WGI AR6 Tables 11.14 and Table 11.15, [[#Seneviratne--2021|Seneviratne et al., 2021]] ). The probability of extended droughts, such as the recently experienced mega-drought (2010–2015), increases to up to 5 events/100 yr ( [[#Bozkurt--2017|Bozkurt et al., 2017]] ). Snow, glaciers, permafrost and ice sheets will decrease with ''high confidence'' (WGI AR6 Table 12.6, [[#Ranasinghe--2021|Ranasinghe et al., 2021]] ). The observed area and the elevation changes indicate that the Echaurren Norte glacier may disappear in the coming years if negative mass balance rates prevail ( ''medium confidence'' ) ( [[#Farías-Barahona--2019|Farías-Barahona et al., 2019]] ).

<div id="12.3.8.2" class="h3-container"></div>

<span id="exposure-7"></span>
==== 12.3.8.2 Exposure ====

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Grasslands make a significant contribution to food security in Patagonia by providing part of the feed requirements of ruminants used for meat, wool and milk production. There is a lack of information regarding the combined effects of climate change and overgrazing and the consequences for pastoral livelihoods that depend on rangelands. Temperature and the amount and seasonal distribution of precipitation were important controls of vegetation structure in Patagonian rangelands ( [[#Gaitán--2014|Gaitán et al., 2014]] ). They found that over two-thirds of the total effect of precipitation on above-ground net primary production (ANPP) was direct, and the other third was indirect (via the effects of precipitation on vegetation structure). Thus, if evapotranspiration and drought stress increase as temperature increases and rainfall decreases in water-limited ecosystems, a greater exposure of ranchers to a reduction in stocking rate and, therefore, family income would be expected ( ''medium confidence'' ). The number of farmers (mainly family enterprises) exposed to climatic hazards (drought) is approximately 70,000–80,000, who have 14–15 million sheep in Argentina ( [[#Peri--2021|Peri et al., 2021]] ).

The main Argentinian Patagonia cities have developed as a result of oil and gas extraction, which requires massive quantities of water due to fracking and drilling techniques. Vaca Muerta is the major region in SA, where those techniques are used to extract oil and gas, and this will lead to an exacerbation of current water scarcity issues and to competition with irrigated agriculture ( [[#Rosa--2019|Rosa and D’Odorico, 2019]] ), which in the context of drought may exacerbate socioenvironmental conflicts ( ''medium confidence'' ).

<div id="12.3.8.3" class="h3-container"></div>

<span id="vulnerability-7"></span>
==== 12.3.8.3 Vulnerability ====

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There are reports related to a decrease in survival, growth and higher vulnerability to drought and fire severity for species of native forests due to climate change and wildfires ( ''high confidence'' ) ( [[#Mundo--2010|Mundo et al., 2010]] ; [[#Landesmann--2015|Landesmann et al., 2015]] ; [[#Whitlock--2015|Whitlock et al., 2015]] ; [[#Jump--2017|Jump et al., 2017]] ; [[#Camarero--2018|Camarero et al., 2018]] ; [[#Venegas-González--2018a|Venegas-González et al., 2018a]] ). A coincidence has been reported between major changes in regional decline in the growth of forests with severe droughts due to climatic variations over northern Patagonia ( [[#Rodríguez-Catón--2016|Rodríguez-Catón et al., 2016]] ). Once the forest decline begins, other contributing factors, such as insects (e.g., defoliator outbreaks), increase forest vulnerability or accelerate the loss of forest health of previously stressed trees ( [[#Piper--2015|Piper et al., 2015]] ). This region hosts unique temperate rainforests, which are particularly rich in endemic and long-lived conifer species (e.g., ''Fitzroya cupressoides'' ) and which may be vulnerable to declines in soil moisture availability ( [[#Camarero--2017|Camarero and Fajardo, 2017]] ). Patagonia will probably be vulnerable to a decrease in precipitation regimes due to climate change, and consequently many species that rely on meadows in an arid environment will also be impacted ( [[#Crego--2014|Crego et al., 2014]] ). The floods triggered by strong ENSOs caused significant changes in crop production ( [[#Isla--2018|Isla et al., 2018]] ).

The development of various human activities and water infrastructure is depleting water sources, changing river basins from exoreic to endoreic and the disappearance of a lake in 2016 ( [[#Scordo--2017|Scordo et al., 2017]] ). Numerous dams for irrigation, some of which are also used for hydropower, have been and are planned to be built despite wind power generation potential ( [[#Silva--2016|Silva, 2016]] ). Oil and gas have played an important role in the rise of Neuquén-Cipolletti as Patagonia’s most populous urban area and in the growth of Comodoro Rivadavia, Punta Arenas and Rio Grande as well.

<div id="12.3.8.4" class="h3-container"></div>

<span id="impacts-7"></span>
==== 12.3.8.4 Impacts ====

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The potential impact of climate change is of special concern in arid and semiarid Patagonia, a &gt;700,000 km 2 region of steppe-like plains in Argentina. Thus, melting snow and ice in the glaciers of Patagonia and the Andes will alter surface runoff into interior wetlands. A SLR of 20–60 cm will destroy coastal marshes, and an increase in extreme events, such as storms, floods and droughts, will affect biodiversity in wet grasslands ( ''medium confidence: low evidence, high agreement'' ) (after Junk et al. 2013; Joyce et al. 2016). Three species of lizard from Patagonia are at risk of extinction as a result of global warming ( [[#Kubisch--2016|Kubisch et al., 2016]] ).

Patagonian ice fields in SA are the largest bodies of ice outside of Antarctica in the Southern Hemisphere. They are losing volume due partly to rapid changes in their outlet glaciers, which end up in lakes or the ocean, becoming the largest contributors to eustatic SLR in the world per unit area ( [[#Foresta--2018|Foresta et al., 2018]] ; [[#Moragues--2019|Moragues et al., 2019]] ; [[#Zemp--2019|Zemp et al., 2019]] ). Most calving glaciers in the southern Patagonia ice field retreated during the last century ( ''high confidence'' ). Upsala glacier retreat generated slope instability, and a landslide movement destroyed the western edge in 2013. The Upsala Argentina Lake has become potentially unstable and may generate new landslides ( [[#Moragues--2019|Moragues et al., 2019]] ). The climate effect on the summer stratification of piedmont lakes is another issue in connection with glacier dynamics ( [[#Isla--2010|Isla et al., 2010]] ).

Between 41° and 56° South latitude, the absolute glacier area loss was 5450 km 2 (19%) in the last approximately 150 years, with an annual area reduction increase of 0.25% yr −1 for the period 2005–2016 ( [[#Meier--2018|Meier et al., 2018]] ). The small glaciers in the northern part of the Northern Patagonian Ice Field had over all periods the highest rates of 0.92% a −1 . In this sub-region, increased melting of ice is leading to changes in the structure and functioning of river ecosystems and in freshwater inputs to coastal marine ecosystems ( ''medium confidence: low evidence, high agreement'' ) ( [[#Aguayo--2019|Aguayo et al., 2019]] ). In addition, in the case of coastal areas, the importance of tides and rising sea levels in the behaviour of river floods has been demonstrated ( [[#Jalón-Rojas--2018|Jalón-Rojas et al., 2018]] ).

Suitable areas for meadows (very productive areas for livestock production) will decrease by 7.85% by 2050 given predicted changes in climate ( ''low confidence'' ) ( [[#Crego--2014|Crego et al., 2014]] ).

A major drought from 1998 to 1999, coincident with a very hot summer, led to extensive dieback in a ''Nothofagus'' species ( [[#Suarez--2004|Suarez et al., 2004]] ). In another dominant ''Nothofagus'' species, several periodic droughts have triggered forest decline since the 1940s ( [[#Rodríguez-Catón--2016|Rodríguez-Catón et al., 2016]] ).

Climate-change-impacted ocean ecosystems by reducing kelp coverage, increasing reproductive failure and chick mortality of penguins and spurring the poleward expansion of saltmarshes in the Atlantic Patagonia. SSA houses the Patagonian Steppe Global-200 terrestrial ecoregion, which is a conservation priority on a global scale, but with a clear lack of studies on likely future climate-change impacts (Section [https://www.ipcc.ch/chapter/12#CCP1.2.2.2 CCP1.2.2.2] ) ( [[#Manes--2021|Manes et al., 2021]] ). The Patagonian Steppe may suffer pronounced expansion in invasive species’ ranges under climate change ( ''low confidence'' ) ( [[#Wang--2017a|Wang et al., 2017a]] ).

Fire has been found to promote or halt biological invasions ( ''medium confidence: medium evidence, high agreement'' ). For example, an analysis of ''Pinus'' spread following wildfires in Patagonia revealed a high risk that pines will become invasive if ignition frequency increases as a result of climate change ( [[#Raffaele--2016|Raffaele et al., 2016]] ). According to Inostroza et al. (2016), the Magellan Region is one of the most fragile regions in Patagonia, and despite its low population densities, it is undergoing a silent process of anthropogenic alteration where between 53.1% and 68.1% of the area needs to be considered to be influenced by humans who are occupying pristine ecosystems, even some with extensive conservation designations ( [[#Inostroza--2016|Inostroza et al., 2016]] ). Fire exposure can result in several health problems for human populations; Table 12.5 shows that SSA is the region with the highest exposure to wildfire danger.

'''Table 12.5 |''' Change in population-weighted exposure to very high or extremely high wildfire risk. Data were derived from Fire Danger Indices (FDIs) produced by the Copernicus Emergency Management Service for the European Forest Fire Information System (EFFIS) (available at Copernicus Emergency Management Service [2021]). High and very high wildfire danger are defined as FDI ≥ 5. Data were derived from Romanello et al. (2021).

{| class="wikitable"
|-
!
! colspan="3"| '''Population-weighted mean days of exposure to extremely high and very high wildfire danger'''

|-
! '''Sub-region'''

! '''2001–2004'''

! '''2017–2020'''

! '''Change from 2001–2004 to 2017–2020'''

|-
| Central America (CA)

| 30.4

| 26.9

| −3.5

|-
| Northwestern South America (NWS)

| 4.2

| 4.6

| 0.5

|-
| Northern South America (NSA)

| 19.7

| 21.2

| 1.5

|-
| South America Monsoon (SAM)

| 16.0

| 27.8

| 11.8

|-
| Northeastern South America (NES)

| 47.9

| 53.3

| 5.4

|-
| Southeastern SouthAmerica (SES)

| 4.2

| 8.2

| 4.0

|-
| Southwestern SouthAmerica (SWS)

| 31.9

| 58.4

| 26.5

|-
| Southern South America (SSA)

| 88.7

| 104.9

| 16.2

|}

<div id="12.4" class="h1-container"></div>

<span id="key-impacts-and-risks"></span>