  Turbulence, namely, irregular fluctuations in space and time characterize
fluid flows in general and atmospheric flows in particular.The irregular,i.e.,
nonlinear space-time fluctuations on all scales contribute to the unpredictable
nature of both short-term weather and long-term 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 power-law
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 long-range spatial correlations.
Atmospheric flows therefore exhibit long-range spatiotemporal correlations,
namely,self-organized criticality,signifying order underlying apparent chaos. 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.The model predictions are in
agreement with continuous periodogram spectral analysis of meteorological data
sets.
