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

=== 12.3.3 Northern South America Sub-region ===

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==== 12.3.3.1 Hazards ====

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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]] ).

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==== 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]] ).

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==== 12.3.3.3 Vulnerability ====

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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]] ).

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==== 12.3.3.4 Impacts ====

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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]] ).

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