1039

Emergent Risks

and Key Vulnerabilities

Coordinating Lead Authors:

Michael Oppenheimer (USA), Maximiliano Campos (Costa Rica), Rachel Warren (UK)

Lead Authors:

Joern Birkmann (Germany), George Luber (USA), Brian O’Neill (USA), Kiyoshi Takahashi

(Japan)

Contributing Authors:

Franz Berkhout (Netherlands), Pauline Dube (Botswana), Wendy Foden (South Africa),

Stefan Greiving (Germany), Solomon Hsiang (USA), Matt Johnston (USA), Klaus Keller (USA),

Joan Kleypas (USA), Robert Kopp (USA), Rachel Licker (USA), Carlos Peres (UK), Jeff Price

(UK), Alan Robock (USA), Wolfram Schlenker (USA), John Richard Stepp (USA), Richard Tol

(UK), Detlef van Vuuren (Netherlands)

Review Editors:

Mike Brklacich (Canada), Sergey Semenov (Russian Federation)

Volunteer Chapter Scientists:

Rachel Licker (USA), Solomon Hsiang (USA)

This chapter should be cited as:

Oppenheimer

, M., M. Campos, R. Warren, J. Birkmann, G. Luber, B. O’Neill, and K. Takahashi, 2014: Emergent risks

and key vulnerabilities. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and

Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental

Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir,

M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken,

P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and

New York, NY, USA, pp. 1039-1099.

1040

Executive Summary ......................................................................................................................................................... 1042

19.1. Purpose, Scope, and Structure of this Chapter ..................................................................................................... 1046

19.1.1. Historical Development of this Chapter .......................................................................................................................................... 1046

19.1.2. The Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation .................. 1047

Box 19-1. Article 2 of the United Nations Framework Convention on Climate Change ........................................................... 1047

Box 19-2. Deﬁnitions ................................................................................................................................................................. 1048

19.1.3. New Developments in this Chapter ................................................................................................................................................ 1049

19.2. Framework for Identifying Key Vulnerabilities, Key Risks, and Emergent Risks ................................................... 1050

19.2.1. Risk and Vulnerability ..................................................................................................................................................................... 1050

19.2.2. Criteria for Identifying Key Vulnerabilities and Key Risks ................................................................................................................ 1050

19.2.2.1. Criteria for Identifying Key Vulnerabilities ....................................................................................................................... 1051

19.2.2.2. Criteria for Identifying Key Risks ...................................................................................................................................... 1051

19.2.3. Criteria for Identifying Emergent Risks ........................................................................................................................................... 1052

19.2.4. Identifying Key and Emergent Risks under Alternative Development Pathways .............................................................................. 1052

19.2.5. Assessing Key Vulnerabilities and Emergent Risks .......................................................................................................................... 1052

19.3. Emergent Risk: Multiple Interacting Systems and Stresses .................................................................................. 1053

19.3.1. Limitations of Previous Approaches Imply Key Risks Overlooked .................................................................................................... 1053

19.3.2. Examples of Emergent Risks ........................................................................................................................................................... 1053

19.3.2.1. Emergent Risks Arising from the Effects of Degradation of Ecosystem Services by Climate Change ............................... 1053

19.3.2.2. Emergent Risk Involving Non-Climate Stressors: The Management of Water, Land, and Energy ...................................... 1054

19.3.2.3. Emergent Risks Involving Health Effects ......................................................................................................................... 1056

19.3.2.4. Spatial Convergence of Multiple Impacts: Areas of Compound Risk ................................................................................ 1057

19.4. Emergent Risk: Indirect, Trans-boundary, and Long-Distance Impacts ................................................................. 1059

19.4.1. Crop Production, Prices, and Risk of Increased Food Insecurity ...................................................................................................... 1059

19.4.2. Indirect, Trans-boundary, and Long-Distance Impacts of Adaptation .............................................................................................. 1060

19.4.2.1. Risks Associated with Human Migration and Displacement ............................................................................................ 1060

19.4.2.2. Risk of Conﬂict and Insecurity ......................................................................................................................................... 1060

19.4.2.3. Risks Associated with Species Range Shifts ..................................................................................................................... 1061

19.4.3. Indirect, Trans-boundary, and Long-Distance Impacts of Mitigation Measures ............................................................................... 1061

19.5. Newly Assessed Risks ........................................................................................................................................... 1062

19.5.1. Risks from Large Global Temperature Rise >4°C above Preindustrial Levels .................................................................................. 1062

19.5.2. Risks from Ocean Acidiﬁcation ....................................................................................................................................................... 1064

19.5.3. Risks from Carbon Dioxide Health Effects ....................................................................................................................................... 1064

19.5.4. Risks from Geoengineering (Solar Radiation Management) ........................................................................................................... 1065

Table of Contents

1041

Emergent Risks and Key Vulnerabilities Chapter 19

19.6. Key Vulnerabilities, Key Risks, and Reasons for Concern ...................................................................................... 1066

19.6.1. Key Vulnerabilities .......................................................................................................................................................................... 1066

19.6.1.1. Dynamics of Exposure and Vulnerability .......................................................................................................................... 1066

19.6.1.2. Differential Vulnerability and Exposure ........................................................................................................................... 1066

19.6.1.3. Trends in Exposure and Vulnerability ............................................................................................................................... 1067

19.6.1.4. Risk Perception ................................................................................................................................................................ 1068

19.6.2. Key Risks ......................................................................................................................................................................................... 1069

19.6.2.1. Assessing Key Risks ......................................................................................................................................................... 1069

19.6.2.2. The Role of Adaptation and Alternative Development Pathways ..................................................................................... 1072

19.6.3. Updating Reasons for Concern ....................................................................................................................................................... 1073

19.6.3.1. Variations in Reasons for Concern across Socioeconomic Pathways ............................................................................... 1074

19.6.3.2. Unique and Threatened Systems ...................................................................................................................................... 1075

19.6.3.3. Extreme Weather Events .................................................................................................................................................. 1076

19.6.3.4. Distribution of Impacts .................................................................................................................................................... 1077

19.6.3.5. Global Aggregate Impacts ............................................................................................................................................... 1078

19.6.3.6. Large-Scale Singular Events: Physical, Ecological, and Social System Thresholds and Irreversible Change ....................... 1079

19.7. Assessment of Response Strategies to Manage Risks .......................................................................................... 1080

19.7.1. Relationship between Adaptation Efforts, Mitigation Efforts, and Residual Impacts ....................................................................... 1080

19.7.2. Limits to Mitigation ........................................................................................................................................................................ 1083

19.7.3. Avoiding Thresholds, Irreversible Change, and Large-Scale Singularities in the Earth System ......................................................... 1084

19.7.4. Avoiding Tipping Points in Social/Ecological Systems ..................................................................................................................... 1085

19.7.5. Limits to Adaptation ....................................................................................................................................................................... 1085

References ....................................................................................................................................................................... 1085

Frequently Asked Questions

19.1: Does science provide an answer to the question of how much warming is unacceptable? ............................................................ 1047

19.2: How does climate change interact with and amplify preexisting risks? .......................................................................................... 1057

19.3: How can climate change impacts on one region cause impacts on other distant areas? ............................................................... 1062

1042

Chapter 19 Emergent Risks and Key Vulnerabilities

Executive Summary

This chapter assesses climate-related risks in the context of Article 2 of the United Nations Framework Convention on Climate

Change (UNFCCC). {Box 19.1} Such risks arise from the interaction of the evolving exposure and vulnerability of human, socioeconomic,

and biological systems with changing physical characteristics of the climate system. {19.2} Alternative development paths influence risk by

changing the likelihood of climatic events and trends (through their effects on greenhouse gases (GHGs) and other emissions) and by altering

vulnerability and exposure. {19.2.4, Figure 19-1, Box 19-2}

Interactions of climate change impacts on one sector with changes in exposure and vulnerability, as well as adaptation and

mitigation actions affecting the same or a different sector are generally not included or well integrated into projections of risk.

However, their consideration leads to the identification of a variety of emergent risks {Box 19-2} that were not previously

assessed or recognized (high confidence). {19.3}

This chapter identifies several such complex system interactions that increase vulnerability

and risk synergistically. For example:

•

The risk of climate change to human systems (e.g., agriculture and water supply) is increased by the loss of ecosystem services

that are supported by biodiversity (e.g., water purification, protection from extreme weather events, preservation of soils, recycling of

nutrients, and pollination of crops) (high confidence). Studies since the Fourth Assessment Report (AR4) broadly confirm that a large

proportion of species are at increased risk of extinction at all but the lowest levels of warming. {19.3.2.1, 19.5.1, 19.6.3.5}

•

Risks result from the management of water, land, and energy in the context of climate change. For example, in some water

stressed regions, as groundwater stores that have historically acted as buffers against impacts of climate variations and change are

depleted, adverse consequences arise for human systems and ecosystems simultaneously undergoing alteration of regional groundwater

resources due to climate change. The production of bioenergy crops to mitigate climate change leads to land conversion (e.g., from food

crops and unmanaged ecosystems to energy crops; high confidence) and in some scenarios, reduced food security as well as additional

GHG emissions over the course of decades or centuries. {19.3.2.2}

•

Climate change has the potential to adversely affect human health by increasing exposure and vulnerability to a variety of

stresses. For example, the interaction of climate change with food security can exacerbate malnutrition, increasing vulnerability of

individuals to a range of diseases (high confidence). {19.3.2.3}

•

The risk of severe harm and loss due to climate change-related hazards and various vulnerabilities is particularly high in large

urban and rural areas in low-lying coastal zones (high confidence). These areas, many characterized by increasing populations, are

exposed to multiple hazards and potential failures of critical infrastructure, generating new systemic risks. Cities in Asian megadeltas,

where populations are subject to sea level rise, storm surge, coastal erosion, saline intrusion, and flooding, provide an example. {19.2.3,

19.3.2.4, 19.4.2.1, 19.6.1.3.1, 19.6.2.1, 19.7.5, Table 19-4}

•

Spatial convergence of impacts in different sectors creates compound risk in many areas (medium confidence). Examples

include the Arctic (where thawing and sea ice loss disrupt land transportation, buildings, other infrastructure, and are projected to disrupt

indigenous culture); and the environs of Micronesia, Mariana Island, and Papua New Guinea (where coral reefs are highly threatened due

to exposure to concomitant sea surface temperature rise and ocean acidification). {19.3.2.4}

Emergent risks also arise from indirect, trans-boundary, and long-distance impacts of climate change. Adaptive responses and

mitigation measures sometimes increase such risks (high confidence). {19.4}

Human or ecological responses to local impacts of climate

change can generate harm at distant places.

• Increasing prices of food commodities on the global market due to local climate impacts, in conjunction with other stressors, decrease food

security and exacerbate food insecurity at distant locations. {19.4.1}

• Climate change will bear significant consequences for human migration flows at particular times and places, creating risks as well as

benefits for migrants and for sending and receiving regions and states (high confidence). {19.4.2.1}

• The effect of climate change on conflict and insecurity is an emergent risk because factors such as poverty and economic shocks that are

associated with a higher risk of violent conflict are themselves sensitive to climate change. In numerous statistical studies, the influence of

climate variability on violent conflict is large in magnitude (medium confidence). {19.4.2.2}

• Many species shift their ranges in response to climate change, adversely affecting ecosystem function and services while presenting new

challenges to conservation efforts (medium confidence). {19.4.2.3}

1043

Emergent Risks and Key Vulnerabilities Chapter 19

• Mitigation measures taken in one location can have long-distance or indirect impacts on biodiversity and/or human systems. For example,

the development of biofuels as energy sources can increase food prices (high confidence) and affect distant land use practices. {19.4.1,

19.4.3}

Additional risks related to particular biophysical impacts of climate change have arisen recently in the literature in sufficient

detail to permit assessment (high confidence). {19.5}

• Risks associated with global temperature rise in excess of 4°C relative to preindustrial levels

arise from severe and widespread

impacts on unique and threatened systems, substantial species extinction, extensive loss of ecosystem functioning, large risks to global and

regional food security, and the combination of high temperature and humidity compromising normal human activities, including growing

food or working outdoors in some areas for parts of the year (high confidence) and the potential for traversing thresholds that lead to

disproportionately large Earth systems responses (medium confidence). {19.5.1}

•

Ocean acidification poses risks to marine ecosystems and the societies that depend on them. For example, ocean acidification is

very likely to lead to changes in coral calcification rates. Reduced coral calcification is projected to have impacts of medium to high

magnitude on some ecosystem services, including tourism and the provisioning of fishing. {19.5.2}

•

There is increasing evidence in the literature that high ambient carbon dioxide (CO

) concentrations in the atmosphere will

affect human health by increasing the production and allergenicity of pollen and allergenic compounds and by decreasing

nutritional quality of important food crops. {19.5.3}

• In addition to providing potential climate change abatement benefits, geoengineering poses widespread risks to society and

ecosystems. For example, in some model experiments the implementation of Solar Radiation Management (SRM) for the purpose of limiting

global warming leads to ozone depletion and reduces precipitation. In addition, the failure or abrupt halting of SRM risks rapid climate

change. {19.5.4}

Global, regional, and local socioeconomic, environmental, and governance trends indicate that vulnerability and exposure of

communities or social-ecological systems to climatic hazards related to extreme events are dynamic and thus vary across temporal

and spatial scales (high confidence).

Effective risk reduction and adaptation strategies consider these dynamics and the inter-linkages between

socioeconomic development pathways and the vulnerability and exposure of people. Changes in poverty or socioeconomic status, ethnic

composition, age structure, and governance had a significant influence on the outcome of past crises associated with climatic hazards. {19.6.1}

Challenges for vulnerability reduction and adaptation actions are particularly high in regions that have shown severe difficulties

in governance. Studies confirm that countries that are classified as failed states and afflicted by violence are often not able to reduce

vulnerability effectively. Unless governance improves in countries with severe governance failure, risk will increase as a result of climate

changes interacting with increased human vulnerability (high confidence). {19.6.1.3.3}

Key risks inform evaluation of “dangerous anthropogenic interference with the climate system,” in the terminology of UNFCCC

Article 2. These are potentially severe adverse consequences for humans and social-ecological systems resulting from the

interaction of hazards linked to climate change and the vulnerability of exposed societies and systems. Key risks were identified

in this assessment based on expert judgments made by authors of the various chapters of this report in light of criteria described

here {19.2.2.2} and consolidated into the following representative list (high confidence).

{19.2.2.2, 19.6.2.1, Table 19-4, Boxes 19-2

and CC-KR} (Roman numerals indicate corresponding entries in Table 19-4; notation at end of each entry indicates corresponding Reasons for

Concern (RFCs), discussed below.)

Levels of global mean temperature change are variously presented in the literature with respect to “preindustrial” temperatures in a specified year or period, e.g., 1850–1900.

Alternatively, the average temperature within a recent period, e.g., 1986–2005, is used as a baseline. In this chapter, we use both, depending on the literature being assessed.

The increase above preindustrial (1850–1900) levels for the period 1986–2005 is estimated at 0.61°C (WGI AR5 Section 11.3.6.3). For example, using these baselines, a 2°C

increase above preindustrial levels corresponds to a 1.39°C increase above 1986–2005 levels. We use other baselines on occasion depending on the literature cited and explicitly

indicate where this is the case. Climate impact studies often report outcomes as a function of regional temperature change, which can differ significantly from changes in global

mean temperature. In most land areas, regional warming is larger than global warming (WGI AR5 Section 10.3.1.1.2). However, given the many conventions in the literature

for baseline periods, readers are advised to check carefully and to adjust baseline levels for consistency when comparing outcomes.

1044

Chapter 19 Emergent Risks and Key Vulnerabilities

i) Risk of death, injury, ill-health, or disrupted livelihoods in low-lying coastal zones and small island developing states and other small islands,

due to storm surges, coastal flooding, and sea level rise. [RFC 1-5]

ii) Risk of severe ill-health and disrupted livelihoods for large urban populations due to inland flooding in some regions. [RFC 2 and 3]

iii) Systemic risks due to extreme weather events leading to breakdown of infrastructure networks and critical services such as electricity,

water supply, and health and emergency services. [RFC 2-4]

iv) Risk of mortality and morbidity during periods of extreme heat, particularly for vulnerable urban populations and those working outdoors

in urban or rural areas. [RFC 2 and 3]

v) Risk of food insecurity and the breakdown of food systems linked to warming, drought, flooding, and precipitation variability and extremes,

particularly for poorer populations in urban and rural settings. [RFC 2-4]

vi) Risk of loss of rural livelihoods and income due to insufficient access to drinking and irrigation water and reduced agricultural productivity,

particularly for farmers and pastoralists with minimal capital in semi-arid regions. [RFC 2 and 3]

vii) Risk of loss of marine and coastal ecosystems, biodiversity, and the ecosystem goods, functions, and services they provide for coastal

livelihoods, especially for fishing communities in the tropics and the Arctic. [RFC 1, 2, and 4]

viii) Risk of loss of terrestrial and inland water ecosystems, biodiversity, and the ecosystem goods, functions, and services they provide for

livelihoods. [RFC 1, 3, and 4]

Climate change risks vary substantially across plausible alternative development pathways and the relative importance of

development and climate change varies by sector, region, and time period; both are important to understanding possible

outcomes (high confidence).

In some cases, there is substantial potential for adaptation to reduce risks, with development pathways playing

a key role in determining challenges to adaptation, including through their effects on ecosystems and ecosystem services. {19.6.2.2}

Assessment of the RFC framework pertinent to Article 2 of the UNFCCC has led to evaluations of risk being updated in light of

the advances since the AR4. {19.6.3}

(All temperature changes are relative to 1986–2005, i.e., “recent.” Numbers are indicative of RFC

designation in key risk enumeration, above.)

Unique and threatened systems: Some unique and threatened systems, including ecosystems and cultures, are already at risk from climate

change (high confidence). The number of such systems at risk of severe consequences is higher with additional warming of around 1°C.

Many species and systems with limited adaptive capacity are subject to very high risks with additional warming of 2°C, particularly Arctic-

sea-ice and coral-reef systems. {19.6.3.2}

Extreme weather events: Climate-change-related risks from extreme events, such as heat waves, extreme precipitation, and coastal

flooding, are already moderate (high confidence) and high with 1°C additional warming (medium confidence). Risks associated with some

types of extreme events (e.g., extreme heat) increase further at higher temperatures (high confidence). {19.6.3.3}

Distribution of impacts: Risks are unevenly distributed and are generally greater for disadvantaged people and communities in countries

at all levels of development. Risks are already moderate because of regionally differentiated climate-change impacts on crop production in

particular (medium to high confidence). Based on projected decreases in regional crop yields and water availability, risks of unevenly

distributed impacts are high for additional warming above 2°C (medium confidence). {19.6.3.4}

Global aggregate impacts: Risks of global aggregate impacts are moderate for additional warming between 1-2°C, reflecting impacts to

both Earth’s biodiversity and the overall global economy (medium confidence). Extensive biodiversity loss with associated loss of ecosystem

goods and services results in high risks around 3°C additional warming (high confidence). Aggregate economic damages accelerate with

increasing temperature (limited evidence, high agreement), but few quantitative estimates have been completed for additional warming

around 3°C or above. {19.3.2.1, 19.5.1, 19.6.3.5}

Large-scale singular events: With increasing warming, some physical systems or ecosystems may be at risk of abrupt and irreversible

changes. Risks associated with such tipping points become moderate between 0-1°C additional warming, due to early warning signs that

both warm-water coral reef and Arctic ecosystems are already experiencing irreversible regime shifts (medium confidence). Risks increase

disproportionately as temperature increases between 1-2°C additional warming and become high above 3°C, due to the potential for a

large and irreversible sea level rise from ice sheet loss. For sustained warming greater than some threshold, near-complete loss of the

Greenland ice sheet would occur over a millennium or more, contributing up to 7 m of global mean sea level rise. {19.6.3.6}

1045

Emergent Risks and Key Vulnerabilities Chapter 19

Impacts of climate change avoided under a range of scenarios for mitigation of GHG emissions are potentially large and increasing

over the 21st century (high confidence). {19.7.1}

Among the impacts assessed here, benefits from mitigation are most immediate for surface

ocean acidification and least immediate for impacts related to sea level rise. Because mitigation reduces the rate as well as the magnitude of

warming, it also increases the time available for adaptation to a particular level of climate change, potentially by several decades.

Only mitigation scenarios in the most stringent category (i.e., with 2100 CO

-eq concentrations of 430 to 480 ppm) maintain

moderately healthy coral reefs (medium confidence). With respect to the RFCs, only the most stringent of scenarios in this category

constrain overall risks to unique and threatened systems, and those associated with extreme weather events to a moderate level,

while the other scenarios in this category create risk in the high range for these two RFCs. The most stringent among these

scenarios constrain the level of risk associated with all other RFCs to the moderate level (high confidence). {19.6.3.2-3, 19.7.1}

The higher part of the range of GHG emission scenarios in the literature, that is, those with 2100 CO

-eq concentrations above

720 ppm create risks associated with extreme weather events and large-scale singular events that are in the high range, and

very high range (reflecting inability to adapt) for unique and threatened systems. Risks associated with the distribution of impacts

increase toward the very high range (high confidence). Risks of global aggregate impacts transition from moderate to high as

-eq concentrations increase from 720 ppm. {19.6.3.2, 19.6.3.4, 19.7.1}

Under any plausible scenario for mitigation and adaptation, some degree of risk from residual damages is unavoidable (very high

confidence).

For example, very few integrated assessment model-based scenarios in the literature demonstrate the feasibility of limiting

warming to a maximum of 1.5°C with at least 50% likelihood. {19.7.1-2}

The risk of crossing tipping points (critical thresholds) in the Earth system or socio-ecological systems is projected to decrease

with reduced GHG emissions {19.7.3}, and the risk of crossing tipping points in socio-ecological systems can also be reduced by

reducing human vulnerability or by preserving ecosystem services, or both (medium confidence). {19.7.4}

The risk of crossing tipping

points is reduced by limiting the level of climate change and/or removing concomitant stresses such as overgrazing, overfishing, and pollution,

but there is low confidence in the level of climate change associated with such tipping points and measures to avoid them.

1046

Chapter 19 Emergent Risks and Key Vulnerabilities

19.1. Purpose, Scope, and Structure

of this Chapter

The objective of this chapter is to assess new literature published

since the Fourth Assessment Report (AR4) on emergent risks and key

vulnerabilities to climate change from the perspective of the distribution

of risk over geographic location, economic sector, time period, and

socioeconomic characteristics of individuals and societies. Frameworks

used in previous IPCC reports to assess risk in the context of Article 2

of the United Nations Framework Convention on Climate Change

(UNFCCC) are updated and extended in light of new literature, and

additional frameworks arising in recent literature are examined. A

focal point of this chapter is the interaction of the changing physical

characteristics of the climate system with evolving characteristics of

socioeconomic and biological systems (exposure and vulnerability) to

produce risk (see Figure 19-1). Given the centrality of Article 2 to this

chapter, the greater emphasis is on harmful outcomes of climate change

rather than potential benefits.

19.1.1. Historical Development of this Chapter

The Third and Fourth Assessment Reports (TAR and AR4, respectively)

each devoted chapters to evaluating the state of knowledge relevant

o Article 2 of the UNFCCC (Smith et al., 2001; Schneider et al., 2007;

see Box 19-1). The TAR sorted and aggregated impacts discussed in

the literature according to a framework called Reasons for Concern

(RFCs), and assessed the level of risk associated with individual impacts

of climate change as well as each category or “reason” as a whole,

generally as a function of global mean warming. This assessment took

account of the distribution of vulnerability across particular regions,

countries, and sectors.

AR4 furthered the discussion relevant to Article 2 by assessing new

literature and developing criteria potentially useful for policy makers in

the determination of key impacts and vulnerabilities, that is, those

meriting particular attention in respect to Article 2. See Box 19-2 for

definitions of Reasons for Concern, Key Vulnerabilities (KVs), and related

terms. Some definitions go beyond those in the Glossary to provide

details especially pertinent to this chapter.

AR4 emphasized the differences in vulnerability between developed

and developing countries but also assessed new literature describing

vulnerability pertaining to various aggregations of people (such as by

ethnic, cultural, age, gender, or income status) and response strategies

for avoiding key impacts. The RFCs were updated and the Synthesis

Report (IPCC, 2007a) noted that they “remain a viable framework to

consider key vulnerabilities” (IPCC, 2007a, Section 5.2). However, their

EMISSIONS

and Land-use Change

Vulnerability

Exposure

RISK

Hazards

Anthropogenic

Climate Change

Socioeconomic

Pathways

Adaptation and

Mitigation

Actions

Governance

IMPACTS

Natural

Variability

SOCIOECONOMIC

PROCESSES

CLIMATE

Key

Emergent

Figure 19-1 | Schematic of the interaction among the physical climate system, exposure, and vulnerability producing risk. The ﬁgure visualizes the different terms and concepts

discussed in this chapter. Risk of climate-related impacts results from the interaction of climate-related hazards (including hazardous events and trends) with the vulnerability and

exposure of human and natural systems. The deﬁnition and use of “key” and “emergent” are indicated in Box 19-2 and the Glossary. Vulnerability and exposure are, as the ﬁgure

shows, largely the result of socioeconomic pathways and societal conditions (although changing hazard patterns also play a role; see Section 19.6.1.1). Changes in both the

climate system (left side) and socioeconomic processes (right side) are central drivers of the different core components (vulnerability, exposure, and hazards) that constitute risk

(modiﬁed version of SREX Figure SPM.1 (IPCC, 2012a)).

1047

Emergent Risks and Key Vulnerabilities Chapter 19

tility was limited by several factors: the lack of a time dimension (i.e.,

representation of impacts arising from timing and rates of climate

change and climate forcing); the focus on risk only as a function of

global mean temperature; lack of a clear distinction between impacts

and vulnerability; and, importantly, incomplete incorporation of the

evolving socioeconomic context, particularly adaptation capacity, in

representing impacts and vulnerability.

19.1.2. The Special Report on Managing the Risks

of Extreme Events and Disasters to Advance

Climate Change Adaptation

The IPCC Special Report on Managing the Risks of Extreme Events and

Disasters to Advance Climate Change Adaptation (SREX; IPCC, 2012a)

provides additional insights with respect to two RFCs (risks associated

with extreme weather events and the distribution of impacts) and

particularly the distribution of capacities to adapt to extreme events

across countries, communities, and other groups, and the limitations on

implementation of these capacities. SREX emphasized the role of the

socioeconomic setting and development pathway (expressed through

exposure and vulnerability) in determining, on the one hand, the

circumstances where extreme events do or do not result in extreme

Box 19-1 | Article 2 of the United Nations

Framework Convention on

Climate Change

Article 2

OBJECTIVE: The ultimate objective of this Convention

and any related legal instruments that the Conference of

the Parties may adopt is to achieve, in accordance with the

relevant provisions of the Convention, stabilization of

greenhouse gas concentrations in the atmosphere at a level

that would prevent dangerous anthropogenic interference

with the climate system. Such a level should be achieved

within a time-frame sufﬁcient to allow ecosystems to adapt

naturally to climate change, to ensure that food production

is not threatened and to enable economic development to

proceed in a sustainable manner.

Frequently Asked Questions

FAQ 19.1 | Does science provide an answer to the question of

how much warming is unacceptable?

No. Careful, critical scientiﬁc research and assessment can provide information to help society consider what levels

of warming or climate change impacts are unacceptable. However, the answer is ultimately a subjective judgment

that depends on values and culture, as well as socioeconomic and psychological factors, all of which inﬂuence how

people perceive risk in general and the risk of climate change in particular. The question of what level of climate

change impacts is unacceptable is ultimately not just a matter of the facts, but of how we feel about those facts.

This question is raised in Article 2 of the UNFCCC. The criterion, in the words of Article 2, is “dangerous anthropogenic

interference with the climate system”—a framing that invokes both scientiﬁc analysis and human values.

Agreements reached by governments since 2009, meeting under the auspices of the UNFCCC, have recognized “the

scientiﬁc view that the increase in global temperature should be below 2 degrees Celsius” (Section 19.1, UNFCCC,

Copenhagen Accord). Still, as informed on the subject as the scientists referred to in this statement may be, theirs

is just one valuable perspective. How each country or community will deﬁne acceptable or unacceptable levels,

essentially deciding what is “dangerous,” is a societal judgment.

Science can certainly help society think about what is unacceptable. For example, science can identify how much

monetary loss might occur if tropical cyclones grow more intense or heat waves more frequent, or identify the land

that might be lost in coastal communities for various levels of higher seas. But “acceptability” depends on how

each community values those losses. This question is more complex when loss of life is involved and yet more so

when damage to future generations is involved. These are highly emotional and controversial value propositions

that science can only inform, not decide.

The purpose of this chapter is to highlight key vulnerabilities and key risks that science has identiﬁed; however, it

is up to people and governments to determine how the associated impacts should be valued, and whether and

how the risks should be acted upon.

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Chapter 19 Emergent Risks and Key Vulnerabilities

Box 19-2 | Deﬁnitions

Exposure: The presence of people, livelihoods, species or ecosystems, environmental functions, services, and resources, infrastructure,

or economic, social, or cultural assets in places and settings that could be adversely affected.

Vulnerability: The propensity or predisposition to be adversely affected. Vulnerability encompasses a variety of concepts and elements

including sensitivity or susceptibility to harm and lack of capacity to cope and adapt.

A broad set of factors such as wealth, social status, and gender determine vulnerability and exposure to climate-related risk.

Impacts: (Consequences, Outcomes) Effects on natural and human systems. In this report, the term impacts is used primarily to refer

to the effects on natural and human systems of extreme weather and climate events and of climate change. Impacts generally refer

to effects on lives, livelihoods, health, ecosystems, economies, societies, cultures, services, and infrastructure due to the interaction of

climate changes or hazardous climate events occurring within a speciﬁc time period and the vulnerability of an exposed society or

system. Impacts are also referred to as consequences and outcomes. The impacts of climate change on geophysical systems, including

ﬂoods, droughts, and sea level rise, are a subset of impacts called physical impacts.

Hazard: The potential occurrence of a natural or human-induced physical event or trend or physical impact that may cause loss of life,

injury, or other health impacts, as well as damage and loss to property, infrastructure, livelihoods, service provision, ecosystems, and

environmental resources. In this report, the term hazard usually refers to climate-related physical events or trends or their physical impacts.

Stressors: Events and trends, often not climate-related, that have an important effect on the system exposed and can increase

vulnerability to climate-related risk.

Risk: The potential for consequences where something of value is at stake and where the outcome is uncertain, recognizing the

diversity of values. Risk is often represented as probability of occurrence of hazardous events or trends multiplied by the impacts if

these events or trends occur.

Risk = (Probability of Events or Trends) × Consequences

Risk results from the interaction of vulnerability, exposure, and hazard (see Figure 19-1). In this report, the term risk is used primarily

to refer to the risks of climate-change impacts.

Key vulnerability, key risk, key impact: A vulnerability, risk, or impact relevant to the deﬁnition and elaboration of “dangerous

anthropogenic interference (DAI) with the climate system,” in the terminology of United Nations Framework Convention on Climate

Change (UNFCCC) Article 2, meriting particular attention by policymakers in that context.

Key risks are potentially severe adverse consequences for humans and social-ecological systems resulting from the interaction of

climate-related hazards with vulnerabilities of societies and systems exposed. Risks are considered “key” due to high hazard or high

vulnerability of societies and systems exposed, or both.

Vulnerabilities are considered “key” if they have the potential to combine with hazardous events or trends to result in key risks.

Vulnerabilities that have little inﬂuence on climate-related risk, for instance, due to lack of exposure to hazards, would not be

considered key.

Key impacts are severe consequences for humans and social-ecological systems.

Continued next page

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Emergent Risks and Key Vulnerabilities Chapter 19

impacts and disasters, and on the other hand, when non-extreme events

may also result in extreme impacts and disasters.

19.1.3. New Developments in this Chapter

With these frameworks already established, and a long list of impacts and

key vulnerabilities enumerated and categorized in previous assessments,

the current chapter has three goals: first, to recognize and assess risks that

arise out of complex interactions involving climate and socio-ecological

systems, called emergent risks (see Boxes 19-2, CC-KR; Table 19-4). In many

cases, scientific literature sufficient to permit assessment of such risks

has become available largely since AR4. In this chapter, we consider

only those emergent risks that are relevant to interpreting Article 2 or

have the potential to become relevant (see criteria in Section 19.2.2)

as additional understanding accumulates. For example, since AR4,

sufficient literature has emerged to allow initial assessment of the

potential relationship between climate change and conflict. The second

goal is to reassess and reorganize the existing frameworks (based on

RFCs and KVs) for evaluating the literature pertinent to Article 2 of the

UNFCCC to address the deficiencies cited in Section 19.1.1, particularly

in light of the advances in SREX and the current report’s discussions of

vulnerability and human security (Chapters 12 and 13) and adaptation

(Chapters 14 to 17 and 20). From this perspective, the objective stated

in Article 2 may be viewed as aiming in part to ensure human security

in the face of climate change. Third, this chapter assesses recent literature

pertinent to additional frameworks for categorizing risk and vulnerability,

focusing on indirect impacts and interaction and concatenation of risk,

including geographic areas of compound risk (Section 19.3).

To clarify the relative roles of characteristics of the physical climate system,

such as increases in temperature, precipitation, or storm frequency, and

Box 19-2 (continued)

Extract from WGII AR4 Chapter 19:

Many impacts, vulnerabilities and risks merit particular attention by policy-makers due to characteristics that might make them ‘key’.

The identiﬁcation of potential key vulnerabilities is intended to provide guidance to decision-makers for identifying levels and rates of

climate change that may be associated with ‘dangerous anthropogenic interference’ (DAI) with the climate system, in the terminology

of United Nations Framework Convention on Climate Change (UNFCCC) Article 2 (see Box 19-1). Ultimately, the deﬁnition of DAI

cannot be based on scientiﬁc arguments alone, but involves other judgments informed by the state of scientiﬁc knowledge.

Emergent Risk: A risk that arises from the interaction of phenomena in a complex system, for example, the risk caused when

geographic shifts in human population in response to climate change lead to increased vulnerability and exposure of populations in

the receiving region. Many of the emergent risks discussed in this report have only recently been analyzed in the scientiﬁc literature

in sufﬁcient detail to permit assessment. In this chapter, the only emergent risks discussed are those that have the potential to become

key risks once sufﬁcient understanding accumulates.

Reasons for Concern: Elements of a classiﬁcation framework, ﬁrst developed in the IPCC Third Assessment Report, which aims to

facilitate judgments about what level of climate change may be “dangerous” (in the language of Article 2 of the UNFCCC) by

aggregating impacts, risks, and vulnerabilities.

Summary of Reasons for Concern (revised from WGII TAR Chapter 19; see also Sections 1.2.3, 18.6.4):

“Reasons for Concern” may aid readers in making their own determination about what is a “dangerous” climate change. Each Reason

for Concern is consistent with a paradigm that can be used by itself or in combination with other paradigms to help determine what

level of climate change is dangerous. The reasons for concern are the relations between global mean temperature increase and:

1. Risks to unique and threatened systems

2. Risks associated with extreme weather events

3. Risks associated with the distribution of impacts

4. Risks associated with global aggregate impacts

5. Risks associated with large-scale singular events

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Chapter 19 Emergent Risks and Key Vulnerabilities

haracteristics of the socioeconomic and biological systems with which

these interact (vulnerability and exposure) to produce risks of particular

consequences (the latter term used interchangeably here with “impacts”

and “outcomes”), we rely heavily on a concept used sparingly in the TAR

and AR4, key risks (see Box 19-2). Furthermore, we emphasize recent

literature pointing to the dynamic character of vulnerability and exposure

based on their intimate relationship to development.

Section 19.2 describes the framework used here for identifying key

vulnerabilities, key risks, and emergent risks. We consider a variety of

types of emergent risks, including in Section 19.3 those arising from

multiple interacting systems and stresses, and in Section 19.4, those

arising from indirect impacts, trans-boundary impacts, and impacts

occurring at a long distance from the location of the climate change

that causes them. One example that illustrates all of these properties

is the extent to which climate change impacts on agriculture, water

resources, and sea level affect human migration flows. These shifts entail

both risks of harm and potential benefits for the migrants, for the regions

where they originate, and for the destination regions (see Sections 12.4,

19.4.2.1). Associated risks include indirect impacts, like the effect of

land use changes on ecosystems occurring at the new locations of

settlement, which may be near the location of the original climate

impact or quite distant. Such distant, indirect effects would compound

the direct consequences of climate change at the locations receiving

the incoming migrants. In Section 19.5, we discuss other risks newly

assessed here, including those arising from ocean acidification. Section

19.6 assesses key risks and vulnerabilities in light of the criteria discussed

here (Section 19.2.2) and in the context of the RFCs, and Section 19.7

assesses response strategies aimed at avoiding key risks.

19.2. Framework for Identifying

Key Vulnerabilities, Key Risks,

and Emergent Risks

19.2.1. Risk and Vulnerability

Definitions and frameworks that systematize hazards, exposure,

vulnerability, risk, and adaptation in the context of climate change are

multiple, overlapping, and often contested (see, e.g., Burton et al., 1983;

Blaikie et al., 1994; Twigg, 2001; Turner et al., 2003a,b; UNISDR, 2004;

Schröter, 2005; Adger, 2006; Birkmann, 2006b; Füssel and Klein, 2006;

Thomalla et al., 2006; Tol and Yohe, 2006; Villagrán de León, 2006; IPCC,

2007a; Cutter and Finch, 2008; Cutter et al., 2008; ICSU-LAC, 2010a,b;

Cardona, 2011; DEFRA, 2012; IPCC, 2012a; Kienberger, 2012; Birkmann

et al., 2013a; Costa and Kropp, 2013). Today, key reports and most

authors differentiate among hazards, vulnerability, risk, and impacts (see,

e.g., Hutton et al., 2011; IPCC, 2012a; Birkmann et al., 2013a). The recent

literature underscores that risks from climate change are not solely ex-

ternally generated circumstances or changes in the climate system to

which societies respond, but rather the result of complex interactions

among societies or communities, ecosystems, and hazards arising from

climate change (Susman et al., 1983; Comfort et al., 1999; Birkmann et

al., 2011a, 2013a; UNISDR, 2011; IPCC, 2012a). The differentiation of

the various aspects of these interactions is an important improvement

since AR4 because it exhibits the social construction of risk through the

concept of vulnerability (IPCC, 2012a). This new framework, growing

ut of SREX, translates information more easily into a risk management

approach that facilitates policy making (de Sherbinin, 2013). The following

section advances this framework in the context of Article 2 of the UNFCCC.

We refer to the characteristics of climate change and its effects on

geophysical systems, such as floods, droughts, deglaciation, sea level

rise, increasing temperature, and frequency of heat waves, as hazards.

In contrast, vulnerability refers primarily to characteristics of human or

social-ecological systems exposed to hazardous climatic (droughts,

floods, etc.) or non-climatic events and trends (increasing temperature,

sea level rise) (UNDRO, 1980; Cardona, 1986, 1990; Liverman, 1990;

Cannon, 1994, 2006; Blaikie et al., 1996; UNISDR, 2004, 2009;

Birkmann, 2006a; Füssel and Klein, 2006; Thywissen, 2006; IPCC, 2012a).

Ecosystems or geographic areas can be classified as vulnerable, which

is of particular concern if human vulnerability increases as a result of

potential impairment of the related ecosystem services. The Millennium

Ecosystem Assessment (MEA), for example, identified ecosystem services

that affect the vulnerability of societies and communities, such as

provision of freshwater resources and air quality (Millennium Ecosystem

Assessment, 2005a,b). Examples in this chapter and other chapters in this

report include the vulnerability of warmwater coral reefs and respective

ecosystem services for coastal communities (see Table 19-4; Box CC-KR).

The new framework used here also underscores that the development

process of a society has significant implications for exposure, vulnerability,

and risk. Climate change is not a risk per se; rather climate changes and

related hazards interact with the evolving vulnerability and exposure

of systems and therewith determine the changing level of risk (see

Figure 19-1; Table 19-4). Identifying key vulnerabilities facilitates

estimating key risks when coupled with information about evolving

hazards associated with climate change. This approach provides the

basis for criteria developed in the following sections.

19.2.2. Criteria for Identifying

Key Vulnerabilities and Key Risks

Vulnerability is dynamic and context specific, determined by human

behavior and societal organization, which influences for example the

susceptibility of people (e.g., by marginalization) and their coping and

adaptive capacities to hazards (see IPCC, 2012a). In this regard coping

mainly refers to capacities that allow a system to protect itself in the

face of adverse consequences, while adaptation—by contrast—denotes

a longer term process that also involves adjustments in the system itself

and refers to learning, experimentation, and change (Yohe and Tol, 2002;

Pelling, 2010; Birkmann et al., 2013a). Perceptions and cognitive

constructs about risks and adaptation options as well as cultural contexts

influence adaptive capacities and thus vulnerability (Grothmann and

Patt, 2005; Rhomberg, 2009; Kuruppu and Liverman, 2011; see Section

19.6.1.4). SREX stressed that the consideration of multiple dimensions

(e.g., social, economic, environmental, institutional, cultural), as well as

different causal factors of vulnerability, can improve strategies to reduce

risks to climate change (see IPCC 2012c, p. 17; Cardona et al., 2012, pp.

17, 67–106).

Key vulnerability and key risk are defined in Box 19-2. Vulnerabilities

that have little influence on overall risk are not considered key. Similarly,

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Emergent Risks and Key Vulnerabilities Chapter 19

he magnitude or other characteristics of climate change-related

hazards, such as glacier melting, sea level rise, or heat waves, are not

by themselves adequate to determine key risks, as the consequences of

climate change also will be determined by the vulnerability of the

exposed society or social-ecological system. Key vulnerabilities and key

risks embody a normative component because different societies might

rank the various vulnerability and risk factors and actual or potential

types of loss and damage differently (see Schneider et al., 2007, p. 785;

Lavell et al., 2012, p. 45). Generally, vulnerability merits particular

attention when the survival of societies, communities, or ecosystems is

threatened (see UNISDR, 2011, 2013; Birkmann et al., 2011a). Climate

change will influence the nature of the climatic hazards people and

ecosystems are exposed to and also contribute to deterioration or

improvement of coping and adaptive capacities of those exposed to

these changes. Consequently, many studies (Wisner et al., 2004; Cardona,

2010; Birkmann et al., 2011a) focus with a priority on the vulnerability

of humans and societies as a central feature, rather than solely on the

level of climatic change and respective hazards.

19.2.2.1. Criteria for Identifying Key Vulnerabilities

We reorganize and further develop criteria for identifying vulnerabilities

as “key” used in AR4 based on the literature (Blaikie et al., 1994; Bohle,

2001; Turner et al., 2003a,b; Birkmann, 2006a, 2011a; Villagrán de León,

2006; Cutter et al., 2008; Cutter and Finch, 2008; ICSU-LAC, 2010a,b;

Cardona, 2011; UNISDR, 2011; IPCC, 2012a; Birkmann et al., 2013a)

and the differentiation of hazard, exposure, and vulnerability presented

here. The criteria in this and succeeding sections were used to identify

key vulnerabilities, key risks, and emergent risks in Sections 19.4 and

19.6.1-2, and in Table 19-4. Not all of the criteria need to be fulfilled to

characterize a vulnerability or risk as key but the characterization of a

phenomenon as a KV or key risk is usually supported by more than one

criterion.

The following five criteria are used to judge whether vulnerabilities are

key:

1) Exposure of a society, community, or social-ecological system to

climatic stressors. While exposure is distinct from vulnerability,

exposure is an important precondition for considering a specific

vulnerability as key. If a system is neither at present nor in the future

exposed to hazardous climatic trends or events, its vulnerability to

such hazards is not relevant in the current context. Exposure can

be assessed based on spatial and temporal dimensions.

2) Importance of the vulnerable system(s). Views on the importance of

different aspects of societies or ecosystems can vary across regions

and cultures (see Kienberger, 2012). However, the identification of

KVs is less subjective when it involves characteristics that are crucial

for the survival of societies or communities or social-ecological

systems exposed to climatic hazards. Defining key vulnerabilities in

the context of particular societal groups or ecosystem services also

takes into account the conditions that make these population

groups or ecosystems highly vulnerable, such as processes of social

marginalization or the degradation of ecosystems (Leichenko and

O’Brien, 2008; O’Brien et al., 2008; IPCC, 2012a).

3) Limited ability of societies, communities, or social-ecological systems

to cope with and to build adaptive capacities to reduce or limit the

dverse consequences of climate-related hazard. Coping and adaptive

capacities are part of the formula that determines vulnerability (see

IPCC, 2012a; Birkmann et al., 2013a). While coping describes actions

taken within existing constraints to protect the current system and

institutional settings, adaptation is a continuous process that

encompasses learning and change of the system exposed, including

changes of rule systems or modes of governance (Smithers and Smit,

1997; Pielke Jr., 1998; Frankhauser et al., 1999; Smit et al., 1999;

Kelly and Adger, 2000; Yohe and Tol, 2002; Adger et al., 2005; Smit

and Wandel, 2006; Pelling et al., 2008; Pelling, 2010; Tschakert and

Dietrich; 2010; IPCC, 2012a; Birkmann et al., 2013a; Garschagen,

2013). Severe limits of coping and adaptation provide criteria

for defining a vulnerability as key, as they are core factors that

increase vulnerability to climatic hazards (see, e.g., Warner et al.,

2012).

4) Persistence of vulnerable conditions and degree of irreversibility of

consequences. Vulnerabilities are considered key when they are

persistent and difficult to alter. This is particularly the case when the

susceptibility is high and coping and adaptive capacities are very

low as a result of conditions that are hard to change. Irreversible

degradation of ecosystems (e.g., warmwater coral reefs), chronic

poverty and marginalization, and insecure land tenure arrangements

are drivers of vulnerability that in combination with climatic hazards

determine risks that often persist over decades (see Box CC-KR), for

example, as observed in the Sahel Zone. In this way, communities

or social-ecological systems (e.g., coastal communities dependent

on fishing or mountain communities dependent on specific soil

conditions) may reach a tipping point (or critical threshold) that

would cause a partial or full collapse of the system, including

displacement (see Renaud et al., 2010; Section 19.4.2.1). Inability

to replace such a system or compensate for potential and actual

losses and damages (i.e., irreversibility) is a critical criterion for

determining what is “key.”

5) Presence of conditions that make societies highly susceptible to

cumulative stressors in complex and multiple-interacting systems.

Conditions that make communities or social-ecological systems

highly susceptible to the imposition of additional climatic hazards

or that impinge on their ability to cope and adapt, such as violent

conflicts (e.g., during drought disaster in Somalia (see Menkhaus,

2010)) are considered under this criterion. Also, the critical

dependence of societies on highly interdependent infrastructures

(e.g., energy/power supply, transport, and health care) (see Rinaldi

et al., 2001; Wang, S. et al., 2012; Atzl and Keller, 2013) leads to key

vulnerabilities regarding multiple-interacting systems where capacity

to cope or adapt to their failure is low (see Copeland, 2005; Reed

et al., 2010; Section 19.6.2.1; Table 19-4).

19.2.2.2. Criteria for Identifying Key Risks

Risks are considered “key” due to high hazard or high vulnerability

(“key vulnerability”) of societies and systems exposed, or both. Criteria

for determining key risks build on the criteria for key vulnerabilities, as

vulnerability is a component of risk. As such, risk is strongly determined

by coping and adaptive capacities. However, the criteria for identifying

key risks also take into account the magnitude, frequency, and intensity

of hazardous events and trends linked to climate change to which

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Chapter 19 Emergent Risks and Key Vulnerabilities

ulnerable systems are exposed. Accordingly, the following four

additional criteria are used to judge whether risks are key:

1) Magnitude. Risks are key if associated harmful consequences have

a large magnitude, determined by a variety of metrics including

human mortality and morbidity, economic loss, losses of cultural

importance, and distributional consequences (see Schneider et al.,

2007; IPCC, 2012a). Magnitude and frequency of the hazard as well

as socioeconomic factors that determine vulnerability and exposure

contribute.

2) Probability that significant risks will materialize and their timing.

Risks are considered key when there is a high probability that the

hazard due to climate change will occur under circumstances where

societies or social-ecological systems exposed are highly susceptible

and have very limited capacities to cope or adapt and consequently

potential consequences are severe. Both the timing of the hazard

and the dynamics of vulnerability and exposure contribute. Risks

that materialize in the near term may be evaluated differently than

risks that materialize in the distant future, as the time available for

building up adaptive capacities is different (Oppenheimer, 2005;

Schneider et al., 2007; see also Section 19.6.3.6).

3) Irreversibility and persistence of conditions that determine risks.

Persistence of risks refers to the fact that underlying drivers and

root causes of these risks, either socioeconomic (e.g., chronic

poverty; see Chapter 13) or physical, cannot be rapidly reduced.

The criteria for assessing key vulnerabilities include the persistence

of socioeconomic conditions contributing to vulnerability that also

apply here (Section 19.2.2.1, point 4). In addition, some hazards

are associated with the potential for persistent physical impacts,

such as loss of an ice sheet causing irreversible sea level rise or

release of methane (CH

) clathrates from the seabed.

4) Limited ability to reduce the magnitude and frequency or other

characteristics of hazardous climatic events and trends and the

vulnerability of societies and social-ecological systems exposed.

Criterion 3 pertaining to key vulnerabilities (Section 19.2.2.1)

discusses limited ability of societies to improve coping and adaptive

capacities in order to manage risk. This criterion also applies here.

In addition, risks are also considered to be key when societies

together have very limited prospects for reducing the magnitude,

frequency, or intensity of the associated climate hazards. For

example, risks that may be reduced or limited by greenhouse gas

(GHG) reductions that reduce the probability of the associated

hazard are less threatening than those for which the likelihood of

the hazard cannot be effectively altered (see also Section 19.7.1).

For example, risks that are already projected to be large during the

next few decades under a range of Representative Concentration

Pathways (RCPs) are much more difficult to influence by reducing

emissions than those projected to become large late in this century

(e.g., see discussion of risk from extreme heat in Section 19.6.3.3).

19.2.3. Criteria for Identifying Emergent Risks

A risk that arises from the interaction of phenomena in a complex system

is defined here as an emergent risk. For example, feedback processes

between climatic change, human interventions involving mitigation and

adaptation, and processes in natural systems can be classified as emergent

risks if they pose a threat to human security. Emergent risks could arise

rom unprecedented situations, such as the increasing urbanization of

low-lying coastal areas that are exposed to sea level rise or where new

pluvial flooding risk emerges due to urbanization of vulnerable areas

not historically populated. Some emergent risks have been identified

or discussed only recently in the scientific literature, and as a result our

ability to assess whether they are key risks is limited. In this chapter,

the only emergent risks discussed are those that have the potential to

become key risks once sufficient understanding accumulates.

19.2.4. Identifying Key and Emergent Risks

under Alternative Development Pathways

Key risks are determined by the interaction of climate-related

hazards with exposure and vulnerabilities of societies or ecosystems.

Development pathways describing possible trends in demographic,

economic, technological, environmental, social, and cultural conditions

(Hallegatte et al., 2011) will affect key risks because they influence both

the likelihood and nature of climate-related hazards, and the societal

and ecological conditions determining exposure and vulnerability.

Therefore some risks could be judged to be key under some development

pathways but not others. Emergent risks can depend on development

pathways as well, because whether or not they become key risks may

be contingent on future socioeconomic conditions.

The effect of development pathways on climate-related hazards occurs

through their effects on emissions and other radiative forcing factors

such as land use change (see WGI AR5 Chapter 12). Components of

development pathways such as economic growth, technical change,

and policy will influence the rates and spatial distributions of emissions

of GHGs and aerosols, and of land use change, and therefore influence

the magnitude, timing, and heterogeneity of hazards (see WGIII AR5

Chapter 5).

Development pathways will also influence the factors determining key

vulnerabilities of human and ecological systems, including exposure,

susceptibility, or sensitivity to impacts, and adaptive capacity (Yohe and

Tol, 2002; Füssel and Klein, 2006; Hallegatte et al., 2011; Birkmann et al.,

2013a; O’Neill et al., 2014). The magnitude of the aggregate exposure

and sensitivity of socio-ecological systems will depend on population

growth and spatial distribution, economic development patterns, and

social systems. The particular elements of the social-ecological system

that are most exposed and sensitive to climate hazards, and that are

considered most important, will depend on spatial development patterns

as well as on cultural preferences, attitudes toward nature/biodiversity,

and reliance on climate-sensitive resources or services, among other

factors (Adger, 2006; Füssel, 2009). The degree to which persistent or

difficult to reverse vulnerabilities are built into social systems, as well

as the degree of inequality in exposure and vulnerability across social

groups or regions, also depend on characteristics of development

pathways (Adger et al., 2009).

19.2.5. Assessing Key Vulnerabilities and Emergent Risks

The criteria above for assessing vulnerability and risk provide a sequence

of potential assessment steps. While the initial assessment phase would

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Emergent Risks and Key Vulnerabilities Chapter 19

xplore whether and how a society or social-ecological system is

exposed to climate-related hazards, the assessment would subsequently

focus on the predisposition of societies or ecosystems to be adversely

affected (vulnerability) and the potential occurrence of severe adverse

consequences for humans and social-ecological systems once the hazard

interacts with the vulnerability of societies and systems exposed. In

addition, the importance of the system at risk and the ability of a society

or system to cope and to adapt to these stressors would be assessed.

Finally, the application of the criteria would also require the assessment

of the irreversibility of the consequences and the persistence of vulnerable

conditions. Hence, the assessment criteria for risks focus on the internal

conditions of a person, a community (e.g., age structure, poverty), or a

social-ecological system and the contextual conditions that influence

their vulnerability (e.g., governance conditions and systems of norms),

in addition to the assessment of hazards, such as storm intensity, heat

waves, and sea level rise, which are directly influenced by climate

change. Examples of such KVs and key risks drawn from other chapters

of this assessment are provided in Section 19.6 and particularly in Table

19-4 and Box CC-KR.

19.3. Emergent Risk: Multiple Interacting

Systems and Stresses

19.3.1. Limitations of Previous Approaches

Imply Key Risks Overlooked

Interactions of climate change impacts on one sector with changes in

exposure and vulnerability, or with adaptation and mitigation actions

affecting the same or a different sector, are generally not included or

well integrated into projections of risk (Warren, 2011). However, their

consideration leads to the identification of a variety of emergent risks

that were not previously assessed or recognized. This chapter identifies

several such complex system interactions that increase vulnerability and

risk synergistically (high confidence; Section 19.3). There are a very large

number of potential interactions, and many important ones have not

yet been quantified, meaning that some key risks have been overlooked

(high confidence). In some cases, literature analyzing these risks is very

recent. The six interaction processes listed below, though not exclusive,

are systemic and may lead to further key vulnerabilities as well as a

larger number of less significant impacts. Several of these are discussed

in more detail in the following sections:

• Biodiversity loss induced by climate change that erodes ecosystem

services, in turn increasing vulnerability and exposure of human

systems dependent on those services (Section 19.3.2.1).

• Alterations in extreme weather events induced by climate change

that affect human systems and ecosystems, increasing vulnerability

and exposure to the effects of mean climate change. Most impacts

projections are based only on changes in mean climate (Rosenzweig

and Hillel, 2008; IPCC, 2012a, Box 3-1).

• The interaction between non-climate stressors such as those related

to land management, water management, air pollution (which has

drivers in common with climate change), and energy production

and climate change (Section 19.3.2.2). Heretofore, mainly climate

interactions with population/economic growth were assessed.

• Climate changes that increase human exposure and vulnerability

to disease (Section 19.3.2.3).

•

Locations where risks in different sectors are compounded because

impacts, hazards, vulnerability, and exposure interact non-additively

(Section 19.3.2.4).

• Mitigation or sectoral adaptation that has unintended consequences

for the functioning of another sector (Section 14.6).

9.3.2. Examples of Emergent Risks

19.3.2.1. Emergent Risks Arising from the Effects of Degradation

of Ecosystem Services by Climate Change

Biodiversity loss is linked to disruption of ecosystem structure, function,

and services (Díaz et al., 2006; Gaston and Fuller, 2008; Cardinale et al.,

2012; Maestre et al., 2012; Midgley, 2012). Terrestrial and freshwater

species face increased extinction risks under projected climate change

during and beyond the 21st century, especially as climate change interacts

with other pressures (high confidence; Section 4.3.2.5). A large number

of modelling studies project that species ranges decline in size as mean

climate changes (Section 4.3.2.5); for example, a global scale study of

50,000 species found that the range sizes of 57 ± 6% of widespread

and common plants and 34 ± 7% of widespread and common animals

are projected to decline by more than 50% by the 2080s if global

temperatures increase by 3.5°C relative to preindustrial times, when

allowing for species to disperse at observed rates to areas that become

newly climatically suitable (Warren et al., 2013a). AR4 (Fischlin et al.,

2007, p. 213) estimated that “Approximately 20 to 30% of plant and

animal species assessed so far (in an unbiased sample) are likely to be at

increasingly high risk of extinction as global mean temperatures exceed

a warming of 2 to 3°C above preindustrial levels (medium confidence).”

Evaluation of various lines of evidence including a range of modeling

approaches and, since AR4, new and/or improved techniques (e.g.,

multifactorial driven species distribution models, species specific

population dynamics, tree- and trait-based modeling (for an overview

see Bellard et al., 2012, Table 1; also Murray et al., 2011; Dullinger et

al., 2012; Staudinger et al., 2012; Foden et al., 2013) imply similar levels

of risk as in AR4 with some new estimates indicating higher fractions

of species at risk. However, there is low agreement on the completeness

of these lines of evidence for assigning specific numerical values

forfraction of species at risk (see Sections 4.3.2.5, 19.5.1).

These extinction risks and possible declines in species richness are

associated with change in mean climate, but ecosystems and species

are also expected to be affected by projected climate change-induced

increases in short-term extreme weather events and increased fire

frequency in some locations (see IPCC, 2012a; WGI AR5 Table SPM.1;

WGI AR5 Sections 6.4.8.1, 12.4.3, 12.4.5). Accordingly, despite the

recognition of additional uncertainties in numerical estimates since AR4

(Section 4.3.2.5), the evidence for risk to a substantial fraction of species

associated with increasing global mean temperature (GMT) is robust.

In both terrestrial and marine environments, the potential for the

disruption of ecosystem functionality as a result of climate change

translates into a key risk of large-scale loss of ecosystem services

(Mooney et al., 2009; Midgley, 2012; Table 19-4). At-risk services include

water purification by wetlands, removal and sequestration of carbon

dioxide (CO

) by forests, crop pollination by insects, coastal protection

1054

Chapter 19 Emergent Risks and Key Vulnerabilities

y mangroves and coral reefs, regulation of pests and disease, and

recycling of waste nutrients (Sections 4.3.4, 22.4.5.6, 27.3.2.1; Box 23-1;

Chivian and Bernstein, 2008). Biodiversity loss can lead to an increase

in the transmission of infectious diseases such as Lyme, schistosomiasis,

and hantavirus in humans, and West Nile virus in birds, creating a newly

identified dimension to the emergent risks resulting from biodiversity

loss (Keesing et al., 2010).

There are a number of examples of projected yield losses in the

agricultural sector due to increased prevalence of pest species under

climate change including Fusarium graminearum (a fungal disease of

wheat), the European corn borer, the Colorado beetle, bakanae disease

and leaf blights of rice, and Western corn root worm (Petzoldt and Seaman,

2006; Huang et al., 2010; Kocmánková et al., 2010; Chakraborty and

Newton, 2011; Magan et al., 2011; Aragón and Lobo, 2012); or declines

in pollinators (Rosenzweig and Hillel, 2008; Abrol, 2012; Bedford et al.,

2012; Giannini et al., 2012; Kuhlmann et al., 2012; see also Section 4.3.4).

Climate change impacts on pollinators places these valuable services

at risk, and affects animals that are dependent on the plants (see

Chapter 4). Although the impacts of CO

fertilization on plant-pathogen

systems is not well understood (Section 7.3.2.3), these processes operate

simultaneously with climate change’s direct effects on yields through

changing temperature, precipitation, and CO

concentrations, creating

an emergent risk. Climate change has caused, or is projected to cause,

range expansion in weeds that have the potential to become invasive

(Bradley et al., 2010; Clements and Ditommaso, 2011). These can damage

agriculture and threaten other species with extinction, with costs to

economies being extremely high (e.g., US$120 billion annually in the

USA; Pimentel et al., 2005; Crowl et al., 2008). Although there are also

examples of projected decreases in insect damage to crops, there is a

tendency for risk of insect damage to plants to increase with climate

change (Section 7.3.2.3). Any one of the above mechanisms could result

in harmful outcomes that act in synergy with existing climate change

impacts on agriculture. Hence, these various susceptibilities to loss of

ecosystem services comprise a KV and, in interaction with climate

change, imply a potential key risk that global scale yields of a number

of crops will be reduced by such interactions.

Severe decline of coral reefs (Section 19.3.2.4) would result in

widespread loss of income for many countries, for example, AU$5.4

billion to the Australian economy from tourism (Box CC-CR). More

generally, for many small island developing states (SIDS), increases in

vulnerability due to loss of such ecosystem services interact with

physical impacts of climate change such as sea level rise to create an

emergent risk (high confidence).

Various studies of ecosystem services, nationally or globally, illustrate

the very large values that are attributed to these services (Table 19-1).

Such costs are represented only very crudely in aggregate global models

of the economic impacts of climate change where “non-market impacts”

are estimated very broadly if at all (Section 19.6.3.5). These costs

contribute to the large magnitude of risks to human systems resulting

from loss of ecosystem services, which in some cases would be irreversible.

Hence the increase in vulnerability due to loss of ecosystem services

interacting with climate change hazards comprises a key risk (high

confidence). In some regions (e.g., South America) payment for ecosystem

services (PES) has been implemented to support landowners to maintain

he provision of services over time (Section 27.6.2; Table 27-7). Studies

on degraded ecosystems examine the cost of restoring ecosystem services.

Willingness to pay to restore degraded services along the Platte River (USA)

(Loomis et al., 2000) greatly exceeded estimated costs of restoration. A

meta-analysis of 89 studies looking at the restoration of ecosystem

services measured using 526 different metrics found that restoration

increased the amount of biodiversity and ecosystem services by 44 and

25% respectively, but restored services were still lower than in intact

ecosystems (Benayas et al., 2009). Restoration of damaged ecosystems

may be cost-effective, but only partially compensates for loss of services.

Concomitant stress from land use change adds to the extinction risk from

climate change, increasing the projected extinction rate (e.g., Şekercioğlu

et al., 2012) and contributing to the emergent risk of ecosystem service

loss (see also Chapter 4). A synthesis of empirical studies across the

globe reveals that ecosystem impacts due to land use change correlate

locally with current maximum temperature and recent precipitation

decline, indicating a potential for climate change to exacerbate the

impacts of land use change (Mantyka-Pringle et al., 2012).

Land clearing releases carbon to the atmosphere and removes carbon

sinks (WGI AR5 Section 6.3.2.2) such as old growth forests which would

otherwise accumulate carbon (Luyssaert et al., 2008). Studies that value

ecosystem services have tended to underestimate the importance of

carbon sinks in ecosystems, owing to a tendency to consider only the

carbon currently stored in the systems and not the fluxes (Anderson-

Teixeira and DeLucia, 2011) and overlooking other aspects such as

changes in albedo (e.g., Betts et al., 2012).

19.3.2.2. Emergent Risk Involving Non-Climate Stressors:

The Management of Water, Land, and Energy

Human management of water, land, and energy interacts with climate

change and its impacts, to profoundly affect risks to the amount of

Ecosystem service Region Value Currency Citation

Pollination of crops Globe 153 billion Euro Gallai et al.

(2009)

Pollination of crops and wild

plants

UK 430 million £ UK NEA

(2011)

Woodland cover increase from

6 to 12%

UK 680 million £ UK NEA

(2011)

ﬁ xation, O

release, nutrient

recycling, soil protection,

water holding capacity, and

environmental puriﬁ cation

Chinese

terrestrial

ecosystems

6.6 trillion Yuan RMB Shi et al.

(2012)

Climate regulation provided

by forests

U S A 1 – 6 b i l l i o n U S $

per year

Krieger

(2001)

Recreation provided by forests USA 1.3 – 110

billion

US$

per year

Krieger

(2001)

Biodiversity supported by

forests

USA 554 billion US$

per year

Krieger

(2001)

Coral reef services Australia 5.4 billion Au$ Section

19.3.2.1;

Box CC-CR

Table 19-1 | Examples of global and national ecosystem service valuation studies. This

table is not intended to be comprehensive. Furthermore, it encompasses studies based

on a wide range of methodologies.

1055

Emergent Risks and Key Vulnerabilities Chapter 19

arbon that can be stored in terrestrial ecosystems, the amount of

water available for use by humans and ecosystems, and the viability of

adaptation plans for cities or protected areas. Failure to manage land,

water, and energy in a synergistic fashion can exacerbate climate

change impacts globally (Searchinger et al., 2008; Wise et al., 2009;

Lotze-Campen et al., 2010; Warren et al., 2011) producing emergent

risks which are also potential key risks. For example, the use of water

by the energy sector, by thermo-electric power generation, hydropower,

and geothermal energy, or biofuel production, can contribute to water

stress in arid regions (Kelic et al., 2009; Pittock, 2011). Some energy

technologies (biofuels, hydropower, thermal power plants), transportation

fuels and modes, and food products (from irrigated crops, in particular

animal protein produced by feeding irrigated crops) require more water

than others (Box CC-WE; Sections 3.7.2, 7.3.2, 10.2, 10.3.5; McMahon

and Price, 2011; Macknick et al., 2012; Ackerman and Fisher, 2013). In

irrigated agriculture, climate, crop choice, and yields determine water

requirements per unit of produced crop, and in areas where water must

be pumped or treated, energy must be provided (Box CC-WE; Gerten et

al., 2011). Recent studies address the energy, water, and land “nexus”

to explore risks to the agricultural and energy sectors (Box CC-WE;

Tidwell et al., 2011; Skaggs et al., 2012; Smith et al., 2013).

Biofuels can potentially mitigate GHG emissions when used in place of

fossil fuels such as gasoline, diesel, and more carbon-intensive fuels

from tar sands and heavy oil (Cherubini et al., 2009). One simulation of

stringent mitigation (e.g., RCP2.6, which constrains radiative forcing to

2.6 W m

–2

and therefore limits global mean temperature increase to

2°C over preindustrial levels during the 21st century) shows an

increased reliance on biofuels (van Vuuren et al., 2011). However, due

to the potential negative consequences of its use as a mitigation

strategy, bioenergy development leads to several emergent risks, which

are summarized in Table 19-2. Systems that may be vulnerable to

bioenergy development are food systems (high confidence, due to

bioenergy feedstocks replacing food crops; see Table 19-2.iii; Section

19.4.1) and ecosystems (high confidence), where biofuel cropping can

directly or indirectly induce land use change, displacing terrestrial

ecosystems such as forests, which can otherwise also act as carbon

sinks (see Table 19-2.i).

While direct land use change (LUC) from impacts of biofuel development

(from crop substitution and/or biofuel feedstock crop expansion) are a

concern, indirect land use change (iLUC) has received more attention

in the literature—both due to the magnitude of its potential impact

(twice as great as direct LUC; Melillo et al., 2009a) and controversy over

the uncertainty in accurately quantifying it. iLUC connotes land use

change resulting from biofuel impacts on agricultural commodity

markets (Fargione et al., 2008; Searchinger et al., 2008). Reductions of

GHG emissions from biofuel production and use (compared to fossil

fuels) may be offset partly or entirely for decades or centuries from iLUC-

induced CO

emissions from deforestation and the draining of peatlands

(medium confidence; Bringezu et al., 2009; van Vuuren et al., 2010; IPCC,

2011, Chapter 2; Miettinen et al., 2012; Smith et al. 2013). In Brazil,

further biofuel expansion would be expected to impinge upon the

Cerrado, the Amazon, and the Atlantic rainforest—all three of which

have high levels of biodiversity (Table 19-2.v) and high levels of

endemism (Lapola et al., 2010). Another study of biofuel production in

Brazil (Barr et al., 2011) found that when pasture is accounted for, direct

xpansion into unexploited forest land is minor, that is, most of

additional cropland is predicted to come from conversion of pastureland.

However, unless the density of livestock operations is increased in

tandem, the latter can also lead to iLUC. To the extent that biofuel

feedstock crops are grown on areas that were previously fallow or

degraded, the iLUC effects might be minimized and CO

potentially

sequestered (Fargione et al., 2010; IPCC, 2011)—although the amount,

alternative uses, and potential productivity of so-called degraded lands

are still contested (Dauber et al., 2012). (For more information on the

effects of biofuel production on terrestrial ecosystems, see Section 4.4.4;

for more information on the effects of land acquisition for biofuel

production on the poor, see Section 13.3.1.4.)

Whether such land management dynamics confound or contribute to

mitigation depends on important interactions with global emissions

mitigation policies (Table 19-2.ii; Van Vuuren et al., 2011). A failure to

include land use change emissions within a carbon mitigation regime—

for example, by applying a carbon price to fossil fuel and industrial

emissions only—has been projected to lead to large-scale deforestation

of natural forests and conversion of many other natural ecosystems by

the end of the 21st century in 450 ppmv CO

-eq and 550 ppmv CO

-eq

scenarios (Melillo et al. 2009b; Wise et al., 2009). This dynamic is due

primarily to enhanced bioenergy production without a corresponding

incentive to limit the resulting land use change emissions. If, instead,

an equal carbon price is applied to terrestrial carbon (which, however,

presents monitoring difficulties) along with fossil and industrial carbon,

deforestation could slow down or even reverse.

That said, there are many equally compelling reasons for a country to

encourage biofuel production including a means to produce downward

pressure on oil prices, rural development, and reduced oil imports—all

of which could be prioritized over biofuels as a GHG mitigation strategy

depending on the country (Cherubini et al., 2009). Per-liter GHG

emissions from biofuels decrease as agriculture is further intensified

through row cropping, fertilizer and pesticide use, and irrigation, while

other per-liter environmental impacts such as eutrophication increase

(Burney et al., 2010; Grassini and Cassman, 2012). This creates an

implicit conflict between alternative development priorities. Second-

generation biofuels, such as those based on non-food crops (grasses,

algae, timber) and agricultural residues, are expected to offer reduced

emissions of GHGs and other air pollutants compared to most first-

generation biofuels. This is due primarily to their having a smaller adverse

interaction with food systems resulting in less LUC and iLUC (Plevin,

2009; Cherubini and Ulgiati, 2010; Fargione, 2010; Sander and Murthy,

2010). Further, bioelectricity and biogas both may be more effective at

mitigating GHG emissions than liquid biofuels (Campbell et al., 2009;

Power and Murphy, 2009).

Other emergent risks from bioenergy development are summarized in

Table 19-2. Nearly all of the risks presented here are driven by the

increased need for raw agricultural feedstocks. Competition for

cultivable lands, irrigation resources (Box CC-WE), and other inputs are

not unique to biofuel-related issues. The approximate doubling of

agricultural demand projected between 2005 and 2050 (Tilman et al.,

2011) similarly increases competition for land and water, and would be

expected to exacerbate GHG emissions from agriculture (see also WGI

AR5 Sections 6.4.3.2, 8.3.5).

1056

Chapter 19 Emergent Risks and Key Vulnerabilities

Projected changes in the hydrological cycle due to climate change

(WGI AR5 Section 12.4.5) combined with increasing water demand

leads to an emergent, potentially key risk of water stress exacerbated

by the reduction of groundwater which serves as “an historical buffer

against climate variability” (Green et al., 2011), and potentially further

exacerbated by existing governance constraints that can act as barriers

to reduce vulnerability. Climate change and increasing food demand

are expected to drive expansion of irrigated cropland (Wada et al.,

2013), increasing the demand for energy intensive extraction and

conveyance of (ground or desalinated sea) water for irrigation (see Box

CC-WE). If water is provided through groundwater extraction, pumping,

or construction and use of de-salinization plants, local energy demand

(and GHG emissions) will increase, although advanced irrigation systems

are available that minimize enhancement of emissions (Rothausen and

Conway, 2011).

A further potential key risk arises from increased water stress due to

unsustainable groundwater extraction, which is expected to increase

as an adaptation to climate change. Groundwater extraction is generally

increasing globally with particularly large extraction in India and China

(Wang, J. et al., 2012). The effects of climate change on groundwater

are varied with some areas expecting decreased recharge while

others are projected to experience increased recharge (Green et al.,

2011; Portmann et al., 2013). Where extraction rates increase or

recharge decreases, water tables will be depleted with potential key risks

to local ecosystems and human systems (such as agriculture, tourism,

and recreation), while water quality will decrease. One projection shows

insufficient water availability in Africa, Latin America, and the Caribbean

to satisfy both agricultural demands and ideal environmental flow

regulations for rivers by 2050, a situation that is exacerbated by climate

change (Strzepek and Boehlert, 2010).

19.3.2.3. Emergent Risks Involving Health Effects

Climate change will act through numerous direct and indirect pathways

to alter the prevalence and distribution of diseases that are climate and

weather sensitive. These effects will differ substantially depending on

baseline epidemiologic profiles, reflecting the level of development and

access to clean and plentiful water, food, and adequate sanitation and

health care resources. Furthermore, the impact of climate change will

differ within and between regions, depending on the adaptive capacity

of public health and medical services and key infrastructure that ensures

access to clean food and water.

A principal emergent global public health risk is malnutrition secondary

to ecological changes and disruptions in food production as a result

of changing rainfall patterns, increases in extreme temperatures (high

confidence; IPCC, 2012a; see also Section 11.6.1), and increased

atmospheric CO

(Taub et al., 2008; Burke and Lobell, 2010; Section

7.3.2.5). Modeling of the magnitude of the effect of climate change on

future under-nutrition in five regions in South Asia and sub-Saharan

Africa in 2050 (using Special Report on Emissions Scenarios (SRES) A2

emissions scenario) suggests an increase in moderate nutritional

stunting, an indicator linked to increased risk of death and poor health

(Black et al., 2008), of 1 to 29%, depending of the region assessed,

compared to a future without climate change, and a much greater

impact on severe stunting for particular regions, such as 23% for central

sub-Saharan Africa and 62% for south Asia (Lloyd et al., 2011). The

impact of climate-induced drought and precipitation changes in Mali

include the southward movement of drought-prone areas which would

result in a loss of critical agriculturally productive land by 2025 and

increase food insecurity (Jankowska et al., 2012).

In densely populated megacities, especially those with a pronounced

urban heat island effect, a principal emergent health risk results

from the synergistic interaction between increased exposure to extreme

heat and degraded air quality with the convergence of increasing

vulnerability of an aging population and a global shift to urbanization

(high confidence; Sections 8.2.3.5, 8.2.4.6, 11.5.3; Box CC-HS). These

trends will increase the risk of relatively higher mortality from exposure

to excessive heat (Knowlton et al., 2007; Kovats and Hajat, 2008; Luber

and McGeehin, 2008). The health risks of such interactions include

increased injuries and fatalities as a result of severe weather events

including heat waves (see Section 19.6.3.3); increased aeroallergen

production in urban areas leading to increases in allergic airway diseases

No. Issue Issue description Nature of emergent risk Reference

Direct and / or indirect land use

change

otential for enhancement of greenhouse gas

emissions

itigation beneﬁ t of biofuels reduced or

negated

elillo et al. (2009a,b); Wise et al. (2009);

Khanna et al. (2011)

i Policies targeting only fossil

carbon

iofuel cropping competes with agricultural

systems and ecosystems for land and water.

itigation beneﬁ t of policies reduced; harmful

interactions with other key systems

earchinger et al. (2008); Mellilo et al. (2009a,b);

Wise et al. (2009); Fargione et al. (2008)

ii Food / fuel competition for land Competition for land driving up food prices Emergent risk of food insecurity due to

mitigation-driven land use change

earchinger et al. (2008); Pimentel et al. (2009);

Hertel et al. (2010)

iv Biofuel production affects

ater resources.

Competition for water affects biodiversity and

ood cropping.

Emergent risk of biodiversity loss and food

nsecurity due to mitigation-driven water stress

Fargione (2010); Fingerman et al. (2010); Poudel

t al. (2012); Yang et al. (2012)

v Biofuel production affects

iodiversity.

Competition for land reduces natural forest and

iodiversity.

Emerging risk of biodiversity loss due to

itigation-driven land use change

Fizherbert et al. (2008); Koh et al. (2009); Lapola

t al. (2010); Fletcher et al. (2011)

vi Land conversion causes air

pollution.

Potential for increased production of

tropospheric ozone from palm / sugarcane-

nduced land use change

Emergent risk of greenhouse gas-mitigation-

driven plant and human health damage caused

y tropospheric ozone

Cançado et al. (2006); Hewitt et al. (2009)

vii Fertilizer application Potential for increased emissions of N

O Offsets some beneﬁ ts of other mitigation

easures

Donner and Kucharik (2008); Searchinger et al.

(

2008); Fargione (2010)

viii Invasive properties of biofuel

rops

Potential to become an invasive species Unintended consequences that damage

griculture and /or biodiversity

Raghu et al. (2006); DiTomaso et al. (2007);

arney and Ditomaso (2008)

Table 19-2 | Emergent risks related to biofuel production as a mitigation strategy.

1057

Emergent Risks and Key Vulnerabilities Chapter 19

(

see Section 19.5.3); and respiratory and cardiovascular morbidity and

mortality secondary to degraded air quality and ozone formation (see

Section 19.6.3.3). While the association between ambient air quality

and health is well established, there is an increasingly robust body of

evidence linking spikes in respiratory diseases to weather events and to

climate change. In New York City, for example, each single degree (Celsius)

increase in summertime surface temperature has been associated with

a 2.7–3.1% increase in same-day hospitalizations due to respiratory

diseases, and an increase of 1.4–3.6% in hospitalizations due to

cardiovascular diseases (Lin et al., 2009). Respiratory health outcomes

will be exacerbated by climate change through increased production and

exposure to ground-level ozone (particularly in urban areas), wildfire

smoke, and increased production of pollen (D’Amato et al., 2010).

19.3.2.4. Spatial Convergence of Multiple Impacts:

Areas of Compound Risk

In this chapter, we define an area of compound risk as a region where

climate change-induced impacts in one sector affects other sectors in

the same region, or a region where climate change impacts in different

sectors are compounded, resulting in extreme or high-risk consequences.

The frequent and ongoing spatial and temporal coincidence of impacts

in different sectors in the same region has consequences that are more

serious than simple summation of the sectoral impacts indicates

(medium confidence). Such synergistic processes are difficult to identify

through sectoral assessment and are apt to be overlooked in spite of

heir potential importance in considering key vulnerabilities and risks.

For example, a large flood in a rural area may damage crop fields

severely, causing food shortages (Stover and Vinck, 2008). The flood

may simultaneously cause a deterioration of hygiene in the region and

the spread of water-borne diseases (Schnitzler et al., 2007; Hashizume

et al., 2008; Kovats and Akhtar, 2008). The coincidence of disease and

malnutrition can thus create an area of compound risk for health

impacts, with the elderly and children most at risk.

As a systematic approach, identification of areas of compound risk could

be achieved by overlaying spatial data of impacts in multiple sectors,

but this cannot indicate synergistic influences and dynamic changes in

these influences quantitatively. For global analysis, certain types of

integrated assessment models that allow spatial analysis of climate

change impacts have been used to identify regions that are affected

disproportionately by climate change (MNP, 2006; Kainuma et al., 2007;

Warren et al., 2008; Füssel, 2010). Recent efforts attempt to collect and

archive spatial data on impact projections and facilitate their public use.

These have created overlays for identifying areas of compound risk with

Web-Geographic Information Systems (GIS) technology (Adaptation

Atlas; Resources for the Future, 2009). There are also efforts to

coordinate impacts assessments adopting identical future climatic

and/or socioeconomic scenarios at various spatial scales (Parry et al.,

2004; Piontek et al., 2014). Areas of compound risk identified by

overlaying spatial data of impacts in multiple sectors can be used as a

starting point for regional case studies on vulnerability and multifaceted

adaptation strategies (Piontek et al., 2014).

Frequently Asked Questions

FAQ 19.2 | How does climate change interact with and amplify preexisting risks?

There are two components of risk: the probability of adverse events occurring and the impact or consequences of

those events. Climate change increases the probability of several types of harmful events that societies and

ecosystems already face, as well as the associated risks. For example, people in many regions have long faced threats

associated with weather-related events such as extreme temperatures and heavy precipitation (which can trigger

ﬂooding). Climate change will increase the likelihood of these two types of extremes as well as others. Climate

change means that impacts already affecting coastal areas, such as erosion and loss of property in damaging storms,

will become more extensive due to sea level rise. In many areas, climate change increases the already high risks to

people living in poverty or to people suffering from food insecurity or inadequate water supplies. Finally, climate

and weather already pose risks for a wide range of economic sectors, including agriculture, ﬁsheries, and forestry:

climate change increases these risks for much of the world.

Climate change can amplify risks in many ways, including through indirect interactions with other risks. These are

often not considered in projections of climate change impacts. For example, hotter weather contributes to increased

amounts of ground level ozone (smog) in polluted areas, exacerbating an existing threat to human health, particularly

for the elderly and the very young and those already in poor health. Also, efforts to mitigate or adapt to climate

change can have negative as well as positive effects. For example, government policies encouraging expansion of

biofuel production from maize have recently contributed to higher food prices for many, increasing food insecurity

for populations already at risk, and threatening the livelihoods of those like the urban poor who are struggling

with the inherent risks of poverty. Increased tapping of water resources for crop irrigation in one region in response

to water shortages related to climate change can increase risks to adjacent areas that share those water resources.

Climate change impacts can also reverberate by damaging critical infrastructure such as power generation,

transportation, or health care systems.

1058

Chapter 19 Emergent Risks and Key Vulnerabilities

Canadian North: Risks to travel,

food security, and infrastructure

due to extreme weather events

(SREX Chapter 9, IPCC, 2012a)

Coastal southeast USA and Gulf

of Mexico: Risks of reef loss,

wetland loss, threats to coastal

infrastructure, and tourism from

sea level rise, storm surges, and

ﬂooding (Sections 26.4.3.2,

26.7)

Southern Europe: Risks to human

health, agriculture, energy

production, transport, tourism, labor

productivity, and built environment

affected by increased frequency and

intensity of heat waves

(Section 23.9)

Asian Arctic: Risk of coastal

erosion due to sea level rise,

changes in permafrost, and the

length of the ice-free season

(Section 24.4.3)

Small island states: Risks to

critical economic sectors such as

agriculture, ﬁsheries, and tourism

due to extreme weather events

and sea level rise (SREX Chapter

9, IPCC, 2012a)

Micronesia, Mariana Island,

Papua New Guinea: Risks to

coral reefs due to sea surface

temperature rise and ocean

acidiﬁcation (Section 19.3.2.4)

Arctic: Risk to human health and

well-being due to climate change

impacts such as injury from changes

in extreme weather and ice/snow

conditions, decreased access to local

foods, compromised freshwater

sources, permafrost and erosion

damage to infrastructure, loss of

traditional livelihood, and

relocation of communities

(Section 28.2.4)

Northern Mexico: Risks to

agriculture and water supplies, due

to projected increases in drought

compounded by salt water intrusion,

in an area with high levels of

poverty (Sections 26.3.2,

26.5.2, 26.5.3, 26.8.3)

São Paulo: Risk of health impacts

from outbreaks of water-borne

diseases due to change in water

quality and availability, rainfall

extremes, urban ﬂash ﬂoods,

landslides (Chapter 27,

Box 27-2)

Sub-Saharan Africa: Risks to crop

yields, ecosystems and food security

in an area with the highest levels of

urban poverty in the world (Sections

7.4.1, 19.5.1, 22.3)

Western Turkmenistan and

Uzbekistan: Risk of exacerbated

desertiﬁcation due to impacts of

droughts on cotton production and

irrigated water demands (Section

24.4.4.3)

Australia: Risks to community

structure of coral reefs due to sea

surface temperature increase and

ocean acidiﬁcation that are

expected to affect tourism

negatively (Sections 25.6.2,

25.7.5.1, Box CC-CR)

New York area: Risk to sanitation,

energy, transportation,

communication network, coastal

infrastructure, from sea level rise

and coastal storms (Section 8.2,

Table 8-6)

South, East, and Southeast

Asia: Risk of displacement due to

coastal ﬂooding, inundation, and

erosion (Section 5.4.3.1)

Dhaka: Risks to housing, food

security, and human health due to

extreme events like cyclones, pluvial

ﬂooding, and heat stress (Section

8.2)

Near-Equatorial Indo-Paciﬁc:

Risks to coral reefs due to sea

surface temperature rise and

ocean acidiﬁcation (Section

19.3.2.4)

Australia: Risks to montane

ecosystems from high temperature,

drying trends, and ﬁre risk (Section

25.6.1)

Mumbai: Risks to commerce and

livelihoods from sea level rise,

coastal erosion, pluvial ﬂooding

and storm surge (Sections 8.2, 8.3,

8.4)

Ecosystem Water

Food production

Human health Natural system Livelihood

Figure 19-2 | Some examples of areas of compound risk identiﬁed in this assessment. Symbols indicate one or two of the main sectors or systems subject to compound risk, but in each case additional sectors and systems are at risk.

1059

Emergent Risks and Key Vulnerabilities Chapter 19

eneral equilibrium economic models (see Chapter 10) may facilitate

quantitative evaluation of synergistic influences. An analysis of the EU

by the PESETA project (Projections of economic impacts of climate

change in sectors of Europe based on bottom-up analysis) showed sub-

regional welfare loss by considering impacts on agriculture, coastal

system, river floods, and tourism together in the Computable General

Equilibrium (CGE) model, which is designed to represent interrelationships

among economic activities of sectors. The result indicated the largest

percentage loss in southern Europe (Ciscar et al., 2011).

The following examples illustrate different types of areas of compound

risk where climate change impacts coincide and interact:

1) Cities in deltas, which are subject to sea level rise, storm surge, coastal

erosion, saline intrusion, and flooding. Extreme weather events can

also disrupt access to food supplies, enhancing malnutrition risk

(Ahmed et al., 2009; see also Section 19.3.2.3). Based on national

population projections, if contemporary rates of effective sea level

rise (a net rate, defined by the combination of eustatic sea level

rise and local contributions from fluvial sediment deposition and

subsidence and subsidence due to groundwater and hydrocarbon

extraction) continue through 2050, more than 6 million people would

be at risk of enhanced inundation and increased coastal erosion in

three megadeltas and 8.7 million in 40 deltas, absent measures to

adapt (Ericson et al., 2006). Examples of urbanized delta areas at

risk include, for example, those where Mumbai and Dhaka are

located (see Chapters 8, 24; Section 19.6.3.4; Table 19-4).

2) The Arctic, where indigenous people (Crowley, 2011) are projected

to be exposed to the disruption, and possible destruction of, their

hunting and food sharing culture (see Chapter 28). Risk arises from

a combination of sea ice loss and the concomitant local extinctions

of the animals dependent on the ice (Johannessen and Miles, 2011).

Thawing ground also disrupts land transportation, buildings, and

infrastructure while exposure of coastal settlements to storms also

increases due to loss of sea ice. Arctic ecosystems are broadly at

risk (Kittel et al., 2011).

3) Coral reefs, which are highly threatened due to the synergistic effects

ofsea surface temperature rise and perturbed ocean chemistry,

reducing calcification and also increasing sensitivity to other impacts

such as the loss of coral symbionts (Chapter 6). The importance of

reef sensitivity to climate change was recently highlighted in the

near-equatorial Indo Pacific, the area of greatest reef diversity

worldwide (Lough, 2012).A second highly diverse reef system at

risk for warming was identified around Micronesia, Mariana Island,

and Papua New Guinea (Meissner et al., 2012).

In Figure 19-2, these and other examples of areas of compound risk

identified in this assessment are indicated on a world map. The map

focuses on the key role that exposure plays in determining risk,

particularly compound risk, rather than vulnerabilities per se.

19.4. Emergent Risk: Indirect, Trans-boundary,

and Long-Distance Impacts

Climate change impacts can have consequences beyond the regions in

which they occur. Global trade systems transmit and mediate a variety

of impacts—the most prominent example of this is the global food

rade system. The competitive market forces which dominate trade do

not account for considerations of justice, and thus can incidentally

diminish or enhance inequality in the distribution of impacts (see Section

19.6.3.4). Where prices on food, land, and other resources increase,

vulnerability increases, ceteris paribus, for those most in need and least

able to pay (see Section 19.6.1.2 on differential vulnerability). In addition,

both mitigation and other adaptation responses have unintended

consequences beyond the locations in which they are implemented

(Oppenheimer, 2013). All of these mechanisms can create emergent

risks (high confidence).

19.4.1. Crop Production, Prices, and Risk

of Increased Food Insecurity

Recent literature indicates that climate trends have already influenced

the yield trends of important crops (e.g., Kucharik and Serbin, 2008; Tao

et al., 2008; Brisson et al., 2010; Lobell et al., 2011). Chapters 7 and 18

provide a detailed overview of these impacts, and have assessed with

medium confidence that the effects of climate trends on maize and

wheat yield trends have been negative in many regions over the past

several decades, and have been small for major rice and soybean

production areas (see Sections 7.2.1.1, 18.4.1.1.). For projected impacts,

“Without adaptation, local temperature increases in excess of about

1°C above preindustrial is projected to have negative effects on yields

for the major crops (wheat, rice, and maize) in both tropical and

temperate regions, although individual locations may benefit (medium

confidence)” (Section 7.4; Figures 7-4, 7-5, 7-7; Chapter 7 ES). Across

all studies projecting crop yield impacts (some of which include both

fertilization and adaptation, and some which account for only one

or neither of these), negative impacts on average yields become likely

from the 2030s (Figure 7-5). Median yield impacts of 0 to –2% per

decade are projected for the rest of the century (compared to yields

without climate change) (Figure 7-7), and after 2050 the risk of more

severe impacts increases (medium confidence) (Chapter 7 ES; Figure

7-5). Among the smaller number of studies that have projected global

yield and price impacts, negative net effects of climate change, CO

increases, and agronomic adaptation on global yields are about as likely

as not by 2050 and likely later in the 21st century (Section 7.4.4).

Climate impacts on crop production influence food prices directly and

through complex interactions with a variety of factors, including biofuel

crop production and mandates, as well as other domestic policies such

as crop export bans (Sections 7.1.2, 7.2.2, 7.4.4). If climate changes

reduce crop yields, international food prices and the number of food-

insecure people are expected to increase globally (limited evidence,

high agreement; Section 7.4.4). For example, global rice prices exhibit

sensitivity both to yield impacts from climate changes as well as the loss

of arable land to sea level rise (Chen et al., 2012). While the evidence

base of how climate change will affect future food consumption patterns

is limited (Section 7.3.3.2), there are large numbers of households that

would be especially vulnerable to a loss of food access if food prices

were to increase, for example, agricultural producers in low-income

countries who are net food buyers (Section 7.3.3.2; Table 7-1).

In addition to the direct impacts of climate change, biofuel production

in service of climate change mitigation may also affect food prices.

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Chapter 19 Emergent Risks and Key Vulnerabilities

ccurately tracking and quantifying the direct and indirect impacts of

biofuel production on the food system has become an intense area of

study since AR4. U.S. ethanol production (for which maize is the primary

feedstock) increased around 720% since 2000, with maize commodity

prices nearly tripling and harvested land growing by more than 10%,

mainly at the expense of soy (Wallander et al., 2011; EIA, 2013). Ethanol

recently consumed one-quarter of U.S. maize production, even after

accounting for feed by-products returned to the market (USDA, 2013).

However, isolating biofuels’ exact contribution to food system changes

from other factors such as extreme weather events, climate change,

changing diets, and increasing population have proven difficult (Zilberman

et al., 2011). Still, estimates of the supply and demand elasticity of basic

grain commodities lead to a prediction that the 2009 U.S. Renewable

Fuel standard could increase commodity prices of maize, wheat, rice,

and soybeans by roughly 20%, ceteris paribus, assuming one-third of

the calories used in ethanol production can be recycled as animal feed

(Roberts and Schlenker, 2013). More generally, there is high confidence

that pressure on land use for biofuels will further increase food prices

(see Table 19-2.iii).

In summary, through the global food trade system, climate change

impacts on agriculture can have consequences beyond the regions in

which those impacts are directly felt. Food access can be inhibited by

rising food price levels and volatility (Sections 7.3.3.1-2), as demonstrated

during the recent 2007–2008 price rise episode that resulted from the

combination of poor weather in certain world regions combined with a

demand for biofuel feedstocks, increased demand for grain-fed meat,

and historically low levels of food stocks (Abbot and Borot de Battisti,

2011; Adam and Ajakaiye, 2011; Figure 7-3). These episodes provide an

analog elucidating how reduced crop yields due to impacts of climate

variability and biofuel cropping work synergistically to create a risk of

increased food insecurity: hence this interaction of climate change and

mitigation actions with the food system via markets comprises an

emergent risk of the impacts of climate change acting at a distance,

affecting the food security of vulnerable households (Section 7.3.3.2).

19.4.2. Indirect, Trans-boundary, and Long-Distance

Impacts of Adaptation

Risk can also arise from unintended consequences of adaptation (see

Section 14.6), and this can act across distance, if for example, there is

migration of people or species from one region to another. Adaptation

responses in human systems can include land use change, which can

have both trans-boundary and long-distance effects, and changes in

water management, which often has downstream consequences.

19.4.2.1. Risks Associated with Human Migration and Displacement

Human migration is one of many possible adaptive strategies or

responses to climate change (Reuveny, 2007; Tacoli, 2009; Piguet, 2010;

McLeman, 2011), assessed in detail in Chapter 12 in the context of the

many other causes of migration. Displacement refers to situations where

choices are limited and movement is more or less compelled by land

loss due to sea level rise or extreme drought, for example (see Section

12.4). A number of studies have linked past climate variability to both

ocal and long-distance migration (see review by Lilleør and Van den

Broeck, 2011). In addition to yielding positive and negative outcomes

for the migrants, migration indirectly transmits consequences of climate

variability and change at one location to people and states in the

regions receiving migrants, sometimes at long distances. Consequences

for receiving regions, which can be assessed by a variety of metrics,

could be both positive and negative, as may also be the case for sending

regions (Foresight, 2011; McLeman, 2011; see Chapter 12). A rapidly

growing literature examines potential changes in migration patterns

due to future climate changes, but projections of specific positive or

negative outcomes are not available. Furthermore, recent literature

underscores risks previously ignored: risks arising from the lack of

mobility in face of a changing climate, and risks entailed by those

migrating into areas of direct climate-related risk, such as low-lying

coastal deltas (Foresight, 2011; see Section 12.4.1.2).

Climate change-induced sea level rise, in conjunction with storm surges

and flooding, creates a threat of temporary and eventually permanent

displacement from low-lying coastal areas, the latter particularly the

case for small island developing states (SIDS) and other small islands

(Pelling and Uitto 2001; see Chapter 12). The distance and permanence

of the displacement will depend on whether governments develop

strategies such as relocating people from highly vulnerable to less

vulnerable areas nearby, and conserving ecosystem services which

provide storm surge protection in addition to so-called “hardening”

including building sea walls and surge barriers (Perch-Nielsen, 2004;

Box CC-EA). Numbers of people at risk from coastal land loss have been

estimated on a regional basis (Ericson et al., 2006; Nicholls and Tol, 2006;

Nicholls et al., 2011) yet projections of resulting anticipatory migration

or permanent versus temporary displacement are not available.

Taken together, these studies indicate that climate change will bear

significant consequences for migration flows at particular times and

places, creating risks as well as benefits for migrants and for sending

and receiving regions and states (high confidence). Urbanization is a

pervasive aspect of recent migration which brings benefits but, in the

climate change context, also significant risks (see Sections 8.2.2.4,

19.2.3, 19.6.1-2, 19.6.3.3). While the literature projecting climate-driven

migration has grown recently (Section 12.4), there is as of yet insufficient

literature to permit assessment of projected region-specific consequences

of such migration. Nevertheless, the potential for negative outcomes from

migration in such complex, interactive situations is an emergent risk of

climate change, with the potential to become a key risk (Box CC-KR).

19.4.2.2. Risk of Conflict and Insecurity

Violent conflict between individuals or groups arises for a variety of

reasons (Section 12.5). Factors such as poverty and economic shocks

that are associated with a higher risk of violent conflict are themselves

sensitive to climate change and variability (high confidence; Sections

12.5.1, 12.5.3, 13.2). In this section, we focus on evidence for the

magnitude of a climate effect on violent conflict to assess its potential

to become a key risk.

The only meta-analysis of the literature (Hsiang et al., 2013), examining

60 quantitative empirical studies generally published since AR4,

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Emergent Risks and Key Vulnerabilities Chapter 19

mplicates climatic events as a contributing factor to the onset or

intensification of several types of personal violence, group conflict, and

social instability in contexts around the world, at temporal scales ranging

from a climatologically anomalous hour to an anomalous millennium

and at spatial scales ranging from the individual level (Vrij et al., 1994;

Ranson, 2012) to the communal level (Hidalgo et al., 2010; O’Loughlin

et al., 2012) to the national level (Burke et al., 2009; Dell et al., 2012) to

the global level (Hsiang et al., 2011). Nevertheless, some individual studies

have been unable to obtain evidence that violence has a statistically

significant association with climate (Buhaug, 2010; Theisen et al., 2011).

In detection and attribution of their impact on human conflict, there is

low confidence that climate change has an effect (Section 18.4.5) and

medium confidence that climate variability has an effect.

Evidence suggests that climatic events over a large range of time and

spatial scales contribute to the likelihood of violence through multiple

pathways discussed in Section 12.5 (Bernauer et al., 2012; Scheffran et

al., 2012; Hsiang and Burke, 2014). Results from modern contexts

(1950–2010) indicate that the frequency of violence between individuals

rises 2.3% and the frequency of intergroup conflict rises 13.2%for each

standard deviation change toward warmer temperatures (Hsiang et al.,

2013). Becauseannual temperatures around the world are expected to

rise 2 to 4 standard deviations (as measured over 1950–2008) above

temperatures in 2000by 2050 (A1B scenario)(Hsiang et al., 2013), there

is potential ceteris paribus for large relative changes to global patterns

of personal violence, group conflict, and social instability in the future.

Social, economic, technological, and political changes that might

exacerbate or mitigate this potential impact are discussed in Chapter

12. These changes may cause future populations to respond to their

climate differently than modern populations; however, the influence of

climate variability on rates of conflict is sufficiently large in magnitude

that such advances may need to be dramatic to offset the potential

influence of future climate changes.

The effect of climate change on conflict and insecurity has the potential

to become a key risk because factors such as poverty and economic

shocks that are associated with a higher risk of violent conflict are

themselves sensitive to climate change (medium confidence; Sections

12.5.1, 12.5.3, 13.2), and in numerous statistical studies the influence

of climate variability on human conflict is large in magnitude (medium

confidence).

19.4.2.3. Risks Associated with Species Range Shifts

One of the primary ways species adapt to climate change is by moving

to more climatically suitable areas (range shifts). These shifts will affect

ecosystem functioning, potentially posing risks to ecosystem services

(medium confidence; Millennium Ecosystem Assessment, 2005a,b;

Dossena et al., 2012), including those related to climate regulation and

carbon storage (Wardle et al., 2011). One example of a key impact is

the warming-driven expansion and intensification of Mountain pine

beetle (Dendroctonus ponderosae) outbreaks in North American pine

forests and its current and projected impacts on carbon regulation and

economies (Sections 26.4.2.1, 26.8.3). Risks also arise from projected

range shifts of important resource species (e.g., marine fishes; Sections

.5.2-3), as well as from potential introductions of diseases to people,

livestock, crops, and native species (see Sections 5.4.3.5, 7.3.2.3, 22.3.5,

23.4.2, 26.6.1.6, 28.2.3). Many newly arrived species prey on, outcompete,

or hybridize with existing biota (e.g., by becoming weeds or pests in

agricultural systems) (Section 4.2.4.6) . The ecological implications of

species reshuffling into novel, no-analog communities largely remain

unknown and pose additional risks that cannot yet be assessed (Root

and Schneider, 2006; Sections 6.5.3, 19.5.1, 21.4.3).

Current legal frameworks and conservation strategies face the challenge

of untangling desirable species range shifts from undesirable invasions

(Webber and Scott, 2012), and identifying circumstances when movement

should be facilitated versus inhibited. New agreements may be needed

recognizing climate change impacts on existing, new, or altered trans-

boundary migration (e.g., under the Convention on the Conservation of

Migratory Species of Wild Animals). As target species and ecosystems

move, protected area networks may become less effective, necessitating

re-evaluation and adaptation, including possible addition of sites,

particularly those important as either “refugia” or migration corridors

(Warren et al., 2013a; Sections 9.4.3.3, 24.4.2.5, 24.5.1). Assisted

colonization—moving individuals or populations from currently occupied

areas to locations with higher probability of future persistence—is

arising as a potential conservation tool for species unable to track changing

climates (Sections 4.4.2.4, 21.4.3). The value of these approaches,

however, is contested and implementation is very limited giving low

confidence that this would be an effective technique (Loss et al., 2011).

Ex situ collections (Section 4.4.2.5) have often been put forward as

fallback resources for conserving threatened species, yet the expense

and the relatively low representation of global species and genetic

diversity (Balmford et al., 2011; Conde et al., 2011) minimize the

effectiveness of this technique.

19.4.3. Indirect, Trans-boundary, and Long-Distance Im-

pacts of Mitigation Measures

Mitigation, too, can have unintended consequences beyond its

boundaries, which may affect natural systems and/or human systems.

If mitigation involves a form of land use change, then regional

implications can ensue in the same way as they can for adaptation

(see Section 14.7).

Mitigation can potentially reduce direct climate change impacts on

biodiversity (Warren et al., 2013a). However, impacts on biodiversity as

a result of land use change induced by biofuel production can offset

benefits associated with biofuels (see Boxes 4-1, 25-10; Sections 4.2.4.1,

4.4.4, 9.3.3.4, 19.3.2.2, 22.6.3, 24.6, 27.2.2.1). Climate change mitigation

through “clean energy” substitution can also have negative impacts on

biodiversity. However, attention to siting and monitoring can decrease

some negative ecological and socioeconomic impacts (medium confidence)

while maximizing positive ones (Section 4.4.4). For example, the U.S.

Government performed an intensive study of suitable sites for solar

power on public lands in the western USA. The end result opened

285,000 acres of public land for large-scale solar deployment while

blocking development on 78 million acres to protect “natural and

cultural” resources (US DOE and BLM, 2012). The construction of large

hydroelectric dams can affect both terrestrial and aquatic ecosystems

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Chapter 19 Emergent Risks and Key Vulnerabilities

along river systems (World Commission on Dams, 2000; see also

Sections 3.7.2.1, 4.4.4, 24.4.2.3, 24.9.1).

Mitigation strategies will have a range of effects on human systems.

Reforestation that properly mimics existing forest ecosystems in structure

and composition would potentially benefit human systems by stabilizing

micro-climatic variation (Canadell and Raupach, 2008) and allowing

benefits from the sustainable harvest of non-timber forest products for

food, medicine, and other marketable commodities (Guariguata et al.,

2010). However, there is a generally longer time frame and greater

expense involved in recreating a diverse forest system. Afforestation

creates a similar set of costs and benefits (Sections 3.7.2.1, 4.4.3,

17.2.7.1, 22.4.5.6-7; Box CC-WE). Mitigation strategies designed to

reduce dependence on carbon-intensive fuels present a very different

set of circumstances in relation to human systems. The development

of bioresources for energy use may have significant economic and

market effects potentially influencing food prices (see also Section

19.4.1). This would especially affect populations that already devote a

considerable portion of their household income to food (Hymans and

Shapiro, 1976).

19.5. Newly Assessed Risks

Newly assessed risks are those for which the evidence base in the sci-

entific literature has only recently become sufficient to allow for assess-

ment. Furthermore, these risks have at least the potential to become

key based on the criteria in Section 19.2.2. Several of the emergent risks

discussed in Sections 19.3 and 19.4, including those associated with

human migration (Section 19.4.2.1) and mitigation measures (Section

19.4. 3), can be considered newly assessed. Others are related to diverse

aspects of climate change, including the impacts of a large temperature

rise, ocean acidification and other direct consequences of CO2 increases,

and the potential impacts of geoengineering implemented as a climate

change response strategy.

19.5.1. Risks from Large Global Temperature Rise

>4°C above Preindustrial Levels

Most climate change impact studies focus on climate change scenarios

corresponding to global mean temperature rises of up to 3.5°C relative

to 1990 (slightly more than 4°C above preindustrial levels), with only a

few examples of assessments of temperature rise significantly above

that level (Parry et al., 2004; Hare, 2006; Warren et al., 2006; Easterling

et al., 2007; Fischlin et al., 2007). Recently the potential for larger

amounts of warming has received increasing attention and preliminary

assessment of impacts above that level of warming is possible for

agriculture, ecosystems, water, health, and large-scale singular events.

In this section, all temperature changes are global and relative to

preindustrial levels. Relevant climate scenarios include those based on

RCP8.5, which in 2081–2100 is projected to result in a temperature rise

of 4.3°C ± 0.7°C with temperature above 4°C as likely as not (WGI AR5

Section 12.4.1, Table 12.3), and some simulations using SRES A2 and

A1FI, which can reach 5.9°C and 6.9°C warming, respectively, by 2100

(WGI AR4 SPM). Literature that uses these scenarios but assumes low

climate sensitivity and hence less than 4°C of warming is excluded.

Relatively few studies have considered impacts on cropping systems for

scenarios where global mean temperatures increase by 4°C or more

(Section 7.4.1). Among these, one indicates substantial reductions in

yields in sub-Saharan Africa (Thornton et al., 2011) and another indicates

reversal of gains in yields and substantial reductions for Finland (Rötter

et al., 2011). Other studies at or below 4 °C anticipate yield losses,

particularly in tropical regions, even when taking agronomic adaptations

into account (Section 7.5.1.1.1). The possibility of compensation for

Frequently Asked Questions

FAQ 19.3 | How can climate change impacts on one region cause impacts

on other distant areas?

People and societies are interconnected in many ways. Changes in one area can have ripple effects around the

world through globally linked systems such as the economy. Globalized food trade means that changed crop

productivity as a result of extreme weather events or adverse climate trends in one area can shift food prices and

food availability for a given commodity worldwide. Depletion of ﬁsh stocks in one region due to ocean temperature

rise can cause impacts on the price of ﬁsh everywhere. Severe weather in one area that interferes with transportation

or shipping of raw or ﬁnished goods, such as reﬁned oil, can have wider economic impacts.

In addition to triggering impacts via globally linked systems like markets, climate change can alter the movement

of people, other species, and physical materials across the landscape, generating secondary impacts in places far

removed from where these particular direct impacts of climate change occur. For example, climate change can create

stresses in one area that prompt some human populations to migrate to adjacent or distant areas. Migration can

affect many aspects of the regions people leave, as well as many aspects of their destination points, including

income levels, land use, and the availability of natural resources, and the health and security of the affected

populations—these effects can be positive or negative. In addition to these indirect impacts, all regions experience

the direct impacts of climate change.

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Emergent Risks and Key Vulnerabilities Chapter 19

hese losses due to other responses of the food system to impacts on

production, such as land use change and adjustment of trade patterns,

cannot yet be adequately assessed for a world with GMT >4°C (Sections

19.4.1, 19.6.3.4).

Assessments of ecological impacts at and above 4°C warming imply a

high risk of extensive loss of biodiversity with concomitant loss of

ecosystem services (high confidence; Section 4.3.2.5; Table 4-3). AR4

estimated that 20 to 30% of species were likely at increasingly high risk

of extinction as global mean temperatures exceed a warming of 2°C to

3°C above preindustrial levels (medium confidence; Fischlin et al., 2007);

hence 4°C warming implies further increases to extinction risks for an

even larger fraction of species. However, there is low agreement on the

numerical assessment because as more realistic details have been

considered in models, it has been shown that extinction risks may be

either under- or overestimated when using the simpler models (Section

4.3.2.5), among other reasons due to the existence of microrefugias or

to delay in population decline leading to extinction debts (e.g., Dullinger

et al., 2012). Additional risks include biome shifts of 400 km (Gonzalez

et al., 2010), the disappearance of analogs of current climates in regions

of exceptional biodiversity in the Himalayas, Mesoamerica, East and

South Africa, the Philippines, and Indonesia (Beaumont et al., 2011),

and loss of more than half of the climatically determined geographic

ranges of 57 ± 6% of plants and 34 ± 7% of animals studied (Warren

et al., 2013a). Widespread coral reef mortality is expected at 4°C due

to the concomitant effects of warming and a projected decline of

ocean pH of 0.43 since preindustrial times (high confidence; WGI AR5

Figure TS.20; Section 5.4.2.4; Boxes CC-CR, CC-OA). The corresponding

concentration in such a scenario is about 900 ppm (WGI AR5

Figure 12.36) whereas the onset of large-scale dissolution of coral

reefs is projected if CO

concentrations reach 560 ppm (Sections 5.4.2.4,

26.4.3.2).

A number of studies project increases in water stress, flood, and drought

in a number of regions with >4°C warming, and decreases in others (Li

et al., 2009; Arnell, 2011; Fung et al., 2011; Dankers et al., 2013; Gerten

et al., 2013; Gosling and Arnell, 2013). For example, projections of the

proportion of global population exposed to water stress due to climate

change range from 5 to 50% (Gosling and Arnell, 2013) by 2100. The

proportion of cropland exposed to drought disaster (one or more

months with Palmer Drought Severity Index (PDSI) drought indicator

below –3) is projected to increase from 15% today to 44 ± 6% by 2100,

based on a range of projections including some that reach or exceed 4°C

global warming (Li et al., 2009). Concurrently irrigation water demand

in currently cultivated areas in the North Hemisphere is projected to

rise by 20% in the summer by 2100 under RCP8.5 due to climate change

alone (Wada et al. 2013), although this could be partly buffered by

decreasing evapotranspiration due to plant physiological responses to

increased atmospheric CO

(Konzmann et al., 2013; Box CC-VW). One

study (Portmann et al., 2013) projects that, by the 2080s under the

RCP8.5 scenario, 27 to 50% (mean 38%) of the global population would

experience at least a 10% decrease in groundwater resources, mostly

in drier areas with high population density where water stress is more

likely to occur. Concurrently, 20 to 45% of the population is projected

to experience at least a 10% increase in groundwater resources under

RCP8.5 in the 2080s. This is projected to occur mostly in wetter areas

or those with low population density where it is less probable that water

tress will be an issue. Another study projects that annual runoff will

fall by up to 75% across the Danube and Mississippi river basins, and

by up to 90% in the Amazon; while runoff is projected to either fall (by

up to 75%) or to rise (by up to 30%) in the Murray Darling, and increase

by up to 150% in the Ganges basin, and up to 80% in the Nile basin

(Fung et al., 2011) with 4°C warming. Both studies are based on an

ensemble of climate model projections. Under RCP8.5 in 2100, nine

global hydrological models driven by five global circulation models

project increases in flood frequency in over half of the land surface, and

decreases in roughly a third of the land surface (Dankers et al., 2013).

According to one study, even if the human population remained

constant in Europe, without adaptation, 3.5°C to 4.8°C global warming

by the 2080s would expose an additional 250,000 to 400,00 people to

river flooding, doubling economic damages since 1961 to 1990, and

expose an additional 850,000 to 5,550,000 to coastal flooding (Ciscar

et al., 2011), compared to 36,000 in 1995.

Under 4°C warming most of the world land area will be experiencing

4°C to 7°C higher temperatures than in the recent past, which means

that important tipping points for health impacts may be exceeded in many

areas of the world during this century, including coping mechanisms for

daily temperature/humidity, seasonally compromising normal human

activities, including growing food or working outdoors (Chapter 11 ES).

Exceedance of human physiological limits is projected in some areas for

a global warming of 7°C, and in most areas for global warming of 11°C

to 12°C (low confidence; Sherwood and Huber, 2010), a temperature

increase that is possible by 2300 (WGI AR5 Figure 12.5).

The risk of large-scale singular events such as ice sheet disintegration,

release from clathrates, and regime shifts in ecosystems (including

Amazon dieback), is higher with increased warming (and therefore

higher above 4°C than below it) although there is low confidence in

the temperature changes at which thresholds might exist for these

processes (Section 19.6.3.6; WGI AR5 Sections 12.5.5, 13.4). There are

also more gradual changes that become large with global temperature

rise of 4°C or more, such as decline in theAtlantic Meridional Overturning

Circulation (AMOC) and release of carbon from thawed permafrost

(CTP). The AMOC is considered very likely to weaken for such warming,

with best estimates of loss over the 21st century under RCP8.5 ranging

from 12 to 54% (WGI AR5 Sections 12.4.7.2, 12.5.5.2). The best

estimated range for CTP by 2100 is from 50 to 250 PgC for RCP8.5

(WGI AR5 Section 6.4.3.4) although there are large uncertainties. Larger

decreases in AMOC and increases in CTP are thus implied for a global

warming of above 4°C. Similarly, because a nearly ice-free Arctic Ocean

in September before mid-century is likely under RCP8.5, by which time

projected GMT rise amounts to 2.0 ± 0.4°C above the 1986–2005

baseline (medium confidence; WGI AR5 Section 12.4.6.1), the likelihood

is even higher for global warming of above 4°C. Regions of the boreal

forest could witness widespread forest dieback (low confidence),

putting at risk the boreal carbon sink (Section 4.3.3.1.1; WGI AR5

Section 12.5.5). Forest susceptibility to fire is projected to increase

substantially in many areas for the high emissions scenario (RCP 8.5;

Section 4.3.3.1; Figure 4-6) and hence larger changes are implied for

global warming above 4°C.

Based on the assessment in this section, we conclude that climate

change impacts at 4°C and above would be of greater magnitude and

1064

Chapter 19 Emergent Risks and Key Vulnerabilities

ore widespread than at lower levels of global temperature rise

(medium evidence, high agreement; high confidence), extending to higher

temperature levels previous findings that risks increase with increasing

global average temperature (WGII AR4 SPM.2; National Research

Council, 2011). Few studies yet consider the interactions between these

effects, which could create significant additional risks (Warren et al.,

2011; Sections 19.3-4).

19.5.2. Risks from Ocean Acidification

Ocean acidification is defined as “a reduction in pH of the ocean over

an extended period, typically decades or longer, caused primarily by the

uptake of carbon dioxide (CO

) from the atmosphere” (WGI AR5 Section

3.3.2, Box 3.2; Box CC-OA; see also Glossary). Acidification is a physical

and biogeochemical impact resulting from CO

emissions that poses risks

to marine ecosystems and the societies that depend on them. Research

on impacts on organisms, ecological responses, and consequences for

ecosystem services is relatively new; the potential for associated risks

to become key is magnified by the fact that acidification is a global

phenomenon and, without a decrease in atmospheric CO

concentration,

it is irreversible on century time scales.

It is virtually certain that ocean acidification is occurring now (WGI AR5

Section 3.9) and will continue to increase in magnitude as long as the

atmospheric CO

concentration increases (National Research Council,

2010). Risks to society and ecosystems result from a chain of consequences

eginning with direct effects on biogeochemical processes and organisms

and extending to indirect effects on ecosystems, ecosystem services,

and society (Figure 19-3). The degree of confidence in assessing risks

decreases along this chain owing to the complexity of interactions

across these scales and the relatively small number of studies available

for quantitative risk assessment.

Most studies have focused on the direct effects of ocean acidification on

marine organisms and biogeochemical processes. The overall effects on

organisms can be assessed with medium confidence (Section 6.3.2; Box

CC-OA), but the effects vary widely across processes (e.g., photosynthesis,

growth, calcification; Section 6.3.2) and across organisms and their life

stages (Section 6.3.2; Box CC-OA).

Far fewer studies have assessed the impacts on ecosystems (Section

6.3.2.5) and ecosystem services (Section 6.4.1), and most of these studies

have focused on the economic impacts on fisheries (Section 6.4.1.1).

For example, changes in overall availability and nutritional value of

desired mollusk species could affect economies (Narita et al., 2012) and

food availability (Section 6.4.1.1). In Table 19-3, we assess the risks to

ecosystem services through the impact of acidification on two key

marine processes, calcification in warmwater corals and nitrogen fixation,

using the criteria for key risks (Section 19.2.2.2).

Based on Table 19-3, the response of coral calcification to ocean

acidification and the resulting consequences for coral reefs constitute

a key risk to important ecosystem services (high confidence). The effect

of ocean acidification on marine N

-fixation could potentially become

a key risk, given that it could have potentially large consequences for

marine ecosystems, but currently there is limited evidence on the

likelihood of this risk materializing.

19.5.3. Risks from Carbon Dioxide Health Effects

There is increasing evidence that the impacts of elevated atmospheric

on plant species will affect health via two distinct pathways: the

increased production and allergenicity of pollen and allergenic compounds,

and the nutritional quality of key food crops. The evidence for these

impacts on plant species is increasingly robust and recent evidence in

the public health literature points to a medium to high confidence in the

potential for these risks to be sufficiently widespread in geographical

scope and large in magnitude of their impact on human health to be

considered key risks.

Climate change is expected to alter the spatial and temporal distribution

of several key allergen-producing plant species (Shea et al., 2008), and

increased atmospheric CO

concentration, independent of climate effects,

has been shown to stimulate pollen production (Rasmussen, 2002; Clot,

2003; Galán et al., 2005; Garcia-Mozo et al., 2006; Ladeau and Clark,

2006; Damialis et al., 2007; Frei and Gassner, 2008). A series of studies

(Ziska and Caulfield, 2000; Ziska et al., 2003; Ziska and Beggs, 2012)

found an association of elevated CO

concentrations and temperature

with faster growing and earlier flowering ragweed species (Ambrosia

artemisiifolia) along with greater production of ragweed pollen (Wayne

et al., 2002; Singer et al., 2005; Rogers et al., 2006), leading, in some

areas, to a measurable increase in hospital visits for allergic rhinitis

Low

Medium

Very highHigh

Conﬁdence in quantifying responses

Biogeochemical

processes

(WGI 3.8.2, Box 3.2;

WGII 6.3.2,

Figure 6-4, Box CC-OA)

Ecosystems

(WGII 6.3.2.5, Figure 6-12,

Box CC-OA)

Ocean

acidiﬁcation

(WGI 3.8.2, Box 3.2;

Box CC-OA)

Organisms

(WGII 6.3.2, Figure 6-10,

Box CC-OA)

Ecosystem

services

(WGII 6.4.1, 6.6.1,

Box CC-OA)

Increases in

atmospheric CO

(WGI 6.3, 6.4)

Society

(WGII 6.4.1, Box CC-OA)

Figure 19-3 | The pathways by which ocean acidiﬁcation affects marine processes,

organisms, ecosystems, and society. The conﬁdence in quantifying the impacts

decreases along the pathway.

1065

Emergent Risks and Key Vulnerabilities Chapter 19

(Breton et al., 2006). Experimental studies have shown that poison

ivy, another common allergenic species, responds to atmospheric CO

enrichment through increased photosynthesis, water use efficiency,

growth, and biomass. This stimulation, exceeding that of most other

woody species, also produces a more potent form of the primary allergenic

compound, urushiol (Mohan et al., 2006).

While climate change and variability are expected to affect crop

production (see Chapter 7), emerging evidence suggests an additional

stressor on the food system: the impact of elevated levels of CO

the nutritional quality of important foods. A prominent example of the

effect of elevated atmospheric CO

is the decrease in the nitrogen

concentration in vegetative plant parts as well as in seeds and grains

and, related to this, the decrease in the protein concentrations (Cotrufo

et al., 1998; Taub et al., 2008; Wieser et al., 2008). Experimental studies

of increasing CO

to 550 ppm demonstrated effects on crude protein,

starch, total and soluble beta-amylase, and single kernel hardiness,

leading to a reduction in crude protein by 4 to 13% in wheat and 11 to

13% in barley (Erbs et al., 2010). Other CO

enrichment studies have

shown changes in the composition of other macro- and micronutrients

(calcium, potassium, magnesium, iron, and zinc) and in concentrations

of other nutritionally important components such as vitamins and sugars

(Idso and Idso, 2001). Declining nutritional quality of important global

crops is a potential risk that would broadly affect rates of protein-energy

and micronutrient malnutrition in vulnerable populations. While there

is medium confidence that this risk has the potential to become key

when judged by its magnitude and other criteria (Sections 19.2.2.1-2)

there is currently insufficient information to assess under what ambient

concentrations this would occur.

19.5.4. Risks from Geoengineering

(Solar Radiation Management)

Geoengineering refers to a set of proposed methods and technologies that

aim to alter the climate system at a large scale to alleviate the impacts of

climate change (see Glossary; IPCC, 2012b; WGI AR5 Sections 6.5, 7.7;

WGIII AR5 Chapter 6). The main intended benefit of geoengineering would

be the reduction of climate change that would otherwise occur, and

the associated reduction in impacts (Shepherd et al., 2009). Here we

focus on risks, consistent with the goal of this chapter. Although

geoengineering is not a new idea (e.g., Rusin and Flit, 1960; Budyko

and Miller, 1974; Enarson and Morrow, 1998; and a long history of

geoengineering proposals as detailed by Fleming, 2010), it has received

increasing attention in the recent scientific literature.

Geoengineering has come to refer to both carbon dioxide removal (CDR;

discussed in detail in WGI AR5 Section 6.5, FAQ 7.3) and Solar Radiation

Management (SRM; Izrael et al., 2009; Lenton and Vaughan, 2009;

Shepherd et al., 2009; discussed in detail in WGI AR5 Section 7.7, FAQ

7.3). These distinct approaches to climate control raise very different

scientific (e.g., Shepherd et al., 2009), ethical (Morrow et al., 2009;

Preston, 2013), and governance (Lloyd and Oppenheimer, 2014) issues.

Many approaches to CDR are considered to more closely resemble

mitigation rather than other geoengineering methods (IPCC, 2012b).

In addition, CDR is thought to produce fewer risks than SRM if the

can be stored safely (Shepherd et al., 2009) and unintended

consequences for land use, the food system, and biodiversity can be

avoided (Section 19.4.3). For these reasons, in addition to the more

substantial recent literature on SRM’s potential impacts, we address only

SRM in this section. SRM is a potential key risk because it is associated

with impacts to society and ecosystems that could be large in magnitude

and widespread. Current knowledge on SRM is limited and our confidence

in the conclusions in this section is low.

Studies of impacts on society and ecosystems have been based on two

of the various SRM schemes that have been suggested: stratospheric

aerosols and marine cloud brightening. These approaches in theory could

produce large-scale cooling (Salter et al., 2008; Lenton and Vaughan,

2009), although it is not clear that it is even possible to produce a

stratospheric sulfate aerosol layer sufficiently optically thick to be

effective (Heckendorn et al., 2009; English et al., 2012). Observations

of volcanic eruptions, frequently used as an analog for SRM (Robock et

al., 2013), indicate that while stratospheric aerosols can reduce the

global average surface air temperature, they can also produce regional

drought (e.g., Oman et al., 2005, 2006; Trenberth and Dai, 2007), cause

ozone depletion (Solomon, 1999), and reduce electricity generation from

solar generators that use focused direct sunlight (Murphy, 2009).

Climate modeling studies show that the risk of ozone depletion depends

in detail on how much and when stratospheric aerosols would be

Criterion for key risk Coral calciﬁ cation Nitrogen ﬁ xation

. Magnitude of consequences for

ecosystem services

cosystem services include supporting habitats, provisioning of ﬁ sh,

regulating shoreline erosion, and tourism. Potential magnitude of

onsequences is medium to high (Box CC-CR).

cosystem services include nitrogen cycling, which supports ecosystem

structure and food chains (Hutchins et al., 2009). Potential magnitude of

onsequences has not been investigated.

2. Likelihood that risks will materialize

nd their timing

A reduction in coral calciﬁ cation rate and an increase in reef dissolution

ates are very likely (Section 6.1.2), so that reefs will progressively shift

oward net dissolution ( medium conﬁ dence; Section 5.4.2.4; Boxes CC-CR

nd CC-OA).

Both increases and decreases in nitrogen ﬁ xation have been observed in

arious N

ﬁ xing organisms (Section 6.3.2.2) but there is limited in situ

vidence and medium agreement on how N

ﬁ xation rates will change

n response to ocean acidiﬁ cation.

3. Irreversibility and persistence of

cean acidiﬁ cation impacts

Decreases in ocean pH will persist as long as atmospheric CO

levels

emain elevated (WGI AR5 Section 3.8.2). Reductions in coral calciﬁ cation

will persist unless corals can physiologically adapt to maintain

calciﬁ cation rates. Reversibility of impacts on ecosystem services of coral

eefs is unknown and depends on ecological factors such as hysteresis.

Decreases in ocean pH will persist as long as atmospheric CO

levels

emain elevated (WGI AR5 Section 3.8.2). Reversibility and persistence

of impacts on nitrogen ﬁ xation are unknown.

4. Limited ability to reduce the

agnitude and frequency or nature of

ocean acidiﬁ cation impacts

Reduction of ocean acidiﬁ cation will require global reductions in

tmospheric CO

Feasibility of mitigating ocean acidiﬁ cation at the local

scale is unknown.

Reduction of ocean acidiﬁ cation will require global reductions in

tmospheric CO

Table 19-3 | An assessment of the risks to ecosystem services posed by the impacts of ocean acidiﬁ cation on warm-water coral calciﬁ cation and nitrogen ﬁ xation, based on the

four criteria for key risks (Section 19.2.2.2).

1066

Chapter 19 Emergent Risks and Key Vulnerabilities

eleased in the stratosphere (Tilmes et al., 2008) and find that global

stratospheric SRM would produce uneven surface temperature responses

and reduced precipitation (Schmidt et al., 2012; Kravitz et al., 2013),

weaken the global hydrological cycle (Bala et al., 2008), and reduce

summer monsoon rainfall relative to current climate in Asia and Africa

(Robock et al., 2008). Hemispheric geoengineering would have even

larger effects (Haywood et al., 2013).

The net effect on crop productivity would depend on the specific

scenario and region (Pongratz et al., 2012). Use of SRM also poses a

risk of rapid climate change if it fails or is halted suddenly (WGI AR5

Section 7.7; Jones et al., 2013), which would have large negative

impacts on ecosystems (high confidence; Russell et al., 2012) and could

offset the benefits of SRM (Goes et al., 2011). There is also a risk of

“moral hazard”; if society thinks geoengineering will solve the global

warming problem, there may be less attention given to mitigation (e.g.,

Lin, 2013). In addition, without global agreements on how and how

much geoengineering to use, SRM presents a risk for international

conflict (Brzoska et al., 2012). Because the direct costs of stratospheric

SRM have been estimated to be in the tens of billions of U.S. dollars per

year (Robock et al., 2009; McClellan et al., 2012), it could be undertaken

by non-state actors or by small states acting on their own (Lloyd and

Oppenheimer, 2014), potentially contributing to global or regional

conflict (Robock, 2008a,b). Based on magnitude of consequences and

exposure of societies with limited ability to cope, geoengineering poses

a potential key risk.

19.6. Key Vulnerabilities, Key Risks,

and Reasons for Concern

In this section, we present key vulnerabilities, key risks, and emergent

risks that have been identified by many of the chapters of this report

based on the material assessed by each in light of criteria discussed in

Sections 19.2.2 and 19.2.3. We then discuss dynamic characteristics

of exposure, vulnerability, and risk, features that are influenced by

development pathways in the past, present, and future. Illustrative

examples of climate-related hazards, key vulnerabilities, key risks, and

emergent risks in Table 19-4 are representative, having been selected

from a larger number provided by the chapters of this report. The table

demonstrates how these four categories are related, as well as how

they differ, and how they interact with non-climate stressors. The table

also provides information on how key risks actually develop due to

changing climatic hazards and vulnerabilities. This knowledge is an

important prerequisite for effective adaptation and risk reduction

strategies that must address climate-related hazards, non-climatic

stressors, and various vulnerabilities that often interact in complex ways

and change over time.

19.6.1. Key Vulnerabilities

Several of the risks discussed in this and other chapters and noted in

Table 19-4 arise because vulnerable people must cope and adapt not

only to changing climate conditions, but also to multiple, interacting

stressors simultaneously (see Sections 19.3-4), which means that effective

adaptation strategies would address these complexities and relationships.

19.6.1.1. Dynamics of Exposure and Vulnerability

This subsection deals with the meaning and the importance of dynamics

of exposure and vulnerability, while Section 19.6.1.3 assesses recent

literature regarding observed trends of vulnerability mostly at a global or

regional scale. The literature provides increasing evidence that structures

and processes that determine vulnerability are dynamic and spatially

variable (IPCC, 2012a; Section 19.6.1.3). SREX states with high confidence

that vulnerability and exposure of communities or social-ecological

systems to climatic hazards related to extreme events are dynamic, thus

varying across temporal and spatial scales due to influences of and

changes in social, economic, demographic, cultural, environmental, and

governance factors (IPCC, 2012c, SPM.B).

Examples of such dynamics in exposure and vulnerability encompass,

for example, population dynamics, such as population growth or

changes in poverty (Table 19-4; Birkmann et al., 2013b) and increasing

exposure of people and settlements in low-lying coastal areas or flood

plains in Asia (see Nicholls and Small, 2002; Fuchs et al., 2011; IPCC,

2012a; Peduzzi et al., 2012). Also, demographic changes, such as aging

of societies, have a significant influence on people’s vulnerability to

heat stress (see Stafoggia et al., 2006; Gosling et al., 2009). Changes in

poverty or socioeconomic status, ethnic compositions, as well as age

structures had a significant influence on the outcome of past crises and

in addition were modified and reinforced through disasters triggered

by climate- and weather-related hazards. For the USA, for example,

Cutter and Finch (2008) found that social vulnerability to natural hazards

increased over time in some areas owing to changes in socioeconomic

status, ethnic composition, age, and density of population. Changes in

the strength of social networks (e.g., resulting in social isolation of

elderly) and physical abilities to cope with such extreme events modify

vulnerability (see, e.g., Khunwishit and Arlikatti, 2012).

In some cases human vulnerability might also change in different

phases of crises and disasters. Hence, the factors that might determine

vulnerability before a crisis or disaster (drought crises, flood disaster)

might differ from those that determine vulnerability thereafter (post-

disaster and recovery phases). Disaster response and reconstruction

processes and policies can modify exposure and vulnerability, for

example, of coastal communities (Birkmann and Fernando, 2008;

Birkmann, 2011). A comprehensive assessment of vulnerability would

account for these dynamics by evaluating long-distance impacts (e.g.,

resulting from migration or global influence of regional crop production

failures following floods) and multiple stressors (e.g., recovery policies

after disasters) that often influence dynamics and generate complex

crises and even emergent risks. Furthermore, SREX also underscores

that the increased intensity, frequency, and duration of some extreme

events as climate continues to change might make adaptation based

only on recent experience or the extrapolation of historical trends

largely ineffective (Lavell et al., 2012, pp. 44–47); hence understanding

the dynamics of vulnerability and its different facets is crucial.

19.6.1.2. Differential Vulnerability and Exposure

Wealth, education, ethnicity, religion, gender, age, class/caste, disability,

and health status exemplify and contribute to the differential exposure

1067

Emergent Risks and Key Vulnerabilities Chapter 19

nd vulnerability of individuals or societies to climate and non-climate-

related hazards (see IPCC, 2012a). Differential vulnerability is, for

example, revealed by the fact that people and communities that are

similarly exposed encounter different levels of harm, damage, and loss

as well as success of recovery (see Birkmann, 2013). The uneven effects

and uneven suffering of different population groups and particularly

marginalized groups is well documented in various studies (Bohle et al.,

1994; Kasperson and Kasperson, 2001; Birkmann, 2006a; Thomalla et

al., 2006; Sietz et al., 2011, 2012). Factors that determine and influence

these differential vulnerabilities to climate-related hazards include, for

example, ethnicity (Fothergill et al., 1999; Elliott and Pais, 2006; Cutter

and Finch, 2008), socioeconomic class, gender, and age (O’Keefe et al.,

1976; Sen, 1981; Peacock, 1997; Jabry, 2003; Wisner, 2006; Bartlett,

2008; Ray-Bennett, 2009), as well as migration experience (Cutter and

Finch, 2008) and homelessness (Wisner, 1998; IPCC, 2012a). Differential

vulnerabilities of specific populations can often be discerned at a particular

scale using quantitative or qualitative assessment methodologies

(Cardona, 2006, 2008; Birkmann et al., 2013b). Various population

groups are differentially exposed to and affected by hazards linked to

climate change in terms of both gradual changes in mean properties

and extreme events. For example, in urban areas, marginalized groups

(particularly as a result of gender or wealth status or ethnicity) often

settle along rivers or canals, where they are highly exposed to flood

hazards or potential sea level rise (see Table 19-4; e.g., Neal and Phillips,

1990; Enarson and Morrow, 1998; Neumayer and Plümper, 2007; Sietz

et al., 2012). Studies emphasize that vulnerability in terms of gender is

not determined through biology, but in most cases by social structures,

institutions, and rule systems; hence women and girls are often (not

always) more vulnerable because they are marginalized from decision

making or experience discrimination in development and reconstruction

efforts (Fordham, 1998; Houghton, 2009; Sultana, 2010; IPCC, 2012a).

19.6.1.3. Trends in Exposure and Vulnerability

Vulnerability and exposure of societies and social-ecological systems to

hazards linked to climate change are dynamic and depend on economic,

social, demographic, cultural, institutional, and governance factors (see

IPCC, 2012c, p. 7). The literature shows that there is a high confidence

that rapid and unsustainable urban development, international financial

pressures, increases in socioeconomic inequalities, failures in governance

(e.g., corruption), and environmental degradation are key trends that

modify vulnerability of societies, communities, and social-ecological

systems (Maskrey, 1993a,b, 1994, 1998; Mansilla, 1996; Cannon, 2006;

Birkmann, 2013; de Sherbinin, 2014) at different scales. Consequently,

many of the factors that reveal and determine differential vulnerability

change over time in terms of their spatial distribution. These dynamics

unfold in different places differently and therefore local or regional

specific strategies are needed that strengthen resilience (Garschagen

and Kraas, 2011; Holdschlag and Ratter, 2013) and reduce exposure and

vulnerability. For example, countries characterized by rapid urbanization

coupled with low economic performance and high social development

barriers face among the highest levels of climate change vulnerability.

However, urbanization in some areas can yield conditions conducive to

building up coping and adaptation capacities particularly when

urban socioeconomic development and risk management is properly

implemented (see Garschagen and Romero-Lankao, 2013). The following

ubsections outline observed trends in vulnerability according to

different thematic dimensions (e.g., socioeconomic, environmental,

institutional), within the constraint that relevant socioeconomic data

are limited.

19.6.1.3.1. Trends in socioeconomic vulnerability

Multi-dimensional poverty is an important factor determining vulnerability

of societies to climate change and extreme events (Section 13.1.4). For

example, risk due to droughts, particularly in sub-Saharan Africa, is

intimately linked to poverty and rural vulnerability (high confidence; see

World Bank, 2010; Birkmann et al., 2011b; UNISDR, 2011, p. 62; Welle

et al., 2012). In interpreting the following estimates, it should be borne

in mind that diverse concepts of poverty lead to different estimates but

that for some regions, e.g., sub-Saharan Africa, the trends are robust.

Recent evaluation of conditions in 119 countries found that at the

international level there had been a clear decrease in global poverty

over the previous 6 years (Chandy and Gertz, 2011). The number of poor

people globally fell, from more than 1.3 billion in 2005 to fewer than

900 million in 2010. This trend is expected to continue (e.g., Hughes et

al., 2009; Chandy and Gertz 2011). However, regional trends vary, as

do differences between emerging and least developed economies. As a

result, there is a growing climate-related risk in some regions associated

with chronic poverty. For example, in 2010, approximately 48.5% of the

population of the highly drought exposed region sub-Saharan Africa

still lives in poverty (poverty headcount ration at $1.25 per day; see

World Bank, 2012) and this area already has been defined as a global

risk hotspot (see Birkmann et al., 2011b; Welle et al., 2012). However,

various national-level poverty statistics provide little information about

the actual distribution of poverty, for example, between rural vs. urban

areas. Income distribution trends show significant increases in inequality

in some countries in Africa, and particularly in Asia, such as in China,

India, Indonesia, and Bangladesh (World Bank, 2012). In Asia and

Southeast Asia this trend overlaps with areas of compound climate risk

(Section 19.3.2.4) in terms of people currently exposed to floods and

tropical cyclones as well as sea level rise (Förster et al., 2011; IPCC,

2012a; Peduzzi et al., 2012). Assessing vulnerability (and risk) in these

countries requires in-depth analysis of trends and distribution patterns

of poverty, income disparities, and exposure of people to changing

climatic hazards.

New socioeconomic vulnerabilities are emerging in some countries, for

example, in developed countries, where the impoverishment of some

population groups is observed. For example, research underscores that

old age increases the risk of poverty in Greece, as the majority of people

working as farmers or in the private sector receive small pensions that

are below the poverty line (Karamessini, 2010, p. 279). These factors might

interact with limited physical means of elderly to cope with climatic

hazards, such as heat waves, and hence increase vulnerability.

Health status of individuals and population groups affects vulnerability

to climate change by limiting capacities to cope and adapt to climate

hazards (see Chapter 11). Although at a global scale the percentage of

people undernourished is decreasing (FAO, 2012) and this trend is

expected to continue (Hughes et al., 2009), the regional and national

differences are significant: during 2010–2012, 870 million people

1068

Chapter 19 Emergent Risks and Key Vulnerabilities

emained chronically undernourished (FAO, 2012). Particularly in certain

regions highly exposed to current and projected climate-related hazards,

the number of people undernourished has increased. In sub-Saharan

Africa, where exposure to drought is episodically high, the number of

undernourished increased by 64 million or about 38% during 2010–

2012 compared to 1990–1992 (Hughes et al., 2009; FAO, 2012, p. 10).

Moreover, at many locations, climate change is expected to reduce the

access to and the quality of natural resources that are important to

sustain rural and urban livelihoods as well as the capacities of states

to provide help to sustain livelihoods (Barnett and Adger, 2007; see also

Section 19.3.2.1). These multi-risk contexts require new approaches for

climate change adaptation.

While these trends mainly point to particularly large exposure and

vulnerability in developing countries, studies regarding extreme heat

vulnerability, for example, underscore that developed countries face

increasing challenges to adaptation as well. Heat waves are projected

to increase in duration, intensity, and extent (WGI AR5 Section 11.3.2).

Advanced age represents one of the most significant risk factors for

heat-related death (Bouchama and Knochel, 2002) because, in addition

to limited thermoregulatory and physiologic heat-adaptation capacities,

elderly have often reduced social contacts, and a higher prevalence of

chronic illness and poor health (Section 11.3.3; Khosla and Guntupalli,

1999; Klinenberg, 2002; O’Neill, 2003). The trend toward an aging

society, for example in Japan or Germany, therefore increases the

vulnerability of these societies to extreme heat stress.

19.6.1.3.2. Trends in environmental vulnerability

Societies depend on ecosystem services for their survival; however, these

ecosystem services and functions (see, e.g., Millenium Ecosystem

Assessment, 2005a,b) are vulnerable to climate change (see Cardona

et al., 2012, pp. 76–77; Table 19-4; Section 19.3.2.1). Various societies

and communities that rely heavily on the quality of ecosystem services,

such as rural populations dependent on rainfed agriculture where drying

is projected (see also Table 19-4), will experience increased risk from

climate change owing to its negative influence on ecosystem services

(high confidence; see Sections 4.3.4, 6.4.1).

Although no global overview is available, recent reports (UNDP, 2007;

IPCC, 2012a) underscore that a number of current environmental trends

threaten human well-being and thus increase human vulnerability

(UNEP, 2007). Many communities that have suffered large losses due

to extreme weather events—for example, coastal flooding—also

experienced earlier degradation of ecosystems providing protective

services. Recent global studies and local studies, such as for the U.S.

East Coast, underscore that intact ecosystems, such as marshes, can

have an important protective role against coastal hazards for example,

by wave attenuation (Shepard et al., 2011; Beck et al., 2013). Hence,

coastal degradation, such as destruction of coral reefs in Asia, is

increasing the exposure of communities to such hazards (Welle et al.,

2012). Moreover, the extinctions of species and the loss of biodiversity

pose a threat of diminution of genetic pools that otherwise buffer the

adaptive capacities of social-ecological systems dependent on these

services in the medium and long run (e.g., in terms of medicine and

agricultural production).

19.6.1.3.3. Trends in institutional vulnerability

Institutional vulnerability refers, among other issues, to the role of

governance. Governance is increasingly recognized as a key factor

that influences vulnerability and adaptive capacity of societies and

communities exposed to extreme events and gradual climate change

(Kahn, 2005; Nordås and Gleditsch, 2007; Welle et al., 2012). People in

countries or places that are facing severe failure of governance, such as

violent conflicts (e.g., Somalia, Afghanistan) are particularly vulnerable

to extreme events and climate change, as they are already exposed to

complex emergency situations and hence have limited capacities to

cope or undertake effective risk management (see Ahrens and Rudolph,

2006; Menkhaus, 2010). Countries classified as failed states are often

not able to guarantee their citizens basic standards of human security

and consequently do not provide adequate or any support in crises or

disaster situations for vulnerable people. The Failed State Index (Foreign

Policy Group, 2012; Fund for Peace, 2012) as well as the Corruption

Perception Index (Transparency International, 2012) are used to

characterize institutional vulnerability and governance failure. Trends

in the Failed State Index from 2006 to 2011 show that countries with

severe problems in the functioning of the state cannot easily shift or

change their situation (persistence of institutional vulnerability). Studies

at the global level also confirm that countries classified as failed states

and affected, for example, by violence are not able to effectively reduce

poverty compared to countries without violence (see World Bank, 2011).

Countries characterized in the literature as substantially failing in

governance or in some particular aspects of governance during some

period, such as Somalia and Ethiopia, Afghanistan, or Haiti have shown

in the past severe difficulties in dealing with extreme events or supporting

people that have to cope and adapt to severe droughts, storms, or floods

(see, e.g., Lautze et al., 2004; Ahrens and Rudolph, 2006; Menkhaus,

2010, pp. 320-341; Heine and Thompson, 2011; Khazai et al., 2011, pp.

30-31). In addition, it is probable that climate change will undermine

the capacity of some states to provide the services and support that

help people to sustain their livelihoods in a changing climate (Barnett

and Adger, 2007). Governance failure and violence as characteristics of

institutional vulnerability have significant influence on socioeconomic

and therefore climatic vulnerability. Furthermore, corruption has been

identified as an important factor that hinders effective adaptation

policies and crisis response strategies (Birkmann et al., 2011b; Welle et

al., 2012). At the local level, various aspects of governance in developing

and developed countries, particularly institutional capacities and self-

organization, as well as political and cultural factors, are critical for social

learning, innovations, and actions that can improve risk management

and adaptation to climate related risks and for empowering highly

vulnerable groups (IPCC, 2012a). Overall, unless governance improves

in countries with severe governance failure, risk will increase and human

security will be further undermined there as a result of climate change

and increased human vulnerability (high confidence; Lautze et al., 2004;

Ahrens and Rudolph, 2006; Barnett and Adger, 2007; Menkhaus, 2010).

19.6.1.4. Risk Perception

Risk perceptions influence the behavior of people in terms of risk

preparedness and adaptation to climate change (Burton et al., 1993;

van Sluis and van Aalst, 2006; IPCC, 2012a). Factors that shape risk

1069

Emergent Risks and Key Vulnerabilities Chapter 19

erceptions and therewith also influence actual and potential responses

(and thus exposure, vulnerability, and risk) include (1) interpretations

of the threat, including the understanding and knowledge of the root

cause of the problem; (2) exposure and personal experience with the

events and respective negative consequences, particularly recently (i.e.,

availability); (3) priorities of individuals; (4) environmental values and

value systems in general (see, e.g., O’Connor et al., 1999; Grothmann

and Patt, 2005; Weber, 2006; Kuruppu and Liverman, 2011). Furthermore,

the perceptions of risk and reactions to such risk and actual events are

also shaped by motivational processes (Weber, 2010). In this context

people will often ignore predictions of climate-related hazards if those

predictions fail to elicit emotional reactions. In contrast, if the event or

forecast of such an event elicits strong emotional feelings of fear, people

may overreact and panic (see Slovic et al., 1982; Slovic, 1993, 2010;

Weber, 2006). Public perceptions of risks are not determined solely by

the “objective” information, but rather are the product of the interaction

of such information with psychological, social, institutional, and cultural

processes and norms that are partly subjective, as demonstrated in

various crises in the context of extreme events (Kasperson et al., 1988;

Funabashi and Kitazawa, 2012). Risk perceptions particularly influence

and increase vulnerability in terms of false perceptions of security

(Cardona et al., 2012, p. 70). Finally, it is important to acknowledge that

everyday concerns and satisfaction of basic needs may prove more

pressing than attention and effort toward actions to address longer-

term risk factors, e.g., climate change (Maskrey, 1989, 2011; Wisner et

al., 2004). Rather, peoples’ worldviews and political ideologies guide

attention toward events that threaten their preferred social order

(Douglas and Wildavsky, 1982; Kahan, 2010).

19.6.2. Key Risks

19.6.2.1. Assessing Key Risks

Key risks arise from the interaction of climate-related hazards and key

vulnerabilities of societies, communities, or systems exposed (see Figure

19-1). Various chapters in this report have assessed key risks from their

particular perspectives. We asked each chapter writing team to provide

Chapter 19 authors with the key risks of highest concern to their chapter

based on the criteria for defining key risks and key vulnerabilities as

outlined in Section 19.2.2. A complete presentation of the key risks

provided is found in Box CC-KR (allowing for some condensation by

authors of Chapter 19 to avoid repetition).

The key risks provided by the chapters represent the issues most

pressing to each set of experts. The list is neither unique nor exhaustive:

other authors might express other preferences; however, this compilation

provides important insights about key risks and their determinants—

hazard, exposure, and vulnerability.

Chapter 19 authors further consolidated these key risks in Table 19-4

in order to produce the following list which, in their judgment (high

confidence), is representative of the range of key risks forwarded. Roman

numerals preceding each key risk correspond with entries in Table 19-4.

Each key risk is followed with a notation in brackets indicating the

Reason(s) for Concern (RFCs; see Section 19.6.3) with which it is aligned.

In addition, a representative set of lines of sight is provided from across

he chapters. Examples of these risks are also displayed geographically

in Figure 19-2:

i) Risk of death, injury, ill-health, or disrupted livelihoods in low-lying

coastal zones and small island developing states and other small

islands, due to storm surges, coastal flooding, and sea level rise.

These risks further increase in regions where the capacity to adapt

long-lived coastal infrastructure (e.g. electricity, water and sanitation

infrastructure) to local sea level rise beyond 1 m is limited. Urban

populations with substandard housing and inadequate insurance,

as well as marginalized rural populations with multidimensional

poverty and limited alternative livelihoods are particularly vulnerable

to these hazards. Inadequate local governmental attention to

disaster risk reduction and adaptation can further increase the

vulnerability of people and also the risk of adverse consequences

(WGI AR5 Sections 3.7, 13.5; WGI AR5 Table 13.5; Sections 5.4.3,

8.1.4, 8.2.3-4, 13.1.4, 13.2.2, 24.4-5, 26.7-8, 29.3.1, 30.3.1; Boxes

25-1, 25-7). [RFC 1, 2, 3, 4, and 5]

ii) Risk of severe ill-health and disrupted livelihoods for large urban

populations due to inland flooding in some regions. Particularly

vulnerable are marginalized and poverty-stricken residents in low-

income informal settlements as well as children, the elderly, and

the disabled that have limited means to cope and adapt. Risks are

increasing due to rapid and unsustainable urbanization especially

in areas where risk governance capacities are constrained or limited

attention is given to risk reduction and adaptation measures. Also,

overwhelmed, aging, poorly maintained, and inadequate infrastructure

(e.g., drainage infrastructure, electricity, water supply, etc.) can further

increase the risk of severe harm and threats to human security in the

case of inland flooding (WGI AR5 FAQ 12.2; Sections 3.2.7, 3.4.8,

8.2.3-4, 13.2.1, 25.10, 26.3, 26.7-8, 27.3.5; Box 25-8). [RFC 2 and 3]

iii) Systemic risks due to extreme weather events leading to breakdown

of infrastructure networks and critical services such as electricity,

water supply, and health and emergency services. Interdependency

of critical infrastructure increases the risk of systemic breakdowns

of vital services, for example, the risk of failure in systems dependent

on electric power (such as drainage systems reliant on electric

pumps) during extreme events. Health and emergency services rely

on critical infrastructure (e.g., telecommunication) that can be

disrupted during such power failures. For example, Hurricane Katrina

left 1220 electricity-dependent drinking water systems in Louisiana,

Mississippi, and Alabama inoperable for several weeks (Copeland,

2005). Overly hazard-specific management planning and infrastructure

design and/or low forecasting capabilities exacerbate such risks

(WGI AR5 Section 11.3.2; Sections 8.1.4, 8.2.4, 10.2-3, 12.6, 23.9,

25.10, 26.7-8). [RFC 2, 3, and 4]

iv) Risk of mortality and morbidity during periods of extreme heat,

particularly for vulnerable urban populations and those working

outdoors in urban or rural areas. Increasing frequency and intensity

of extreme heat (including exposure to the urban heat island

effect and air pollution) interacts with an inability of some local

organizations that provide health, emergency, and social services

to adapt to new risk levels for vulnerable groups. In addition, the

impact of heat stress on aging populations, such as during the

heat wave disaster in 2003 in Europe, shows how changing climatic

conditions interact with trends in population structure, health

conditions, and social isolation (characteristics of vulnerability) to

create key risks (WGI AR5 Section 11.3.2; Sections 8.2.3, 11.3,

1070

Chapter 19 Emergent Risks and Key Vulnerabilities

1.4.1, 13.2, 23.5.1, 24.4.6, 25.8.1, 26.6, 26.8; Box CC-HS). [RFC 2

and 3]

v) Risk of food insecurity and the breakdown of food systems linked to

warming, drought, flooding, and precipitation variability and extremes,

particularly for poorer populations in urban and rural settings. This

risk is a particular concern for farmers who are net food buyers and

people in low-income, agriculturally dependent economies that are

net food importers). Climatic hazards and the vulnerability of people

(see above) may exacerbate malnutrition, giving rise to a larger

burden of disease in these groups, especially among elderly and

emale-headed households having limited ability to cope. The

reversal of progress in reducing malnutrition is a potential outcome

(WGI AR5 Section 11.3.2; Sections 7.3-5, 11.3, 11.6.1, 13.2.1-2,

19.3.2, 19.4.1, 22.3.4, 24.4, 26.8, 27.3.4). [RFC 2, 3 and 4]

vi) Risk of loss of rural livelihoods and income due to insufficient

access to drinking and irrigation water and reduced agricultural

productivity, particularly for farmers and pastoralists with minimal

capital in semi-arid regions. Interaction of warming and drought

with lack of alternative sources of income, and the presence of

regional and national conditions that lead to a breakdown of food

No. Hazard Key vulnerabilities Key risks Emergent risks

Sea level rise, coastal ﬂ ooding

including storm surges

(

WGI AR5 Sections 3.7 and

13.5; WGI AR5 Table 13.5;

Sections 5.4.3, 8.1.4, 8.2.3,

8.2.4, 13.1.4, 13.2.2, 24.4,

24.5, 26.7, 26.8, 29.3.1, and

30.3.1; Boxes 25-1, 25-7)

igh exposure of people, economic activity, and

infrastructure in low-lying coastal zones, Small Island

eveloping States (SIDS), and other small islands

eath, injury, and disruption to

livelihoods, food supplies, and drinking

ater

Loss of common-pool resources, sense

of place and identity, especially among

indigenous populations in rural coastal

zones

nteraction of rapid urbanization, sea

level rise, increasing economic activity,

isappearance of natural resources,

and limits of insurance; burden of risk

management shifted from the state

to those at risk, leading to greater

inequality

Urban population unprotected due to substandard

housing and inadequate insurance. Marginalized

rural population with multidimensional poverty and

limited alternative livelihoods

Insufﬁ cient local governmental attention to disaster

risk reduction

ii Extreme precipitation and

inland ﬂ ooding

(WGI AR5 FAQ 12.2; Sections

3.2.7, 3.4.8, 8.2.3, 8.2.4,

13.2.1, 25.10, 26.3, 26.7, 26.8,

and 27.3.5; Box 25-8)

Large numbers of people exposed in urban areas

to ﬂ ood events, particularly in low-income informal

settlements

Death, injury, and disruption of human

security, especially among children,

elderly, and disabled persons

Interaction of increasing frequency of

intense precipitation, urbanization,

and limits of insurance; burden of risk

management shifted from the state

to those at risk, leading to greater

inequality, eroded assets due to

infrastructure damage, abandonment

of urban districts, and the creation of

high-risk / high-poverty spatial traps

Overwhelmed, aging, poorly maintained, and

inadequate urban drainage infrastructure and limited

ability to cope and adapt due to marginalization,

high poverty, and culturally imposed gender roles

Inadequate governmental attention to disaster risk

reduction

iii Novel hazards yielding

systemic risks

(WGI AR5 Section 11.3.2;

Sections 8.1.4, 8.2.4, 10.2,

10.3, 12.6, 23.9, 25.10, 26.7,

and 26.8)

Populations and infrastructure exposed and lacking

historical experience with these hazards

Failure of systems coupled to electric

power system, e.g., drainage systems

reliant on electric pumps or emergency

services reliant on telecommunications.

Collapse of health and emergency

services in extreme events

Interactions due to dependence on

coupled systems lead to magniﬁ cation

of impacts of extreme events. Reduced

social cohesion due to loss of faith in

management institutions undermines

preparation and capacity for response.

Overly hazard-speciﬁ c management planning

and infrastructure design, and/or low forecasting

capability

iv Increasing frequency and

intensity of extreme heat,

including urban heat island

effect

(WGI AR5 Section 11.3.2;

Sections 8.2.3, 11.3, 11.4.1,

13.2, 23.5.1, 24.4.6, 25.8.1,

26.6, and 26.8; Box CC-HS)

Increasing urban population of the elderly, the very

young, expectant mothers, and people with chronic

health problems in settlements subject to higher

temperatures

Increased mortality and morbidity

during periods of extreme heat

Interaction of changes in regional

temperature extremes, local heat

island, and air pollution, with

demographic shifts

Overloading of health and emergency

services. Higher mortality, morbidity,

and productivity loss among manual

workers in hot climates

Inability of local organizations that provide health,

emergency, and social services to adapt to new risk

levels for vulnerable groups

v Warming, drought, and

precipitation variability

(WGI AR5 Section 11.3.2;

Sections 7.3, 7.4, 7.5, 11.3,

11.6.1, 13.2.1, 13.2.2, 19.3.2,

19.4.1, 22.3.4, 24.4, 26.8, and

27.3.4)

Poorer populations in urban and rural settings are

susceptible to resulting food insecurity; includes

particularly farmers who are net food buyers and

people in low-income, agriculturally dependent

economies that are net food importers. Limited

ability to cope among the elderly and female-headed

households

Risk of harm and loss of life due

to reversal of progress in reducing

malnutrition

Interactions of climate changes,

population growth, reduced

productivity, biofuel crop cultivation,

and food prices with persistent

inequality and ongoing food insecurity

for the poor increase malnutrition,

giving rise to larger burden of disease.

Exhaustion of social networks reduces

coping capacity.

Table 19-4 | A selection of the hazards, key vulnerabilities, key risks, and emergent risks identiﬁ ed in various chapters in this report (Chapters 4, 6, 7, 8, 9, 11, 13, 19, 22,

23, 24, 25, 26, 27, 28, 29, and 30). Key risks are determined by hazards interacting with vulnerability and exposure of human systems and of ecosystems or species. The table

underscores the complexity of risks determined by various climate-related hazards, non-climatic stressors, and multifaceted vulnerabilities. The examples show that underlying

phenomena, such as poverty or insecure land tenure arrangements, unsustainable and rapid urbanization, other demographic changes, failure in governance and inadequate

governmental attention to risk reduction, and tolerance limits of species and ecosystems that often provide important services to vulnerable communities, generate the context in

which climatic change–related harm and loss can occur. The table illustrates that current global megatrends (e.g., urbanization and other demographic changes) in combination

and in speciﬁ c development contexts (e.g., in low-lying coastal zones) can generate new systemic risks in their interaction with climate hazards that exceed existing adaptation

and risk management capacities, particularly in highly vulnerable regions, such as dense urban areas of low-lying deltas. Roman numerals correspond with key risks listed in

Section 19.6.2.1. A representative set of lines of sight is provided from across WGI AR5 and WGII AR5. See Section 19.6.2.1 for a full description of the methods used to select

these entries.

Continued next page

1071

Emergent Risks and Key Vulnerabilities Chapter 19

distribution and storage systems, increase risk. Especially vulnerable

are those with limited ability to compensate for losses in water-

dependent farming and pastoral systems, as well as those subject

to conflict over natural resources. In addition, insufficient supply of

water due to droughts and institutional vulnerabilities (e.g., lack of

state capacities, conflicts) for both industry and urban populations

lacking running water, yielding severe economic impacts and other

harms (WGI AR5 Sections 12.4.1, 12.4.5; Sections 3.2.7, 3.4.8, 3.5.1,

8.2.3-4, 9.3.3, 9.3.5, 13.2.1, 19.3.2.2, 24.4). [RFC 2 and 3]

vii) Risk of loss of marine and coastal ecosystems, biodiversity, and the

ecosystem goods, functions, and services they provide for coastal

livelihoods, especially for fishing communities in the tropics and

the Arctic. These resources are especially at risk due to rising water

temperature and the increase of stratification and ocean acidification.

Loss of Arctic sea ice and degradation of coral reefs, as well as other

natural barriers, presents a high risk to ecosystem services where

many people are exposed to coastal hazards and also depend on

coastal resources for livelihoods, such as Alaska, the Philippines,

and Indonesia (WGI AR5 Section 11.3.3; Sections 5.4.2, 6.3.1-2,

7.4.2, 9.3.5, 22.3.2.3, 24.4, 25.6, 27.3.3, 28.2-3, 29.3.1, 30.5-6;

Boxes CC-OA, CC-CR). [RFC 1, 2, and 4]

viii) Risk of loss of terrestrial and inland water ecosystems, biodiversity,

and the ecosystem goods, functions, and services they provide for

livelihoods. Biodiversity and terrestrial ecosystem services are

important for rural and urban communities globally. These services

are at risk due to rising temperatures, changes in precipitation

patterns, and extreme weather events. Risks are high for communities

whose livelihoods depend on provisioning services. Human and

natural systems are susceptible to loss of provisioning services such

as food and fiber, regulating services such as water quality, fire, and

erosion, and cultural services such as aesthetic values and tourism

(WGI AR5 Section 11.3.2.5; Sections 4.3.4, 19.3.2.1, 22.4.5.6,

27.3.2.1; Boxes 23-1, CC-WE; FAQs 4.5, 4.7). [RFC 1, 3 and 4]

An important common characteristic of all key risks associated with

anthropogenic climate change is that they are determined by hazards due

to changing climatic conditions on the one hand and the vulnerability

of exposed societies, communities, and social-ecological systems, for

No. Hazard Key vulnerabilities Key risks Emergent risks

vi Drought

(

WGI AR5 Sections 12.4.1 and

12.4.5; Sections 3.2.7, 3.4.8,

.5.1, 8.2.3, 8.2.4, 9.3.3, 9.3.5,

13.2.1, 19.3.2.2, and 24.4)

Urban populations with inadequate water services.

xisting water shortages (and irregular supplies),

and constraints on increasing supplies

Insufﬁ cient water supply for people

nd industry, yielding severe harm and

economic impacts

Interaction of urbanization,

nfrastructure insufﬁ ciency,

groundwater depletion

Lack of capacity and resilience in water management

egimes including rural–urban linkages

Poorly endowed farmers in drylands or pastoralists

ith insufﬁ cient access to drinking and irrigation

water

Loss of agricultural productivity and/

r income of rural people. Destruction

of livelihoods, particularly for those

epending on water-intensive

agriculture. Risk of food insecurity

Interactions across human

ulnerabilities: deteriorating

livelihoods, poverty traps, heightened

ood insecurity, decreased land

productivity, rural outmigration,

and increase in new urban poor in

ow- and middle-income countries.

Potential tipping point in rain-fed

arming system and/or pastoralism

imited ability to compensate for losses in water-

dependent farming and pastoral systems, and

onﬂ ict over natural resources

ack of capacity and resilience in water

management regimes, inappropriate land policy,

and misperception and undermining of pastoral

ivelihoods

vii Rising ocean temperature,

cean acidiﬁ cation, and loss

f Arctic sea ice

(WGI AR5 Section 11.3.3;

ections 5.4.2, 6.3.1, 6.3.2,

7.4.2, 9.3.5, 22.3.2.3, 24.4,

5.6, 27.3.3, 28.2, 28.3,

29.3.1, 30.5, and 30.6; Boxes

CC-OA and CC-CR)

High susceptibility of warm water coral reefs

nd respective ecosystem services for coastal

ommunities; high susceptibility of polar systems,

e.g., to invasive species

Loss of coral cover, Arctic species, and

ssociated ecosystems with reduction

f biodiversity and potential losses

of important ecosystem services. Risk

f loss of endemic species, mixing

of ecosystem types, and increased

dominance of invasive organisms

Interactions of stressors such as

cidiﬁ cation and warming on

alcareous organisms enhancing risk

usceptibility of coastal and SIDS ﬁ shing

communities depending on these ecosystem services;

nd of Arctic settlements and culture

viii Rising land temperatures,

changes in precipitation

patterns, and frequency and

intensity of extreme heat

(WGI AR5 Section 11.3.2.5;

Sections 4.3.4, 19.3.2.1,

22.4.5.6, and 27.3.2.1; FAQs

4.5 and 4.7; Boxes 23-1 and

CC-WE)

Susceptibility of societies to loss of provisioning,

regulation, and cultural services from terrestrial

ecosystems

Reduction of biodiversity and potential

losses of important ecosystem services.

Risk of loss of endemic species, mixing

of ecosystem types, and increased

dominance of invasive organisms

Interaction of social-ecological

systems with loss of ecosystem

services upon which they depend

Susceptibility of human systems, agro-ecosystems,

and natural ecosystems to (1) loss of regulation of

pests and diseases, ﬁ re, landslide, erosion, ﬂ ooding,

avalanche, water quality, and local climate; (2) loss

of provision of food, livestock, ﬁ ber, bioenergy; (3)

loss of recreation, tourism, aesthetic and heritage

values, and biodiversity

Social

vulnerability

Economic

vulnerability

Environmental

vulnerability

Institutional

vulnerability

Exposure

Table 19-4 (continued)

1072

Chapter 19 Emergent Risks and Key Vulnerabilities

xample, in terms of livelihoods, infrastructure, ecosystem services and

management/governance systems on the other (see Table 19-4). The

compilation of key risks underscores that effective adaptation and risk

reduction measures would address all three components of risk (high

confidence).

9.6.2.2. The Role of Adaptation

and Alternative Development Pathways

As discussed in Section 19.2.4, the identification of key risks depends

in part on the underlying socioeconomic conditions assumed to occur

in the future, which can differ widely across alternative development

pathways. This section assesses literature that compares impacts across

development pathways, compares the contributions of anthropogenic

climate change and socioeconomic development (through changes in

vulnerability and exposure) to climate-related impacts, and examines the

potential for adaptation to reduce those impacts. Based on this assessment,

risks vary substantially across plausible alternative development pathways

and the relative importance of development and climate change varies

by sector, region, and time period, but in general both are important to

understanding possible outcomes (high confidence). In some cases, there

is substantial potential for adaptation to reduce risks, with development

pathways playing a critical role in determining challenges to adaptation,

including through their effects on ecosystems and ecosystem services

(Rothman et al., 2014).

Direct comparison of impacts across alternative development pathways

shows, for example, that socioeconomic conditions are an important

determinant of the impacts of climate change on food security, water

stress, and the consequences of extreme events and sea level rise. The

additional effect of climate change and CO

fertilization on the number

of people at risk from hunger by 2080 generally spans a range of ± 10 to

30 million across the four marker SRES scenarios, each of which assumes

different socioeconomic futures. However, in a scenario (A2) with high

population growth and slow economic growth, this effect becomes as

high as 120 to 170 million in some analyses (Schmidhuber and Tubiello,

2007). Similarly, the number of people exposed to water scarcity in a

global study is sensitive to population growth assumptions (Gosling

and Arnell, 2013), as are projected water resources in the Middle

East under an A1B climate change scenario (Chenoweth et al., 2011).

Assessments of the risks from river flooding depend on alternative future

population and land use assumptions (Bouwer et al., 2010; te Linde et

al., 2011), and sea level rise impacts depend on development pathways

through their effect on the exposure of both the population and

economic assets to coastal impacts, as well as on the capacity to invest

in protection (Anthoff et al., 2010).

The view that development pathways are an important determinant of

risk related to climate change impacts is further supported by two other

types of studies: those that examine the vulnerability of subgroups of

the current population, and those that compare the relative importance

of climate and socioeconomic changes to future impacts. The first type

finds that variation in current socioeconomic conditions explains some

of the variation in risks associated with climate and climate change,

supporting the idea that alternative development pathways, which

describe different patterns of change in these conditions over time,

hould influence the future risks of climate change. For example,

socioeconomic conditions have been found to be a key determinant of

risks to low-income households due to climate change effects on

agriculture (Ahmed et al., 2009; Hertel et al., 2010), to sub-populations

due to exposure to heterogeneous regional climate change (Diffenbaugh

et al., 2007), and to low-income coastal populations due to storm surges

(Dasgupta et al., 2009). Assessments of environmentally induced migration

have concluded that migration responses are mediated by a number

of social and governance characteristics that can vary widely across

societies (Warner, 2010; see Sections 12.4, 19.4.2.1).

The second type of study finds that, within a given projection of future

climate change and change in socioeconomic conditions, typically both

are important to determining risks. In fact, the effect of the physical

impacts of climate change on globally aggregated changes in food

consumption or risk of hunger have been found to be small relative to

changes in these metrics driven by socioeconomic development alone

(Schmidhuber and Tubiello, 2007; Nelson et al., 2010; Wiltshire et al.,

2013). Similarly, future population growth is found to be an equally

(Murray et al., 2012) or more (Fung et al., 2011; Schewe et al., 2013)

important determinant of globally aggregated water stress as the level

of climate change, and population growth, economic growth, and

urbanization are expected to largely drive potential future damages to

coastal cities due to flooding (high confidence; Section 5.4.3.1; Hallegatte

et al., 2013) and to be important determinants of damages from tropical

cyclones (Bouwer et al., 2007; Pielke Jr., 2007; Mendelsohn et al., 2012).

At the regional level, socioeconomic development has also been found

to be equally or more important than climate change to impacts in

Europe due to sea level rise, through coastal development (Hinkel et al.,

2010); heat stress, especially when acclimatization (Watkiss and Hunt,

2012) or aging (Lung et al., 2013) is taken into account; and flood

risks, through exposure due to land use and distributions of buildings

and infrastructure (Feyen et al., 2009; Bouwer et al., 2010). Climate

change was the dominant driver of flood risks in Europe when future

changes in the value of buildings and infrastructure at risk were

excluded from the analysis (te Linde et al., 2011; Lung et al., 2013) or

when biophysical impacts such as stream discharge, rather than its

consequences, were assessed (Ward et al., 2011).

Land use is another socioeconomic factor that can affect risks in addition

to climate change, but until recently few studies have addressed the

combined impacts of climate change and land use on ecosystems

(Warren et al., 2011). Studies including multiple drivers of extinction

find that although land use change remains the dominant driver out to

2100, climate change is the next most important driver (Sala et al., 2000;

Millenium Ecosystem Assessment, 2005b). A study of land bird extinction

risk found some sensitivity to four alternative land use scenarios,

but by 2100 risk was dominated by the climate change scenario

(Şekercioğlu, 2008). A study of European land use found that while

land use outcomes were more sensitive to the assumed socioeconomic

scenario, consequences for species depended more on the climate

scenario (Berry et al., 2006).

Explicit assessments of the potential for adaptation to reduce risks have

indicated that there is substantial scope for reducing impacts of several

types, but the capacity to undertake this adaptation is dependent on

underlying development pathways. Assessments of the impacts of

1073

Emergent Risks and Key Vulnerabilities Chapter 19

ea level rise, for example, show that if development pathways allow

for substantial investment of resources in adaptation through coastal

protection, as opposed to accommodation or abandonment strategies,

reducing impacts by investing in coastal protection can be an economically

rational response for large areas of coastline globally (Nicholls et al.,

2008a,b; Anthoff et al., 2010; Nicholls and Cazenave, 2010; Hallegatte

et al., 2013) and in Europe (Bosello et al., 2012b). For the specific case

of sea level rise impacts in Europe, adaptation in the form of increasing

dike heights and nourishing beaches, at a cost reaching about €3 billion

per year by 2100, was found to reduce the number of people affected by

coastal flooding in 2100 from hundreds of thousands to a few thousand

per year depending on the socioeconomic and sea level rise scenario

(A2 vs. B1), and total economic damages from about €17 billion to

about €2 billion per year (Hinkel et al., 2010). In contrast, in some areas

with higher current and anticipated future vulnerability such as low-

lying island states and parts of Africa and Asia, impacts are expected

to be greater and adaptation more difficult (Nicholls et al., 2011).

Similarly, the risk to food security in many regions could be reduced if

development pathways increase the capacity for policy and institutional

eform, although most impact studies have focused on agricultural

production and accounted for adaptation to a limited and varying degree

(Lobell et al., 2008; Nelson et al., 2009; Ziervogel and Ericksen, 2010).

A study of response options in sub-Saharan Africa identified some scope

for adapting to climate change associated with a global warming of

2°C above preindustrial levels (Thornton et al., 2011), given substantial

investment in institutions, infrastructure, and technology, but was

pessimistic about the prospects of adapting to a world with 4°C of

warming (Thornton et al., 2011; see also Section 19.7.1). Improved water

use efficiency and extension services have been identified as the highest

priority agricultural adaptation options available in Europe (Iglesias et

al., 2012), and a potentially large role for expanded desalination has

been identified for the Middle East (Chenoweth et al., 2011).

19.6.3. Updating Reasons for Concern

The RFCs are the relationship between global mean temperature increase

and five categories of impacts that were introduced in the IPCC TAR

(Smith et al., 2001) in order to facilitate interpretation of Article 2

°C

-0.61

(

C relative to 1986–2005)

Global mean temperature change

Risks to

some

Moderate

risk

Loss to

biodiversity

and global

economy.

Low risk

Risks to

many,

limited

adaptation

capacity

Severe and

widespread

impacts

Increased

risk for

most

regions

Risk of

extensive

biodiversity

loss and

global

economic

damages

High

risk

Increased

risk for some

regions

Positive and

negative

impacts

(RFC1) Risks

to unique

and

threatened

systems

(RFC2) Risks

associated

with extreme

weather

events

(RFC3) Risks

associated

with the

distribution

of impacts

(RFC4) Risks

associated

with global

aggregate

impacts

(RFC5) Risks

associated

with

large-scale

singular

events

Level of additional risk due to climate change

Undetectable

Very high

White Yellow Red Purple

White to

yellow

Yellow to

red

Red to

purple

Moderate

High

°C

(

C relative to 1850–1900, as an

approximation of preindustrial levels)

2003

–2012

Recent

(1986–2005)

Figure 19-4 | The dependence of risk associated with the Reasons for Concern (RFCs) on the level of climate change, updated from the Third Assessment Report and Smith et al.

(2009). The color shading indicates the additional risk due to climate change when a temperature level is reached and then sustained or exceeded. The shading of each ember

provides a qualitative indication of the increase in risk with temperature for each individual “reason.” Undetectable risk (white) indicates no associated impacts are detectable

and attributable to climate change. Moderate risk (yellow) indicates that associated impacts are both detectable and attributable to climate change with at least medium

conﬁdence, also accounting for the other speciﬁc criteria for key risks. High risk (red) indicates severe and widespread impacts, also accounting for the other speciﬁc criteria for

key risks. Purple, introduced in this assessment, shows that very high risk is indicated by all speciﬁc criteria for key risks. Comparison of the increase of risk across RFCs indicates

the relative sensitivity of RFCs to increases in GMT. In general, assessment of RFCs takes autonomous adaptation into account, as was done previously (Smith et al., 2001, 2009;

Schneider et al., 2007). In addition, this assessment took into account limits to adaptation in the case of RFC1, RFC3, and RFC5, independent of the development pathway. The

rate and timing of climate change and physical impacts, not illustrated explicitly in this diagram, were taken into account in assessing RFC1 and RFC5. Comments superimposed

on RFCs provide additional details that were factored into the assessment. The levels of risk illustrated reﬂect the judgments of Chapter 19 authors.

1074

Chapter 19 Emergent Risks and Key Vulnerabilities

(

Section 1.2.2; Box 19-2). In AR4, new literature related to the five RFCs

was assessed, leading in most cases to confirmation or strengthening of

the judgments about their relevance to defining dangerous anthropogenic

interference based on evidence that some impacts were already

apparent, higher likelihoods of some climate-related hazards, and

improved identification of currently vulnerable populations (Schneider

et al., 2007; Smith et al., 2009).

RFCs are related to the framework of key risks, climate-related hazards,

and vulnerabilities used in this chapter because each RFC is understood

to represent a broad category of key risks to society or ecosystems

associated with a specific type of hazard (extreme weather events,

large-scale singular events), system at risk (unique and threatened

systems), or characteristic of risk to social-ecological systems (global

aggregate impacts on those systems, distribution of impacts to those

systems). For example, the RFC for extreme weather events implies a

concern for risks to society and ecosystems posed by extreme events,

rather than a concern for extreme events per se. Accordingly, in this

chapter we have reworded the definition of RFCs to emphasize risk.

In this section we assess new literature related to each of the RFCs,

concluding that, compared to judgments presented in AR4 and in Smith

et al. (2009), levels of risk associated with extreme weather events and

distribution of impacts can be assessed with higher confidence and are

higher for large temperature rise than previously assessed; risks associated

ith global aggregate impacts are similar to AR4 and Smith et al (2009)

and confidence in the assessment unchanged; and risks to unique and

threatened systems and those associated with large-scale singular

events are higher above 2°C (compared to a 1986–2005 baseline) than

assessed previously. These judgments are illustrated in Figure 19-4, an

updated version of the “burning embers” diagram that describes how

the additional risk due to climate change for each RFC changes with

increasing GMT. We retain the color scheme employed in previous

versions of this figure (Smith et al., 2001, 2009) with some refinement.

White, yellow, and red indicate undetectable, moderate, and high

additional risk, respectively. Risk is low in the transition between white

and yellow, and substantial in the transition between yellow and red. We

add a new color (purple) indicating very high risk as elaborated below.

The following subsections assess risks for each RFC and locate transitions

between colors using the criteria for key risks as a guide (Section 19.2.2.2).

The transition from white to yellow is partly defined by the GMT at

which there is at least medium confidence that impacts associated with

a given risk are both detectable and attributable to climate change,

while also accounting for the magnitude of the risk. We draw on Section

18.6.4 to inform the placement of this transition relative to recent GMT.

The transition from yellow to red is defined by increasing magnitude

(including pervasiveness) or likelihood of impacts, with high risk (red

color) defined as risk of severe and widespread impacts that is judged

to be high on one or more criteria for assessing key risks (Section

19.2.2.2). Purple, introduced in this assessment, shows that very high

risk is indicated by all specific criteria for key risks, including limited

ability to adapt. As was true in the TAR and Smith et al. (2009), transitions

are fuzzy owing to uncertainties in a variety of factors determining the

relation between GMT and risk, including the rate of climate change, the

time at which the temperature is reached, and the extent and agreement

of the evidence base in the literature.

We also clarify the concept of RFCs: because risks depend not only on

physical impacts of climate change but also on exposure and vulnerability

of societies and ecosystems to those impacts, RFCs as a reflection of

those risks depend on both factors as well (see also Section 19.1).

19.6.3.1. Variations in RFCs across Socioeconomic Pathways

The determination of key risks as reflected in the RFCs has not previously

been distinguished across alternative development pathways. In the

TAR and AR4, RFCs took only autonomous adaptation into account

(Smith et al., 2001; Schneider et al., 2007; WGII AR4 Chapter 19).

However, the RFCs represent risks that are determined by both climate-

related hazards and the vulnerability and exposure of social and

ecological systems to climate change stressors. Figure 19-5 illustrates

this dependence on vulnerability and exposure in a modified version of

Low HighMedium

Exposure and vulnerability

Burning

Ember

A2,

2050

A2, 2100

B1, 2100

Figure 19-5 | Illustration of the dependence of risk associated with a Reason for

Concern (RFC) on the level of climate change and exposure and vulnerability (E&V) of

society. This ﬁgure is schematic; the degree of risk associated with particular levels of

climate change or E&V has neither been based on a literature assessment nor

associated with a particular RFC (the “burning ember” in the ﬁgure refers generically

to any of the embers in Figure 19-4). The E&V axis is relative rather than absolute.

“Medium” E&V indicates a future development path in which E&V changes over time

are driven by moderate trends in socioeconomic conditions. “Low” and “High” E&V

indicate futures that are substantially more optimistic or pessimistic, respectively,

regarding exposure and vulnerability. Judgments made in other burning ember

diagrams of the RFCs (Smith et al., 2001, 2009) including Figure 19-4, which do not

explicitly take changes in E&V into account, are consistent with Medium future E&V.

Arrows and dots illustrate the use of Special Report on Emission Scenarios

(SRES)-based literature to locate particular impact or risk assessments on the ﬁgure

according to the evolution of climate and socioeconomic conditions over time. This

ﬁgure does not explicitly address issues related to the rates of climate change or when

impacts might be realized.

Level of additional risk due to climate change

Undetectable

Very high

White Yellow

Red

Purple

White to

yellow

Yellow to

red

Red to

purple

Moderate

High

°C

-0.61

(

C relative to 1986–2005)

Global mean temperature change

°C

(

C relative to 1850–1900, as an

approximation of preindustrial levels)

2003

–2012

Recent

(1986–2005)

A2, 2100

with

mitigation

1075

Emergent Risks and Key Vulnerabilities Chapter 19

he burning embers diagram. Current literature is not sufficient to support

confident assessment of specific RFCs using this approach.

As literature accumulates, it could inform new versions of this figure

applied to specific RFCs. For example, studies that employ particular

scenarios of socioeconomic conditions could be categorized according

to the levels of vulnerability represented by those scenarios (van Vuuren

et al., 2012) to locate results along the horizontal axes, while climate

conditions assumed in those studies would locate results along the

vertical axis. As with previous versions of the burning embers, however,

this new figure does not explicitly address issues related to rates of

climate change or to when impacts might be realized. The updates

of RFCs in 19.6.3.2 to 19.6.3.6 that follow (and are illustrated in Figure

19-4) do not account for differences in vulnerability across development

paths; rather, they are based on the same assessment framework as

used in AR4 and Smith et al. (2009), but with additional elaboration.

19.6.3.2. Unique and Threatened Systems

Unique and threatened systems include a wide range of physical,

biological, and human systems that are restricted to relatively narrow

geographical ranges and are threatened by future changes in climate

(Smith et al., 2001). Where consequences are irreversible and importance

to society and other systems is high, the potential for loss of or damage

to such systems constitutes a key risk. AR4 stated with high confidence

that a warming of up to 2°C above preindustrial levels would result

in significant impacts on many unique and vulnerable systems and

would increase the endangered status of many threatened species, with

increasing adverse impacts (and increasing confidence in this conclusion)

at higher temperatures (Schneider et al., 2007). Since AR4, there is a

growing body of literature suggesting that the number of threatened

systems and species is greater than previously thought.

Chapters 4, 22, 23, 24, 25, 26, and 27 highlight areas where unique and

threatened systems are particularly vulnerable to climate change.

Evidence for severe and widespread impacts to humans and social

systems, ecosystems, and species in polar regions as warming progresses

has continued to accrue (Sections 4.3.3.4, 28.2). Projections of Arctic

sea ice melt rates have increased since AR4 (WGI AR5 Section 12.4.6),

increasing risks to the Inuit and the sea ice-dependent ecosystems upon

which they subsist. CMIP5 model runs for September with all RCPs show

substantial additional losses of Arctic Ocean ice for a global warming

of 1°C relative to 1986–2005 and a nearly ice-free Arctic Ocean for

global warming greater than 2°C (WGI AR5 Figure 12.30). Furthermore,

a nearly ice-free Arctic Ocean in September before mid-century is likely

under RCP8.5 (medium confidence; WGI AR5 Section 12.4.6).

Coral reef ecosystems are still considered amongst the most vulnerable

of unique marine systems (Sections 5.4.2.4, 19.3.2.4), with corals’

evolutionary responses being outpaced by climate change (Hoegh-

Guldberg, 2012) resulting in projections of extensive reef decline

throughout the 21st century. Globally, large-scale reef dissolution may

occur if CO

concentrations reach 560 ppm (Section 5.4.2.4) due to the

combined effects of warming and ocean acidification. Even if global

temperature rise in the 2090s is constrained to 1.2 to 2.0°C above

preindustrial levels (WGI AR5 Table 12.3, RCP2.6), and assuming rapid

daptation rates in corals, 9 to 60% of reefs are projected to be subject

to long-term degradation, while 30 to 88% of reefs are projected to

eventually degrade if global temperature rises in the 2090s by 1.9 to

2.9°C above preindustrial levels (RCP4.5; Box CC-CR; temperatures from

WGI AR5 Table 12.3). Loss of corals and mangrove ecosystems would

endanger the livelihoods of unique human communities and cause

economic damage (Section 4.3.3 for global discussion; Sections

22.3.2.3, 24.4.3, 25.6 for Africa, Asia, and Australia; Section 26.4 for

North America; Section 27.3.3.1 for South America).

There is a large and increasing amount of evidence for escalating risks

of species range loss, extirpation, and extinction based on studies for

global temperatures exceeding 2°C above preindustrial levels (1.4°C

above 1986–2005; Warren et al., 2011; Şekercioğlu et al., 2012, Foden

et al., 2013; Warren et al., 2013a). An assessment of 16,857 species

(Foden et al., 2013) found that with approximately 2°C of warming

above preindustrial (A1B, 2050s), 24 to 50% of the birds, 22 to 44% of

the amphibians, and 15 to 32% of the corals were highly vulnerable to

climate change defined as having high sensitivity, high exposure, and

low adaptive capacity.

An increasing number of threatened systems has been identified, in the

form of projected species range losses and extinction risks, although

without yet tying risks to specific levels of warming. Evidence of climate

risks to unique mountain ecosystems and their numerous endemic

alpine species has continued to accrue in Europe, Asia, Australia, and

South America (Sections 23.6.4, 24.4.2.3, 25.6.1, 27.3.2.1). Siberian,

tropical, and desert ecosystems in Asia (Section 24.4.2.3), Africa (Warren

et al., 2013a), and Mediterranean areas in Europe (Klausmeyer and

Shaw, 2009; Maiorano et al., 2011), the Queensland rainforest, Kakadu

National Park, and the southwestern region of Australia (Section 25.6.1),

Amazonian ecosystems in South America (Foden et al., 2013; Warren et

al., 2013a), and freshwater ecosystems in Africa (specifically Ethiopia,

Malawi, Mozambique, Zambia, and Zimbabwe) (Section 22.3.2.2) are

particularly at risk, as are the Fynbos and succulent Karoo areas of South

Africa (Midgley and Thuiller, 2011; Kuhlmann et al., 2012; Huntley and

Barnard, 2012) and dune systems in temperate climates (Section 23.6.5).

Recent research has identified risks to highly biodiverse tropical wet

and dry forests (Sections 4.3.3, 24.4.2.3; Kearney et al., 2009, Wright et

al., 2009; Toms et al., 2012) and tropical island endemics (Fordham and

Brook, 2010). Globally amphibians were found to be the most vulnerable

of vertebrate taxa (Stuart et al., 2004; Brito, 2008; Rohr and Raffel, 2010;

Liu et al., 2013; Warren et al., 2013a).

Owing to higher projections of sea level rise than in AR4 (WGI AR5 Sections

13.5-7), risk of partial inundation of small island states has increased.

“Since AR4, almost all glaciers worldwide have continued to shrink as

revealed by the time series of measured changes in glacier length, area,

volume and mass (very high confidence)” (WGI AR5 Chapter 4 ES). There

is substantial new evidence that, across most of Asia, glaciers have been

shrinking, except in some areas in the Karakorum and Pamir (Section

18.5.3). In the Andes, glacier loss threatens to reduce the water and

electricity supplies of large cities and hydropower projects, as well as the

agricultural and tourism sectors (Sections 27.3.1.1-2, 27.6.1; Table

27-3). Model simulations show a large projected loss of glacier ice

volume in central Asia by end of the century: in particular, estimates for

1076

Chapter 19 Emergent Risks and Key Vulnerabilities

CP8.5 and RCP4.5 suggest the potential for loss of most of the 2006

ice volume (Section 24.9.2). Loss of glacial cover has been projected to

significantly reduce water supplies in meltwater-dependent arid regions

(Kaser et al., 2010), potentially threatening the food security of 60 million

people in the Brahmaputra and Indus basins by the 2050s (Immerzeel

et al., 2010). However, recent work has suggested the glacier melt rates

in two Himalayan watersheds, Baltoro and Langtang, were previously

overestimated and, since precipitation is projected to concurrently increase,

runoff may actually rise until 2050 in these particular watersheds

(Immerzeel et al., 2013). Large uncertainties in projections of Himalayan

ice cover and runoff dynamics remain (Bolch et al., 2012).

In Figure 19-4, we locate the transition to moderate risk (white to

yellow) below recent global temperatures because there is at least

medium confidence in attribution of a major role for climate change for

impacts on at least one each of ecosystems, physical systems, and

human systems (Section 18.6.4). A transition to purple is located around

2°C above 1986–2005 levels to reflect the very high risk to species and

ecosystems projected to occur beyond that level as well as limited ability

to adapt to impacts on coral reef systems and on Arctic sea ice-dependent

systems (Chapters 4, 5, 6, 28) if that level of warming were exceeded

(high confidence). A transition to red is located around 1°C above 1986–

2005 levels, midway between current temperature and the transition

to purple, indicating the increasing risk to unique and threatened systems,

including Arctic sea ice and coral reefs, as well as threatened species

as temperature increases over this range.

19.6.3.3. Extreme Weather Events

Extreme weather events (e.g., heat waves, intense precipitation, drought,

tropical cyclones) trigger impacts that can pose key risks to societies

that are exposed and vulnerable (Lavell et al., 2012). With regard to

the physical hazard aspect of risk, AR5 assesses a higher likelihood of

attribution of heat waves and extreme hot days and nights to human

activity than AR4. WGI AR5 states, “We assess that it is very likely

that human influence has contributed to the observed changes in the

frequency and intensity of daily temperature extremes on the global

scale since the mid-20th century” (WGI AR5 Section 10.6.1.1) and “it

is likely that human influence has substantially increased the probability

of occurrence of heat waves in some locations” (WGI AR5 Section

10.6.2). WGI finds medium confidence in attribution of intensification

of heavy precipitation over land areas with sufficient data (WGI AR5

Section 10.6.1.2), and “low confidence in detection and attribution of

changes in drought over global land areas” (WGI AR5 Section 10.6.1.3)

and global changes in tropical cyclone activity (WGI AR5 Section

10.6.1.5) to human influence. There is high confidence in attribution of

impacts of weather extremes (as opposed to the physical hazards alone)

on coral reef systems (Sections 18.6.4, 19.6.3.2; Table 18-10), with

evidence for impact attribution limited and highly localized otherwise.

The likelihood of projected 21st century changes in extremes has not

changed markedly since AR4 (WGI AR5 Chapters 10, 12), but for the

first time near-term changes (for the period 2016–2035 relative to

1986–2005) are assessed (WGI AR5 Chapter 1), a period during which

the increase in the model and scenario averaged GMT is projected to

remain below 1°C relative to 1986–2005 (WGI AR5 Figure 11.8; WGI

R5 Section 11.3.6.3). Among the conclusions are, “In most land regions

the frequency of warm days and warm nights will likely increase in the

next decades, while that of cold days and cold nights will decrease”

(WGI AR5 Chapter 11 ES). Specifically, about 15% of currently observed

maximum daily temperatures exceed the historical 90th percentile

values (rather than the historical 10%) and, by about 2035, 25 to 30%

of daily maximums are projected to exceed the historical 90th percentile

value (WGI AR5 Figure 11.17). WGI also notes that “Models project

near-term increases in the duration, intensity and spatial extent of heat

waves and warm spells” (WGI AR5 Chapter 11 ES; WGI AR5 Table

SPM.1). With regard to extreme precipitation events, WGI finds “The

frequency and intensity of heavy precipitation events over land will

likely increase on average in the near term. However, this trend will not

be apparent in all regions because of natural variability and possible

influences of anthropogenic aerosols” (WGI AR5 Chapter 11 ES). In

addition, SREX (IPCC, 2012a, Figure SPM.4B) projects a reduction in return

period for historical once-in-20-year precipitation events globally (land

only) to about once-in-14-year or less by 2046–2065.

With regard to the vulnerability and exposure aspects of risk, SREX

reviewed literature on the relationship between changes in these factors

and the risk of extreme events (IPCC, 2012a, Sections 4.5.4, 4.5.6).

Increases in local vulnerability and exposure to extreme precipitation

can lead to a disproportionate increase in overall risk (IPCC, 2012a,

Sections 4.3.5.1, 9.2.8; Douglas et al., 2008; Douglas, 2009; Hallegate

et al., 2011; Ranger, 2011). For example, growth of megacities both

concentrates exposure and vulnerability and can generate “synchronous

failure” that spreads beyond the immediate vicinity of extreme events.

Megacities increase nighttime temperature extremes via the urban heat

island effect (Section 8.2.3.1; IPCC, 2012a, Sections 4.3.5.1, 4.4.5.2)

while also enhancing exposure to high air pollution levels (IPCC, 2012a,

Sections 4.3.5.1, 9.2.1.2.3; Fang et al., 2013) and consequent health

effects (Sections 11.5.3.2, 11.5.3.4), with widespread impacts by mid-

century in some studies. Densely populated areas of East and South

Asia and North America are projected to be especially affected by

climate-related air pollution (Fang et al., 2013).

Projections of the global socioeconomic (Mendelsohn et al., 2012) impact

of tropical cyclones demonstrate increasing risk due to interactions of

increasing storm intensity with exposure. Hazard projection suggests a

disproportionate increase in exposure to tropical cyclone risk with

increasing temperature at New York City due to combined effects of

storm intensification and sea level rise (Lin et al., 2012). Other studies

(Jongman et al., 2012; Hallegate et al., 2013; Preston, 2013) project

increasing coastal flood risk due to increasing exposure, although the

first two do not disaggregate to specific types of extreme events. Taken

together, this evidence supports a conclusion of disproportionate

increase in risk associated with extreme events as temperature, and in

many cases, exposure and vulnerability increase as well.

Based on the above assessments of the physical hazard alone, we find

increased confidence in the AR4 assessment of the risk from extreme

weather events.Based on the attribution of heat and precipitation

extremes to anthropogenic climate change, the attribution to climate

change of impacts of climate extremes on one unique and threatened

system, and the current vulnerability of other exposed systems, we

assign a yellow level of risk at recent temperatures in Figure 19-4 (high

1077

Emergent Risks and Key Vulnerabilities Chapter 19

onfidence), consistent with Smith et al. (2009). We assign a transition

to red beginning below 1°C compared to 1986–2005 (also consistent

with Smith et al. (2009)) based primarily on the magnitude and likelihood

and timing (see Section 19.2.2.2) of the projected change in hazard of

extreme weather events, indicating that impacts will become more

severe and widespread over the next few decades (medium confidence).

Risks associated with some types of extreme events (e.g., extreme heat)

increase further at higher temperatures (high confidence).

19.6.3.4. Distribution of Impacts

The distribution of impacts is a category of climate change consequences

that includes key risks to particular societies and social-ecological systems

that may be disproportionately affected due to unequal distribution of

hazards, exposure, or vulnerability. AR4 concluded that there is high

confidence that low-latitude, less-developed areas are generally at

greatest risk and found that, because vulnerability to climate change

is also highly variable within countries, some population groups in

developed countries are also highly vulnerable even to a warming of less

than 2°C above 1990–2000 (Schneider et al., 2007). These conclusions

remain valid and are now supported by a limited number of impact

studies that explicitly consider differences in socioeconomic conditions

that affect vulnerability across regions or populations (Mougou et al.,

2011; Müller et al., 2011; Gosling and Arnell, 2013; Schewe et al., 2013).

Furthermore, we have increased confidence in the AR4 assessment of

the risk arising in the near term from the distribution of impacts from

extreme weather events because, by their very nature, these events

change in a locally and temporally variable fashion with, for example,

a larger change in extreme temperatures at higher latitudes (IPCC,

2012a, Figure SPM.4A).

Impacts of climate change on food security depend on both production

and non-production aspects of the food system, including not just

yield effects but also changes in the amount of land in production and

adjustments in trade patterns (Section 7.1.1). Effects on prices are often

taken as an indicator of impacts on food security, and the combined

effect of climate and CO

change (but ignoring O

and pest and disease

impacts) appears about as likely as not to increase prices by 2050,

with few new studies examining prospects at longer time horizons

(Section 7.4.4). Most studies have focused on geographical differences

in the effects of climate change on crop yields. With regard to such

distributional consequences, yields of maize and wheat begin to decline

with about 1°C to 2°C of local warming in the tropics, with or without

adaptation taken into account (Figure 7-4). Temperate maize and

tropical rice yields are less clearly affected at these temperatures, but

significantly affected with local warming of 3 to 5°C particularly without

adaptation (based on studies with various baselines, see Section 7.3.2.1).

These data confirm AR4 findings that even small warming will decrease

yields in low-latitude regions (medium evidence, high agreement; Section

7.3.2.1.1), and increase the risk assigned to yields in mid- to high-latitude

regions (compared to AR4), suggesting thattemperate wheat yield

decreases are about as likely as not for moderate warming.

Risks of climate change related to freshwater systems, such as water

scarcity and flooding, increase with global mean temperature rise

(medium evidence, high agreement; Chapter 3 ES; Table 3-2). Climate

hange is projected to reduce renewable surface water and groundwater

resources significantly in most dry subtropical regions (high agreement,

robust evidence; Section 3.5; Chapter 3 ES). One study using multiple

climate and hydrological models to simulate impacts of scenario RCP8.5

and Shared Socioeconomic Pathway 2 (SSP2) project that global warming

of 1.7°C above preindustrial will reduce water resources by more than

one standard deviation, or by more than 20%, for 8% of the global

population, while for warming of 2.7°C above preindustrial this

increases to 14% (model range 10 to 30%), and for warming of 3.7°C

above preindustrial it reaches a mean of 17% across models (Schewe

et al., 2013). In addition, in another study (Gosling and Arnell, 2013),

climate change amplifies water scarcity by 30 to 40% for 1.7 to 2.7°C

of warming, with around 40% of the global population under increased

water stress. In one model, exposure to water scarcity increases steeply

up to 2.3°C above preindustrial in North and East Africa, Arabia, and

South Asia (Gosling and Arnell, 2013). In Africa water resources risks

are “medium-high” at 2°C and “high-very high” at 4°C (Table 22-6).

Model projections generally agree that discharge will decrease in the

Mediterranean and in large parts of North and South America (Schewe et

al., 2013). However, there are opportunities for adaptation in the water

resources sector, particularly for municipal water supply (Section 3.6).

The first global scale analysis of climate change impacts on almost

50,000 species of plants and animals has highlighted that risks are not

distributed equally, with sub-Saharan Africa, Central America, Amazonia,

and Australia at risk for plants and animals, and North Africa, Central

Asia, and southeastern Europe for many plants (Warren et al., 2013a).

A traits-based analysis of more than 16,000 species identified Amazonia

and Mesoamerica as being at risk for birds and amphibians; central

Eurasia, the Congo Basin, the Himalayas, and Sundaland for birds; and

the Coral Triangle region for corals (Foden et al., 2013).

In summary, since AR4, new evidence has emerged highlighting the

magnitude of risk for particular regions, for example, in relation to the

potential for regional impacts on ecosystems (see Section 19.6.3.2),

megadeltas (see Section 8.2.3.3; Chapter 5), and agricultural systems,

which is exacerbated by the potential for changes in the monsoon systems

(see WGI AR5 Sections 12.5.5, 14.2). Overall there is increased evidence

that low-latitude and less developed areas generally face greater risk

than higher latitude and more developed countries (Smith et al., 2009).

At the same time, there has been an increase in appreciation for

vulnerability (e.g., to extreme events) in developed countries, especially,

localized issues of differential vulnerability in particular areas of the

developed world (IPCC, 2012a, Section 2.5.1.2).

Regionally differentiated impacts on crop production have been detected

and attributed to climate change with medium to high confidence

(Section 18.4.1.1), and we interpret this as an early warning sign of

attributable impacts on food security. For this reason, as well as for

reasons of timing and likelihood and magnitude of these risks, we

assign a yellow level of risk at recent temperatures in Figure 19-4. Based

on risks to regional crop production and water resources the transition

from yellow to red is assessed to occur between 1°C and 2°C above

the 1986–2005 global mean temperature (medium confidence). Both

assessments are consistent with Smith et al. (2009). Furthermore, given

evidence that agronomic adaptations would be more than offset for

tropical wheat and maize where increases in local temperature of more

1078

Chapter 19 Emergent Risks and Key Vulnerabilities

han 3°C above preindustrial occur (limited evidence, medium agreement;

Chapter 7 ES; Section 7.5.1.1.1; Figure 7-4), the intensity of red increases

nonlinearly toward purple in recognition of the temperature sensitivity

of crop productivity and limited efficacy of agronomic adaptation above

2°C compared to 1986–2005.

9.6.3.5. Global Aggregate Impacts

The RFC pertaining to aggregate impacts includes risks that are aggregated

globally into a single metric, such as monetary damages, lives affected,

lives lost, or species or ecosystems lost. Estimates of the aggregate,

economy-wide risks of climate change since AR4 continue to exhibit a

low level of agreement. Studies at the sectoral level have been refined

with new data and models, and have assessed new sectors.

AR4 stated with medium confidence that approximately 20 to 30% of

the plant and animal species assessed to date are likely at increasing

risk of extinction as global mean temperatures exceed a warming of

2°C to 3°C above preindustrial levels (Fischlin et al., 2007). There is high

confidence that climate change will contribute to increased extinction

risk for terrestrial, freshwater, and marine species over the coming century

(Sections 4.3.2.5, 30.5; Box CC-CR). Since AR4 a substantial amount of

additional work has been done, looking at many more species and using

new and/or improved modeling and traits-based techniques, strengthening

the evidence of increasing risk of extinction with increasing temperature

(e.g., Lenoir et al., 2008; Amstrup et al., 2010; Hunter et al., 2010; Bálint

et al., 2011; Pearman et al., 2011; Barnosky et al., 2012; Bellard et al., 2012;

Norberg et al., 2012; Foden et al., 2013). More studies have scrutinized

caveats to previous studies and assessed their role in either under- or

overestimating extinction risks (e.g., Beale et al., 2008; Cressey, 2008;

Randin et al., 2009; He and Hubbell, 2011; Harte and Kitzes, 2012),

including the role of evolution (Norberg et al., 2012), while others have

carefully examined risk considering other species traits (looking at

exposure, sensitivity, and potential adaptive capacity for large numbers

of species; Foden et al., 2013). Literature incorporating multiple new

assessment techniques quantifying extinction risks supports the

conclusion that the dependence between increasing extinction risk and

temperature is robust (medium confidence), albeit varying across

biota.However, there is low agreement on assigning specific numerical

values for species at risk (Sections 19.3.2.1, 19.5.1). Since AR4 it has

been found that not only endemics (which have tended to be the focus

of many previous studies) but species geographically widespread are

at risk (Warren et al., 2013a), implying a significant and widespread

potential loss of ecosystem services (Section 4.3.2.5; Gaston and Fuller,

2008; Allesina et al., 2009; Staudinger et al., 2012), comprising a new

emergent risk (Table 19-4). At a global temperature rise of 3.5°C to 4°C

above preindustrial, Foden et al. (2013) estimated that 30 to 60% of

the birds and amphibians and 40 to 62% of the corals studied are highly

vulnerable to climate change. Taking this estimate conservatively as a

maximum (i.e., assuming all species not studied are able to adapt at

least as well as the groups investigated), and combining this estimate

with the finding of ≥50% loss of potential range in 57% of plants and

34% of animals studied globally for a global temperature rise of 3.5°C

to 4°C by the 2080s allowing for realistic dispersal rates (Warren et al.,

2013a), there is high confidence that climate change will significantly

affect biodiversity and related ecosystem services.

uch new work has focused on future projected synergistic impacts of

climate change-induced increases in fire, drought, disease, and pests

(Flannigan et al., 2009; Hegland et al., 2009; Koeller et al., 2009;

Krawchuk et al., 2009; Garamszegi, 2011). New work has demonstrated

that the expected large turnovers of more than 60% in marine species

assemblages by the 2050s in response to climate change (under SRES

scenarios A1B and B1), combined with shrinkage of fish body weight of

14 to 24% (SRES A2; Cheung et al., 2009, 2013), put marine ecosystem

functioning at risk with negative consequences for fishing industries,

coastal communities, and wildlife that are dependent on marine resources

(Lam et al., 2012).

Consistent with AR4, global aggregate economic impacts from climate

change are highly uncertain, with most estimates a small fraction of gross

world product up until at least 2.5°C of warming above preindustrial

(Section 10.9.2; Figure 10-1). Some studies suggest net benefits of

climate change at 1°C of warming (Section 10.9.2; Figure 10-1). Little

is known about global aggregate damages above 3°C (Sections 10.9.2,

19.5.1; Figure 10-1; Ackerman et al., 2010; Weitzman, 2010; Ackerman

and Stanton, 2012; Kopp et al., 2012). Aggregate damages vary with

alternative development pathways, but the relationship between

development pathway and aggregate damages is not well explored. In

many sectors, damages as a fraction of output are expected to be larger

in low-income economies, although monetized damages are expected

to be larger in high-income economies (e.g., Anthoff and Tol, 2010).

Adaptation is treated differently across modeling studies (Hope, 2006;

de Bruin et al., 2009; Bosello et al., 2010; Füssel, 2010; Patt et al., 2010)

and affects aggregate damage estimates in ambiguous ways.

Estimates of global aggregate economic damages omit a number of

factors (Yohe and Tirpak, 2008; Kopp and Mignone, 2012). While some

studies of aggregate economic damages include market interactions

between sectors in a computable general equilibrium framework (e.g.,

Bosello et al., 2012a; Roson and van der Mensbrugghe, 2012), none

treat non-market interactions between impacts (Warren, 2011), such as

the effects of the loss of biodiversity among pollinators and wild crops

on agriculture or the effects of land conversions owing to shifts in

agriculture on terrestrial ecosystems (see Sections 19.3-4). They do not

include the effects of the degradation of ecosystem services by climate

change (Section 19.3.2.1) and ocean acidification (Section 19.5.2), and

in general assume that market services can substitute perfectly for

degraded environmental services (Sterner and Persson, 2008; Weitzman,

2010; Kopp et al., 2012). The global aggregate damages associated with

large-scale singular events (Section 19.6.3.6) are not well explored

(Kopp and Mignone, 2012; Lenton and Ciscar, 2013).

The risk associated with global aggregate impacts is similar to that

expressed in AR4 and Smith et al. (2009), as indicated in Figure 19-4,

with risk based primarily on economic damages and confidence in the

assessment unchanged. For aggregate economic impacts, there is low

to medium confidence in attribution of climate change influence on a

few sectors (Section 18.6.4; Table 18-11) so that this RFC is still shaded

white at recent temperature in Figure 19-4. Risks of global aggregate

impacts are moderate for additional warming between 1°C to 2°C

compared to 1986–2005, reflecting impacts to both Earth’s biodiversity

and the overall global economy (medium confidence). Extensive

biodiversity loss with associated loss of ecosystem goods and services

1079

Emergent Risks and Key Vulnerabilities Chapter 19

esults in high risks around 3°C additional warming (high confidence).

Aggregate economic damages accelerate with increasing temperature

(limited evidence, high agreement) but few quantitative estimates have

been completed for additional warming around 3°C or above.

19.6.3.6. Large-Scale Singular Events: Physical, Ecological,

nd Social System Thresholds and Irreversible Change

Large-scale singular events (sometimes called “tipping points,” or critical

thresholds) are abrupt and drastic changes in physical, ecological, or

social systems in response to smooth variations in driving forces (Smith

et al., 2001, 2009; McNeall et al., 2011). Combined with widespread

vulnerability and exposure, they pose key risks because of the potential

magnitude of the consequences; the rate at which they would occur;

and, depending on this rate, the limited ability of society to cope with

them. Research on the societal impacts associated with such events is

limited; we focus in this section on physical hazards and ecological

thresholds.

Regarding singular events in physical systems, AR4 expressed medium

confidence that at least partial deglaciation of the Greenland ice sheet,

and possibly the West Antarctic ice sheet (WAIS), would occur over a

period of time ranging from centuries to millennia for a global average

temperature increase of 1°C to 4°C (relative to 1990–2000), causing a

contribution to sea level rise of 4 to 6 m or more (Schneider et al., 2007).

Studies since AR4 are consistent with these judgments but provide a

more detailed view (see WGI AR5 Chapter 13). The Greenland ice sheet

(very likely) and the Antarctic ice sheet (medium confidence) contributed

to the 5 m higher than present (very high confidence) to 10 m above

present (high confidence) sea level rise that occurred during the Last

Interglacial (WGI AR5 SPM; Kopp et al., 2009; McKay et al., 2011; Dutton

and Lambeck, 2012). This period provides a partial analog for the

magnitude of mid- to late-21st century warming because GMT was not

more than 2°C warmer than preindustrial (medium confidence; WGI

AR5 SPM; Section 5.3.4). However, the resulting sea level rise may have

taken millennia to complete.

With regard to projection, WGI AR5 finds that “There is high confidence

that sustained warming greater than some threshold would lead to the

near-complete loss of the Greenland ice sheet over a millennium or more,

causing a global mean sea level rise of up to 7 m. Current estimates

indicate that the threshold is greater than about 1°C (low confidence)

but less than about 4°C (medium confidence) global mean warming

with respect to preindustrial” (WGI AR5 SPM). A threshold for the

disintegration of WAIS remains difficult to identify due to shortcomings

in various aspects of ice sheet modeling, including representation of

the dynamical component of ice loss and ocean processes. For RCP8.5,

projected sea level rise is 1 to more than 3 m (medium confidence) by

2300. Beyond 2300, “Sustained mass loss by ice sheets would cause larger

sea level rise, and some part of the mass loss might be irreversible”

(WGI AR5 SPM). Extreme exposure and vulnerability to the magnitude

of sea level rise associated with loss of a significant fraction of either

ice sheet is found worldwide (Nicholls and Tol, 2006) but millennial time

scales for ice loss allow greater opportunities to adapt successfully than

do century scales, so timing is a critical and highly uncertain factor in

assessing the risk.

here is also additional evidence regarding singular events in other

physical systems. Feedback processes in the Earth system could cause

accelerated emissions of CH

from wetlands, permafrost, and ocean

hydrates. There are large uncertainties in the size of carbon stores, the

time scales of release, and the fate of the carbon once released. The

probability of substantial carbon release in the form of CH

or CO

increases with warming (Archer et al., 2009; O’Connor et al., 2010). WGI

AR5 finds “low confidence in modelling abilities to simulate transient

changes in hydrate inventories, but large CH

release to the atmosphere

during this century is unlikely” (WGI AR5 Section 6.4.7.3). Owing to such

uncertainties, the existence of a tipping point cannot be ascertained.

AR4 stated that Arctic summer sea ice disappears almost entirely in

some projections by the end of the century (WGI AR4 Section 10.3); WGI

AR5 finds that a “nearly ice-free Arctic Ocean (sea ice extent less than

1 × 10

for at least 5 consecutive years) in September before mid-

century is likely under RCP8.5 (medium confidence).” Furthermore,

“There is little evidence in global climate models of a tipping point (or

critical threshold) in the transition from a perennially ice-covered to a

seasonally ice-free Arctic Ocean beyond which further sea ice loss is

unstoppable and irreversible” (WGI AR5 Chapter 12 ES). Whether or not

the physical process is reversible, effects of ice loss on biodiversity may

not be.

Large uncertainties remain in estimating the probability of a shutdown

of the AMOC. One expert elicitation finds the chance of a shutdown to

be between 0 and 60% for global average warming between 2°C and

4°C, and between 5 and 95% for 4°C to 8°C of warming relative to 2000

(Zickfeld et al., 2007; Kriegler et al., 2009). AR5 judges that “It is very

unlikely that the AMOC will undergo an abrupt transition or collapse in

the 21st century for the scenarios considered. There is low confidence

in assessing the evolution of the AMOC beyond the 21st century because

of the limited number of analyses and equivocal results. However, a

collapse beyond the 21st century for large sustained warming cannot

be excluded” (WGI AR5 SPM).

Regarding regime shifts in ecosystems, there are “early warning signs”

from detection and attribution analysis that both Arctic and warmwater

coral reef systems are experiencing irreversible regime shifts (Section

18.6.4). Recent observational evidence confirms the susceptibility of the

Amazon to drought and fire (Adams et al., 2009; Phillips et al., 2009),

and recent improvements to models provide increased confidence in

the existence of a tipping point in the Amazon from humid tropical forest

to seasonal forest or grassland as the dominant ecosystem (Jones et

al., 2009; Lapola et al., 2009; Malhi et al., 2009; Section 4.3.3.1; Figure

4-8; Box 4-3). In contrast, one recent study suggests that the Amazon

may be less susceptible to crossing a tipping point than previously

thought (Cox et al., 2013), although this is contingent on the uncertain

role of CO

fertilization being as strong as models project. Overall, recent

“multi-model estimates based on different CMIP3 climate scenarios and

different dynamic global vegetation models predict a moderate risk of

tropical forest reduction in South America and even lower risk for

African and Asian tropical forests” (WGI AR5 Section 12.4.8.2).

Based on the weight of the above evidence, we judge that the overall

risk from large-scale singular events is somewhat higher than assessed

in AR4 and indicated by Smith et al. (2009). The position of the transition

1080

Chapter 19 Emergent Risks and Key Vulnerabilities

rom white to yellow between 0°C and 1°C compared to 1986–2005

remains as before but with higher confidence due to the existence of

early warning signs regarding regime shifts in Arctic and warmwater

coral reef systems. The transition from yellow to red occurs over a range

from 1°C to more than 3°C, consistent with Smith et al. (2009) and

based primarily on the uncertainty in the warming level associated with

eventual ice sheet loss. However, we assess a faster increase in risk as

temperature increases between 1°C and 2°C compared to 1986–2005,

largely determined by the risk arising from a very large sea level rise due

to ice sheet loss as occurred during the Last Interglacial when GMT was

no more than 2°C warmer than preindustrial (medium confidence; WGI

AR5 Sections 5.3.4, 5.6.2). This assessment of risk is based primarily on

the magnitude and irreversibility of such sea level rise and the widespread

exposure and vulnerability to it. However, as noted, the slower the rate

of rise, the more feasible becomes adaptation to reduce vulnerability

and exposure. Owing to this uncertainty in timing, we refrain from

imposing a transition to purple in Figure 19-4.

19.7. Assessment of Response Strategies

to Manage Risks

The management of key and newly identified risks of climate change

can include mitigation that reduces the likelihood of climate changes

and physical impacts and adaptation that reduces the exposure and

vulnerability of society and ecosystems to both. Key risks, impacts, and

vulnerabilities to which societies and ecosystems may be subject will

depend in large part on the mix of mitigation and adaptation measures

undertaken, as will the evaluation of RFCs (Section 19.6.3). This section

therefore assesses relationships between mitigation, adaptation, and

the residual impacts that generate key risks. It also considers limits to

both mitigation and adaptation responses, because understanding

where these limits lie is critical to anticipating risks that may be

unavoidable. Potential impacts involving thresholds for large changes

in physical, ecological, and social systems (Section 19.6.3.6) are

particularly important elements of key risks, and the section therefore

assesses response strategies aimed at avoiding them or adapting to

crossing them.

19.7.1. Relationship between Adaptation Efforts,

Mitigation Efforts, and Residual Impacts

Under any plausible scenario for mitigation and adaptation, some degree

of risk from residual damages is unavoidable (very high confidence).

Evaluating potential mixes of mitigation, adaptation, and impacts requires

joint consideration of outcomes for climate change and socioeconomic

development. A principal way in which these different mixes are assessed

is comparing the impacts that result from scenarios with little or no

mitigation (and therefore more climate change) to those with substantial

mitigation (and less climate change). Climate change mitigation costs

have been extensively explored (WGIII AR5 Chapter 6), but there has

been less work on quantifying the impacts avoided by mitigation and,

with the exception of studies of the impacts of sea level rise (Nicholls

et al., 2011), treatment of adaptation has been limited and uneven. In

this section, unless otherwise stated, global temperature rise is given

relative to preindustrial (1850–1900) levels.

mpact studies generally indicate that mitigation can reduce a large

proportion of climate change impacts that would otherwise occur

(high confidence). In one study, mitigation that stabilizes global CO

concentrations at 550 ppm reduces by 80 to 95% the number of people

additionally at risk of hunger (largely in Africa) in 2080 under an SRES

A2 scenario with CO

concentrations of 800 ppm, creating an estimated

benefit of US$23 to 34 billion of agricultural output compared to the

un-mitigated case (Tubiello and Fischer, 2007). In Africa, there are

much greater impacts on crop productivity, freshwater resources, and

ecosystems at 4°C than at 2°C, with adaptation failing to reduce risk

below a “high” level at 4°C (“very high” for crop productivity), whereas

at 2°C risks are lower and adaptation could reduce these risks to a

“medium” level (Table 22-6). In North America, with 4°C warming,

adaptation is not expected to reduce risks below “high” for urban flooding

(both riverine and coastal) or for fire damage in ecosystems, or below

“medium” for heat-related human mortality. Without adaptation, risk

is “very high” for these sectors. In contrast, at 2°C risks are at the

“high” level for urban flooding and heat-related human mortality, but

the risk of fire in ecosystems is still “very high.” At 2°C, adaptation is

expected to reduce urban flooding risk to “medium” and heat-related

human mortality risk to “low” (Table 26-1). Impacts on water resources

would also be reduced (Table 3-2). Fung et al. (2011) and Gosling and

Arnell et al. (2013) both found that climate change-induced increases

in water stress (defined as persons with <1700 or <1000 m

per capita

per year respectively in the two studies) globally would be reduced

significantly were global temperature rise to be constrained to 2°C

rather than 4°C. Reducing climate change from an RCP8.5 scenario to

an RCP2.6 scenario reduces the proportion of the global population

that experiences >10% declines in available groundwater from 27 to

50% to 11 to 39% (Portmann et al., 2013).

Figure 19-6 highlights results from three studies that estimated the

global avoided impacts for multiple sectors when global average

temperature is limited to 2°C rather than following scenarios with no

mitigation, such as the SRES A1B or A1FI baseline scenarios in which

global average temperature reaches 4°C and 5.6°C, respectively (Arnell

et al., 2013; Warren et al., 2013a,b). The studies isolate the effects of

climate change by using common socioeconomic assumptions in

mitigation and baseline scenarios. Overall, sector-specific impacts were

reduced by 20 to 80%, with aggregate global economic damages

reduced by about one-half (Warren et al., 2013b). The largest impacts

avoided were for crop productivity, drought in cropland, biodiversity,

exposure to coastal and pluvial flooding, and energy use for cooling,

while avoided impacts were smaller for water resources stress. Because

some areas become wetter and others drier (WGI AR5 Section 12.4.5),

there are regions where climate change results in decreases in flood,

drought, or water stress, which may be beneficial. (Note that reduced

water stress is not necessarily beneficial; for example, if increased

precipitation occurs in a small number of isolated heavy rainfall events,

water cannot easily be stored and can cause flooding). This means that

as well as avoiding a large amount of negative impacts, mitigation is

projected to result in the avoidance of some benefits that are projected

to result from climate change, although these avoided benefits are much

smaller than the avoided impacts. There are shown as the blue bars in

Figure 19-6. Avoided impacts are significantly larger when an A1FI

baseline is used compared to an A1B baseline (Figure 19-6) because

emissions and global temperature rise are greater in the A1FI baseline

1081

Emergent Risks and Key Vulnerabilities Chapter 19

cenario. All of these studies employed an ensemble of climate change

projections based on emulation of seven different Global Climate

Models (GCMs). The proportion of impacts avoided at the global scale

was relatively robust to uncertainties in regional climate projection,

but the magnitude of avoided impacts varied considerably with climate

projection uncertainty.

he timing of emissions reductions strongly affects impacts. In general

fewer impacts can be avoided when mitigation is delayed(Arnellet al.,

2013; Warren et al., 2013a,b; Figure 19-6b) because there are limits to

how fast emissions can be reducedsubsequently to compensate for the

delay (Section 19.7.2). For example, if global emissions peak in 2016 and

are then reduced at 5% annually, one half of global aggregate economic

(dark red)

(light brown)

(yellow)

(orange)

(dark blue)

(bright blue)

(light blue)

(purple)

020406080100

% changes avoided in 2100

relative to SRES A1B baseline

020406080100

Change in average annual people ﬂooded in coastal ﬂoods

Climatic range loss in plants

Climatic range loss in animals

xposure to increased river ﬂood frequency

Change in residential cooling energy demand

hange in soybean productivity

otal economic damages (without equity weights)

Total economic damages (with equity weights)

hange in spring wheat productivity

Increased exposure to drought in croplands

Decline in crop suitability

Change in coastal wetland extent

Increased exposure to water resources stress

Improvement in crop suitability

Exposure to decreased river ﬂood frequency

Decreased exposure to water resources stress

Decreased exposure to drought in croplands

Change in residential heating energy demand

% changes avoided in 2100 relative to SRES A1B

or A1FI baseline in rapid early mitigation

% impacts

avoided

% beneﬁts

avoided

Rapid early mitigation

Slow early mitigation

Rapid late mitigation

A1B

A1FI

(a) (b)

Impacts avoidedBeneﬁt avoided

% impacts

avoided

% beneﬁts

avoided

Figure 19-6 | (a) Climate change impacts avoided by an early, rapid mitigation scenario in which global emissions peak in 2016 and are reduced at 5% thereafter, compared to

two no-mitigation baseline cases, Special Report on Emission Scenarios (SRES) A1B (dark red bars) and SRES A1FI (orange bars). Impacts avoided are larger if the A1FI baseline

scenario is used than if the A1B baseline is used, because greenhouse gas emissions in A1FI exceed those in A1B (see Section 19.7.1). Since the literature does not provide

estimates of avoided impacts relative to the A1FI baseline for all sectors considered here, some bars are absent from the panel (a). (b) The dependence of the potential to avoid

climate change impacts upon the timing of emission reductions is illustrated. Climate change impacts avoided by the same early, rapid mitigation scenario compared to the

no-mitigation baseline case SRES A1B (dark red bars) are shown. The information displayed is identical to the orange bars in (a), but a comparison is now made with the impacts

avoided from two other less stringent mitigation scenarios. Impacts avoided if global emissions peak in 2016 but are subsequently reduced more slowly (2% annually) are lower

(light brown bars compared to dark red bars). However, if mitigation occurs later, so that global emissions do not peak until 2030, even if emissions are subsequently reduced at

5% annually, the avoided impacts are smaller than in either of the other two cases (yellow bars compared to dark red and light brown bars). Both panels show the uncertainty

range (error bars) due to regional climate change projected with seven global climate models. Errors due to uncertainty within impacts models are not shown. Uncertainties

associated with sea level rise related impacts are not provided because the models used a single sea level rise projection. Because increases and decreases in water stress, ﬂood

risks, and crop suitability are not co-located and affect different regions, these effects are not combined. Since some areas become wetter and others drier (WGI AR5 Section

12.4.5), there are regions where climate change results in decreases in ﬂood, drought, or water stress, which may be beneﬁcial. This means that avoided beneﬁts of climate

change, as well as avoided impacts of climate change, are also shown here, as the shorter blue bars. Overall the avoided impacts greatly outweigh the avoided beneﬁts (Arnell et

al., 2013; Warren et al., 2013a,b).

1082

Chapter 19 Emergent Risks and Key Vulnerabilities

mpacts might be avoided (Figure 19-6b, orange bars), or around 43%

if emissions are reduced more slowly at 2% annually (Figure 19-6b, pink

bars); compared to only one-third if emissions peak in 2030 even if

emissions are reduced at 5% thereafter(Warren et al., 2013b, Figure

19-6b, brown bars). This applies irrespective of whether or not equity

weighting is used in the impact valuation process.

Avoided impacts vary significantly across regions as well as sectors

(high confidence) due to (1) differing levels of regional climate change,

(2) differing numbers of people and levels of resources at risk in different

regions, and (3) differing sensitivities and adaptive capacities of

humans, species, or ecosystems (Tubiello and Fischer, 2007; Ciscar et al.,

2011; Arnell et al., 2013; Section 25.10.1). The length of time it takes

for avoided impacts to accrue is determined partly by the nature of the

climate system. Benefits accrue least rapidly for impacts associated with

sea level rise such as coastal flooding and loss of mangroves and coastal

wetlands because sea level rise responds very slowly to mitigation

efforts (Meehl et al., 2012). Nevertheless, mitigation may limit 21st

century impacts of increased coastal flood damage, dry land loss, and

wetland loss substantially (limited evidence, medium agreement) albeit

there is little agreement on the exact magnitude of this reduction

(Section 5.4.3.1). Benefits accrue more rapidly for impacts associated

with global temperature change (WGI AR5 Section 12.5.2, Figure 12.44)

and those associated with reduced ocean acidification because surface

pH responds relatively quickly to changes in emissions of CO

(FAQ

30.1).

n WGIII AR5 Chapter 6, the emission scenarios in the literature (as

collected in the AR5 database) have been categorized on the basis of the

2100 radiative forcing (in a total of seven categories). Most Integrated

Assessment Models (IAMs) provide information on concentration,

forcing, and temperature. However, as the climate components of the

IAMs differ, all scenarios were reanalyzed in the simple climate model

Model for the Assessment of Greenhouse-gas Induced Climate Change

(MAGICC; Meinshausen et al., 2011) using its probabilistic set-up. The

results of this categorization can be used to connect emission trajectories

to climate outcomes (Figure 19-7a) and impacts and risks (Figure 19-7b;

Table 19-4).

Mitigationscenarios in category 1 with a 2100 CO

-eq concentration

of 430 to 480 ppm result in a median projected 2100 global temperature

rise of between 1.5°C and 1.7°C above preindustrial (10–90% range

1.0–2.8°C) (Figure 19-7a; WGIII AR5 Table 6.3). These scenarios correspond

to a 2011–2100 cumulative emission level of around 630–1180 GtCO

(WGIII AR5 Table 6.3). Under these scenarios, based on the MAGICC

calculations, warming is likely to stay below 2°C and very likely to stay

below 3°C during the 21st century. This significantly reduces the key

risks listed in Table 19-4, as well as others discussed in this chapter.

Constraining global temperature rise to 2°C would constrain the risks

associated with global aggregate impacts and large-scale singular

events to the yellow or moderate level and the risks associated with

the distribution of impacts, extreme weather events, and to unique and

threatened systems to the lower part of the red or high level. If global

(a) (b)

2100 CO

-eq concentration level (ppm)

Figure 19-7 | Relationship between mitigation scenario categories considered in WGIII AR5, in terms of their CO

-eq concentrations and global temperature rise outcomes in

2100, and level of risk associated with Reasons for Concern. (a) The projected increase in global mean temperature in 2100 compared to pre-industrial and recent (1986–2005),

calculated using the Model for the Assessment of Greenhouse-gas Induced Climate Change (MAGICC) climate model for the scenario categories deﬁned in WGIII AR5 Chapter 6,

indicating the uncertainty range resulting both from the range of emission scenario projections within each category (10–90th percentile) and the uncertainty in the climate

system as represented by MAGICC (16–84th percentile) (data taken from WGIII AR5 Chapter 6). (b) Reproduction of Figure 19-4 for ease of comparison. Beyond 2100,

temperature, and therefore risk, decreases in most of the lowest three scenarios and increases further in most of the others.

°C

-0.61

Risks to

some

Moderate

risk

Loss to

biodiversity

and global

economy.

Low risk

Risks to

many,

limited

adaptation

capacity

Severe and

widespread

impacts

Increased

risk for

most

regions

Risk of

extensive

biodiversity

loss and

global

economic

damages

High

risk

Increased

risk for some

regions

Positive and

negative

impacts

Level of additional risk due to climate change

Undetectable Very high

White Yellow Red Purple

White to

yellow

Yellow to

red

Red to

purple

Moderate

High

°C

2003

–2012

Recent

(1986–2005)

(RFC1) Risks

to unique

and

threatened

systems

(RFC2) Risks

associated

with extreme

weather

events

(RFC3) Risks

associated

with the

distribution

of impacts

(RFC4) Risks

associated

with global

aggregate

impacts

(RFC5) Risks

associated

with

large-scale

singular

events

1000

20-

580-

530

-5

80-

430

-4

(

C relative to 1986–2005)

Global mean temperature change

(

C relative to 1850–1900, as an

approximation of preindustrial levels)

(

C relative to 1986–2005)

Global mean temperature change

(

C relative to 1850–1900, as an

approximation of preindustrial levels)

°C

-0.61

°C

1083

Emergent Risks and Key Vulnerabilities Chapter 19

emperature rise were 1.5–1.7°C only, risks to unique and threatened

systems and risks associated with extreme weather events would be

further constrained to the transition between moderate and high risk

levels. The temperature levels in the RCP2.6 scenario are 1.2°C to 2.0°C

(WGI AR5 Table 12.2) matching closely the scenarios in this category.

Mitigation scenarios in category 2 with a concentration of 480 to 530

ppm CO

-eq in 2100 correspond to a median projected 2100 global

temperature rise of between 1.7°C and 2.1°C (10–90% range 1.2–3.3°C)

in the MAGICC calculations. These scenarios correspond to a cumulative

emission level over the 2011–2100 period on the order of 960–1550

GtCO

(WGIII AR5 Table 6.3) and are as likely as not to stay below 2°C,

but are still very unlikely to rise above 3°C. Thus, scenarios in category

2 also reduce risks, but to a lesser extent than for category 1. If global

temperature rise reaches 2.5°C in 2100, levels of risk due to extreme

weather events are at the red or high level, while those to unique and

threatened systems now reach the very high or purple level reflecting

inability to adapt. Risks associated with global aggregate impacts reach

the transition zone from yellow or moderate level to red or high risk,

while risks associated with the distribution of impacts and large-scale

singular events reach the red or high level.

Mitigation scenarios in category 3 with 530 to 580 ppm CO

-eq in 2100

correspond to a median projected temperature rise of between 2.0°C

and 2.3°C (range 1.4–3.6°C) above preindustrial levels (WGIII AR5

Table 6.3) such that it is very unlikely that temperature rise would

stay below 1.5°C, and less probable than category 2 to remain below

2°C, affording little protection to coral reefs. In this category, risks to

unique and threatened systems remain high or very high indicating

inability to adapt. Risks associated with extreme weather events remain

at the high level. Risks associated with the distribution of impacts,

global aggregate impacts and large-scale singular events range from

moderate to high.

Mitigation scenarios in category 4 with 580 to 720 ppm CO

-eq in 2100

result in a range of possible temperature outcomes between 2.3°C and

2.9°C (10–90% range 1.5–4.5°C) above preindustrial levels, affording

no protection to coral reefs. In these scenarios, it is more likely than not

that global temperature rise would exceed 2°C (WGIII AR5 Table 6.3)

so that risks to unique and threatened systems remain high or very high

indicating inability to adapt. Risks associated with extreme weather

events and the distribution of impacts are high. Levels of risk associated

with global aggregate impacts and large-scale singular events may be

moderate or high (high confidence). Global temperature rise in RCP4.5

in 2100 is 1.9 to 2.9°C above preindustrial levels (WGI AR5 Table 12.2),

matching the low scenarios in this category.

Onset of large-scale dissolution of coral reefs is projected if CO

concentrations reach 560 ppm (Sections 5.4.1.6, 5.4.2.4, 19.6.3.2,

26.4.3.2; Silverman, 2009), due to the combined effects of warming and

ocean acidification. However, already at 450 ppm, reef growth rates are

projected to be reduced by more than 60% globally and by at least 20%

globally at 380 ppm (Silverman, 2009). Coral organisms themselves are

projected to be damaged by warming at concentrations below 560 ppm:

specifically, even with optimistic assumptions regarding the ability of

corals to rapidly adapt to thermal stress, RCP4.5 is projected to result

in long-term degradation of two-thirds of coral reefs, compared with

ne-third of them under RCP3PD (Box CC-CR). Hence, maintenance of

moderately healthy coral reefs is consistent only with scenarios in the

scenarios in the 430 to 480 ppm CO

-eq category; while some reef

protection is achieved with scenarios in the category 480 to 530 ppm

-eq. A low level of protection exists for the category 530 to 580 ppm

-eq while all other categories exceed the 560 ppm level.

Finally, scenarios in category 6 with a concentration level of >1000 ppm

-eq are projected to result in median 2100 temperature rise of 4.1°C

to 4.8°C (range 2.8–7.8°C) above preindustrial with negligible chances

to constrain it below 2°C above preindustrial (Figure 19-7a) and would

allow significant key risks to persist in all the areas listed in Table 19-4.

Risk is at the red level for all RFCs except unique and threatened systems,

where risk is at the purple level indicating infeasibility of adaptation.

For the distribution of impacts, risk reaches the transition to purple if

temperatures rise in excess of 4°C above preindustrial levels. For the

scenarios with a concentration level between 720 ppm and 1000 ppm

(category 5) outcomes for risk levels are high or very high, except that

risk of global aggregate impacts ranges from the transition zone from

moderate risk up to high risk.

Scenarios with rapid, early mitigation (particularly those with a 2100

-eq concentration of 430 to 480 ppm) generally delay the onset of a

given global annual mean temperature rise until several decades later in

the century than is the case for scenarios with slower, delayed mitigation

or no mitigation (such as those with a 2100 CO

-eq concentration of 720

to 1000 ppm), thus allowing impacts to be further reduced by adaptation

during this time.

19.7.2. Limits to Mitigation

Mitigation possibilities, such as those implicit in scenarios discussed in

Section 19.7.1, are not unlimited. Assessment of maximum feasible

mitigation (and lowest feasible emissions pathways) must account for the

fact that feasibility is a subjective concept encompassing technological,

economic, political, and social dimensions (Hare et al., 2010). Most

mitigation studies have focused on technical feasibility, for example,

demonstrating that it is possible to reduce emissions enough to have

at least a 50% chance of limiting warming to less than 2°C relative to

preindustrial (den Elzen and van Vuuren, 2007; Clarke et al., 2009;

Edenhofer et al., 2010; Hare et al., 2010; O’Neill et al., 2010), taking into

account uncertainty in climate and carbon cycle response to emissions

(see WGI AR5 Section 12.5.4 for a discussion of uncertainties in the

relationship between emissions and long-term climate stabilization

targets). RCP2.6, based on an integrated assessment model-based

mitigation scenario (van Vuuren et al., 2012), is unlikely to produce more

than 2°C of warming relative to preindustrial (medium confidence; WGI

AR5 Section 12.4.1.1). Such scenarios lead to pathways in which global

emissions peak within the next 1 to 2 decades and decline to 50 to 85%

below 2000 levels by 2050 (or 40 to 70% compared to 1990 levels), and

in some cases exhibit negative emissions before the end of the century

(den Elzen et al., 2007, 2010; IPCC, 2007b; van Vuuren et al. 2012). Very

few integrated assessment model-based scenarios in the literature

demonstrate the feasibility of limiting warming to a maximum of 1.5°C

with at least 50% likelihood (Rogelj et al., 2012); most 1.5°C scenarios

have been based on stylized emissions pathways (Hare et al.., 2010;

1084

Chapter 19 Emergent Risks and Key Vulnerabilities

anger et al., 2012). The highest emission reduction rate considered in

most integrated modeling studies that attempt to minimize mitigation

cost is typically between 3 and 4% but with larger values not ruled out

although some studies find that for an additional cost higher rates may

be achievable (den Elzen et al., 2010; O’Neill et al., 2010).

However, most studies of feasibility include a number of idealized

assumptions, including availability of a wide range of mitigation

technologies such as carbon capture and storage (CCS) and large-scale

renewable and biomass energy. Most also assume universal participation

in mitigation efforts beginning immediately, economically optimal

reductions (i.e., reductions are made wherever they are cheapest), and no

constraints on policy implementation. Any deviation from these idealized

assumptions can significantly limit feasible mitigation reductions (Knopf

et al., 2010; Rogelj et al., 2012). For example, delayed participation

in reductions by non-Organisation for Economic Co-operation and

Development (OECD) countries made concentration limits such as not

exceeding 450 ppm CO

-eq (roughly consistent with a 50% chance of

remaining below 2°C relative to preindustrial), and in some cases even

550 ppm CO

-eq, unachievable in some models unless temporary

overshoot of these targets (Izrael and Semenov, 2006) were allowed

(Clarke et al., 2009), but not in others (Waldhoff and Fawcett, 2011).

Technology limits, such as unavailability of CCS or limited expansion of

renewables or biomass, makes stabilization at 450 ppm CO

-eq (or 2°C

with a 50% chance) unachievable in some models (Krey and Riahi, 2009;

van Vliet et al., 2012). Similarly, if the political will to implement

coordinated mitigation policies within or across a large number of

countries were limited, peak emissions and subsequent reductions

would be delayed (Webster, 2008).

These considerations have led some analysts to doubt the plausibility

of limiting warming to 2°C (Anderson and Bows, 2008, 2011; Tol, 2009).

“Emergency mitigation” options have also been considered that would

go beyond the measures considered in most mitigation analyses (Swart

and Marinova, 2010). These include drastic emissions reductions achieved

through limits on energy consumption (Anderson and Bows, 2011) or

geoengineering through management of the Earth’s radiation budget

(Section 19.5.4; WGI AR5 Chapters 6, 7).

19.7.3. Avoiding Thresholds, Irreversible Change, and

Large-Scale Singularities in the Earth System

Section 19.6.3.6 discussed the RFC related to nonlinear changes in the

Earth system (“large-scale singular events”), whereby anthropogenic

forcings might cause irreversible and potentially rapid transitions over

a wide range of time scales (see Section 19.6.3; WGI AR5 SPM, TS, TFE.5,

Section 12.5; Lenton et al., 2008). The risk of triggering such transitions

generally increases with increasing anthropogenic climate forcings/

climate change (Lenton et al., 2008; Kriegler et al., 2009; Levermann et

al., 2012). Reducing GHG emissions is projected to reduce the risks of

triggering such transitions (medium confidence). Adaptation could reduce

their potential consequences, but the efficacy of adaptation might be

limited, for example for rapid transitions (Section 19.7.5).

Several studies have sought to identify levels of atmospheric GHG

concentrations or global average temperature change that would limit

he risks of triggering these transitions (e.g., Keller et al., 2005, 2008;

Lenton et al., 2008; Kriegler et al., 2009). Section 19.6.3.6 assesses

evidence regarding the relationship between global average temperature

and risks of disintegration of major ice sheets, loss of Arctic sea ice,

shutdown of the AMOC, carbon releases from temperature-related

feedback processes, and regime shifts in ecosystems. Additional aspects

of these risks are important to mitigation strategies. For example, it is

important to distinguish between triggering and experiencing a

threshold response because model simulations suggest that there can be

sizable delays between the two (e.g., Lenton et al., 2008). The location

of these trigger points can be difficult to determine from process-based

models alone, as some of these models lack potentially important

processes (see e.g., WGI AR5 Chapter 13).

In this situation, expert elicitations can provide additional useful

information for risk assessments. One such assessment based on

expert elicitation (Lenton et al., 2008) finds that limiting global mean

temperature increase to approximately 3°C above recent (1980–1999)

values would considerably reduce the risks of triggering some nonlinear

responses. In general, there is low confidence in the location of such

temperature limits owing to disagreements among experts. Estimates of

such temperature limits can change over time (Oppenheimer et al., 2008)

and may be subject to overconfidence that can introduce a downward

bias in risk estimates of low-probability events (Morgan and Henrion,

1990). The climate threshold responses can interact (e.g., Kriegler et al.,

2009). Other climate change metrics (e.g., rates of changes or atmospheric

concentrations) can also be important in the consideration of

response strategies aimed at reducing the risk of crossing thresholds

(McAlpine et al., 2010; Lenton, 2011a).

Several analyses have performed risk- and decision-analyses for specific

thresholds, mostly focusing on a persistent weakening or collapse of the

AMOC (Zickfeld and Bruckner, 2008; Urban and Keller, 2010; Bahn et

al., 2011; McInerney et al., 2012). Experiencing AMOC collapse has been

assessed as very unlikely in this century and there is low confidence in

assessing the AMOC beyond the 21st century (WGI AR5 SPM). However,

owing to lags in the ocean system, the probability of triggering an

eventual collapse differs from that of experiencing such an outcome

(Urban and Keller, 2010). A probabilistic analysis sampling a subset of

the relevant uncertainties concluded that reducing the probability of a

collapse within the next few centuries to one in ten requires emissions

reductions of roughly 60% relative to a business-as-usual strategy by

2050 (McInerney and Keller, 2008). Bruckner and Zickfeld (2009) show

that, under their worst-case assumptions about key parameter values,

emissions mitigation would need to begin within the next 2 decades to

avoid reducing the overturning rate by more than 50%.

Threshold risk estimates and evaluations of risk-management strategies

are sensitive to factors such as the representation of uncertainties and

the decision-making frameworks used (Polasky et al., 2011; McInerney

et al., 2012). Several analyses have examined how the consideration of

threshold events affects response strategies. For example, the design of

risk-management strategies could be informed by observation and

projection systems that would provide an actionable early warning

signal of an approaching threshold response. Learning about key

uncertain parameters (e.g., climate sensitivity or impacts of a threshold

response) can considerably affect risk-management strategies and have

1085

Emergent Risks and Key Vulnerabilities Chapter 19

sizable economic value of information (Keller et al., 2004; Lorenz et

al., 2012). However, there is limited evidence about the feasibility and

requirements for such systems owing to the small number of studies

and their focus on highly simplified situations (Keller and McInerney,

2008; Lenton, 2011b; Lorenz et al., 2012). In some decision-analytic

frameworks, knowing that a threshold has been crossed can lead to

reductions in emissions mitigation and a shift of resources toward

adaptation and/or geoengineering (Keller et al., 2004; Guillerminet and

Tol, 2008; Swart and Marinova, 2010; Lenton, 2011b).

19.7.4. Avoiding Tipping Points

in Social/Ecological Systems

Tipping points (see Glossary) in socio-ecological systems are defined as

thresholds beyond which impacts increase nonlinearly to the detriment

of both human and natural systems. These can be initiated rapidly,

inducing a need for rapid response. For example, regime shifts have

already occurred in marine food webs (Byrnes et al., 2007; Green et

al., 2008; Alheit, 2009; Section 6.3.6) due to (observed) changes in sea

surface temperature, changes in salinity, natural climate variability,

and/or overfishing.

Because human and ecological systems are linked by the services that

ecosystems provide to society (McLeod and Leslie, 2009; Lubchenco and

Petes, 2010), tipping points may be crossed when either the ecosystem

services are disrupted and/or social/economic networks are disrupted

(Renaud et al., 2010). Climate change provides a stress that increases

the risk that tipping points will be crossed, although they may be

crossed due to other types of stresses even in the absence of climate

change. For example, in dryland ecosystems, overgrazing has caused

grassland-to-desert transitions (Pimm, 2009).

The likelihood of crossing tipping points due to climate change may be

reduced by preserving ecosystem services through (1) limiting the level

and rate of climate change (medium confidence) and/or (2) removing

concomitant stresses such as overgrazing, fishing, habitat destruction,

and pollution. Most literature currently focuses on strategy (2), and

there is limited information about the exact levels and rates of climate

change that specific coupled socioeconomic systems can withstand.

Examples of strategy (2) include maintaining resilience of coral reefs

and cephalopod or piscivorous seabird populations by removal of

concomitant stress from fishing (Andre et al., 2010; Anthony et al., 2011;

see also Sections 6.3.6, 30.6.2) or expanding protected area networks

(Brodie et al., 2012). Removal of concomitant stress such as nutrient

loading can reduce the chance of a regime shift (Jurgensone et al., 2011)

in coral reef ecosystems (De’ath et al., 2012). Sometimes management

can reverse the crossing of a tipping point, for example, by adding

sediment to a submerged salt marsh (Stagg and Mendelssohn, 2010).

Strategy (2) is enhanced by resilience-based management approaches

in ecosystems (Walker and Salt, 2006; Lubchenco and Petes, 2010; Allen

et al., 2011; Selig et al., 2012). A high level of biodiversity increases

ecosystem resilience and can enable recovery after crossing a tipping

point (Brierley and Kingsford, 2009; Lubchenco and Petes, 2010).

Strategy (2) generally becomes ineffective once climate changes beyond

an uncertain and spatially variable threshold; also successive thresholds

may be crossed as stress increases (Renaud et al., 2010).

onitoring that aims to detect a slowdown in the recovery of systems

from small changes (van Nes and Scheffer, 2005) or to measure an

appropriate indicator (Biggs et al., 2008) may give warning that a system

is a approaching a regime shift, justifying intervention of type (2) (Guttal

and Jayaprakash, 2009; Brock and Carpenter, 2010). Such indicators

have been identified for the desertification process in the Mediterranean

(Kéfi et al., 2007) and for landscape fire dynamics (Zinck et al., 2011;

McKenzie and Kennedy, 2012).

19.7.5. Limits to Adaptation

Sections 16.2 and 16.4 provide a thorough assessment of the literature

on limits to adaptation. Discussions are beginning on the nature of such

limits, for example, in terms of different dimensions of the limits to

adaptation, including financial or economic limits to adapt, but also

social and political or cognitive limits of adaptation. Limits to adaptation

(see, e.g., Adger et al., 2009) are also recognized in terms of specific

geographies, for example, SIDS and their limited ability to adapt to

increasing impacts of sea level rise, the limits to adaptation of urban

agglomerations and urban dwellers in low-lying coastal zones (see,

e.g., Birkmann et al., 2010), or in relation to loss of water supplies as a

result of glacier retreat (Orlove, 2009). Overall, the concept of limits to

adaptation is closely related to key vulnerabilities and key risks including

those identified in Table 19-4 and Box CC-KR, because this concept

helps define residual risk.

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