Summary
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 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 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.
The model provides universal quantification for
self-organized criticality
in terms of the statistical normal distribution. Model predictions are
in agreement with a majority of observed spectra of time series of several
standard climatological data sets representative of disparate climate regimes.
Universal spectrum for natural climate variability rules out linear trends.
Man-made greenhouse gas related atmospheric warming will result in intensification
of natural climate variability, seen immediately in high frequency fluctuations
such as QBO (quasibiennial oscillation) and ENSO (El Nino-Southern
Oscillation) and even shorter timescales. Model concepts and results of
analyses are discussed with reference to possible prediction of climate
change.