HR: 15:25h
AN: GC53A-08    [Abstracts]
TI: Questioning the Relevance of Model-Based Probability Statements on Extreme Weather and Future Climate
AU: * Smith, L A
EM: lenny@maths.ox.ac.uk
AF: London School of Economics and Political Science, Centre for the Analysis of Time Series (CATS), Houghton Street, London, WC2A 2AE, United Kingdom
AB: We question the relevance of climate-model based Bayesian (or other) probability statements for decision support and impact assessment on spatial scales less than continental and temporal averages less than seasonal. Scientific assessment of higher resolution space and time scale information is urgently needed, given the commercial availability of "products" at high spatiotemporal resolution, their provision by nationally funded agencies for use both in industry decision making and governmental policy support, and their presentation to the public as matters of fact. Specifically we seek to establish necessary conditions for probability forecasts (projections conditioned on a model structure and a forcing scenario) to be taken seriously as reflecting the probability of future real-world events. We illustrate how risk management can profitably employ imperfect models of complicated chaotic systems, following NASA's study of near-Earth PHOs (Potentially Hazardous Objects). Our climate models will never be perfect, nevertheless the space and time scales on which they provide decision- support relevant information is expected to improve with the models themselves. Our aim is to establish a set of baselines of internal consistency; these are merely necessary conditions (not sufficient conditions) that physics based state-of-the-art models are expected to pass if their output is to be judged decision support relevant. Probabilistic Similarity is proposed as one goal which can be obtained even when our models are not empirically adequate. In short, probabilistic similarity requires that, given inputs similar to today's empirical observations and observational uncertainties, we expect future models to produce similar forecast distributions. Expert opinion on the space and time scales on which we might reasonably expect probabilistic similarity may prove of much greater utility than expert elicitation of uncertainty in parameter values in a model that is not empirically adequate; this may help to explain the reluctance of experts to provide information on "parameter uncertainty." Probability statements about the real world are always conditioned on some information set; they may well be conditioned on "False" making them of little value to a rational decision maker. In other instances, they may be conditioned on physical assumptions not held by any of the modellers whose model output is being cast as a probability distribution. Our models will improve a great deal in the next decades, and our insight into the likely climate fifty years hence will improve: maintaining the credibility of the science and the coherence of science based decision support, as our models improve, require a clear statement of our current limitations. What evidence do we have that today's state-of-the-art models provide decision-relevant probability forecasts? What space and time scales do we currently have quantitative, decision-relevant information on for 2050? 2080?
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