Climate projections are increasingly being presented in terms of uncertainties and probability distributions rather than median or ‘most-likely’ values. The current national UK climate change projections, UKCP09, provide 10,000 probabilistic projections (PP) and 11 spatially coherent projections (11SCP) for three future emission scenarios. In contrast, previous iterations such as UKCIP02 provided only a single ‘most-likely’ (deterministic) projection for each. This move from deterministic to probabilistic methods of communicating climate change information, whilst increasing the wealth of the data, complicates the process of adaptation planning by communicating extra uncertainty to the public and decision-makers. This paper examines the application of probabilistic climate change projections and explores the impact of uncertainty on decision-making, using a case study of irrigation reservoir design at three sites in the UK. The implications of sub-sampling the PP using both simple random and Latin-hypercube sampling are also explored. The study found that the choice of dataset has a much larger impact on irrigation reservoir design than emission uncertainty. The study confirmed the dangers of inadequate sample size, particularly when applying decision criteria based on extreme events, and found that more advanced stratified sampling techniques did not noticeably improve the reproducibility of decision outcomes.

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