3.6.6. Application of Scenarios
3.6.6.1. Simple Scenarios
Simple scenarios are based on one or several estimates of sea-level rise consistent
with IPCC-projected ranges of global sea-level rise for a particular date. Usually
a mid-range or upper estimate is chosen. The application of a eustatic scenario,
where a relative scenario is required, discounts the impact of regional sea-level
change and local land movements, although it is possible to add the latter explicitly
where estimates exist (Gambolati et al., 1999). Assessments that use
simple scenarios usually test whether a coastal region is sensitive and/or vulnerable
to a plausible upper limit of climate change (e.g., Zeidler, 1996; El Raey et
al., 1997; Olivo, 1997).
3.6.6.2. Projected Ranges
A range of global sea-level rise can be applied, bounded by its upper and lower
extremes, for a particular date (e.g., Ali, 1996; Nicholls et al., 1999).
This will project a likely range of impacts but without any reference to the
likelihood of that range or specific changes within that range (Section
2.5). The major disadvantage of this technique is the large range of uncertainty
that is produced, making it difficult for policymakers and planners to decide
on a concrete response.
3.6.6.3. Risk and Integrated Assessment
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Box 3-2. The Global Impact of Climate Change on Five
Sectors (Parry and Livermore, 1999)
In this assessment, the prospective effects of unmitigated climate change
during the 21st century are estimated at a global scale in five sectoral
studies (see Table 3-8). Each study has different
scenario requirements, though some are common to several studies. For
example, the ecosystems study estimates potential biomass on the basis
of scenarios of climate, CO2 concentration, and nitrogen deposition,
but it ignores future land-cover and land-use changes that would be expected
regardless of climate change. In contrast, the study on food security
examines the effects on crop productivity of the same scenarios of climate
(though for fewer variables) and CO2 concentration; it too
ignores likely land-cover and land-use changes and does not consider effects
of nitrogen deposition, although it adopts a range of socioeconomic and
technological scenarios to evaluate the number of persons at risk from
hunger.
Notably, across all of the studies the scenarios adopted are designed
to be mutually consistent. For instance, the population and GDP scenarios
are those adopted in constructing the IS92a emissions scenario (Leggett
et al., 1992). An approximation of the IS92a emissions scenario
is used to force the HadCM2 and HadCM3 GCMs that were employed to construct
the climate and sea-level scenarios (Hulme et al., 1999b). Other
scenarios are chosen to be broadly consistent with these assumptions.
The scenarios are required as inputs to global impact models, and results
from these are described elsewhere in this report. Finally, it also should
be noted that although these studies are compatible and consistent, they
are not integrated across sectors. For example, climate-induced changes
in water resources for irrigation are not accounted for in estimates of
future food security.
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Risk assessment aims to produce meaningful outcomes under
conditions of high uncertainty. For sea-level rise, two approaches to risk assessment
have been reported. The first approach is to construct a probability distribution
for a single outcome. For example, Titus and Narayanan (1996) conclude that
a sea-level rise of 10–65 cm by 2100 has an 80% probability of occurring;
the 99th percentile was associated with a 104-cm rise. The second approach is
to calculate the probability of exceedance above a given threshold identified
as a hazard. Pittock and Jones (2000) suggest the use of critical thresholds,
which link an unacceptable level of harm with a key climatic or climate-related
variable. For coastal impacts, the critical threshold is then linked to a projected
range of sea-level scenarios, through key climatic and marine variables, and
its risk of exceedance calculated (Jones et al., 1999).
IAMs attempt to represent the interaction of human activities with socioeconomic
and biophysical systems on a global scale (see Section 3.3.2.3).
In the TARGETS model (Rotmans and de Vries, 1997), various human activities
that affect a succession of phenomena are simulated to produce scenarios of
sea-level rise, which then lead to calculations of people and capital at risk
in low-lying coastal regions (Hoekstra, 1997). The IMAGE 2 integrated model
applies baseline scenarios of global environmental change (Alcamo et al., 1996)
to project several global outcomes, one of which is sea-level rise. Yohe and
Schlesinger (1998) used a model of global economic activity to produce emissions
profiles, which they then used to calculate temperature and sea-level changes
and integrated with an economic damages model for the U.S. coastline. The scenarios
of sea-level rise were probabilistically weighted from a sample of 280 to calculate
the 10th and 90th percentiles and the median estimate, producing several ranges
similar in magnitude to that of Titus and Narayanan (1996).
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