Interest in attributing the risk of damaging weather-related events to anthropogenic climate change is increasing1. Yet climate models used to study the attribution problem typically do not resolve the weather systems associated with damaging events2 such as the UK floods of October and November 2000. Occurring during the wettest autumn in England and Wales since records began in 17663,4, these floods damaged nearly 10,000 properties across that region, disrupted services severely, and caused insured losses estimated at £1.3 billion (refs 5, 6). Although the flooding was deemed a ‘wake-up call’ to the impacts of climate change at the time7, such claims are typically supported only by general thermodynamic arguments that suggest increased extreme precipitation under global warming, but fail8,9 to account fully for the complex hydrometeorology4,10 associated with flooding. Here we present a multi-step, physically based ‘probabilistic event attribution’ framework showing that it is very likely that global anthropogenic greenhouse gas emissions substantially increased the risk of flood occurrence in England and Wales in autumn 2000. Using publicly volunteered distributed computing11,12, we generate several thousand seasonal-forecast-resolution climate model simulations of autumn 2000 weather, both under realistic conditions, and under conditions as they might have been had these greenhouse gas emissions and the resulting large-scale warming never occurred. Results are fed into a precipitation-runoff model that is used to simulate severe daily river runoff events in England and Wales (proxy indicators of flood events). The precise magnitude of the anthropogenic contribution remains uncertain, but in nine out of ten cases our model results indicate that twentieth-century anthropogenic greenhouse gas emissions increased the risk of floods occurring in England and Wales in autumn 2000 by more than 20%, and in two out of three cases by more than 90%.
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We thank all members of the public who ran HadAM3-N144 climate model simulations using the BOINC open source computing platform, and the climateprediction.net team for their technical support; M. Collins, N. Rayner, A. Jones and C. Johnson for originally supplying the HadAM3-N144 model, observed SSTs and sea ice, sulphate fields, and ozone fields, respectively; G. Meehl and T. Knutson for permission to use NCARPCM1 and GFDLR30 temperature data, respectively; S. Hay and I. Tracey for assistance with beta tests; C. Lee for technical advice; and W. Ingram and D. Rowlands for scientific advice. P.P. was supported by a NERC CASE studentship with Risk Management Solutions Ltd, by WWF International, and by NCCR climate. D.A.S. and P.P. received partial support from the UK Department for Environment, Food, and Rural Affairs. P.A.S. was supported by the Joint DECC and Defra Integrated Climate Programme — DECC/Defra (GA01101). T.N. was supported by the Japanese Ministry of Education, Culture, Sports, Science and Technology. D.A.S. and M.R.A. received additional support from the Climate Change Detection and Attribution Project jointly funded by the US National Oceanic and Atmospheric Administration’s Office of Global Programs and the US Department of Energy. This research was also supported by the European Union (FP6) funded Integrated Project WATCH (contract number 036946) and the Smith School of Enterprise and the Environment.
The file contains Supplementary Methods, additional references, Supplementary Figures 1-6 with legends and Supplementary Tables 1-2.
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Extreme Flood Events over the Past 300 Years Inferred from Lake Sedimentary Grain Sizes in the Altay Mountains, Northwestern China
Chinese Geographical Science (2018)