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Uncertainty representations of mean sea-level change: a telephone game?

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Abstract

For the long-term management of coastal flood risks, investment and policy strategies need to be developed in light of the full range of uncertainties associated with mean sea-level rise (SLR). This, however, remains a challenge due to deep uncertainties involved in SLR assessments, many ways of representing uncertainties and a lack of common terminology for referring to these. To contribute to addressing these limitations, this paper first develops a typology of representations of SLR uncertainty by categorising these at three levels: (i) SLR scenarios versus SLR predictions, (ii) the type of variable that is used to represent SLR uncertainty, and (iii) partial versus complete uncertainty representations. Next, it is analysed how mean SLR uncertainty is represented and how representations are converted within the following three strands of literature: SLR assessments, impact assessments and decision analyses. We find that SLR assessments mostly produce partial or complete precise probabilistic scenarios. The likely ranges in the report of the Intergovernmental Panel on Climate Change are a noteworthy example of partial imprecise probabilistic scenarios. SLR impact assessments and decision analyses mostly use deterministic scenarios. In conversions of uncertainty representations, a range of arbitrary assumptions are made, for example on functional forms of probability distributions and relevant confidence levels. The loss of quality and the loss of information can be reduced by disregarding deterministic and complete precise probabilistic predictions for decisions with time horizons of several decades or centuries and by constructing imprecise probabilistic predictions and using these in approaches for robust decision-making.

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Notes

  1. An interval projection becomes a complete precise probabilistic projection if a uniform or other probability distribution function is defined. Non-probabilistic decision analyses, for example minimax analysis, do not use probabilistic information.

  2. Imprecise probability theories include possibility and necessity measures, plausibility and belief functions and various other mathematical models (Walley 2000). Le Cozannet et al. (2017) convert partial probabilistic projections for RCP 8.5 into possibilistic projections (“probability-possibility transformation”). These are then converted into imprecise probabilities (“possibility-probability transformation”).

  3. Classical ROA methods analyse the value of investment flexibility in stochastic settings (Dixit and Pindyck 1994). Recent methods have started to explore imprecise probabilistic concepts, such as probability thresholds (Lempert et al. 2012) or multiple SLR priors (Dawson et al. 2018).

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Acknowledgements

We thank three anonymous reviewers for their very helpful comments.

Funding

This work was supported in part through grant SEASCAPE from the Deutsche Forschungsgemeinschaft (DFG) as part of the Special Priority Program (SPP)-1889 “Regional Sea Level Change and Society”. We thankfully acknowledge funding from the Horizon 2020 RISES-AM project (Grant 603396), GREEN-WIN project (Grant 642018) and Project INSeaPTION. The latter is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by BMBF (DE), MINECO (ES), NOW (NL) and ANR (FR) with co-funding by the European Union (Grant 690462).

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    Thomas David van der Pol & Jochen Hinkel

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van der Pol, T.D., Hinkel, J. Uncertainty representations of mean sea-level change: a telephone game?. Climatic Change 152, 393–411 (2019). https://doi.org/10.1007/s10584-018-2359-z

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