HR: 10:20h
AN: NG32A-01 INVITED    [Abstracts]
TI: The Phase-Space Approach to Low-Frequency Climate Variability: Particles and Waves, Model Hierarchies and Data Sets
AU: * Ghil, M
EM: ghil@atmos.ucla.edu
AF: Ecole Normale Superieure, 24 rue Lhomond, Paris, 75231-05, France
AB: Four decades ago, E. N. Lorenz provided some approximate limits to atmospheric predictability. The details---in space and time---of atmospheric flow fields are lost after about 10 days. Certain gross flow features, however, persist at times for longer and recur from time to time, giving hope for their extended prediction. Over the last two decades, numerous attempts have been made to describe, understand and predict these persistent and recurrent features. The attempts have involved, on the one hand, systematic improvements in numerical weather prediction (NWP) by increasing the spatial resolution and physical faithfulness in the detailed models used for this prediction. On the other hand, theoretical attempts motivated by the same goal have involved the study of the large-scale atmospheric motions' phase space and of its inhomogeneities. These "coarse-graining" studies have addressed observed as well as simulated atmospheric data sets. Two distinct approaches have been used in these studies: the episodic or intermittent and the oscillatory or periodic. The intermittency approach describes multiple-flow (or weather) regimes, their persistence and recurrence, and the Markov chain of transitions among them. The periodicity approach studies intraseasonal oscillations, with periods of 15--70 days, and their predictability. We review these two approaches, "particles" vs. "waves," in the quantum physics analogy alluded to in the title of this talk, discuss their complementarity, and outline unsolved problems. Considerable emphasis is also put on the complementarity between dynamical and statistical aspects of describing, understanding and predicting the state space structure of the atmosphere and oceans, across a hierarchy of models and in observations. Recent results point to the robustness of Markov chains of regimes across a hierarchy of models and in multi- annual, reanalysis-based data sets. Preferential exit directions from several regimes (i) confirm the importance of unstable fixed points as the dynamical origin of these regimes; and (ii) help extended prediction by advanced statistical algorithms. Combining such statistical predictions with the increased skill of state-of-the-art NWP models is an interesting challenge for nonlinear dynamicists, numerical modelers and data assimilators alike.
UR: http://www.atmos.ucla.edu/tcd/
DE: 3305 Climate change and variability (1616, 1635, 3309, 4215, 4513)
DE: 3315 Data assimilation
DE: 3319 General circulation (1223)
DE: 4410 Bifurcations and attractors
DE: 4460 Pattern formation
SC: Nonlinear Geophysics [NG]
MN: 2007 Fall Meeting