HR: 10:20h
AN: NG22A-01 INVITED     [Abstracts]
TI: Predicting Tomorrow's Weather Next Week: The Roles of Uncertainty, Probability, and Model Inadequacy in Weather and in Climate
AU: * Smith, L
EM: lenny@maths.ox.ac.uk
AF: CATS London School of Economics, Dept of Statistics Houghton Street, London, WC2A 2AE United Kingdom
AB: The aim of constructing a forecast from the best model(s) simulations should be distinguished from the aim of improving the model(s) whenever possible. The common confusion of these distinct aims in earth system science sometimes results both in the misinterpretation of results and in a less than ideal experimental design. The motivation, resource distribution, and scientific goals of these two aims almost always differ in the earth sciences. The goal of this talk is to illustrate these differences in the contexts of operational weather forecasting and that of climate modelling. We adopt the mathematical framework of indistinguishable states (Judd and Smith, Physica D, 2001 & 2004), which allows us to clarify fundamental limitations on any attempt to extract accountable (physically relevant) probability forecasts from imperfect models of any physical system, even relatively simple ones. Operational weather forecasts from ECMWF and NCEP are considered in the light of THORPEX societal goals. Monte Carlo experiments in general, and ensemble systems in particular, generate distributions of simulations, but the interpretation of the output depends on the design of the ensemble, and this in turn is rather different if the aim is to better understand the model rather than to better predict electricity demand. Also, we show that there are alternatives to interpreting the ensemble as a probability forecast, alternatives that are sometime more relevant to industrial applications. Extracting seasonal forecasts from multi-model, multi-initial condition ensembles of simulations is also discussed. Finally, two different approaches to interpreting ensembles of climate model simulations are discussed. Our main conclusions reflect the need to distinguish the ways and means of using geophysical ensembles for model improvement from their applications to socio-economic risk management and policy, and to verify the physical relevance of requested deliverables like probability forecasts.
DE: 3200 MATHEMATICAL GEOPHYSICS (New field)
DE: 3210 Modeling
DE: 3220 Nonlinear dynamics
SC: Nonlinear Geophysics [NG]
MN: 2004 AGU Fall Meeting