Representing uncertainty is central to this task. Some assessments have used heuristic schemes to communicate uncertainties by assigning judgmental degrees of confidence to their main concluding statements. This approach can be used in any assessment, including one integrated by less formal means than models. But in a model-based assessment, more systematic analysis and communication of uncertainty is also possible. Because component models in assessments are deterministic, uncertainty is normally treated through some form of meta-model analysis.
Several approaches are possible.
In a model, where uncertainty is analyzed through repeated runs with variation, there is a tradeoff between the size and complexity of the basic model and how much uncertainty analysis is feasible. Specifying a set of future scenarios is one simple way of presenting uncertainty, with the scenarios selected to span a judgmentally determined range of plausible, representative futures. The difficulties of scenario analysis are that the origin and meaning of the range bounded by the scenarios cannot normally be explored precisely. Sensitivity analysis is a more systematic method of studying uncertainty, through which the sensitivity of outputs to variation in key input parameters, or to discrete changes in assumed models or policies, can be examined. The difficulty of a sensitivity-analysis approach is that one cannot examine sensitivity to every input, and determining which key ones to select for analysis must reflect some prior judgment.