The potential to narrow uncertainty in regional climate predictions

Ed Hawkins and Rowan Sutton, NCAS-Climate & Walker Institute, University of Reading

These interactive webpages are associated with two papers (temperature, precipitation) on the sources of uncertainty in IPCC climate predictions. As it was not possible to present the full set of results in the papers, the sensitivity of the results for different regions, temporal means, etc, can be explored using these webpages. The papers explain how the plots were calculated, and should be read first. Maybe start with the FAQ.

Other variables:
Similar analyses to quantify uncertainty have been performed on Amazonian dieback projections and ozone projections.

Ed Hawkins ( or Rowan Sutton (


'The potential to narrow uncertainty in regional climate predictions', 2009, BAMS

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Faced by the realities of a changing climate, decision makers in a wide variety of organisations are increasingly seeking quantitative predictions of regional and local climate. An important issue for these decision makers, and for organisations that fund climate research, is what is the potential for climate science to deliver improvements - especially reductions in uncertainty - in such predictions? Uncertainty in climate predictions arises from three distinct sources: internal variability, model uncertainty and scenario uncertainty. Using data from a suite of climate models we separate and quantify these sources. For predictions of changes in surface air temperature on decadal timescales and regional spatial scales, we show that uncertainty for the next few decades is dominated by sources (model uncertainty and internal variability) that are potentially reducible through progress in climate science. Furthermore, we find that model uncertainty is of greater importance than internal variability. Our findings have implications for managing adaptation to a changing climate. Because the costs of adaptation are very large, and greater uncertainty about future climate is likely to be associated with more expensive adaptation, reducing uncertainty in climate predictions is potentially of enormous economic value. We highlight the need for much more work to compare: a) the cost of various degrees of adaptation given current levels of uncertainty; and b) the cost of new investments in climate science to reduce current levels of uncertainty. Our study also highlights the importance of targeting climate science investments on the most promising opportunities to reduce prediction uncertainty. doi: 10.1175/2009BAMS2607.1


'The potential to narrow uncertainty in projections of regional precipitation change', 2010, Climate Dynamics

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We separate and quantify the sources of uncertainty in projections of regional precipitation changes for the 21st century using the CMIP3 multi-model ensemble, allowing a direct comparison with a similar analysis for regional temperature changes. For decadal means of seasonal precipitation, internal variability is the dominant uncertainty for predictions of the first decade everywhere, and for many regions until the third decade ahead. Model uncertainty is generally the dominant source of uncertainty for longer lead times. Scenario uncertainty is found to be small or negligible for all regions and lead times, apart from close to the poles at the end of the century. For the global mean, model uncertainty dominates at all lead times. The signal-to-noise ratio (S/N) of the precipitation projections is highest at the poles but less than 1 almost everywhere else, and is far lower than for temperature projections. In particular, the tropics have the highest S/N for temperature, but the lowest for precipitation. We also estimate a `potential S/N' by assuming that model uncertainty could be reduced to zero, and show that, for regional precipitation, the gains in S/N are fairly modest, especially for predictions of the next few decades. This finding suggests that adaptation decisions will need to be made in the context of high uncertainty concerning regional changes in precipitation. For regional temperature projections we find a far greater potential to narrow uncertainty. doi: 10.1007/s00382-010-0810-6

Copyright: The images on these pages may be used with the appropriate citations:
 Hawkins & Sutton, 2009, BAMS or Hawkins & Sutton, 2010, Climate Dynamics

University of
ReadingNCASWalker Institute