Figure 13.3: Some alternative data sources and procedures for constructing climate scenarios for use in impact assessment. Highlighted boxes indicate the baseline climate and common types of scenario (see text for details). Grey shading encloses the typical components of climate scenario generators.

Four types of climate scenario that have been applied in impact assessments are introduced in this section. The most common scenario type is based on outputs from climate models and receives most attention in this chapter. The other three types have usually been applied with reference to or in conjunction with model-based scenarios, namely: incremental scenarios for sensitivity studies, analogue scenarios, and a general category of “other scenarios”. The origins of these scenarios and their mutual linkages are depicted in Figure 13.3.

The suitability of each type of scenario for use in policy-relevant impact assessment can be assessed according to five criteria adapted from Smith and Hulme (1998):

1. Consistency at regional level with global projections. Scenario changes in regional climate may lie outside the range of global mean changes but should be consistent with theory and model-based results.
2. Physical plausibility and realism. Changes in climate should be physically plausible, such that changes in different climatic variables are mutually consistent and credible.
3. Appropriateness of information for impact assessments. Scenarios should present climate changes at an appropriate temporal and spatial scale, for a sufficient number of variables, and over an adequate time horizon to allow for impact assessments.
4. Representativeness of the potential range of future regional climate change.
5. Accessibility. The information required for developing climate scenarios should be readily available and easily accessible for use in impact assessments.

A summary of the major advantages and disadvantages of different scenario development methods, based on these criteria, is presented in Table 13.1. The relative significance of the advantages and disadvantages is highly application dependent.

Table 13.1: The role of various types of climate scenarios and an evaluation of their advantages and disadvantages according to the five criteria described in the text. Note that in some applications a combination of methods may be used (e.g., regional modelling and a weather generator).
Scenario type or tool	Description/Use	Advantagesa	Disadvantagesa
Incremental	Testing system sensitivity Identifying key climate threshold	Easy to design and apply (5) Allows impact response surfaces to be created (3)	Potential for creating unrealistic scenarios (1, 2) Not directly related to greenhouse gas forcing (1)
Analogue:
Palaeoclimatic	Characterising warmer periods in past	A physically plausible changed climate that really did occur in the past of a magnitude similar to that predicted for ~2100 (2)	Variables may be poorly resolved in space and time (3, 5) Not related to greenhouse gas forcing (1)
Instrumental	Exploring vulnerabilities and some adaptive capacities	Physically realistic changes (2) Can contain a rich mixture of well-resolved, internally consistent, variables (3) Data readily available (5)	Not necessarily related to greenhouse gas forcing (1) Magnitude of the climate change usually quite small (1) No appropriate analogues may be available (5)
Spatial	Extrapolating climate/ecosystem relationships Pedagogic	May contain a rich mixture of well-resolved variables (3)	Not related to greenhouse gas forcing (1, 4) Often physically implausible (2) No appropriate analogues may be available (5)
Climate model based:
Direct AOGCM outputs	Starting point for most climate scenarios Large-scale response to anthropogenic forcing	Information derived from the most comprehensive, physically-based models (1, 2) Long integrations (1) Data readily available (5) Many variables (potentially) available (3)	Spatial information is poorly resolved (3) Daily characteristics may be unrealistic except for very large regions (3) Computationally expensive to derive multiple scenarios (4, 5) Large control run biases may be a concern for use in certain regions (2)
High resolution/stretched grid (AGCM)	Providing high resolution information at global/continental scales	Provides highly resolved information (3) Information is derived from physically-based models (2) Many variables available (3) Globally consistent and allows for feedbacks (1,2)	Computationally expensive to derive multiple scenarios (4, 5) Problems in maintaining viable parametrizations across scales (1,2) High resolution is dependent on SSTs and sea ice margins from driving model (AOGCM) (2) Dependent on (usually biased) inputs from driving AOGCM (2)
Regional models	Providing high spatial/temporal resolution information	Provides very highly resolved information (spatial and temporal) (3) Information is derived from physically-based models (2) Many variables available (3) Better representation of some weather extremes than in GCMs (2, 4)	Computationally expensive, and thus few multiple scenarios (4, 5) Lack of two-way nesting may raise concern regarding completeness (2) Dependent on (usually biased) inputs from driving AOGCM (2)
Statistical downscaling	Providing point/high spatial resolution information	Can generate information on high resolution grids, or non-uniform regions (3) Potential,for some techniques, to address a diverse range of variables (3) Variables are (probably) internally consistent (2) Computationally (relatively) inexpensive (5) Suitable for locations with limited computational resources (5) Rapid application to multiple GCMs (4)	Assumes constancy of empirical relationships in the future (1, 2) Demands access to daily observational surface and/or upper air data that spans range of variability (5) Not many variables produced for some techniques (3, 5) Dependent on (usually biased) inputs from driving AOGCM (2)
Climate scenario generators	Integrated assessments Exploring uncertainties Pedagogic	May allow for sequential quantification of uncertainty (4) Provides ‘integrated’ scenarios (1) Multiple scenarios easy to derive (4)	Usually rely on linear pattern scaling methods (1) Poor representation of temporal variability (3) Low spatial resolution (3)
Weather generators	Generating baseline climate time-series Altering higher order moments of climate Statistical downscaling	Generates long sequences of daily or sub-daily climate (2, 3) Variables are usually internally consistent (2) Can incorporate altered frequency/intensity of ENSO events (3)	Poor representation of low frequency climate variability (2, 4) Limited representation of extremes (2, 3, 4) Requires access to long observational weather series (5) In the absence of conditioning, assumes constant statistical characteristics (1, 2)
Expert judgment	Exploring probability and risk Integrating current thinking on changes in climate	May allow for a ‘consensus’ (4) Has the potential to integrate a very broad range of relevant information (1, 3, 4) Uncertainties can be readily represented (4)	Subjectivity may introduce bias (2) A representative survey of experts may be difficult to implement (5)
a Numbers in parentheses under Advantages and Disadavantages indicate that they are relevant to the criteria described. The five criteria are: (1) Consistency at regional level with global projections; (2) Physical plausibility and realism, such that changes in different climatic variables are mutually consistent and credible, and spatial and temporal patterns of change are realistic; (3) Appropriateness of information for impact assessments (i.e., resolution, time horizon, variables); (4) Representativeness of the potential range of future regional climate change; and (5) Accessibility for use in impact assessments.

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