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11 IMSC Session Program Statistical inference for space-time extremes Friday - Plenary Session 7 A. C. Davison EPFL, Switzerland Statistical inference for extremes of univariate time series is now well-established and widely used in climate applications. However many problems involve extremes that are dependent in space or time or both, and inference for these is much less well developed. In this talk I shall describe approaches to modelling such extremes, illustrated with rainfall and with temperature data. The ingredients are composite likelihood, Gaussian processes, max-stable models, peaks over thresholds and block maxima models for extremes, and of course data. The work is joint with Mehdi Gholamrezaee, Raphaël Huser, Simone Padoan and Mathieu Ribatet, and is supported by the Swiss National Science Foundation and the Competence Centre for Environmental Sustainability. (http://www.cces.ethz.ch/projects/hazri/EXTREMES) Abstracts 297
11 IMSC Session Program Anthropogenic greenhouse gas contribution to UK autumn flood risk Friday - Plenary Session 7 Pardeep Pall 1,2 , Tolu Aina 3 , Dáithí A. Stone 1,4 , Peter A. Stott 5 , Toru Nozawa 6 , Arno G.J. Hilberts 7 , Dag Lohmann 7 and Myles R. Allen 1 1 Atmospheric, Oceanic & Planetary Physics, Department of Physics, University of Oxford, Oxford, UK 2 Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland 3 Oxford e-Research Centre, University of Oxford, Oxford, UK 4 Tyndall Centre Oxford, Oxford University Centre for the Environment, Oxford, UK 5 Met Office Hadley Centre, Exeter, UK 6 National Institute for Environmental Studies, Tsukuba, Japan 7 Risk Management Solutions Ltd., London, UK Interest in attributing the risk of damaging weather-related events to anthropogenic climate change is increasing 1 . Yet climate models typically used to study the attribution problem do not resolve weather at scales causing damage 2 . Here we present the first multi-step study that attributes increasing risk of a damaging regional weather-related event to global anthropogenic greenhouse gas emissions. The event was the UK flooding of October and November 2000, occurring during the wettest autumn in England & Wales since records began in 1766 3 and inundating several river catchments 4 . Nearly 10,000 properties were flooded and transport services and power supplies severely disrupted, with insured losses estimated at £1.3bn 5,6 . Though the floods were deemed a ‘wake up call’ to the impacts of climate change 7 , were anthropogenic drivers to blame? Typically offered thermodynamic arguments do suggest increased precipitation extremes in a warming climate, but are too simple 8,9 to fully account for the complex hydrometeorology 4,10 associated with the flooding. Instead we use a Probabilistic Event Attribution framework to more rigorously estimate the contribution of anthropogenic greenhouse gas emissions to England & Wales Autumn 2000 flood risk. This involves comparing an unprecedented number of daily river runoff realisations for the region – under Autumn 2000 scenarios both with and without the emissions. These realisations are produced using publicly volunteered distributed computing power to generate several thousand seasonal-forecast resolution climate model simulations 11,12 that are then fed into a precipitation-runoff model. Autumn 2000 flooding is characterised by realisations exceeding the highest daily river runoff for that period, derived from the observational-based ERA-40 reanalysis 13 . Our model results indicate 20 th century anthropogenic greenhouse gas emissions significantly (at the 10% level) increased England & Wales flood risk in Autumn 2000 and most probably about trebled it. 1. Hegerl, G.C. et al. Understanding and attributing climate change. In Climate change 2007: The physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [eds Solomon, S. et al.] (Cambridge University Press, United Kingdom and New York, NY, USA) (2007). Abstracts 298
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