2.4.2 The State of the Art
Treatment of impacts in these models also varies greatly. Generally, however,
impacts are one of the weakest parts of IAMs. To a large extent this is a reflection
of the state of the art of the underlying research, but it also reflects the
high complexity of the task at hand (see Tol and Fankhauser, 1998, for a survey).
Despite the growing number of country-level case studies, our knowledge about
climate change and climate change impacts at the regional level remains limited.
A coherent global picture, based on a uniform set of assumptions, has yet to
emerge. The basis of most global impact assessments remain studies undertaken
in developed countries (often the United States), which are then extrapolated
to other regions. Such extrapolation is difficult and will be successful only
if regional circumstances are carefully taken into account, including differences
in geography, level of development, value systems, and adaptive capacity. Not
all analyses are equally careful in undertaking this task, and not all models
rely on the latest available information in calibrating their damage functions.
The actual functional relationships applied in many integrated models remain
simple and often ad hoc. This reflects our still poor understanding of how impacts
change over time and as a function of climate parameters. Impacts usually are
a linear or exponential function of absolute temperature, calibrated around
static "snapshot" estimates (such as 2xCO2) without distinguishing
the different dynamics that may govern impacts in different sectors. Developing
a better understanding of these relationships is one of the most important challenges
for integrated model development.
Baseline trendssuch as economic development, population growth, technological
progress, changes in values, natural climate fluctuations, and increased stress
on natural ecosystemshave strong repercussions for climate change vulnerability
(e.g., Mendelsohn and Neumann, 1999). They must be better understood and their
effect incorporated in the models. Unfortunately, these trends are inherently
difficult, if not impossible, to predict over the longer term. This generic
problem will not go away, but it can be overcome, at least partly, through broad
scenario and sensitivity analysis.
Another key challenge is taking adaptation into account. Adaptation can significantly
reduce people's vulnerability to climate change, as shown in Chapter
18. However, adaptation can take many different forms and is correspondingly
difficult to model (see Section 19.4). To date there are
no IAMs available that can adequately represent or guide the full range of adaptation