Emissions Scenarios - IPCC

184 An Overview of Scenarios
particular scenario storyline compared to the previous scenario
categories. In some cases, differences in interpretation relate to
uncertainties in rates of change - "other" scenarios yield
similar global demographic and economic outcomes by 2100
(e.g. the B2-ASF scenario compared to the B2 marker), but
illustrate different dynamics of how these could unfold. In
other cases, the "other" scenario category comprises scenario
quantifications that deliberately explore alternative
interpretations of a scenario storyline in terms of global
population and GDP growth altogether (e.g. in the A2-A1-
MiniCAM scenario). The reason is to indicate that quantitative
scenario descriptions entail a high degree of uncertainty (and
subjectivity from different modeling teams) when it comes to
interpret the four different qualitative SRES scenario storylines
and to translate them into the quantitative assumptions that
drive emission models. When comparing GHG emissions
results for the four SRES marker scenarios (see Chapter 5)
with those of the other SRES scenarios, it is illustrative to
distinguish the effects of different model methodologies and
parametrizations from variations of important scenario drivers
that often serve as exogenous input to models.
Of the total of 40 SRES scenarios, 29 (including the marker
scenarios) satisfy the harmonization criteria for population on
the world level and for all four SRES regions, 12 scenarios are
harmonized for population and GDP, and U (13 including the
AIT scenario group) scenarios are harmonized for population,
GDP and final energy (see Table 4-1). Also, 35 scenarios are
harmonized for population on the world level and 26 scenarios
are harmonized for global population and GDP (see Table 4-1).
The status of harmonization is also relatively stable to changes
in the harmonization criteria. For example, if the above
harmonization criteria were increased by 50% (i.e. GDP for the
four SRES regions may differ by up to ±38% from the
respective GDP of the marker scenario), the sample of 11
harmonized scenarios does not change; however, the number of
scenarios harmonized on the global level increases from 15 to
20.
Thus, as mentioned above not all scenario quantifications
comply with the adopted harmonization criteria differences in
regional coverage and definition among models. In some
instances modeling teams also deliberately chose not to follow
harmonized input assumptions, but instead explored scenario
sensitivities by emphasizing alternative developments than
suggested in the marker scenario quantification. The writing
team recognizes that this increases the number of scenarios as
well as complexity in the interpretation of resuhs. These
additional scenarios are the result of the SRES terms of
reference of proceeding via an open process soliciting as wide
participation and viewpoints as possible and also serve the
purpose of highlighting important uncertainties of the future
that are necessarily compressed by limiting scenario
quantification to four illustrative marker scenarios. Thus, while
unharmonized scenarios illustrate the impact on GHG
emissions of expanding the uncertainty range of main scenario
drivers within any particular scenario family, the "globally
harmonized" scenarios indicate the range of GHG emissions
uncertainty that remains after most important global driving
force assumptions (population and GDP) have been
harmonized. (Finally, the range of GHG emissions resulting
from comparing "fully harmonized" scenarios is indicative of
the uncertainty of internal model parametrizations such as
energy technology change, dietary patterns, and agricultural
productivity changes that influence structural changes in energy
supply and end-use and land-use changes, see Table 4-1.)
Harmonization of input assumptions increases the
comparability across scenarios and can serve as an additional
guide for choosing a particular SRES scenaiio subset, and to
illustrate different degrees of scenario uncertainty. The latter is
an important aspect, considering the different user
communities of SRES scenarios. Given the comparatively
naiTow variation as defined by the harmonization criteria,
differences in population, GDP, and final energy use between
harmonized scenarios of the same scenario family need not to
be considered in subsequent analyses and are also not
discussed separately below.
In the AI scenario family, the scenarios within one group were
also harmonized. In one Al scenario group the transition away
from conventional oil and gas either leads to a massive
development of unconventional oil and gas resources (AIG) or
to a large-scale synfuel economy based on coal (AlC). Please
note that AlC and AIG were combined into one fossil
intensive group AlFI in the Summary for Policymakers during
its approval process (see also footnote I). GHG emissions in
these scenarios approach emissions characteristic of the A2
scenario family (i.e. are much higher than in the case of the Al
marker scenario). In another Al scenario group, dwindling
conventional oil and gas resources lead to fast development of
post-fossil alternatives and enhanced energy conservation. In
this technology-intensive scenario group (AIT), energy
demands are lower than in the other Al scenario groups and,
because of radical technological change in energy systems,
GHG emissions are much lower than in the other Al scenario
groups (including the AIB marker scenario), approaching
those of the В1 scenario family.
The six modeling teams also produced other scenarios as part
of the SRES open process. These modeling runs were generally
not harmonized and are presented as appropriate later in the
report.
Table 4-3 gives an overview of the 40 SRES scenario
quantifications as they were developed to describe the four
scenario famities and the seven different scenario groups.
4.4.2. Translation of Storylines into Scenario Drivers
Table 4-4 gives a summary overview of the main scenario
assumptions and characteristics (see also Table 4-2 above). To
facilitate comparability, the summary format adopted is similar
to the previous IS92 scenario series (Pepper et al., 1992).
Specific assumptions about the quantification of particular