Potential influences on the United Kingdom's floods of winter 2013/14

During the winter of 2013/14, much of the UK experienced repeated intense rainfall events and flooding. This had a considerable impact on property and transport infrastructure. A key question is whether the burning of fossil fuels is changing the frequency of extremes, and if so to what extent. We a...

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Bibliographic Details
Main Authors: Chris Huntingford, Terry Marsh, Adam A. Scaife, Elizabeth J. Kendon, Jamie Hannaford, Alison L. Kay, Mike Lockwood, Christel Prudhomme, Nick S. Reynard, Simon Parry, Jason A. Lowe, James A. Screen, Helen C. Ward, Malcolm Roberts, Peter A. Stott, Vicky A. Bell, Mark Bailey, Alan Jenkins, Tim Legg, Friederike E.L. Otto, Neil Massey, Nathalie Schaller, Julia Slingo, Myles R. Allen
Format: Article
Language:English
Subjects:
Atmospheric dynamics
Atmospheric Sciences
Atmospheric sciences not elsewhere classified
Earth Sciences not elsewhere classified
Hydrology
Other earth sciences not elsewhere classified
Water resources
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Summary:During the winter of 2013/14, much of the UK experienced repeated intense rainfall events and flooding. This had a considerable impact on property and transport infrastructure. A key question is whether the burning of fossil fuels is changing the frequency of extremes, and if so to what extent. We assess the scale of the winter flooding before reviewing a broad range of Earth system drivers affecting UK rainfall. Some drivers can be potentially disregarded for these specific storms whereas others are likely to have increased their risk of occurrence. We discuss the requirements of hydrological models to transform rainfall into river flows and flooding. To determine any general changing flood risk, we argue that accurate modelling needs to capture evolving understanding of UK rainfall interactions with a broad set of factors. This includes changes to multiscale atmospheric, oceanic, solar and sea-ice features, and land-use and demographics. Ensembles of such model simulations may be needed to build probability distributions of extremes for both pre-industrial and contemporary concentration levels of atmospheric greenhouse gases.