Background on uncertainty assessment supporting climate adaptation decision-making

Although trends in climate change are expected to continue, there is considerable uncertainty about the precise rate of change and its concrete impact. A key element in decision-making on climate adaptation is how to deal with this uncertainty. This chapter provides the background information on dealing with uncertainties: descriptions of uncertainty typology, methods of assessment, as well as a framework for dealing with uncertainty in climate adaptation decision-making. The chapter highlights that the classical elements used in uncertainty assessment (statistics, scenarios and recognised ignorance) can be expanded toward five principal uncertainty dimensions that are crucial for informing/supporting adaptation decision-making: location, level, nature, qualification of knowledge base, and value-ladenness. In practice, to deal with uncertainties, but also because of time and budget constraints, uncertainty assessments may follow a three step approach: (1) identify and characterise sources of uncertainty; (2) weigh, appraise, and prioritise uncertainties; and (3) select and apply methods for dealing with uncertainties in policy. Based on political and societal preferences, adaptation strategies could either use top-down or bottom-up approaches considering adaptation action based on the best prediction, robustness, or resilience. Adaptation policies that focus on enhancing the system’s and society’s capability of dealing with possible future changes, uncertainties and surprises (e.g. through resilience, flexibility, and adaptive capacity) seem most appropriate. For potential climate-related effects for which rough risk estimates are available, ‘robust’ measures are recommended. For potential climate effects with limited societal and/or political relevance, ‘no-regret’ measures are recommended. For highly relevant potential climate-related effects, precautionary measures can be considered. The chapter provides also links to the uncertainty approaches in the case studies described in Chap. 4.