Signatures of a Universal Spectrum for Atmospheric Interannual Variability in Some Disparate Climatic Regimes

Details Signatures of a Universal Spectrum for Atmospheric Interannual Variability in Some Disparate Climatic Regimes Signatures of a Universal Spectrum for Atmospheric Interannual Variability in Some Disparate Climatic Regimes

1998-05-28

arxiv.org/abs/chao-dyn/9805028v1

Nonlinear Sciences

Chaotic Dynamics

26 pages, 11 figures. Meteorology and Atmospheric Physics, 1998 (in Press) (Springer-Verlag, Austria)

Scientific paper

10.1007/BF01030450

Atmospheric flows exhibit long-range spatiotemporal correlations manifested as the fractal geometry to the global cloud cover pattern concomitant with inverse power law form for power spectra of temporal fluctuations on all space-time scales ranging from turbulence(centimeters-seconds) to climate(kilometers-years). Long-range spatiotemporal correlations are ubiquitous to dynamical systems in nature and are identified as signatures of self-organized criticality. Standard models in meteorological theory cannot explain satisfactorily the observed self-organized criticality in atmospheric flows. Mathematical models for simulation and prediction of atmospheric flows are nonlinear and do not possess analytical solutions. Finite precision computer realizations of nonlinear models give unrealistic solutions because of deterministic chaos, a direct consequence of round-off error growth in iterative numerical computations. Recent studies show that round-off error doubles on an average for each iteration of iterative computations. Round-off error propagates to the main stream computation and gives unrealistic solutions in numerical weather prediction (NWP) and climate models which incorporate thousands of iterative computations in long-term numerical integration schemes. An alternative non-deterministic cell dynamical system model for atmospheric flows described in this paper predicts the observed self-organized criticality as intrinsic to quantumlike mechanics governing flow dynamics.

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