Most climate scenarios for impact assessments were developed by using outputs from AOGCMs (Hulme and Brown, 1998). Often these results are scaled toward the desired emission levels with simple climate models (Hulme et al., 1995; Harvey et al., 1997; see Section 3.8.3). In this simple approach, most interactions are neglected.

The only models that can be used to develop more consistent scenarios that incorporate most of the important interactions are IAMs (see Section 3.3.2.3). IAMs have been developed with different levels of complexity, from extremely simple to highly complex (Harvey et al., 1997). Different interactions are included, although no single model provides a fully comprehensive treatment. The models are most commonly used for emission scenario development and mitigation policy assessment (Schimel et al., 1997a; Alcamo et al., 1998a; Pepper et al., 1998). All simulate a causal chain (e.g., human activities, emissions, climate change, sea-level rise, and other impacts). Emissions, climate change, impact, and mitigation scenarios derived from these models have been published (Schimel et al., 1997a; Leemans et al., 1998; Pepper et al., 1998) and collected in several databases (Alcamo et al., 1995; Nakicenovic et al., 2000). Unfortunately, it is not always clear which interactions are explicitly included in individual IAMs. This reduces the comparability of individual IAM-derived scenarios and thus their utility.

Depending on assumed interactions during scenario development, a wide range of estimates of climate change and its impacts is possible (see Table 3-7). However, within this range certain responses are more likely than others. To define appropriate and realistic levels of interactions, expert judgment and sensitivity experiments with models could be very valuable (van der Sluijs, 1997). Innovative, objective, and systematic approaches have to be developed to evaluate underlying scenario assumptions and to validate the scenario results. This is still an immature area of scenario development.

One difficulty faced by authors in attempting to summarize and synthesize the results of impact studies for previous IPCC assessments (i.e., IPCC, 1996b, 1998) has been a lack of consistency in projections. Different climate projections have been adopted in different studies, in different regions (or within the same region), and in different sectors. Moreover, even where the same climate projections are assumed, they might not be applied in the same way in different impact studies. Finally, some studies also are inconsistent in their methods of projecting changes in climate alongside concurrent changes in related socioeconomic and environmental conditions.

For example, GHG concentrations often are transformed into CO₂-equivalent concentrations to determine radiative forcing levels and climate change. The GCM community often presents climate change simulations as "doubled CO₂" anomalies. Depending on the scenario, however, 5-40% of the forcing is caused by non-CO₂ GHGs (30% in 1990). The doubled-CO₂ scenarios often are interpreted as CO₂ only (e.g., Cramer et al., 1997); others add an explicit distinction between CO₂ and non-CO₂ gases (e.g., Downing et al., 1999). In determining the impacts of direct CO₂ effects and climate change, this can easily lead to inconsistencies. Similar discrepancies exist for other types of interactions.

Finally, it is a significant challenge to integrate climate or sea-level rise scenarios, with a time horizon of decades to hundreds of years, with nonclimatic scenarios of social, economic, and technological systems that can change rapidly over a time scale of years. For instance, it is difficult to devise credible socioeconomic scenarios that extend beyond the lifetime of current infrastructure and institutions. Moreover, social/economic actors who need to be involved in the scenario development process (e.g., business, governments) often find long time horizons difficult to contemplate. Box 3-2 illustrates a recent example of an attempt to harmonize climate change, sea level, atmospheric composition, and socioeconomic scenarios in a multi-sectoral global impact assessment.

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