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9 - Uncertainties

from Part I - Background and Fundamentals

Published online by Cambridge University Press:  27 December 2017

Douglas Maraun
Affiliation:
Karl-Franzens-Universität Graz, Austria
Martin Widmann
Affiliation:
University of Birmingham
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Summary

Simulations of future climate are affected by inherent uncertainties – an iconic illustration of at least part of these uncertainties is the ensemble projections of global mean temperature (Collins et al. 2013, see Figure 9.1). Understanding, quantifying and attributing these uncertainties is of genuine scientific interest as they reflect both our limited understanding of the system and fundamental limits of predictability. But it is furthermore indispensable if credible and salient climate information is required for decision making (see Chapter 5). In this chapter we will discuss different types of uncertainties in climate projections (Section 9.1) and how they can be assessed by ensembles of climate models (Section 9.1). The actual interpretation of uncertainties will be discussed in Chapter 18.

Types of Uncertainties in Climate Projections

Uncertainties are often categorised into epistemic and aleatory (O'Hagan 2004). Epistemic uncertainty refers to our limited knowledge – it is thus in principle reducible. Aleatory uncertainty stems from unknowable knowledge and describes the limited ability to predict the behaviour of a system because of randomness intrinsic to the system itself – it is therefore often called ontic or ontologic uncertainty (van Asselt et al. 2002, Foley 2010). As it is a fundamental property of the considered system, aleatory uncertainty is inherently irreducible.

In practice the characterisation of uncertainty as either epistemic or aleatory is not clear cut but often reflects limitations in our current knowledge as well as practical constraints. For instance, the high uncertainty in state-of-the-art climate predictions from seasons to decades ahead may not be fully attributable to intrinsic stochasticity of the climate system but may be partly caused by our insufficient knowledge of initial conditions as well as model biases. Thus, part of the uncertainty characterised as aleatory might turn out to be epistemic and therefore in principle reducible (Hawkins and Sutton 2009).

Before discussing different sources of uncertainty in more detail, it might be helpful to recall how simulations of future climate – at the global and regional scale – are generated. As discussed in Chapter 8, climate models are numerical representations of our theories about atmosphere-ocean dynamics and thermodynamics as well as interactions with other components of the climate system.

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Publisher: Cambridge University Press
Print publication year: 2018

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  • Uncertainties
  • Douglas Maraun, Karl-Franzens-Universität Graz, Austria, Martin Widmann, University of Birmingham
  • Book: Statistical Downscaling and Bias Correction for Climate Research
  • Online publication: 27 December 2017
  • Chapter DOI: https://doi.org/10.1017/9781107588783.010
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  • Uncertainties
  • Douglas Maraun, Karl-Franzens-Universität Graz, Austria, Martin Widmann, University of Birmingham
  • Book: Statistical Downscaling and Bias Correction for Climate Research
  • Online publication: 27 December 2017
  • Chapter DOI: https://doi.org/10.1017/9781107588783.010
Available formats
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Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Uncertainties
  • Douglas Maraun, Karl-Franzens-Universität Graz, Austria, Martin Widmann, University of Birmingham
  • Book: Statistical Downscaling and Bias Correction for Climate Research
  • Online publication: 27 December 2017
  • Chapter DOI: https://doi.org/10.1017/9781107588783.010
Available formats
×