Coral Reefs

Jean-Pierre Gattuso (France), Ove Hoegh-Guldberg (Australia), Hans-Otto Pörtner (Germany)

Coral reefs are shallow-water ecosystems that consist of reefs made of calcium carbonate which

is mostly secreted by reef-building corals and encrusting macroalgae. They occupy less than 0.1%

of the ocean ﬂoor yet play multiple important roles throughout the tropics, housing high levels

of biological diversity as well as providing key ecosystem goods and services such as habitat

for ﬁsheries, coastal protection, and appealing environments for tourism (Wild et al., 2011).

About 275 million people live within 30 km of a coral reef (Burke et al., 2011) and derive some

beneﬁts from the ecosystem services that coral reefs provide (Hoegh-Guldberg, 2011), including

provisioning (food, livelihoods, construction material, medicine), regulating (shoreline protection,

water quality), supporting (primary production, nutrient cycling), and cultural (religion, tourism)

services. This is especially true for the many coastal and small island nations in the world’s

tropical regions (Section 29.3.3.1).

Coral reefs are one of the most vulnerable marine ecosystems (high conﬁdence; Sections

5.4.2.4, 6.3.1, 6.3.2, 6.3.5, 25.6.2, and 30.5), and more than half of the world’s reefs are under

medium or high risk of degradation (Burke et al., 2011). Most human-induced disturbances to

coral reefs were local until the early 1980s (e.g., unsustainable coastal development, pollution,

nutrient enrichment, and overﬁshing) when disturbances from ocean warming (principally mass

coral bleaching and mortality) began to become widespread (Glynn, 1984). Concern about the

impact of ocean acidiﬁcation on coral reefs developed over the same period, primarily over the

implications of ocean acidiﬁcation for the building and maintenance of the calcium carbonate

reef framework (Box CC-OA).

A wide range of climatic and non-climatic drivers affect corals and coral reefs and negative

impacts have already been observed (Sections 5.4.2.4, 6.3.1, 6.3.2, 25.6.2.1, 30.5.3, 30.5.6).

Bleaching involves the breakdown and loss of endosymbiotic algae, which live in the coral tissues

and play a key role in supplying the coral host with energy (see Section 6.3.1. for physiological

details and Section 30.5 for a regional analysis). Mass coral bleaching and mortality, triggered

by positive temperature anomalies (high conﬁdence), is the most widespread and conspicuous

impact of climate change (Figure CR-1A and B, Figure 5-3; Sections 5.4.2.4, 6.3.1, 6.3.5, 25.6.2.1,

30.5, and 30.8.2). For example, the level of thermal stress at most of the 47 reef sites where

bleaching occurred during 1997–1998 was unmatched in the period 1903–1999 (Lough, 2000).

Ocean acidiﬁcation reduces biodiversity (Figure CR-1C and D) and the calciﬁcation rate of corals

(high conﬁdence; Sections 5.4.2.4, 6.3.2, 6.3.5) while at the same time increasing the rate of

dissolution of the reef framework (medium conﬁdence; Section 5.2.2.4) through stimulation of

biological erosion and chemical dissolution. Taken together, these changes will tip the calcium

carbonate balance of coral reefs toward net dissolution (medium conﬁdence; Section 5.4.2.4).

Cross-Chapter Box

Coral Reefs

Ocean warming and acidiﬁcation have synergistic effects in several reef-builders (Section 5.2.4.2, 6.3.5). Taken together, these changes will

erode habitats for reef-based ﬁsheries, increase the exposure of coastlines to waves and storms, as well as degrading environmental features

important to industries such as tourism (high conﬁdence; Section 6.4.1.3, 25.6.2, 30.5).

2011

Partitioned annual mortality (% cover)

Coral cover (%)

(a) Before bleaching

(e) (f)

(d) Low pH

(b) After bleaching

1986 1991 1996 2001 2006

1986 1991 1996 2001 20061986 1991 1996 2001 2006 2011

Crown-of-thorns starﬁsh

Cyclones

Bleaching

mean ±2 standard errors

mean

N=214

Figure CR-1 | (a, b) The same coral community before and after a bleaching event in February 2002 at 5 m depth, Halfway Island, Great Barrier Reef. Approximately 95% of the

coral community was severely bleached in 2002 (Elvidge et al., 2004). Corals experience increasing mortality as the intensity of a heating event increases. A few coral species

show the ability to shufﬂe symbiotic communities of dinoﬂagellates and appear to be more tolerant of warmer conditions (Berkelmans and van Oppen, 2006; Jones et al., 2008).

(c, d) Three CO

seeps in Milne Bay Province, Papua New Guinea show that prolonged exposure to high CO

is related to fundamental changes in the ecology of coral reefs

(Fabricius et al., 2011), including reduced coral diversity (–39%), severely reduced structural complexity (–67%), lower density of young corals (–66%), and fewer crustose

coralline algae (–85%). At high CO

sites (d; median pH

~7.8, where pH

is pH on the total scale), reefs are dominated by massive corals while corals with high morphological

complexity are underrepresented compared with control sites (c; median pH

~8.0). Reef development ceases at pH

values below 7.7. (e) Temporal trend in coral cover for the

whole Great Barrier Reef over the period 1985–2012 (N=number of reefs, De'ath et al., 2012). (f) Composite bars indicate the estimated mean coral mortality for each year, and

the sub-bars indicate the relative mortality due to crown-of-thorns starﬁsh, cyclones, and bleaching for the whole Great Barrier Reef (De'ath et al., 2012). (Photo credit: R.

Berkelmans (a and b) and K. Fabricius (c and d).)

Coral Reefs

Cross-Chapter Box

A growing number of studies have reported regional scale changes in coral calciﬁcation and mortality that are consistent with the scale and

impact of ocean warming and acidiﬁcation when compared to local factors such as declining water quality and overﬁshing (Hoegh-Guldberg

et al., 2007). The abundance of reef building corals is in rapid decline in many Paciﬁc and Southeast Asian regions (very high conﬁdence, 1 to

2% per year for 1968–2004; Bruno and Selig, 2007). Similarly, the abundance of reef-building corals has decreased by more than 80% on many

Caribbean reefs (1977–2001; Gardner et al., 2003), with a dramatic phase shift from corals to seaweeds occurring on Jamaican reefs (Hughes,

1994). Tropical cyclones, coral predators, and thermal stress-related coral bleaching and mortality have led to a decline in coral cover on the

Great Barrier Reef by about 51% between 1985 and 2012 (Figure CR-1E and F). Although less well documented, benthic invertebrates other

than corals are also at risk (Przeslawski et al., 2008). Fish biodiversity is threatened by the permanent degradation of coral reefs, including in a

marine reserve (Jones et al., 2004).

Future impacts of climate-related drivers (ocean warming, acidiﬁcation, sea level rise as well as more intense tropical cyclones and rainfall

events) will exacerbate the impacts of non-climate–related drivers (high conﬁdence). Even under optimistic assumptions regarding corals being

able to rapidly adapt to thermal stress, one-third (9 to 60%, 68% uncertainty range) of the world’s coral reefs are projected to be subject to

long-term degradation (next few decades) under the Representative Concentration Pathway (RCP)3-PD scenario (Frieler et al., 2013). Under

the RCP4.5 scenario, this fraction increases to two-thirds (30 to 88%, 68% uncertainty range). If present-day corals have residual capacity to

acclimate and/or adapt, half of the coral reefs may avoid high-frequency bleaching through 2100 (limited evidence, limited agreement; Logan

et al., 2014). Evidence of corals adapting rapidly, however, to climate change is missing or equivocal (Hoegh-Guldberg, 2012).

Damage to coral reefs has implications for several key regional services:

• Resources: Coral reefs account for 10 to 12% of the ﬁsh caught in tropical countries, and 20 to 25% of the ﬁsh caught by developing

nations (Garcia and de Leiva Moreno, 2003). More than half (55%) of the 49 island countries considered by Newton et al. (2007) are

already exploiting their coral reef ﬁsheries in an unsustainable way and the production of coral reef ﬁsh in the Paciﬁc is projected to

decrease 20% by 2050 under the Special Report on Emission Scenarios (SRES) A2 emissions scenario (Bell et al., 2013).

• Coastal protection: Coral reefs contribute to protecting the shoreline from the destructive action of storm surges and cyclones (Sheppard

et al., 2005), sheltering the only habitable land for several island nations, habitats suitable for the establishment and maintenance of

mangroves and wetlands, as well as areas for recreational activities. This role is threatened by future sea level rise, the decrease in coral

cover, reduced rates of calciﬁcation, and higher rates of dissolution and bioerosion due to ocean warming and acidiﬁcation (Sections

5.4.2.4, 6.4.1, 30.5).

• Tourism: More than 100 countries beneﬁt from the recreational value provided by their coral reefs (Burke et al., 2011). For example, the

Great Barrier Reef Marine Park attracts about 1.9 million visits each year and generates A$5.4 billion to the Australian economy and

54,000 jobs (90% in the tourism sector; Biggs, 2011).

Coral reefs make a modest contribution to the global gross domestic product (GDP) but their economic importance can be high at the country

and regional scales (Pratchett et al., 2008). For example, tourism and ﬁsheries represent 5% of the GDP of South Paciﬁc islands (average for

2001–2011; Laurans et al., 2013). At the local scale, these two services provided in 2009–2011 at least 25% of the annual income of villages in

Vanuatu and Fiji (Pascal, 2011; Laurans et al., 2013).

Isolated reefs can recover from major disturbance, and the beneﬁts of their isolation from chronic anthropogenic pressures can outweigh the

costs of limited connectivity (Gilmour et al., 2013). Marine protected areas (MPAs) and ﬁsheries management have the potential to increase

ecosystem resilience and increase the recovery of coral reefs after climate change impacts such as mass coral bleaching (McLeod et al., 2009).

Although they are key conservation and management tools, they are unable to protect corals directly from thermal stress (Selig et al., 2012),

suggesting that they need to be complemented with additional and alternative strategies (Rau et al., 2012; Billé et al., 2013). While MPA

networks are a critical management tool, they should be established considering other forms of resource management (e.g., ﬁshery catch limits

and gear restrictions) and integrated ocean and coastal management to control land-based threats such as pollution and sedimentation. There

is medium conﬁdence that networks of highly protected areas nested within a broader management framework can contribute to preserving

coral reefs under increasing human pressure at local and global scales (Salm et al., 2006). Locally, controlling the input of nutrients and

sediment from land is an important complementary management strategy (Mcleod et al., 2009) because nutrient enrichment can increase the

susceptibility of corals to bleaching (Wiedenmann et al., 2013) and coastal pollutants enriched with fertilizers can increase acidiﬁcation (Kelly

et al., 2011). In the long term, limiting the amount of ocean warming and acidiﬁcation is central to ensuring the viability of coral reefs and

dependent communities (high conﬁdence; Section 5.2.4.4, 30.5).

Bell, J.D., A. Ganachaud, P.C. Gehrke, S.P. Grifﬁths, A.J. Hobday, O. Hoegh-Guldberg, J.E. Johnson, R. Le Borgne, P. Lehodey, J.M. Lough, R.J. Matear, T.D. Pickering, M.S.

Pratchett, A. Sen Gupta, I. Senina and M. Waycott, 2013: Mixed responses of tropical Paciﬁc ﬁsheries and aquaculture to climate change. Nature Climate Change,

3(6), 591-599.

Berkelmans, R. and M.J.H. van Oppen, 2006: The role of zooxanthellae in the thermal tolerance of corals: a ‘nugget of hope’ for coral reefs in an era of climate change.

Proceedings of the Royal Society B: Biological Sciences, 273(1599), 2305-2312.

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Coral Reefs

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This cross-chapter box should be cited as:

Ecosystem-Based

Approaches to Adaptation—

Emerging Opportunities

Rebecca Shaw (USA), Jonathan Overpeck (USA), Guy Midgley (South Africa)

101

Ecosystem-based adaptation (EBA), deﬁned as the use of biodiversity and ecosystem services as

part of an overall adaptation strategy to help people to adapt to the adverse effects of climate

change (CBD, 2009), integrates the use of biodiversity and ecosystem services into climate

change adaptation strategies (e.g., CBD, 2009; Munroe et al., 2011; see IPCC AR5 WGII Chapters

3, 4, 5, 8, 9, 13, 14, 15, 16, 19, 22, 25, and 27). EBA is implemented through the sustainable

management of natural resources and conservation and restoration of ecosystems, to provide

and sustain services that facilitate adaptation both to climate variability and change (Colls et al.,

2009). It also sets out to take into account the multiple social, economic, and cultural co-beneﬁts

for local communities (CBD COP 10 Decision X/33).

EBA can be combined with, or even serve as a substitute for, the use of engineered infrastructure

or other technological approaches. Engineered defenses such as dams, sea walls, and levees

adversely affect biodiversity, potentially resulting in maladaptation due to damage to ecosystem

regulating services (Campbell et al., 2009; Munroe et al., 2011). There is some evidence that the

restoration and use of ecosystem services may reduce or delay the need for these engineering

solutions (CBD, 2009). EBA offers lower risk of maladaptation than engineering solutions in

that their application is more ﬂexible and responsive to unanticipated environmental changes.

Well-integrated EBA can be more cost effective and sustainable than non-integrated physical

engineering approaches (Jones et al., 2012), and may contribute to achieving sustainable

development goals (e.g., poverty reduction, sustainable environmental management, and even

mitigation objectives), especially when they are integrated with sound ecosystem management

approaches (CBD, 2009). In addition, EBA yields economic, social, and environmental co-beneﬁts

in the form of ecosystem goods and services (World Bank, 2009).

EBA is applicable in both developed and developing countries. In developing countries where

economies depend more directly on the provision of ecosystem services (Vignola et al., 2009),

EBA may be a highly useful approach to reduce risks to climate change impacts and ensure that

development proceeds on a pathways that are resilient to climate change (Munang et al., 2013).

EBA projects may be developed by enhancing existing initiatives, such as community-based

adaptation and natural resource management approaches (e.g., Khan et al., 2012, Midgley et al.,

2012; Roberts et al., 2012).

Examples of ecosystem based approaches to adaptation include:

• Sustainable water management, where river basins, aquifers, ﬂood plains, and their

associated vegetation are managed or restored to provide resilient water storage and

Cross-Chapter Box

Ecosystem-Based Approaches to Adaptation–Emerging Opportunities

102

enhanced baseﬂows, ﬂood regulation and protection services, reduction of erosion/siltation rates, and more ecosystem goods (e.g.,

Opperman et al., 2009; Midgley et al., 2012)

• Disaster risk reduction through the restoration of coastal habitats (e.g., mangroves, wetlands, and deltas) to provide effective measure

against storm-surges, saline intrusion, and coastal erosion (Jonkman et al., 2013)

• Sustainable management of grasslands and rangelands to enhance pastoral livelihoods and increase resilience to drought and ﬂooding

• Establishment of diverse and resilient agricultural systems, and adapting crop and livestock variety mixes to secure food provision.

Traditional knowledge may contribute in this area through, for example, identifying indigenous crop and livestock genetic diversity, and

water conservation techniques.

• Management of ﬁre-prone ecosystems to achieve safer ﬁre regimes while ensuring the maintenance of natural processes

Application of EBA, like other approaches, is not without risk, and risk/beneﬁt assessments will allow better assessment of opportunities

offered by the approach (CBD, 2009). The examples of EBA are too few and too recent to assess either the risks or the beneﬁts comprehensively

at this stage. EBA is still a developing concept but should be considered alongside adaptation options based more on engineering works or

social change, and existing and new cases used to build understanding of when and where its use is appropriate.

Climate mitigation Climate change impacts

Ecosystem protection

and restoration

Sustainable

economies with

reduced risk of

climate impacts

Increase in human well-being

Sustained ecosystem

services delivery

Biodiversity retention,

ecosystem resilience, and

reduced vulnerability

Degradation of

ecological processes

and loss of biodiversity

Loss of

ecosystem

services

Loss of human

well-being

Increased pressure

on ecosystems/

natural capital

With ecosystem-based

adaptation

Without ecosystem-based

adaptation

Figure EA-1 |

Adapted from Munang et al. (2013). Ecosystem-based adaptation (EBA) uses the capacity of nature to buffer human systems from the adverse impacts of climate

change. Without EBA, climate change may cause degradation of ecological processes (central white panel) leading to losses in human well-being. Implementing EBA (outer blue

panel) may reduce or offset these adverse impacts resulting in a virtuous cycle that reduces climate-related risks to human communities, and may provide mitigation beneﬁts.

Campbell, A., V. Kapos, J. Scharlemann, P. Bubb, A. Chenery, L. Coad, B. Dickson, N. Doswald, M. Khan, F. Kershaw, and M. Rashid, 2009: Review of the Literature on

the Links between Biodiversity and Climate Change: Impacts, Adaptation and Mitigation. CBD Technical Series No. 42, Secretariat of the Convention on Biological

Diversity (CBD), Montreal, QC, Canada, 124 pp.

CBD, 2009: Connecting Biodiversity and Climate Change Mitigation and Adaptation: Report of the Second Ad Hoc Technical Expert Group on Biodiversity and Climate

Change. CBD Technical Series No. 41, Secretariat of the Convention on Biological Diversity (CBD), Montreal, QC, Canada, 126 pp.

Colls, A., N. Ash, and N. Ikkala, 2009: Ecosystem-Based Adaptation: A Natural Response to Climate Change. International Union for Conservation of Nature and Natural

Resources (IUCN), Gland, Switzerland, 16 pp.

Jones, H.P., D.G. Hole, and E.S. Zavaleta, 2012: Harnessing nature to help people adapt to climate change. Nature Climate Change, 2(7), 504-509.

Jonkman, S.N., M.M. Hillen, R.J. Nicholls, W. Kanning, and M. van Ledden, 2013: Costs of adapting coastal defences to sea-level rise – new estimates and their

implications. Journal of Coastal Research, 29(5), 1212-1226.

Khan, A.S., A. Ramachandran, N. Usha, S. Punitha, and V. Selvam, 2012: Predicted impact of the sea-level rise at Vellar-Coleroon estuarine region of Tamil Nadu coast in

India: mainstreaming adaptation as a coastal zone management option. Ocean & Coastal Management, 69, 327-339.

Midgley, G.F., S. Marais, M. Barnett, and K. Wågsæther, 2012: Biodiversity, Climate Change and Sustainable Development – Harnessing Synergies and Celebrating

Successes. Final Technical Report, The Adaptation Network Secretariat, hosted by Indigo Development & Change and The Environmental Monitoring Group,

Nieuwoudtville, South Africa. 70 pp.

Munang, R, I. Thiaw, K. ALverson, M. Mumba, J. Liu, and M. Rivington, 2013: Climate change and ecosystem-based adaptation: a new pragmatic approach to buffering

climate change impacts. Current Opinion in Environmental Sustainability, 5(1), 67-71.

Munroe, R., N. Doswald, D. Roe, H. Reid, A. Giuliani, I. Castelli, and I. Moller, 2011: Does EbA Work? A Review of the Evidence on the Effectiveness of Ecosystem-Based

Approaches to Adaptation. Research collaboration between BirdLife International, United Nations Environment Programme-World Conservation Monitoring Centre

(UNEP-WCMC), and the University of Cambridge, Cambridge, UK, and the International Institute for Environment and Development (IIED), London, UK, 4 pp.

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Cross-Chapter Box

103

Opperman, J.J., G.E. Galloway, J. Fargione, J.F. Mount, B.D. Richter, and S. Secchi, 2009: Sustainable ﬂoodplains through large-scale reconnection to rivers. Science,

326(5959), 1487-1488.

Roberts, D., R. Boon, N. Diederichs, E. Douwes, N. Govender, A. McInnes, C. McLean, S. O’Donoghue, and M. Spires, 2012: Exploring ecosystem-based adaptation in

Durban, South Africa: “learning-by-doing” at the local government coal face. Environment and Urbanization, 24(1), 167-195.

Vignola, R., B. Locatelli, C. Martinez, and P. Imbach, 2009: Ecosystem-based adaptation to climate change: what role for policymakers, society and scientists? Mitigation

and Adaptation Strategies for Global Change, 14(8), 691-696.

Shaw, M.R., J.T. Overpeck, and G.F. Midgley, 2014: Cross-chapter box on ecosystem based approaches to adaptation—emerging opportunities. 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. 101-103.

This cross-chapter box should be cited as:

Gender and Climate Change

Katharine Vincent (South Africa), Petra Tschakert (U.S.A.), Jon Barnett (Australia), Marta G.

Rivera-Ferre (Spain), Alistair Woodward (New Zealand)

105

Gender, along with sociodemographic factors of age, wealth, and class, is critical to the ways

in which climate change is experienced. There are signiﬁcant gender dimensions to impacts,

adaptation, and vulnerability. This issue was raised in WGII AR4 and SREX reports (Adger et

al., 2007; IPCC, 2012), but for the AR5 there are signiﬁcant new ﬁndings, based on multiple

lines of evidence on how climate change is differentiated by gender, and how climate change

contributes to perpetuating existing gender inequalities. This new research has been undertaken

in every region of the world (e.g. Brouwer et al., 2007; Buechler, 2009; Nelson and Stathers,

2009; Nightingale, 2009; Dankelman, 2010; MacGregor, 2010; Alston, 2011; Arora-Jonsson, 2011;

Omolo, 2011; Resureccion, 2011).

Gender dimensions of vulnerability derive from differential access to the social and environmental

resources required for adaptation. In many rural economies and resource-based livelihood

systems, it is well established that women have poorer access than men to ﬁnancial resources,

land, education, health, and other basic rights. Further drivers of gender inequality stem

from social exclusion from decision-making processes and labor markets, making women in

particular less able to cope with and adapt to climate change impacts (Paavola, 2008; Djoudi

and Brockhaus, 2011; Rijkers and Costa, 2012). These gender inequalities manifest themselves in

gendered livelihood impacts and feminisation of responsibilities: whereas both men and women

experience increases in productive roles, only women experience increased reproductive roles

(Resureccion, 2011; Section 9.3.5.1.5, Box 13-1). A study in Australia, for example, showed how

more regular occurrence of drought has put women under increasing pressure to earn off-farm

income and contribute to more on-farm labor (Alston, 2011). Studies in Tanzania and Malawi

demonstrate how women experience food and nutrition insecurity because food is preferentially

distributed among other family members (Nelson and Stathers, 2009; Kakota et al., 2011).

AR4 assessed a body of literature that focused on women’s relatively higher vulnerability to

weather-related disasters in terms of number of deaths (Adger et al., 2007). Additional literature

published since that time adds nuances by showing how socially constructed gender differences

affect exposure to extreme events, leading to differential patterns of mortality for both men and

women (high conﬁdence; Section 11.3.3, Table 12-3). Statistical evidence of patterns of male and

female mortality from recorded extreme events in 141 countries between 1981 and 2002 found

that disasters kill women at an earlier age than men (Neumayer and Plümper, 2007; see also

Box 13-1). Reasons for gendered differences in mortality include various socially and culturally

determined gender roles. Studies in Bangladesh, for example, show that women do not learn to

swim and so are vulnerable when exposed to ﬂooding (Röhr, 2006) and that, in Nicaragua, the

construction of gender roles means that middle-class women are expected to stay in the house,

Cross-Chapter Box

Gender and Climate Change

106

even during ﬂoods and in risk-prone areas (Bradshaw, 2010). Although the differential vulnerability of women to extreme events has long

been understood, there is now increasing evidence to show how gender roles for men can affect their vulnerability. In particular, men are often

expected to be brave and heroic, and engage in risky life-saving behaviors that increase their likelihood of mortality (Box 13-1). In Hai Lang

district, Vietnam, for example, more men died than women as a result of their involvement in search and rescue and protection of ﬁelds during

ﬂooding (Campbell et al., 2009). Women and girls are more likely to become victims of domestic violence after a disaster, particularly when

they are living in emergency accommodation, which has been documented in the USA and Australia (Jenkins and Phillips, 2008; Anastario et

al., 2009; Alston, 2011; Whittenbury, 2013; see also Box 13-1).

Heat stress exhibits gendered differences, reﬂecting both physiological and social factors (Section 11.3.3). The majority of studies in European

countries show women to be more at risk, but their usually higher physiological vulnerability can be offset in some circumstances by relatively

lower social vulnerability (if they are well connected in supportive social networks, for example). During the Paris heat wave, unmarried men

were at greater risk than unmarried women, and in Chicago elderly men were at greatest risk, thought to reﬂect their lack of connectedness

in social support networks which led to higher social vulnerability (Kovats and Hajat, 2008). A multi-city study showed geographical variations

in the relationship between sex and mortality due to heat stress: in Mexico City, women had a higher risk of mortality than men, although the

reverse was true in Santiago and São Paulo (Bell et al., 2008).

Recognizing gender differences in vulnerability and adaptation can enable gender-sensitive responses that reduce the vulnerability of women

and men (Alston, 2013). Evaluations of adaptation investments demonstrate that those approaches that are not sensitive to gender dimensions

and other drivers of social inequalities risk reinforcing existing vulnerabilities (Vincent et al., 2010; Arora-Jonsson, 2011; Figueiredo and Perkins,

2012). Government-supported interventions to improve production through cash-cropping and non-farm enterprises in rural economies, for

example, typically advantage men over women because cash generation is seen as a male activity in rural areas (Gladwin et al., 2001; see

also Section 13.3.1). In contrast, rainwater and conservation-based adaptation initiatives may require additional labor, which women cannot

necessarily afford to provide (Baiphethi et al., 2008). Encouraging gender-equitable access to education and strengthening of social capital

are among the best means of improving adaptation of rural women farmers (Goulden et al., 2009; Vincent et al., 2010; Below et al., 2012) and

could be used to complement existing initiatives mentioned above that beneﬁt men. Rights-based approaches to development can inform

adaptation efforts as they focus on addressing the ways in which institutional practices shape access to resources and control over decision-

making processes, including through the social construction of gender and its intersection with other factors that shape inequalities and

vulnerabilities (Tschakert and Machado, 2012; Bee et al., 2013; Tschakert, 2013; see also Section 22.4.3 and Table 22-5).

Adger, W.N., S. Agrawala, M.M.Q. Mirza, C. Conde, K. O’Brien, J. Pulhin, R. Pulwarty, B. Smit, and K. Takahashi, 2007: Chapter 17: Assessment of adaptation practices,

options, constraints and capacity. In: Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report

of the Intergovernmental Panel on Climate Change [Parry, M.L., O.F. Canziani, J.P. Palutikof, P.J. van der Linden, and C.E. Hanson (ed.)]. IPCC, Geneva, Switzerland, pp.

719-743.

Alston, M., 2011: Gender and climate change in Australia. Journal of Sociology, 47(1), 53-70.

Alston, M., 2013: Women and adaptation. Wiley Interdisciplinary Reviews: Climate Change, (4)5, 351-358.

Anastario, M., N. Shebab, and L. Lawry, 2009: Increased gender-based violence among women internally displaced in Mississippi 2 years post-Hurricane Katrina. Disaster

Medicine and Public Health Preparedness, 3(1), 18-26.

Arora-Jonsson, S., 2011: Virtue and vulnerability: discourses on women, gender and climate change. Global Environmental Change, 21, 744-751.

Baiphethi, M.N., M. Viljoen, and G. Kundhlande, 2008: Rural women and rainwater harvesting and conservation practices: anecdotal evidence from the Free State and

Eastern Cape. Agenda, 22(78), 163-171.

Bee, B., M. Biermann, and P. Tschakert, 2013: Gender, development, and rights-based approaches: lessons for climate change adaptation and adaptive social protection.

In: Research, Action and Policy: Addressing the Gendered Impacts of Climate Change [Alston, M. and K. Whittenbury (eds.)]. Springer, Dordrecht, Netherlands, pp.

95-108.

Bell, M.L., M.S. O’Neill, N. Ranjit, V.H. Borja-Aburto, L.A. Cifuentes, and N.C. Gouveia, 2008: Vulnerability to heat-related mortality in Latin America: a case-crossover study

in Sao Paulo, Brazil, Santiago, Chile and Mexico City, Mexico. International Journal of Epidemiology, 37(4), 796-804.

Below, T.B., K.D. Mutabazi, D. Kirschke, C. Franke, S. Sieber, R. Siebert, and K. Tscherning, 2012: Can farmers’ adaptation to climate change be explained by socio-economic

household-level variables? Global Environmental Change, 22(1), 223-235.

Bradshaw, S., 2010: Women, poverty, and disasters: exploring the links through Hurricane Mitch in Nicaragua. In: The International Handbook of Gender and Poverty:

Concepts, Research, Policy [Chant, S. (ed.)]. Edward Elgar Publishing, Cheltenham, UK, pp. 627-632.

Brouwer, R., S. Akter, L. Brander, and E. Haque, 2007: Socioeconomic vulnerability and adaptation to environmental risk: a case study of climate change and ﬂooding in

Bangladesh. Risk Analysis, 27(2), 313-326.

Campbell, B., S. Mitchell, and M. Blackett, 2009: Responding to Climate Change in Vietnam. Opportunities for Improving Gender Equality. A Policy Discussion Paper,

Oxfam in Viet Nam and United Nations Development Programme-Viet Nam (UNDP-Viet Nam), Ha noi, Viet Nam, 62 pp.

Dankelman, I., 2010: Introduction: exploring gender, environment, and climate change. In: Gender and Climate Change: An Introduction [Dankelman, I. (ed.)]. Earthscan,

London, UK and Washington, DC, USA, pp. 1-18.

Djoudi, H. and M. Brockhaus, 2011: Is adaptation to climate change gender neutral? Lessons from communities dependent on livestock and forests in northern Mali.

International Forestry Review, 13(2), 123-135.

Figueiredo, P. and P.E. Perkins, 2012: Women and water management in times of climate change: participatory and inclusive processes. Journal of Cleaner Production,

60(1), 188-194.

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Cross-Chapter Box

107

Gladwin, C.H., A.M. Thomson, J.S. Peterson, and A.S. Anderson, 2001: Addressing food security in Africa via multiple livelihood strategies of women farmers. Food Policy,

26(2), 177-207.

Goulden, M., L.O. Naess, K. Vincent, and W.N. Adger, 2009: Diversiﬁcation, networks and traditional resource management as adaptations to climate extremes in rural

Africa: opportunities and barriers. In: Adapting to Climate Change: Thresholds, Values and Governance [Adger, W.N., I. Lorenzoni, and K. O’Brien (eds.)]. Cambridge

University Press, Cambridge, UK, pp. 448-464.

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Intergovernmental Panel on Climate Change [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M.

Tignor, and P.M. Midgley (eds.)], Cambridge University Press, Cambridge, UK and New York, NY, USA, 582 pp.

Jenkins, P. and B. Phillips, 2008: Battered women, catastrophe, and the context of safety after Hurricane Katrina. NWSA Journal, 20(3), 49-68.

Kakota, T., D. Nyariki, D. Mkwambisi, and W. Kogi-Makau, 2011: Gender vulnerability to climate variability and household food insecurity. Climate and Development, 3(4),

298-309.

Kovats, R. and S. Hajat, 2008: Heat stress and public health: a critical review. Public Health, 29, 41-55.

MacGregor, S., 2010: ‘Gender and climate change’: from impacts to discourses. Journal of the Indian Ocean Region, 6(2), 223-238.

Nelson, V. and T. Stathers, 2009: Resilience, power, culture, and climate: a case study from semi-arid Tanzania, and new research directions. Gender & Development, 17(1),

81-94.

Neumayer, E. and T. Plümper, 2007: The gendered nature of natural disasters: the impact of catastrophic events on the gender gap in life expectancy, 1981-2002. Annals

of the Association of American Geographers, 97(3), 551-566.

Nightingale, A., 2009: Warming up the climate change debate: a challenge to policy based on adaptation. Journal of Forest and Livelihood, 8(1), 84-89.

Omolo, N., 2011: Gender and climate change-induced conﬂict in pastoral communities: case study of Turkana in northwestern Kenya. African Journal on Conﬂict

Resolution, 10(2), 81-102.

Paavola, J., 2008: Livelihoods, vulnerability and adaptation to climate change in Morogoro, Tanzania. Environmental Science & Policy, 11(7), 642-654.

Resurreccion, B.P., 2011: The Gender and Climate Debate: More of the Same or New Pathways of Thinking and Doing? Asia Security Initiative Policy Series, Working Paper

No. 10, RSIS Centre for Non-Traditional Security (NTS) Studies, Singapore, 19 pp.

Rijkers, B. and R. Costa, 2012: Gender and Rural Non-Farm Entrepreneurship. Policy Research Working Paper 6066, Macroeconomics and Growth Team, Development

Research Group, The World Bank, Washington, DC, USA, 68 pp.

Röhr, U., 2006: Gender and climate change. Tiempo, 59, 3-7.

Tschakert, P., 2013: From impacts to embodied experiences: tracing political ecology in climate change research. Geograﬁsk Tidsskrift/Danish Journal of Geography,

112(2), 144-158.

Tschakert, P. and M. Machado, 2012: Gender justice and rights in climate change adaptation: opportunities and pitfalls. Ethics and Social Welfare, 6(3), 275-289, doi:

10.1080/17496535.2012.704929.

Vincent, K., T. Cull, and E. Archer, 2010: Gendered vulnerability to climate change in Limpopo province, South Africa. In: Gender and Climate Change: An Introduction

[Dankelman, I. (ed.)]. Earthscan, London, UK and Washington, DC, USA, pp. 160-167.

Whittenbury, K., 2013: Climate change, women’s health, wellbeing and experiences of gender-based violence in Australia. In: Research, Action and Policy: Addressing the

Gendered Impacts of Climate Change [Alston, M. and K. Whittenbury (eds.)]. Springer Science, Dordrecht, Netherlands, pp. 207-222.

K.E. Vincent, P. Tschakert, Barnett, J., M.G. Rivera-Ferre, and A. Woodward, 2014: Cross-chapter box on gender and climate change. 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,

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Kingdom and New York, NY, USA, pp. 105-107.

This cross-chapter box should be cited as:

Heat Stress and Heat Waves

Lennart Olsson (Sweden), Dave Chadee (Trinidad and Tobago), Ove Hoegh-Guldberg (Australia),

Michael Oppenheimer (USA), John Porter (Denmark), Hans-O. Pörtner (Germany), David

Satterthwaite (UK),Kirk R. Smith (USA), Maria Isabel Travasso (Argentina), Petra Tschakert (USA)

109

According to WGI, it is very likely that the number and intensity of hot days have increased

markedly in the last three decades and virtually certain that this increase will continue into

the late 21st century. In addition, it is likely (medium conﬁdence) that the occurrence of heat

waves (multiple days of hot weather in a row) has more than doubled in some locations, but

very likely that there will be more frequent heat waves over most land areas after mid-century.

Under a medium warming scenario, Coumou et al. (2013) predicted that the number of monthly

heat records will be more than 12 times more common by the 2040s compared to a non-

warming world. In a longer time perspective, if the global mean temperature increases to +7°C

or more, the habitability of parts of the tropics and mid-latitudes will be at risk (Sherwood and

Huber, 2010). Heat waves affect natural and human systems directly, often with severe losses

of lives and assets as a result, and may act as triggers of tipping points (Hughes et al., 2013).

Consequently, heat stress plays an important role in several key risks noted in Chapter 19 and

CC-KR.

Economy and Society (Chapters 10, 11, 12, 13)

Environmental heat stress has already reduced the global labor capacity to 90% in peak months

with a further predicted reduction to 80% in peak months by 2050. Under a high warming

scenario (RCP8.5), labor capacity is expected to be less than 40% of present-day conditions in

peak months by 2200 (Dunne et al., 2013). Adaptation costs for securing cooling capacities and

emergency shelters during heat waves will be substantial.

Heat waves are associated with social predicaments such as increasing violence (Anderson,

2012) as well as overall health and psychological distress and low life satisfaction (Tawatsupa

et al., 2012). Impacts are highly differential with disproportional burdens on poor people, elderly

people, and those who are marginalized (Wilhelmi et al., 2012). Urban areas are expected to

suffer more due to the combined effect of climate and the urban heat island effect (Fischer et al.,

2012; see also Section 8.2.3.1). In low- and medium-income countries, adaptation to heat stress

is severely restricted for most people in poverty and particularly those who are dependent on

working outdoors in agriculture, ﬁsheries, and construction. In small-scale agriculture, women and

children are particularly at risk due to the gendered division of labor (Croppenstedt et al., 2013).

The expected increase in wildﬁres as a result of heat waves (Pechony and Shindell, 2010) is a

concern for human security, health, and ecosystems. Air pollution from wildﬁres already causes an

estimated 339,000 premature deaths per year worldwide (Johnston et al., 2012).

Cross-Chapter Box

Heat Stress

110

Human Health (Chapter 11)

Morbidity and mortality due to heat stress is now common all over the world (Barriopedro et al., 2011; Nitschke et al., 2011; Rahmstorf

and Coumou, 2011; Diboulo et al., 2012; Hansen et al., 2012). Elderly people and people with circulatory and respiratory diseases are also

vulnerable even in developed countries; they can become victims even inside their own houses (Honda et al., 2011). People in physical work are

at particular risk as such work produces substantial heat within the body, which cannot be released if the outside temperature and humidity

is above certain limits (Kjellstrom et al., 2009). The risk of non-melanoma skin cancer from exposure to UV radiation during summer months

increases with temperature (van der Leun, et al., 2008). High temperatures are also associated with an increase in air-borne allergens acting as

triggers for respiratory illnesses such as asthma, allergic rhinitis, conjunctivitis, and dermatitis (Beggs, 2010).

Ecosystems (Chapters 4, 5, 6, 30)

Tree mortality is increasing globally (Williams et al., 2013) and can be linked to climate impacts, especially heat and drought (Reichstein et al.,

2013), even though attribution to climate change is difﬁcult owing to lack of time series and confounding factors. In the Mediterranean region,

higher ﬁre risk, longer ﬁre season, and more frequent large, severe ﬁres are expected as a result of increasing heat waves in combination with

drought (Duguy et al., 2013; see also Box 4.2).

Marine ecosystem shifts attributed to climate change are often caused by temperature extremes rather than changes in the average (Pörtner

and Knust, 2007). During heat exposure near biogeographical limits, even small (<0.5°C) shifts in temperature extremes can have large effects,

often exacerbated by concomitant exposures to hypoxia and/or elevated CO

levels and associated acidiﬁcation (medium conﬁdence; Hoegh-

Guldberg et al., 2007; see also Figure 6-5; Sections 6.3.1, 6.3.5, 30.4, 30.5; CC-MB).

Most coral reefs have experienced heat stress sufﬁcient to cause frequent mass coral bleaching events in the last 30 years, sometimes

followed by mass mortality (Baker et al., 2008). The interaction of acidiﬁcation and warming exacerbates coral bleaching and mortality (very

high conﬁdence).Temperate seagrass and kelp ecosystems will decline with the increased frequency of heat waves and through the impact of

invasive subtropical species (high conﬁdence; Sections 5, 6, 30.4, 30.5, CC-CR, CC-MB).

Agriculture (Chapter 7)

Excessive heat interacts with key physiological processes in crops. Negative yield impacts for all crops past +3°C of local warming without

adaptation, even with beneﬁts of higher CO

and rainfall, are expected even in cool environments (Teixeira et al., 2013). For tropical systems

where moisture availability or extreme heat limits the length of the growing season, there is a high potential for a decline in the length of the

growing season and suitability for crops (medium evidence, medium agreement; Jones and Thornton, 2009). For example, half of the wheat-

growing area of the Indo-Gangetic Plains could become signiﬁcantly heat-stressed by the 2050s.

There is high conﬁdence that high temperatures reduce animal feeding and growth rates (Thornton et al., 2009). Heat stress reduces

reproductive rates of livestock (Hansen, 2009), weakens their overall performance (Henry et al., 2012), and may cause mass mortality of

animals in feedlots during heat waves (Polley et al., 2013). In the USA, current economic losses due to heat stress of livestock are estimated at

several billion US$ annually (St-Pierre et al., 2003).

Anderson, C.A., 2012: Climate change and violence. In: The Encyclopedia of Peace Psychology [Christie, D.J. (ed.)]. John Wiley & Sons/Blackwell, Chichester, UK, pp. 128-

132.

Baker, A.C., P.W. Glynn, and B. Riegl, 2008: Climate change and coral reef bleaching: an ecological assessment of long-term impacts, recovery trends and future outlook.

Estuarine, Coastal and Shelf Science, 80(4), 435-471.

Barriopedro, D., E.M. Fischer, J. Luterbacher, R.M. Trigo, and R. García-Herrera, 2011: The hot summer of 2010: redrawing the temperature record map of Europe. Science,

332(6026), 220-224.

Beggs, P.J., 2010: Adaptation to impacts of climate change on aeroallergens and allergic respiratory diseases. International Journal of Environmental Research and Public

Health, 7(8), 3006-3021.

Coumou, D., A. Robinson, and S. Rahmstorf, 2013: Global increase in record-breaking monthly-mean temperatures. Climatic Change, 118(3-4), 771-782.

Croppenstedt, A., M. Goldstein, and N. Rosas, 2013: Gender and agriculture: inefﬁciencies, segregation, and low productivity traps. The World Bank Research Observer,

28(1), 79-109.

Diboulo, E., A. Sie, J. Rocklöv, L. Niamba, M. Ye, C. Bagagnan, and R. Sauerborn, 2012: Weather and mortality: a 10 year retrospective analysis of the Nouna Health and

Demographic Surveillance System, Burkina Faso. Global Health Action, 5, 19078, doi:10.3402/gha.v5i0.19078.

Duguy, B., S. Paula, J.G. Pausas, J.A. Alloza, T. Gimeno, and R.V. Vallejo, 2013: Effects of climate and extreme events on wildﬁre regime and their ecological impacts. In:

Regional Assessment of Climate Change in the Mediterranean, Volume 3: Case Studies [Navarra, A. and L. Tubiana (eds.)]. Advances in Global Change Research

Series: Vol. 52, Springer, Dordrecht, Netherlands, pp. 101-134.

Dunne, J.P., R.J. Stouffer, and J.G. John, 2013: Reductions in labour capacity from heat stress under climate warming. Nature Climate Change, 3, 563-566.

Fischer, E., K. Oleson, and D. Lawrence, 2012: Contrasting urban and rural heat stress responses to climate change. Geophysical Research Letters, 39(3), L03705,

doi:10.1029/2011GL050576.

Hansen, J., M. Sato, and R. Ruedy, 2012: Perception of climate change. Proceedings of the National Academy of Sciences of the United States of America, 109(37),

E2415-E2423.

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Henry, B., R. Eckard, J.B. Gaughan, and R. Hegarty, 2012: Livestock production in a changing climate: adaptation and mitigation research in Australia. Crop and Pasture

Science, 63(3), 191-202.

Hoegh-Guldberg, O., P. Mumby, A. Hooten, R. Steneck, P. Greenﬁeld, E. Gomez, C. Harvell, P. Sale, A. Edwards, and K. Caldeira, 2007: Coral reefs under rapid climate

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Hughes, T.P., S. Carpenter, J. Rockström, M. Scheffer, and B. Walker, 2013: Multiscale regime shifts and planetary boundaries. Trends in Ecology & Evolution, 28(7), 389-

395.

Johnston, F.H., S.B. Henderson, Y. Chen, J.T. Randerson, M. Marlier, R.S. DeFries, P. Kinney, D.M. Bowman, and M. Brauer, 2012: Estimated global mortality attributable to

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Jones, P.G. and P.K. Thornton, 2009: Croppers to livestock keepers: livelihood transitions to 2050 in Africa due to climate change. Environmental Science & Policy, 12(4),

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Kjellstrom, T., R. Kovats, S. Lloyd, T. Holt, and R. Tol, 2009: The direct impact of climate change on regional labor productivity. Archives of Environmental & Occupational

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Nitschke, M., G.R. Tucker, A.L. Hansen, S. Williams, Y. Zhang, and P. Bi, 2011: Impact of two recent extreme heat episodes on morbidity and mortality in Adelaide, South

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This cross-chapter box should be cited as:

Birkmann, Joern (Germany), Rachel Licker (USA), Michael Oppenheimer (USA), Maximiliano

Campos (Costa Rica), Rachel Warren (UK), George Luber (USA), Brian O’Neill (USA), and Kiyoshi

Takahashi (Japan)

113

A Selection of the Hazards,

Key Vulnerabilities, Key

Risks, and Emergent Risks

Identiﬁed in the WGII

Contribution to the Fifth

Assessment Report

The accompanying table provides a selection of the hazards, key vulnerabilities, key risks, and

emergent risks identiﬁed in various chapters in this report (Chapters 4, 6, 7, 8, 9, 11, 13, 19, 22,

23, 24, 25, 26, 27, 28, 29, 30). Key risks are determined by hazards interacting with vulnerability

and exposure of human systems, and 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 speciﬁc development context (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. A representative set of lines of sight is provided from across WGI and WGII.

See Section 19.6.2.1 for a full description of the methods used to select these entries.

Cross-Chapter Box

114

Hazards, Key Vulnerabilities, Key Risks, and Emergent Risks

Continued next page

Hazard Key vulnerabilities Key risks Emergent risks

Terrestrial and

Inland Water

Systems

(Chapter 4)

Rising air, soil, and

water temperature

(Sections 4.2.4, 4.3.2,

4.3.3)

Exceedance of eco-physiological climate

tolerance limits of species (limited coping and

adaptive capacities), increased viability of

alien organisms

Risk of loss of native biodiversity, increase in

non-native organism dominance

Cascades of native species loss due to

interdependencies

Health response to spread of temperature-

sensitive vectors (insects)

Risk of novel and /or much more severe pest and

pathogen outbreaks

Interactions among pests, drought, and ﬁre

can lead to new risks and large negative

impacts on ecosystems.

Change in seasonality

of rain

(Section 4.3.3)

Increasing susceptibility of plants and

ecosystem services, due to mismatch between

plant life strategy and growth opportunities

Changes in plant functional type mix leading

to biome change with respective risks for

ecosystems and ecosystem services

Fire-promoting grasses grow in winter-

rainfall areas and provide fuel in dry

summers.

Ocean

Systems

(Chapter 6)

Rising water

temperature, increase

of (thermal and haline)

stratiﬁcation, and

marine acidiﬁcation

(Section 6.1.1)

Tolerance limits of endemic species surpassed

(limited coping and adaptive capacities),

increased abundance of invasive organisms,

high susceptibility and sensitivity of warm

water coral reefs and respective ecosystem

services for coastal communities (Sections

6.3.1, 6.4.1)

Risk of loss of endemic species, mixing of

ecosystem types, increased dominance of

invasive organisms.

Increasing risk of loss of coral cover and

associated ecosystem with reduction of

biodiversity and ecosystem services (Section 6.3.1)

Enhancement of risk as a result of

interactions, e.g., acidiﬁcation and warming

on calcareous organisms (Section 6.3.5)

New vulnerabilities can emerge as a result

of shifted productivity zones and species

distribution ranges, largely from low to high

latitudes (Sections 6.3.4, 6.5.1), shifting

ﬁshery catch potential with species migration

(Sections 6.3.1, 6.5.2, 6.5.3)

Risks due to unknown productivity and services

of new ecosystem types (Sections 6.4.1, 6.5.3)

Enhancement of risk due to interactions of

warming, hypoxia, acidiﬁcation, new biotic

interactions (Sections 6.3.5, 6.3.6)

Expansion of oxygen

minimum zones and

coastal dead zones

with stratiﬁcation and

eutrophication

(Section 6.1.1)

Increasing susceptibility because hypoxia

tolerance limits of larger animals surpassed,

habitat contraction and loss for midwater

ﬁshes and benthic invertebrates (Section

6.3.3)

Risk of loss of larger animals and plants, shifts to

hypoxia-adapted, largely microbial communities

with reduced biodiversity (Section 6.3.3)

Enhancement of risk due to expanding

hypoxia in warming and acidifying oceans

(Section 6.3.5)

Enhanced harmful

algal blooms in coastal

areas due to rising

water temperature

(Section 6.4.2.3)

Increasing susceptibility and limited adaptive

capacities of important ecosystems and

valuable services due to already existing

multiple stresses (Sections 6.3.5, 6.4.1)

Increasing risk due to enhanced frequency of

dinoﬂagellate blooms and respective potential

losses and degradations of coastal ecosystems

and ecosystem services (Section 6.4.2)

Disproportionate enhancement of risk due

to interactions of various stresses (Section

6.3.5)

Food Security

and Food

Production

Systems

(Chapter 7)

Rising average

temperatures and

more frequent extreme

temperatures

(Sections 7.1, 7.2,

7.4, 7.5)

Susceptibility of all elements of the food

system from production to consumption,

particularly for key grain crops

Risk of crop failures, breakdown of food

distribution and storage processes

Increase in the global population to about

9 billion combined with rising temperatures

and other trace gases such as ozone

affecting food production and quality. Upper

temperature limit to the ability of some food

systems to adapt

Extreme precipitation

and droughts (Section

7.4)

Crops, pasture, and husbandry are susceptible

and sensitive to drought and extreme

precipitation.

Risk of crop failure, risk of limited food access

and quality

Flood and droughts affect crop yields and

quality, and directly affect food access in

most developing countries. (Section 7.4)

Urban Areas

(Chapter 8)

Inland ﬂooding

(Sections 8.2.3, 8.2.4)

Large numbers of people exposed in urban

areas to ﬂood events. Particularly susceptible

are people in low-income informal settlements

with inadequate infrastructure (and often on

ﬂood plains or along river banks). These bring

serious environmental health consequences

from overwhelmed, aging, poorly maintained,

and inadequate urban drainage infrastructure

and widespread impermeable surfaces. Local

governments are often unable or unwilling to

give attention to needed ﬂood-related disaster

risk reduction. Much of the urban population

unable to get or afford housing that protects

against ﬂooding, or insurance. Certain

groups are more sensitive to ill health from

ﬂood impacts, which may include increased

mosquito- and water-borne diseases.

Risks of deaths and injuries and disruptions to

livelihoods / incomes, food supplies, and drinking

water

In many urban areas, larger and more

frequent ﬂooding impacting much larger

population. No insurance available or

impacts reaching the limits of insurance.

Shift in the burden of risk management

from the state to those at risk, leading

to greater inequality and property blight,

abandonment of urban districts, and the

creation of high-risk / high-poverty spatial

traps

Coastal ﬂooding

(including sea level

rise and storm surge)

(Sections 8.1.4, 8.2.3,

8.2.4)

High concentrations of people, businesses, and

physical assets including critical infrastructure

exposed in low-lying and unprotected coastal

zones. Particularly susceptible is the urban

population that is unable to get or afford

housing that protects against ﬂooding or

insurance. The local government is unable or

unwilling to give needed attention to disaster

risk reduction.

Risks from deaths and injuries and disruptions to

livelihoods / incomes, food supplies, and drinking

water

Additional 2 billion or so urban dwellers

expected over the next three decades

Sea level rise means increasing risks over

time, yet with high and often increasing

concentrations of population and economic

activities on the coasts. No insurance

available or reaching the limits of insurance;

shift in the burden of risk management from

the state to those at risk leading to greater

inequality and property blight, abandonment

of urban districts, and the creation of high-

risk / high-poverty spatial traps

Table KR-1 | Examples of hazards /stressors, key vulnerabilities, key risks, and emergent risks.

Cross-Chapter Box

115

Hazards, Key Vulnerabilities, Key Risks, and Emergent Risks

Hazard Key vulnerabilities Key risks Emergent risks

Urban Areas

(continued)

(Chapter 8)

Heat and cold

(including urban heat

island effect)

(Section 8.2.3)

Particularly susceptible is a large and often

increasing urban population of infants, young

children, older age groups, expectant mothers,

people with chronic diseases or compromised

immune system in settlements exposed

to higher temperatures (especially in heat

islands) and unexpected cold spells. Inability of

local organizations for health, emergency, and

social services to adapt to new risk levels and

set up needed initiatives for vulnerable groups

Risk of mortality and morbidity increasing,

including shifts in seasonal patterns and

concentrations due to hot days with higher

or more prolonged high temperatures or

unexpected cold spells. Avoiding risks often most

difﬁcult for low-income groups

Duration and variability of heat waves

increasing risks over time for most locations

owing to interactions with multiple stressors

such as air pollution

Water shortages and

drought in urban

regions

(Sections 8.2.3, 8.2.4)

Lack of piped water to homes of hundreds

of millions of urban dwellers. Many urban

areas subject to water shortages and irregular

supplies, with constraints on increasing

supplies. Lack of capacity and resilience

in water management regimes including

rural–urban linkages. Dependence on water

resources in energy production systems

Risks from constraints on urban water provision

services to people and industry with human and

economic impacts. Risk of damage and loss to

urban ecology and its services including urban

and peri-urban agriculture.

Cities’ viability may be threatened by loss or

depletion of freshwater sources—including

for cities dependent on distant glacier

melt water or on depleting groundwater

resources.

Changes in urban

meteorological

regimes lead to

enhanced air pollution.

(Section 8.2.3)

Increases in exposure and in pollution

levels with impacts most serious among

physiologically susceptible populations.

Limited coping and adaptive capacities, due

to lacking implementation of pollution control

legislation of urban governments

Increasing risk of mortality and morbidity,

lowered quality of life. These risks can also

undermine the competitiveness of global cities

to attract key workers and investment.

Complex and compounding health crises

Geo-hydrological

hazards (salt water

intrusion, mud / land

slides, subsidence)

(Sections 8.2.3, 8.2.4)

Local structures and networked infrastructure

(piped water, sanitation, drainage,

communications, transport, electricity, gas)

particularly susceptible. Inability of many

low-income households to move to housing

on safer sites.

Risk of damage to networked infrastructure. Risk

of loss of human life and property

Potential for large local and aggregate

impacts

Knock-on effects for urban activities and

well-being

Wind storms with

higher intensity

(Sections 8.1.4, 8.2.4)

Substandard buildings and physical

infrastructure and the services and functions

they support particularly susceptible. Old and

difﬁcult to retroﬁt buildings and infrastructure

in cities

Local government unable or unwilling to give

attention to disaster risk reduction (limited

coping and adaptive capacities)

Risk of damage to dwellings, businesses, and

public infrastructure. Risk of loss of function

and services. Challenges to recovery, especially

where insurance is absent

Challenges to individuals, businesses,

and public agencies where the costs of

retroﬁtting are high and other sectors

or interests capture investment budgets;

potential for tensions between development

and risk reduction investments

Changing hazard

proﬁle including

novel hazards and

new multi-hazard

complexes

(Sections 8.1.4, 8.2.4)

Newly exposed populations and infrastructure,

especially those with limited capacity for

multi-hazard risk forecasting and where

risk reduction capacity is limited, e.g.,

where risk management planning is overly

hazard speciﬁc including where physical

infrastructure is predesigned in anticipation

of other risks (e.g., geophysical rather than

hydrometeorological)

Risks from failures within coupled systems, e.g.,

reliance of drainage systems on electric pumps,

reliance of emergency services on roads and

telecommunications. Potential of psychological

shock from unanticipated risks

Loss of faith in risk management

institutions. Potential for extreme impacts

that are magniﬁed by a lack of preparation

and capacity in response

Compound slow-onset

hazards including

rising temperatures

and variability in

temperature and water

(Sections 8.2.2, 8.2.4)

Large sections of the urban population in low-

and middle-income nations with livelihoods or

food supplies dependent on urban and peri-

urban agriculture are especially susceptible.

Risk of damage to or degradation of soils, water

catchment capacity, fuel wood production, urban

and peri-urban agriculture, and other productive

or protective ecosystem services. Risk of knock-

on impacts for urban and peri-urban livelihoods

and urban health

Collapsing of peri-urban economies and

ecosystem services with wider implications

for urban food security, service provision,

and disaster risk reduction

Climate change–

induced or intensiﬁed

hazard of more

diseases and exposure

to disease vectors

(Sections 8.2.3, 8.2.4)

Large urban population that is exposed to

food-borne and water-borne diseases and

to malaria, dengue, and other vector-borne

diseases that are inﬂuenced by climate change

Risk due to increases in exposure to these

diseases

Lack of capacity of public health system to

simultaneously address these health risks

with other climate-related risks such as

ﬂooding

Rural Areas

(Chapter 9)

Drought in pastoral

areas

(Sections 9.3.3.1,

9.3.5.2)

Increasing vulnerability due to encroachment

on pastoral rangelands, inappropriate land

policy, misperception and undermining of

pastoral livelihoods, conﬂict over natural

resources, all driven by remoteness and lack

of voice

Risk of famine

Risk of loss of revenues from livestock trade

Increasing risks for rural livelihoods through

animal disease in pastoral areas combined

with direct impacts of drought

Effects of climate

change on artisanal

ﬁsheries

(Sections 9.3.3.1,

9.3.5.2)

Artisanal ﬁsheries affected by pollution and

mangrove loss, competition from aquaculture,

and the neglect of the sector by governments

and researchers as well as complex property

rights

Risk of economic losses for artisanal ﬁsherfolk,

due to declining catches and incomes and

damage to ﬁshing gear and infrastructure

Reduced dietary protein for those

consuming artisanally caught ﬁsh, combined

with other climate-related risks

Table KR-1 (continued)

Continued next page

Cross-Chapter Box

Hazards, Key Vulnerabilities, Key Risks, and Emergent Risks

116

Hazard Key vulnerabilities Key risks Emergent risks

Rural Areas

(continued)

(Chapter 9)

Water shortages and

drought in rural areas

(Section 9.3.5.1.1)

Rural people lacking access to drinking and

irrigation water. High dependence of rural

people on natural resource-related activities.

Lack of capacity and resilience in water

management regimes (institutionally driven).

Increased water demand from population

pressure

Risk of reduced agricultural productivity of rural

people, including those dependent on rainfed

or irrigated agriculture, or high-yield varieties,

forestry, and inland ﬁsheries. Risk of food

insecurity and decrease in incomes. Decreases in

household nutritional status (Section 9.3.5.1)

Impacts on livelihoods driven by interaction

with other factors (water management

institutions, water demand, water used

by non-food crops), including potential

conﬂicts for access to water. Water-related

diseases

Human

Health

(Chapter 11)

Increasing frequency

and intensity of

extreme heat

Older people living in cities are most

susceptible to hot days and heat waves,

as well as people with preexisting health

conditions. (Section 11.3)

Risk of increased mortality and morbidity during

hot days and heat waves. (Section 11.4.1) Risk

of mortality, morbidity, and productivity loss,

particularly among manual workers in hot

climates

The number of elderly people is projected

to triple from 2010 to 2050. This can result

in overloading of health and emergency

services.

Increasing

temperatures,

increased variability in

precipitation

Poorer populations are particularly susceptible

to climate-induced reductions in local

crop yields. Food insecurity may lead to

undernutrition. Children are particularly

vulnerable. (Section 11.3)

Risk of a larger burden of disease and increased

food insecurity for particular population groups.

Increasing risk that progress in reducing

mortality and morbidity from undernutrition may

slow or reverse. (Section 11.6.1)

Combined effects of climate impacts,

population growth, plateauing productivity

gains, land demand for livestock, biofuels,

persistent inequality, and ongoing food

insecurity for the poor

Increasing

temperatures,

changing patterns of

precipitation

Non-immune populations who are exposed

to water- and vector-borne diseases that are

sensitive to meteorological conditions (Section

11.3)

Increasing health risks due to changing spatial

and temporal distribution of diseases strains

public health systems, especially if this occurs in

combination with economic downturn. (Section

11.5.1)

Rapid climate and other environmental

change may promote emergence of new

pathogens.

Increased variability in

precipitation

People exposed to diarrhea aggravated by

higher temperatures, and unusually high or

low precipitation (Section 11.3)

Risk that the progress to date in reducing

childhood deaths from diarrheal disease is

compromised (Section 11.5.2)

Increased rate of failure of water and

sanitation infrastructure due to climate

change leading to higher diarrhea risk

Livelihoods

and Poverty

(Chapter 13)

Increasing frequency

and severity of

droughts, coupled with

decreasing rainfall

and / or increased

unpredictability of

rainfall

(Sections 13.2.1.2,

13.2.1.4, 13.2.2.2)

Poorly endowed farmers (high and persistent

poverty), particularly in drylands, are

susceptible to these hazards, since they have

a very limited ability to compensate for losses

in water-dependent farming systems and /or

livestock.

Risk of irreversible harm due to short time

for recovery between droughts, approaching

tipping point in rainfed farming system and /or

pastoralism

Deteriorating livelihoods stuck in poverty

traps, heightened food insecurity, decreased

land productivity, outmigration, and new

urban poor in LICs and MICs

Floods and ﬂash

ﬂoods in informal

urban settlements

and mountain

environments,

destroying physical

assets (e.g., homes,

roads, terraces,

irrigation canals)

(Sections 13.2.1.1,

13.2.1.3, 13.2.1.4)

High exposure and susceptibility of people,

particularly children and elderly, as well as

disabled in ﬂood-prone areas. Inadequate

infrastructure, culturally imposed gender roles,

and limited ability to cope and adapt due

to political and institutional marginalization

and high poverty adds to the susceptibility of

these people in informal urban settlements;

limited political interest in development and

building adaptive capacity

Risk of high morbidity and mortality due to

ﬂoods and ﬂash ﬂoods. Factors that further

increase risk may include a shift from transient

to chronic poverty due to eroded human and

economic assets (e.g., labor market) and

economic losses due to infrastructure damage.

Exacerbated inequality between better-

endowed households able to invest in

ﬂood-control measures and /or insurance

and increasingly vulnerable populations

prone to eviction, erosion of livelihoods, and

outmigration

Increased variability

of precipitation; shifts

in mean climate and

extreme events

(Sections 13.2.1.1,

13.2.1.4)

Limited ability to cope owing to exhaustion of

social networks, especially among the elderly

and female-headed households; mobilization

of labor and food no longer possible

Hazard combines with vulnerability to shift

populations from transient to chronic poverty

due to persistent and irreversible socioeconomic

and political marginalization. In addition, the

lack of governmental support, as well as limited

effectiveness of response options, increase the

risk.

Increasing yet invisible multidimensional

vulnerability and deprivation at the

convergence of climatic hazards and

socioeconomic stressors

Successive and

extreme events (ﬂoods,

droughts) coupled

with increasing

temperatures and

rising water demand

(Sections 13.2.1.1,

13.2.1.5)

Rural communities are particularly susceptible,

due to the marginalization of rural water users

to the beneﬁt of urban users, given political

and economic priorities (e.g., Australia, Andes,

Himalayas, Caribbean).

Risk of loss of rural livelihoods, severe economic

losses in agriculture, and damage to cultural

values and identity; mental health impacts

(including increased rates of suicide)

Loss of rural livelihoods that have existed

for generations, heightened outmigration to

urban areas; emergence of new poverty in

MICs and HICs

Sea level rise

(Sections 13.1.4,

13.2.1.1, 13.2.2.1,

13.2.2.3)

High number of people exposed in low-lying

areas coupled with high susceptibility due to

multidimensional poverty, limited alternative

livelihood options among poor households,

and exclusion from institutional decision-

making structures

Risk of severe harm and loss of livelihoods.

Potential loss of common-pool resources;

of sense of place, belonging, and identity,

especially among indigenous populations

Loss of livelihoods and mental health

risks due to radical change in landscape,

disappearance of natural resources, and

potential relocation; increased migration

Increasing

temperatures and heat

waves

(Sections 13.2.1.5,

13.2.2.3, 13.2.2.4)

Agricultural wage laborers, small-scale

farmers in areas with multidimensional

poverty and economic marginalization,

children in urban slums, and the elderly are

particularly susceptible.

Risk of increased morbidity and mortality due

to heat stress, among male and female workers,

children, and the elderly, limited protection due

to socioeconomic discrimination and inadequate

governmental responses

Declining labor pool for agriculture coupled

with new challenges for rural health care

systems in LICs and MICs; aging and low-

income populations without safety nets in

HICs at risk

Table KR-1 (continued)

Continued next page

Hazards, Key Vulnerabilities, Key Risks, and Emergent Risks

Cross-Chapter Box

117

Hazard Key vulnerabilities Key risks Emergent risks

Livelihoods

and Poverty

(continued)

(Chapter 13)

Increased variability

of rainfall and/ or

extreme events (ﬂoods,

droughts, heat waves)

(Sections 13.2.1.1,

13.2.1.3, 13.2.1.4,

13.2.1.5)

People highly dependent on rainfed

agriculture are particularly at risk. Persistent

poverty among subsistence farmers and urban

wage laborers who are net buyers of food

with limited coping mechanisms

Risk of crop failure, spikes in food prices,

reduction in consumption to protect household

assets, risk of food insecurity, shifts from

transient to chronic poverty due to limited ability

to reduce risks

Food riots, child food poverty, global food

crises, limits of insurance and other risk-

spreading strategies

Changing rainfall

patterns (temporally

and spatially)

Households or people with a high dependence

on rainfed agriculture and little access to

alternative modes of income

Risks of crop failure, food shortage, severe

famine

Coincidence of hazard with periods of

high global food prices leads to risk of

failure of coping strategies and adaptation

mechanisms such as crop insurance (risk

spreading).

Stressor from soaring

demand (and prices)

for biofuel feedstocks

due to climate policies

Farmers and groups that have unclear and / or

insecure land tenure arrangements are

exposed to the dispossession of land due to

land grabbing in developing countries.

Risk of harm and loss of livelihoods for some

rural residents due to soaring demand for biofuel

feedstocks and insecure land tenure and land

grabbing

Creation of large groups of landless farmers

unable to support themselves. Social unrest

due to disparities between intensive energy

production and neglected food production

Increasing frequency

of extreme events

(droughts, ﬂoods),

e.g., if 1:20 year

drought / ﬂood

becomes 1:5 year

drought / ﬂood

Pastoralists and small farmers subject to

damage to their productive assets (e.g., herds

of livestock; dykes, fences, terraces)

Risk of the loss of livelihoods and harm due to

shorter time for recovery between extremes.

Pastoralists restocking after a drought may take

several years; in terraced agriculture, need to

rebuild terraces after ﬂood, which may take

several years

Collapse of coping strategies with risk

of collapsing livelihoods. Adaptation

mechanisms such as insurance fail due to

increasing frequency of claims.

Emergent

Risks and Key

Vulnerabilities

(Chapter 19)

Warming and

drying (precipitation

changes of uncertain

magnitude)

(WGI AR5 TS 5.3; SPM;

Sections 11.3, 12.4)

Limits to coping capacity to deal with reduced

water availability; increasing exposure

and demand due to population increase;

conﬂicting demands for alternative water

uses; sociocultural constraints on some

adaptation options (Sections 19.2.2, 19.3.2.2,

19.6.1.1, 19.6.3.4)

Risk of harm and loss due to livelihood

degradation from systematic constraints on

water resource use that lead to supply falling far

below demand. In addition, limited coping and

adaptation options increase the risk of harm and

loss. (Sections 19.3.2.2, 19.6.3.4)

Competition for water from diverse sectors

(e.g., energy, agriculture, industry) interacts

with climate changes to produce locally

severe shortages. (Sections 19.3.2.2,

19.6.3.4)

Changes in regional

and seasonal

temperature and

precipitation over land

(WGI AR5 TS 5.3; SPM;

Sections 11.3, 12.4)

Communities highly dependent on ecosystem

services (Sections 19.2.2.1, 19.3.2.1) which

are negatively affected by changes in regional

and seasonal temperature

Risk of large-scale species richness loss over

most of the global land surface. 57 ± 6% of

widespread and common plants and 34 ± 7% of

widespread and common animals are expected

to lose ≥50% of their current climatic range by

the 2080s leading to loss of services. (Section

19.3.2.1)

Widespread loss of ecosystem services,

including: provisioning, such as food and

water;regulating, such as the control of

climate and disease;supporting, such

as nutrient cycles and crop pollination;

andcultural, such as spiritual and

recreational beneﬁt (Sections 19.3.2.1,

19.6.3.4)

Africa

(Chapter 22)

Increasing temperature Children, pregnant women, and those with

compromised health status are particularly

at risk for temperature-related changes in

diarrheal and vector-borne diseases, and for

temperature-related reductions in crop yields.

Outdoor workers, older adults, and young

children are most susceptible to hot weather

and heat waves. (Sections 22.3.5.2, 22.3.5.4)

Risk of changes in the geographic distribution,

seasonality, and incidence of infectious diseases,

leading to increases in the health burden. Risk

of increased burdens of stunting in children. Risk

of increase in morbidity and mortality during hot

days and heat waves

Interactions among factors lead to emerging

and re-emerging epidemics.

Populations dependent on aquatic systems

and aquatic ecosystem services that are

sensitive to increased water temperatures

Loss of aquatic ecosystems and risks for people

who might depend on these resources; reduction

in freshwater ﬁsheries production (Sections

22.3.2.2, 22.3.4.4)

Risk of loss of livelihoods due to

interactions of loss of ecosystem services

and other climate-related stressors on poor

communities

Rural and urban populations whose food and

livelihood security is diminished

Risk of harm and loss due to increased heat

stress on crops and livestock resulting in reduced

productivity; increased food storage losses due

to spoilage (Sections 22.3.4.1, 22.3.4.2)

Range expansion of crop pests and diseases

to high-elevation agroecosystems (Section

22.3.4.3)

Extreme events, e.g.,

ﬂoods and ﬂash ﬂoods

(and drought)

Population groups living in informal

settlements in highly exposed urban areas;

women and children often the most vulnerable

to disaster risk (Sections 22.3.6, 22.4.3)

Increasing risk of mortality, harm and losses

due to water logging triggered by heavy rainfall

events

Compounded risk of epidemics including

diarrheal diseases (e.g., cholera)

Susceptible groups include those who

experience diminished access to food resulting

from reduced capacity to transport, store, and

market food, such as the urban poor.

Risk of food shortages and of damages to the

food system due to storms and ﬂooding

Food price spikes due to convergence of

climatic and non-climatic forces that reduce

food access for the poor whose income is

disproportionately spent on food (Section

22.3.4.5)

Children, pregnant women, and those with

compromised health status are particularly

vulnerable to reduced access to safe water

and improved sanitation and increasing food

insecurity. (Sections 22.3.5.2, 22.3.5.3)

Risk of crop and livestock losses from drought

Risk of reduced water supply and quality for

household use. (Sections 22.3.4.1, 22.3.4.2) Risk

of increased incidence of food- and water-borne

diseases (e.g., cholera) and undernutrition.

Risk of drinking water contamination due to

heavy precipitation events and ﬂooding (Section

22.3.5.2)

Compound effects of high temperature and

changes in rainfall on human and natural

systems. Increased incidence of stunting in

children (Section 22.3.5.3)

Table KR-1 (continued)

Continued next page

Cross-Chapter Box

Hazards, Key Vulnerabilities, Key Risks, and Emergent Risks

118

Hazard Key vulnerabilities Key risks Emergent risks

Europe

(Chapter 23)

Extreme weather

events

(Section 23.9)

Sectors with limited coping and adaptive

capacity as well as high sensitivity to these

extreme events, such as transport, energy, and

health, are particularly susceptible.

Risk of new systemic threats due to stress

on multiple and interconnected sectors. Risk

of failure of service provision of one or more

sectors

Disproportionate intensiﬁcation of risk due

to increasing interdependencies

Climate change

increases the spatial

distribution and

seasonality of pests

and diseases.

(Section 23.4.1, 23.4.3,

23.4.4)

High susceptibility of plants and animals that

are exposed to pests and diseases

Risk of increases in crop losses and animal

diseases or even fatalities of livestock

Increasing risks due to limited response

options and various feedback processes

in agriculture, e.g., use of pesticides or

antibiotics to protect plants and livestock

increases resistance of disease vectors

Extreme weather

events and reduced

water availability due

to climate change

(Section 23.3.4)

Low adaptive capacity of power systems

might lead to limited energy supply as well

as higher supply costs during such extreme

events and conditions.

Increasing risk of power shortages due to limited

energy supply, e.g., of nuclear power plants due

to limited cooling water during heat stress

Continued underinvestment in adaptive

energy systems might increase the risk of

mismatches between limited energy supply

during these events and increased demands,

e.g., during a heat wave.

Asia

(Chapter 24)

Rising average

temperatures and

more frequent extreme

temperatures, as well

as changing rainfall

patterns (temporally

and spatially)

Food systems and food production systems

for key grain crops, particularly rice and

other cereal crop farming systems, are highly

susceptible. (Section 24.4.4.3)

Risk of crop failures and lower crop yield also

can increase the risk of major losses for farmers

and rural livelihoods. (Section 24.4.4.3)

Increase in Asian population combined

with rising temperatures affecting food

production. Upper temperature limit to the

ability of some food systems to adapt could

be reached.

Rising sea level Paddy ﬁelds and farmers near the coasts are

particularly susceptible. (Section 24.4.4.3)

Risk of loss of arable areas due to submergence

(Section 24.4.4.3)

Migration of farming communities to higher

elevation areas entails risks for migrants

and receiving regions.

Projected increase in

frequency of various

extreme events (heat

wave, ﬂoods, and

droughts) and sea

level rise

Increasing exposure due to convergence

of livelihood and properties into coastal

megacities. People in areas that are not

sufﬁciently protected against natural hazards

are particularly susceptible.

Risk of loss of life and assets due to coastal

ﬂoods accompanied by increasing vulnerabilities.

Projected increase in disruptions of basic

services such as water supply, sanitation,

energy provision, and transportation

systems, which themselves could increase

vulnerabilities

Australasia

(Chapter 25)

Rising air and sea

surface temperatures,

drying trends, reduced

snow cover, increased

intensity of severe

cyclones, ocean

acidiﬁcation

(Section 25.2; Table

25-1; Figure 25-4; WGI

AR5 Chapter 14 and

Atlas)

Species that live in a limited climatic range

and that suffer from habitat fragmentation

as well as from external stressors (pollution,

runoff, ﬁshing, tourism, introduced predators,

and pests) are especially susceptible. (Sections

25.6.1, 25.6.2)

Risk of signiﬁcant change in community

composition and structure of coral reefs and

montane ecosystems and risk of loss of some

native species in Australia (Sections 25.6.1,

25.6.2, 25.10.2)

Increasing risk from compound extreme

events across time and space, and

cumulative adaptation needs, with recovery

and risk reduction measures hampered

further by impacts and responses reaching

across different levels of government

(Sections 25.10.2, 25.10.3; Box 25-9)

Increased extreme

rainfall related to ﬂood

risk in many locations

(Section 25.2; Table

25-1)

Adaptation deﬁcit of existing infrastructure

and settlements to current ﬂood risk;

expansion and densiﬁcation of urban areas;

effective adaptation includes transformative

changes such as land-use controls and retreat.

(Sections 25.3, 25.10.2; Box 25-8)

Increased frequency and intensity of ﬂood

damage to infrastructure and settlements in

Australia and New Zealand (Box 25-8; Section

25.10.2)

Continuing sea level

rise, with projections

spanning a particularly

large range and

continuing beyond

2100, even under

mitigation scenarios

(Section 25.2; Box 25-1;

WGI AR5 Chapter 13)

Long-lived and high asset value coastal

infrastructure and low-lying ecosystems

are highly susceptible. Expansion of coastal

populations and assets into coastal zones

increases the exposure. Conﬂicting priorities

constrain adaptation options and limit

effective response strategies. (25.3, Box 25-1)

Increasing risks to coastal infrastructure and

low-lying ecosystems in Australia and New

Zealand, with widespread damages toward

the upper end of projected ranges (Box 25-1;

Sections 25.6.1, 25.6.2, 25.10.2)

North

America

(Chapter 26)

Increases in frequency

and /or intensity of

extreme events, such

as heavy precipitation,

river and coastal

ﬂoods, heat waves,

and droughts

(Sections 26.2.2,

26.3.1, 26.8.1)

Physical infrastructure in a declining state

in urban areas particularly susceptible. Also

increases in income disparities and limited

institutional capacities might result in larger

proportions of people susceptible to these

stressors due to limited economic resources.

(Sections 26.7, 26.8.2)

Risk of harm and loss in urban areas, particularly

in coastal and dry environments due to

enhanced vulnerabilities of social groups,

physical systems, and institutional settings

combined with the increases of extreme weather

events (Section 26.8.1)

Inability to reduce vulnerability in many

areas results in an increase in risk more so

than change in physical hazard. (Section

26.8.3)

Higher temperatures,

decreases in runoff,

and lower soil

moisture due to

climate change

(Sections 26.2, 26.3)

Vulnerability of small rural landholders,

particularly in Mexican agriculture, and of

the poor in rural settlements (Sections 26.5,

26.8.2.2)

Risk of increased losses and decreases in

agricultural production. Risk of food and job

insecurity for small landholders and social

groups in regions exposed to these phenomena

(Sections 26.5, 26.8.2.2)

Increasing risks of social instability and

local economic disruption due to internal

migration (Sections 26.2.1, 26.8.3)

Table KR-1 (continued)

Continued next page

Hazards, Key Vulnerabilities, Key Risks, and Emergent Risks

Cross-Chapter Box

119

Hazard Key vulnerabilities Key risks Emergent risks

North

America

(continued)

(Chapter 26)

Wildﬁres and drought

conditions

(Box 26-2)

Indigenous groups, low-income residents in

peri-urban areas, and forest systems (Box

26-2; Section 26.8.2)

Risk of loss of ecosystem integrity, property loss,

human morbidity, and mortality due to wildﬁres

(Box 26-2; Section 26.8.3)

Extreme storm and

heat events, air

pollution, pollen, and

infectious diseases

(Section 26.6.1)

Susceptibility of individuals is determined by

factors such as economic status, preexisting

illness, age, and access to assets. (Section

26.6.1)

Increasing risk of extreme temperature-, storm-,

pollen-, and infectious diseases–related human

morbidity or mortality (Section 26.6.2)

River and coastal

ﬂoods, and sea level

rise

(Sections 26.2.2,

26.4.2, 26.8.1)

Increasing exposure of populations, property,

as well as ecosystems, partly resulting from

overwhelmed drainage networks. Groups and

economic sectors that highly depend on the

functioning of different supply chains, public

health institutions that can be disrupted, and

groups that have limited coping capacities

to deal with supply chain interruptions and

disruptions to their livelihoods are particularly

susceptible. (Sections 26.7, 26.8.1)

Risk of property damage, supply chain

disruption, public health, water quality

impairment, ecosystem disruption, infrastructure

damage, and social system disruption from

urban ﬂooding due to river and coastal ﬂoods

and ﬂoods of drainage networks (Sections

26.4.2, 26.8.1)

Multiple risks from interacting hazards on

populations’ livelihoods, infrastructure, and

services (Sections 26.7, 26.8.3)

Central

and South

America

(Chapter 27)

Reduced water

availability in semi-arid

regions and regions

dependent on glacier

meltwater; ﬂooding

in urban areas due to

extreme precipitation

(Sections 27.2.1,

27.3.3)

Groups that cannot keep agricultural

livelihoods and are forced to migrate are

especially vulnerable. Limited infrastructure

and planning capacity can further increase the

lack of coping and adaptive capacities to rapid

changes expected (precipitation), especially

in large cities.

Risk of loss of human lives, livelihood, and

property

Increase in infectious diseases. Economic

impacts due to reallocation of populations

Ocean acidiﬁcation

and warming

(Section 27.3.3; Box

CC-OA)

Sensitivity of coral reef systems to ocean

acidiﬁcation and warming

Risk of loss of biodiversity (species) and risk of a

reduced ﬁshing capacity with respective impacts

for coastal livelihoods

Economic losses and impact on food

(ﬁshery) production in certain regions

Extremes of drought /

precipitation

(Sections 27.2.1,

27.3.4)

Elevated CO

decreases nutrient contents

in plants, especially nitrogen in relation to

carbon in food products.

Risk of loss of (food) production and productivity

in some regions where extreme events may

occur. Need to adjust diet due to decrease in

food quality (e.g., less protein due to lower

nitrogen assimilation). Decrease in bioenergy

production

Strong economic impacts related to the

need to move crops to more suitable

regions. Teleconnections (related to food

quality) related to the intense exportation

of food by the region. Impacts on energy

system and carbon emissions with

consequent increase in fossil fuel demand.

Higher temperatures

and humidity lead to a

spread of vector-borne

diseases in altitude

and latitude.

(Section 27.3.7)

People exposed and vulnerable to vector-

borne diseases and an increase in mosquito

biting rates that increase the probability of

human infections

Risk of increase in morbidity and in disability-

adjusted life years (DALYs); risk of loss of human

lives; risk of decrease in school and labor

productivity

High economic impacts owing to the

necessity to increase the ﬁnancing of

health programs, as well as the costs of

DALYs, increase in hospitals and medical

infrastructure adequate to cope with

increasing disease incidence rates, and the

spread of diseases to newer regions

Polar Regions

(Chapter 28)

Loss of multi-year

ice and reductions in

the spatial extent of

summer sea ice

(Sections 28.2.5,

28.3.2, 28.4.1)

Indigenous communities that depend on sea

ice for traditional livelihoods are vulnerable

to this hazard, particularly due to loss of

breeding and foraging platforms for marine

mammals.

Risk of loss of traditional livelihoods and food

sources.

Top-down shifts in food webs

Ecosystems are vulnerable owing to the shifts

in the distribution and timing of ice algal and

ocean phytoplankton blooms.

Risk of disruption of synchronized timing of

zooplankton ontogeny and availability of prey.

Increased variability in secondary production

while zooplankton adapt to shifts in timing.

Risks also to local marine food webs.

Bottom up shifts in food webs. Potential

changes in pelagic and benthic coupling

Ocean acidiﬁcation

(Sections 28.2.2,

28.3.2)

Tolerance limits of endemic species surpassed.

Impacts on exoskeleton formation for some

species and alteration of physiological

and behavioral properties during larval

development

Localized loss of endemic species, local impacts

on marine food webs

Localized declines in commercial ﬁsheries.

Local declines in ﬁsh, shellﬁsh, seabirds, and

marine mammals

Shifts in boundaries

of marine eco-regions

due to rising water

temperature, shifts

in mixed layer

depth, changes in

the distribution and

intensity of ocean

currents

(Sections 28.2.2,

28.3.2)

Marine organisms that are susceptible to

spatial shifts are particularly vulnerable.

Risk of changes in the structure and function of

marine systems and potentially species invasions

Disputes over international ﬁsheries and

shared stocks

Table KR-1 (continued)

Continued next page

Cross-Chapter Box

Hazards, Key Vulnerabilities, Key Risks, and Emergent Risks

120

Hazard Key vulnerabilities Key risks Emergent risks

Polar Regions

(continued)

(Chapter 28)

Declining sea ice,

changes in snow

and ice timing and

state, decreasing

predictability of

weather

(Sections 28.1, 28.4.1)

Many traditional subsistence food sources—

especially for indigenous peoples—such as

Arctic marine and land mammals, ﬁsh, and

waterfowl. Various traditional livelihoods are

susceptible to these hazards.

Risk of loss of habitats and changes in migration

patterns of marine species

Enhancement of risk to food security and

basic nutrition—especially for indigenous

peoples—from loss of subsistence foods

and increased risk to subsistence hunters’,

herders’, and ﬁshers’ health and safety in

changing ice conditions

Increased river and

coastal ﬂooding and

erosion and thawing

of permafrost

(Sections 28.2.4,

28.3.1, 28.3.4)

Rural and remote communities as well as

urban communities in low-lying Arctic areas

are exposed. Susceptibility and limited coping

capacity of community water supplies due to

potential damages to infrastructure.

Community and public health infrastructure

damaged resulting in disease from

contamination and sea water intrusion

Reduced water quality and quantity may

result in increased rates of infection, other

medical problems, and hospitalizations.

Extreme and rapidly

changing weather,

intense weather and

precipitation events,

rapid snow and ice

melt, changing river

and sea ice conditions,

permafrost thaw

(Section 28.2.4)

People living from subsistence travel and

hunting, herding, and ﬁshing, for example

indigenous peoples in remote and isolated

communities, are particularly susceptible.

Accidents, physical /mental injuries, death, and

cold-related exposure, injuries, and diseases

Enhanced risks to safe travel or subsistence

hunting, herding, ﬁshing activities affect

livelihoods and well-being.

Diminished sea

ice; earlier sea ice

melt-out; faster sea

ice retreat; thinner,

less predictable ice

in general; greater

variability in snow

melt /freeze; ice,

weather, winds,

temperatures,

precipitation

(Sections 28.2.5,

28.2.6, 28.4.1)

Livelihoods of many indigenous peoples (e.g.,

Inuit and Saami) depend upon subsistence

hunting and access to and favorable

conditions for animals. These livelihoods

are susceptible. Also marine ecosystems are

susceptible (e.g., marine mammals).

Risk of loss of livelihoods and damage due to,

e.g., more difﬁcult access to marine mammals

associated with diminishing sea ice (a risk to

the Inuit), and loss of access by reindeer to their

forage under snow due to ice layers formed

by warming winter temperatures and “rain on

snow” (a risk to the Saami).

Enhanced risk of loss of livelihoods and

culture of increasing numbers of indigenous

peoples, exacerbated by increasing loss

of lands and sea ice for hunting, herding,

ﬁshing due to enhanced petroleum and

mineral exploration, and increased maritime

trafﬁc

Small Islands

(Chapter 29)

Increases in intensity

of tropical cyclones

(WGI AR5 Sections

14.6, 14.8.4)

Various countries and communities are

vulnerable to these hazards because of their

high dependence on natural and ecological

systems for security of settlements and

tourism (Section 29.3.3.1), human health

(Section 29.3.3.2), and water resources

(Section 29.3.2).

Risk of loss of ecosystems, settlements, and

infrastructure, as well as negative impacts on

human health and island economies (Figure 29-4)

Increased risk of interactions of damages to

ecosystems, settlements, island economies,

and risks to human life (Section 29.6; Figure

29-4)

Ocean warming and

acidiﬁcation leading to

coral bleaching

(Sections 29.3.1.2,

30.5.4.2, 30.5.6.1.1,

30.5.6.2)

Tropical island communities are highly

dependent on coral reef ecosystems for

subsistence life styles, food security, coastal

protection and beach, and reef-based tourist

economic activity, and hence are highly

susceptible to the hazard of coral bleaching.

(Sections 29.3.1.2, 30.6.2.1.2)

Risk of decline and possible loss of coral reef

ecosystems through thermal stress. Risk of

serious harm and loss of subsistence lifestyles.

Risk of loss of coastal protection and beaches,

risk of loss of tourist revenue (Sections 29.3.1.1,

29.3.1.2)

Impacts on human health and loss of

subsistence lifestyles. Potential increase in

internal migration /urbanization (Section

29.3.3.3; Chapter 9)

Sea level rise

(Sections 29.3.1.1,

30.3.1.2; WGI AR5

Section 3.7.1)

Many small island communities and

associated settlements and infrastructure are

in low-lying coastal zones (high exposure) and

are also vulnerable to increasing inundation,

erosion and wave incursion. (Sections 5.3.2,

29.3.1.1; Figure 29-2)

Risk of loss and harm due to sea level rise in

small island communities. Global mean sea

level is likely to increase by 0.35 to 0.70 m for

Representative Concentration Pathway (RCP) 4.5

during the 21st century, threatening low-lying

coastal areas and atoll islands. (Section 29.4.3,

Table 29-1; WGI AR5 Section 13.5.1, Table 13.5)

Incremental upwards shift in sea-level

baselines results in increased frequency and

extent of marine ﬂooding during high tides

and episodic storm surges. These events

could render soils and fresh groundwater

resources unﬁt for human use before

permanent inundation of low-lying areas.

(Sections 29.3.1.1, 29.3.2, 29.3.3.1, 29.5.1)

Table KR-1 (continued)

Continued next page

Hazards, Key Vulnerabilities, Key Risks, and Emergent Risks

Cross-Chapter Box

121

Hazard Key vulnerabilities Key risks Emergent risks

The Ocean

(Chapter 30)

Increasing ocean

temperatures.

Increased frequency of

thermal extremes

Corals and other organisms whose tolerance

limits are exceeded are particularly susceptible

(especially CBS, STG, SES, and EUS ocean

regions). (Sections 6.2.2.1, 6.2.2.2, 30.5.2,

30.5.4, 30.5.5; Boxes CC-CR, 30.5.6, CC-OA)

Risk of increased mass coral bleaching and

mortality (loss of coral cover) with severe

risks for coastal ﬁsheries, tourism, and coastal

protection (Sections 6.3.2. 6.3.5, 5.4.2.4, 7.2.1.2,

6.4.1.4, 29.3.1.2, 30.5.2, 30.5.3, 30.5.4, 30.5.5;

Box CC-CR)

Loss of coastal reef systems, risk of

decreased food security and reduced

livelihoods, and reduced coastal protection

(Sections 7.2.1.2, 30.6.2.1, 30.6.5)

Marine species and ecosystems as well as

ﬁsheries and coastal livelihoods and tourism

that cannot cope or adapt to changing

temperatures and changes in the distribution

are particularly vulnerable, especially for HLSBS,

CBS, STG, and EBUE. (Sections 6.3.2, 6.3.4,

7.3.2.6, 30.5; Box CC-BIO)

Risk for ﬁshery and coastal livelihoods. Fishery

opportunity changes as stock abundance may

rise or fall; increased risk of disease and invading

species impacting ecosystems and ﬁsheries

(Sections 6.3.5, 6.4.1.1, 6.5.3, 7.3.2.6, 7.4.2,

29.5.3, 29.5.4)

Signiﬁcant risk of ﬁshery collapse may

develop as the capacity of ﬁsheries to resist

the following is exceeded: a) fundamental

change to ﬁshery composition, and b) the

increased migration of disease and other

organisms. (Sections 6.5.3, 7.5.1.1.3)

Coastal ecosystems and communities that

might be exposed to phenomena of elevated

rates of microbial respiration leading to

reduced oxygen at depth and increased spread

of dead zones are particularly vulnerable

(particularly for EBUE, SES, EUS).

Risk of loss of habitats and ﬁshery resources

as well as losses of key ﬁsheries species.

Oxygen levels decrease, leading to impacts on

ecosystems (e.g., loss of habitat) and organisms

(e.g., physiological performance of ﬁsh) resulting

in reduced capture of key ﬁsheries species.

Increasing risk of loss of livelihoods

Deep sea life is sensitive to hazards and to

change given the very constant conditions

under which it has evolved. (30.1.3.1.3,

30.5.2, 30.5.5)

Risk of fundamental changes in conditions

associated with deep sea (e.g., oxygen, pH,

carbonate, CO

, temperature) drive fundamental

changes that result in broad-scale changes

throughout the ocean. (Sections 30.1.3.1.3,

30.5.2, 30.5.5; Boxes CC-UP, CC-NPP)

Changes in the deep ocean may be a

prelude to ocean wide changes with

planetary implications.

Rising ocean

acidiﬁcation

Reef systems, corals, and coastal ecosystems

that are exposed to a reduced rate of

calciﬁcation and greater decalciﬁcation

leading to potential loss of carbonate reef

systems, corals, molluscs, and other calciﬁers

in key regions, such as the CBS, STG (Section

6.2.2.2)

Risk of the alteration of ecosystem services

including risks to food provisioning with impacts

on ﬁsheries and aquaculture (Sections 6.2.5.3,

7.2.1.2, 7.3.2, 7.4.2,)

Income and livelihoods for communities

are reduced as productivity of ﬁsheries and

aquaculture diminish. (Sections 7.5.1.1.3,

30.6)

Marine organisms that are susceptible to

changes in pH and carbonate chemistry imply

a large number of changes to the physiology

and ecology of marine organisms (particularly

in CBS, STG, SES regions). (Sections 6.2.5,

6.3.4, 30.3.2.2)

Risk of fundamental shifts in ecosystems

composition as well as organism function

occur, leading to broad scale and fundamental

change. Income and livelihoods from dependent

communities are affected as ecosystem goods

and services decline, with the prospect that

recovery may take tens of thousands of years.

(Section 6.1.1.2)

Risk to ecosystems and livelihoods is

increased by the potential for interaction

among ocean warming and acidiﬁcation to

create unknown impacts. (Section CC-OA)

Coastal systems are increasingly exposed

to upwelling in some areas, which results in

periods of high CO

, low O

and pH. (Box CC-

UP; Sections 6.2.2.2, 6.2.5.3)

Risk of loss and harm to ﬁshery and aquaculture

operations and respective livelihoods (e.g.,

oyster cultivation), especially those exposed

periodically to harmful conditions during

elevated upwelling, which trigger adaptation

responses. (Section 30.6.2.1.4)

Background pH and carbonate chemistry

are also such that harmful conditions

are always present (avoiding impacts via

adaptation not possible any more). (Section

30.6.2.1.4)

Increased stratiﬁcation

as a result of ocean

warming; reduced

ventilation

Ocean ecosystems are vulnerable due to the

reduced regeneration of nutrients as mixing

between the ocean and its surface is reduced

(EUS, STG, and EBUE). (Sections 6.2, 6.3, 6.5,

30.5.2, 30.5.4, 30.5.5)

Risk of productivity losses of oceans and

respective negative impacts on ﬁsheries. The

concentration of inorganic nutrients in the upper

layers of the ocean is reduced, leading to lower

rates of primary productivity. (Box CC-NPP)

Reduced primary productivity of the ocean

impacts ﬁsheries productivity leading to

lower catch rates and effects on livelihoods

(Section 6.4.1.1; Box CC-NPP)

Ecosystems and organisms that are sensitive

to decreasing oxygen levels (Sections 30.5.2,

30.5.3, 30.5.5, 30.5.6, 30.5.7)

Increased risk of dead (hypoxic) zones reducing

key ecosystems and ﬁsheries habitat (Sections

6.1.1.3, 30.3.2.3)

Changes to wind,

wave height, and

storm intensity

Shipping and industrial infrastructure is

vulnerable to wave and storm intensity.

(Section 30.6.2)

Risk of increasing losses and damages to

shipping and industrial infrastructure

Risk of accidents increases for enterprises

such as shipping, as well as deep sea oil gas

and mineral extraction.

Table KR-1 (continued)

CBS = Coastal Boundary Systems; EBUE = Eastern Boundary Upwelling Ecosystems; EUS = Equatorial Upwelling Systems; HIC, LIC, MIC = high-, low-, and medium-income

countries; HLSBS = High-Latitude Spring Bloom Systems; SES = Semi-Enclosed Seas; STG = Sub-Tropical Gyres.

Birkmann, J., R. Licker, M. Oppenheimer, M. Campos, R. Warren, G. Luber, B.C. O’Neill, and K. Takahashi, 2014: Cross-chapter box on a selection of the

hazards, key vulnerabilities, key risks, and emergent risks identiﬁed in the WGII contribution to the ﬁfth assessment report. 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. 113-121.

This cross-chapter box should be cited as:

Observed Global Responses

of Marine Biogeography,

Abundance, and Phenology

to Climate Change

Elvira Poloczanska (Australia), Ove Hoegh-Guldberg (Australia), William Cheung (Canada), Hans-

Otto Pörtner (Germany), Michael T. Burrows (UK)

123

IPCC WGII AR4 presented the detection of a global ﬁngerprint on natural systems and its

attribution to climate change (AR4, Chapter 1, SPM Figure 1), but studies from marine systems

were mostly absent. Since AR4, there has been a rapid increase in studies that focus on climate

change impacts on marine species, which represents an opportunity to move from more

anecdotal evidence to examining and potentially attributing detected biological changes within

the ocean to climate change (Section 6.3; Figure MB-1). Recent changes in populations of marine

species and the associated shifts in diversity patterns are resulting, at least partly, from climate

change–mediated biological responses across ocean regions (robust evidence, high agreement,

high conﬁdence; Sections 6.2, 30.5; Table 6-7).

Poloczanska et al. (2013) assess a potential pattern in responses of ocean life to recent climate

change using a global database of 208 peer-reviewed papers. Observed responses (n = 1735)

were recorded from 857 species or assemblages across regions and taxonomic groups, from

phytoplankton to marine reptiles and mammals (Figure MB-1). Observations were deﬁned as

those where the authors of a particular paper assessed the change in a biological parameter

(including distribution, phenology, abundance, demography, or community composition) and, if

change occurred, the consistency of the change with that expected under climate change. Studies

from the peer-reviewed literature were selected using three criteria: (1) authors inferred or

directly tested for trends in biological and climatic variables; (2) authors included data after 1990;

and (3) observations spanned at least 19 years, to reduce bias resulting from biological responses

to short-term climate variability.

The results of this meta-analysis show that climate change has already had widespread

impacts on species’ distribution, abundance, phenology, and subsequently, species richness and

community composition across a broad range of taxonomic groups (plankton to top predators).

Of the observations that showed a response in either direction, changes in phenology, distribution

and abundance were overwhelmingly (81%) in a direction that was consistent with theoretical

responses to climate change (Section 6.2). Knowledge gaps exist, especially in equatorial sub-

regions and the Southern Hemisphere (Figure MB-1).

The timing of many biological events (phenology) had an earlier onset. For example, over the last

50 years, spring events shifted earlier for many species with an average advancement of 4.4 ± 0.7

days per decade (mean ± SE) and summer events by 4.4 ± 1.1 days per decade (robust evidence,

high agreement, high conﬁdence) (Figure MB-2). Phenological observations included in the study

range from shifts in peak abundance of phytoplankton and zooplankton, to reproduction and

migration of invertebrates, ﬁshes, and seabirds (Sections 6.3.2, 30.5).

Cross-Chapter Box

Observed Global Responses of Marine Biogeography, Abundance, and Phenology to Climate Change

124

The distributions of benthic, pelagic, and demersal species and communities have shifted by up to a thousand kilometers, although the

range shifts have not been uniform across taxonomic groups or ocean regions (Sections 6.3.2, 30.5) (robust evidence, high agreement, high

conﬁdence). Overall, leading range edges expanded in a poleward direction at 72.0 ± 13.5 km per decade and trailing edges contracted in a

poleward direction at 15.8 ± 8.7 km per decade (Figure MB-2), revealing much higher current rates of migration than the potential maximum

rates reported for terrestrial species (Figure 4-6) despite slower warming of the ocean than land surface (WGI Section 3.2).

Poleward distribution shifts have resulted in increased species richness in mid- to high-latitude regions (Hiddink and ter Hofstede, 2008) and

changing community structure (Simpson et al., 2011; see also Section 28.2.2). Increases in warm-water components of communities concurrent

with regional warming have been observed in mid- to high-latitude ocean regions including the Bering Sea, Barents Sea, Nordic Sea, North

Sea, and Tasman Sea (Box 6.1; Section 30.5). Observed changes in species composition of catches from 1970–2006 that are partly attributed to

long-term ocean warming suggest increasing dominance of warmer water species in subtropical and higher latitude regions, and reduction in

abundance of subtropical species in equatorial waters (Cheung et al., 2013), with implications for ﬁsheries (Sections 6.5, 7.4.2, 30.6.2.1).

The magnitude and direction of distribution shifts can be related to temperature velocities (i.e., the speed and direction at which isotherms

propagate across the ocean’s surface (Section 30.3.1.1; Burrows et al., 2011). Pinsky et al. (2013) showed that shifts in both latitude and depth

of benthic ﬁsh and crustaceans could be explained by climate velocity with remarkable accuracy, using a database of 128 million individuals

across 360 marine taxa from surveys of North American coastal waters conducted over 1968–2011. Poloczanska et al. (2013) found that

faster distribution shifts generally occur in regions of highest surface temperature velocity, such as the North Sea and sub-Arctic Paciﬁc Ocean.

Observed marine species shifts, since approximately the 1950s, have generally been able to track observed velocities (Figure MB-3), with

phyto- and zooplankton distribution shifts vastly exceeding climate velocities observed over most of the ocean surface, but with considerable

variability within and among taxonomic groups (Poloczanska et al., 2013).

Change consistent with climate change

Type of observed change

No change

Change not consistent with climate change

222

221

772

132

Regions with large numbers of observations

Proportion of observed changes

Total number of observations within each region / locality41

Figure MB-1

| 1735 observed responses to climate change from 208 single- and multi-species studies. Data shown include changes that are attributed (at least partly) to

climate change (blue), changes that are inconsistent with climate change (red), and no change (orange). Each circle represents the center of a study area. Where points fall on

land, it is because they are centroids of distributions that surround an island or peninsula. Studies encompass areas from single sites (e.g., seabird breeding colony) to large

ocean regions (e.g., continuous plankton recorder surveys in north-east Atlantic). For regions (indicated by blue shading) and localities with large numbers of observations, pie

charts summarize the relative proportions of the three types of observed changes (consistent with climate change, inconsistent with climate change, and no change) in those

regions or localities. The numbers indicate the total observations within each region or locality. Note: 57% of the studies included were published since AR4. (From Poloczanska

et al., 2013).

Observed Global Responses of Marine Biogeography, Abundance, and Phenology to Climate Change

Cross-Chapter Box

125

Biogeographic shifts are also inﬂuenced by other factors such as currents, nutrient and stratiﬁcation changes, light levels, sea ice, species’

interactions, habitat availability and ﬁshing, some of which can be independently inﬂuenced by climate change (Section 6.3). Rate and pattern

of biogeographic shifts in sedentary organisms and benthic macroalgae are complicated by the inﬂuence of local dynamics and topographic

features (islands, channels, coastal lagoons, e.g., of the Mediterranean (Bianchi, 2007), coastal upwelling e.g., (Lima et al., 2007)). Geographical

barriers constrain range shifts and may cause a loss of endemic species (Ben Rais Lasram et al., 2010), with associated niches ﬁlled by alien

species, either naturally migrating or artiﬁcially introduced (Philippart et al., 2011).

Whether marine species can continue to keep pace as rates of warming, hence climate velocities, increase (Figure MB-3b) is a key uncertainty.

Climate velocities on land are expected to outpace the ability of many terrestrial species to track climate velocities this century (Section 4.3.2.5;

Figure 4-6). For marine species, the observed rates of shift are generally much faster than those for land species, particularly for primary

producers and lower trophic levels (Poloczanska et al., 2013). Phyto- and zooplankton communities (excluding larval ﬁsh) have extended

distributions at remarkable rates (Figure MB-3b), such as in the Northeast Atlantic (Section 30.5.1) with implications for marine food webs.

Geographical range shifts and depth distribution vary between coexisting marine species (Genner et al., 2004; Perry et al., 2005; Simpson et

al., 2011) as a consequence of the width of species-speciﬁc thermal windows and associated vulnerabilities (Figure 6-5). Warming therefore

causes differential changes in growth, reproductive success, larval output, early juvenile survival, and recruitment, implying shifts in the relative

performance of animal species and, thus, their competitiveness (Pörtner and Farrell, 2008; Figure 6-7A). Such effects may underlie abundance

losses or local extinctions, “regime shifts” between coexisting species, or critical mismatches between predator and prey organisms, resulting

in changes in local and regional species richness, abundance, community composition, productivity, energy ﬂows, and invasion resistance.

Even among Antarctic stenotherms, differences in biological responses related to mode of life, phylogeny and associated metabolic capacities

exist (Section 6.3.1.4). As a consequence, marine ecosystem functions may be substantially reorganized at the regional scale, potentially

triggering a range of cascading effects (Hoegh-Guldberg and Bruno, 2010). A focus on understanding the mechanisms underpinning the nature

and magnitude of responses of marine organisms to climate change can help forecast impacts and the associated costs to society as well as

facilitate adaptive management strategies formitigating these impacts (Sections 6.3, 6.4).

Cooler watersWarmer waters

111

359

106

Distribution shift (km per decade)

–20 200 100 400

Benthic algae

Benthic cnidarians

Benthic mollusks

Benthic crustacea

Benthic invertebrates

(other)

Phytoplankton

Zooplankton

Larval bony ﬁshes

Non-bony ﬁshes

Bony ﬁshes

All taxa

Number of

observations

Mean Standard

error

Standard

error

Direction of shift consistent with climate change (warming)

Distribution shifts towards:

Rates of change in

distribution measured at

leading edges

trailing edges

regardless of

range location

Figure MB-2 |

Rates of change in distribution (kilometers per decade) for marine taxonomic groups, measured at the leading edges (red) and trailing edges (green). Average

distribution shifts were calculated using all data, regardless of range location, and are in dark blue. Distribution shifts have been square-root transformed; standard errors may be

asymmetric as a result. Positive distribution changes are consistent with warming (into previously cooler waters, generally poleward). Means ± standard error are shown, along

with number of observations. Non-bony ﬁshes include sharks, rays, lampreys, and hagﬁsh. (From Poloczanska et al., 2013).

Cross-Chapter Box

Observed Global Responses of Marine Biogeography, Abundance, and Phenology to Climate Change

126

Slow areas

Global median

Fast areas

Figure MB-3 |

(a) Rate of climate change for the ocean (sea surface temperature (SST) °C yr

-1

). (b) Corresponding climate velocities for the ocean and median velocity from land

(adapted from Burrows et al.

, 2011). (c) Observed rates of displacement of marine taxonomic groups based on observations over 1900–2010. The dotted bands give an example

of interpretation. Rates of climate change of 0.01 °C yr

-1

correspond to approximately 3.3 km yr

-1

median climate velocity in the ocean. When compared to observed rates of

displacement (c), many marine taxonomic groups have been able to track these velocities. For phytoplankton and zooplankton the rates of displacement greatly exceed median

climate velocity for the ocean and, for phytoplankton exceed velocities in fast areas of the ocean approximately 10.0 km yr

-1

. All values are calculated for ocean surface with the

exclusion of polar seas (Figure 30-1a). (a) Observed rates of climate change for ocean SST (green line) are derived from the Hadley Centre Interpolated SST 1.1 (HadISST1.1)

data set, and all other rates are calculated based on the average of the Coupled Model Intercomparison Project Phase 5 (CMIP5) climate model ensembles (Table SM30-3) for the

historical period and for the future based on the four Representative Concentration Pathway (RCP) scenarios. Data were smoothed using a 20-year sliding window. (b) Median

climate velocity over the global ocean surface (light blue line; excluding polar seas) calculated from HadSST1.1 data set over 1960–2009 using the methods of Burrows et al.

(2011). Median velocities representative of ocean regions of slow velocities such as the Paciﬁc subtropical gyre (dark blue line) and of high velocities such as the Coral Triangle or

the North Sea (purple line) shown. Median rates over global land surface (red line) over 1960–2009 calculated using Climate Research Unit data set CRU TS3.1. Figure 30-3

shows climate velocities over the ocean surface calculated over 1960–2009. (c) Rates of displacement for marine taxonomic groups estimated by Poloczanska et al. (2013) using

published studies. Note the displacement rates for phytoplankton exceed the axis, so values are given.

0 2 4 6 8 10 12

0 2 4 6 8 10 4240

4240

0 2 4 6 8 10

55.0

15.7

35.8

Historical Projected

(a) Climate change scenarios

(km yr

–1

)

(km yr

–1

)

Rate of climate change for the ocean (sea surface temperature °C yr

–1

)

(°C yr

–1

)

Observed

Historical

RCP2.6

RCP4.5

RCP6.0

RCP8.5

−0.02

−0.01

0.00

0.01

0.02

0.05

0.04

0.03

0.06

0.00

0.01

0.02

0.05

0.04

0.03

0.06

0.00

0.01

0.02

0.05

0.04

0.03

0.06

1900 1950 2000 2050 2100

Lower bound

(25th percentile)

Upper bound

(75th percentile)

Estimated speed at which species group has moved

example of interpretation

Median

Unable to keep up Able to keep up

(a) Rate of climate change

(b) Estimate of climate velocity to determine rate of displacement

Land global median

Ocean slow areas

(e.g., subtropical gyres)

Ocean global median

Ocean fast areas

(e.g., equatorial and high latitudes)

Benthic algae

Benthic cnidarians

Benthic mollusks

Benthic crustacea

Other benthic

invertebrates

Phytoplankton

Zooplankton

Larval bony ﬁshes

Non-bony ﬁshes

Bony ﬁshes

Observed Global Responses of Marine Biogeography, Abundance, and Phenology to Climate Change

Cross-Chapter Box

127

Ben Rais Lasram, F., F. Guilhaumon, C. Albouy, S. Somot, W. Thuiller, and D. Mouillot, 2010: The Mediterranean Sea as a ‘cul-de-sac’ for endemic ﬁshes facing climate

change. Global Change Biology, 16, 3233-3245.

Bianchi, C.N., 2007: Biodiversity issues for the forthcoming Mediterranean Sea. Hydrobiologia, 580, 7-21.

Burrows, M.T., D. S. Schoeman, L.B. Buckley, P.J. Moore, E.S. Poloczanska, K. Brander, K, C.J. Brown, J.F. Bruno, C.M. Duarte, B.S. Halpern, J. Holding, C.V. Kappel, W.

Kiessling, M.I. O’Connor, J.M. Pandolﬁ, C. Parmesan, F. Schwing, W.J. Sydeman, and A.J. Richardson, 2011: The pace of shifting climate in marine and terrestrial

ecosystems. Science, 334, 652-655.

Cheung, W.W.L., R. Watson, and D. Pauly, 2013: Signature of ocean warming in global ﬁsheries catch. Nature, 497(7449), 365-368.

Genner, M.J., D.W. Sims, V.J. Wearmouth, E.J. Southall, A.J. Southward, P.A. Henderson, and S.J. Hawkins, 2004: Regional climatic warming drives long-term community

changes of British marine ﬁsh. Proceedings of the Royal Society B, 271(1539), 655-661.

Hiddink, J.G. and R. ter Hofstede, 2008: Climate induced increases in species richness of marine ﬁshes. Global Change Biology, 14, 453-460.

Hoegh-Guldberg, O. and J.F. Bruno, 2010: The impact of climate change on the world’s marine ecosystems. Science, 328, 1523-1528.

Lima, F.P., P.A. Ribeiro, N. Queiroz, S.J. Hawkins, and A.M. Santos, 2007: Do distributional shifts of northern and southern species of algae match the warming pattern?

Global Change Biology, 13, 2592-2604.

Perry, A.L., P.J. Low, J.R. Ellis, and J.D. Reynolds, 2005: Climate change and distribution shifts in marine ﬁshes. Science, 308(5730), 1912-1915.

Philippart, C.J.M., R. Anadon, R. Danovaro, J.W. Dippner, K.F. Drinkwater, S.J. Hawkins, T. Oguz, G. O’Sullivan, and P.C. Reid, 2011: Impacts of climate change on European

marine ecosystems: observations, expectations and indicators. Journal of Experimental Marine Biology and Ecology, 400, 52-69.

Pinksy, M.L., B. Worm, M.J. Fogarty, J.L. Sarmiento, and S.A. Levin, 2013: Marine taxa track local climate velocities. Science, 341, 1239-1242.

Pörtner, H.O. and A.P. Farrell, 2008: Physiology and climate change. Science, 322(5902), 690-692.

Poloczanska, E.S., C.J. Brown, W.J. Sydeman, W. Kiessling, D.S. Schoeman, P.J. Moore, K. Brander, J.F. Bruno, L.B. Buckley, M.T. Burrows, C.M. Duarte, B.S. Halpern, J.

Holding, C.V. Kappel, M.I. O’Connor, J.M. Pandolﬁ, C. Parmesan, F. Schwing, S.A.Thompson, and A.J. Richardson, 2013: Global imprint of climate change on marine

life. Nature Climate Change, 3, 919-925.

Simpson, S.D., S. Jennings, M.P. Johnson, J.L. Blanchard, P.J. Schon, D.W. Sims, and M.J. Genner, 2011: Continental shelf-wide response of a ﬁsh assemblage to rapid

warming of the sea. Current Biology, 21, 1565-1570.

References

Poloczanska, E.S., O. Hoegh-Guldberg, W. Cheung, H.-O. Pörtner, and M. Burrows, 2014: Cross-chapter box on observed global responses of marine biogeogra-

phy, abundance, and phenology to climate change. 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. 123-127.

This cross-chapter box should be cited as:

Ocean Acidiﬁcation

Jean-Pierre Gattuso (France), Peter G. Brewer (USA), Ove Hoegh-Guldberg (Australia), Joan A.

Kleypas (USA), Hans-Otto Pörtner (Germany), Daniela N. Schmidt (UK)

129

Anthropogenic ocean acidiﬁcation and global warming share the same primary cause, which is

the increase of atmospheric CO

(Figure OA-1A; WGI, Section 2.2.1). Eutrophication, loss of sea ice,

upwelling and deposition of atmospheric nitrogen and sulfur all exacerbate ocean acidiﬁcation

locally (Sections 5.3.3.6, 6.1.1, 30.3.2.2).

Chemistry and Projections

The fundamental chemistry of ocean acidiﬁcation is well understood (robust evidence, high

agreement). Increasing atmospheric concentrations of CO

result in an increased ﬂux of CO

into a

mildly alkaline ocean, resulting in a reduction in pH, carbonate ion concentration, and the capacity

of seawater to buffer changes in its chemistry (very high conﬁdence). The changing chemistry of

the surface layers of the open ocean can be projected at the global scale with high accuracy using

projections of atmospheric CO

levels (Figure CC-OA-1B). Observations of changing upper ocean

chemistry over time support this linkage (WGI Table 3.2 and Figure 3.18; Figures 30-8, 30-9).

Projected changes in open ocean, surface water chemistry for the year 2100 based on representative

concentration pathways (WGI, Figure 6.28) compared to pre-industrial values range from a pH

change of –0.14 units with Representative Concentration Pathway (RCP)2.6 (421 ppm CO

, +1°C,

22% reduction of carbonate ion concentration) to a pH change of –0.43 units with RCP8.5 (936

ppm CO

, +3.7ºC, 56% reduction of carbonate ion concentration). Projections of regional changes,

especially in the highly complex coastal systems (Sections 5.3.3.5, 30.3.2.2), in polar regions (WGI

Section 6.4.4), and at depth are more difﬁcult but generally follow similar trends.

Biological, Ecological, and Biogeochemical Impacts

Investigations of the effect of ocean acidiﬁcation on marine organisms and ecosystems have a

relatively short history, recently analyzed in several meta-analyses (Sections 6.3.2.1, 6.3.5.1). A wide

range of sensitivities to projected rates of ocean acidiﬁcation exists within and across diverse groups

of organisms, with a trend for greater sensitivity in early life stages (high conﬁdence; Sections

5.4.2.2, 5.4.2.4, 6.3.2). A pattern of positive and negative impacts emerges (high conﬁdence; Figure

OA-1C) but key uncertainties remain in our understanding of the impacts on organisms, life histories,

and ecosystems. Responses can be inﬂuenced, often exacerbated by other drivers, such as warming,

hypoxia, nutrient concentration, and light availability (high conﬁdence; Sections 5.4.2.4, 6.3.5).

Growth and primary production are stimulated in seagrass and some phytoplankton (high

conﬁdence; Sections 5.4.2.3, 6.3.2.2, 6.3.2.3, 30.5.6). Harmful algal blooms could become more

frequent (limited evidence, medium agreement). Ocean acidiﬁcation may stimulate nitrogen ﬁxation

(limited evidence, low agreement; 6.3.2.2). It decreases the rate of calciﬁcation of most, but not

Cross-Chapter Box

Ocean Acidiﬁcation

130

all, sea ﬂoor calciﬁers (medium agreement, robust evidence) such as reef-building corals (Box CC-CR), coralline algae, bivalves, and gastropods,

reducing the competitiveness with non-calciﬁers (Sections 5.4.2.2, 5.4.2.4, 6.3.2.5). Ocean warming and acidiﬁcation promote higher rates of

calcium carbonate dissolution resulting in the net dissolution of carbonate sediments and frameworks and loss of associated habitat (medium

conﬁdence; 5.4.2.4, 6.3.2.5, 6.3.5.4). Some corals and temperate ﬁshes experience disturbances to behavior, navigation, and their ability to tell

conspeciﬁcs from predators (Section 6.3.2.4). However, there is no evidence for these effects to persist on evolutionary time scales in the few

groups analyzed (Section 6.3.2).

Some phytoplankton and molluscs displayed adaptation to ocean acidiﬁcation in long-term experiments (limited evidence, medium agreement;

Section 6.3.2.1), indicating that the long-term responses could be less than responses obtained in short-term experiments. However, mass

extinctions in Earth history occurred during much slower rates of ocean acidiﬁcation, combined with other drivers changing, suggesting that

evolutionary rates are not fast enough for sensitive animals and plants to adapt to the projected rate of future change (medium conﬁdence;

Section 6.1.2).

Projections of ocean acidiﬁcation effects at the ecosystem level are made difﬁcult by the diversity of species-level responses. Differential

sensitivities and associated shifts in performance and distribution will change predator–prey relationships and competitive interactions (Sections

RCP8.5

RCP2.6

Historical

7.7

7.8

7.9

8.0

8.1

8.2

pH (total scale)

1850 1900 1950 2000 2050 2100

Burning of fossil

fuels, cement

manufacture,

and land use

change

(WGI 2.2.1)

(WGI 3.8.2;

WGII 5.3.3, 30.3.2)

(WGII 5.4.2. 6.3.2, 30.5)

(WGII 5.4.2.2, 5.4.2.4,

30.6.2, Box CC-CR)

(WGII 30.6.4, 30.7.1)

Increase in

atmospheric

High certainty Low certainty

• Increased CO

bicarbonate

ions, and acidity

• Decreased

carbonate ions

and pH

• Reduced shell and

skeleton production

• Changes in

assemblages, food

webs, and ecosystems

• Biodiversity loss

• Changes in biogas

production and

feedback to climate

• Fisheries,

aquaculture, and

food security

• Coastal protection

• Tourism

• Climate regulation

• Carbon storage

• UN Framework Convention on

Climate Change: Conference

of the Parties, IPCC,

Conference on Sustainable

Development (Rio+20)

• Convention on Biological

Diversity

• Geoengineering

• Regional and local acts, laws,

and policies to reduce other

stresses

(a)

(b)

–0.75 –0.50 –0.25 0 0.25

Mean effect size (lnRR)Year

Abundance

Calciﬁcation

Development

Growth

Metabolism

Photosynthesis

Survival

(72)

(110)

(24)

(173)

(32)

(82)

(69)

(c)

Ocean acidiﬁcation

Atmospheric

change

Changes to organisms

and ecosystems

Socioeconomic impacts

Ocean warming and deoxgenation

relevant sections

(WGI 6.3.2)

Policy options for actionDriver

Figure OA-1 |

(a) Overview of the chemical, biological, and socio-economic impacts of ocean acidiﬁcation and of policy options (adapted from Turley and Gattuso, 2012). (b) Multi-model

simulated time series of global mean ocean surface pH (on the total scale) from Coupled Model Intercomparison Project Phase 5 (

CMIP5) climate model simulations from 1850 to 2100.

Projections are shown for emission scenarios Representative Concentration Pathway (RCP)2.6 (blue) and RCP8.5 (red) for the multi-model mean (solid lines) and range across the

distribution of individual model simulations (shading). Black (gray shading) is the modeled historical evolution using historical reconstructed forcings. The models that are included are those

from CMIP5 that simulate the global carbon cycle while being driven by prescribed atmospheric CO

concentrations (WGI AR5 Figures SPM.7 and TS.20). (c) Effect of near-future

acidiﬁcation (

seawater pH reduction of ≤0.5 units) on major response variables estimated using weighted random effects meta-analyses, with the exception of survival, which is not

weighted (Kroeker et al., 2013). The log-transformed response ratio (lnRR) is the ratio of the mean effect in the acidiﬁcation treatment to the mean effect in a control group. It indicates

which process is most uniformly affected by ocean acidiﬁcation, but large variability exists between species. Signiﬁcance is determined when the 95% bootstrapped conﬁdence interval

does not cross zero. The number of experiments used in the analyses is shown in parentheses. The * denotes a statistically signiﬁcant effect.

Ocean Acidiﬁcation

Cross-Chapter Box

131

6.3.2.5, 6.3.5, 6.3.6), which could impact food webs and higher trophic levels (limited evidence, high agreement). Natural analogues at CO

vents

indicate decreased species diversity, biomass, and trophic complexity of communities (Box CC-CR; Sections 5.4.2.3, 6.3.2.5, 30.3.2.2, 30.5). Shifts in

community structure have also been documented in regions with rapidly declining pH (Section 5.4.2.2).

Owing to an incomplete understanding of species-speciﬁc responses and trophic interactions, the effect of ocean acidiﬁcation on global

biogeochemical cycles is not well understood (limited evidence, low agreement) and represents an important knowledge gap. The additive,

synergistic, or antagonistic interactions of factors such as temperature, concentrations of oxygen and nutrients, and light are not sufﬁciently

investigated yet.

Risks, Socioeconomic Impacts, and Costs

The risks of ocean acidiﬁcation to marine organisms, ecosystems, and ultimately to human societies, include both the probability that ocean

acidiﬁcation will affect fundamental physiological and ecological processes of organisms (Section 6.3.2.1), and the magnitude of the resulting

impacts on ecosystems and the ecosystem services they provide to society (Box 19-2). For example, ocean acidiﬁcation under RCP4.5 to RCP8.5

will impact formation and maintenance of coral reefs (high conﬁdence; Box CC-CR, Section 5.4.2.4) and the goods and services that they provide

such as ﬁsheries, tourism, and coastal protection (limited evidence, high agreement; Box CC-CR; Sections 6.4.1.1,19.5.2, 27.3.3, 30.5, 30.6). Ocean

acidiﬁcation poses many other potential risks, but these cannot yet be quantitatively assessed because of the small number of studies available,

particularly on the magnitude of the ecological and socioeconomic impacts (Section 19.5.2).

Global estimates of observed or projected economic costs of ocean acidiﬁcation do not exist. The largest uncertainty is how the impacts on lower

trophic levels will propagate through the food webs and to top predators. However, there are a number of instructive examples that illustrate

the magnitude of potential impacts of ocean acidiﬁcation. A decrease of the production of commercially exploited shelled molluscs (Section

6.4.1.1) would result in a reduction of USA production of 3 to 13% according to the Special Report on Emission Scenarios (SRES) A1FI emission

scenario (low conﬁdence). The global cost of production loss of molluscs could be more than US$100 billion by 2100 (limited evidence, medium

agreement). Models suggest that ocean acidiﬁcation will generally reduce ﬁsh biomass and catch (low conﬁdence) and that complex additive,

antagonistic, and/or synergistic interactions will occur with other environmental (warming) and human (ﬁsheries management) factors (Section

6.4.1.1). The annual economic damage of ocean-acidiﬁcation–induced coral reef loss by 2100 has been estimated, in 2012, to be US$870 and 528

billion, respectively for the A1 and B2 SRES emission scenarios (low conﬁdence; Section 6.4.1). Although this number is small compared to global

gross domestic product (GDP), it can represent a very large GDP loss for the economies of many coastal regions or small islands that rely on the

ecological goods and services of coral reefs (Sections 25.7.5, 29.3.1.2).

Mitigation and Adaptation

Successful management of the impacts of ocean acidiﬁcation includes two approaches: mitigation of the source of the problem (i.e., reduce

anthropogenic emissions of CO

) and/or adaptation by reducing the consequences of past and future ocean acidiﬁcation (Section 6.4.2.1).

Mitigation of ocean acidiﬁcation through reduction of atmospheric CO

is the most effective and the least risky method to limit ocean acidiﬁcation

and its impacts (Section 6.4.2.1). Climate geoengineering techniques based on solar radiation management will not abate ocean acidiﬁcation

and could increase it under some circumstances (Section 6.4.2.2). Geoengineering techniques to remove CO

from the atmosphere could directly

address the problem but are very costly and may be limited by the lack of CO

storage capacity (Section 6.4.2.2). In addition, some ocean-

based approaches, such as iron fertilization, would only relocate ocean acidiﬁcation from the upper ocean to the ocean interior, with potential

ramiﬁcations on deep water oxygen levels (Sections 6.4.2.2, 30.3.2.3, 30.5.7). A low-regret approach, with relatively limited effectiveness, is to

limit the number and the magnitude of drivers other than CO

, such as nutrient pollution (Section 6.4.2.1). Mitigation of ocean acidiﬁcation at

the local level could involve the reduction of anthropogenic inputs of nutrients and organic matter in the coastal ocean (Section 5.3.4.2). Some

adaptation strategies include drawing water for aquaculture from local watersheds only when pH is in the right range, selecting for less sensitive

species or strains, or relocating industries elsewhere (Section 6.4.2.1).

Kroeker, K., R.C. Kordas, A. Ryan, I. Hendriks, L. Ramajo, G. Singh, C. Duarte, and J.-P. Gattuso, 2013: Impacts of ocean acidiﬁcation on marine organisms: quantifying

sensitivities and interaction with warming. Global Change Biology, 19, 1884-1896.

Turley, C. and J.-P. Gattuso, 2012: Future biological and ecosystem impacts of ocean acidiﬁcation and their socioeconomic-policy implications. Current Opinion in

Environmental Sustainability, 4, 278-286.

References

Gattuso, J.-P., P.G. Brewer, O. Hoegh-Guldberg, J.A. Kleypas, H.-O. Pörtner, and D.N. Schmidt, 2014: Cross-chapter box on ocean acidiﬁcation. 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 King-

dom and New York, NY, USA, pp. 129-131.

This cross-chapter box should be cited as:

Net Primary Production in

the Ocean

Philip W. Boyd (New Zealand), Svein Sundby (Norway), Hans-Otto Pörtner (Germany)

133

Net Primary Production (NPP) is the rate of photosynthetic carbon ﬁxation minus the fraction of

ﬁxed carbon used for cellular respiration and maintenance by autotrophic planktonic microbes

and benthic plants (Sections 6.2.1, 6.3.1). Environmental drivers of NPP include light, nutrients,

micronutrients, CO

, and temperature (Figure PP-1a). These drivers, in turn, are inﬂuenced by

oceanic and atmospheric processes, including cloud cover; sea ice extent; mixing by winds, waves,

and currents; convection; density stratiﬁcation; and various forms of upwelling induced by eddies,

frontal activity, and boundary currents. Temperature has multiple roles as it inﬂuences rates

of phytoplankton physiology and heterotrophic bacterial recycling of nutrients, in addition to

stratiﬁcation of the water column and sea ice extent (Figure PP-1a). Climate change is projected

to strongly impact NPP through a multitude of ways that depend on the regional and local

physical settings (WGI AR5, Chapter 3), and on ecosystem structure and functioning (medium

conﬁdence; Sections 6.3.4, 6.5.1). The inﬂuence of environmental drivers on NPP causes as much

as a 10-fold variation in regional productivity with nutrient-poor subtropical waters and light-

limited Arctic waters at the lower range and productive upwelling regions and highly eutrophic

coastal regions at the upper range (Figure PP-1b).

The oceans currently provide ~50 × 10

g C yr

–1

, or about half of global NPP (Field et al., 1998).

Global estimates of NPP are obtained mainly from satellite remote sensing (Section 6.1.2),

which provides unprecedented spatial and temporal coverage, and may be validated regionally

against oceanic measurements. Observations reveal signiﬁcant changes in rates of NPP when

environmental controls are altered by episodic natural perturbations, such as volcanic eruptions

enhancing iron supply, as observed in high-nitrate low-chlorophyll waters of the Northeast Paciﬁc

(Hamme et al., 2010). Climate variability can drive pronounced changes in NPP (Chavez et al.,

2011), such as from El Niño to La Niña transitions in Equatorial Paciﬁc, when vertical nutrient and

trace element supply are enhanced (Chavez et al., 1999).

Multi-year time series records of NPP have been used to assess spatial trends in NPP in recent

decades. Behrenfeld et al. (2006), using satellite data, reported a prolonged and sustained global

NPP decrease of 190 × 10

g C yr

–1

, for the period 1999–2005—an annual reduction of 0.57%

of global NPP. In contrast, a time series of directly measured NPP between 1988 and 2007 by

Saba et al. (2010) (i.e., in situ incubations using the radiotracer

C-bicarbonate) revealed an

increase (2% yr

–1

) in NPP for two low-latitude open ocean sites. This discrepancy between in situ

and remotely sensed NPP trends points to uncertainties in either the methodology used and/

or the extent to which discrete sites are representative of oceanic provinces (Saba et al., 2010,

2011). Modeling studies have subsequently revealed that the <15-year archive of satellite-

Cross-Chapter Box

Net Primary Production in the Ocean

134

Nutrient

recycling

•

Nutrients

•

Trace metals

Euphotic zone (0–100 m)

•

Upwelling

•

Vertical mixing

•

Stratiﬁcation

(a)

Light

•

Zooplankton

•

Microbes

NPP (g C m

–

² y

–

¹)

300 250 200 150 100 50 0

(b)

Latitude

Season

Cloud cover

Figure PP-1 |

(a) Environmental factors controlling Net Primary Production (NPP). NPP is controlled mainly by three basic processes: (1) light conditions in the surface ocean, that

is, the photic zone where photosynthesis occurs; (2) upward ﬂux of nutrients and micronutrients from underlying waters into the photic zone, and (3) regeneration of nutrients and

micronutrients via the breakdown and recycling of organic material before it sinks out of the photic zone. All three processes are inﬂuenced by physical, chemical, and biological

processes and vary across regional ecosystems. In addition, water temperature strongly inﬂuences the upper rate of photosynthesis for cells that are resource-replete. Predictions of

alteration of primary productivity under climate change depend on correct parameterizations and simulations of each of these variables and processes for each region. (b) Annual

composite map of global areal NPP rates (derived from Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua satellite climatology from 2003–2012; NPP was calculated

with the Carbon-based Productivity Model (CbPM; Westberry et al., 2008)). Overlaid is a grid of (thin black lines) that represent 51 distinct global ocean biogeographical provinces

(after Longhurst, 1998 and based on Boyd and Doney, 2002). The characteristics and boundaries of each province are primarily set by the underlying regional ocean physics and

chemistry. White areas = no data. (Figure courtesy of Toby Westberry (OSU) and Ivan Lima (WHOI), satellite data courtesy of NASA Ocean Biology Processing Group.)

Copepod pellets

Microzooplankton

mini-pellets

Particles

Coccolithophorids

Diatoms

Other

Phytoplankton

Copepods

Plankton

Temperature

Net Primary Production in the Ocean

Cross-Chapter Box

135

Arrigo, K.R. and G.L. van Dijken, 2011: Secular trends in Arctic Ocean net primary production. Journal of Geophysical Research, 116(C9), C09011,

doi:10.1029/2011JC007151.

Beaulieu, C., S.A. Henson, J.L. Sarmiento, J.P. Dunne, S.C. Doney, R.R. Rykaczewski, and L. Bopp, 2013: Factors challenging our ability to detect long-term trends in ocean

chlorophyll. Biogeosciences, 10(4), 2711-2724.

Behrenfeld, M.J., R.T. O’Malley, D.A. Siegel, C.R. McClain, J.L. Sarmiento, G.C. Feldman, A.J. Milligan, P.G. Falkowski, R.M. Letelier, and E.S. Boss, 2006: Climate-driven

trends in contemporary ocean productivity. Nature, 444(7120), 752-755.

Bopp, L., L. Resplandy, J.C. Orr, S.C. Doney, J.P. Dunne, M. Gehlen, P. Halloran, C. Heinze, T. Ilyina, R. Séférian, J. Tijiputra, and M. Vichi, 2013: Multiple stressors of ocean

ecosystems in the 21st century: projections with CMIP5 models. Biogeosciences, 10, 6225-6245.

Boyce, D.G., M.R. Lewis, and B. Worm, 2010: Global phytoplankton decline over the past century. Nature, 466(7306), 591-596.

Boyd, P.W. and S.C. Doney, 2002: Modelling regional responses by marine pelagic ecosystems to global climate change. Geophysical Research Letters, 29(16), 53-1–53-4,

doi:10.1029/2001GL014130.

Boyd, P.W., R. Strzepek, F.X. Fu, and D.A. Hutchins, 2010: Environmental control of open-ocean phytoplankton groups: now and in the future. Limnology and

Oceanography, 55(3), 1353-1376.

Chavez, F.P., P.G. Strutton, C.E. Friederich, R.A. Feely, G.C. Feldman, D.C. Foley, and M.J. McPhaden, 1999: Biological and chemical response of the equatorial Paciﬁc Ocean

to the 1997-98 El Niño. Science, 286(5447), 2126-2131.

References

derived NPP is insufﬁcient to distinguish climate-change mediated shifts in NPP from those driven by natural climate variability (Henson et al.,

2010; Beaulieu et al., 2013). Although multi-decadal, the available time series of oceanic NPP measurements are also not of sufﬁcient duration

relative to the time scales of longer-term climate variability modes as for example Atlantic Multi-decadal Oscillation (AMO), with periodicity of

60-70 years, Figure 6-1). Recent attempts to synthesize longer (i.e., centennial) records of chlorophyll as a proxy for phytoplankton stocks (e.g.,

Boyce et al., 2010) have been criticized for relying on questionable linkages between different proxies for chlorophyll over a century of records

(e.g., Rykaczewski and Dunne, 2011).

Models in which projected climate change alters the environmental drivers of NPP provide estimates of spatial changes and of the rate of

change of NPP. For example, four global coupled climate–ocean biogeochemical Earth System Models (WGI AR5 Chapter 6) projected an

increase in NPP at high latitudes as a result of alleviation of light and temperature limitation of NPP, particularly in the high-latitude biomes

(Steinacher et al., 2010). However, this regional increase in NPP was more than offset by decreases in NPP at lower latitudes and at mid-

latitudes due to the reduced input of macronutrients into the photic zone. The reduced mixed-layer depth and reduced rate of circulation may

cause a decrease in the ﬂux of macronutrients to the euphotic zone (Figure 6-2). These changes to oceanic conditions result in a reduction in

global mean NPP by 2 to 13% by 2100 relative to 2000 under a high emission scenario (Polovina et al., 2011; SRES (Special Report on Emission

Scenarios) A2, between RCP6.0 and RCP8.5). This is consistent with a more recent analysis based on 10 Earth System Models (Bopp et al.,

2013), which project decreases in global NPP by 8.6 (±7.9), 3.9 (±5.7), 3.6 (±5.7), and 2.0 (±4.1) % in the 2090s relative to the 1990s, under

the scenarios RCP8.5, RCP6.0, RCP4.5, and RCP2.6, respectively. However, the magnitude of projected changes varies widely between models

(e.g., from 0 to 20% decrease in NPP globally under RCP 8.5). The various models show very large differences in NPP at regional scales (i.e.,

provinces, see Figure PP-1b).

Model projections had predicted a range of changes in global NPP from an increase (relative to preindustrial rates) of up to 8.1% under an

intermediate scenario (SRES A1B, similar to RCP6.0; Sarmiento et al., 2004; Schmittner et al., 2008) to a decrease of 2-20% under the SRES A2

emission scenario (Steinacher et al., 2010). These projections did not consider the potential contribution of primary production derived from

atmospheric nitrogen ﬁxation in tropical and subtropical regions, favoured by increasing stratiﬁcation and reduced nutrient inputs from mixing.

This mechanism is potentially important, although such episodic increases in nitrogen ﬁxation are not sustainable without the presence of

excess phosphate (e.g., Moore et al., 2009; Boyd et al., 2010). This may lead to an underestimation of NPP (Mohr et al., 2010; Mulholland et al.,

2012; Wilson et al., 2012), however, the extent of such underestimation is unknown (Luo et al., 2012).

Care must be taken when comparing global, provincial (e.g., low-latitude waters, e.g., Behrenfeld et al., 2006) and regional trends in NPP

derived from observations, as some regions have additional local environmental inﬂuences such as enhanced density stratiﬁcation of the upper

ocean from melting sea ice. For example, a longer phytoplankton growing season, due to more sea ice–free days, may have increased NPP

(based on a regionally validated time-series of satellite NPP) in Arctic waters (Arrigo and van Dijken, 2011) by an average of 8.1x10

g C yr

−1

between 1998 and 2009. Other regional trends in NPP are reported in Sections 30.5.1 to 30.5.6. In addition, although future model projections

of global NPP from different models (Steinacher et al., 2010; Bopp et al., 2013) are comparable, regional projections from each of the models

differ substantially. This raises concerns as to which aspect(s) of the different model NPP parameterizations are responsible for driving regional

differences in NPP, and moreover, how accurate model projections are of global NPP.

From a global perspective, open ocean NPP will decrease moderately by 2100 under both low- (SRES B1 or RCP4.5) and high-emission

scenarios (medium conﬁdence; SRES A2 or RCPs 6.0, 8.5, Sections 6.3.4, 6.5.1), paralleled by an increase in NPP at high latitudes and

a decrease in the tropics (medium conﬁdence). However, there is limited evidence and low agreement on the direction, magnitude and

differences of a change of NPP in various ocean regions and coastal waters projected by 2100 (low conﬁdence).

Cross-Chapter Box

Net Primary Production in the Ocean

136

Boyd, P.W., S. Sundby, and H.-O. Pörtner, 2014: Cross-chapter box on net primary production in the ocean. In: Climate Change 2014: Impacts, Adaptation, and

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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. 133-136.

Chavez, F.P., M. Messié, and J.T. Pennington, 2011: Marine primary production in relation to climate variability and change. Annual Review of Marine Science, 3(1), 227-

260.

Field, C.B., M.J. Behrenfeld, J.T. Randerson, and P. Falkowski, 1998: Primary production of the biosphere: integrating terrestrial and oceanic components. Science,

281(5374), 237-240.

Hamme, R.C., P.W. Webley, W.R. Crawford, F.A. Whitney, M.D. DeGrandpre, S.R. Emerson, C.C. Eriksen, K.E. Giesbrecht, J.F.R. Gower, M.T. Kavanaugh, M.A. Peña, C.L.

Sabine, S.D. Batten, L.A. Coogan, D.S. Grundle, and D. Lockwood, 2010: Volcanic ash fuels anomalous plankton bloom in subarctic northeast Paciﬁc. Geophysical

Research Letters, 37(19), L19604, doi:10.1029/2010GL044629.

Henson, S.A., J.L. Sarmiento, J.P. Dunne, L. Bopp, I. Lima, S.C. Doney, J. John, and C. Beaulieu, 2010: Detection of anthropogenic climate change in satellite records of ocean

chlorophyll and productivity. Biogeosciences, 7(2), 621-640.

Longhurst, A.R., 1998: Ecological Geography of the Sea. Academic Press, San Diego, CA, USA, 560 pp.

Luo, Y.-W., S.C. Doney, L.A. Anderson, M. Benavides, I. Berman-Frank, A. Bode, S. Bonnet, K.H. Boström, D. Böttjer, D.G. Capone, E.J. Carpenter, Y.L. Chen, M.J. Church, J.E.

Dore, L.I. Falcón, A. Fernández, R.A. Foster, K. Furuya, F. Gómez, K. Gundersen, A.M. Hynes, D.M. Karl, S. Kitajima, R.J. Langlois, J. LaRoche, R.M. Letelier, E. Marañón,

D.J. McGillicuddy Jr., P.H. Moisander, C.M. Moore, B. Mouriño-Carballido, M.R. Mulholland, J.A. Needoba, K.M. Orcutt, A.J. Poulton, E. Rahav, P. Raimbault, A.P. Rees, L.

Riemann, T. Shiozaki, A. Subramaniam, T. Tyrrell, K.A. Turk-Kubo, M. Varela, T.A. Villareal, E.A. Webb, A.E. White, J. Wu, and J.P. Zehr, 2012: Database of diazotrophs in

global ocean: abundances, biomass and nitrogen ﬁxation rates. Earth System Science Data, 4, 47-73, doi:10.5194/essd-4-47-2012.

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Mulholland, M.R., P.W. Bernhardt, J.L. Blanco-Garcia, A. Mannino, K. Hyde, E. Mondragon, K. Turk, P.H. Moisander, and J.P. Zehr, 2012: Rates of dinitrogen ﬁxation and the

abundance of diazotrophs in North American coastal waters between Cape Hatteras and Georges Bank. Limnology and Oceanography, 57(4), 1067-1083.

Polovina, J.J., J.P. Dunne, P.A. Woodworth, and E.A. Howell, 2011: Projected expansion of the subtropical biome and contraction of the temperate and equatorial upwelling

biomes in the North Paciﬁc under global warming. ICES Journal of Marine Science, 68(6), 986-995.

Rykaczewski, R.R. and J.P. Dunne, 2011: A measured look at ocean chlorophyll trends. Nature, 472(7342), E5-E6, doi:10.1038/nature09952.

Saba, V.S., M.A.M. Friedrichs, M.-E. Carr, D. Antoine, R.A. Armstrong, I. Asanuma, O. Aumont, N.R. Bates, M.J. Behrenfeld, V. Bennington, L. Bopp, J. Bruggeman, E.T.

Buitenhuis, M.J. Church, A.M. Ciotti, S.C. Doney, M. Dowell, J. Dunne, S. Dutkiewicz, W. Gregg, N. Hoepffner, K.J.W. Hyde, J. Ishizaka, T. Kameda, D.M. Karl, I. Lima,

M.W. Lomas, J. Marra, G.A. McKinley, F. Mélin, J.K. Moore, A. Morel, J. O’Reilly, B. Salihoglu, M. Scardi, T.J. Smyth, S.L. Tang, J. Tjiputra, J. Uitz, M. Vichi, K. Waters, T.K.

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marine primary productivity in coastal and pelagic regions across the globe. Biogeosciences, 8(2), 489-503.

Sarmiento, J.L., R. Slater, R. Barber, L. Bopp, S.C. Doney, A.C. Hirst, J. Kleypas, R. Matear, U. Mikolajewicz, P. Monfray, V. Soldatov, S.A. Spall, and R. Stouffer, 2004: Response

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Schmittner, A., A. Oschlies, H.D. Matthews, and E.D. Galbraith, 2008: Future changes in climate, ocean circulation, ecosystems, and biogeochemical cycling simulated for a

business-as-usual CO2 emission scenario until year 4000 AD. Global Biogeochemical Cycles, 22(1), GB1013, doi:10.1029/2007GB002953.

Steinacher, M., F. Joos, T.L. Frölicher, L. Bopp, P. Cadule, V. Cocco, S.C. Doney, M. Gehlen, K. Lindsay, J.K. Moore, B. Schneider, and J. Segschneider, 2010: Projected 21st

century decrease in marine productivity: a multi-model analysis. Biogeosciences, 7(3), 979-1005.

Westberry, T., M.J. Behrenfeld, D.A Siegel, and E. Boss, 2008: Carbon-based primary productivity modeling with vertically resolved photoacclimation. Global

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Applied and Environmental Microbiology, 78(18), 6516-6523.

This cross-chapter box should be cited as:

Regional Climate

Summary Figures

Noah Diffenbaugh (USA), Dáithí Stone (Canada/South Africa/USA), Peter Thorne (USA/Norway /UK,

Filippo Giorgi (Italy), Bruce Hewitson (South Africa), Richard Jones (UK), Geert Jan van Oldenborgh

(Netherlands)

137

Information about the likelihood of regional climate change, assessed by Working Group I (WGI),

is foundational for the Working Group II assessment of climate-related risks. To help communicate

this assessment, the regional chapters of WGII present a coordinated set of regional climate

ﬁgures, which summarize observed and projected change in annual average temperature and

precipitation during the near term and the longer term for RCP2.6 and RCP8.5. These WGII regional

climate summary ﬁgures use the same temperature and precipitation ﬁelds that are assessed in

WGI Chapter 2 and WGI Chapter 12, with spatial boundaries, uncertainty metrics, and data classes

tuned to support the WGII assessment of climate-related risks and options for risk management.

Additional details on regional climate and regional climate processes can be found in WGI Chapter

14 and WGI Annex 1.

The WGII maps of observed annual temperature and precipitation use the same source data,

calculations of data sufﬁciency, and calculations of trend signiﬁcance as WGI Chapter 2 and WGI

Figures SPM.1 and SPM.2. (A full description of the observational data selection and signiﬁcance

testing can be found in WGI Box 2.2.) Observed trends are determined by linear regression

over the 1901–2012 period of Merged Land–Ocean Surface Temperature (MLOST) for annual

temperature, and over the 1951–2010 period of Global Precipitation Climatology Centre (GPCC)

for annual precipitation. Data points on the maps are classiﬁed into three categories, reﬂecting the

categories used in WGI Figures SPM.1 and SPM.2:

1) Solid colors indicate areas where (a) sufﬁcient data exist to permit a robust estimate of the

trend (i.e., only for grid boxes with greater than 70% complete records and more than 20%

data availability in the ﬁrst and last 10% of the time period), and (b) the trend is signiﬁcant

at the 10% level (after accounting for autocorrelation effects on signiﬁcance testing).

2) Diagonal lines indicate areas where sufﬁcient data exist to permit a robust estimate of the

trend, but the trend is not signiﬁcant at the 10% level.

3) White indicates areas where there are not sufﬁcient data to permit a robust estimate of the

trend.

The WGII maps of projected annual temperature and precipitation are based on the climate model

simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5; Taylor et al., 2012),

which also form the basis for the ﬁgures presented in WGI (including WGI Chapters 12, 14, and

Annex I). The CMIP5 archive includes output from Atmosphere–Ocean General Circulation Models

(AOGCMs), AOGCMs with coupled vegetation and/or carbon cycle components, and AOGCMs with

coupled atmospheric chemistry components. The number of models from which output is available,

and the number of realizations of each model, vary between the different CMIP5 experiments.

The WGII regional climate maps use the same source data as WGI Chapter 12 (e.g., Box 12.1 Figure

Cross-Chapter Box

Regional Climate Summary Figures

138

1), including the WGI multi-model mean values; the WGI individual model values; the WGI measure of baseline (“internal”) variability; and the

WGI time periods for the reference (1986–2005), mid-21st century (2046–2065), and late-21st century (2081–2100) periods. The full description

of the selection of models, the selection of realizations, the deﬁnition of internal variability, and the interpolation to a common grid can be found

in WGI Chapter 12 and Annex I.

In contrast to the Coupled Model Intercomparison Project Phase 3 (CMIP3) (Meehl et al., 2007), which used the IPCC Special Report on Emission

Scenarios (SRES) emission scenarios (IPCC, 2000), CMIP5 uses the Representative Concentration Pathways (RCPs) (van Vuuren et al., 2011) to

characterize possible trajectories of climate forcing over the 21st century. The WGII regional climate projection maps include RCP2.6 and RCP8.5,

which represent the high and low end of the RCP range at the end of the 21st century. Projected changes in global mean temperature are

similar across the RCPs over the next few decades (Figure RC-1; WGI Figure 12.5). During this near-term era of committed climate change, risks

will evolve as socioeconomic trends interact with the changing climate. In addition, societal responses, particularly adaptations, will inﬂuence

near-term outcomes. In the second half of the 21st century and beyond, the magnitude of global temperature increase diverges across the RCPs

(Figure RC-1; WGI Figure 12.5). For this longer-term era of climate options, near-term and longer-term mitigation and adaptation, as well as

development pathways, will determine the risks of climate change. The beneﬁts of mitigation and adaptation thereby occur over different but

overlapping time frames, and present-day choices thus affect the risks of climate change throughout the 21st century.

The projection maps plot differences in annual average temperature and precipitation between the future and reference periods (Figures RC-2

and RC-3), categorized into four classes. The classes are constructed based on the IPCC uncertainty guidance, providing a quantitative basis for

assigning likelihood (Mastrandrea et al., 2010), with likely deﬁned as 66 to 100% and very likely deﬁned as 90 to 100%.

Observed

RCP2.6

RCP8.5

Overlap

1900 1950 2000 2050 2100

–2

(˚C relative to

1986–2005

)

Global mean temperature change

Figure RC-1 | Observed and projected changes in global annual average temperature. Values are expressed relative to 1986–2005. Black lines show the Goddard

Institute for Space Studies Surface Temperature Analysis (GISTEMP), National Climate Data Center Merged Land–Ocean Surface Temperature (NCDC-MLOST), and

Hadley Centre/Climatic Research Unit gridded surface temperature data set 4.2 (HadCRUT4.2) estimates from observational measurements. Blue and red lines and

shading denote the ensemble mean and ±1.64 standard deviation range, based on Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations from 32

models for Representative Concentration Pathway (RCP) 2.6 and 39 models for RCP8.5.

The classiﬁcations in the WGII regional climate projection ﬁgures are based on two aspects of likelihood (e.g., WGI Box 12.1 and Knutti et al.,

2010). The ﬁrst is the likelihood that projected changes exceed differences arising from internal climate variability (e.g., Tebaldi et al., 2011). The

second is agreement among models on the sign of change (e.g., Christensen et al., 2007; and IPCC, 2012).

The four classiﬁcations of projected change depicted in the WGII regional climate maps are:

1) Solid colors indicate areas with very strong agreement, where the multi-model mean change is greater than twice the baseline variability

(natural internal variability in 20-year means), and greater than or equal to 90% of models agree on sign of change. These criteria (and the

areas that fall into this category) are identical to the highest conﬁdence category in WGI Box 12.1. This category supersedes other categories

in the WGII regional climate maps.

2) Colors with white dots indicate areas with strong agreement, where 66% or more of models show change greater than the baseline

variability, and 66% or more of models agree on sign of change.

3) Gray indicates areas with divergent changes, where 66% or more of models show change greater than the baseline variability, but fewer

than 66% agree on sign of change.

4) Colors with diagonal lines indicate areas with little or no change, where fewer than 66% of models show change greater than the baseline

variability. It should be noted that areas that fall in this category for the annual average could still exhibit signiﬁcant change at seasonal,

monthly, and/or daily time scales.

Regional Climate Summary Figures

Cross-Chapter Box

139

Difference from

1986–2005 mean (

Projected Temperature Change

0 2 4 6

–0.5 11.7

Solid Color

Strong

agreement

Very strong

agreement

Little or

no change

Gray

Divergent

changes

Diagonal Lines

White Dots

Observed Temperature Change

Based on trend over

1901–2012 (

C over period)

Diagonal Lines

Trend not

statistically

signiﬁcant

White

Insufﬁcient

data

Solid Color

Signiﬁcant

trend

0 2 4 6

–0.5 11.7

RCP2.6 mid 21st century RCP8.5 mid 21st century

RCP2.6 late 21st century RCP8.5 late 21st century

Figure RC-2 |

Observed and projected changes in annual average surface temperature. (A) Map of observed annual average temperature change from 1901 to 2012, derived

from a linear trend where sufﬁcient data permit a robust estimate (i.e., only for grid boxes with greater than 70% complete records and more than 20% data availability in the

ﬁrst and last 10% of the time period); other areas are white. Solid colors indicate areas where trends are signiﬁcant at the 10% level (after accounting for autocorrelation

effects on signiﬁcance testing). Diagonal lines indicate areas where trends are not signiﬁcant. Observed data (range of grid-point values: –0.53 to +2.50°C over period) are

from WGI AR5 Figures SPM.1 and 2.21. (B) Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model mean projections of annual average temperature changes for

2046–2065 and 2081–2100 under Representative Concentration Pathway (RCP) 2.6 and 8.5, relative to 1986–2005. Solid colors indicate areas with very strong agreement,

where the multi-model mean change is greater than twice the baseline variability (natural internal variability in 20-year means) and ≥90% of models agree on sign of change.

Colors with white dots indicate areas with strong agreement, where ≥66% of models show change greater than the baseline variability and ≥66% of models agree on sign of

change. Gray indicates areas with divergent changes, where ≥66% of models show change greater than the baseline variability, but <66% agree on sign of change. Colors

with diagonal lines indicate areas with little or no change, where <66% of models show change greater than the baseline variability, although there may be signiﬁcant change

at shorter timescales such as seasons, months, or days. Analysis uses model data from WGI AR5 Figure SPM.8, Box 12.1, and Annex I. The range of grid-point values for the

multi-model mean is: +0.19 to +4.08˚C for mid 21st century of RCP2.6; +0.06 to +3.85˚C for late 21st century of RCP2.6; +0.70 to +7.04˚C for mid 21st century of RCP8.5;

and +1.38 to +11.71°C for late 21st century of RCP8.5.

Cross-Chapter Box

Regional Climate Summary Figures

140

Projected Precipitation Change

RCP2.6 mid 21st century RCP8.5 mid 21st century

RCP2.6 late 21st century RCP8.5 late 21st century

–20 0 20 40

Observed Precipitation Change

–

5 0 5 25102.5

–

2.5 50

–

100

Diagonal Lines

Trend not

statistically

signiﬁcant

White

Insufﬁcient

data

Solid Color

Signiﬁcant

trend

Solid Color

Strong

agreement

Very strong

agreement

Little or

no change

Gray

Divergent

changes

Diagonal Lines

White Dots

Difference from

1986–2005 mean (%)

Trend in annual

precipitation

over 1951–2010

(mm/year per decade)

Figure RC-3 | Observed and projected changes in annual average precipitation. (A) Map of observed annual precipitation change from 1951–2010, derived from a linear trend

where sufﬁcient data permit a robust estimate (i.e., only for grid boxes with greater than 70% complete records and more than 20% data availability in the ﬁrst and last 10% of

the time period); other areas are white. Solid colors indicate areas where trends are signiﬁcant at the 10% level (after accounting for autocorrelation effects on signiﬁcance

testing). Diagonal lines indicate areas where trends are not signiﬁcant. Observed data (range of grid-point values: –185 to +111 mm/year per decade) are fr

om WGI AR5 Figures

SPM.2 and 2.29. (B) Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model average percent changes in annual mean precipitation for 2046–2065 and

2081–2100 under Representative Concentration Pathway (RCP) 2.6 and 8.5, relative to 1986–2005. Solid colors indicate areas with very strong agreement, where the

multi-model mean change is greater than twice the baseline variability (natural internal variability in 20-yr means) and ≥90% of models agree on sign of change. Colors with

white dots indicate areas with strong agreement, where ≥66% of models show change greater than the baseline variability and ≥66% of models agree on sign of change. Gray

indicates areas with divergent changes, where ≥66% of models show change greater than the baseline variability, but <66% agree on sign of change. Colors with diagonal lines

indicate areas with little or no change, where <66% of models show change greater than the baseline variability, although there may be signiﬁcant change at shorter timescales

such as seasons, months, or days. Analysis uses model data from WGI AR5 Figure SPM.8, Box 12.1, and Annex I. The range of grid-point values for the multi-model mean is: –10

to +24% for mid 21st century of RCP2.6; –9 to +22% for late 21st century of RCP2.6; –19 to +57% for mid 21st century of RCP8.5; and –34 to +112% for late 21st century

of RCP8.5.

Regional Climate Summary Figures

Cross-Chapter Box

141

Christensen, J.H., B. Hewitson, A. Busuioc, A. Chen, X. Gao, I. Held, R. Jones, R.K. Kolli, W.-T. Kwon, R. Laprise, V. Magaña Rueda, L. Mearns, C.G. Menéndez, J. Räisänen, A.

Rinke, A. Sarr, and P. Whetton, 2007: Regional climate projections. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth

Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor, and H.L. Miller

(eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 847-940.

IPCC, 2000: Special Report on Emissions Scenarios [Nakicenovic, N. and R. Swart (eds.)]. Cambridge University Press, Cambridge, UK, 570 pp.

IPCC, 2012: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the

Intergovernmental Panel on Climate Change [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M.

Tignor, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA, 582 pp.

Knutti, R., R. Furrer, C. Tebaldi, J. Cermak, and G.A. Meehl, 2010: Challenges in combining projections from multiple climate models. Journal of Climate, 23(10), 2739-2758.

Mastrandrea, M.D., C.B. Field, T.F. Stocker, O. Edenhofer, K.L. Ebi, D.J. Frame, H. Held, E. Kriegler, K.J. Mach, P.R. Matschoss, G.-K. Plattner, G.W. Yohe, and F.W. Zwiers, 2010:

Guidance Note for Lead Authors of the IPCC Fifth Assessment Report on Consistent Treatment of Uncertainties. Intergovernmental Panel on Climate Change (IPCC),

www.ipcc.ch/pdf/supporting-material/uncertainty-guidance-note.pdf.

Meehl, G.A., C. Covey, K.E. Taylor, T. Delworth, R.J. Stouffer, M. Latif, B. McAvaney, and J.F.B. Mitchell, 2007: The WCRP CMIP3 multimodel dataset – a new era in climate

change research. Bulletin of the American Meteorological Society, 88(9), 1383-1394.

Taylor, K.E., R.J. Stouffer, and G.A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society, 93(4), 485-498.

Tebaldi, C., J.M. Arblaster, and Reto Knutti, 2011: Mapping model agreement on future climate projections. Geophysical Research Letters, 38(23), L23701,

doi:10.1029/2011GL049863.

van Vuuren, D.P., J. Edmonds, M. Kainuma, K. Riahi, A. Thomson, K. Hibbard, G.C. Hurtt, T. Kram, V. Krey, J.-F. Lamarque, T. Masui, M. Meinshausen, N. Nakicenovic, S.J.

Smith, and S.K. Rose 2011: The representative concentration pathways: an overview. Climatic Change, 109(1-2), 5-31.

References

Diffenbaugh, N.S., D.A. Stone, P. Thorne, F. Giorgi, B.C. Hewitson, R.G. Jones, and G.J. van Oldenborgh, 2014: Cross-chapter box on the regional climate summary

ﬁgures. 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. 137-141.

This cross-chapter box should be cited as:

Impact of Climate Change on

Freshwater Ecosystems due to

Altered River Flow Regimes

Petra Döll (Germany), Stuart E. Bunn (Australia)

143

It is widely acknowledged that the ﬂow regime is a primary determinant of the structure and

function of rivers and their associated ﬂoodplain wetlands, and ﬂow alteration is considered to be

a serious and continuing threat to freshwater ecosystems (Bunn and Arthington, 2002; Poff and

Zimmerman, 2010; Poff et al., 2010). Most species distribution models do not consider the effect

of changing ﬂow regimes (i.e., changes to the frequency, magnitude, duration, and/or timing of

key ﬂow parameters) or they use precipitation as proxy for river ﬂow (Heino et al., 2009).

There is growing evidence that climate change will signiﬁcantly alter ecologically important

attributes of hydrologic regimes in rivers and wetlands, and exacerbate impacts from human

water use in developed river basins (medium conﬁdence; Xenopoulos et al., 2005; Aldous et al.,

2011). By the 2050s, climate change is projected to impact river ﬂow characteristics such as

long-term average discharge, seasonality, and statistical high ﬂows (but not statistical low ﬂows)

more strongly than dam construction and water withdrawals have done up to around the year

2000 (Figure RF-1; Döll and Zhang, 2010). For one climate scenario (Special Report on Emission

Scenarios (SRES) A2 emissions, Met Ofﬁce Hadley Centre climate prediction model 3 (HadCM3)),

15% of the global land area may be negatively affected, by the 2050s, by a decrease of ﬁsh

species in the upstream basin of more than 10%, as compared to only 10% of the land area that

has already suffered from such decreases due to water withdrawals and dams (Döll and Zhang,

2010). Climate change may exacerbate the negative impacts of dams for freshwater ecosystems

but may also provide opportunities for operating dams and power stations to the beneﬁt of

riverine ecosystems. This is the case if total runoff increases and, as occurs in Sweden, the annual

hydrograph becomes more similar to variation in electricity demand, that is, with a lower spring

ﬂood and increased runoff during winter months (Renofalt et al., 2010).

Because biota are often adapted to a certain level of river ﬂow variability, the projected larger

variability of river ﬂows that is due to increased climate variability is likely to select for generalist

or invasive species (Ficke et al., 2007). The relatively stable habitats of groundwater-fed streams in

snow-dominated or glacierized basins may be altered by reduced recharge by meltwater and as a

result experience more variable (possibly intermittent) ﬂows (Hannah et al., 2007). A high-impact

change of ﬂow variability is a ﬂow regime shift from intermittent to perennial or vice versa. It is

projected that until the 2050s, river ﬂow regime shifts may occur on 5 to 7% of the global land

area, mainly in semiarid areas (Döll and Müller Schmied, 2012; see Table 3-2 in Chapter 3).

In Africa, one third of ﬁsh species and one ﬁfth of the endemic ﬁsh species occur in eco-regions

that may experience a change in discharge or runoff of more than 40% by the 2050s (Thieme et

al., 2010). Eco-regions containing more than 80% of Africa’s freshwater ﬁsh species and several

Cross-Chapter Box

Impact of Climate Change on Freshwater Ecosystems due to Altered River Flow Regimes

144

outstanding ecological and evolutionary phenomena are likely to experience hydrologic conditions substantially different from the present,

with alterations in long-term average annual river discharge or runoff of more than 10% due to climate change and water use (Thieme et al.,

2010).

As a result of increased winter temperatures, freshwater ecosystems in basins with signiﬁcant snow storage are affected by higher river

ﬂows in winter, earlier spring peak ﬂows, and possibly reduced summer low ﬂows (Section 3.2.3). Strongly increased winter peak ﬂows may

lead to a decline in salmonid populations in the Paciﬁc Northwest of the USA of 20 to 40% by the 2050s (depending on the climate model)

due to scouring of the streambed during egg incubation, the relatively pristine high-elevation areas being affected most (Battin et al., 2007).

Reductions in summer low ﬂows will increase the competition for water between ecosystems and irrigation water users (Stewart et al.,

2005). Ensuring environmental ﬂows through purchasing or leasing water rights and altering reservoir release patterns will be an important

adaptation strategy (Palmer et al., 2009).

Mean annual river ﬂow Low ﬂow Q

Monthly river ﬂow exceeded in 9 out of 10 months

Impact of climate change at least twice as strong as impact of water withdrawals and dams on natural ﬂow

Impact of water withdrawals and dams on natural ﬂow at least twice as strong as impact of climate change

None of the two impacts is more than twice as strong as the other

Information not computable

Climate change exacerbates past impacts of water withdrawals and dams on natural ﬂow that reduced ﬂow

Climate change exacerbates past impacts of water withdrawals and dams on natural ﬂow that increased ﬂow

Climate change mitigates past impacts of water withdrawals and dams on natural ﬂow that reduced ﬂow

Climate change mitigates past impacts of water withdrawals and dams on natural ﬂow that increased ﬂow

Past impacts < 1% or information not computable

Figure RF-1 |

Impact of climate change relative to the impact of water withdrawals and dams on natural ﬂows for two ecologically relevant river ﬂow characteristics (mean annual river

ﬂow and monthly low ﬂow Q

), computed by a global water model (Döll and Zhang, 2010). Impact of climate change is the percent change of ﬂow between 1961–1990 and 2041–2070

according to the emissions scenario A2 as implemented by the global climate model Met Ofﬁce Hadley Centre Coupled Model, version 3 (HadCM3). Impact of water withdrawals and

reservoirs is computed by running the model with and without water withdrawals and dams that existed in 2002. Please note that the ﬁgure does not reﬂect spatial differences in the

magnitude of change.

Observations and models suggest that global warming impacts on glacier and snow-fed streams and rivers will pass through two contrasting

phases (Burkett et al., 2005; Vuille et al., 2008; Jacobsen et al., 2012). In the ﬁrst phase, when river discharge is increased as a result of

intensiﬁed melting, the overall diversity and abundance of species may increase. However, changes in water temperature and stream ﬂow may

have negative impacts on narrow range endemics (Jacobsen et al., 2012). In the second phase, when snowﬁelds melt early and glaciers have

shrunken to the point that late-summer stream ﬂow is reduced, broad negative impacts are foreseen, with species diversity rapidly declining

once a critical threshold of roughly 50% glacial cover is crossed (Figure RF-2).

River discharge also inﬂuences the response of river temperatures to increases of air temperature. Globally averaged, air temperature increases

of 2°C, 4°C, and 6°C are estimated to lead to increases of annual mean river temperatures of 1.3°C, 2.6°C, and 3.8°C, respectively (van Vliet

Impact of Climate Change on Freshwater Ecosystems due to Altered River Flow Regimes

Cross-Chapter Box

145

Aldous, A., J. Fitzsimons, B. Richter, and L. Bach, 2011: Droughts, ﬂoods and freshwater ecosystems: evaluating climate change impacts and developing adaptation

strategies. Marine and Freshwater Research, 62(3), 223-231.

Battin, J., M.W. Wiley, M.H. Ruckelshaus, R.N. Palmer, E. Korb, K.K. Bartz, and H. Imaki, 2007: Projected impacts of climate change on salmon habitat restoration.

Proceedings of the National Academy of Sciences of the United States of America, 104(16), 6720-6725.

Bunn, S.E. and A.H. Arthington, 2002: Basic principles and ecological consequences of altered ﬂow regimes for aquatic biodiversity. Environmental Management, 30(4),

492-507.

Burkett, V., D. Wilcox, R. Stottlemyer, W. Barrow, D. Fagre, J. Baron, J. Price, J. Nielsen, C. Allen, D. Peterson, G. Ruggerone, and T. Doyle, 2005: Nonlinear dynamics in

ecosystem response to climatic change: case studies and policy implications. Ecological Complexity, 2(4), 357-394.

Döll, P. and H. Müller Schmied, 2012: How is the impact of climate change on river ﬂow regimes related to the impact on mean annual runoff? A global-scale analysis.

Environmental Research Letters, 7(1), 014037, doi:10.1088/1748-9326/7/1/014037.

Döll, P. and J. Zhang, 2010: Impact of climate change on freshwater ecosystems: a global-scale analysis of ecologically relevant river ﬂow alterations. Hydrology and Earth

System Sciences, 14(5), 783-799.

Ficke, A.D., C.A. Myrick, and L.J. Hansen, 2007: Potential impacts of global climate change on freshwater ﬁsheries. Reviews in Fish Biology and Fisheries, 17(4), 581-613.

Hannah, D.M., L.E. Brown, A.M. Milner, A.M. Gurnell, G.R. McGregord, G.E. Petts, B.P.G. Smith, and D.L. Snook, 2007: Integrating climate-hydrology-ecology for alpine river

systems. Aquatic Conservation: Marine and Freshwater Ecosystems, 17(6), 636-656.

Heino, J., R. Virkalla, and H. Toivonen, 2009: Climate change and freshwater biodiversity: detected patterns, future trends and adaptations in northern regions. Biological

Reviews, 84(1), 39-54.

Jacobsen, D., A.M. Milner, L.E. Brown, and O. Dangles, 2012: Biodiversity under threat in glacier-fed river systems. Nature Climate Change, 2(5), 361-364.

Palmer, M.A., D.P. Lettenmaier, N.L. Poff, S.L. Postel, B. Richter, and R. Warner, 2009: Climate change and river ecosystems: protection and adaptation options.

Environmental Management, 44(6), 1053-1068.

Poff, N.L. and J.K.H. Zimmerman, 2010: Ecological responses to altered ﬂow regimes: a literature review to inform the science and management of environmental ﬂows.

Freshwater Biology, 55(1), 194-205.

Poff, N.L., B.D. Richter, A.H. Arthington, S.E. Bunn, R.J. Naiman, E. Kendy, M. Acreman, C. Apse, B.P. Bledsoe, M.C. Freeman, J. Henriksen, R.B. Jacobson, J.G. Kennen, D.M.

Merritt, J.H. O’Keeffe, J.D. Olden, K. Rogers, R.E. Tharme, and A. Warner, 2010: The ecological limits of hydrologic alteration (ELOHA): a new framework for developing

regional environmental ﬂow standards. Freshwater Biology, 55(1), 147-170.

References

Glacier cover in catchment (%)

Accumulated regional species loss

100 80 60 40 20 0

European Alps

Alaskan Coastal Range

Ecuadorian Andes

Figure RF-2 |

Accumulated loss of regional species richness (gamma diversity) of macroinvertebrates as a function of glacial cover in catchment. Obligate glacial river

macroinvertebrates begin to disappear from assemblages when glacial cover in the catchment drops below approximately 50%, and 9 to 14 species are predicted to be lost with

the complete disappearance of glaciers in each region, corresponding to 11, 16, and 38% of the total species richness in the three study regions in Ecuador, Europe, and Alaska.

Data are derived from multiple river sites from the Ecuadorian Andes and Swiss and Italian Alps, and a temporal study of a river in the Coastal Range Mountains of southeast

Alaska over nearly three decades of glacial shrinkage. Each data point represents a river site (Europe or Ecuador) or date (Alaska), and lines are Lowess ﬁts. (Adapted by

permission from Jacobsen et al., 2012.)

et al., 2011). Discharge decreases of 20% and 40% are computed to result in additional increases of river water temperature of 0.3° C and

0.8°C on average (van Vliet et al., 2011). Therefore, where rivers will experience drought more frequently in the future, freshwater-dependent

biota will suffer not only directly by changed ﬂow conditions but also by drought-induced river temperature increases, as well as by related

decreased oxygen and increased pollutant concentrations.

Cross-Chapter Box

Impact of Climate Change on Freshwater Ecosystems due to Altered River Flow Regimes

146

Döll, P. and S.E. Bunn, 2014: Cross-chapter box on the impact of climate change on freshwater ecosystems due to altered river ﬂow regimes. 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 King-

dom and New York, NY, USA, pp. 143-146.

This cross-chapter box should be cited as:

Renofalt, B.M., R. Jansson, and C. Nilsson, 2010: Effects of hydropower generation and opportunities for environmental ﬂow management in Swedish riverine

ecosystems. Freshwater Biology, 55(1), 49-67.

Stewart, I., D. Cayan, and M. Dettinger, 2005: Changes toward earlier streamﬂow timing across western North America. Journal of Climate, 18(8), 1136-1155.

Thieme, M.L., B. Lehner, R. Abell, and J. Matthews, 2010: Exposure of Africa’s freshwater biodiversity to a changing climate. Conservation Letters, 3(5), 324-331.

van Vliet, M.T.H., F. Ludwig, J.J.G. Zwolsman, G.P. Weedon, and P. Kabat, 2011: Global river temperatures and sensitivity to atmospheric warming and changes in river

ﬂow. Water Resources Research, 47(2), W02544, doi:10.1029/2010WR009198.

Vuille, M., B. Francou, P. Wagnon, I. Juen, G. Kaser, B.G. Mark, and R.S. Bradley, 2008: Climate change and tropical Andean glaciers: past, present and future. Earth-Science

Reviews, 89(3-4), 79-96.

Xenopoulos, M., D. Lodge, J. Alcamo, M. Marker, K. Schulze, and D. Van Vuuren, 2005: Scenarios of freshwater ﬁsh extinctions from climate change and water withdrawal.

Global Change Biology, 11(10), 1557-1564.

Building Long-Term

Resilience from Tropical

Cyclone Disasters

Yoshiki Saito (Japan), Kathleen McInnes (Australia)

147

Tropical cyclones (also referred to as hurricanes and typhoons in some regions) cause powerful

winds, torrential rains, high waves, and storm surge, all of which can have major impacts on

society and ecosystems. Bangladesh and India suffer 86% of mortality from tropical cyclones

(Murray et al., 2012), which occurs mainly during the rarest and most severe storm categories (i.e.,

Categories 3, 4, and 5 on the Safﬁr–Simpson scale).

About 90 tropical cyclones occur globally each year (Seneviratne et al., 2012) although interannual

variability is large. Changes in observing techniques, particularly after the introduction of satellites

in the late 1970s, confounds the assessment of trends in tropical cyclone frequencies and

intensities, which leads to low conﬁdence that any observed long-term (i.e., 40 years or more)

increases in tropical cyclone activity are robust, after accounting for past changes in observing

capability (Seneviratne et al., 2012; Chapter 2). There is also low conﬁdence in the detection and

attribution of century scale trends in tropical cyclones. Future changes to tropical cyclones arising

from climate change are likely to vary by region. This is because there is medium conﬁdence

that for certain regions, shorter-term forcing by natural and anthropogenic aerosols has had a

measurable effect on tropical cyclones. Tropical cyclone frequency is likely to decrease or remain

unchanged over the 21st century, while intensity (i.e., maximum wind speed and rainfall rates) is

likely to increase (WGI AR5 Section 14.6). Regionally speciﬁc projections have lower conﬁdence

(see WGI AR5 Box 14.2).

Longer-term impacts from tropical cyclones include salinization of coastal soils and water supplies

and subsequent food and water security issues from the associated storm surge and waves (Terry

and Chui, 2012). However, preparation for extreme tropical cyclone events through improved

governance and development to reduce their impacts provides an avenue for building resilience to

longer-term changes associated with climate change.

Asian deltas are particularly vulnerable to tropical cyclones owing to their large population density

in expanding urban areas (Nicholls et al., 2007). Extreme cyclones in Asia since 1970 caused more

than 0.5 million fatalities (Murray et al., 2012), for example, cyclones Bhola in 1970, Gorky in

1991, Thelma in 1998, Gujarat in 1998, Orissa in 1999, Sidr in 2007, and Nargis in 2008. Tropical

cyclone Nargis hit Myanmar on May 2, 2008 and caused more than 138,000 fatalities. Several-

meter high storm surges widely ﬂooded densely populated coastal areas of the Irrawaddy Delta

and surrounding areas (Revenga et al., 2003; Brakenridge et al., 2013). The ﬂooded areas were

captured by a NASA Moderate Resolution Imaging Spectrometer (MODIS) image on May 5, 2008

(see Figure TC-1).

Cross-Chapter Box

Building Long-Term Resilience from Tropical Cyclone Disasters

148

Murray et al. (2012) compared the response to cyclone

Sidr in Bangladesh in 2007 and Nargis in Myanmar in

2008 and demonstrated how disaster risk reduction

methods could be successfully applied to climate change

adaptation. Sidr, despite being of similar strength to

Nargis, caused far fewer fatalities (3400 compared to more

than 138,000) and this was attributed to advancement

in preparedness and response in Bangladesh through

experience in previous cyclones such as Bhola and Gorky.

The responses included the construction of multistoried

cyclone shelters, improvement of forecasting and warning

capacity, establishing a coastal volunteer network,

and coastal reforestation of mangroves. Disaster risk

management strategies for tropical cyclones in coastal

areas create protective measures, anticipate and plan for

extreme events, and increase the resilience of potentially

exposed communities. The integration of activities relating

to education, training, and awareness-raising into relevant

ongoing processes and practices is important for the long-

term success of disaster risk reduction and management

(Murray et al., 2012). However, Birkmann and Teichman

(2010) caution that while the combination of risk reduction

and climate change adaptation strategies may be desirable,

different spatial and temporal scales, norm systems, and

knowledge types and sources between the two goals can

confound their effective combination.

Birkman, J. and K. von Teichman, 2010: Integrating disaster risk reduction and climate change adaptation: key challenges – scales, knowledge and norms. Sustainability

Science, 5, 171-184.

Brakenridge, G.R., J.P.M. Syvitski, I. Overeem, S.A. Higgins, A.J. Kettner, J.A. Stewart-Moore, and R. Westerhoff, 2013: Global mapping of storm surges and the assessment

of delta vulnerability. Natural Hazards, 66, 1295-1312, doi:10.1007/s11069-012-0317-z.

Murray V., G. McBean, M. Bhatt, S. Borsch, T.S. Cheong, W.F. Erian, S. Llosa, F. Nadim, M. Nunez, R. Oyun, and A.G. Suarez, 2012: Case studies. In: Managing the Risks

of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate

Change [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)].

Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 487-542.

Nicholls, R.J., 2007: Adaptation Options for Coastal Areas and Infrastructure: An Analysis for 2030. Report to the United Nations Framework Convention on Climate

Change (UNFCCC), UNFCCC Secretariat, Bonn, Germany, 35 pp.

Revenga, C., J. Nackoney, E. Hoshino, Y. Kura, and J. Maidens, 2003: Watersheds of Asia and Oceania: AS 12 Irrawaddy. In: Water Resources eAtlas: Watersheds of the

World. A collaborative product of the International Union for Conservation of Nature (IUCN), the International Water Management Institute (IWMI), the Ramsar

Convention Bureau, and the World Resources Institute (WRI), WRI, Washington, DC, USA, pdf.wri.org/watersheds_2003/as13.pdf.

Seneviratne, S.I., N. Nicholls, D. Easterling, C.M. Goodess, S. Kanae, J. Kossin, Y. Luo, J. Marengo, K. McInnes, M. Rahimi, M. Reichstein, A. Sorteberg, C. Vera, and X. Zhang,

2012: Changes in climate extremes and their impacts on the natural physical environment. In: Managing the Risks of Extreme Events and Disasters to Advance

Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change [Field, C.B., V. Barros, T.F. Stocker, D. Qin,

D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, UK and New

York, NY, USA, pp. 109-230.

Terry, J. and T.F.M. Chui, 2012: Evaluating the fate of freshwater lenses on atoll islands after eustatic sea level rise and cyclone driven inundation: a modelling approach.

Global and Planetary Change, 88-89, 76-84.

Saito, Y. and K.L. McInnes, 2014: Cross-chapter box on building long-term resilience from tropical cyclone disasters. 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. 147-148.

References

Kyangin

Monyo

Minhla

Gyobingauk

Zigon

Nattalin

Paungde

Henzada

Ngathainggyaung

Yegyi

Kyonpyaw

Kangyidaung

Dassein

Kyaunggon

Wakema

Bogale

Pyapon

Kyaiklat

Ma-ubin

Yandoon

Danubyu

Taikkyi

Thonze

Tharrawaddy

Syriam

Rangoon

Mingaladon

Pyuzu

Kayan

Thetkala

Onhne

Satthwadaw

Intagwa

Thanatpin

Kamase

Pegu

Hmawbi

Hlegu

Pyinyegyi

Pyuntaza

Dai-k

Dabeinzu

Poungdawthi

Payagyi

Thaiktugon

Nyaungkashe

Pyu

Kanna banlaung

Kyaukkyi

Papun

Kyaikto

Kyaikpi

Naunggala

Naungbo

Kathapa-anauk

Thaton

Mutkyi

Khindan

Moulmein

Wagaru

Kale

Kada

0 40 80 km

Tropical cyclone Nargis storm surge in 2008

Flooded areas in previous years

18ºN

16ºN

Figure TC-1 | The intersection of inland and storm surge ﬂooding. Red shows May 5, 2008

Moderate Resolution Imaging Spectrometer (MODIS) mapping of the tropical cyclone Nargis storm

surge along the Irrawaddy Delta and to the east, Myanmar. The purple areas to the north were

ﬂooded by the river in prior years. (Source: Brakenridge et al., 2013.)

This cross-chapter box should be cited as:

Uncertain Trends in Major

Upwelling Ecosystems

Salvador E. Lluch-Cota (Mexico), Ove Hoegh-Guldberg (Australia), David Karl (USA), Hans O.

Pörtner (Germany), Svein Sundby (Norway), Jean-Pierre Gattuso (France)

149

Upwelling is the vertical transport of cold, dense, nutrient-rich, relatively low-pH and often

oxygen-poor waters to the euphotic zone where light is abundant. These conditions trigger high

levels of primary production and a high biomass of benthic and pelagic organisms. The driving

forces of upwelling include wind stress and the interaction of ocean currents with bottom

topography. Upwelling intensity also depends on water column stratiﬁcation. The major upwelling

systems of the planet, the Equatorial Upwelling System (EUS; Section 30.5.2, Figure 30.1A) and

the Eastern Boundary Upwelling Ecosystems (EBUE; Section 30.5.5, Figure 30.1A), represent only

10% of the ocean surface but contribute nearly 25% to global ﬁsh production (Figure 30.1B, Table

SM30.1).

Marine ecosystems associated with upwelling systems can be inﬂuenced by a range of “bottom-

up” trophic mechanisms, with upwelling, transport, and chlorophyll concentrations showing

strong seasonal and interannual couplings and variability. These, in turn, inﬂuence trophic transfer

up the food chain, affecting zooplankton, foraging ﬁsh, seabirds, and marine mammals.

There is considerable speculation as to how upwelling systems might change in a warming and

acidifying ocean. Globally, the heat gain of the surface ocean has increased stratiﬁcation by

4% (WGI Sections 3.2, 3.3, 3.8), which means that more wind energy is needed to bring deep

waters to the surface. It is as yet unclear to what extent wind stress can offset the increased

stratiﬁcation, owing to the uncertainty in wind speed trends (WGI Section 3.4.4). In the tropics,

observations of reductions in trade winds over several decades contrast more recent evidence

indicating their strengthening since the late 1990s (WGI Section 3.4.4). Observations and

modeling efforts in fact show diverging trends in coastal upwelling at the eastern boundaries

of the Paciﬁc and the Atlantic. Bakun (1990) proposed that the difference in rates of heat gain

between land and ocean causes an increase in the pressure gradient, which results in increased

alongshore winds and leads to intensiﬁed offshore transport of surface water through Ekman

pumping and the upwelling of nutrient-rich, cold waters (Figure CC-UP). Some regional records

support this hypothesis; others do not. There is considerable variability in warming and cooling

trends over the past decades both within and among systems, making it difﬁcult to predict

changes in the intensity of all Eastern EBUEs (Section 30.5.5).

Understanding whether upwelling and climate change will impact resident biota in an additive,

synergistic, or antagonistic manner is important for projections of how ecological goods and

services provided for human society will change. Even though upwellings may prove more

resilient to climate change than other ocean ecosystems because of their ability to function

under extremely variable conditions (Capone and Hutchins, 2013), consequences of their shifts

Cross-Chapter Box

Uncertain Trends in Major Upwelling Ecosystems

150

are highly relevant because these systems provide a signiﬁcant portion of global primary productivity and ﬁshery catch (Figure 30.1 A, B;

Table SM30.1). Increased upwelling would enhance ﬁsheries yields. However, the export of organic material from surface to deeper layers of

the ocean may increase and stimulate its decomposition by microbial activity, thereby enhancing oxygen depletion and CO

enrichment in

deeper water layers. Once this water returns to the surface through upwelling, benthic and pelagic coastal communities will be exposed to

acidiﬁed and deoxygenated water which may combine with anthropogenic impact to negatively affect marine biota and ecosystem structure

of the upper ocean (high conﬁdence; Sections 6.3.2, 6.3.3, 30.3.2.2, 30.3.2.3). Extreme hypoxia may result in abnormal mortalities of ﬁshes

and invertebrates (Keller et al., 2010), reduce ﬁsheries’ catch potential, and impact aquaculture in coastal areas (Barton et al., 2012; see also

Sections 5.4.3.3, 6.3.3, 6.4.1, 30.5.1.1.2, 30.5.5.1.3). Shifts in upwelling also coincide with an apparent increase in the frequency of submarine

eruptions of methane and hydrogen sulﬁde gas, caused by enhanced formation and sinking of phytoplankton biomass to the hypoxic or anoxic

sea ﬂoor. This combination of factors has been implicated in the extensive mortality of coastal ﬁshes and invertebrates (Bakun and Weeks,

2004; Bakun et al., 2010), resulting in signiﬁcant reductions in ﬁshing productivity, such as Cape hake (Merluccius capensis), Namibia’s most

valuable ﬁshery (Hamukuaya et al., 1998).

Reduced upwelling would also reduce the productivity of important pelagic ﬁsheries, such as for sardines, anchovies and mackerel, with

major consequences for the economies of several countries (Section 6.4.1, Chapter 7, Figure 30.1A, B, Table S30.1). However, under projected

scenarios of reduced upward supply of nutrients due to stratiﬁcation of the open ocean, upwelling of both nutrients and trace elements may

become increasingly important to maintaining upper ocean nutrient and trace metal inventories. It has been suggested that upwelling areas

may also increase nutrient content and productivity under enhanced stratiﬁcation, and that upwelled and partially denitriﬁed waters containing

excess phosphate may select for N

-ﬁxing microorganisms (Deutsch et al., 2007; Deutsch and Weber, 2012), but ﬁeld observations of N

ﬁxation

in these regions have not supported these predictions (Fernandez et al., 2011; Franz et al., 2012). The role of this process in global primary

production thus needs to be validated (low conﬁdence).

The central question therefore is whether or not upwelling will intensify, and if so, whether the effects of intensiﬁed upwelling on O

and CO

inventories will outweigh its beneﬁts for primary production and associated ﬁsheries and aquaculture (low conﬁdence). In any case increasing

atmospheric CO

concentrations will equilibrate with upwelling waters that may cause them to become more corrosive, depending on pCO

the upwelled water, and potentially increasingly impact the biota of EBUEs.

Uncertain trend in upwelling

Increasing stratiﬁcation Uncertain trend in wind stress

Increase Decrease

Increased upwelling-

favorable winds

Intensifying low

pressure over land

Increasing atmospheric pressure gradient

Increasing offshore transport of surface water

Increasing vertical ﬂux of cold, nutrient-rich

water (may be enhanced by stratiﬁcation-

induced limitation of mixing across the

thermocline at basin scales)

Increase in organic input,

decomposition, and

hypoxia in bottom waters

Increased coastal fauna

exposure to hypoxic,

low-pH waters

Decrease in food

availability for ﬁshes

Decreased pelagic

ﬁsheries

Decreased coastal

ﬁsheries

Enhanced

pelagic ﬁsheries

Increase in nutrient enrichment and

primary productivity

(a)

(b)

Figure UP-1 | (a) Hypothetic mechanism of increasing coastal wind–driven upwelling at Equatorial and Eastern Boundary upwelling systems (EUS, EBUE, Figure 30-1), where differential

warming rates between land and ocean results in increased land–ocean (1) pressure gradients that produce (2) stronger alongshore winds and (3) offshore movement of surface water

through Ekman transport, and (4) increased upwelling of deep cold nutrient rich waters to replace it. (b) Potential consequences of climate change in upwelling systems. Increasing

stratiﬁcation and uncertainty in wind stress trends result in uncertain trends in upwelling. Increasing upwelling may result in higher input of nutrients to the euphotic zone, and increased

primary production, which in turn may enhance pelagic ﬁsheries, but also decrease coastal ﬁsheries due to an increased exposure of coastal fauna to hypoxic, low pH waters. Decreased

upwelling may result in lower primary production in these systems with direct impacts on pelagic ﬁsheries productivity.

Uncertain Trends in Major Upwelling Ecosystems

Cross-Chapter Box

151

Lluch-Cota, S.E., O. Hoegh-Guldberg, D. Karl, H.-O. Pörtner, S. Sundby, and J.-P. Gattuso, 2014: Cross-chapter box on uncertain trends in major upwelling

ecosystems. 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. 149-151.

This cross-chapter box should be cited as:

Bakun, A., 1990: Global climate change and intensiﬁcation of coastal ocean upwelling. Science, 247(4939), 198-201.

Bakun, A. and S.J. Weeks, 2004: Greenhouse gas buildup, sardines, submarine eruptions and the possibility of abrupt degradation of intense marine upwelling ecosystems.

Ecology Letters, 7(11), 1015-1023.

Bakun, A., D. B. Field, A. N. A. Redondo-Rodriguez, and S. J. Weeks, 2010: Greenhouse gas, upwelling-favorable winds, and the future of coastal ocean upwelling

ecosystems. Global Change Biology 16:1213-1228.

Barton, A., B. Hales, G.G. Waldbusser, C. Langdon, and R.A. Feely, 2012: The Paciﬁc oyster, Crassostrea gigas, shows negative correlation to naturally elevated carbon

dioxide levels: implications for near-term ocean acidiﬁcation effects. Limnology and Oceanography, 57(3), 698-710.

Capone, D.G. and D.A. Hutchins, 2013: Microbial biogeochemistry of coastal upwelling regimes in a changing ocean. Nature Geoscience, 6(9) 711-717.

Deutsch, C. and T. Weber, 2012: Nutrient ratios as a tracer and driver of ocean biogeochemistry. Annual Review of Marine Science, 4, 113-141.

Deutsch, C., J.L. Sarmiento, D.M. Sigman, N. Gruber, and J.P. Dunne, 2007: Spatial coupling of nitrogen inputs and losses in the ocean. Nature, 445(7124), 163-167.

Fernandez, C., L. Farías, and O. Ulloa, 2011: Nitrogen ﬁxation in denitriﬁed marine waters. PLoS ONE, 6(6), e20539, doi:10.1371/journal.pone.0020539.

Franz, J., G. Krahmann, G. Lavik, P. Grasse, T. Dittmar, and U. Riebesell, 2012: Dynamics and stoichiometry of nutrients and phytoplankton in waters inﬂuenced by the

oxygen minimum zone in the eastern tropical Paciﬁc. Deep-Sea Research Part I: Oceanographic Research Papers, 62, 20-31.

Hamukuaya, H., M.J. O’Toole, and P.M.J. Woodhead, 1998: Observations of severe hypoxia and offshore displacement of Cape hake over the Namibian shelf in 1994.

South African Journal of Marine Science, 19(1), 57-59.

Keller, A.A., V. Simon, F. Chan, W.W. Wakeﬁeld, M.E. Clarke, J.A. Barth, D. Kamikawa and E.L. Fruh, 2010: Demersal ﬁsh and invertebrate biomass in relation to an offshore

hypoxic zone along the US West Coast. Fisheries Oceanography, 19, 76-87.

References

Urban–Rural Interactions –

Context for Climate Change

Vulnerability, Impacts, and

Adaptation

John Morton (UK), William Solecki (USA), Purnamita Dasgupta (India), David Dodman

(Jamaica), Marta G. Rivera-Ferre (Spain)

153

Rural areas and urban areas have always been interconnected and interdependent, but recent

decades have seen new forms of these interconnections: a tendency for rural–urban boundaries

to become less well deﬁned, and new types of land use and economic activity on those

boundaries. These conditions have important implications for understanding climate change

impacts, vulnerabilities, and opportunities for adaptation. This box examines three critical

implications of these interactions:

1) Climate extremes in rural areas resulting in urban impacts— teleconnections of resources

and migration streams mean that climate extremes in non-urban locations with associated

shifts in water supply, rural agricultural potential, and the habitability of rural areas will have

downstream impacts in cities.

2) Events speciﬁc to the rural–urban interface— given the highly integrated nature of rural–

urban interface areas and overarching demand to accommodate both rural and urban

demands in these settings, there is a set of impacts, vulnerabilities, and opportunities

for adaptation speciﬁc to these locations. These impacts include loss of local agricultural

production, economic marginalization resulting from being neither rural or urban, and stress

on human health.

3) Integrated infrastructure and service disruption—as urban demands often take preference,

interdependent rural and urban resource systems place nearby rural areas at risk, because

during conditions of climate stress, rural areas more often suffer resource shortages or

other disruptions to sustain resources to cities. For example, under conditions of resource

stress associated with climate risk (e.g., droughts) urban areas are at an advantage because

of political, social, and economic requirements to maintain service supply to cities to the

detriment of relatively marginal rural sites and settlements.

Urban areas historically have been dependent on the lands just beyond their boundaries for

most of their critical resources including water, food, and energy. Although in many contexts,

the connections between urban settlements and surrounding rural areas are still present, long

distance, teleconnected, large-scale supply chains have been developed particularly with respect

to energy resources and food supply (Güneralp et al., 2013). Extreme event disruptions in distant

resource areas or to the supply chain and relevant infrastructure can negatively impact the urban

areas dependent on these materials (Wilbanks et al., 2012). During the summer of 2012, for

instance, an extended drought period in the central United States led to signiﬁcantly reduced river

levels on the Mississippi River that led to interruptions of barge trafﬁc and delay of commodity

ﬂows to cities throughout the country. Urban water supply is also vulnerable to droughts in

predominantly rural areas. In the case of Bulawayo, Zimbabwe, periodic urban water shortages

over the last few decades have been triggered by rural droughts (Mkandla et al., 2005).

Cross-Chapter Box

Urban–Rural Interactions – Context for Climate Change Vulnerability, Impacts, and Adaptation

154

A further teleconnection between rural and urban areas is rural–urban migration. There have been cases where migration and urbanization

patterns have been to attributed to climate change or its proxies such as in parts of Africa (Morton, 1989; Barrios et al., 2006). However,

as recognized by Black et al. (2011), life in rural areas across the world typically involves complex patterns of rural–urban and rural–rural

migration, subject to economic, political, social, and demographic drivers, patterns that are modiﬁed or exacerbated by climate events and

trends rather than solely caused by them.

Globally, an increased blending of urban and rural qualities has occurred. Simon et al. (2006, p. 4) assert that the simple dichotomy between

“rural” and “urban” has “long ceased to have much meaning in practice or for policy-making purposes in many parts of the global South.”

One approach to reconciling this is through the increasing application of the concept of “peri-urban areas” (Simon et al., 2006; Simon, 2008).

These areas can be seen as rural locations that have “become more urban in character” (Webster, 2002, p. 5); as sites where households

pursue a wider range of income-generating activities while still residing in what appear to be “largely rural landscapes” (Learner and Eakin,

2010, p. 1); or as locations in which rural and urban land uses coexist, whether in contiguous or fragmented units (Bowyer-Bower, 2006). The

inhabitants of “core” urban areas within cities have also increasingly turned to agriculture, with production of staple foods, higher value crops

and livestock (Bryld, 2003; Devendra et al., 2005; Lerner and Eakin, 2010; Lerner et al., 2013). Bryld (2003) sees this as driven by rural–urban

migration and by structural adjustment (e.g., withdrawal of food price controls and food subsidies). Lerner and Eakin (2011; also Lerner et al.,

2013) explored reasons why people produce food in urban environments, despite high opportunity costs of land and labor: buffering of risk

from insecure urban labor markets; response to consumer demand; and the meeting of cultural needs.

Livelihoods and areas on the rural–urban interface suffer highly speciﬁc forms of vulnerability to disasters, including climate-related disasters.

These may be summarized as speciﬁcally combining urban vulnerabilities of population concentration, dependence on infrastructure, and social

diversity limiting social support with rural traits of distance, isolation, and invisibility to policymakers (Pelling and Mustafa, 2010). Increased

connectivity can also encourage land expropriation to enable commercial land development (Pelling and Mustafa, 2010). Vulnerability may

arise from the coexistence of rural and urban perspectives, which may give rise to conﬂicts between different social/interest groups and

economic activities (Masuda and Garvin, 2008; Solona-Solona 2010; Darly and Torre, 2013).

Additional vulnerability of peri-urban areas is on account of the re-constituted institutional arrangements and their structural constraints

(Iaquinta and Drescher, 2000). Rapid declines in traditional informal institutions and forms of collective action, and their imperfect replacement

with formal state and market institutions, may also increase vulnerability (Pelling and Mustafa, 2010).

Peri-urban areas and livelihoods have low visibility to policymakers at both local and national levels, and may suffer from a lack of necessary

services and inappropriate and uncoordinated policies. In Tanzania and Malawi, national policies of agricultural extension to farmer groups, for

example, do not reach peri-urban farmers (Liwenga et al., 2012). In peri-urban areas around Mexico City (Eakin et al., 2013), management of

the substantial risk of ﬂooding is led de facto by agricultural and water agencies, in the absence of capacity within peri-urban municipalities

and despite clear evidence that urban encroachment is a key driver of ﬂood risk. In developed country contexts, suburban–exurban fringe areas

often are overlooked in the policy arena that traditionally focuses on rural development and agricultural production, or urban growth and

services (Hanlon et al., 2010). The environmental function of urban agriculture, in particular, in protection against ﬂooding, will increase in the

context of climate change (Aubry et al., 2012).

However, peri-urban areas and mixed livelihoods more generally on rural–urban interfaces, also exhibit speciﬁc factors that increase their

resilience to climate shocks (Pelling and Mustafa, 2010). Increased transport connectivity in peri-urban areas can reduce disaster risk by

providing a greater diversity of livelihood options and improving access to education. The expansion of local labor markets and wage labor in

these areas can strengthen adaptive capacity through providing new livelihood opportunities (Pelling and Mustafa, 2010). Maintaining mixed

portfolios of agricultural and non-agricultural livelihoods also spreads risk (Lerner et al., 2013).

In high-income countries, practices attempting to enhance the ecosystem services and localized agriculture more typically associated with

lower density areas have been encouraged. In many situations these practices are focused increasingly on climate adaptation and mitigating

the impacts of climate extremes such as those associated with heating and the urban heat island effect, or wetland restoration efforts to limit

the impact of storm surge wave action (Verburg et al., 2012).

The dramatic growth of urban areas also implies that rural areas and communities are increasingly politically and economically marginalized

within national contexts, resulting in potential infrastructure and service disruptions for such sites. Existing rural–urban conﬂicts for the

management of natural resources (Castro and Nielsen, 2003) such as water (Celio et al., 2011) or land use conversion in rural areas, for

example, wind farms in rural Catalonia (Zografos and Martínez-Alier, 2009); industrial coastal areas in Sweden (Stepanova and Bruckmeier,

2013); or conversion of rice land into industrial, residential, and recreational uses in the Philippines (Kelly, 1998) have been documented, and it

is expected that stress from climate change impacts on land and natural resources will exacerbate these tensions. For instance, climate-induced

reductions in water availability may be more of a concern than population growth or increased per capita use for securing continued supplies

of water to large cities (Jenerette and Larsen, 2006), which requires an innovative approach to address such conﬂicts (Pearson et al., 2010).

Urban–Rural Interactions – Context for Climate Change Vulnerability, Impacts, and Adaptation

Cross-Chapter Box

155

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This cross-chapter box should be cited as:

Active Role of Vegetation in

Altering Water Flows under

Climate Change

Dieter Gerten (Germany), Richard Betts (UK), Petra Döll (Germany)

157

Climate, vegetation, and carbon and water cycles are intimately coupled, in particular via

the simultaneous transpiration and CO

uptake through plant stomata in the process of

photosynthesis. Hence, water ﬂows such as runoff and evapotranspiration are affected not only

directly by anthropogenic climate change as such (i.e., by changes in climate variables such as

temperature and precipitation), but also indirectly by plant responses to increased atmospheric

concentrations. In addition, effects of climate change (e.g., higher temperature or altered

precipitation) on vegetation structure, biomass production, and plant distribution have an indirect

inﬂuence on water ﬂows. Rising CO

concentration affects vegetation and associated water

ﬂows in two contrasting ways, as suggested by ample evidence from Free Air CO

Enrichment

(FACE), laboratory and modeling experiments (e.g., Leakey et al., 2009; Reddy et al., 2010; de

Boer et al., 2011). On the one hand, a physiological effect leads to reduced opening of stomatal

apertures, which is associated with lower water ﬂow through the stomata, that is, lower leaf-

level transpiration. On the other hand, a structural effect (“fertilization effect”) stimulates

photosynthesis and biomass production of C

plants including all tree species, which eventually

leads to higher transpiration at regional scales. A key question is to what extent the climate- and

-induced changes in vegetation and transpiration translate into changes in regional and global

runoff.

The physiological effect of CO

is associated with an increased intrinsic water use efﬁciency (WUE)

of plants, which means that less water is transpired per unit of carbon assimilated. Records of

stable carbon isotopes in woody plants (Peñuelas et al., 2011) verify this ﬁnding, suggesting an

increase in WUE of mature trees by 20.5% between the early 1960s and the early 2000s. Increases

since pre-industrial times have also been found for several forest sites (Andreu-Hayles et al.,

2011; Gagen et al., 2011; Loader et al., 2011; Nock et al., 2011) and in a temperate semi-natural

grassland (Koehler et al., 2010), although in one boreal tree species WUE ceased to increase

after 1970 (Gagen et al., 2011). Analysis of long-term whole-ecosystem carbon and water ﬂux

measurements from 21 sites in North American temperate and boreal forests corroborates a

notable increase in WUE over the two past decades (Keenan et al., 2013). An increase in global

WUE over the past century is supported by ecosystem model results (Ito and Inatomi, 2012).

A key inﬂuence on the signiﬁcance of increased WUE for large-scale transpiration is whether

vegetation structure and production has remained approximately constant (as assumed in the

global modeling study by Gedney et al., 2006) or has increased in some regions due to the

structural CO

effect (as assumed in models by Piao et al., 2007; Gerten et al., 2008). While ﬁeld-

based results vary considerably among sites, tree ring studies suggest that tree growth did not

increase globally since the 1970s in response to climate and CO

change (Andreu-Hayles et al.,

Cross-Chapter Box

Active Role of Vegetation in Altering Water Flows under Climate Change

158

2011; Peñuelas et al., 2011). However, basal area measurements at more than 150 plots across the tropics suggest that biomass and growth

rates in intact tropical forests have increased in recent decades (Lewis et al., 2009). This is also conﬁrmed for 55 temperate forest plots, with a

suspected contribution of CO

effects (McMahon et al., 2010). Satellite observations analyzed in Donohue et al. (2013) suggest that an increase

in vegetation cover by 11% in warm drylands (1982–2010 period) is attributable to CO

fertilization. Owing to the interplay of physiological

and structural effects, the net impact of CO

increase on global-scale transpiration and runoff remains rather poorly constrained. This is also true

because nutrient limitation, often omitted in modeling studies, can suppress the CO

fertilization effect (see Rosenthal and Tomeo, 2013).

Therefore, there are conﬂicting views on whether the direct CO

effects on plants already have a signiﬁcant inﬂuence on evapotranspiration

and runoff at global scale. AR4 reported work by Gedney et al. (2006) that suggested that the physiological CO

effect (lower transpiration)

contributed to a supposed increase in global runoff seen in reconstructions by Labat et al. (2004). However, a more recent analysis based on

a more complete data set (Dai et al., 2009) suggested that river basins with decreasing runoff outnumber basins with increasing runoff, such

that a small decline in global runoff is likely for the period 1948–2004. Hence, detection of vegetation contributions to changes in water ﬂows

critically depends on the availability and quality of hydrometeorological observations (Haddeland et al., 2011; Lorenz and Kunstmann, 2012).

Overall, the evidence since AR4 suggests that climatic variations and trends have been the main driver of global runoff change in the past

decades; both CO

increase and land use change have contributed less (Piao et al., 2007; Gerten et al., 2008; Alkama et al., 2011; Sterling et al.,

2013). Oliveira et al. (2011) furthermore pointed to the importance of changes in incident solar radiation and the mediating role of vegetation;

according to their global simulations, a higher diffuse radiation fraction during 1960–1990 may have increased evapotranspiration in the tropics

by 3% due to higher photosynthesis from shaded leaves.

It is uncertain how vegetation responses to future increases in CO

and to climate change will modulate the impacts of climate change on

freshwater ﬂows. Twenty-ﬁrst century continental- and basin-scale runoff is projected by some models to either increase more or decrease less

when the physiological CO

effect is included in addition to climate change effects (Betts et al., 2007; Murray et al., 2012). This could somewhat

ease the increase in water scarcity anticipated in response to future climate change and population growth (Gerten et al., 2011; Wiltshire et

al., 2013). In absolute terms, the isolated effect of CO

has been modeled to increase future global runoff by 4 to 5% (Gerten et al., 2008) up

to 13% (Nugent and Matthews, 2012) compared to the present, depending on the assumed CO

trajectory and whether feedbacks of changes

in vegetation structure and distribution to the atmosphere are accounted for (they were in Nugent and Matthews, 2012). In a global model

intercomparison study (Davie et al., 2013), two out of four models projected stronger increases and, respectively, weaker decreases in runoff

when considering CO

effects compared to simulations with constant CO

concentration (consistent with the above ﬁndings, though magnitudes

differed between the models), but two other models showed the reverse. Thus, the choice of models and the way they represent the coupling

between CO

, stomatal closure, and plant growth is a source of uncertainty, as also suggested by Cao et al. (2009). Lower transpiration due to

rising CO

concentration may also affect future regional climate change itself (Boucher et al., 2009) and enhance the contrast between land

and ocean surface warming (Joshi et al., 2008). Overall, although physiological and structural effects will inﬂuence water ﬂows in many regions,

precipitation and temperature effects are likely to remain the prime inﬂuence on global runoff (Alkama et al., 2010).

An application of a soil–vegetation–atmosphere–transfer model indicates complex responses of groundwater recharge to vegetation-mediated

changes in climate, with computed groundwater recharge being always larger than would be expected from just accounting for changes in

rainfall (McCallum et al., 2010). Another study found that even if precipitation slightly decreased, groundwater recharge might increase as a

net effect of vegetation responses to climate change and CO

rise, that is, increasing WUE and either increasing or decreasing leaf area (Crosbie

et al., 2010). Depending on the type of grass in Australia, the same change in climate is suggested to lead to either increasing or decreasing

groundwater recharge in this location (Green et al., 2007). For a site in the Netherlands, a biomass decrease was computed for each of eight

climate scenarios indicating drier summers and wetter winters (A2 emissions scenario), using a fully coupled vegetation and variably saturated

hydrological model. The resulting increase in groundwater recharge up-slope was simulated to lead to higher water tables and an extended

habitat for down-slope moisture-adapted vegetation (Brolsma et al., 2010).

Using a large ensemble of climate change projections, Konzmann et al. (2013) put hydrological changes into an agricultural perspective and

suggested that the net result of physiological and structural CO

effects on crop irrigation requirements would be a global reduction (Figure

VW-1). Thus, adverse climate change impacts on irrigation requirements and crop yields might be partly buffered as WUE and crop production

improve (Fader et al., 2010). However, substantial CO

-driven improvements will be realized only if proper management abates limitation of

plant growth by nutrient availability or other factors.

Changes in vegetation coverage and structure due to long-term climate change or shorter-term extreme events such as droughts (Anderegg

et al., 2013) also affect the partitioning of precipitation into evapotranspiration and runoff, sometimes involving complex feedbacks with

the atmosphere such as in the Amazon region (Port et al., 2012; Saatchi et al., 2013). One model in the study by Davie et al. (2013) showed

regionally diverse climate change effects on vegetation distribution and structure, which had a much weaker effect on global runoff than the

structural and physiological CO

effects. As water, carbon, and vegetation dynamics evolve synchronously and interactively under climate change

(Heyder et al., 2011; Gerten et al., 2013), it remains a challenge to disentangle the individual effects of climate, CO

, and land cover change on

the water cycle.

Active Role of Vegetation in Altering Water Flows under Climate Change

Cross-Chapter Box

159

–20 to –40<–40 –5 to 20 –5 to 5 5 to 20 20 to 40 >40 No irrigation

(a) Impact of climate change including physiological and structural crop responses to increased

atmospheric CO

(b) Impact of climate change only

Percentage change in net

irrigation requirements

Figure VW-1 | Percentage change in net irrigation requirements of 11 major crops from 1971–2000 to 2070–2099 on areas currently equipped for irrigation, assuming current

management practices. (a) Impact of climate change including physiological and structural crop responses to increased atmospheric CO

concentration (co-limitation by nutrients

not considered). (b) Impact of climate change only. Shown is the median change derived from climate change projections by 19 General Circulation Models (GCMs; based on the

Special Report on Emission Scenarios (SRES) A2 emissions scenario) used to force a vegetation and hydrology model. (Modiﬁed after Konzmann et al., 2013.)

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Active Role of Vegetation in Altering Water Flows under Climate Change

Cross-Chapter Box

161

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This cross-chapter box should be cited as:

The Water–Energy–Food/

Feed/Fiber Nexus as Linked

to Climate Change

Douglas J. Arent (USA), Petra Döll (Germany), Kenneth M. Strzepek (USA), Blanca Elena Jiménez

Cisneros (Mexico), Andy Reisinger (New Zealand), Ferenc Toth (Hungary), Taikan Oki (Japan)

163

Water, energy, and food/feed/ﬁber are linked through numerous interactive pathways and

subject to a changing climate, as depicted in Figure CC-WE-1. The depth and intensity of those

linkages vary enormously among countries, regions, and production systems. Energy technologies

(e.g., biofuels, hydropower, thermal power plants), transportation fuels and modes, and food

products (from irrigated crops, in particular animal protein produced by feeding irrigated crops

and forages) may require signiﬁcant amounts of water (Sections 3.7.2, 7.3.2, 10.2,10.3.4,

22.3.3, 25.7.2; Allan, 2003; King and Weber, 2008; McMahon and Price, 2011; Macknick et al.,

2012a). In irrigated agriculture, climate, irrigating procedure, crop choice, and yields determine

water requirements per unit of produced crop. In areas where water (and wastewater) must be

pumped and/or treated, energy must be provided (Metcalf & Eddy, Inc. et al., 2007; Khan and

Hanjra, 2009; EPA, 2010; Gerten et al., 2011). While food production, refrigeration, transport,

and processing require large amounts of energy (Pelletier et al., 2011), a major link between food

and energy as related to climate change is the competition of bioenergy and food production

for land and water (robust evidence, high agreement; Section 7.3.2, Box 25-10; Diffenbaugh et

al., 2012; Skaggs et al., 2012). Food and crop wastes, and wastewater, may be used as sources

of energy, saving not only the consumption of conventional nonrenewable fuels used in their

traditional processes, but also the consumption of the water and energy employed for processing

or treatment and disposal (Schievano et al., 2009; Oh et al., 2010; Olson, 2012). Examples of this

can be found in several countries across all income ranges. For example, sugar cane byproducts

are increasingly used to produce electricity or for cogeneration (McKendry, 2002; Kim and Dale,

2004) for economic beneﬁts, and increasingly as an option for greenhouse gas mitigation.

Most energy production methods require signiﬁcant amounts of water, either directly (e.g., crop-

based energy sources and hydropower) or indirectly (e.g., cooling for thermal energy sources or

other operations) (robust evidence, high agreement; Sections 10.2.2, 10.3.4, 25.7.4; and van Vliet

et al., 2012; Davies et al., 2013. Water for biofuels, for example, under the International Energy

Agency (IEA) Alternative Policy Scenario, which has biofuels production increasing to 71 EJ in

2030, has been reported by Gerbens-Leenes et al. (2012) to drive global consumptive irrigation

water use from 0.5% of global renewable water resources in 2005 to 5.5% in 2030, resulting

in increased pressure on freshwater resources, with potential negative impacts on freshwater

ecosystems. Water is also required for mining (Section 25.7.3), processing, and residue disposal of

fossil and nuclear fuels or their byproducts. Water for energy currently ranges from a few percent

in most developing countries to more than 50% of freshwater withdrawals in some developed

countries, depending on the country (Kenny et al., 2009; WEC, 2010). Future water requirements

will depend on electricity demand growth, the portfolio of generation technologies and water

management options employed (medium evidence, high agreement; WEC, 2010; Sattler et al.,

Cross-Chapter Box

The Water–Energy–Food/Feed/Fiber Nexus as Linked to Climate Change

164

2012). Future water availability for energy production will change due to climate change (robust evidence, high agreement; Sections 3.4, 3.5.1,

3.5.2.2.

Water may require signiﬁcant amounts of energy for lifting, transport, and distribution and for its treatment either to use it or to depollute it.

Wastewater and even excess rainfall in cities requires energy to be treated or disposed. Some non-conventional water sources (wastewater

or seawater) are often highly energy intensive. Energy intensities per m

of water vary by about a factor of 10 between different sources,

for example, locally produced potable water from ground/surface water sources versus desalinated seawater (Box 25-2, Tables 25-6, 25-7;

Macknick et al., 2012b; Plappally and Lienhard, 2012). Groundwater (35% of total global water withdrawals, with irrigated food production

being the largest user; Döll et al., 2012) is generally more energy intensive than surface water. In India, for example, 19% of total electricity

use in 2012 was for agricultural purposes (Central Statistics Ofﬁce, 2013), with a large share for groundwater pumping. Pumping from greater

depth increases energy demand signiﬁcantly—electricity use (kWh m

–3

of water) increases by a factor of 3 when going from 35 to 120 m depth

(Plappally and Lienhard, 2012). The reuse of appropriate wastewater for irrigation (reclaiming both water and energy-intense nutrients) may

increase agricultural yields, save energy, and prevent soil erosion (medium conﬁdence; Smit and Nasr, 1992; Jiménez-Cisneros, 1996; Qadir et

al., 2007; Raschid-Sally and Jayakody, 2008). More energy efﬁcient treatment methods enable poor quality (“black”) wastewater to be treated

to quality levels suitable for discharge into water courses, avoiding additional freshwater and associated energy demands (Keraita et al., 2008).

If properly treated to retain nutrients, such treated water may increase soil productivity, contributing to increased crop yields/food security in

regions unable to afford high power bills or expensive fertilizer (high conﬁdence; Oron, 1996; Lazarova and Bahri, 2005; Redwood and Huibers,

2008; Jiménez-Cisneros, 2009).

Linkages among water, energy, food/feed/ﬁber, and climate are also strongly related to land use and management (robust evidence, high

agreement; Section 4.4.4, Box 25-10). Land degradation often reduces efﬁciency of water and energy use (e.g., resulting in higher fertilizer

demand and surface runoff), and compromises food security (Sections 3.7.2, 4.4.4). On the other hand, afforestation activities to sequester

carbon have important co-beneﬁts of reducing soil erosion and providing additional (even if only temporary) habitat (see Box 25-10) but

may reduce renewable water resources. Water abstraction for energy, food, or biofuel production or carbon sequestration can also compete

with minimal environmental ﬂows needed to maintain riverine habitats and wetlands, implying a potential conﬂict between economic and

other valuations and uses of water (medium evidence, high agreement; Sections 25.4.3, 25.6.2, Box 25-10). Only a few reports have begun to

evaluate the multiple interactions among energy, food, land, and water and climate (McCornick et al., 2008; Bazilian et al., 2011; Bierbaum and

Matson, 2013), addressing the issues from a security standpoint and describing early integrated modeling approaches. The interaction among

each of these factors is inﬂuenced by the changing climate, which in turn impacts energy and water demand, bioproductivity, and other factors

(see Figure CC-WE-1 and Wise et al., 2009), and has implications for security of supplies of energy, food, and water; adaptation and mitigation

pathways; and air pollution reduction, as well as the implications for health and economic impacts as described throughout this Assessment

Report.

Water

Energy Food/feed/ﬁber

Water for energy

• Cooling of thermal power plants

• Hydropower

• Irrigation of bioenergy crops

• Extraction and reﬁning

Energy for water

• Extraction and transportation

• Water treatment/desalination

• Wastewater, drainage,

treatment, and disposal

Energy for food/feed/ﬁber

Energy – Water – Food/Feed/Fiber – Climate change

GHG

emissions/

climate change

Nutritionally appropriate low-meat diet or

low-water-consuming vegetarian diet

generally reduces water and energy demand

as well as GHG emissions per person.

Use of agricultural, livestock, and food waste

may reduce conventional energy use and GHG

emissions.

Climate change tends to increase energy

demand for cooling as well as water demand.

Figure WE-1

| The water–energy–food nexus as related to climate change. The interlinkages of supply/demand, quality and quantity of water, and energy and food/feed/ﬁber with

changing climatic conditions have implications for both adaptation and mitigation strategies.

• Crop and livestock production

• Processing and transport

• Food consumption

• Energy for irrigated crops

Food/feed/ﬁber for energy production

Competition between (bio)energy and

food/ﬁber production for water and land

Water for food/feed/ﬁber

Impact of food/feed/ﬁber

production on water

quality and runoff

generation

• Irrigation

• Livestock water use

• Water use for food processing

The Water–Energy–Food/Feed/Fiber Nexus as Linked to Climate Change

Cross-Chapter Box

165

The interconnectivity of food/ﬁber, water, land use, energy, and climate change, including the perhaps not yet well understood cross-sector

impacts, are increasingly important in assessing the implications for adaptation/mitigation policy decisions. Fuel–food–land use–water–

greenhouse gas (GHG) mitigation strategy interactions, particularly related to bioresources for food/feed, power, or fuel, suggest that

combined assessment of water, land type, and use requirements, energy requirements, and potential uses and GHG impacts often epitomize

the interlinkages. For example, mitigation scenarios described in the IPCC Special Report on Renewable Energy Sources and Climate Change

Mitigation (IPCC, 2011) indicate up to 300 EJ of biomass primary energy by 2050 under increasingly stringent mitigation scenarios. Such high

levels of biomass production, in the absence of technology and process/management/operations change, would have signiﬁcant implications

for land use, water, and energy, as well as food production and pricing. Consideration of the interlinkages of energy, food/feed/ﬁber, water,

land use, and climate change is increasingly recognized as critical to effective climate resilient pathway decision making (medium evidence,

high agreement), although tools to support local- and regional-scale assessments and decision support remain very limited.

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This cross-chapter box should be cited as: