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Climatic Change

, Volume 79, Issue 3–4, pp 243–257 | Cite as

A Demonstration of the Uncertainty in Projections of UK Climate Change Resulting from Regional Model Formulation

  • David P. RowellEmail author
Article

Abstract

Regional climate models (RCMs) are now commonly used to downscale climate change projections provided by global coupled models to resolutions that can be utilised at national and finer scales. Although this extra tier of complexity adds significant value, it inevitably contributes a further source of uncertainty, due to the regional modelling uncertainties involved. Here, an initial attempt is made to estimate the uncertainty that arises from typical variations in RCM formulation, focussing on changes in UK surface air temperature (SAT) and precipitation projected for the late twenty-first century. Data are provided by a relatively large suite of RCM and global model integrations with widely varying formulations. It is found that uncertainty in the formulation of the RCM has a relatively small, but non-negligible, impact on the range of possible outcomes of future UK seasonal mean climate. This uncertainty is largest in the summer season. It is also similar in magnitude to that of large-scale internal variations of the coupled climate system, and for SAT, it is less than the uncertainty due to the emissions scenario, whereas for precipitation it is probably larger. The largest source of uncertainty, for both variables and in all seasons, is the formulation of the global coupled model. The scale-dependency of uncertainty due to RCM formulation is also explored by considering its impact on projections of the difference in climate change between the north and south of the UK. Finally, the implications for the reliability of UK seasonal mean climate change projections are discussed.

Keywords

Emission Scenario Springer Climatic Change Global Couple Model Couple Climate System Reliability Ensemble Average 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media B.V. 2006

Authors and Affiliations

  1. 1.Met OfficeHadley Centre for Climate Prediction and ResearchExeterUK

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