4.2.2. Scenarios

All SRES scenarios were designed as quantitative "interpretations" (quantifications) of the SRES qualitative storylines. Each scenario is a particular quantification of one of the four storylines. The quantitative inputs for each scenario involved, for instance, regionalized measures of population, economic development, and energy efficiency, the availability of various forms of energy, agricultural productivity, and local pollution controls. Each participating modeling group (see previous page) used computer models and their experience in the assessment of long-range development of economic, technological, and environmental systems to generate quantifications of the storylines. The models used to develop the scenarios are:

• Asian Pacific Integrated Model (AIM) from the National Institute of Environmental Studies (NIES) in Japan (Morita et al., 1994).
• Atmospheric Stabilization Framework Model (ASF) from ICF Consulting in the US (Lashof and Tirpak, 1990; Pepper et al., 1998; Sankovski et al., 2000).
• Integrated Model to Assess the Greenhouse Effect (IMAGE) from the National Institute for Public Health and Hygiene (RIVM) in the Netherlands (Alcamo et al., 1998; de Vries et al., 1994, 1999, 2000), used in connection with the Central Planning Bureau (CPB) WorldScan model (de Jong and Zalm, 1991), the Netherlands.
• Multiregional Approach for Resource and Industry Allocation (MARIA) from the Science University of Tokyo in Japan (Mori and Takahashi, 1999; Mori, 2000).
• Model for Energy Supply Strategy Alternatives and their General Environmental Impact (MESSAGE) from the International Institute of Applied Systems Analysis (IIASA) in Austria (Messner and Strubegger, 1995; Riahi and Roehrl, 2000).
• The Mini Climate Assessment Model (MiniCAM) from the Pacific Northwest National Laboratory (PNNL) in the USA (Edmonds et al., 1994, 1996a, 1996b).

A more detailed description of the modeling approaches is given in Appendix IV. Some modeling teams developed scenarios that reflected all four storylines, while some presented scenarios for fewer storylines. Some scenarios share harmonized² input assumptions of main scenario drivers, such as population, economic growth, and final energy use, with their respective designated marker scenarios of the four scenario families and underlying storylines (see Section 4.4.1). Others explore scenario sensitivities in these driving forces through alternative interpretations of the four scenario storylines. Table 4-1 lists all SRES scenarios, by modeling group and by scenario family, and indicates which scenarios share harmonized input assumptions of important driving forces of emissions at the global level and at the level of the four SRES regions. Altogether, the six modeling teams formulated 40 alternative SRES scenarios

All the qualitative and quantitative features of scenarios that belong to the same family were set to conform to the corresponding features of the underlying storyline. Quantitative storyline targets recommended for use in all scenarios within a given family included, in particular, population and GDP growth assumptions. Most scenarios developed within a given family follow these storyline recommendations, but some scenarios offer alternative interpretations. Scenarios within each family vary quite substantially in such characteristics as the assumptions about availability of fossil-fuel resources, the rate of energy-efficiency improvements, the extent of renewable-energy development, and, hence, resultant GHG emissions. This variation reflects the modeling teams' alternative views on the plausible global and regional developments and also stems from differences in the underlying modeling approaches. After the modeling teams had quantified the key driving forces and made an effort to harmonize them with the storylines by adjusting control parameters, possible diversity still remained (see Section 4.4.1).

Figure 4-3: Schematic illustration of the multidimensional classification space of SRES scenarios. The set of scenarios consists of four scenario families (A1, A2, B1, and B2), each of which consists of a number of scenarios. Some of these have "harmonized" inputs - they share similar pre-specified global population and GDP trajectories. They are marked as "HS" for (globally) harmonized scenarios. All other scenarios of the same family based on the quantification of the storyline chosen by the modeling team are marked as "OS." The A1 family is divided into four scenario groups that explore alternative developments in the future energy sector. These were merged into three groups in the SPM (see also footnote 1). Finally, one of the harmonized scenarios is designated as the characteristic representative of each family and is the marker scenario.

In addition, the A1 scenario family developed into different distinct scenario groups, each based on the A1 storyline that describes alternative developments in future energy systems, from carbon-intensive development to decarbonization (see footnote 1). (Similar storyline variations were considered for other scenario families, but were pursued only to a limited degree in scenario sensitivity analysis in order to limit the number of scenarios.) This further increased the richness in different GHG emissions paths, because this variation in the structure of the future energy system in itself resulted in a range of emissions almost as large as that generated through the variation of other main driving forces, such as population and economic development. The differentiation into various scenario groups was introduced into the A1 storyline because of its "high growth with high technology" nature, in which differences in alternative technology developments translate into large differences in future GHG emission levels.

As for the storylines, no single scenario was treated as more or less "probable" than others belonging to the same family. However, after requests from various user communities to reduce the number of scenarios to a manageable size, a single scenario within a family was selected as a representative case to illustrate a particular storyline on the basis of the modeling teams' consensus. These scenarios were named "marker scenarios" or simply "markers" and were put on the SRES open process webpage for review. The marker scenario for the A1 scenario storyline was developed using the AIM model; for the A2 storyline using the ASF model; for the B1 storyline using the IMAGE model; and finally for the B2 storyline using the MESSAGE model (see Table 4-1).

The choice of the markers was based on extensive discussion within the SRES team:

• Which of the initial quantifications (by the models) reflected the story best.
• Preference of some of the modeling teams and features of specific models.
• Range of emissions across all the markers.
• Use of different models for the four markers.

In 1998, the preliminary descriptions and quantifications of the marker scenarios were posted on the SRES website for the open process and, in accordance with a decision of the IPCC Bureau, were in this way made available to climate modelers for their input in the Third Assessment Report. As a result of the inputs and comments received through the open process and by the entire writing team, the marker scenarios have been successively refined and improved without changing their fundamental characteristics in terms of important scenario driving forces (population, GDP) and order of magnitude of GHG emissions. Subsequently, additional scenarios within each scenario family were developed to explore the sensitivity of adopting alternative quantitative scenario input assumptions on future GHG emissions. As a result the markers are not necessarily the median or mean of a scenario family (nor would it be possible to construct such a median or mean scenario by taking all salient scenario characteristics and regional results into account). The markers are simply those scenarios considered by the SRES writing team as illustrative of a particular storyline. They are not singled out as more likely than alternative quantitative interpretations of a particular scenario family and its underlying storyline. Perhaps they may be best described as "first among equals." However, as a result of time and resource limitations the marker scenarios have received the closest scrutiny from the entire writing team and through the SRES open process compared to other scenario quantifications. The marker scenarios are also the SRES scenarios most intensively tested in terms of reproducibility. As a rule, at least four different models were used in attempts to replicate the model quantification of a particular marker scenario. Available time and resources did not allow a similar exercise to be conducted for all SRES scenarios, albeit a more limited effort was devoted to reproduce the A1 scenario groups (next to the A1 marker) with different models.

To enable a comparison of the resultant GHG emissions, the writing team decided to define a subset of harmonized scenarios within each family that share common main scenario driving-force assumptions, such as population or GDP growth. Two harmonization criteria were developed (see also Section 4.4.1). This procedure and the harmonization criteria were adopted in a joint agreement among the six SRES modeling teams.

"Fully harmonized" scenarios are those that share important driving force variables, including population, GDP, and final energy use for each of the four SRES regions and the world (according to the quantitative criteria listed in Table 4-1). Fully harmonized scenarios by definition include the respective marker scenario. From 40 scenarios 11 are classified as scenarios with "full harmonization." For each scenario family at least two scenarios are harmonized using the most restrictive criteria. This also applies to the scenario groups within the A1 scenario family, which correspondingly has the highest number of fully harmonized scenarios. This subset of "fully harmonized" scenarios serves to provide a better correspondence between the development of the three main driving forces and the resultant GHG emissions. The "fully harmonized" scenarios thus demonstrate the degree by which a particular marker scenario is reproducible by alternative modeling approaches. Therefore, "fully harmonized" scenarios are not independent from each other within a particular scenario family (or scenario group in case of A1).

"Globally harmonized" scenarios are those that share global population and GDP profiles within the agreed upon bounds of 5% and 10%, respectively, over the period 1990-2100³ (see Table 4-1). Altogether 26 ⁴ scenarios are categorized into this category and can be considered to capture the main global development characteristics over time for each respective scenario family and storyline. Again these 26 scenarios are not independent from each other, constituting seven distinct scenario groups (see also footnote 1).

Thus, there are three different types of scenarios within each family:

• One marker and a set of "fully harmonized" scenarios that attempt to replicate the marker scenario quantification.
• A set of "globally harmonized" scenarios.
• A set of non-harmonized scenarios.

In addition, two illustrative scenarios have been selected in the Summary for Policymakers (SPM) from the additional A1 scenario groups (see also footnote 1).

For the sake of simplicity, the term "harmonized" is used herein to describe global harmonization of population and GDP growth. "Fully harmonized" scenarios, for which the main objective is to assess the reproducibility of particular marker scenario quantifications and any remaining uncertainty in GHG emissions from internal model parametrizations, are referred to in the text where appropriate. Figure 4-3 shows the SRES scenarios as a set consisting of subsets that correspond to the four families. The A1 family is in addition divided into different groups of scenarios. The detailed descriptions of inputs and outputs (other than GHG emissions) of the SRES marker scenarios, other harmonized scenarios, and other scenarios are presented in Section 4.4 (see Appendix VII for further numeric detail). The emissions of GHGs and other radiatively important species of gases are described in Chapter 5 (more detail is again presented in Appendix VII).

Table 4-2 summarizes the main characteristics of the four SRES scenario families and their scenario groups, and gives an overview about the number of scenarios that were developed for each scenario group (see also Table 4-1 and Section 4.4.1).

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