Scenarios are images of the future, or alternative futures. They are neither predictions nor forecasts. Rather, each scenario is one alternative image of how the future might unfold. A set of scenarios assists in the understanding of possible future developments of complex systems. Some systems, those that are well understood and for which complete information is available, can be modeled with some certainty, as is frequently the case in the physical sciences, and their future states predicted. However, many physical and social systems are poorly understood, and information on the relevant variables is so incomplete that they can be appreciated only through intuition and are best communicated by images and stories. Prediction is not possible in such cases (see Box 1-1 on uncertainties inherent in scenario analysis).
In general, there are three types of uncertainty: uncertainty in quantities, uncertainty about model structure and uncertainties that arise from disagreements among experts about the value of quantities or the functional form of the model (Morgan and Henrion, 1990). Sources of uncertainty could be statistical variation, subjective judgment (systematic error), imperfect definition (linguistic imprecision), natural variability, disagreement among experts and approximation (Morgan and Henrion, 1990). Others (Funtowicz and Ravetz, 1990) distinguish three main sources of uncertainty: "data uncertainties,""modeling uncertainties" and "completeness uncertainties." Data uncertainties arise from the quality or appropriateness of the data used as inputs to models. Modeling uncertainties arise from an incomplete understanding of the modeled phenomena, or from approximations that are used in formal representation of the processes. Completeness uncertainties refer to all omissions due to lack of knowledge. They are, in principle, non-quantifiable and irreducible.
Scenarios help in the assessment of future developments in complex systems that are either inherently unpredictable, or that have high scientific uncertainties. In all stages of the scenario-building process, uncertainties of different nature are encountered. A large uncertainty surrounds future emissions and the possible evolution of their underlying driving forces, as reflected in a wide range of future emissions paths in the literature. The uncertainty is further compounded in going from emissions paths to climate change, from climate change to possible impacts and finally from these driving forces to formulating adaptation and mitigation measures and policies. The uncertainties range from inadequate scientific understanding of the problems, data gaps and general lack of data to inherent uncertainties of future events in general. Hence the use of alternative scenarios to describe the range of possible future emissions.
For the current SRES scenarios, the following sources of uncertainties
Translation of the Understanding of Linkages between Driving Forces
into Quantitative Inputs for Scenario Analysis. Often the understanding
of the linkages is incomplete or qualitative only. This makes it difficult
for modelers to implement these linkages in a consistent manner.
Different Sources of Data. Data differ from a variety of well-acknowledged scientific studies, since "measurements" always provide ranges and not exact values. Therefore, modelers can only choose from ranges of input parameters for. For example:
Inherent Uncertainties. These uncertainties stem from the fact that unexpected "rare" events or events that a majority of researchers currently consider to be "rare future events" might nevertheless occur and produce outcomes that are fundamentally different from those produced by SRES model runs.
Scenarios can be viewed as a linking tool that integrates qualitative narratives or stories about the future and quantitative formulations based on formal modeling. As such they enhance our understanding of how systems work, behave and evolve. Scenarios are useful tools for scientific assessments, for learning about complex systems behavior and for policy making (Jefferson, 1983; Davis, 1999). In scientific assessments, scenarios are usually based on an internally consistent and reproducible set of assumptions or theories about the key relationships and driving forces of change, which are derived from our understanding of both history and the current situation. Often scenarios are formulated with the help of numeric or analytic formal models.
Future levels of global GHG emissions are the products of a very complex, ill-understood dynamic system, driven by forces such as population growth, socio-economic development, and technological progress; thus to predict emissions accurately is virtually impossible. However, near-term policies may have profound long-term climate impacts. Consequently, policy-makers need a summary of what is understood about possible future GHG emissions, and given the uncertainties in both emissions models and our understanding of key driving forces, scenarios are an appropriate tool for summarizing both current understanding and current uncertainties. For such scenarios to be useful for climate models, impact assessments and the design of mitigation and adaptation policies, both the main outputs of the SRES scenarios (emissions) and the main inputs or driving forces (population growth, economic growth, technological, e.g., as it affects energy and land-use) are equally important.
GHG emissions scenarios are usually based on an internally consistent and reproducible set of assumptions about the key relationships and driving forces of change, which are derived from our understanding of both history and the current situation. Often these scenarios are formulated with the help of formal models. Such scenarios specify the future emissions of GHGs in quantitative terms and, if fully documented, they are also reproducible. Sometimes GHG emissions scenarios are less quantitative and more descriptive, and in a few cases they do not involve any formal analysis and are expressed in qualitative terms. The SRES scenarios involve both qualitative and quantitative components; they have a narrative part called "storylines" and a number of corresponding quantitative scenarios for each storyline. Figure 1-1 illustrates the interrelated nature of these alternative scenario formulations.
Although no scenarios are value free, it is often useful to distinguish between normative and descriptive scenarios. Normative (or prescriptive) scenarios are explicitly values-based and teleologic, exploring the routes to desired or undesired endpoints (utopias or dystopias). Descriptive scenarios are evolutionary and open-ended, exploring paths into the future. The SRES scenarios are descriptive and should not be construed as desirable or undesirable in their own right. They are built as descriptions of possible, rather than preferred, developments. They represent pertinent, plausible, alternative futures. Their pertinence is derived from the need for policy makers and climate-change modelers to have a basis for assessing the implications of future possible paths for GHG and SO2 emissions, and the possible response strategies. Their plausibility is based on an extensive review of the emissions scenarios available in the literature, and has been tested by alternative modeling approaches, by peer review (including the "open process" through the IPCC web site), and by the IPCC review and approval processes. Good scenarios are challenging and court controversy, since not everybody is comfortable with every scenario, but used intelligently they allow policies and strategies to be designed in a more robust way.
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