The knowledge that is used in IPCC assessments predominantly stems from a wide variety of academic disciplines. Given the high scientific and political profile of the IPCC, the production of knowledge in disciplines is impacted by the existence and dynamics of the IPCC assessment process. In some cases, the dynamics between academic disciplines and the IPCC is characterised by the presence of positive feedback loops, where the production of knowledge is structured and programmed by the IPCC. The subsequent findings then receive a preeminent role in later IPCC assessments, and so the cycle continues. It is important to critically reflect on these dynamics, in order to determine whether visions of climate change’s past, present, and future – for example, pathways for the climate-change problem and its potential solutions, as far as they exist – have not been unduly constrained by the IPCC process. The IPCC runs the risk of unreflexively foregrounding some scientific and policy approaches at the expense of other approaches.

Introduction

This chapter deals with the implications of uncertainty in the practice of climate modelling for communicating model-based findings to decision-makers, particularly high-resolution predictions intended to inform decision-making on adaptation to climate change. Our general claim is that methodological reflections on uncertainty in scientific practices should provide guidance on how their results can be used more responsibly in decision support. In the case of decisions that need to be made to adapt to climate change, societal actors, both public and private, are confronted with deep uncertainty. In fact, it has been argued that some of the questions these actors may ask ‘cannot be answered by science’. In this chapter, the notions of ‘reliability’ are examined critically; in particular the manner(s) in which the reliability of climate model findings pertaining to model-based high-resolution climate predictions is communicated. A broader discussion of these issues can be found in the chapter by Beck, in this volume.

Findings can be considered ‘reliable’ in many different ways. Often only a statistical notion of reliability is implied, but in this chapter we consider wider variations on the meaning of ‘reliability’, some more relevant to decision support than the mere uncertainty in a particular calculation.

Introduction

Policy decisions in many areas involving science, including the environment and public health, are both complex and contested. Typically there are no facts that entail a unique correct policy. Furthermore, political decisions on these problems will need to be made before conclusive scientific evidence is available. Decision stakes are high: The impacts of wrong decisions based on the available limited knowledge can be huge. Actors disagree on the values that should guide the decision-making. The available knowledge bases are typically characterised by imperfect understanding (and imperfect reduction into models) of the complex systems involved. Models, scenarios and assumptions dominate assessment of these problems, and many (hidden) value loadings reside in problem frames, indicators chosen and assumptions made.

The evidence that is embodied in scientific policy advice under such post-normal (Funtowicz and Ravetz 1993) conditions requires quality assessment. Advice should be relevant to the policy issue, scientifically tenable and robust under societal scrutiny. Governmental and intergovernmental agencies that inform policy and the public about complex risks increasingly recognise that uncertainty and disagreement can no longer be suppressed or denied, but need to be dealt with in a transparent and effective manner. In response to emerging needs, several institutions that interface science and policy have adopted knowledge quality assessment approaches, where knowledge refers to any information that is accepted into a debate (UK Strategy Unit 2002; EPA 2003; MNP/UU 2003; IPCC 2005).