Nonlinear Dynamics and Chaos: Applications for Prediction of Weather and Climate
Abstract
Turbulence, namely, irregular fluctuations in space and time characterize fluid flows in general and atmospheric flows in particular.The irregular,i.e., nonlinear spacetime fluctuations on all scales contribute to the unpredictable nature of both shortterm weather and longterm climate.It is of importance to quantify the total pattern of fluctuations for predictability studies. The power spectra of temporal fluctuations are broadband and exhibit inverse power law form with different slopes for different scale ranges. Inverse powerlaw form for power spectra implies scaling (self similarity) for the scale range over which the slope is constant. Atmospheric flows therefore exhibit multiple scaling or multifractal structure.Standard meteorological theory cannot explain satisfactorily the observed multifractal structure of atmospheric flows.Selfsimilar spatial pattern implies longrange spatial correlations. Atmospheric flows therefore exhibit longrange spatiotemporal correlations, namely,selforganized criticality,signifying order underlying apparent chaos. A recently developed nondeterministic cell dynamical system model for atmospheric flows predicts the observed selforganized criticality as intrinsic to quantumlike mechanics governing flow dynamics.The model predictions are in agreement with continuous periodogram spectral analysis of meteorological data sets.
 Publication:

arXiv eprints
 Pub Date:
 April 2001
 arXiv:
 arXiv:physics/0104056
 Bibcode:
 2001physics...4056P
 Keywords:

 Physics  General Physics
 EPrint:
 4 pages