Impacts and risks under future climate
Processes affecting vulnerability to climate change
Processes affecting adaptation and adaptive capacity
Interactions and feedbacks between multiple drivers and impacts
Actions to reduce risks
Actions to reduce vulnerability
Actions to improve adaptation
Global policy options and costs
Standard approach to CCIAV Drivers-pressure-state-impact-response (DPSIR) methods Hazard-driven risk assessment
Vulnerability indicators and profiles Past and present climate risks Livelihood analysis Agent-based methods Narrative methods Risk perception including critical thresholds Development/sustainability policy performance Relationship of adaptive capacity to sustainable development
Integrated assessment modelling Cross-sectoral interactions Integration of climate with other drivers
Stakeholder discussions Linking models across types and scales Combining assessment approaches/methods
Top-down Global Local
Bottom-up Local Regional (macro-economic approaches are top-down)
Linking scales Commonly global/regional Often grid-based
Exploratory scenarios of climate and other factors (e.g., SRES) Normative scenarios (e.g., stabilisation)
Socio-economic conditions Scenarios or inverse methods
Baseline adaptation Adaptation analogues from history, other locations, other activities
Exploratory scenarios: exogenous and often endogenous (including feedbacks) Normative pathways
and scenarios assuming no climate policy to restrict greenhouse gas (GHG) emissions have been contrasted with those assuming GHG stabilisation (e.g., Parry et al., 2001; see also Sections 18.104.22.168 and 22.214.171.124). The use of probabilities in impact assessments, presented as proof-of-concept examples in the TAR (Mearns et al., 2001), is now more firmly established (see examples in Section 2.4.8). Some other notable advances in impact assessment include: a reassessment of bioclimatic niche-based modelling, meta-analyses summarising a range of assessments, and new dynamic methods of analysing economic damages. Nevertheless, the climate-sensitive resources of many regions and sectors, especially in developing countries, have not yet been subject to detailed impact assessments.
Recent observational evidence of climatic warming, along with the availability of digital species distribution maps and greatly extended computer power has emboldened a new generation of bioclimatic niche-based modellers to predict changes in species distribution and prevalence under a warming climate using correlative methods (e.g., Bakkenes et al., 2002; Thomas et al., 2004; see also Chapter 4, Section 4.4.11). However, the application of alternative statistical techniques to the same data sets has also exposed significant variations in model performance that have recently been the subject of intensive debate (Pearson and Dawson, 2003; Thuiller et al., 2004; Luoto et al., 2005; Araujo and Rahbek, 2006) and should promote a more cautious application of these models for projecting future biodiversity.
A global-scale, meta-analysis of a range of studies for different sectors was conducted by Hitz and Smith (2004) to evaluate the aggregate impacts at different levels of global mean temperature. For some sectors and regions, such as agriculture and the coastal zone, sufficient information was available to summarise aggregated sectoral impacts as a function of global warming. For other sectors, such as marine biodiversity and energy, limited information allowed only broad conclusions of low confidence.
Dynamic methods are superseding statistical methods in some economic assessments. Recent studies account, for example, for the role of world markets in influencing climate change impacts on global agriculture (Fischer et al., 2002), the effect on damage from sea-level rise when assuming optimal adaptation measures (Neumann et al., 2000; Nicholls and Tol, 2006), the added costs for adapting to high temperatures due to uncertainties in projected climate (Hallegatte et al., 2007), and increasing long-term costs of natural disasters when explicitly accounting for altered extreme event distributions (Hallegatte et al., 2006). The role of economic dynamics has also been emphasised (Fankhauser and Tol, 2005; Hallegatte, 2005; Hallegatte et al., 2006). Some new studies suggest damage overestimations by previous assessments, while others suggest underestimations, leading to the conclusion that uncertainty is likely to be larger than suggested by the range of previous estimates.
Significant advances in adaptation assessment have occurred, shifting its emphasis from a research-driven activity to one where stakeholders participate in order to improve decision-making. The key advance is the incorporation of adaptation to past and present climate. This has the advantage of anchoring the assessment in what is already known, and can be used to explore adaptation to climate variability and extremes, especially if scenarios of future variability are uncertain or unavailable (Mirza, 2003b; UNDP, 2005). As such, adaptation assessment has accommodated a wide range of methods used in mainstream policy and planning. Chapter 17 of this volume discusses adaptation practices, the processes and determinants of adaptive capacity, and limits to adaptation, highlighting the difficulty of establishing a general methodology for adaptation assessment due to the great diversity of analytical methods employed. These include the following approaches and methods.
• The scenario-based approach (e.g., IPCC, 1994; see also Section 2.2.1), where most impact assessments consider future adaptation as an output.
• Normative policy frameworks, exploring which adaptations are socially and environmentally beneficial, and applying diverse methods, such as vulnerability analysis, scenarios, cost-benefit analysis, multi-criteria analysis and technology risk assessments (UNDP, 2005).
• Indicators, employing models of specific hypothesised components of adaptive capacity (e.g., Moss et al., 2001; Yohe and Tol, 2002; Brooks et al., 2005; Haddad, 2005).
• Economic modelling, anthropological and sociological methods for identifying learning in individuals and organisations (Patt and Gwata, 2002; Tompkins, 2005; Berkhout et al., 2006).
• Scenarios and technology assessments, for exploring what kinds of adaptation are likely in the future (Dessai and Hulme, 2004; Dessai et al., 2005a; Klein et al., 2005).
• Risk assessments combining current risks to climate variability and extremes with projected future changes, utilising cost-benefit analysis to assess adaptation (e.g., ADB, 2005).
Guidance regarding methods and tools to use in prioritising adaptation options include the Compendium of Decision Tools (UNFCCC, 2004), the Handbook on Methods for Climate Change Impact Assessment and Adaptation Strategies (Feenstra et al., 1998), and Costing the Impacts of Climate Change (Metroeconomica, 2004). A range of different methods can also be used with stakeholders (see Section 2.3.2).
The financing of adaptation has received minimal attention. Bouwer and Vellinga (2005) suggest applying more structured decision-making to future disaster management and adaptation to climate change, sharing the risk between private and public sources. Quiggin and Horowitz (2003) argue that the economic costs will be dominated by the costs of adaptation, which depend on the rate of climate change, especially the occurrence of climate extremes, and that many existing analyses overlook these costs (see also Section 2.2.2).
Since the TAR, the IPCC definition of vulnerability3 has been challenged, both to account for an expanded remit by including social vulnerability (O'Brien et al., 2004a) and to reconcile it with risk assessment (Downing and Patwardhan, 2005).
Different states of vulnerability under climate risks include: vulnerability to current climate, vulnerability to climate change in the absence of adaptation and mitigation measures, and residual vulnerability, where adaptive and mitigative capacities have been exhausted (e.g., Jones et al., 2007). A key vulnerability has the potential for significant adverse affects on both natural and human systems, as outlined in the United Nations Framework Convention on Climate Change (UNFCCC), thus contributing to dangerous anthropogenic interference with the climate system (see Chapter 19). Füssel and Klein (2006) review and summarise these developments.
Vulnerability is highly dependent on context and scale, and care should be taken to clearly describe its derivation and meaning (Downing and Patwardhan, 2005) and to address the uncertainties inherent in vulnerability assessments (Patt et al.,
2005). Frameworks should also be able to integrate the social and biophysical dimensions of vulnerability to climate change (Klein and Nicholls, 1999; Polsky et al., 2003; Turner et al., 2003a). Formal methods for vulnerability assessment have also been proposed (Ionescu et al., 2005; Metzger and Schröter,
2006) but are very preliminary.
The methods and frameworks for assessing vulnerability must also address the determinants of adaptive capacity (Turner et al., 2003a; Schröter et al., 2005a; O'Brien and Vogel, 2006; see also Chapter 17, Section 17.3.1) in order to examine the potential responses of a system to climate variability and change. Many studies endeavour to do this in the context of human development, by aiming to understand the underlying causes of vulnerability and to further strengthen adaptive capacities (e.g., World Bank, 2006). In some quantitative approaches, the indicators used are related to adaptive capacity, such as national economic capacity, human resources, and environmental capacities (Moss et al., 2001; see also Section 2.2.3). Other studies include indicators that can provide information related to the conditions, processes and structures that promote or constrain adaptive capacity (Eriksen et al., 2005).
Vulnerability assessment offers a framework for policy measures that focus on social aspects, including poverty reduction, diversification of livelihoods, protection of common property resources and strengthening of collective action (O'Brien et al., 2004b). Such measures enhance the ability to respond to stressors and secure livelihoods under present conditions, which can also reduce vulnerability to future climate change. Community-based interactive approaches for identifying coping potentials provide insights into the underlying causes and structures that shape vulnerability (O'Brien et al., 2004b). Other methods employed in recent regional vulnerability studies include stakeholder elicitation and survey (Eakin et al., 2006; Pulhin et al., 2006), and multi-criteria modelling (Wehbe et al., 2006).
Traditional knowledge of local communities represents an important, yet currently largely under-used resource for CCIAV assessment (Huntington and Fox, 2005). Empirical knowledge from past experience in dealing with climate-related natural
3 The degree to which a system is susceptible to, or unable to cope with, adverse effects of climate change, including climate variability and extremes. Vulnerability is a function of the character, magnitude, and rate of climate variation to which a system is exposed, its sensitivity, and its adaptive capacity (IPCC, 2001b, Glossary).
disasters such as droughts and floods (Osman-Elasha et al., 2006), health crises (Wandiga et al., 2006), as well as longer-term trends in mean conditions (Huntington and Fox, 2005; McCarthy and Long Martello, 2005), can be particularly helpful in understanding the coping strategies and adaptive capacity of indigenous and other communities relying on oral traditions.
Integrated assessment represents complex interactions across spatial and temporal scales, processes and activities. Integrated assessments can involve one or more mathematical models, but may also represent an integrated process of assessment, linking different disciplines and groups of people. Managing uncertainty in integrated assessments can utilise models ranging from simple models linking large-scale processes, through models of intermediate complexity, to the complex, physically explicit representation of Earth systems. This structure is characterised by trade-offs between realism and flexibility, where simple models are more flexible but less detailed, and complex models offer more detail and a greater range of output. No single theory describes and explains dynamic behaviour across scales in socioeconomic and ecological systems (Rotmans and Rothman, 2003), nor can a single model represent all the interactions within a single entity, or provide responses to questions in a rapid turn-around time (Schellnhuber et al., 2004). Therefore, integration at different scales and across scales is required in order to comprehensively assess CCIAV. Some specific advances are outlined here; integration to assess climate policy benefits is considered in Section 2.2.6.
Cross-sectoral integration is required for purposes such as national assessments, analysis of economic and trade effects, and joint population and climate studies. National assessments can utilise nationally integrated models (e.g., Izaurralde et al., 2003; Rosenberg et al., 2003; Hurd et al., 2004), or can synthesise a number of disparate studies for policy-makers (e.g., West and Gawith, 2005). Markets and trade can have significant effects on outcomes. For example, a study assessing the global impacts of climate change on forests and forest products showed that trade can affect efforts to stabilise atmospheric carbon dioxide (CO2) and also affected regional welfare, with adverse effects on those regions with high production costs (Perez-Garcia et al., 2002). New economic assessments of aggregated climate change damages have also been produced for multiple sectors (Tol, 2002a, b; Mendelsohn and Williams, 2004; Nordhaus, 2006). These have highlighted potentially large regional disparities in vulnerability to impacts. Using an integrated assessment general equilibrium model, Kemfert (2002) found that interactions between sectors acted to amplify the global costs of climate change, compared with single-sector analysis.
Integration yields results that cannot be produced in isolation. For example, the Millennium Ecosystem Assessment assessed the impact of a broad range of stresses on ecosystem services, of which climate change was only one (Millennium Ecosystem Assessment, 2005). Linked impact and vulnerability assessments can also benefit from a multiple stressors approach. For instance, the AIR-CLIM Project integrated climate and air pollution impacts in Europe between 1995 and 2100, concluding that that while the physical impacts were weakly coupled, the costs of air pollution and climate change were strongly coupled. The indirect effects of climate policies stimulated cost reductions in air pollution control of more than 50% (Alcamo et al., 2002). Some of the joint effects of extreme weather and air pollution events on human health are described in Chapter 8, Section 8.2.6.
Earth system models of intermediate complexity that link the atmosphere, oceans, cryosphere, land system, and biosphere are being developed to assess impacts (particularly global-scale, singular events that may be considered dangerous) within a risk and vulnerability framework (Rial et al., 2004; see also Section 2.4.7). Global climate models are also moving towards a more complete representation of the Earth system. Recent simulations integrating the atmosphere with the biosphere via a complete carbon cycle show the potential of the Amazon rainforest to suffer dieback (Cox et al., 2004), leading to a positive feedback that decreases the carbon sink and increases atmospheric CO2 concentrations (Friedlingstein et al., 2006; Denman et al., 2007).
Risk management is defined as the culture, processes and structures directed towards realising potential opportunities whilst managing adverse effects (AS/NZS, 2004). Risk is generally measured as a combination of the probability of an event and its consequences (ISO/IEC, 2002; see also Figure 2.1), with several ways of combining these two factors being possible. There may be more than one event, consequences can range from positive to negative, and risk can be measured qualitatively or quantitatively.
To date, most CCIAV studies have assessed climate change without specific regard to how mitigation policy will influence those impacts. However, the certainty that some climate change will occur (and is already occurring - see Chapter 1) is driving adaptation assessment beyond the limits of what scenario-driven methods can provide. The issues to be addressed include assessing current adaptations to climate variability and extremes before assessing adaptive responses to future climate, assessing the limits of adaptation, linking adaptation to sustainable development, engaging stakeholders, and decision-making under uncertainty. Risk management has been identified as a framework that can deal with all of these issues in a manner that incorporates existing methodologies and that can also accommodate other sources of risk (Jones, 2001; Willows and Connell, 2003; UNDP, 2005) in a process known as mainstreaming.
The two major forms of climate risk management are the mitigation of climate change through the abatement of GHG emissions and GHG sequestration, and adaptation to the consequences of a changing climate (Figure 2.1). Mitigation reduces the rate and magnitude of changing climate hazards; adaptation reduces the consequences of those hazards (Jones, 2004). Mitigation also reduces the upper bounds of the range of potential climate change, while adaptation copes with the lower bounds (Yohe and Toth, 2000). Hence they are complementary
. Time horizon of vulnerability and adaptation approaches
2050 2070 2090
Time horizon of impact approach-
. Time horizon of vulnerability and adaptation approaches
2050 2070 2090
Time horizon of impact approach-
Time horizon of integrated approaches
Was this article helpful?
Disasters: Why No ones Really 100 Safe. This is common knowledgethat disaster is everywhere. Its in the streets, its inside your campuses, and it can even be found inside your home. The question is not whether we are safe because no one is really THAT secure anymore but whether we can do something to lessen the odds of ever becoming a victim.