HR: 15:25h
AN: GC13B-08 INVITED    [Abstracts]
TI: Near-real-time attribution of extreme weather events
AU: * Allen, M R
EM: myles.allen@physics.ox.ac.uk
AF: University of Oxford Department of Physics, Clarendon Laboratory Parks Road, Oxford, OX1 3PU, United Kingdom
AU: Pall, P
EM: pall@atm.ox.ac.uk
AF: University of Oxford Department of Physics, Clarendon Laboratory Parks Road, Oxford, OX1 3PU, United Kingdom
AU: Stone, D
EM: stoned@atm.ox.ac.uk
AF: University of Oxford Department of Physics, Clarendon Laboratory Parks Road, Oxford, OX1 3PU, United Kingdom
AU: Stott, P
EM: peter.stott@metoffice.gov.uk
AF: The Met Office Reading Unit, University of Reading Department of Meteorology, Reading, RG6 6BB, United Kingdom
AU: Lohmann, D
EM: dag.lohmann@rms.com
AF: Risk Management Solutions Ltd, Peninsular House 30 Monument Street, London, EC3R 8NB, United Kingdom
AB: As the impacts of global climate change become increasingly evident, there is growing demand for a quantitative and objective answer the the question of what is "to blame" for observed extreme weather phenomena. In addition to considerable public interest, understanding how external drivers, particularly secular trends such as anthropogenic greenhouse gas forcing, is important for the correct quantification of current weather-related risks for the insurance industry. We propose a method of quantifying the contribution of external drivers to weather-related risks based on a twinned ensemble design. Under this approach, a large ensemble of simulations with a forecast-resolution atmospheric model is driven with observed sea surface temperatures and atmospheric composition over the period of interest. A second ensemble is then generated with the influence of a particular external agent, such as anthropogenic greenhouse gases, removed through modification of composition and surface temperatures. Conventional detection and attribution techniques are used to allow for uncertainty in the magnitude and pattern of the signal removed. The frequency of occurrence of the weather event in question can then be compared between the two ensembles. For the exploration of changing risks of the most extreme events, very large ensembles (thousands of members, unprecedented for a model of this resolution) are needed, requiring a novel distributed computing approach, relying on computing resources donated by the general public: see http://attribution.cpdn.org. We focus as an example on the events of Autumn 2000 which brought widespread flooding to many regions of the UK. Precipitation from the twin ensembles is used to force an empirical run-off model to provide an estimate of its contribution to flood risk. Results are summarized in the form of an estimated fraction attributable risk for the anthropogenic contribution to the flooding events of that year.
UR: http://attribution.cpdn.org
DE: 1626 Global climate models (3337, 4928)
DE: 1630 Impacts of global change (1225)
SC: Global Environmental Change [GC]
MN: 2007 Fall Meeting