Box 21 Definitions of future characterisations

Figure 2.4 illustrates the relationships among the categories of future characterisations most commonly used in CCIAV studies. Because definitions vary across different fields, we present a single consistent typology for use in this chapter. Categories are distinguished according to comprehensiveness and plausibility.

Comprehensiveness indicates the degree to which a characterisation of the future captures the various aspects of the socioeconomic/biophysical system it aims to represent. Secondarily, it indicates the detail with which any single element is characterised.

Plausibility is a subjective measure of whether a characterisation of the future is possible. Implausible futures are assumed to have zero or negligible likelihood. Plausible futures can be further distinguished by whether a specific likelihood is ascribed or not.


Artificial experiments

Implausible futures

Zero or negligible likelihood





Sensitivity analysis

Probabilistic Futures


Sensitivity analysis

Plausible futures

Without ascribed likelihood

With ascribed likelihood

Artificial experiment. A characterisation of the future constructed without regard to plausibility (and hence often implausible) that follows a coherent logic in order to study a process or communicate an insight. Artificial experiments range in comprehensiveness from simple thought experiments to detailed integrated modelling studies.

Figure 2.4. Characterisations of the future.

Sensitivity analysis. Sensitivity analyses employ characterisations that involve arbitrary or graduated adjustments of one or several variables relative to a reference case. These adjustments may be plausible (e.g., changes are of a realistic magnitude) or implausible (e.g., interactions between the adjusted variables are ignored), but the main aim is to explore model sensitivity to inputs, and possibly uncertainty in outputs.

Analogues. Analogues are based on recorded conditions that are considered to adequately represent future conditions in a study region.These records can be of past conditions (temporal analogues) or from another region (spatial analogues). Their selection is guided by information from sources such as AOGCMs; they are used to generate detailed scenarios which could not be realistically obtained by other means. Analogues are plausible in that they reflect a real situation, but may be implausible because no two places or periods of time are identical in all respects.

Scenarios. A scenario is a coherent, internally consistent, and plausible description of a possible future state of the world (IPCC, 1994; Nakicenovic et al., 2000; Raskin et al., 2005). Scenarios are not predictions or forecasts (which indicate outcomes considered most likely), but are alternative images without ascribed likelihoods of how the future might unfold. They may be qualitative, quantitative, or both. An overarching logic often relates several components of a scenario, for example a storyline and/or projections of particular elements of a system. Exploratory (or descriptive) scenarios describe the future according to known processes of change, or as extrapolations of past trends (Carter et al., 2001). Normative (or prescriptive) scenarios describe a pre-specified future, either optimistic, pessimistic, or neutral (Alcamo, 2001), and a set of actions that might be required to achieve (or avoid) it. Such scenarios are often developed using an inverse modelling approach, by defining constraints and then diagnosing plausible combinations of the underlying conditions that satisfy those constraints (see Nakicenovic et al., 2007).

Storylines. Storylines are qualitative, internally consistent narratives of how the future may evolve. They describe the principal trends in socio-political-economic drivers of change and the relationships between these drivers. Storylines may be stand-alone, but more often underpin quantitative projections of future change that, together with the storyline, constitute a scenario.

Projection. A projection is generally regarded as any description of the future and the pathway leading to it. However, here we define a projection as a model-derived estimate of future conditions related to one element of an integrated system (e.g., an emission, a climate, or an economic growth projection). Projections are generally less comprehensive than scenarios, even if the projected element is influenced by other elements. In addition, projections may be probabilistic, while scenarios do not ascribe likelihoods.

Probabilistic futures. Futures with ascribed likelihoods are probabilistic. The degree to which the future is characterised in probabilistic terms can vary widely. For example, conditional probabilistic futures are subject to specific and stated assumptions about how underlying assumptions are to be represented. Assigned probabilities may also be imprecise or qualitative.

associated with radiative forcing in 2000 of about 0.6°C by 2100 (Meehl et al., 2007). Sea-level rise due to thermal expansion of the oceans responds much more slowly, on a time-scale of millennia; committed sea-level rise is estimated at between 0.3 and 0.8 m above present levels by 2300, assuming concentrations stabilised at A1B levels in 2100 (Meehl et al., 2007). However, these commitment runs are unrealistic because the instantaneous stabilisation of radiative forcing is implausible, implying an unrealistic change in emission rates (see Nakicenovic et al., 2007). They are therefore only suitable for setting a lower bound on impacts seen as inevitable (Parry et al., 1998).

2.4.3 Sensitivity analysis

Sensitivity analysis (see Box 2.1) is commonly applied in many model-based CCIAV studies to investigate the behaviour of a system, assuming arbitrary, often regularly spaced, adjustments in important driving variables. It has become a standard technique in assessing sensitivity to climatic variations, enabling the construction of impact response surfaces over multi-variate climate space (e.g., van Minnen et al., 2000; Miller et al., 2003). Response surfaces are increasingly constructed in combination with probabilistic representations of future climate to assess risk of impact (see Section 2.4.8). Sensitivity analysis sampling uncertainties in emissions, natural climate variability, climate change projections, and climate impacts has been used to evaluate the robustness of proposed adaptation measures for water resource management by Dessai (2005). Sensitivity analysis has also been used as a device for studying land-use change, by applying arbitrary adjustments to areas, such as +10% forest, -10% cropland, where these area changes are either spatially explicit (Shackley and Deanwood, 2003) or not (Ott and Uhlenbrook, 2004; van Beek and van Asch, 2004; Vaze et al., 2004).

2.4.4 Analogues

Temporal and spatial analogues are applied in a range of CCIAV studies. The most common of recently reported temporal analogues are historical extreme weather events. These types of event may recur more frequently under anthropogenic climate change, requiring some form of adaptation measure. The suitability of a given climate condition for use as an analogue requires specialist judgement of its utility (i.e., how well it represents the key weather variables affecting vulnerability) and its meteorological plausibility (i.e., how well it replicates anticipated future climate conditions). Examples of extreme events judged likely or very likely by the end of the century (see Table 2.2) that might serve as analogues include the European 2003 heatwave (see Chapter 12, Section 12.6.1) and flooding events related to intense summer precipitation in Bangladesh (Mirza, 2003a) and Norway (Nffiss et al., 2005). Other extreme events suggested as potential analogues, but about which the likelihood of future changes is poorly known (Christensen et al., 2007a), include El Niño-Southern Oscillation (ENSO)-related events (Glantz, 2001; Heslop-Thomas et al., 2006) and intense precipitation and flooding events in central Europe (Kundzewicz et al., 2005). Note also that the suitability of such analogue events should normally be considered along with information on accompanying changes in mean climate, which may ease or exacerbate vulnerability to extreme events.

Spatial analogues have also been applied in CCIAV analysis. For example, model-simulated climates for 2071 to 2100 have been analysed for selected European cities (Hallegatte et al., 2007). Model grid boxes in Europe showing the closest match between their present-day mean temperatures and seasonal precipitation and those projected for the cities in the future were identified as spatial analogues. These 'displaced' cities were then used as a heuristic device for analysing economic impacts and adaptation needs under a changing climate. A related approach is to seek projected climates (e.g., using climate model simulations) that have no present-day climatic analogues on Earth ('novel' climates) or regions where present-day climates are no longer to be found in the future ('disappearing' climates: see Ohlemuller et al., 2006; Williams et al., 2007). Results from such studies have been linked to risks to ecological systems and biodiversity.

2.4.5 Storylines

Storylines for CCIAV studies (see Box 2.1) are increasingly adopting a multi-sectoral and multi-stressor approach (Holman et al., 2005a, b) over multiple scales (Alcamo et al., 2005; Lebel et al., 2005; Kok et al., 2006a; Westhoek et al., 2006b) and are utilising stakeholder elicitation (Kok et al., 2006b). As they have become more comprehensive, the increased complexity and richness of the information they contain has aided the interpretation of adaptive capacity and vulnerability (Metzger et al., 2006). Storyline development is also subjective, so more comprehensive storylines can have alternative, but equally plausible, interpretations (Rounsevell et al., 2006). The concept of a 'region', for example, may be interpreted within a storyline in different ways - as world regions, nation states, or subnational administrative units. This may have profound implications for how storylines are characterised at a local scale, limiting their reproducibility and credibility (Abildtrup et al., 2006). The alternative is to link a locally sourced storyline, regarded as credible at that scale, to a global scenario.

Storylines can be an endpoint in their own right (e.g., Rotmans et al., 2000), but often provide the basis for quantitative scenarios. In the storyline and simulation (SAS) approach (Alcamo, 2001), quantification is undertaken with models for which the input parameters are estimated through interpretation of the qualitative storylines. Parameter estimation is often subjective, using expert judgement, although more objective methods, such as pairwise comparison, have been used to improve internal consistency (Abildtrup et al., 2006). Analogues and stakeholder elicitation have also been used to estimate model parameters (e.g., Rotmans et al., 2000; Berger and Bolte, 2004; Kok et al., 2006a). Moreover, participatory approaches are important in reconciling long-term scenarios with the short-term, policy-driven requirements of stakeholders (Velazquez et al., 2001; Shackley and Deanwood, 2003; Lebel et al., 2005).

2.4.6 Scenarios

Advances in scenario development since the TAR address issues of consistency and comparability between global drivers of change, and regional scenarios required for CCIAV assessment (for reviews, see Berkhout et al., 2002; Carter et al., 2004; Parson et al., 2006). Numerous methods of downscaling from global to sub-global scale are emerging, some relying on the narrative storylines underpinning the global scenarios.

At the time of the TAR, most CCIAV studies utilised climate scenarios (many based on the IS92 emissions scenarios), but very few applied contemporaneous scenarios of socio-economic, land-use, or other environmental changes. Those that did used a range of sources to develop them. The IPCC Special Report on Emissions Scenarios (SRES: see Nakicenovic et al., 2000) presented the opportunity to construct a range of mutually consistent climate and non-climatic scenarios. Originally developed to provide scenarios of future GHG emissions, the SRES scenarios are also accompanied by storylines of social, economic, and technological development that can be used in CCIAV studies (Box 2.2).

There has been an increasing uptake of the SRES scenarios since the TAR, and a substantial number of the impact studies assessed in this volume that employed future characterisations made use of them.7 For this reason, these scenarios are highlighted in a series of boxed examples throughout Section 2.4. For some other studies, especially empirical analyses of adaptation and vulnerability, the scenarios were of limited relevance and were not adopted.

While the SRES scenarios were specifically developed to address climate change, several other major global scenario-

building exercises have been designed to explore uncertainties and risks related to global environmental change. Recent examples include: the Millennium Ecosystem Assessment scenarios to 2100 (MA: see Alcamo et al., 2005), Global Scenarios Group scenarios to 2050 (GSG: see Raskin et al., 2002), and Global Environment Outlook scenarios to 2032 (GEO-3: see UNEP 2002). These exercises were reviewed and compared by Raskin et al. (2005) and Westhoek et al. (2006a), who observed that many applied similar assumptions to those used in the SRES scenarios, in some cases employing the same models to quantify the main drivers and indicators. All the exercises adopted the storyline and simulation (SAS) approach (introduced in Section 2.4.5). Furthermore, all contain important features that can be useful for CCIAV studies; with some exercises (e.g., MA and GEO-3) going one step further than the original SRES scenarios by not only describing possible emissions under differing socio-economic pathways but also including imaginable outcomes for climate variables and their impact on ecological and social systems. This helps to illustrate risks and possible response strategies to deal with possible impacts.

Five classes of scenarios relevant to CCIAV analysis were distinguished in the TAR: climate, socio-economic, land-use and land-cover, other environmental (mainly atmospheric composition), and sea-level scenarios (Carter et al., 2001). The following sections describe recent progress in each of these classes and in four additional categories: technology scenarios, adaptation scenarios, mitigation scenarios, and scenario integration.

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