HR: 14:10h
AN: A13C-03    [Abstracts]
TI: Towards Defining Probability Forecasts of Likely Climate Change
AU: * Smith, L A
EM: lenny@maths.ox.ac.uk
AF: CATS, London School of Economics, Department of Statistics Houghton Street, London, WC2A 2AE United Kingdom
AU: Allen, M R
EM: m.allen1@physics.ox.ac.uk
AF: AOPP University of Oxford, Parks Road, Oxford, OX1 1DW United Kingdom
AU: Stainforth, D A
EM: d.stainforth1@physics.ox.ac.uk
AF: AOPP University of Oxford, Parks Road, Oxford, OX1 1DW United Kingdom
AB: There is strong desire for probabilistic forecasts of climate change, both for policy making and risk management, as well as scientific interest. The extent to which this desire can be satisfied scientifically is unclear. The aim of this paper is to explore (i) current methods for extracting probability forecasts and (ii) alternative deliverables which have a firm scientific basis given the current limitations to the state of the art. Even ``physics-based" models contain empirically determined paramters and parameterizations. While it is straightforward to make `ensembles' over initial conditions, parameter values and even several model structures, the interpretation of the resulting ensemble of simulations requires some care. Methods for extracting probability forecasts from ensembles of model simulations will be discussed in terms of their relevance and internal consistency, a particular example being provided by Murphy et al (Nature, 2004). This approach will be compared and contrasted with one proposed by climateprediction.net (Stainforth et al, Nature, in review), which strives to produce policy relevant information when no coherent probability forecast can be extracted from the limited ensembles available in practice. The role of the Bayesian paradigm will be considered in both cases. Extracting probability distributions in the context of climate change requires consideration of a discrete sample drawn from a high dimensional space. The analysis of small ensembles requires further assumptions of linearity and smoothness which must be verified explicitly; even when ``large" ensembles are to hand, the analysis of the collection of simulations requires combining mutually exclusive runs, sampling a restricted region of the parameter space, under a set of models with related shortcoming. Historical observations serve to lift some of these difficulties, but attempts to fold observations into the analysis (say, in terms of weighting sets of simulations differently) are complicated by the fact that we are trying to model climate (a distribution) while we have only one realization of the earth system, and that we are extrapolating into regions where we have no empirical data (in CO2 concentration, for example). We conclude that while new ensembles of model simulations provide significant insights into the future of the earth system, current attempts to extract physically relevant, objective probability distributions are not coherent. Nevertheless, policy and risk management decisions can be better informed through a coherent interpretation of these simulations, which also provide useful guidance toward the design of improved climate studies.
DE: 3339 Ocean/atmosphere interactions (0312, 4504)
DE: 3344 Paleoclimatology
DE: 3200 MATHEMATICAL GEOPHYSICS (New field)
DE: 3210 Modeling
DE: 3220 Nonlinear dynamics
SC: Atmospheric Sciences [A]
MN: 2004 AGU Fall Meeting