If climate adaptation were like most other risk management issues,
where the use of standard scientific, modelling, and decision-analytic
methods to characterize probabilities and payoffs is possible or even
routine, then conventional approaches to decision making under
uncertainty could be highly relevant for climate adaptation. But given
the deep and irreducible uncertainties in the processes, rate, and
implications of climate change, along with the many ethical and
institutional complexities climate adaptation decisions entail, standard
tools of policy analysis are often not up to the task (Cullen and Small,
2004; Morgan, et al., 1999).
"Deep" and "irreducible" uncertainty could be referred to by other terms such as ambiguity, model uncertainty, "Knightian" uncertainty or unknowable futures. In other words, the terms refer to situations in which standard use of probability to characterize uncertainty is profoundly challenging if not impossible, particularly for long term problems.
Several kinds of approaches have been developed as responses to address deep uncertainties in climate adaptation choices. Some of the approaches adapted in research by the Climate Decision Making Center (CDMC) and other climate researchers include the following:
1. Relying on scenarios without probabilities to characterize a range of potential futures given climate change, and the societal conditions that could lead to future emission scenarios. This approach is apparent in the scenario structure of GCM modeling done to support the Intergovernmental Panel on Climate Change (IPCC) consensus report process. Some researchers have questioned the standard use of scenarios in climate mitigation and adaptation research, arguing that current practice may make some paths seem far more likely than others, simply in terms of depth of description, and criticize the lack of focus on decisions to be addressed (Morgan and Keith, 2008).
2. Attempting to characterize specific, well defined uncertainties through expert elicitations, to show the sensitivity of the climate system to various levels of forcing, in order to characterize broad vulnerability of the climate system and components within it. Expert elicitation is feasible even under deep uncertainty if the quantities to be considered in the assessment are extremely well defined, if there is an effort to understand and communicate the range of received and emerging science underlying the judgment task, and if state of the art practice is followed in eliciting and exploring the implications of the uncertainties. Several papers and cases regarding expert elicitation are available in the Publications section under "Irreducible Uncertainty".
3. Turning long term decisions into a series of shorter term decisions. Keeney and McDaniels (2000) discuss the importance of breaking very long term decisions regarding climate change into shorter term decisions, and emphasize the importance of treating learning over time as a key objective for short term policies. Several researchers in the CDMC have prepared analyses of specific climate adaptation decisions in the energy sector, such as the viability of investing in carbon capture and storage in new coal fired power plants. The issue is the timing of new regulations and the potential price of carbon offsets. These papers are listed under "Irreducible Uncertainty" in the Publications section.
4. Seeking robust and flexible alternatives. This approach proceeds from the notion that picking a single optimum as the alternative for a given climate adaptation decision may be flawed, if the uncertainties are large and the outcomes are sensitive to the uncertainties. In that case, seeking alternatives that are expected to perform reasonably well over a wide range of futures (i.e., are robust to key uncertainties) and can be changed over time as new information is learned and experience is gained is an attractive approach. Some of these efforts may involve creating better institutional structures to address opportunities as well as regulatory gaps or mismatches. Hence, institutional capacity to create and implement alternatives is an important means to achieving more fundamental objectives.