  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 of all scales
ranging from turbulence (millimeters-seconds) to climate (thousands of
kilometers-years). Long-range spatiotemporal correlations are ubiquitous to
dynamical systems in nature and are identified as signatures of self-organized
criticality. Standard models for turbulent fluid flows in meteorological theory
cannot explain satisfactorily the observed multifractal (space-time) structures
in atmospheric flows. Numerical models for simulation and prediction of
atmospheric flows are subject to deterministic chaos and give unrealistic
solutions. Deterministic chaos is a direct consequence of round-off error
growth in iterative computations. Round-off error of finite precision
computations doubles on an average at each step of iterative computations.
Round-off error will propagate to the mainstream computation and give
unrealistic solutions in numerical weather prediction and climate models which
incorporate thousands of iterative computations in long-term numerical
integration schemes. A recently developed non-deterministic cell dynamical
system model for atmospheric flows predicts the observed self-organized
criticality as intrinsic to quantumlike mechanics governing flow dynamics.
Further, the fractal space-time structure to the stringlike atmospheric flow
trajectory is resolved into a continuum of eddies. The eddy circulations obey
Kepler third law of planetary motion and therefore eddy inertial masses obey
Newton inverse square law of gravitation on all scales from microscopic to
macroscale.
