An Incomplete Overview
5. Challenges Ahead
There are three different challenges on the horizon for integrated assessment studies of climate and global change. The first of these is related t o the basic science which provides the fundamental information used in developing IA frameworks. The second is in the methodologies for integrated assessment.
The third is in learning what matters t o decision makers and designing inte- grated assessments so that they inform the decision making process and are adopted by decision makers in their activities.
Basic Scientific Challenges
In the modeling of socioeconomic aspects of IAs, we suffer a dearth of ba- sic d a t a outside democratic countries. For example, there are no incentives for collection of representative demographic data outside democratic soci- eties. A spectacular example of this issue was found in Nigeria where in anticipation of democratic rule a new census was taken in 1991. This found 88.5 million Nigerians rather than the UNs estimate of 126 million (Central Intelligence Agency, 1992). Furthermore, the models we have developed t o describe social dynamics have only been tested in the context of the demo- cratic subset of the world. More generally, our knowledge of key dynamics of social systems is limited. These limitations include, but are not limited to:
What brings about demographic transition, and how may population changes be predicted over the next century or more?
What are the roots of technological innovation and diffusion?
W h a t has led t o rapid industrialization in some countries and how have other countries failed to grow?
How are preferences formed and do they evolve through time?
Finally, a question common t o all these issues. Can these be manipulated through specific initiatives?
In the realm of the natural systems we are faced with similar problems.
The dynamics of the climate system continue t o be far from well understood.
The real difficulty arises from the dearth knowledge about the internal dy- namics of this system. After all, greenhouse gases (other than water vapor and ozone) contribute about 5% of the global warming effect that permits life on earth. The remaining 95% is due t o water vapor and ozone whose behavior is internal t o the climate system. The challenge in climate model- ing is two fold: (i) establishing some measure of confidence about the state of the climate system in the absence of anthropogenic influences, and (ii) predicting the response of the 95% t o perturbations in the 5%. The nascent nature of climate science is typified by a continuing stream of "surprise"
findings and continuing disappointment in solving what were once thought t o be tractable problems. For example:
We are still a t a loss as to how t o model clouds (Cess et al., 1990).
Balancing the "carbon-cycle" remains a challenge, made more difficult with recent evidence of a new reservoir of organic carbon in the oceans (Benner et al., 1992; Toggweiler, 1992).
CFCs, once thought t o be the most potent greenhouse gases, are now believed t o have a negligible net warming effect (Wigley and Raper, 1992).
Fuel and biomass burning as well as biogenic sources lead t o emission of greenhouse gases and aerosols. The former lead to long wave radiation being trapped in the atmosphere and "warming." The latter lead t o reflection of short-wave radiation. Estimating the magnitude of this cooling effect continues t o be a difficult challenge (Kaufman e t al., 1991;
Charlson et al., 1992).
We have long known about the central role of ocean circulation in the global climate, but new evidence calls into question long held beliefs on the cause and effect in that relationship (Zahn, 1992).
And finally, there is paleoclimatic evidence of abrupt climate change and multiple stable states of climate (at least on a regional and possibly on the global scale) but there is insufficient data on what may have triggered these (Dansgaard et al., 1993; Veum et al., 1992).
In plant response studies, we have learned about the importance of COz fertilization effect on plants. However, even the first steps towards an un- derstanding the ecological consequence of this matter are yet t o be com- pleted. A sensitivity analysis of a leading plant physiology model suggests the impact of changed COz concentration t o be greater than the impact of climate change (temperature, precipitation, and photosynthetically active radiation) (Shevliakova et al., 1993). However, the most advanced global
ecosystem modeling efforts continue t o seek impacts on ecosystem distribu- tions as a consequence of changes in temperature and precipitation (Smith and Shugart, 1993). This is an unsatisfactory situation when it is not even clear if our present characterization of ecosystems would persist.
Methodological Challenges
In the realm of methodological challenges, there are three frontiers t o push back. The first is the frontier of computational techniques for probability and uncertainty analysis in large integrated models. A typical challenge may involve a model such as ICAM-1 being used t o explore the issue of research prioritization. The value of research will be dependent on the path of the discovery and concurrent path of investments in mitigation and adaptation activities. All of these factors are uncertain. This makes the optimization possibilities a large combinatorics problem and a computational nightmare.
We need t o develop efficient algorithms and robust heuristics for solving such problems.
T h e second problem is that of elicitation of knowledge from experts where the quantified models are unsatisfactory. This is unquestionably a potentially powerful tool. However, the successful practitioners exercise a black a r t and myriad basic and other problems have never been systemati- cally investigated.
T h e third challenge is in representation of ignorance in models. This is one step beyond the consideration of uncertainties. To date, most of the major models Global 2100, Edmonds Reilly, CETA, and DICE have been run with stochastic sampling of their input parameters. However, there are only two climate IA models that have considered uncertainties in their de- sign (PAGE and ICAM-1). Furthermore, strict Bayesian theory does not permit the definition of ignorance about a parameter. Some mechanisms exist for getting around the definition of a parameter about which one may be partially ignorant. We need t o capture both uncertainties and ignorance in IA models. We especially need t o capture ignorance where we have devel- oped well-behaved models of processes (over a limited range of observations) and suspect non-linearities, discontinuities, or bifurcations just around the corner.
The Challenge of Meeting Policy Maker Needs
Two issues need t o be considered before integrated assessments can be made more useful t o policy makers. The first is t o recognize that climate change is
one of many possible issues decision makers must grapple with. The second is that policy makers do not seem t o have made their decisions on the basis of cost-effectiveness or cos t-benefit analyses in the past. Past evidence suggests that absolute costs and their distribution matter. Who the beneficiaries are also matters. Finally, policy responses are often triggered by extreme events and rarely by secular trends in key parameters.
These observations suggest that model predictions need t o be presented alongside measures of other global change and their impacts. This will help decision makers calibrate their level of effort and reactions to climate change issues. In addition, integrated assessments need to predict distributional characteristics of costs and benefits. Finally, non-linearities and bifurcations need t o be incorporated into the assessments. This is a tall order, but aiming t o be valuable t o the point of being indispensable is a lofty goal.
6. Conclusion
In the preceding discussions, an overview of the history and philosophy of integrated assessment has been presented. A number of conclusions can be drawn:
Support integrated assessments of climate change has already materi- alized in Europe for such projects as IMAGE, ESCAPE, and PAGE.
Support for similar North American integrated assessment efforts is promised for 1995.
More satisfactory and representative of the dynamics of social systems, ecological systems, and natural systems are needed before integrated assessments can be made more realistic. In addition, non-linearities, bifurcations, and ignorance about systems need t o be incorporated into integrated assessment frameworks.
We do not know how far integrated assessments are from providing the information decision makers use. While this is the case, success of this powerful tool in the policy arena will be a matter of chance.
So the agenda is set for the various parties. The disciplinary scientists need t o develop better models of the dynamics of processes they study;
integrated assessment teams need t o study the decision making of policy makers; and policy makers need to decide if integrated assessment is a useful tool that they would like t o endorse and use more widely.
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