19.7.4. Aggregate Approaches
Advantages: Aggregate analyses synthesize climate change impacts in an internally
consistent manner, using relatively comprehensive global indicators or metrics.
These often are expressed in U.S. dollars (e.g., Tol, 2001b) or other common
metrics such as changes in vegetation cover (Alcamo et al., 1998). This enables
direct comparisons of impacts among sector systems and regions and with other
environmental problems and emission control costs. Some aggregate analyses have
assessed differences in relative impacts in developed and developing regions
of the world and have shown that regional differences in impacts may be substantial.
Disadvantages: Aggregate analyses lack richness of detail. Partly this is inherent
because aggregation explicitly seeks to synthesize complex information. Partly
this is because aggregate analyses tend to rely on reduced-form models. Condensing
the diverse pattern of impacts into a small number of damage indicators is difficult.
Some metrics may not accurately capture the value of certain impacts; for example,
nonmarket impacts such as mortality and loss of species diversity or cultural
heritage often are not well captured in monetization approaches, and change
in vegetation cover may not clearly indicate threats to biodiversity. Other
complicating issues concern comparison of impacts across time (impact today
and several generations from now) and between regions (e.g., impact in developing
and developed countries), as well as how much importance to assign to different
effects. In addition, many aggregate studies examine a static world rather than
a dynamic one and do not consider the effects of changes in extreme events or
large-scale discontinuities. The aggregation process is not possible without
value judgments, and different ethical views imply different aggregate measures
across socioeconomic groupings and generations (see Azar and Sterner, 1996;
Fankhauser et al., 1997). Choice of discount rates can affect valuation of damages.
In addition, general shortcomings that affect all reasons for concern are particularly
prominent in aggregate analysis (e.g., accounting for baseline development,
changes in variability and extreme events, and costs and benefits of adaptation).
Uncertainties: Uncertainties include whether all climate change impacts (positive
and negative) are included, the implications of various aggregation and valuation
methods, and implicit or explicit assumptions of methods, including possible
mis-specifications of nonlinearities and interaction effects.
Research Needs: The next generation of aggregate estimates will have to account
better for baseline developments, transient effects, climate variations, and
multiple stresses. Further progress also is still needed in the treatment of
adaptation. A broader set of primary studies on impacts in developing countries
and nonmarket sectors would reduce the need for difficult extrapolation. More
work also is needed on the ethical underpinnings of aggregation and on alternative
aggregation schemes. Work on reflecting information from the other reasons for
concern into the aggregate approach is underway, but proceeding slowly.
19.7.5. Integrated Assessment Frameworks
Advantages: Integrated assessment frameworks or models provide a means of structuring
the enormous amount of and often conflicting data available from disaggregated
studies. They offer internally consistent and globally comprehensive analysis
of impacts; provide "vertical integration" (i.e., cover the entire
"causal chain" from socioeconomic activities giving rise to GHG emissions
to concentration, climate, impacts, and adaptations); provide "horizontal
integration" (i.e., account for interlinkages between different impact
categories, adaptations, and exogenous factors such as economic development
and population growth); and allow for consistent treatment of uncertainties.
IAMs have been used primarily for benefit-cost and inverse (or threshold) analyses.
The latter have the advantage of being directly related to Article 2 because
they define impacts that may be considered "dangerous" (through specification
of thresholds related to, e.g., harm to unique and threatened systems or the
probability of large-scale discontinuities).
Disadvantages: The main disadvantages with most IAMs are those associated with
aggregate approaches: reliance on a single or a limited number of universal
measures of impacts. These may not adequately measure impacts in meaningful
ways. This is partly because IAMs rely on reduced-form equations to represent
the complexities of more detailed models. Their usefulness is highly dependent
on how well they are able to capture the complexities of more disaggregated
approaches. Some of the IAMs used for benefit-cost analyses have considered
large-scale irregularities (e.g., Gjerde et al., 1999), but inclusion of such
outcomes is preliminary. Few have accounted for loss of or substantial harm
to unique and threatened systems. Although inverse (or threshold) approaches
allow researchers to overcome these problems, the disadvantages of this kind
of analysis include the difficulty of explicitly specifying thresholds and combining
them within and across sectors and regions.
Uncertainties: Uncertainties are the same as those for the aggregate approach
or for unique and threatened systems, depending on the structure and objectives
of the model. This also would include the effects of different assumptions,
methods, and value choices.
Research Needs: Among the biggest challenges facing integrated assessment modelers
(see Weyant et al., 1996) are developing a credible way to represent and value
the impacts of climate change; a credible way to handle low-probability but
potentially catastrophic events; a credible way to incorporate changes in extreme
weather events; and realistic representations of changes in socioeconomic and
institutional conditions, particularly in developing countries. In addition,
they must decide how to allow explicitly for effects of different value choices,
systems, and assumptions; how to quantify uncertainties; and how to credibly
incorporate planned adaptation, including costs and limitations.
19.7.6. Extreme Events
Advantages: Extreme events are recognized as major contributors to the impacts
of climate variability now and to potential impacts of climate change in the
future. Thus, realistic climate change impact assessments must take them into
account even though they may change in complex wayssuch as in frequency,
magnitude, location, and sequences (e.g., increased variability may lead to
more frequent floods and droughts). Better understanding of changes in extreme
events and adaptation measures for coping with them also will help in coping
with present variability.
Disadvantages: Extreme events are more difficult to model and characterize
than average climates. Changes in extreme events will be complex and uncertain,
in part because extremes occur in a chaotic manner even in the present climate.
Large data series are needed to characterize their occurrence because, by definition,
they are rare events. This means that long time scale model simulations are
needed to develop relevant statistics from long time slices or multiple realizations.
Extreme events need to be considered in terms of probabilities or risks of occurrence
rather than predictions. This chaotic element adds to other sources of uncertainty.
It means that engineering or other design standards based on climatology that
normally use long data series of observations will need a synthetic data set
that simulates potential changes in future climate. It also makes adaptation
to changes in extremes more difficult because planned adaptation must rely on
necessarily uncertain projections into the future from theory and thus requires
greater faith in the science before the information will be acted on.
Uncertainties and Research Needs: Better knowledge of the behavior of extremes
will require long or multiple simulations at finer spatial and temporal scales,
to capture the scale, intensity, and frequency of the events. Some types of
extreme events (e.g., hail and extreme wind bursts) are poorly simulated at
present; others, such as ENSO and tropical cyclones, are extremely complex and
only now are beginning to be better simulated. Arguments for changes in their
behavior are still often largely theoretical, qualitative, or circumstantial,
rather than well based in verified models. Moreover, much more work is needed
on how they will affect natural and human systems and how much of the recent
trend to greater damages from extreme events is related to changes in exposure
(e.g., greater populations, larger investments, more insurance cover, or greater
reporting) rather than changes in the number and intensity of those extremes.
More work is needed on how best to adapt to changes in extreme events, especially
on how planners and decisionmakers can best take information on projected changes
in extremes into consideration. This may be done best by focusing on projected
change in the risk of exceeding prescribed natural, engineering, or socioeconomic
19.7.7. Large-Scale Singular Events
Advantages: Consideration of strongly nonlinear or even disruptive effects
accompanying climate change is a critical component of the "dangerous interference"
debate. The basic idea is to corroborate any non-negligible probability for
high-consequence impacts that may be triggered by human climate perturbations.
The political process to avoid high-consequence impacts may be facilitated by
the global scope of such effects (e.g., disintegration of the WAIS generating
a planetary sea-level rise of approximately 5m). Inclusion of extreme events
in the analysis helps, in general, to pursue all other reasons for concern in
a realistic way because irregular impacts may dominate impacts on unique and
threatened systems, distributional impacts, and aggregate impacts.
Disadvantages: This is an emerging area of research, facing several serious
challenges because of the complexity of nonlinear interactions to be considered.
The prevailing lack of knowledge is reflected in use of the term "surprises"
for disruptive events. The potentials for climate change-induced transformations
of extreme events regimes and for large-scale discontinuities in the Earth system
are still highly uncertain. The search for irregularities might turn out to
be futile and distract scientific resources from other important topics, such
as the distributional aspects of regular climate change impacts.
Uncertainties and Research Needs: By definition, uncertainties are most severe
in this realm of impact research. At present, there is no way of estimating
the probabilities of certain disruptive events or assigning confidence levels
to those probabilities. As a consequence, a strong research program should be
launched that combines the best paleoclimate observations with the strongest
simulation models representing full and intermediate complexity.
19.7.8. Looking across Analytic Approaches
Looking across the different analytic approaches (implicitly, the different
reasons for concern), it is clear that to a great extent they complement and
in many respects do not overlap each other. Combining these approaches into
an integrated framework is the ambition of IAMs, at least in principle. However,
this process is just starting. Because observed evidence has not been incorporated
in the other analytic approaches, impacts to unique and threatened systems have
not been accounted for in aggregate and IAM approaches, they are difficult to
sum, and large-scale irregular impacts have only begun to be addressed, it does
not appear to be feasible yet to combine these approaches into a comprehensive
analytic approach. Thus, those who are seeking to implement climate policies
must currently do their own integration of information from the alternative
lines of inquiry.