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MEDIATION D2.1 Review of existing methods and metrics for assessing and
quantifying impacts and vulnerability identifying key shortcomings and suggesting
improvements
Status: Final
Version 4
Date: 17 March 2011
Authors: Timothy R. Carter and Kirsi Mäkinen
Address: Finnish Environment Institute (SYKE), Box 140, FI-00251 Helsinki, Finland
Email: [email protected]
Revised title
Approaches to climate change impact, adaptation and vulnerability
assessment: towards a classification framework to serve decision-making
Acknowledgements
We are grateful for helpful comments on earlier versions of this report from Jochen Hinkel,
Reinhard Mechler and Rob Swart.
Please cite as:
Carter, T.R. and Mäkinen, K. 2011. Approaches to climate change impact, adaptation and
vulnerability assessment: towards a classification framework to serve decision-making.
MEDIATION Technical Report No. 2.1, Finnish Environment Institute (SYKE), Helsinki,
Finland, 70 pp.
i
Table of contents
1 Introduction ...................................................................................................................... 2
1.1 Scope of the review ............................................................................................................. 2
2 Frameworks for assessment ............................................................................................ 3
2.1 Early perspectives on climate and society .......................................................................... 3
2.2 From impacts to risk management ...................................................................................... 4
2.2.1 Impacts of anthropogenic climate change ................................................................. 4
2.2.2 IPCC seven-step guidelines to assessment ................................................................. 4
2.2.3 Top-down and bottom-up approaches........................................................................ 5
2.2.4 Contrasting vulnerability assessment and adaptation assessment ............................ 7
2.2.5 A risk management framework ................................................................................... 7
3 Vulnerability assessment ............................................................................................... 10
3.1 Vulnerability indicators and indices .................................................................................. 11
3.2 Distinguishing current and future vulnerability ................................................................ 12
3.3 Critiques of vulnerability assessment ................................................................................ 13
3.4 Stakeholder-driven processes ............................................................................................ 15
3.5 Vulnerability assessment in a wider context ..................................................................... 15
4 Impact assessment .......................................................................................................... 17
4.1 Experimental simulation of impacts .................................................................................. 17
4.2 Detection and attribution of observed impacts of climate change .................................... 18
4.3 Modelling of biophysical impacts ..................................................................................... 20
4.4 Modelling of socio-economic impacts .............................................................................. 23
5 Adaptation assessment ................................................................................................... 25
5.1 Identification of adaptation measures ............................................................................... 25
5.2 Evaluation of adaptation options ....................................................................................... 28
5.3 Mapping adaptation research ............................................................................................ 31
5.4 Adaptation problem types ................................................................................................. 31
6 Integrated assessment .................................................................................................... 34
6.1 Integrated assessment models (IAMs) .............................................................................. 34
6.1.1 Types of IAMs and their representation of impacts ................................................. 34
6.1.2 Treatment of adaptation in IAMs ............................................................................. 36
6.1.3 Weaknesses of IAMs and research needs ................................................................. 38
6.2 Model-based integrated analysis ....................................................................................... 40
6.3 Qualitative integrated assessment ..................................................................................... 41
ii
7 Operationalising CCIAV assessment methods ............................................................ 43
7.1 The UKCIP risk framework .............................................................................................. 43
7.2 Towards a framework for classifying CCIAV assessments to support decision-making . 44
7.3 Assessment type, purpose and target audience ................................................................. 48
7.3.1 Purpose ..................................................................................................................... 48
7.3.2 Beneficiaries ............................................................................................................. 48
7.3.3 Approaches ............................................................................................................... 49
7.4 Dimensions of the assessment ........................................................................................... 49
7.4.1 Sectoral or thematic focus ........................................................................................ 49
7.4.2 Region and spatial scale .......................................................................................... 49
7.4.3 Time horizon and resolution .................................................................................... 49
7.5 Methods and participation ................................................................................................. 49
7.5.1 Methods and tools .................................................................................................... 49
7.5.2 Stakeholder involvement .......................................................................................... 50
7.6 Information management .................................................................................................. 50
7.6.1 Guidance on data and scenarios .............................................................................. 50
7.6.2 Qualitative information ............................................................................................ 51
7.6.3 Quantified variables and their sources .................................................................... 52
7.6.4 Characterising future climate .................................................................................. 52
7.6.5 Characterising other environmental and socio-economic futures ........................... 54
7.6.6 Scenarios as integrating devices .............................................................................. 54
7.6.7 Addressing low probability, high consequence events ............................................. 55
7.6.8 Treatment of uncertainty .......................................................................................... 55
7.7 Assessment outputs ........................................................................................................... 57
7.7.1 Metrics ...................................................................................................................... 57
7.7.2 Presentation of results .............................................................................................. 58
7.7.3 Documentation and publications ............................................................................. 58
8 Conclusion: en route to a common platform for MEDIATION ................................. 59
9 References ....................................................................................................................... 61
1
Approaches to climate change impact, adaptation and vulnerability
assessment: towards a classification framework to serve decision-making
Timothy R. Carter and Kirsi Mäkinen
Finnish Environment Institute (SYKE)
Box 140, FI-00251 Helsinki, Finland
Summary
This paper offers a review of research approaches, methods and tools that are available for
assessing the potential impacts of climate change on natural and human systems, for
identifying vulnerable sectors and populations, and for examining and evaluating adaptation
options. Assessments of climate change impacts, adaptation and vulnerability (CCIAV) are
essential for informing decisions on the design and implementation of adaptation strategies
for responding to future climate change.
After a short recap in section 2 of the historical evolution of approaches from the scenario-
driven impact assessments of the early 1980s to the adaptation-orientated, risk management
frameworks of today, sections 3-6 present reviews of four major approaches to CCIAV:
Vulnerability Assessment, Impact Assessment, Adaptation Assessment and Integrated
Assessment. Each of these approaches is divided into important sub-categories, and some
examples of the methods and tools that might accompany each of them are provided. Section
7 then takes up the challenge of devising an organisational framework for classifying CCIAV
assessments to support decision-making for climate change adaptation. This comprises a
simple checklist of attributes that would need to be considered in an assessment in order to
fulfil a given purpose defined by the expected beneficiaries of the work. The purpose of the
assessment determines the emphasis that is required; its dimensions influence the suitability
of approaches, methods, tools and information to be deployed. The utility of this framework
remains to be tested using case studies from the project and elsewhere. An eventual goal is to
map out possible pathways leading to the development of a common platform for knowledge
sharing.
2
1 Introduction
1.1 Scope of the review
This paper offers a review of research approaches, methods and tools that are available for
assessing the potential impacts of climate change on natural and human systems, for
identifying vulnerable sectors and populations, and for examining and evaluating adaptation
options. Assessments of climate change impacts, adaptation and vulnerability (CCIAV) are
essential for informing decisions on the design and implementation of adaptation strategies
for responding to future climate change.
Variations in climate occur naturally over a wide range of time scales, and have affected
society throughout history. A knowledge of natural climate variability is therefore of
fundamental importance in understanding sensitivities and adaptations to climate. However,
superimposed on these natural variations are anthropogenically-induced changes in climate
which are now established as ongoing (IPCC, 2007, p. 10) and very likely to accelerate as a
consequence of unabated increases in atmospheric greenhouse gas concentrations (IPCC,
2007, p. 13). Moreover, a range of impacts consistent with a warming climate are already
being confidently attributed to human causes (e.g. Root et al., 2003; Rosenzweig et al., 2007;
Rosenzweig et al., 2008). The approaches reviewed in this paper address impacts of both
natural variability and anthropogenic climate change.
The review embraces approaches for describing the risks posed by climate change,
approaches for estimating the likely impacts of the projected changes and approaches for
identifying those regions, sectors and communities that are potentially vulnerable to such
impacts. A major motivating force for undertaking such assessments is to alert society to the
threats and opportunities presented by climate change, so that adaptive measures can be put in
place to avert the damaging consequences and exploit any beneficial effects1. As such, the
review also extends to classifying research approaches that are available for identifying and
evaluating adaptation options.
Both sets of approaches – for assessing impacts and vulnerability and for assessing adaptation
– are fundamental for informing decisions about adaptation. Hence, they also occupy a central
role in any common platform to be developed in the MEDIATION project in particular, or by
agencies charged with providing guidance on adaptation to European nation states in the
context of the EU White Paper on Adaptation (CEC, 2009).
1Adaptation measures will be needed in addition to mitigation policies for reducing the greenhouse gas
emissions that are the underlying source of the problem and must be reduced in order to avert the most
damaging, costly and potentially irreversible impacts of climate change.
3
2 Frameworks for assessment
2.1 Early perspectives on climate and society2
The review builds on previous efforts to organise research methods and metrics to serve
policy needs. Prior to the 1990s, the earliest reviews were aimed primarily at academic
researchers. The first major overview of methods of climate impact assessment was an
authoritative collection of chapters compiled for the SCOPE 27 (Scientific Committee on
Problems of the Environment) volume, including four methodological Overviews, five sector-
based chapters on Biophysical Impacts, eight chapters illustrating methods and case studies of
Social and Economic Impacts and Adjustments and five chapters devoted to different
approaches to Integrated Assessment (Kates et al., 1985).
As one of the contributions to that volume, Riebsame (1985) distinguished four views of
climate that were prevalent in the early 1980s, some of which still influence approaches to
climate impact assessment today. They are: (i) climate as setting, (ii) climate as determinant,
(iii) climate as hazard, and (iv) climate as natural resource.
Climate as setting refers to a climatological perspective that describes the earth's climate as a
context for the processes of natural and human systems. "Nature provides the climate and
climate provides the setting" (Riebsame, 1985). Most statistical analysis of climate (e.g.
through World Meteorological Organization 30-year climatological "normals") and climate
classification and mapping (Köppen and Geiger, 1930; Palmer, 1965) can be linked to a view
of climate as stationary or only slowly varying.
Climate as determinant alludes to the search for cause and effect relationships between
climate and the behaviour of biophysical or human systems. It has its origins in climatic
determinism, which posited a dominant influence of climate on nature and society. It can be
traced back to the Greeks and Romans, and was extended in the early twentieth century by
Ellsworth Huntington and colleagues (e.g. Huntington, 1915). Though largely discredited as a
framework for study of human responses, given the innate capacity of human society for
adjustment and adaptation to climatic variations, it nevertheless re-emerged in the 1980s in
the form of mathematical models relating climate to a range of biophysical and socio-
economic outcomes (e.g. Emanuel et al., 1985; Kauppi and Posch, 1985; WMO, 1985; WMO,
1988). Such tools are now widely applied to provide information on the sensitivity of
different systems to climate.
Climate as hazard is also a well-established area of inquiry, viewing climate as a hazard to be
suffered, accommodated or mitigated. Research tended to focus on extreme climate events
such as droughts, cold waves, heat waves or wet seasons (Heathcote, 1985), reasoning that
vulnerability to climate is most strongly manifest through such extremes. Hazard research
paid special attention to the relationship between impacts and predisposing social
vulnerability (e.g. Burton et al., 1978). It also focused on strategies for adjusting to and
coping with climatic variability. It can be argued that there was an in-built bias in this
research towards the adverse impacts of climate, with less attention paid to the positive
impacts of favourable conditions, which also play a role in longer-term societal adaptation to
climate. Furthermore, it is questionable whether it is possible to interpolate impacts of more
moderate climatic variations from studies of extreme cases.
2 This section draws heavily on a review prepared for the Workshop The Future Climatic Window: Local
Impacts of Climate Change, 25-27 January 2007, Seggau Castle, Leibnitz, Austria
4
Climate as natural resource can be regarded as a development of the view of climate as
setting, but with the difference that climate is regarded as an exhaustible resource that has
value and can be allocated, managed or manipulated. The concept emerged along with ideas
about the limits to the carrying capacity of global environmental commons such as air, water
and soils (Meadows et al., 1972). The atmosphere was both a receptacle and disperser of
industrial pollutants, it was a commodity that farmers, foresters and water engineers needed to
manage, and it was even a resource that might be manipulated (e.g. experiments with cloud
seeding began at this time). Finally, with a recognition that humans were inadvertently
modifying the atmosphere, and with it the climate, came the realisation that the resource was
not a fixed one but subject to change, with potential associated risks and benefits.
Riebsame pointed out that these four viewpoints are not mutually exclusive, but together
capture many of the issues being addressed today when confronting climate change adaptation
(Riebsame, 1985). The next sections show how these ideas have developed in recent years.
2.2 From impacts to risk management
2.2.1 Impacts of anthropogenic climate change
In the 1960s it was already recognised from observations that humans were altering the
composition of the atmosphere, and that this might affect the radiation balance of the earth.
With the advent of general circulation models (GCMs) that could be used to simulate the
response of the global climate to radiative forcing, it soon became clear that increases in
anthropogenic greenhouse gases could have a significant impact on global climate. Thus,
while previous study of climate-society interactions was based almost exclusively on
observed or reconstructed past events, the use of projected climates from GCMs opened up a
whole new area of investigation of potential future impacts.
A notable benchmark in this development occurred at an international workshop in Villach,
Austria in 1983, where climate modellers and impact assessors came together to present some
early estimates of GCM-based climate change and its projected impacts (WMO/UNEP/ICSU,
1984; Parry, 1985). This was one in a series of international scientific workshops on
anthropogenic climate change that persuaded policy makers of the global significance of the
issue, and led up to the formation (in 1988) of the Intergovernmental Panel on Climate
Change (IPCC) and the adoption of the United Nations Framework Convention on Climate
Change (UNFCCC) at the Earth Summit in Rio de Janeiro, in June 1992. New research
programmes were initiated in many countries to evaluate the potential impacts of climate
change, providing an input to policy making through periodic assessments by the IPCC as
well as National Communications to the UNFCCC.
2.2.2 IPCC seven-step guidelines to assessment
Most early impact and adaptation studies followed a sequential approach that focused on
evaluating the impacts of projected climate change under given GCM-based scenarios of
future climate, and then considered the adaptation responses that might be required to reduce
any resulting vulnerability to climate risks. The approach was presented as a seven-step
analytical framework in a document prepared for the first UNFCCC Conference of the Parties
(COP) in 1995 (IPCC, 1994). The seven steps comprised:
5
1. Definition of the problem, requiring identification of the specific goals of the assessment,
the sectors, systems and regions of interest, and the time horizon and data needs of the
study.
2. Selection of the methods, which depends on the availability of resources, models and data,
ranging from qualitative and descriptive to quantitative and prognostic.
3. Testing the method, which involved model validation, sensitivity testing and uncertainty
analysis to ensure the credibility of the tools applied in the assessment.
4. Development of the scenarios, requiring first, the projection of conditions expected to
exist over the study period in the absence of climate change, and second, the projection of
conditions associated with possible future changes in climate.
5. Assessment of potential impacts, which involves estimating, for the sectors and regions of
interest, the differences in environmental and socio-economic conditions projected to
occur with and without climate change.
6. Assessment of autonomous adjustments, which implies the analysis of responses to climate
change that generally occur in an automatic or unconscious (spontaneous) manner.
7. Evaluation of adaptation strategies, involving the analysis of different means of reducing
damage costs through exogenous or planned adaptation, requiring deliberate policy
decisions. The following steps were proposed for addressing adaptation (IPCC, 1994, p.
33): (i) define the objectives, (ii) specify the climate impacts of importance, (iii) identify
the adaptation options, (iv) examine the constraints, (v) quantify measures and formulate
alternative strategies, (vi) weight objectives and evaluate trade-offs, and (vii) recommend
adaptation measures. Though adaptation was paid much less attention than impacts in the
1990s, with the recent emergence of policy interest in adaptation, these steps have begun
to re-appear in frameworks that focus on adaptation (see section 5, below).
The seven-step framework has been a dominant mode of analysis in studies of climate change
impacts, adaptation and vulnerability (CCIAV) during the past two decades. Variants of the
framework are found in guidance documents supporting a number of early research
programmes on climate change impacts and adaptation, including the US EPA national
assessment (Smith and Tirpak, 1989), the US Country Studies Program (Smith et al., 1996)
and the United Nations Environment Programme Country Studies (Feenstra et al., 1998). It is
also reflected in the structure of the IPCC assessment reports themselves, which have
conventionally dealt, in three volumes, first with the science of climate change (working from
GHG emissions to GHG concentrations to observed and then projected climate and sea level
change), second with impacts, adaptation and vulnerability (typically impacts of climate
change and then adaptation as a potential response to these), and third with mitigation
responses (with respect to different options for reducing GHG emissions).
2.2.3 Top-down and bottom-up approaches
The seven-step framework is sometimes cited as the "standard" or "first generation" approach
to climate impact assessment (UNFCCC, 2005). Its analytical focus is on the identification
and quantification of impacts of climate change, often on a particular sector or a region. It
places a large reliance on the scenarios of future climate and related developments selected at
step 4. Characterised as a "top-down" approach (e.g. Dessai and Hulme, 2004) because it
proceeds from global climate projections that are subsequently downscaled and applied to
assess regional impacts, it has been criticised for treating adaptation as a residual at the end of
the analysis, and for over-simplifying the role of adaptation in responding to multiple stresses.
For example, many studies ignore adaptation altogether (i.e. climate as determinant, as
6
described above, or the "dumb farmer" approach3). Hence results may exaggerate the impacts
of climate change (Füssel and Klein, 2006). The approach tends to concentrate on biophysical
effects of climate change that can be readily quantified, and treats higher-order socio-
economic impacts only if quantitative models are available to link to the biophysical effects.
Therefore, the main output from such studies that can be used to inform policy is an
assessment of physical vulnerability for a time period in the future (right hand side of Figure
1).
Figure 1. Top-down and bottom-up approaches for addressing climate adaptation policy (adapted from
Dessai and Hulme, 2004).
An alternative approach that focuses on social vulnerability is characterised by Dessai and
Hulme (2004) as a "bottom-up" approach (left hand side of Figure 1). Here the unit of study is
local, commonly households or communities, and the temporal scale tends to be more
immediate and near term than in top-down approaches as decision-making on adaptation must
address both current and future vulnerabilities and actions. Vulnerability to climate is
addressed largely as a problem of climate variability now (i.e. closely following the
perspective of climate as hazard, described above), and focuses on social vulnerability (Figure
1). The great majority of assessments that follow this approach are found in developing
countries, where vulnerability to present-day climatic variability is commonly perceived to be
more of a threat than long-term climate change. In contrast, developed countries are often
3 An agricultural metaphor is sometimes employed to explain different levels of adaptation assumed when
assessing impacts of climate change (e.g. Smit et al., 1996). This alludes to four types of farmer: (i) the "dumb"
farmer does not react at all to changing climatic conditions; (ii) the typical farmer adjusts management practices
only to persistent climate change; (iii) the smart farmer uses all available information on expected climate
conditions to adjust management practices proactively; and (iv) the clairvoyant farmer has perfect knowledge of
the future climate and has no constraints on implementing adaptation measures. Any impacts remaining after this
optimal adaptation can be regarded as "unavoidable".
Global
Local
Economic resources
Infrastructure
Institutions
Technology
Information & skills
Equity
Indicators based on:
Adaptive capacity
Vulnerability
(social)
Bottom-up approach
Economic resources
Infrastructure
Institutions
Technology
Information & skills
Equity
Indicators based on:
Adaptive capacity
Vulnerability
(social)
Bottom-up approach
Impacts
Regionalisation
Global climate models
Global greenhouse gases
Vulnerability
(physical)
World development
Top-down approach
Impacts
Regionalisation
Global climate models
Global greenhouse gases
Vulnerability
(physical)
World development
Top-down approach
Past Present Future
Climate
adaptation
policy
Climate
adaptation
policy
7
regarded as resilient to variability, and vulnerability studies of this type have tended to be
ignored until recently (e.g. O'Brien et al., 2006a). Bottom-up approaches are closely
connected with other frameworks dealing with resource management, disaster management
and sustainable development, which offer opportunities for integrating climate change
considerations into existing decision-making and management contexts.
2.2.4 Contrasting vulnerability assessment and adaptation assessment
The bottom-up approach can also be divided into two types of assessments: vulnerability
assessments and adaptation assessments. Vulnerability assessments focus on the risk of
damage from climate change (i.e. the propensity to be harmed or the "downside" of risk),
seeking to maximise potential benefits and minimise or reverse potential losses (Adger,
2006). In contrast, adaptation assessments concentrate on the adaptive capacity and adaptation
measures required to improve the resilience or robustness of a system exposed to climate
change (the "upside" of risk – Smit and Wandel, 2006). The two approaches are closely
related, especially with respect to adaptive capacity, which can be thought of as the potential
to reduce vulnerability (but also to exploit opportunities) through adaptation.
2.2.5 A risk management framework
Chapters in the IPCC Working Group II assessment reports have provided updates of CCIAV
methods and tools, with an array of methods covered in the Third Assessment Report (e.g.
Ahmad et al., 2001; Carter et al., 2001; Smit et al., 2001). In the Fourth Assessment Report
(AR4), a four-fold classification of approaches to CCIAV assessments is proposed (Carter et
al., 2007): scenario-driven impact approaches, vulnerability-based approaches, adaptation-
based approaches and an integrated approach. Some of the main characteristics of these
approaches are summarised in Table 1.
Table 1. Characteristics of different approaches to CCIAV assessments (Carter et al., 2007)
A suggestion for mapping these approaches onto a two-dimensional framework is presented
by Jones and Preston (2011), who combine the top-down/bottom-up scales of analysis of
8
Dessai and Hulme (2004 – Figure 1) with alternative approaches to assessing risk (Figure 2).
A "prescriptive", "event" or "natural hazard" orientation is forward-looking and seeks to
predict cause and effect processes in order to define vulnerabilities to a range of sectoral
hazards (e.g. flood risk, changes in agricultural productivity – cf. the sequential approach
outlined in section 2.2.2). A "diagnostic", "inverse", "goal-oriented" or "critical threshold"
orientation expresses consequences in terms of risks of exceeding valued outcomes of climate
change (e.g. species extinctions, critical damage to infrastructure or excess mortality) and
looks backward to seek adaptive and mitigative solutions that reduce these risks.
Figure 2. Selected reference points for assessments mapped on top-down/bottom-up and prescriptive/
diagnostic axes (for explanation, see text). Source: Jones and Preston (2011).
On the basis of this framing, Carter et al. (2007) identified risk management as a framework
capable of dealing with some of the emerging issues of interest for decision-making. These
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
decision-making under uncertainty
Jones and Preston (2011) contend that all of these goals are compatible with the international
risk management standard ISO 31000 (ISO, 2009). Moreover, a risk management framework
can also be used to integrate approaches to adaptation with complementary approaches
directed towards mitigation. Mitigation reduces the rate and magnitude of changing climate
hazards; adaptation reduces the consequences of those hazards (Jones, 2004). Adaptation can
have immediate or near-term effects, whereas benefits from mitigation accumulate over
longer time scales.
Top-down
Bottom-up
Prescriptive Diagnostic
Global
scenariosAvoiding
dangerous climate change
Future
vulnerability –critical thresholds
Current
vulnerability –critical thresholds
Adaptation
across scales
(space &
time)
Standard
approach
Sustainable
regions
Livelihood
approaches
Sustainable
communities
Local
scenarios
9
Jones and Preston (2011) update ideas emerging out of Carter et al. (2007) by equating the
evolution of key policy questions to five stages of a risk management process and the
methodological approaches and scenario requirements that are associated with them (Table 2).
The first three stages: (i) scoping and risk identification, (ii) risk analysis and (iii) risk
evaluation can be related to earlier policy questions as reflected in contemporary IPCC
assessments. They argue that recent and ongoing attention is focused on assessing the
effectiveness of adaptation through stages (iv) risk management and (v) implementation and
monitoring. These ideas are carried forward later in this report into a suggestion for
classifying methods of CCIAV assessment for the MEDIATION common platform (see
section 7.2).
Table 2. The evolution of risk assessment as applied to climate change, particularly adaptation, since the
establishment of the IPCC in 1988. Source: Jones and Preston (2011).
Assessment Policy
Question
Stage of Risk
Assessment
Methodological
Approaches
Scenario
Requirement
Years
First
generation
Is climate change
a problem?
Scoping the
question, risk
identification
Sensitivity
analysis
Incremental scenarios
for primary climate
variables
1988-1992
Second
generation
What are the
potential impacts
of unmanaged
climate change?
Risk analysis Scenario-driven
impact assessment
Climate model
derived scenarios for
multiple variables at
global and regional
scale
1988-2001
Third
generation
How do we
effectively adapt
to climate
change?
Risk
evaluation
Risk assessment
Vulnerability
assessment
Model derived
scenarios for many
variables, consistent
with other scenarios,
integration at a range
of scales
1995-2007
Fourth
generation
Which adaptation
options are the
most effective?
Risk
management
Risk management
Mainstreaming
adaptation
Dynamic scenarios of
climate and other key
drivers, conditional
probabilities
2001
ongoing
Fifth
generation
Are we seeing
the benefits?
Implementing
and monitoring
Implementation,
monitoring and
review
Updating scenarios
through observation
and learning by doing
2007
ongoing
The remainder of this paper reviews in more detail some of the methods and tools applied in
CCIAV assessments. Four main approaches to assessment are identified: (i) vulnerability
assessment, (ii) impact assessment, (iii) adaptation assessment and (iv) integrated assessment.
These approaches are discussed in the following sections, along with specific methods and
tools that are commonly associated with each approach, alternative ways of classifying them
and some examples of relevance for assessments in Europe. Section 7 then introduces a
suggestion for a framework that might be applied to classify CCIAV studies in support of
decision-making.
10
3 Vulnerability assessment
Vulnerability to climate change is defined by the IPCC as "the degree to which a system is
susceptible to, or unable to cope with, adverse effects of climate change, including climate
variability and extremes (IPCC, 2001 Adaptation,, Glossary). However, the concept is a
theoretical one, and the IPCC definition is but one of a plethora of overlapping, though often
distinctively differing interpretations of the term, leading Cutter et al. (2003) to conclude that
"little agreement has been reached beyond that there are competing conceptualisations of
vulnerability and that vulnerability is place-based and context-specific". In fact, vulnerability
cannot be measured or observed directly; rather it has to be deduced, as it relates to how the
functioning of a system is likely to be impaired in the future compared with today (Patt et al.,
2009).
Methods of vulnerability assessment are defined by the type of exposure unit thought to be
vulnerable (e.g. natural resources, region, people), the nature of the climate change hazard
leading to that vulnerability, and the specific nature of the vulnerability being described (e.g.
loss of biodiversity, damage to infrastructure, loss of earnings). Four purposes are identified
by Patt et al. (2009) for undertaking a vulnerability assessment: (i) to improve adaptation
planning, (ii) to frame climate change mitigation as an urgent problem (by contrasting impacts
of unmitigated and mitigated climate change) (iii) to address social injustice, by exposing the
differential burden of vulnerability borne by the socially disadvantaged, or (iv) to improve
basic scientific understanding of vulnerability and improve the methods and tools used in its
evaluation.
On much the same lines, Malone and Engle (2011) distinguish three "framings" of
vulnerability assessment:
as an extension of research on impacts of climate change focusing mainly on physical
risks of climate change (e,g, studies of numbers of people at risk from climate change)
as an offshoot of sustainable development research, focusing on social aspects of
vulnerability to climate change, determined by factors such as governance and human
capital
as a development of hazards and disasters research and newer research related to
resilience, which focuses on flexible responses that integrate both physical and societal
capacities to cope in the short term and adapt in the longer term (see Box 1).
***************************************************************************
Box 1. Resilience frameworks
Resilience frameworks provide an additional angle to assessing adaptation. Their origins lie in research traditions
on population, landscape ecology and resource management and consequently their methodological foundation is
strongly mathematical and modelling-based. Nelson et al. (2007) examine how adaptation is treated in the wider
literature on environmental change and point to additional views offered by resilience frameworks. Key elements
that set resilience frameworks apart from the mainstream adaptation literature include a systems-oriented rather
than agent-based view in examining adaptation and the fundamental assumption that the natural state of a system
is one of change rather than equilibrium. Thus adaptation assessments should not aim at reducing vulnerability in
relation to static risks at a given point in time but focus on maintaining system flexibility and its ability to
respond to and to take advantage of the opportunities arising from change. These frameworks also emphasise the
coupled nature of socio-ecological systems, which is also increasingly recognised in other assessment
frameworks. Their interest focuses on the relationships between system elements, context and feedbacks rather
than individual elements of a system.
***************************************************************************
11
Regardless of the framing, quantitative vulnerability research to date has been dominated by
the use of indicators and their combination into vulnerability indices.
3.1 Vulnerability indicators and indices
Measures of vulnerability (V) typically include three components: exposure to climate change
(E), sensitivity to its effects (S) and adaptive capacity for coping with the effects (AC), such
that:
V = f (E, S, AC) (1)
Typically, attempts are made to quantify each of these components, usually by identifying
appropriate indicators for each, and then combining them into indices, and subsequently
combining the components into an integrated index of vulnerability. Some of the indicators
(primarily of exposure and sensitivity) are from the biophysical realm; others (mainly
describing adaptive capacity) are drawn from socio-economic statistical sources4.
As a means of comparing the diversity of vulnerability studies, Polsky (2007) propose a
vulnerability scoping diagram (VSD – Figure 3). This identifies the common dimensions of
vulnerability (exposure, sensitivity and adaptive capacity – cf. Schröter et al., 2005), the
components of these dimensions (i.e. the attributes that characterise them, for a given human-
environment system) and measures of the components (i.e. quantitative or qualitative metrics
that individually or collectively describe their contribution to vulnerability).
(a) (b)
Figure 3. Vulnerability scoping diagram (VSD) for facilitating comparison of vulnerability assessments:
(a) generalised form with hazard and exposure unit unspecified; (b) hypothetical example for a generic
community water system exposed to the hazard of drought. Source: (Polsky et al., 2007)
A number of recent European climate change assessments that have employed vulnerability
indicators and indices are listed in Table 3.
4 It has also been argued that expression (1) covers only those aspects that can be quantified using objective
measures, and ignores some important elements of social vulnerability. For instance, overall vulnerability may
be strongly influenced by individuals' perceptions of their vulnerability to climate change, which is highly
subjective and difficult to measure.
12
Table 3. A selection of recent studies applying indicators and composite indices of vulnerability to climate
change for different focal regions and sectors in Europe.
Vulnerability study Focal region and
sector(s)
Determinants
of vulnerability
Metric(s)
O'Brien et al. (2004) Agriculture and winter
tourism in Norway
(municipality)
E, AC Normalised indices;
vulnerability not defined
Schröter et al. (2005);
Metzger (2005)
Ecosystem services in
Europe (NUTS-2)
PI, AC Normalised indices;
composite vulnerability
Yohe et al. (2006) Multiple sectors,
globally (national)
E, AC Quotient of exposure
(global warming) and
globally normalised
composite AC
ESPON study (Peltonen
et al., 2010)
Various sectors in
Europe (NUTS-2)
AC Normalised indices
Carter et al. (2010) Agriculture and the
elderly in theNordic
region (municipality)
E/S, AC Normalised indices;
composite vulnerability
(user defined/weighted)
3.2 Distinguishing current and future vulnerability
Vulnerability assessment as defined in this review concerns the future propensity for harmful
impacts. This distinguishes it from impact assessment (section 4), which embraces past,
present and future impacts of all types (harmful, beneficial or neutral). Even with this narrow
definition of vulnerability assessment, it is useful to make a further distinction between
assessments of vulnerability that are determined by current or future conditions.
Preston and Stafford-Smith (2009) present a diagram depicting these two types as "present
vulnerability" and "future vulnerability", each comprising biophysical and social
determinants. To avoid using the term present vulnerability, given that vulnerability refers to
potential harm in the future, we have modified their schema to refer to vulnerability to climate
variability and vulnerability to climate change (Figure 4), which recognises that there are two
different time horizons of interest in framing vulnerability, especially with respect to the
implementation of adaptation responses.
An apparent dichotomy can be identified in previous vulnerability studies between
approaches that focus on social determinants that contribute to the adaptive capacity and
vulnerability of communities or systems today (box 3 in Figure 4), and biophysical changes
that will affect exposures and vulnerability in the future (box 2). Indeed, most vulnerability
assessments that combine social and biophysical components also typically superimpose
future biophysical exposures onto current adaptive capacities (e.g. O'Brien et al., 2006b).
It has been argued that adaptations that are robust under projected biophysical changes will
also be robust for existing vulnerabilities (sometimes known as no regret or low regret
measures – Willows and Connell, 2003 Uncertainty, and Decision-making). On the other
hand, it is also argued that present-day social determinants of vulnerability should guide
adaptation, and that such interventions will then drive future development pathways that are
also less vulnerable to climate change. This tendency to superimpose projected exposure on
13
current adaptive capacity also reflects international funding goals, which have tended to target
capacities to cope with climatic conditions experienced today, rather than projected longer-
term climate change. Interestingly, future socio-economic changes are rarely explored as a
guide for targeting adaptation in anticipation of future vulnerabilities, one argument being that
such anticipatory adaptation may not necessarily adequately address current vulnerabilities
(Preston and Stafford-Smith, 2009). However, this argument appears somewhat lame, given
the few serious attempts that have been made to project future adaptive capacity. Moreover,
the argument could equally apply to the use of biophysical scenarios to anticipate future
exposure, where there is still considerable scope for maladaptation (e.g. through ill-advised
interventions that may inadvertently enhance current or future exposure).
Another reason for the readiness to adopt biophysical but not socio-economic scenarios has
been the heavy bias of scientific investment into climate prediction in recent decades (see, for
example, Biesbroek et al., 2010). There is clearly a need for more attention to be paid to the
development of integrated scenarios that can be applied in studies of both future biophysical
and future social vulnerability. Suggestions of how to undertake such studies of future
vulnerability, as well as how their results might be linked to adaptation policy as a component
of the UNDP Adaptation Policy Framework, are provided by Downing and Patwardhan
(2005).
Figure 4. Current and future determinants of vulnerability to climate variability and climate change
(modified from Preston and Stafford-Smith, 2009). A notable gap in knowledge relates to adaptation that
targets future changes in social determinants of vulnerability (box 4).
3.3 Critiques of vulnerability assessment
The value of vulnerability indices is disputed in the literature (Patt et al., 2009). For example,
indicators can generally portray only a measure of relative vulnerability (e.g. between places
or between time periods). If more specific information is required by decision-makers, then
estimates of impacts might be more appropriate. Indeed, Metzger and Schröter (2006) suggest
that potential impacts can be estimated by combining the exposure and sensitivity terms in
expression (1), and this formulation was applied in several of the studies of ecosystem service
vulnerability to climate change in Europe in the A-TEAM project (Schröter et al., 2005).
Other criticisms levelled at this approach to assessment are that individual indicators can
never properly capture the tremendous heterogeneity of vulnerability (e.g. aspects of social
vulnerability discussed above), and that uncertainties tend to be poorly conveyed in an
indicator-based assessment.
Climate variability
TopographyLand UseDefences
Infrastructural ageBuilding Material
Wealth
TechnologyEducation
EntitlementsSocial Capital
BiophysicalDeterminants
SocialDeterminants
Current State + = Vulnerability to
climate variability
Temperature changeRainfall change
Evaporation changeWind change
Humidity changeSea-level rise
Population growthEconomic growthChanging values
Changing governance
New policy decisions
FutureChange +
+ += Vulnerability to
climate change
1
43
2
14
In a recent detailed critique of vulnerability assessment, Hinkel (2011) identified six purposes
that might be served by vulnerability indicators for informing decision-making and then
offered a verdict on whether each is fit for purpose (shown in parentheses as an interpretation
of Hinkel's arguments) based on evidence gathered from the literature:
(i) To identify mitigation targets (Verdict: No. Global scale vulnerability indicators, even if
they could be defined, would be useless for setting mitigation targets, which involve
describing trade-offs between different dimensions of global impacts. These impacts need to
be estimated explicitly using impact models)
(ii) To identify particularly vulnerable people, regions or sectors (Verdict: Yes at local scales,
where exposure units are narrowly defined by a few variables. No over large regions and
globally, as the generalisations required in developing a composite index will inevitably
obscure the complexity of regional processes that can be modelled explicitly using impact
models)
(iii) To raise awareness of climate change (Verdict: No, because the presentation of indicators
on their own is meaningless without effective techniques for communicating the information
they contain in a form that is readily understandable by stakeholders. It is argued that the
vagueness surrounding the concept of vulnerability can only add to the challenge of
communicating the key messages)
(iv) To allocate adaptation funds to particular vulnerable regions, sectors or groups of people
(Verdict: No, both internationally and at national scale. The key issue here is the inherent
subjectivity involved in selecting indicators and then combining them into indices. Since the
scientific methods employed can readily be contested, there would be ample opportunity to
offer counter-proposals for funding priorities to suit different parties. At national scale, it is
likely that more advanced knowledge would be available from impact models than can be
gleaned from composite indices)
(v) To monitor the performance of adaptation policy (Verdict: No. The success of adaptation
policy cannot usefully be measured in terms of vulnerability. Instead, it could be monitored as
an outcome indicator of the amount of harm observed (i.e. observed impacts) and/or as a
process indicator of the progress in implementing adaptation measures)
(vi) To conduct scientific research (Verdict: No. Scientific inquiry can only be taken forward
by formulating specific research hypotheses about cause and effect that can be tested by
measurement or modelling rather than by applying arbitrary methods in an attempt to quantify
the unquantifiable).
In conclusion, Hinkel recommends that if vulnerability indicators are to be developed, they
should only serve as high level entry points to further more detailed information behind. A
concerted effort should be made to make the problems addressed and methodologies applied
more explicit, which also requires that more specific terminology be employed as well, in
particular avoiding all but the most qualified use of the all-embracing label "vulnerability"
(Hinkel, 2011).
15
3.4 Stakeholder-driven processes
An alternative or complementary approach to quantitative indicator studies elicits the
involvement of stakeholders in agreeing upon the main issues and responses of importance for
assessing vulnerability to climate change (Malone and Engle, 2011). In regions where the
availability of quantitative data may be poor, expert opinion of regional stakeholders or even
use of analogues (examples from different but related settings), may offer alternative sources
of information about climate change vulnerability (Downing and Patwardhan, 2005). Here,
community stakeholders communicate what they are vulnerable to, who is vulnerable, how
future vulnerability may be characterised, and at what scales. Stakeholders can also provide
invaluable information about non-climate stimuli that are important in mediating the potential
impacts of climate change.
Malone and Engle lists several challenges posed by involving stakeholders in assessments,
including the need: (i) to balance over-prescriptive guidance against lack of direction, (ii) to
employ skilled facilitators when engaging stakeholders; (iii) to schedule sufficient time for
gaining consensus, and (iv) to be aware of power relations among stakeholders (Malone and
Engle, 2011).
Stakeholder participation may also undermine at least one of the conclusions put forward in
Hinkel's critique of vulnerability assessment (Hinkel, 2011 – see above). The argument that
vulnerability indicators are not the right means to raise awareness of climate change because
this is primarily an issue of risk communication, seems to downplay the importance of having
useful information to convey. Hinkel dismisses the usefulness of most vulnerability indicators
due to the vagueness of the concept they express and because any information conveyed by
the indicators has to be of relevance to stakeholders. However, he seems to be referring to
indices (arbitrary combinations of indicators, often vaguely defined) rather than indicators,
which are observed or modelled and usually fairly tangible. Moreover, emerging new
vulnerability tools may finesse these arguments somewhat by offering users the flexibility to
explore indicators themselves (albeit from a pre-selected list, though stakeholders can also
help to define that list) as well as combining and weighting them according to their interest
(e.g. Carter et al., 2010). Such tools place the definition of vulnerability firmly and more
transparently in the hands of the user rather than the researcher, whose role is simply to
compile the requisite data for analysis.
Whether the development of interactive vulnerability tools and the "democratic learning" they
can promote should be regarded as scientific research (the sixth purpose of assessment listed
by Hinkel) may also merit further attention. Causality does not necessarily need to be
explicitly represented by researchers to describe vulnerability; it can also be inferred
subjectively, but still usefully by an expert user (for example, by comparing patterns of a
given impact with patterns of candidate indicators that might contribute to those impacts).
Moreover, study of user-decisions in such an environment might yield very useful insights
into how stakeholders actually perceive vulnerability to climate change in the specific context
in which they work.
3.5 Vulnerability assessment in a wider context
Whilst paying heed to Hinkel's admonishments concerning the "Babylonian confusion"
surrounding definitions of vulnerability, it is worth pointing out that wider definitions of
vulnerability assessment than those encapsulated in the above discussion of indicators can
also be found in the literature. For example, the concept of vulnerability has been combined
16
with probabilistic analysis into a framework for assessing climate risk (Preston and Stafford-
Smith, 2009) that encapsulates many of the standard methods of vulnerability and impact
analysis as well as a multiplicity of metrics (Figure 5). Climate vulnerability is characterised
as a function of both social and biophysical vulnerabilities, each defined by variants of the
three common dimensions: exposure, sensitivity (and their combination as potential impact)
and adaptive capacity. These dimensions are themselves characterised by a range of attributes
and associated metrics (cf. Figure 4). Collectively, climate vulnerability is associated with the
potential for harm or various adverse consequences. When combined with likelihood of
occurrence, climate vulnerability can be redefined in terms of climate risk.
This depiction of vulnerability assessment links directly to the wider issues of risk assessment
and risk management that are increasingly being seen as an essential framework for
mainstreaming climate change considerations into existing policy frameworks.
Figure 5. A suggestion for combining concepts of climate vulnerability with those of climate risk. Source:
Preston and Stafford Smith (2009).
17
4 Impact assessment
Climate change impact assessment characteristically poses the question: "If the climate
changes what will its impacts be on a given exposure unit5?" It presumes some kind of causal
relationship between climate and the system exposed, though establishment of such causality
may itself be the focus of an assessment. Several broad categories of climate change impact
assessment can be identified, each involving the deployment of methods and tools drawn from
a formidable and ever-expanding range of options. For the purposes of this report, four
categories can be distinguished:
Experimental simulation of impacts
Detection and attribution of observed impacts of climate change
Modelling of biophysical impacts
Modelling of social and economic systems
Each of these is now treated separately and some examples provided of methods and tools
applied in assessment studies.
4.1 Experimental simulation of impacts
In order to understand how an exposure unit may respond to future environmental conditions
that are unique and often outside the range of anything observed historically, there may be
opportunities to conduct controlled physical experiments that simulate these future conditions.
The most common examples of such experiments concern studies of plant responses to
combinations of atmospheric carbon dioxide concentration, temperature and moisture
conditions that have been altered to replicate projected conditions that differ significantly
from those found at the present-day. Experiments may be carried out in the laboratory (e.g.
photosynthetic responses of leaves to different levels of light and temperature conditions – ),
in greenhouses (e.g. pot experiments with crop plants grown under various combinations of
CO2 concentration, temperature and soil moisture) or in the field, using specially designed
open top chambers (e.g. for subjecting forest trees to prolonged multi-year exposure to
elevated CO2) or free-air concentration enrichment (FACE) experiments, which are not
constrained by a chamber but where CO2 is released into the open air over a circumscribed
area of a crop stand at a rate controlled to maintain a target level relative to the surrounding
ambient concentration.
Such experiments may also extend to consideration of other stressors, including air pollutants
like ozone (e.g. Fumagalli et al., 2001; Pleijel et al., 2002; Mills et al., 2011), sulphur
dioxide (e.g. Bender and Weigel, 2010) or soil characteristics such as nutrients (e.g. Brouder
and Volenec, 2008) or salinity (e.g. Pérez-Lópeza et al., 2009). Nor is experimentation
limited to plant growth alone; comparable experiments have been reported for coral (e.g.
Atkinson and Cuet, 2008), permafrost (e.g. Dutta et al., 2006), insect behaviour (e.g. Sudderth
et al., 2005), human health (e.g. Beggs, 2010) and building materials (e.g. Andrady et al.,
2011).
Experimentation can be highly instructive in providing new information about responses to
environmental changes that are projected for the future but have never been recorded directly
5 An exposure unit is the activity, group, region or resource exposed to significant climatic variations (IPCC,
1994).
18
(e.g. elevated CO2 concentrations). Such insights can be used in developing and refining
process-based impact models (see below). For instance, the strong photosynthetic response of
C3 plants exposed to elevated CO2 concentrations during a single growing season, was
probably exaggerated in early experiments with growth chambers, due to artificial
modification of growth conditions of wind, humidity and radiation introduced by the
chambers themselves (the so-called "chamber effect"), with FACE experiments producing
reduced (though still significant) enhancement of photosynthesis (Long et al., 2006). There
are suggestions, therefore, that early model-based estimates of the contribution of CO2
fertilization to future crop yields, combined with their inability to account for negative effects
of tropospheric ozone, may have provided a too optimistic prognosis of crop yields compared
to estimates based on recent results from FACE experiments (Long et al., 2005), though the
extent of this exaggeration is disputed (Ewert et al., 2007).
Similarly, of considerable interest for perennial species such as trees, is the phenomenon of
downregulation, which refers to the apparent gradual acclimation of photosynthetic response
(i.e. a reduction towards present-day levels levels) in trees following prolonged, multi-year
exposure to elevated CO2 concentrations (Rasmussen et al., 2002; Körner et al., 2005).
Discovery of this phenomenon has required significant investment of equipment and
resources to undertake long-term experiments during the past two decades. However, were
forest growth models to rely solely on short-term, single season experiments, then it is
probable that estimates of future tree growth in response to elevated CO2 would be greatly
exaggerated. As it is, most contemporary models that consider CO2 response nowadays
account for downregulation based on the results from long-term experiments (Medlyn et al.,
2011).
The above examples point to a need for practitioners to be wary of undertaking controlled
experiments that attempt to simulate possible future conditions. Some pitfalls to be aware of
include:
Chamber effect. Use of an enclosure to maintain CO2 and climate control can produce
inadvertent modifications of other environmental conditions compared to natural
conditions (e.g. impaired transmission of radiation through plastic or glass, altered
humidity, enhanced susceptibility to pest or pathogen damage)
Scale. The applicability of results even from field experiments can be affected by the scale
at which the environmental modification is being carried out. For example, edge effects
may distort results of plant responses obtained from plots that are too small. Moreover,
there are significant challenges in inferring responses over large regions from results
obtained from small experimental plots.
Statistical significance. A multifactorial experimental design is commonly employed to
test responses to different combinations of environmental driving variables, and for each
combination typically two to four replicates are required to ensure statistical significance
of results. Due to the high cost of instrumenting and monitoring experimental
installations, there is often a trade-off to consider between the number of combinations
that can be tested versus the number of replicates required to obtain robust results (Filion
et al., 2000).
4.2 Detection and attribution of observed impacts of climate change
In its strictest sense, detection of observed impacts of climate change refers to a confirmation
that impacts of climate on an exposure unit during a given period depart significantly from
19
those impacts that would have been expected due to natural climatic variations, due to a
change in the underlying climate. No judgement is made about the likely causes of the climate
change. Note that detection studies are distinct from other empirical studies of societal
responses to climate variability, including extreme events, which relate primarily to weather
hazards.
Joint attribution refers both to the demonstrated link between observed impacts and regional
climate change (detection) and the ability to assign or attribute a measurable proportion of
either the regional climate change or the associated observed impacts to anthropogenic causes,
beyond natural variability (Rosenzweig et al., 2007). Joint attribution studies can hence offer
confirmation that anthropogenic climate change is already affecting natural and human
systems. Such evidence is important scientifically, as a test of the predictions of impact
models. Perhaps more importantly, by providing evidence of ongoing and often detrimental
impacts of anthropogenic climate change, these studies may help to communicate the urgent
need for implementing mitigation and adaptation policies.
An expanding body of literature has appeared over the past two decades, documenting
responses of physical and biological systems to regional warming during the twentienth
century (Rosenzweig et al., 2007; Rosenzweig et al., 2008), with a large number of studies
reported from Europe (e.g. Menzel et al., 2006a; EEA, 2008; Schleip et al., 2009). Observed
impacts have been documented for physical systems (e.g. glacier volume and extent; sea ice
duration, thickness and extent; freeze and thaw dates of lakes and rivers; timing of the spring
discharge peak in rivers; permafrost depth and extent; snow cover and volume; coastal
erosion) and for biological systems (e.g. plant development and phenology; timing of plant
photosynthetic activity; bird nesting and breeding times; insect distributions, numbers of
generations and fecundity).
There are fewer studies that have demonstrated observed impacts of climate change in human
systems. This is partly because of the many confounding factors that may have influenced
system responses in addition to climate. For example, management and technology have
exerted a strong influence on agricultural and forestry productivity during the past 50-100
years. However, some studies have shown convincingly that changes in climate have affected
these activities, for example through changes in growing season length and phenology
(Kaukoranta and Hakala, 2008), although some human responses appear not fully to be
tracking the pace of change (Menzel et al., 2006b).
The main methods applied in these detection studies require the use of statistical techniques to
identify trends, including regression, correlation and time series analysis. Some of the
statistical challenges confronting analysts include:
Discontinuous time series. Identification and treatment of discontinuities in records (e.g.
abrupt changes, split time series)
Explanatory variables. Identification of climate variables thought to be of importance in
explaining system responses
Scale issues. Matching observations of key climate variables to observed impacts across
the same region
Alternative explanations. Examination of alternative explanations for the observed
impacts (e.g. land use change, air pollution effects)
Sample biases. Biases in sampling of observed impacts, including over-reporting of
climate-sensitive biological species versus less sensitive species or publication bias
20
towards results showing positive associations with climate and away from results
exhibiting no long-term change.
Attribution studies require additional statistical analyses to determine whether the pattern and
strength of regional climate changes and observed impacts can be explained by natural
variability alone, or if anthropogenic forcing of the climate system needs to be invoked. Two
methods of joint attribution are applied by Rosenzweig et al. (2007): climate model based and
spatial analysis.
Climate model-based methods. This type of analysis requires the use of climate models,
that can simulate climate over the historical period. If the pattern of observed impacts
departs from the pattern inferred from climate model simulations of natural variability
(assuming no change in atmospheric composition from pre-industrial levels), this would
suggest that some other mechanism was affecting the pattern. If the pattern is closer to
that expected on the basis of simulated climate assuming observed anthropogenically-
induced changes in atmospheric greenhouse gas and aerosol concentrations, then unless
some other explanation is forthcoming (e.g. land use change) such a finding can be used
as evidence for a human cause of observed regional impacts.
Spatial analysis. This method divides the globe into a regular grid, determines the
direction of impacts on natural systems in each grid box that would be expected assuming
natural variability alone (based on climate model information), and then compares this
null hypothesis with the direction of impacts actually observed. Significant departures
from the null hypothesis (calculated using a chi-squared test) would indicate that natural
variability alone cannot explain the pattern of observed impacts. Combining such results
with the attribution of observed climate trends to anthropogenic causes at both global and
continental scales (e.g. IPCC, 2007) would hence provide strong evidence for a significant
anthropogenic impact.
4.3 Modelling of biophysical impacts
A large proportion of climate change impact assessments make use of predictive models
describing causal relationships between climate and an exposure unit. It would probably be
reasonable to assert that for any exposure unit that is valued to society and known to be
sensitive to climate, there is likely to be at least one model available that has been developed
to describe this sensitivity. Models vary enormously in their complexity, in the spatial and
temporal scale of their application, in their data requirements, and in their applicability for
addressing different questions. Some models have built-in assumptions about autonomous
adaptation (i.e. human adjustments in the management of systems that could be expected to
occur in response to a changing climate), while others do not.
It has been common, especially following the sequential top-down approach (cf. section
2.2.3), to discriminate between models that represent the direct physical or biological
responses of systems to climate, sometimes referred to as first-order or biophysical models,
and models that estimate the socio-economic implications of such biophysical impacts,
namely higher-order or socio-economic models. Since these are usually quite different types
of models, socio-economic models are treated separately in the following section.
Biophysical impact models range in complexity from simple monotonic relationships
established between a single climate variable and a single type of response (e.g. between
mean daily temperature and excess mortality – Keatinge et al., 2000) through to complex
21
simulation models where developers have attempted to incorporate all of the processes
thought to be of importance in determining system responses (examples include dynamic
vegetation models or basin-scale hydrological models).
All biophysical models rely at some scale of analysis on empirical relationships between
driving variables and system responses, but the level of empiricism varies enormously. In
process-based models many of the equations describing physical or biological processes are
well-established theoretically and have been verified empirically (e.g. photosynthetic
processes in plants or water flow in soils). Other processes may be less well established, and
are subject to greater uncertainty. Taken together, the description of interacting processes
allows for a deeper understanding of the behaviour of different components of a complex
system and hence a better appreciation of the reasons for a given response of a system.
However, such models tend to be very demanding of data, expertise and time for model
testing and application, which may limit their use in different regions. Model estimates of
future impacts increasingly rely not only on climate projections, but also on scenarios of other
conditions that either affect impacts directly (e.g. changes in atmospheric composition or sea
level) or precondition sensitivity to impacts (e.g. population, income, land use and land cover
change, technology). Increasingly, process-based models with a potentially wide application
are being packaged in user-friendly decision support systems (DSS), where users are able to
tailor the impact model to the needs of their own assessment, being provided with detailed
guidance on data collection and procedures for model calibration and testing, as well as
advice and built-in graphical and statistical tools for the analysis and interpretation of model
outputs.
In contrast, at the other end of the spectrum are simple empirical-statistical models that are
based on a statistical association between the overall response of an exposure unit and a set of
climatic predictors, without consideration of the intermediate process that might have
produced a given response. Here, statistical associations are sought between responses to
climatic variations observed over long time periods or across geographical or altitudinal
climatic gradients. Impacts of future climate change are estimated by applying the same
statistical relationships observed in the past and assuming they can be extrapolated to future
conditions represented using climate scenarios. The advantages of such models include
minimal data requirements (usually only observations and scenarios of readily accessible
climate variables) and speed of application. However, there can be major pitfalls in relying on
extrapolation of statistical relationships to represent responses under future conditions6.
In order to provide an overview of the types of biophysical impact models commonly applied
in CCIAV assessments, it is helpful to classify models thematically, by sector. Table 4 lists
some model types along with examples of their application in climate change impact
assessments. They are organised according to the following nine sectors or focal areas:
agriculture, biodiversity and natural ecosystems, coastal zones, cryosphere, energy
production, forestry, human health, tourism and water resources and hydrology.
6 To illustrate the problems of extrapolation, consider an example of the effects of climate warming on wheat
yield in central Europe. Simple regression of wheat yield and temperature might reveal a negative association
between wheat yield and temperature (decreased yields in warmer years and higher yields in cooler years).
Applying such a statistical relationship with scenarios of future warming would hence predict reduced crop
yields. However, use of a process-based model that incorporated not only the negative effects of increased
temperature on yield, but also positive effects of future CO2-fertilization as well as effects of changes in soil
moisture might produce yield responses that are quite different for a scenario with the same warming but also
increased CO2 concentration and precipitation changes.
22
Table 4. Some examples of biophysical impact models applied in sectoral or thematic climate change
assessments in Europe.
Biophysical impact models Examples of assessments (brief description and reference)
Agriculture Crop suitability ISTO programme: National crop suitability maps for Finland
(Peltonen-Sainio et al., 2009)
Yield forecasting PRUDENCE project: Statistical wheat yield emulator of DAISY
model (e.g. Olesen et al., 2007)
Crop yield simulation COST 734 project: Wheat/barley model intercomparison study for
Europe (e.g. Palosuo et al., 2011)
Pest and disease risk Insect pests in the UK (Cannon, 1998)
Biodiversity and natural ecosystems Climate envelope/niche-based ALARM project: Plant species distibutions in Europe (Thuiller et
al., 2005)
Earth system (carbon cycle) A-TEAM project: Terrestrial carbon storage in Europe (Zaehle et
al., 2007)
Coastal zones Coastal erosion Coastal cliff erosion in the eastern England (Dickson et al., 2007 )
Fisheries Population dynamics of North Sea cod (Clark et al., 2003)
Flood simulation Implications of sea-level rise for Ireland (Devoy, 2008)
Storm surge simulation Projected North Sea storm surge extremes (Woth et al., 2005)
Cryosphere Dynamical sea ice Baltic sea ice distribution (Meier et al., 2004)
Statistical sea ice Baltic sea ice extent (Jylhä et al. 2008)
Permafrost presence/absence PALSALARM project: palsa distribution in Fennoscandia (Fronzek
et al. 2010)
Energy production & consumption Heating and cooling demand Energy demand in the Mediterranean (Giannakopoulos et al., 2009)
Hydro-power potential Impacts on hydro-power potential in Europe (Lehner et al., 2005)
Forestry (Medlyn et al., 2011) Process-based stand Forest growth impacts in the Mediterranean region (Sabate et al.,
2002)
Terrestrial biogeochemical Comparison of four model estimates of impacts on carbon balance
of terrestrial biosphere (McGuire et al., 2001)
Hybrid Regionally optimised forest management (Nuutinen et al., 2006)
Carbon accounting European forest resources (Eggers et al., 2008)
Gap Impacts on forests in Switzerland (Fuhrer et al., 2006)
Dynamic global vegetation Terrestrial biosphere carbon storage (Schaphoff et al., 2006)
Human health Spatio-correlative Tick-borne encephalitis range in Europe (Randolph and Rogers,
2000)
Dose-response PESETA project: heat and cold excess mortality in Europe (Watkiss
et al., 2009)
Tourism Snow depth and duration PRUDENCE project: snow duration and liquid water content in
European regions (Jylhä et al., 2008)
Human comfort PESETA project: tourism climate index (Amelung and Moreno,
2009)
Water resources and hydrology Hydrological PRUDENCE project: Modelled hydrological responses in northern
Sweden (Graham et al., 2007)
Water availability Flood and drought risk in Europe (Lehner et al., 2006)
Nitrogen leaching Nitrogen leaching in Finnish river basins (Rankinen et al., 2009)
An exhaustive listing and classification of model types is beyond the scope of this review and
the list in Table 4 is designed to be illustrative. Compilation of a more comprehensive list
23
awaits the populating of a common platform with methods and models applied in European
case studies.
4.4 Modelling of socio-economic impacts
Higher-order effects of climate change on human society are most commonly expressed in
terms of economic cost, though other metrics may also be employed (e.g. numbers of persons
affected or at risk – Parry et al., 1998). In a recent review of economic assessments of
adaptation costs in Europe for the ClimateCost project, Watkiss and Hunt (2010) observe that
the boundary between assessment of impacts (damage) and adaptation costs is drawn
differently depending on the study authors. They also identify a number of variations in
approaches to assessment, including whether:
future socio-economic change is adequately accounted for in cost estimates for future
impacts (cf. section 3.2);
climate changes are sufficiently distinguished from present-day climate variability
and the so-called "current adaptation deficit", which relates to the (in)effectiveness of
current adaptation to account for ongoing climate variability;
costs of climate change should be weighed against possible benefits and reported as
"net costs" (e.g. where increased energy costs of summer cooling are assessed
alongside reduced costs of heating).
Some of the main methods of assessment of economic costs, examples of their application
(expanded in this review) along with their advantages and other issues are summarised in
Table 5. In addition, Watkiss and Hunt (2010) identify four decision support tools that are
commonly used in quantitative assessments: cost-benefit analysis, cost-effectiveness analysis,
multi-criteria analysis, and pathway/real options assessment. These are described in more
detail in a related review for the MEDIATION project (Zhu and van Ierland, 2010).
24
Table 5. Methodological frameworks and models for economic assessment of climate change and
adaptation. Source: Modified from Watkiss and Hunt (2010).
Approach Description
Examples Advantages Issues
Economic
Integrated
Assessment
Models
(IAM)
Aggregated economic
models. Values in future
periods, expressed £ and
%GDP and values over
time (PVs)
Global studies (e.g.
de Bruin et al.,
2009a) that provide
outputs for Europe.
Provide headline values
for raising awareness.
Very flexible – wide range
of potential outputs (future
years, PV, CBA).
Aggregated and low
representation of impacts,
generally exclude extreme
events and do not capture
adaptation in any realistic
form. Not suitable for detailed
national planning.
Investment
and Financial
Flows (I&FF)
Financial analysis. Costs
of adaptation (increase
against future baseline)
Global studies (e.g.
UNFCCC, 2007;
Parry et al., 2009).
National studies, e.g.
Sweden (SOU,
2007) uses I&FF
type approach.
Costs of adaptation in
short-term policy time-
scale. Easier to apply even
without detailed analysis
of climate change.
No specific linkage with
climate change or adaptation
(though can be included). No
analysis of adaptation benefits
or residual impacts.
Computable
General
Equilibrium
models (GCE)
Multi-sectoral economic
analysis.
National level –
Germany (Kemfert,
2007); EU review
(Osberghaus and
Reif, 2010).
Capture cross-sectoral
linkages in economy wide
models (not in other
approaches).
Can represent global and
trade effects.
Aggregated representation of
impacts and only capture
adaptation in market form.
Issues with projections of
sectoral inkages. Omits non-
market effects. Not suitable
for detailed national planning.
Impact
assessment
(scenario
based
assessment)
Physical effects and
economic costs of CC with
sectoral models in future
periods, and costs and
benefits of adaptation or in
cost-effectiveness analysis
Multi-sectoral
PESETA study
(Ciscar et al., 2009).
National scale:
Flooding in the UK
(Thorne et al., 2007)
and Finland (Perrels
et al., 2010)
More sector specific
analysis. Provides physical
impacts as well as
economic values –
therefore can capture gaps
and non-market sectors.
Not able to represent cross-
sectoral, economy-wide
effects. Tends to treat
adaptation as a menu of hard
(technical) adaptation options.
Less relevant for short-term
policy.
Impact
assessment -
shocks
Use of historic damage
loss relationships
(statistics and
econometrics) applied to
future projections of
shocks combined with
adaptation costs (and
sometimes benefits)
Sector level, e.g.
EAC study (NAO,
2009) in the UK and
FINADAPT study in
Finland (Perrels et
al., 2005)
Allow consideration of
future climate variability
(in addition to future
trends)
Issues of applying historical
relationships to the future.
Issues with high uncertainty in
predicting future extremes.
Impact
assessment -
econometric
based
Relationships between
economic production and
climate parameters derived
with econometric analysis
and applied to future
scenarios – and to consider
adaptation. Ricardian
analysis relates regional
land prices to climate and
other factors.
National level
Household level or
sector.
Ricardian analysis
has been applied in
agriculture (e.g.
Lippert et al., 2009)
Can provide information
on overall economic
growth and allow analysis
of longer-term effects.
Provide greater
sophistication with level of
detail.
Mostly focused on
autonomous or non-specified
adaptation. Very simplistic
relationships to represent
complex parameters. No
information on specific
attributes. Issues on whether
relationships are applicable to
future time periods.
Risk
management
Current and future risks to
climate variability.
Probabilistic approach.
Flood risk studies
(coastal and river).
Well suited for current and
future risks and
uncertainty, Often used
with Cost-effectiveness.
Has been applied in
adaptive management and
iterative analysis.
Extra dimension of complexity
associated with probabilistic
approach. Limited
applicability: focused on
thresholds (e.g. risk of
flooding).
Adaptation
assessments
Risks over a range of
policy / planning horizons.
Often linked risk
management and adaptive
capacity.
No real economic
examples. Emerging
number of
adaptation
assessments.
Stronger focus on
immediate adaptation
policy needs and decision
making under uncertainty
and greater consideration
of diversity of adaptation
(including soft options)
and adaptive capacity.
Less explored in relation to
economic assessment
25
5 Adaptation assessment
With the approval at COP-11 in 2005 of the five-year Nairobi work programme (NWP) on
impacts, vulnerability, and adaptation to climate change, there has been an increased demand
by governments and international agencies for practical guidance on methods of adaptation
assessment and the development of appropriate analytical tools. The United Nations
Development Programme (UNDP) published its Adaptation Policy Framework (APF) which,
like the IPCC Guidelines, is organised as a series of steps (Figure 6). The APF process
proceeds upwards from the bottom of the diagram, beginning with a scoping phase and
moving through an assessment of current vulnerability, future climate risks and on to the
formulation of an adaptation strategy and its review, monitoring and evaluation. Some of
these steps are covered in earlier sections of this report; we focus here on the analysis of
adaptation.
Figure 6. Outline of the UNDP Adaptation Policy Framework process (UNDP, 2005).
Approaches to the assessment of adaptation options to serve decision-making can be broadly
categorised according to two main steps of analysis (Willows and Connell, 2003 Uncertainty;
Niang-Diop and Bosch, 2005): (i) the identification of adaptation measures and (ii) the
evaluation of adaptation options. Each step embraces a multitude of methods and tools and
potentially draws upon a wide diversity of research disciplines.
5.1 Identification of adaptation measures
There is surprisingly little systematic treatment in the scientific literature of methods for
identifying alternative measures of adapting to climate change. While this analytical step is
included as an integral part of most adaptation methodologies, the comprehensiveness of its
26
treatment is frequently stymied by the nature of the assessment framework in which it is
addressed.
Top-down CCIAV assessments conventionally estimate potential impacts for a range of
climate change scenarios (often using quantitative models) and then consider possible
adaptation responses that might ameliorate or exploit such impacts. In some cases, the
effectiveness of different adaptation responses might also be modelled. The identification of
adaptive measures may or may not include input from key stakeholders, depending on the
target audience for the results of the study, but assessments rarely seek a comprehensive
survey of available adaptation options, and seldom relate these measures – designed for
addressing long-term climate change – to their suitability for dealing with present-day climate
variability.
In contrast, bottom-up assessments are commonly framed around the adaptive measures
already in place to cope with climate variability, which may be inventoried and analysed in
conjunction with key stakeholders in a given sector or region. Explicit treatment of adaptation
to climate change is often only considered in the context of coping with known climate
hazards, so may fail to address features of a changed climate that are well beyond local
experience.
A more comprehensive blueprint for the identification of adaptation options would seem to
demand some kind of hybrid approach that acknowledges both the need for effective
measures to cope with present-day climate variability, but also the requirement for adaptive
management to account for longer-term shifts in the climate resource base.
For example, the World Resources Institute's Pilot National Adaptive Capacity (NAC)
Framework (WRI, 2009) draws attention to a number of distinguishing features of alternative
adaptation options that might assist in organising and compiling a checklist:
"Hard" engineering options (e.g. construction of sea walls, dykes or levees) versus
"soft" options that shift incentives and reduce barriers to action (e.g. zoning
regulations, permits, taxes, land tenure rights or insurance premiums)
Options that entail changing practices in the areas of infrastructure, natural resources
management or social protection
Options originating from existing adaptation and/or risk reduction projects, including
indigenous knowledge and ecological management techniques
Adaptation measures are organised somewhat differently in Finland's National Strategy for
adaptation to climate change (Marttila et al., 2005), first distinguishing public from private
measures, and then sub-dividing these into the following categories (by sector):
Administration and planning
Research and information
Economic-technical measures
Normative framework (mainly covering legislative measures)
Measures falling in each of these categories are further classified as being either anticipatory
or reactive.
27
The UKCIP guidance note on identifying adaptation options distinguishes four targets for
measures and strategies that contribute either to building adaptive capacity or delivering
adaptation actions (UKCIP, 2008):
1. Accepting the impacts and bearing losses, which reflects a conscious decision that no
action is needed to address foreseeable climate hazards, either because the hazards
themselves represent a small or acceptable risk with existing measures, or because the
exposure unit is not judged worth sustaining and alternatives will need to be considered.
2. Preventing effects or reducing risks, which involves the introduction of new measures
designed to reduce exposure of assets to new or heightened risks. Such an approach pre-
supposes that the exposure unit is of sufficient value to warrant some degree of protection.
3. Offsetting losses by spreading or sharing risks or losses, which implies using insurance or
establishing partnerships or co-operatives to reduce financial or social losses and
minimise expsoure to risks.
4. Exploiting positive opportunities, which might involve the introduction of new activities
or behaviour to take advantage of reduced climate risks or a move to a new location to
exploit favourable climate shifts.
These alternative strategies are mapped against different evaluation criteria in Figure 7, noting
that in practice a mixture of strategies is commonly adopted, the precise combination
determined by the specific case in question.
Figure 7. Framework for identifying adaptation options (Source: UKCIP, 2008)
The pivotal role of dialogue between researchers and stakeholders becomes apparent when
considering the formal analytical methods commonly applied for identifying potential
28
adaptation options. These include (Willows and Connell, 2003): brainstorming, consultation
exercises, focus groups, analysis of interconnected decision areas (AIDA), problem mapping
tools, checklists, screening, free-form gaming, and policy exercises.
5.2 Evaluation of adaptation options
Once a set of adaptation options has been identified, the next logical step of analysis is to
evaluate these options as a basis for guiding decisions on the eventual selection and
implementation of adaptation measures. The World Resources Institute and World Bank
guidance documents list a number of evaluation criteria for assessing the suitability of an
adaptation option for contributing to a stated objective (WRI, 2009 World Bank, 2009,
Identifying appropriate):
Cost analysis, including total costs and cost effectiveness
Environmental implications
Secondary or cross-sectoral impacts, externalities or co-benefits
Social implications, including implications for women and marginalized groups
Short-, medium-, and long-term efficacy
Effectiveness at reducing impacts of extreme events (e.g. floods, droughts, strong winds)
Effectiveness under different scenarios of future climate
Limiting factors for implementation or sustainability (e,g, resource constraints)
Consultation with a broad set of stakeholders
Provision for reviewing options based on changing assessments of risk (referred to as
flexible or adaptive management in UKCIP, 2008)
Transparency in the process and justification of options selection
One approach to the classification of adaptation options is to rank them into "levels of regret",
which alludes to the consequences of making a wrong decision, which can vary according to
the degree of complexity, cost and risks. Four categories emerge from sources applying this
classification (Niang-Diop and Bosch, 2005; UKCIP, 2008; World Bank, 2009): win-win, no-
regret, low-regret, and high-regret adaptation options.
Win-win options not only fulfil the desired goals relating to reducing risks or exploiting
opportunities under a changing climate, but also result in other environmental, social or
economic benefits. They may or may not have been introduced with the primary purpose
of reducing climate risks. Examples include modifying buildings to reduce extremes of
temperature, making them both more habitable as well as less energy intensive, or
replacement of spring sown with winter sown agricultural crops, which are heavier
yielding in a warmer climate, provide prolonged ground cover to reduce nitrogen leaching
and soil erosion, and may also enhance soil carbon retention.
No-regret options have socio-economic benefits that exceed their costs regardless of the
course of future climate. These measures would be expected to deliver immediate benefits
under present-day climatic variability as well as addressing risks associated with projected
changes in climate. Although they might require investment, any costs would be more
than outweighed by the benefits to be gained from reduced climate risks. Typical
examples might include avoidance of building in high risk areas such as flood plains,
altered crop management to conserve water or enhanced surveillance of vulnerable people
during heatwaves.
29
Low- or limited-regret options incur fairly low costs but may realise relatively large
benefits under projected future climate change with only a small risk of a negative
outcome. For instance, development of drought-resistant crops in anticipation of an
increased risk of future drought would offer benefits in drought situations regardless of
climate change, which could be greatly enhanced under a drying climate. The only
grounds for possible regret would be a reduction in drought frequency. Others examples
might be the designation of agricultural land in flood plains as back-up storage areas to
accommodate water during flood events, or the design of structures such as bridges to
allow for periodic modifications that account for sea level rise or increasing peak river
discharges.
High-regret options usually involve large-scale planning (perhaps requiring resettlement
of communities), or investment decisions with high irreversibility (e.g. major, long-lived
investments such as the construction of dams, large reservoirs or coastal defences along
low-lying coasts). These are options that demand careful scrutiny of the uncertainties in
climate projections as well as the potential consequences of either over- or under-
estimating the extent of changing future climate risks.
These are shown schematically for an agricultural example in Figure 8.
Figure 8. Considerations of uncertainty and potential levels of regret for adaptation investments in
agriculture. Source: modified from World Bank (2009).
A raft of tools and techniques for evaluating and prioritising adaptation options is presented
by Willows and Connell (2003 Uncertainty, and Decision-making) and reproduced in Table 6.
These are broadly classified into three types:
Multi-cropping
Win-win N-leaching?
Mixed farming and
livstock systems
Win-win biodiversity?
Meteorological /
seasonal climate
forecasts
Rotating or shifting
production of crops
and livestock
Conservation and
sustainable use of
natural resources
(e.g. land
conservation)
Win-win erosion?
Extension services
targeted to new
crops / water saving
technologies
Diversifying
community sources
of income
New water
harvesting
infrastructure
No-regret Low-regret High-regret
Low impact of
future uncertainty
High impact of
future uncertainty
30
1. Qualitative methods, employing a systematic qualitative analysis
2. Alternative methods, commonly employing a semi-quantitative analysis in order to
compare different attributes or parameters, and
3. Quantitative and economics-based methods, usually employing a fully quantitative
analysis of risks, costs and benefits.
Table 6. Tools and techniques for the appraisal of adaptation options. C indicates the complexity of the
tool and D the data requirements (L = low; M = medium; H = high). Source: Willows and Connell (2003
Uncertainty, and Decision-making).
Tool/technique Qualitative Alternative Quantitative C D
Consultation Exercises X M M
Focus Groups X M M
Ranking/Dominance Analysis X L M
Screening X L M
Scenario Analysis X X X M M
Cross-Impact Analysis X M M
Pairwise Comparison X L M
Sieve Mapping X H H
Maximax, Maximin, Minimax, Regret X M M
Expected Value X M H
Cost-Effectiveness Analysis X L M
Cost-Benefit Analysis X H H
Decision Analysis X H H
Bayesian Methods X H H
Decision Conferencing X H H
Discounting X L H
Environmental Impact Assessment/ Strategic Environmental Assessment
X H H
Multi-Criteria Analysis (Scoring and Weighting)
X M M
Risk-Risk Analysis X M M
Contingent Valuation
• Revealed performance
• Stated performance
X
X
X
H
H
H
H
Fixed Rule-based Fuzzy Logic X X X H M
Financial Analysis X M M
Partial Cost-Benefit Analysis X X H M
Preference Scales X M L
Free-form Gaming X M M
Policy Exercise X M M
31
However, other classifications can also be used. For example, a Dutch review of methods and
tools for evaluating climate change adaptation options in water management offers a threefold
classification: (i) methods based on (social) cost benefit analyses (CBA), (ii) tools focusing on
optimization, and (iii) impact-focused models (Ludwig and Swart, 2010).
5.3 Mapping adaptation research
Research is an essential ingredient for informing the identification of adaptation options, and
recent years have witnessed a burgeoning of adaptation-orientated research in Europe. In
order to gain an impression of the contemporary focus of adaptation research, major themes
and example studies have been sampled from abstracts accepted for two large internatinal
conferences held in 2010 (Table 7). These are divided here between research supporting the
formulation of adaptation strategies (research for adaptation) and research investigating the
process of adaptation (research on adaptation), though the distinctions are not always clearcut.
This qualitative survey, though far from systematic, does serve to illustrate the diversity of
contemporary climate change adaptation research, including some of the potential focal areas
of attention in assessments designed to support policy.
5.4 Adaptation problem types
As mentioned earlier, the twofold classification of approaches to adaptation assessment
described in sections 5.1 and 5.2 is targeted at decision makers looking to the practical
implementation of adaptation. However, not all adaptation research is directly applicable to
practice, yet it is still important to represent this research in a general typology. The
distinction made in section 5.3 between research on adaptation and research for adaptation
offers one inclusive approach, but this typology is too crude to be useful for identifying
specific assessment methods and tools. An alternative classification procedure looks to
identify adaptation "problem types" (Hinkel, 2010). The classification distinguishes three
problem types, from the perspective of the actors involved in the assessment of adaptation:
single perspective problems, facilitation problems and collective action problems.
Single perspective problems address adaptation issues at different levels of awareness and
detail from the point of view of the individual actor (similar to the phases of adaptation
categorised in sections 5.1 and 5.2), focusing either on determination of vulnerability,
identification of possible actions to reduce vulnerability or appraisal of the most effective
choices to reduce vulnerability.
Facilitation problems involve the actions of a facilitator, either interacting with a vulnerable
actor directly or influencing that actor's institutional environment. Direct interaction may
address the same problems discussed for single perspective problems, but offering third party
assistance, either through awareness raising, communication of adaptation options (sometimes
referred to as capacity building), or communication of effective choices, where expert advice
can assist actors in taking decisions. Institutional design involves a facilitator influencing the
institutional setting that an actor operates within, with a view to encouraging adaptive
behaviour (e.g. through incentives). Such institutional adjustment might serve an adaptation
objective even if it is not explicitly targeted at climate change.
32
Table 7. Summary of research themes and example studies from abstracts accepted at two large
international conferences on climate change adaptation held in 20107.
Research for adaptation Research on adaptation
Scenarios for adaptation planning (e.g. worst
case; surprises; lags; anticipatory planning;
extreme events; climate scenarios)
Technology (building regulations; climate
proofing; social media and technology
innovation; engineering solutions; improved
weather forecasting)
Analysis of adaptation strategies (e.g. ecosystem
approaches; conservation planning; reserve
networks; urban; tourism development;
relocation suitability; identifying regions or
communities at risk; local climate impacts
profiles; indicators; metropolitan area strategies)
Adaptive capacity (species distribution; coastal
urban areas; soft infrastructure; social networks;
social resilience; diversity and adaptability;
mapping of indicators; electricity sector; water
supply and wastewater; gender perspectives)
Integrated assessment modelling (impacts and
adaptation)
Mainstreaming (e.g. development plans; spatial
planning; extreme weather lessons for civil
protection; national forestry planning; strategic
planning)
Adaptation costs (economic assessment; C/B in
a portfolio decision framework; equity in
financing; climate resilient urban infrastructure;
politics of local adaptation)
Support for adaptation (e.g. institutional support
to decision making; good practice guidance;
science, policy and private sector integration;
climate change portals; GIS methods; toolbox
for adaptation; role of scientific knowledge)
Tools for adaptation (e.g. raising awareness on
adaptation needs; developing policy for
implementing strategies; operationalising
adaptive management strategies)
Scenarios of adaptation (e.g. agent-based
models; transition dynamics)
Perception of climate risks (e.g.
communication; opinions; attitudes; atlas;
shared learning; traditional knowledge;
media studies; local risk perceptions)
Institutions (e.g. legislation; regulations;
regional governance; legal responses;
disaster planning; participatory
approaches; governance of adaptation;
insurance sector)
Limits and opportunities (coastal islands;
Bayesian analysis of barriers to adaptive
capacity; effectiveness of regional
adaptation)
Analysis of the process of adaptation (e.g.
sustainable livelihoods and community
adaptation; comparative studies of
adaptation processes)
Analysis of adaptation strategies (e.g.
community-based; disaster and
evacuation planning; sustainable
livelihoods and community adaptation;
actual adaptation versus adaptive
capacity; reactive and proactive
adaptation)
Implementation of adaptation (e.g.
maladaptation; monitoring; indicators;
observed adaptation; migration; process
case studies; timing of adaptation)
Social justice (e.g. equity in adaptation
financing; indigenous peoples;
empowering community response)
7 Climate Adaptation Futures: 2010 International Climate Change Adaptation Conference, 29 June - 1 July
2010, Gold Coast, Australia (http://www.nccarf.edu.au/conference2010/)
Climate Adaptation in the Nordic Countries: Science, Practice, Policy, 8-10 November 2010, Stockholm,
Sweden ( http://www.nordicadaptation2010.net/)
33
Collective action problems address situations in which the effectiveness of individual
adaptation actions depends on the actions of others. Where there is a collective action that is
likely to be optimal for all actors, a solution can best be reached through co-ordination.
However, in situations where no collective action can be identified that is optimal for all
parties, but where all actors can still benefit through some collective action, solutions need to
sought through negotiation.
At a larger scale, collective action problems might also be regarded as a subset of a possible
fourth adaptation problem type: governance problems. The concept of problem types remains
to be fleshed out in more detail, and is still under development as part of another Work
Package of the MEDIATION project (Hinkel, 2010).
34
6 Integrated assessment
The fourth major approach used in CCIAV analysis is integrated assessment, which attempts
to represent complex interactions across spatial and temporal scales, processes and activities.
It may involve the use of one or more mathematical models, of varied complexity – either
multiple models applied separately but with outputs combined in various ways, or individual,
integrated asessment models (IAMs) that seek to represent the main driving factors, sectors,
processes and feedbacks of interest in a given interactive system. On the other hand, it may
also represent an integrated process of assessment, linking different disciplines and groups of
people, often using qualitative or semi-quantitative information (Carter et al., 2007). Here we
distinguish three different classes of analysis, involving: (i) integrated assessment models, (ii)
model-based integrated analysis and (iii) qualitative integrated assessment.
6.1 Integrated assessment models (IAMs)
IAMs have been applied to study anthropogenic climate change for over three decades, and
vary enormously in their purpose, disciplinary coverage, scale of application, and level of
process complexity. They represent key features of human systems, such as demography,
energy use, technology, economic conditions, agriculture, forestry and land use as well as
simplified representations of the climate system, ecosystems and, in some cases, climate
impacts (Moss et al., 2010). Their breadth of coverage facilitates the integration of
information needed to study interactions in human systems and the processes that affect
climate change and its impacts. They can also be used to examine the implications of
alternative climate policies. IAMs typically divide the world into a large world regions and
undertake simulations with time steps of about a decade. They have been used to develop
emissions scenarios, estimate the potential economic impacts of climate change and the costs
and benefits of mitigation, simulate feedbacks, and evaluate uncertainties. Here, we focus on
their contribution to CCIAV assessment by first, discussing how they treat impacts of climate
change, second considering how they address adaptation, and finally identifying some critical
weaknesses that require urgent attention.
6.1.1 Types of IAMs and their representation of impacts
Until recently, IAMs were regarded primarily as policy-orientated tools designed to integrate
the causes, impacts, feedbacks and policy implications of climate change within a modelling
framework. Essentially, this required bringing together expertise and the latest models from
different disciplines, simplifying these within a common architecture, programming the
linkages and interactions between them, and comparing the outputs both with historical real
world data as well as with detailed offline models of the individual system components. Two
main approaches to the construction of IAMs have been prevalent until recently (IPCC, 1994;
Goodess et al., 2003):
1. An aggregate cost-benefit approach, which used macro-economic methods and tools to
determine future paths of supply and demand in commodities that can affect greenhouse
gas emissions, on the basis of projected population and economic development. The
models use price to determine the relative competitiveness of different energy
technologies. While originally developed to address costs of mitigation, they increasingly
also treat the cost of climate impacts, usually through sectoral damage functions that relate
global damage to global mean temperature change. Examples of such models include
DICE (Nordhaus, 1992), MERGE (Manne and Richels, 2001), MiniCAM (Edmonds et
al., 1997; Smith and Wigley, 2006), MESSAGE (Rao and Riahi, 2006), PAGE (Hope,
35
2006) and FUND (Tol, 2002; Tol, 2008 development), with the last of these offering the
most explicit representation of a range of impacts.
2. A regionalised process-based approach, which attempted to model the sequence of cause-
effect processes originating from scenarios of future greenhouse gas emissions, through
atmospheric composition, radiative forcing, global temperature change, regional climate
change, regional impacts and feedbacks from impacts to each of the other components
(e.g. through the carbon and hydrological cycles). In contrast to the aggregate cost-benefit
approach the estimates of the biophysical impacts in these models are quantitative and
regionally explicit. The models emerged from the applied natural sciences, and the
economic and global trade components of such models were commonly integrated at a
later stage. Examples include simple energy balance models (EBMs) such as MAGICC
(Wigley, 1994) and Earth System Models of Intermediate Complexity (EMICs), which
link climate models at lower resolution than GCMs with detailed economic models (e.g.
MIT IGSM – Sokolov et al., 2005 and IMAGE – ).
As early as 1994, it was noted that the two approaches were already rapidly converging,
presumably an acknowledgement of the need for a more comprehensive, explicit and balanced
treatment of the major processes influencing human-environmental interactions (IPCC, 1994).
Moreover, in recent years, the distinction between science- and policy-orientated models has
become more blurred, as science-based global climate models have evolved from atmosphere-
only general circulation models (GCMs) towards fully coupled Earth System Models (ESMs),
incorporating detailed representations of atmospheric, oceanic, cryospheric and land surface
processes and interactions, including some aspects of hydrological, global vegetation and
carbon cycle impacts and feedbacks (e.g. see Friedlingstein et al., 2006; Betts et al., 2007;
Somerville et al., 2007). Similarly, economic models vary in nature – some optimise over the
full future time period of a simulation while others calculate general equilibrium solutions for
a given time period; most models are deterministic, but some include stochastic processes
(Sarofim and Reilly, 2011).
The overlap area between IAMs, climate models and impact and adaptation models is
expanding as models become ever more comprehensive (Figure 9). However, the price of
increased complexity is often model transparency. Acknowledging this problem, Risbey et al.
(1996) proceed to list a number of discipline and process based considerations that IA
practitioners should focus on to ensure the credibility of IAMs for supporting policy. These
include:
Clarity about the goals of their research and their confidence in the suitability of the
models for use as explanatory and forecasting tools;
The limitations of the tools and subcomponents in IA models, and the appropriateness of
applying tools developed in particular disciplinary, geographical, or temporal contexts in
the broader contexts inherent in IA studies
Potential bias in the selection of tools and concepts towards those which are most easily
quantified in a model. The corollary is that it is also important to pursue non-model IA
approaches, free from model-induced biases.
The need for evaluation of IA models to establish their credibility. This requires designing
experiments and gathering data to compare purported IA model insights with analogous
processes in the real world. It can also include open discussions of modelling problems.
Diversity in funding pattems and sources, disciplinary composition and study regions,
which may help avoid stagnation in the ideas and directions emerging from IA.
36
Figure 9. Schematic representation of the domains occupied by climate modelling, analysis of impacts,
adaptation and vulnerability and integrated assessment modelling. Source: (Moss et al., 2010).
6.1.2 Treatment of adaptation in IAMs
Of particular relevance to CCIAV assessment has been a recent shift in emphasis of some
IAM applications towards the treatment of climate change adaptation (Patt et al., 2010).
Following the publication of the Stern Review (Stern, 2007), with its estimates of potential
damage (expressed in terms of the "social cost of carbon") that exceeded those in most
previous studies (US$310 per tonne of carbon compared to a range of US$-10 to US$+350 in
the literature assessed by the IPCC in Yohe et al., 2007), there has been renewed scrutiny of
how impacts and adaptation are actually handled by IAMs. Moreover, there is a much greater
policy imperative to sharpen the cost estimates, as financial support for adaptation in
developing countries under the UNFCCC has risen up the international policy agenda.
Until recently, most IAMs calculated adaptation implicitly, as an optimal solution that
minimises climate damages (Patt et al., 2010) – see Box 2. One method of calculating
damages applied simple accounting methods (e.g. by estimating coastal property and
infrastructure losses due to sea level rise and the costs of coastal protection to avoid losses,
arriving at a solution that produced the lowest cost – Nicholls and Tol, 2006). Another
method was through "Ricardian analysis" (commonly applied to land use and agricultural
production), which related land values to patterns of present-day and historical climate (cross-
sectional analysis) and assumed that these relationships would also hold in the future,
adjusting land values according to the altered pattern of climate (see discussion by
Mendelsohn, 2007). Early models applied single damage functions globally, but following
criticism that these functions must vary in different regions of the world later model versions
adopted regional damage functions and then aggregated the regional results globally.
However, all models except PAGE (Hope, 2006) assume that adaptation is optimal.
By treating adaptation explicitly, as a control variable (namely, a binary choice between no
adaptation or aggressive adaptation), the PAGE model yielded a result that aggressive
adaptation could decrease initial climate damages by as much as 90% for economic impacts in
OECD countries, 50% for economic impacts in the rest of the world, and 25% of the non-
37
economic impacts (Patt et al., 2010). This was criticised as being overly optimistic and
inconsistent with empirical observations (de Bruin, 2009). The same set of authors have
developed a new version of the DICE model, AD-DICE, which specifies sectoral damages as
quadratic functions of global mean temperature rise, making use of selected impact studies
that assumed a cost-minimising mix of adaptation and residual damage costs (cf. Figure 11a,
Box 2).
***************************************************************************
Box 2: The costs of adaptation (based on Stern, 2007; Patt et al., 2010)
Adverse impacts of climate change are conventionally treated quantitatively by economists as costs of climate
change (Figure 10). Adaptation reduces these impacts, but there is nearly always some residual damage, which
might be very large. The gross benefit of adaptation is the damage avoided. The net benefit of adaptation is the
damage avoided minus the cost of adaptation. The cost of climate change after adaptation is the sum of the
residual cost of climate damage and the cost of adaptation.
Figure 10. The role of adaptation in reducing climate change damages. Note that linear relationships
between cost and temperature are used here only for illustrative purposes (indeed, there is reason to
believe that the relationships are non-linear). Source. Stern (2007).
As might be inferred intuitively, the cost of adaptation to small amounts of damage can be negligible or low, but
for each incremental increase in damage, the cost of additional adaptation rises, resulting in a non-linear
adaptation cost curve (e.g. de Bruin et al., 2009b). A similar curve is obtained when considering the costs of
reducing emissions. IAMs are eminently suitable tools for comparing these two types of costs, both directed
towards reducing climate change damages.
Figure 11a shows how an adaptation cost curve (green) can be related to a corresponding residual damage cost
curve (blue) assuming a given level of climate change. The sum of the two costs is the total impact cost curve
(red). The point where the total impact costs are at their lowest (vertical dashed line) is the optimal level of
adaptation.
Figure 11b illustrates how optimal adaptation affects the choice of mitigation targets. Moving from left to right
along the (blue-green) mitigation cost curve, increases mitigation costs but decreases warming. Total impacts fall
in a range bounded by two (red) adaptation cost curves, the lower curve optimal, the upper curve least optimal.
This narrows as mitigation increases and the level of climate change declines. Total climate costs (violet) are the
sum of mitigation costs and total impact costs. Optimal mitigation targets are delimited by the two dashed lines.
Optimal mitigation costs are minimised when adaptation levels are optimal (left hand line), and at their highest
when adaptation is least optimal (right hand line).
Gross benefit
of adaptation
Net benefit
of adaptation
Total cost of
climate change
after adaptation
Cost of climate
change without
adaptation
Cost of adaptation
+ residual climate
change damage
Residual
climate change
damage
Co
sts
of
clim
ate
ch
an
ge
Global mean temperature change
38
Figure 11. Identifying optimal levels of: (a) adaptation and (b) mitigation, using cost curves. Source: (Patt
et al., 2010). For explanation, see text.
***************************************************************************
Overall, results from both AD-DICE and PAGE imply that adaptation is an important policy
option for limiting the total costs of climate change. AD-DICE offers further insights,
suggesting that the majority of damage cost reduction is generated by adaptation before 2100,
and by mitigation after 2100 (Patt et al., 2010).
6.1.3 Weaknesses of IAMs and research needs
One of the fundamental weaknesses of the great majority of IAMs is their economic
assumptions concerning costs of impacts and adaptation. In most models adaptation is
assumed to occur as an optimal cost minimisation response to climate change. Unfortunately,
on the ground the reality of adaptation is quite different. Patt et al. (2010) offer six reasons to
question the current treatment of impacts and adaptation in IAMs.
1. Adaptation is mostly bottom-up, meaning that targets for adaptation are case-specific,
determined by private actors, local communities, regional and national governments, with
the benefits largely accruing at the local scale. This is in contrast to mitigation, where the
actors may be much the same, but the targets and benefits are global. The ability to adapt
optimally needs to be informed first by the spatial distribution of adaptation costs and
benefits, but even more importantly, by local "frictional costs" describing obstacles to
action, such as perceptions of risk, conflicting goals, attitudes to coping and loss and
institutional and behavioural inertia.
2. Adaptation does not occur in response to gradual changes in means, though most IAMs
compute damage as a function of global mean temperature. First, the ability to adapt can
be compromised by the rate of climate change. Some measures can be implemented
rapidly, but other more substantial interventions, such as major infrastructural projects,
spatial planning approaches or technological developments like plant breeding, can take
many decades to realise. Indeed, investments in such projects (often with highly uncertain
returns and sensitive to discounting) may be judged unfavourably by most private actors,
leaving the public sector to cover the adaptation deficit. Second, changes in mean climate
Adaptation level
Co
sts
None Full
Residual
damage costs
Adaptation costs
Total impact costs
Mitigation level
Co
sts
None Full
Total
impact
costs
Mitigation costs
Total
climate
costs
(a) (b)
39
are expected to be accompanied by shifts in climate variability and extreme climate
events, such as flooding, drought and severe storms. Many adaptations are made in
reaction to the impacts of such events, which reveal existing vulnerabilities. However,
private and public actors are known to have cognitive difficulties in interpreting
information about low probability, high consequence events, let alone including this
explicitly and effectively in "rational" decision-making.
3. Adaptation costs and benefits are scale dependent, involving a range of processes and
actors operating at local, regional and global scales. However, IAMs treat impacts and
responses as aggregate damage functions applicable globally or over large regions and
insulated from background economic behaviour. In reality, rational economic responses at
the local scale might not scale up to rational aggregate behaviour (e.g. increasing
production to improve profits might be a successful strategy among some individual
actors, but the same response by multiple actors over large regions could depress prices
through over-supply, hence reducing profits). Communities dependent on a single,
climate-sensitive sector might be disproportionately affected by climate change compared
to communities with diverse economies. Moreover, the motivations for implementing
adaptation are place-specific, depending on institutional, economic and polictical factors
as well as historical experiences of climate-related damages. These local factors are not
readily captured in aggregate measures of damage.
4. Non-market costs and benefits play a vital role, and these are difficult to represent in
current IAMs. For instance, local actors may have complex social, cultural or
psychological reasons for responding to climate risks in the way that they do, and without
in depth local research, these crucial motivating (or frictional) factors will not be
accounted for in models.
5. Information is seldom used optimally, although this is an implicit assumption in most
IAMs. In reality, decision-makers usually do not make maximum use of available
information, often because the information is not conveyed to them in a form that they can
readily appreciate and act upon.This points to the importance of stakeholder participation
in the communication of scientific information, to ensure that it is salient, credible and
legitimate. However, even the most progressive policy environments are unlikely to yield
the optimum responses assumed in IAMs.
6. Uncertainty is a defining feature of adaptation analysis, probably more so than for
mitigation because of the more distributed nature of adaptation decision-making and the
geographical heterogeneity of climate change risks. Although models such as PAGE are
starting to treat uncertainties using stochastic methods, these are still too spatially
aggregated to capture the regional differences in uncertainty that govern local adaptive
responses to changing climate risks and socio-economic development. As uncertainty
increases, so the argument for delaying action until improved information becomes
available may become more persuasive. "Prevarication" lag times also need to be
factored-in to IAMs.
To provide a basis for discussion regarding the levels of investment needed for adaptation,
there have been several recent assessments of the global costs of climate change impacts and
adaptation, mainly based on aggregate cost-benefit IAMs (e.g. World Bank, 2006; Oxfam,
2007; Stern, 2007; UNDP, 2007). The UNFCCC itself commissioned a set of studies
producing results for six sectors that are summarised in Table 8 (UNFCCC, 2007).
40
Table 8. UNFCCC estimate of additional annual investment need and financial flow needed by 2030 to
cover costs of adaptation to climate change (billion dollars per annum; 2005 values). After Parry et al.
(2009); source information: UNFCCC (2007). Note that annual costs of enhancing the global terrestrial
protected areas network of $12–22 billion were estimated by UNFCCC, but this omitted costs for marine
protected areas and adaptation in the wider landscape.
Sector
Global
cost
Developed
countries
Developing
countries
Agriculture, Forestry and
Fisheries
14 7 7
Water supply 11 2 9
Human health 5 Not estimated 5
Coastal zones 11 7 4
Infrastructure 8-130 6-88 2-41
Total (billion US$/annum) 49-171 22-105 27-66
A recent re-assessment of these studies concluded that previous estimates of the costs of
adaptation may have been too low (Parry et al., 2009; Fankhauser, 2010). In particular, the
UNFCCC study may have under-estimated the costs of adaptation up to 2030 by 2-3 times for
the six sectors considered (agriculture, forestry and fisheries, water supply, human health,
coastal zones, infrastructure and ecosystems); more if impacts on sectors not hitherto
considered are accounted for.
In conclusion, Patt et al. (2010) offer two compelling arguments for improving the
representation of adaptation in IAMs. First, as they analyse in their review, and as
exemplified by Parry et al. (2009), adaptation could be more difficult and costly than current
models suggest. Second, if the total climate damages are higher than have hitherto been
assumed in most existing models (e.g. as expressed in the range of estimates presented by
Yohe et al., 2007), then different adaptation strategies could influence mitigation targets to a
much greater extent than previously modelled.
6.2 Model-based integrated analysis
Another form of integrated assessment also makes use of quantitative models, but rather than
attempting to represent all processes within a single IAM, these are run independently and in
parallel, or are "soft-linked", with outputs of one model serving as inputs for another, in order
to explore relationships between components of an integrated system. Many multi-sector
impact assessments have been conducted using this type of model framework, often using a
common set of exogenous climate and socio-economic scenarios to ensure consistency and
promote synthesis across the modelling exercises.
Examples at different scales include a global study of climate change impacts on food
security, water stress, coastal flood risk and wetland loss, exposure to malaria risk and
terrestrial ecosystems (e.g. Arnell et al., 2004), an evaluation of climate change and
ecosystem services in Europe, including models of species biodiversity, water resources,
forest growth, terrestrial carbon cycling and land use change (Schröter et al., 2005), and
modelling of regional climate change impacts on agriculture, biodiversity, coastal zones and
water resources in East Anglia and north-west England in the REGIS project (Holman et al.,
2005a; Holman et al., 2005b).
41
A development of this approach, positioned at the boundary with IAMs proper, is the class of
models represented by the Community Integrated Assessment System (CIAS – Warren et al.,
2008). The CIAS seeks to address some of the challenges to IA modellers posed by Risbey et
al. (1996) by:
Connecting together alternative sets of component modules (Figure 12). Each connected
set of component modules is broadly equivalent to an IAM. It is flexible and multi-
modular to allow a range of policy questions to be addressed, thus facilitating iterative
interaction with stakeholders.
Operating a distributed model system deployed across a wide range of institutions in
different countries, which promotes greater diversity and comprehensiveness of modelling
components, drawing on a wide range of international expertise
Enabling models to communicate with each other regardless of operating system or
computer language.
Various combinations of the modules depicted in Figure 12 can be used to address different
policy questions (Warren et al., 2008).
Figure 12. Operational components of the Community Integrated Assessment System (CIAS) at the time
of reporting, with distributed contributors in parentheses (Warren et al., 2008). Note that the Tyndall
Centre is itself distributed among eight research institutions throughout the United Kingdom
6.3 Qualitative integrated assessment
A third alternative approach to integration, where quantitative analysis is not possible, applies
qualitative methods. An iconic example of such an analysis, based on expert elicitation for the
IPCC Third Assessment Report, is the so-called "burning embers" diagram. This colour codes
the levels of risk associated with global mean temperature rise in relation to five reasons for
concern (Smith et al., 2001) and updated (Smith et al., 2009). An interesting variant of this
approach was used to assess climate change risks for different sectors in Australia and New
Zealand (Hennessy et al., 2007), incorporating expert judgement not only of the projected
vulnerability to climate change but also of the current coping range as well as the potential for
42
future adaptation to ameliorate projected adverse impacts (Figure 13). There is no reason why
a similar type of assessment could not be undertaken for regions of Europe.
Figure 13. Vulnerability to climate change aggregated for key sectors in the Australia and New Zealand
region, allowing for current coping range and adaptive capacity. Right-hand panel shows relative coping
range, adaptive capacity and vulnerability. Left-hand panel shows global temperature change under a
variety of SRES and stabilisation scenarios. Source: Hennessy et al. (2007).
43
7 Operationalising CCIAV assessment methods
7.1 The UKCIP risk framework
The IPCC AR4 summary table introduced in section 2.2.5 (Table 1) includes categories that
can be mapped onto all of the approaches and methods discussed above. It also attempts to
distinguish some of the motivations, aim and objectives underpinning various CCIAV
assessments. However, the framing it is still a little too generic and "scientific" to be directly
useable as an organising device for categorising all of the methods and tools that might be
applied in assessments designed to inform decisions at different scales of governance in
Europe. For this, the diversity of research approaches encapsulated in the table needs to be
related to the practical needs of policy.
Another way of looking at CCIAV assessments is captured in a framework developed by the
UK Climate Impacts Programme (UKCIP) and already touched upon in earlier sections. The
UKCIP framework provides a broader support structure for decision-making on climate
adaptation throughout the policy cycle, thus including CCIAV assessments but going beyond
them to include additional elements relevant to decision-making (Willows and Connell,
2003). This framework is useful in conceptualising the science-policy interface and
identifying linkages between CCIAV research efforts and their utilisation by decision-makers
(Figure 14).
Figure 14. The UKCIP framework for climate change adaptation decision-making. Stages 3-5 correspond
with three stages commonly associated with assessments: problem analysis, identification of options and
evaluation of options. Source: Willows and Connell (2003).
CCIAV assessments can be conceptualised in three stages: problem analysis, identification of
options to address these problems, and evaluation of options. These three stages correspond to
stages 3-5 of the UKCIP framework: climate change risk assessment, identification of options
and appraisal of options. However, assessments also presuppose information from stages 1-2
44
of the UKCIP framework: problem identification and framing including objective setting and
criteria selection.
Activities in the first three work packages (WPs) and case studies of the MEDIATION project
can also be related directly to the UKCIP framework:
WP 1 (Decision-making context) touches on aspects of stages 1, 2, 4 and 5
WP 2 (Methods and metrics for assessing impacts and vulnerability) maps onto stage 3
WP 3 (Methods and metrics for socio-economic evaluation of adaptation strategies
including cost-effectiveness) mainly covers stage 5
MEDIATION case studies offer a diverse set of examples of assessments proceeding
through some or all of stages 1-5
Work Packages 4-6 in MEDIATION represent an attempt to move beyond a UKCIP-type
framework in order to develop a generic methodology for CCIAV assessment applicable for
informing adaptation policy in Europe. The practical offshoot of the methodology is a
common platform, developed to provide access to the information, methods and tools
analysed in MEDIATION (though theoretically extendible to any other CCIAV studies that
might be undertaken in Europe), providing training in its use and designed by means of a
continuous dialogue with stakeholders.
Within this context, and as part of our review of methods and metrics of CCIAV assessment,
we now offer an initial suggestion for a unified framework that might be developed into a
basis for the common platform. Operating with such a framework greatly simplifies the task
of cataloguing and describing different methods, tools and metrics. The framework is
introduced in the next section, and subsequent sections then follow its proposed structure.
7.2 Towards a framework for classifying CCIAV assessments to support decision-
making
Examination of the IPCC table (Table 1) and UKCIP framework (Figure 14) reveals points of
connection which are useful in embedding CCIAV assessments into a broader decision-
making framework. The left-hand side of Table 1, presenting impacts and vulnerability-based
assessment according to the IPCC typology, concentrates on assessing climate impacts and
associated risks and thus connects to stage 3 of the UKCIP framework (climate risk
assessment). Alternatively, the right-hand side of Table 1 (adaptation-based and integrated
assessment) offers more of an adaptation emphasis and thus links more closely with UKCIP
stages 4 and 5 (identification and evaluation of adaptation options). We suggest that these two
emphases – climate change risks and climate change adaptation – offer the most obvious
distinction between most assessments, though with some studies inevitably straddling the
divide.
Policy insights offered by integrated assessment models on the global costs of impacts,
adaptation and mitigation, serve a distinctive set of purposes that are somewhat aloof from the
more regionally-focused CCIAV approaches embraced in the UKCIP framework and pursued
in MEDIATION. However, we suggest that the over-arching risk management framework
proposed by Jones and Preston (2011, – cf. Figure 2) offers such a global dimension. Figure
15 represents an attempt to map the two emphases and four approaches described in the
present review onto that framework, adding a third emphasis concerned with global policy
analysis. Note that IAMs are also applied extensively at regional scale in Europe (e.g. to
45
analyse land use change, species shifts and bioenergy potential), with an emphasis that is not
necessarily focused on global policy.
Figure 15. A mapping of the main emphases (blue) and approaches of CCIAV assessment discussed in
this report onto the axes proposed by Jones and Preston (2011 – compare with Figure 2). Locations of
assessment approaches are schematic; in reality the dimensions of different approaches are overlapping.
We propose using these connections as a starting point in developing a framework for
classifying CCIAV assessments in a meaningful way to support decision-making for climate
change adaptation. Table 9 introduces elements of CCIAV assessments that are considered
relevant for such a structure. These elements are discussed in more detail below, and in other
sections of the report denoted in the table. After separating out approaches according to the
underlying purpose and emphasis of the assessment, we would suggest applying the same
classification framework to risk-, adaptation- and global policy-orientated CCIAV
assessments.
The key elements of this framework are also depicted schematically as a decision tree in
Figure 16. Here, the purpose of an assessment largely determines its emphasis and hence the
type of approach(es) most suitable for addressing the purpose. The pursuit of particular
approach will involve the deployment of one or more specific methods, and associated with
each method could be a suite of tools, either models or procedures already available from
earlier work, or developed as part of the assessment itself. Some tools are specific to a single
method (e.g. specific impact indices); others may embrace multiple methods and approaches
(e.g. decision support systems). Similarly, some methods are applicable to most types of
approach (e.g. scenario development), whereas others are more closely tied to a single
approach (e.g. vulnerability mapping).
Top-down
Bottom-up
Prescriptive Diagnostic
Global
scenarios
Integrated
assessment
Vulnerability
assessment (future)
Vulnerability
assessment(present-day)
Adaptation
assessment(identifying and
evaluating
options across scales)
Impact
assessment
Local
scenarios
GLOBAL POLICY ANALYSIS
46
Ultimately, a common platform should enable the interested user to trace the linkages
between a given tool and the method(s) and approach(es) it might be used to inform (moving
from right to left in Figure 16). Conversely, it should also be possible to determine the range
of methods and associated tools that might be deployed in applying a given approach to
address a given purpose or problem type (i.e. moving from left to right in the diagram).
Figure 16. A simple decision tree for categorising approaches, methods and tools applied in CCIAV
assessment (schematic).
Purpose of assessment
Global climate policy analysis
Integrated assessment
Climate change risks
Impact assessment
Climate change adaptation
Vulnerability assessment
Adaptation assessment
Purpose Emphasis Approach Method Tool
oooooooooooooooooooooooooooooooo
47
Table 9. Elements of a proposed classification framework for CCIAV assessments with sections in which
they are described in this paper in parentheses.
Element Description
Assessment type, purpose and target audience
Study name Full name and acronym
Purpose of
assessment
The primary motivation of an assessment (7.3.1)
Emphasis of
assessment
Main orientation of assessment: Climate Risks, Adaptation or Global Policy
Analysis (7.2)
Beneficiaries The intended target audience and other potential interested parties (7.3.2)
Approach Main approach of assessment: Vulnerability, Impact, Adaptation, Integrated
Assessment (7.3.3)
Dimensions of the assessment
Sectoral/
thematic focus
Thematic focus of the assessment, e.g. certain sector(s), population groups or
communities (7.4.1)
Region/spatial
scale
Region and spatial scale(s) for which analysis is carried out and results are valid.
Where relevant, spatial resolution applied in a study (7.4.2)
Time horizon
and resolution
Whether past and future perspectives are included; for future-looking studies
also time period(s) and resolution considered in the assessment (7.4.3)
Methods and participation
Methods and
tools
Specific analytical methods and tools applied in an assessment as well as details
of their application in an assessment (7.5.1)
Involvement of
stakeholders
Key stakeholder groups who have formally influenced the assessment results
through their inputs and the format of their involvement (7.5.2)
Information management
Data and
scenarios
Data and methods applied to characterise the past, present and future in an
assessment (e.g. for climate, other environmental, land-use, socio-economic and
technological conditions – 7.6.1-7.6.7)
Treatment of
uncertainty
Techniques to address and represent different sources of uncertainties in an
assessment (7.6.8)
Assessment outputs
Metric(s) Specific measures/measurements and units in terms of which the results of an
assessment are presented (7.7.1)
Presentation of
results
Approach for displaying and documenting background information, methods,
results and conclusions of an assessment to users (7.7.2)
Documentation
& publications
Peer reviewed articles, technical reports, other reports, web descriptions (7.7.3)
48
7.3 Assessment type, purpose and target audience
The multiplicity of approaches, methods and tools available to CCIAV analysts points to the
need for a way to shortlist the most promising candidates for a given assessment. It is the
purpose of an assessment that will determine its emphasis and the most appropriate
approaches and methods. Not all methods are suitable for answering all questions and some
may be more suitable than others for producing relevant knowledge to inform an intended
audience.
7.3.1 Purpose
To date, there has been no systematic analysis of CCIAV assessments from the perspective of
categorising their purposes; indeed, studies are rarely explicit about the intended use of their
results in the sphere of decision-making. However, there are a few studies that have
recognised how different study purposes can give rise to varying information needs, which in
turn can have an integral role in guiding the selection of approach and associated methods of
assessment (e.g. Füssel and Klein, 2006; Smit and Wandel, 2006; Hinkel, 2011).
We have compiled some of the more common purposes cited for undertaking CCIAV
assessments into the following list (based in part on Carter et al., 2007; Füssel, 2007; Hinkel,
2011). Purposes include:
Identification and prioritisation of research priorities
Improving process understanding (basic research)
Raising awareness of climate problems
Building capacity for future vulnerability and adaptation assessments
Developing and testing new research methods and tools
Determining the effectiveness of interventions
Identifying vulnerable sectors and communities
Estimating potential impacts of changes in mean climate and variability
Identifying potential adaptation measures
Prioritisation and costing of adaptation measures
Exploring trade-offs between adaptation and mitigation policies
Mainstreaming climate into wider policy agendas (e.g. sustainable development)
The list is by no means exhaustive, but its diversity points to some of the boundary conditions
and constraints imposed on researchers, which may have an important bearing on the types of
results obtained and their applicability, if any, for decision-making.
7.3.2 Beneficiaries
Closely linked to the purpose, is the target audience for the assessment, which we have listed
as "beneficiaries" in Table 9. Assessment beneficiaries are actors and/or stakeholder groups
who are the intended users of the assessment results (i.e. those who may benefit from
knowledge generated by the research). These may be implicit or explicit in an assessment.
The category also covers potential beneficiaries who may use the results indirectly or for
whom the benefit is unclear. Typical examples of assessment beneficiaries include researchers
and research-linked communities such as funding agencies, decision-makers at different
levels and key sectoral stakeholders. Beneficiaries may or may not also take an active role in
the design and conduct of an assessment, but this aspect of an assessment is treated separately
under stakeholder participation.
49
7.3.3 Approaches
The purpose and target beneficiaries will largely determine the required outputs of an
assessment, which in turn dictate the overall emphasis of a study and the types of approaches
that can be deployed to achieve its goals. Four main approaches, concerned with impacts,
vulnerability, adaptation and integrated assessment are detailed at length in sections 3-6.
7.4 Dimensions of the assessment
The focus of an assessment can be defined with respect to three main dimensions: its topical
theme or focal sector(s), regional scope and spatial resolution, and time horizon over which
the study is applicable.
7.4.1 Sectoral or thematic focus
Impact and vulnerability assessments typically focus on particular sectors, such as agriculture,
water resources, tourism or human health. In other cases, especially in bottom-up studies of
vulnerability and adaptation, assessments may focus instead on specific communities or
population groups. Increasingly, especially for large national or regional CCIAV assessment
programmes with multiple projects drawing on different disciplines and traditions, assessors
are called upon to address interactions and linkages between different sectors or populations,
as climate change may bring about critical synergies, co-benefits, conflicts and trade-offs that
are of importance and interest to actors operating in a complex decision environment.
7.4.2 Region and spatial scale
Defining the regional scope of an assessment places a geographical and environmental stamp
on a study that might be of interest to other researchers working in comparable environments
or seeking analogues of their own region under a future climate. From a decision-making
perspective, it is important to know what area(s) and scale(s) the results represent, be it a
country, geographical region spanning multiple countries or a sub-national region. The spatial
resolution of the assessment will offer clues about the level of detail a study has gone into,
and the types of data and analytical tools that were required to achieve this.
7.4.3 Time horizon and resolution
Finally, the time horizon of an assessment will indicate whether it focused on present-day
conditions and/or looked backward (such as for detection and attribution) or forward in time,
over what time span the analysis extends, and whether the study focused on fixed periods
(time slices) or considered change in a time-dependent (transient) mode. The time resolution
at which analyses are conducted also has implications for the data and tools that were applied.
7.5 Methods and participation
With the purpose, emphasis and dimensions of an assessment specified, analysts are presented
with an array of choices regarding the most suitable methods to apply. The role of
stakeholders is an important component in this choice.
7.5.1 Methods and tools
For each of the four assessment approaches and their sub-categories identified in sections 3–6,
there are numerous methods available to address specific questions through a specific type of
50
analysis. The methods may be quantitative or qualitative, and they often have to be tailored to
the particular circumstances of a given study. The description of a method should cover both
the generic method type (e.g. statistical, modelling, decision-support, participatory, indicator-
based, economic valuation, etc.) as well as details of its application in the particular case.
Some examples of typical methods are described and/or listed in sections 3–6. For a wider
selection, a number of sources of online information on methods and tools have appeared in
recent years. A notable example is the online "Compendium on methods and tools to evaluate
impacts of, and vulnerability and adaptation to, climate change", established in 1999 by the
UNFCCC to assist users "in selecting the most appropriate methodology for assessments of
impacts and vulnerability, and preparing for adaptation to climate change" (UNFCCC, 2005)8.
The compendium has been adopted by the Nairobi Work Programme and updated several
times (most recently in 2009). New methods and tools can be communicated to the UNFCCC
Secretariat for inclusion in the Compendium by completing a standard form. This organises
each method or tool by sector, theme and types, each offering a set of options. These options
are currently non-exhaustive (for example, only a few sectors are mentioned explicitly, while
methods or tools for others would need to be included under more generic headings) and will
be modified as new examples appear. Each entry is also accompanied by a short description,
information on appropriate use, scope, key outputs and inputs, ease of use, need for and
availability of training, computer requirements, documentation, applications and contact
details.
7.5.2 Stakeholder involvement
The value of participatory research methods for ensuring the saliency, legitimacy and
credibility of CCIAV assessments is repeatedly stressed in the published literature (e.g., see
sections 3.4, 5.1 and 5.2), so the active involvement of stakeholders also needs to be recorded.
This category should specify the key stakeholder groups who have formally influenced the
assessment results through their inputs as well as describing the format of their involvement.
7.6 Information management
Central to almost all CCIAV assessments are questions relating to the availability, quality and
application of information used to represent different aspects of the environment and human
society affected by climate change. Methods of characterising the future, typically through the
use of scenarios, are important for determining the risks posed by climate change and for
exploring mitigation and adaptation responses that might ameliorate adverse impacts. All of
these choices are accompanied by uncertainties that need to be identified and effectively
documented and communicated.
7.6.1 Guidance on data and scenarios
There is a formidable literature on the use of data and scenarios in CCIAV assessments, and
this report does not seek to repeat earlier extensive reviews (e.g. see Carter et al., 2001;
Mearns et al., 2001; Carter et al., 2007; Rounsevell and Metzger, 2010). In 1996 the IPCC
formed TGICA9, a special cross-Working Group committee, to address these questions.
TGICA has prepared a number of guidance documents on data and scenarios (e.g. IPCC-
TGICA, 2007; Nicholls et al., 2011), regional workshops have been arranged on the topic
8 http://unfccc.int/adaptation/nairobi_workprogramme/knowledge_resources_and_publications/items/5457.php
9 IPCC Task Group on Data and Scenario Support for Impact and Climate Analysis
51
(e.g. Leary et al., 2009) and the IPCC Data Distribution Centre10
was established to facilitate
the timely distribution of consistent data and scenarios for use in CCIAV and mitigation
assessments that can ultimately feed into the IPCC assessment process.
Furthermore, many governments, national and international agencies are now investing
heavily in the provision of data and scenario support for climate change research. Examples in
Europe include:
Denmark: climate data and scenarios supplied by the Danish Meteorological Institute
through the Climate Change Adaptation portal11
Finland: data and scenarios of climate, sea-level, atmospheric composition, acid
deposition and socio-economic scenarios from the FINSKEN portal (Carter et al., 2004)12
,
out of the FINADAPT (Carter et al., 2005) and ACCLIM (Jylhä et al., 2009) projects, and
shortly from a new national climate change portal13
Germany: The Federal Environment Agency (Umweltbundesamt) web portal KomPass
(Kompetenzzentrum, Klimafolgen und Anpassung)14
and the government-funded Climate
Service Center – Germany15
Netherlands: Climate services, including climate scenarios, provided by the Royal
Netherlands Meteorological Institute (KNMI)16
and accessible from the Dutch climate
change portal Platform Communication on Climate Change (PCCC)17
Norway: Climate scenarios provided by the Norwegian Meteorological Institute18
and
accessible from the Norwegian Climate Change Adaptation Programme web portal hosted
by the Norwegian Ministry of the Environment19
Spain: Climate scenarios prepared by the State Meteorological Agency of Spain20
in
support of the Spanish National Climate Change Adaptation Plan
United Kingdom: the UK Government has produced five sets of official climate
projections since 1991, the most recent being UKCP09 (Murphy et al., 2009), and one set
of socio-economic scenarios in 2001 (UKCIP, 2001). These are distributed by UKCIP21
.
There have also been critical reviews of the effectiveness of both the climate scenarios
(Hulme and Dessai, 2008) and socio-economic scenarios (Hughes et al., 2009).
The following sections describe a number of issues to consider in reporting the use of data
and scenarios in assessments, along with supporting literature offering additional information.
7.6.2 Qualitative information
Qualitative descriptions of past, present or future conditions can be very effective ways of
conveying information to non-specialists, though these descriptions may be based on
quantitative data. Moreover, narrative descriptions of possible future developments
10
http://www.ipcc-data.org/ 11
http://www.klimatilpasning.dk/en-US/Sider/ClimateChangeAdaptation.aspx 12
http://www.finessi.info/finsken/ 13
http://climateguide.fi/ 14
http://www.anpassung.net/cln_110/nn_1134902/DE/Klimaprojektionen/klimaprojektionen__node.html?__nnn=true 15
http://www.climate-service-center.de/ 16
http://www.knmi.nl/research/climate_services/ 17
http://www.klimaatportaal.nl/pro1/general/home.asp 18
http://senorge.no/ 19
http://www.regjeringen.no/en/dep/md/kampanjer/engelsk-forside-for-klimatilpasning.html?id=539980 20
http://www.aemet.es/es/elclima/cambio_climat/proyeccione 21
http://www.ukcip.org.uk/tools/
52
(storylines), by virtue of not specifying precise numbers, can be useful devices for framing the
future that allow analysts some flexibility in interpreting future regional trends (Rounsevell
and Metzger, 2010). Through dialogue and negotiation, they can also allow for direct
stakeholder participation and eventual buy-in to an agreed set of storylines (Alcamo, 2001).
7.6.3 Quantified variables and their sources
As climate change is the central focus of study, most CCIAV assessments, especially those
employing models, make use of climate information for a wide range of variables (e.g. near
surface air temperature, precipitation, solar radiation, wind speed and humidity are the most
common). Data may also be required to describe all relevant attributes of a system or activity
exposed to climate change, or preconditioning human responses to climate change. Data may
be physical (e.g. forest productivity, river flow, water quality or soil nutrient status),
economic (e.g. income, or price), social (e.g. population, employment, education) or technical
(e.g.irrigation, forest equipment, building materials). Potential sources of data are highly case
specific.
Some data are observed or collected operationally, such as weather, streamflow, sea level and
wave heights, population, economic activity. These are commonly available from national or
international agencies and government statistical offices. They might also be collected
especially for a study, in targeted experiments or surveys. Climate information from the past
may have been inferred from proxy information such as historical accounts, tree rings or ice
cores. Information on regional climate can also be simulated using climate models. Some
climate information can also be derived from other climate variables (e.g. accumulated
temperatures, evaporation, number of air frosts). Data are often re-formatted to suit the needs
of users, for example, by aggregating population data by regional units, or by interpolating
observed climate data from weather stations to a regular spatial grid.
7.6.4 Characterising future climate
Projections of future climate that are applied in CCIAV assessment are conventionally
referred to as climate scenarios (Mearns et al., 2001), which distinguishes them from climate
predictions or forecasts, to which probabilities can be attached. However, this distinction is
becoming blurred as climate scientists have moved towards expressing future climate in terms
of conditional probabilities (cf. Table 2 and discussion below). A useful recent comparison of
different methods of climate scenario development for use in CCIAV is provided by Wilby et
al.(2009). Table 10 combines elements of that review into a summary of different scenario
construction methods, their resource needs and potential applications.
The most credible and sophisticated tools for simulating the response of the Earth's climate to
increasing emissions of greenhouse gases and aerosols are coupled atmosphere-ocean general
circulation models (AOGCMs – cf. Figure 9. There is agreement among all models that the
planet will warm, globally, though the magnitude varies from model to model. There is less
unanimity in the projected regional pattern of changes in different climate variables, and the
spatial resolution is quite coarse (grid box dimensions are seldom finer than 150 km). Since
most impacts of climate change will be manifest locally, there have been great efforts to
downscale AOGCM projections to a finer spatial resolution (Fowler et al., 2007), either
using numerical models (Mearns et al., 2003; Rummukainen, 2010) or statistical techniques
(Wilby et al., 2004), and sometimes involving the use of stochastic weather generators
(Wilks, 2010). There have been several major research projects conducted to this end in
53
Europe, such as PRUDENCE (Christensen et al., 2007) and ENSEMBLES (van der Linden
and Mitchell, 2009).
Table 10. Selected methods of climate scenario development classified according to their resource needs
and potential applications for adaptation planning (based on tables in Wilby et al., 2009 and amended,
with major additions in italics).
Level of
resource needs
Methods Spatial application and
input requirements
Applications for adaptation
planning
Limited
Sensitivity analysis Local (site/area)
(Observed climate data)
Resource management, Sectoral
Climate analogues Communication, Institutional,
Sectoral
Trend extrapolation New infrastructure (coastal)
Modest
"Delta" change
Regional
(AOGCM and simpler
global model outputs)
Most adaptation activities
Pattern-scaling Institutional, Sectoral
Stochastic weather
generation
Resource management,
Retrofitting, Behavioural
Empirical/statistical
downscaling
New infrastructure, Resource
management, Behavioural
High
Dynamical
downscaling
(RCM)
Regional-global
(AOGCM outputs)
New infrastructure, Resource
management, Behavioural,
Communication
Coupled AOGCMs Regional-global Communication, Financial
Probabilistic Global-regional-local
(Multiple sources)
New infrastructure, Resource
management, Communication
An alternative method used to generate climate scenarios involves identifying spatial
analogues (climates in other regions) or temporal analogues (climates from the past) that may
resemble anticipated future conditions in a region (Ford et al., 2010). Other simple techniques
involve adjusting present-day climate by fixed increments (e.g. warming in increments of
1°C; precipitation changes in increments of ±5%) to explore the sensitivity of exposure units
to a changing climate (IPCC, 1994), or applying simple extrapolation of past trends (Wilby et
al., 2009).
Perhaps the most common technique for applying climate scenarios in CCIAV studies is the
so-called "delta change" method, whereby changes between modelled reference and future
periods are appended as factors (or "deltas") to the climate observed during the reference
period. This technique recognises the common biases found in model representations of
present-day climate (e.g. Fronzek and Carter, 2007). Pattern-scaling is a method often applied
in IAMs for relating the regional patterns of changes in climate derived from individual
AOGCM simulations to global mean annual temperature (which can be computed in simple
climate models). The same pattern can then be scaled up or down according to the simple
model's temperature projections for a wide range of emissions scenarios and future time
periods (Mitchell, 2003). Finally, as computer power has improved, so multiple ensemble
simulations with climate models have become feasible, allowing different model uncertainties
to be explored and encouraging climate scientists to attach likelihoods to climate projections.
54
The new UKCP09 projections are probabilistic (Murphy et al., 2009) as are recent projections
for Finland (Räisänen and Ruokolainen, 2006) and for Europe (e.g. Harris et al., 2010).
7.6.5 Characterising other environmental and socio-economic futures
Previous sections have already outlined the importance of characterising, in parallel with
climate, future environmental and societal conditions that influence vulnerability (section
3.2), impacts (section 4.3) and risk management in general (Table 2). Scenarios of these other
factors have been categorised by Carter et al. (2007) and are summarised in Table 11 along
with some examples of their application in European CCIAV assessments. Many of the same
issues as for climate, regarding data availability and temporal and spatial dimensions (e.g. van
Vuuren et al., 2010) also apply to these scenarios.
Table 11. Types of scenarios of future environmental and societal developments adopted for CCIAV
assessments and examples of their application.
Type of scenario Examples of scenario
development methods
Examples of scenario applications
Atmospheric
composition
CO2 concentration (IMAGE-team,
2001)
Impacts on ecosystems and agriculture
(Schröter et al., 2005)
Sea-level Guidance on sea-level scenario
development (Nicholls et al.,
2011)
Economic impacts on coastal systems in
Europe (Richards and Nicholls, 2009)
Socio-economic Population (O'Neill, 2005) Human health impacts in Europe (Watkiss
et al., 2009)
Land-use Land use scenarios for Europe
(Audsley et al., 2006)
Vulnerability of agricultural land use and
natural species (Berry et al., 2006)
Technology Crop yield potential (Ewert et al.,
2006)
Crop productivity and agricultural land
use in Europe (Rounsevell et al., 2005)
Adaptation Optimal crop management
(Iglesias et al., 2009)
Crop productivity in Europe (Iglesias et
al., 2009)
7.6.6 Scenarios as integrating devices
The selection of common scenarios can be a useful device for imposing consistency and
comparability across CCIAV assessments. During the past decade, most model projections of
climate in the 21st century have been based on the IPCC Special Report on Emissions
Scenarios (SRES) set of six marker scenarios (Nakićenović et al., 2000)22
. The narrative
storylines describing future worlds giving rise to the SRES emissions, and the demographic
and economic assumptions that acompanied them, therefore offer a consistent framework for
characterising other environmental and socio-economic scenarios to be used alongside SRES-
based climate projections. Several European assessments have developed scenarios using
SRES as an integrating framework (e.g. Arnell et al., 2004; Carter et al., 2004; Holman et al.,
2005b; Rounsevell et al., 2006; Spangenberg et al., 2011). Other global scenario exercises
that have also been matched to SRES emissions, include those developed for the Millennium
Ecosystem Assessment (Millennium Ecosystem Assessment, 2005) and the United Nations
22
These are labelled, in increasing order of their radiative forcing of the atmosphere, B1, B2, A1T, A1B, A2 and
A1FI, with CO2-equivalent concentrations of approximately 600, 700, 800, 850, 1250 and 1550 ppm,
respectively (IPCC, 2007).
55
Environment Programme, Global Environmental Outlook 4 (UNEP, 2007). A new generation
of global scenarios (socio-economic, technological, land use and climate) are currently under
preparation by the international research community ahead of the IPCC AR5 (Moss et al.,
2010). These appear very likely gradually to supersede the SRES set as they become
available to CCIAV researchers during the next few years.
7.6.7 Addressing low probability, high consequence events
There is a separate class of future environmental changes that might have the potential to
trigger large-scale and severe (possibly irreversible) consequences for natural systems and
society, but while theoretically plausible, are too poorly understood for their likelihood to be
determined. Examples include an abrupt re-organisation of the north Atlantic thermohaline
circulation (THC), sea level rise due to melting of the Greenland and West Antarctic Ice
Sheets, and sudden release of methane from thawing permafrost. Such large-scale
singularities (Schneider et al., 2007) commonly involve the crossing of critical thresholds or
"tipping points" of magnitude, temporal rates or spatial gradients of climate change
(Schellnhuber et al., 2006; Lenton et al., 2008; Lenton, 2011). Although their physical
mechanisms are intensively studied, often in the context of "key vulnerabilities" (Schneider et
al., 2007) there are relatively few examples of CCIAV assessments that have applied
scenarios of such events. These include, for Europe, scenarios (Wood et al., 2003; Fronzek et
al., 2011), ecological (Higgins and Vellinga, 2004) and land use (Reginster et al., 2010)
implications of a THC shutdown, and potential impacts of extreme sea-level rise (Arnell et
al., 2005; Tol et al., 2006).
7.6.8 Treatment of uncertainty
Understanding of climate change, as with all scientific knowledge, will always contain "some
level of uncertainty, and any actions based on scientific evidence inevitably involves an
assessment of risk and a process of risk management" (Shapiro, 2010). Therefore, it is
important to describe the most important sources of uncertainty in the results of an
assessment, if possible providing explicit quantification of these or at least indicating the
various lines of evidence invoked to make judgements about confidence in findings.
Some common sources of uncertainty in climate change assessments include (Moss and
Schneider, 2000):
Problems with data
Missing components or errors in the data
"Noise" in the data associated with biased or incomplete observations
Random sampling error and biases (non-representativeness) in a sample
Problems with models
Known processes but unknown functional relationships or errors in the structure of the
model
Known structure but unknown or erroneous values of some important parameters
Known historical data and model structure, but reasons to believe parameters or model
structure will change over time
Uncertainty regarding the predictability (e.g., chaotic or stochastic behavior) of the system
or effect
Uncertainties introduced by approximation techniques used to solve a set of equations that
characterize the model.
56
Other sources of uncertainty
Ambiguously defined concepts and terminology
Inappropriate spatial/temporal units
Inappropriateness of/lack of confidence in underlying assumptions
Uncertainty due to projections of human behavior (e.g., future consumption patterns, or
technological change), which is distinct from uncertainty due to "natural" sources (e.g.,
climate sensitivity, chaos)
Various methods have been proposed for representing different sources of uncertainty in
assessments, and these have recently received intense scrutiny following debate surrounding
the methods applied in the IPCC AR4 (Morgan et al., 2009; e.g. Swart et al., 2009; IAC,
2010, Climate). Out of this discussion, including a formal review of IPCC procedures by the
InterAcademy Council (IAC, 2010), a new set of best practice guidelines have been prepared
ahead of the IPCC Fifth Assessment (AR5 – Mastrandrea et al., 2010). The AR5 will employ
two metrics of confidence:
1. Confidence in the validity of a finding, based on the type, amount, quality, and
consistency of evidence (e.g., mechanistic understanding, theory, data, models, expert
judgment) and the degree of agreement. Confidence is expressed qualitatively.
2. Quantified measures of uncertainty in a finding expressed probabilistically (based on
statistical analysis of observations or model results, or expert judgment).
There are scales of confidence or likelihood recommended for each of these (Mastrandrea et
al., 2010). Swart et al. (2009) present a schematic representation of how uncertainty can be
communicated in relation to key climate change issues (Figure 17). The two metrics described
above would be positioned at the right hand part of the figure.
Finally, as well as affecting confidence in the results of an assessment, uncertainty is also a
important factor to consider in the choice of an assessment approach (Jones and Preston,
2011). Uncertainties in prediction and confidence in the likely success of specific actions will
bot influence the choice of approach (Table 12) with complex systems possibly warranting
the application of a range of approaches (Jones and Preston, 2011).
Table 12. Matching assessment approaches to uncertainties in prediction and in taking action. Source:
Jones and Preston (2011).
Uncertainty in prediction
Low High
Uncertainty in
taking action
Low Forecasting, IPCC standard
approach, add climate change to
existing risk management
practice
Contingency planning,
vulnerability assessment, regular
review of measures taken
(especially of system stress)
High Standard approach, develop with
institutional, social and financial
capacity, monitoring to see
whether measures are having
planned effect
Narrative scenarios, visioning
exercises, investigate current,
and future vulnerability
57
Figure 17. Relationships between the dominant uncertainty philosophy, precision of information,
statements about key climate change issues, and approaches to the communication of confidence. Source:
Swart et al. (2009).
7.7 Assessment outputs
The final category in the proposed classification of CCIAV assessments concerns the outputs
of the assessment. Two components of the outputs are of potential interest and importance to
decision makers: the metrics in which results are expressed, and the various media and
devices used to present these results.
7.7.1 Metrics
Metrics refer to the measurement units in which the results of an assessment are presented.
For many forward looking impact and integrated assessments, the metrics deployed are
usually quantitative descriptions of modelled changes in valued features of an exposure unit.
Some of these are of quantities that are also measurable on the ground (e.g. changes in tree
growth, crop yield, river discharge, flood damage costs). Other quantities might be inferred,
but are not so readily measurable (e.g. regional carbon balance, winter chilling of fruit trees,
economic costs of non-monetary impacts such as losses to biodiversity).
More problematic are assessments of vulnerability, a concept that lacks both an operational
definition and agreed metrics. Klein (2009) notes how the research community, to date, has
failed to agree on a systematic way to assess, measure and compare the vulnerability of
countries, although challenged to identify particularly vulnerable nations in the framework of
the UNFCCC. More widely, a lack of agreed metrics defies generalisations regarding
58
vulnerability assessments and hinders comparisons of different assessments (Polsky et al.,
2007). Indicators are becoming increasingly popular for measuring vulnerability, but they are
a contested method (especially when combined into multi-factorial indices) that can spawn
multiple misconceptions and areas of confusion that may limit their usefulness (Hinkel, 2011
– see section 3.3).
In a review of possible metrics for adaptation cost-effectiveness assessment (CEA), Watkiss
and Hunt (2011) emphasise that the selection of relevant metrics is closely tied to the
objectives of an assessment, its level of aggregation (e.g. national versus local) or time scale
(short- versus long-term). Even where the same units are applied, if acceptable levels of risk
differ between regions, then this may complicate the application of methods such as CEA.
Nevertheless, they list a set of metrics that might serve CEA in a European context for the
following sectors or focal issues (Watkiss and Hunt, 2011): health, sea level rise and other
flood risks, agriculture, water resources, ecosystems and biodiversity, business and industry
as well as for a cross cutting theme of extreme events (including infrastructure).
7.7.2 Presentation of results
The methods adopted to present and communicate assessment outcomes play an important
role in determining how and by whom the results can be used in decision-making. Results can
be communicated using narratives, graphics such as different types of maps and charts, or
tables for numerical information. For graphics, additional aspects could include whether the
results are presented in a static or interactive format or whether maps are presented in two- or
three-dimensions.
Another important medium for dissemination of assessment results is through direct
engagement with key stakeholder groups, for example in workshops, conferences, public
meetings and debates. Another avenue is via newspapers, television, radio, the internet and
other various social media.
This category also makes provision for tools or other materials developed with the specific
purpose of disseminating new information (e.g. interactive web-based or stand-alone
computer-based tools, web portals, science exhibitions, posters, brochures, or even through
visual and performance art).
7.7.3 Documentation and publications
A final category concerns references to published materials relating to an assessment. These
may include peer-reviewed publications, technical documentation, online descriptions, or any
other sources that the authors of an assessment judge to be worthy of citation.
59
8 Conclusion: en route to a common platform for MEDIATION
The previous section has described a possible framework for classifying CCIAV assessments
to support decision-making for climate change adaptation. This comprises a simple checklist
of attributes that would need to be considered in an assessment in order to fulfil a given
purpose defined by the expected beneficiaries of the work. The purpose of the assessment
determines the emphasis that is needed; its dimensions influence the suitability of approaches,
methods, tools and information to be deployed. Examples of these have been reviewed in
detail in sections 3-6.
The next step is to test the concept by beginning to populate the framework. Logically, this
could be done by requesting case study participants in the project to attempt to locate their
assessments within the matrix. Table 13 illustrates how this might work, using an example of
the northern European study of vulnerability of the elderly.
This review has attempted to cover most of the major approaches to CCIAV assessment that
might be adopted in individual studies conducted at different scales in Europe. Some
approaches, methods and tools are described in considerably more detail in other
MEDIATION deliverables, but they are still referred to here to provide as comprehensive
coverage as possible. Although this coverage extends beyond our original remit (originally
addressing methods and metrics for impacts and vulnerability), our justification for doing this
is twofold. First, one element of CCIAV assessment that was not included in the review
process described in the original description of work, but which is fundamental to the
objectives of MEDIATION, is methods of adaptation assessment. We have tried to plug this
gap. Second, it is clear that such an overview would have been required at some stage in order
to begin to define an overarching integrated methodology. This review presented an obvious
receptacle for launching this process. Moreover, in so doing it can also offer initial insights
into some of the possible pathways the project might choose to follow in developing a
common platform for dissemination and knowledge sharing.
60
Table 13. A proposed framework for classifying CCIAV assessments for supporting decision-making
Study Name and
contact details
(responsible
researcher and
institution)
Purpose of
assessment
Emphasis of
assessment
(Climate Risks,
Adaptation, Global
Policy Analysis)
Beneficiaries
(intended and
potential)
Approach (Vulnerability,
Impact, Adaptation
Options, Adaptation
Appraisal, Integrated
Assessment)
Sectoral /
thematic focus
Spatial scale /
resolution
Time horizon and
resolution
Methods and tools
(analytical methods
and tools and their
application)
Involvement of
stakeholders (i.e.
role as participants in
the assessment)
Data and scenarios
(methods for
characterising past,
present and future
conditions)
Treatment of
uncertainty (methods for
describing and
representing uncertainties
in data, models and
inferences)
Metric(s) (measurement
units used to quantify
results)
Presentation of
results (media for
communicating
assessment results)
Documentation
and Publications
CARAVAN case
study; Timothy
Carter, Finnish
Environment
Institute (SYKE)
Identification of
differential regional
vulnerability of the
elderly to climate
change
Climate Risks National planners
in public health
and social welfare
Indicator-based
vulnerability assessment
The elderly;
human health
Fenno-scandia /
municipality
Present to medium-
term (2005-2050);
period-mean results
Development and
combination of
indicators, informed
through stakeholder
dialogue
National and regional
care providers for the
elderly
Climate;
demographic;
economic; social
Quantitative for climate
(preliminary)
Observed/modelled
indicators (various units);
relative indices of
exposure/sensitivity,
adaptive capacity,
vulnerability
Interactive mapping;
web-based tool;
scientific articles
Carter et al. (2010);
Rosentrater et al.
(2010)
MEDIATION
Case study 2
MEDIATION
case study 3
MEDIATION
case study n
MEDIATION
case study n+1
Assessment outputsAssessment type, purpose and target audience Dimensions of the assessment Methods & participation Information management
61
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