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Reducing doubt about uncertainty: Guidance for IPCC’s third assessment

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Abstract

It is usually necessary to apply incomplete and uncertain information to inform policy and decision making, creating the need to characterize the state of knowledge and identify when more certain information may be available. After all, some information is better than none and conversely, even perfect information is of no use if it is available only after a decision has been made. In scientific assessments for global change, the challenges are particularly acute because of scientific complexity, long time horizons, and large political and economic stakes, among other factors. Moss and Schneider prepared uncertainty guidelines for the Third Assessment Report (TAR) of the Intergovernmental Panel on Climate Change (IPCC) that recommended a process to make expert judgments of levels of confidence and uncertainty more systematic and transparent. The guidance provided calibrated uncertainty terms to improve communication of findings to users and urged preparation of a traceable account of the authors’ assessment of the evidence for each major finding. This article reviews the recommendations and their effectiveness and highlights ensuing critiques and the evolution of uncertainty guidance for subsequent assessment reports. It discusses emerging challenges in providing science for decision making in the era of increasing model resolution and complexity and burgeoning interest in information to inform adaptation and mitigation at regional and finer scales.

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Notes

  1. Approaches to evaluating and communicating uncertainty in expert-judgment based scientific assessments are a small subset of quantitative and qualitative approaches to uncertainty quantification, propagation and characterization applied to evaluating uncertainties in research and analysis. Uncertainty characterization and communication is essential to ensure that a scientific conclusion represents a valid and defensible insight. This article does not address this broader set of approaches for evaluating uncertainty in the science of complex systems.

  2. In the frequentist statistical approach, probability is defined as the relative frequency that something would occur in a limitless number of independent, identical trials. In practice, random sampling or other thought experiments are used since there are relatively few phenomena that can be expected to occur in an infinite, independent, and identical fashion. A Bayesian approach involves a rigorous mathematical methodology for evaluating the probability of an event conditional on the available information, interpreted as degree of belief, which is updated as new evidence becomes available.

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Authors and Affiliations

  1. Joint Global Change Research Institute, University of Maryland, Pacific Northwest National Laboratory, Maryland, USA

    Richard H. Moss

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  1. Richard H. Moss
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Correspondence to Richard H. Moss.

Additional information

The author is Senior Staff Scientist at Pacific Northwest National Laboratory’s Joint Global Change Research Institute at the University of Maryland. He dedicates this article to the memory of Stephen H. Schneider, the captain of the “uncertainty cops”. He also acknowledges with gratitude PNNL colleagues Jennie Rice, Michael Scott, and Stephen Unwin, whose knowledge and ideas have given shape to a number of recommendations in this article. He also thanks Elizabeth Malone, Granger Morgan, Nathan Engle, and Caroline Boules for comments on an earlier draft.

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Moss, R.H. Reducing doubt about uncertainty: Guidance for IPCC’s third assessment. Climatic Change 108, 641 (2011). https://doi.org/10.1007/s10584-011-0182-x

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