HR: 15:10h
AN: GC53A-07    [Abstracts]
TI: Applications and Limitations of Using Large State-of-the-Art Climate Ensembles for Risk Management
AU: * Tredger, E R
EM: e.r.tredger@lse.ac.uk
AF: Centre for the Analysis of Time Series, London School of Economics Houghton Street, LONDON, WC2A 2AE, United Kingdom
AU: Stainforth, D A
EM: das@atm.ox.ac.uk
AF: Centre for the Analysis of Time Series, London School of Economics Houghton Street, LONDON, WC2A 2AE, United Kingdom
AU: Stainforth, D A
EM: das@atm.ox.ac.uk
AF: SOGAER, University of Exeter, The University of Exeter The Queen's Drive, Exeter, EX4 4QJ, United Kingdom
AU: Smith, L A
EM: lenny@maths.ox.ac.uk
AF: Centre for the Analysis of Time Series, London School of Economics Houghton Street, LONDON, WC2A 2AE, United Kingdom
AB: Climate model output is increasingly being offered to both policy makers and industry in support of detailed risk management and decision making strategies. Ensemble techniques illuminate the impact of some sources of uncertainty in future climate scenarios, and may provide valuable information to risk managers in their attempts to make robust decisions. Ensemble climate experiments are designed to explore variability in model behaviour due to differences in initial conditions, model parameters, and to a more limited extent model structure and other uncertainties. After discussing the strengths and limitations of this approach, an unprecedented range of behaviour is shown to arise in an ensemble of 45,000 General Circulation Model (HaDSM3) climateprediction.net runs. Initial condition uncertainty is shown to play a significant role if this model were to be used for risk management, especially in terms of estimating extremes. In terms of global behaviour, both low (less than 1 degree Celsius) and high (over 16 degrees Celsius) climate sensitivity runs are observed in models versions with comparable performance in 1xCO2 simulations. The data set is produced using a perturbed physics grand ensemble generated by the climateprediction.net experiment, a publically distributed computing experiment. Over 10,000 different model versions (the same structural model with different parameter values) are run, allowing for an assessment of parametric uncertainty. For each of these model versions, an initial condition ensemble is run, providing an estimate of each model versions' climate distribution. A grand ensemble of runs gives the opportunity for a better understanding of the state of climate modeling science. The wide range of behaviour raises important questions of how to interpret climate predictions for policy makers and how state-of-the-art (2007) climate modeling experiments might be related to the Earth's climate. While ensembles contain useful information, the limits on the potential for extracting decision relevant probability distributions from models of limited realism is discussed, and the coherence of attempting to weight empirically inadequate models in the absence of a relevant forecast-verification archive is questioned. Challenges to the evaluation of model performance are shown and the consequences for constraining/weighting ensembles are discussed.
UR: http://www.lse.ac.uk/collections/cats/
DE: 1616 Climate variability (1635, 3305, 3309, 4215, 4513)
DE: 1620 Climate dynamics (0429, 3309)
DE: 1622 Earth system modeling (1225)
DE: 1626 Global climate models (3337, 4928)
DE: 1630 Impacts of global change (1225)
SC: Global Environmental Change [GC]
MN: 2007 Fall Meeting