Signatures of a Universal Spectrum for Atmospheric Interannual Variability in Coads Surface Pressure Time Series

Int. J. Bifurcation Chaos

2013

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Atmospheric flows exhibit fractal fluctuations and inverse power law form for power spectra indicating an eddy continuum structure for the selfsimilar fluctuations. A general systems theory for fractal fluctuations developed by the author is based on the simple visualisation that large eddies form by space-time integration of enclosed turbulent eddies, a concept analogous to Kinetic Theory of Gases in Classical Statistical Physics. The ordered growth of atmospheric eddy continuum is in dynamical equilibrium and is associated with Maximum Entropy Production. The model predicts universal (scale-free) inverse power law form for fractal fluctuations expressed in terms of the golden mean. Atmospheric particulates are held in suspension in the fractal fluctuations of vertical wind velocity. The mass or radius (size) distribution for homogeneous suspended atmospheric particulates is expressed as a universal scale-independent function of the golden mean, the total number concentration and the mean volume radius. Model predicted spectrum is in agreement (within two standard deviations on either side of the mean) with total averaged radius size spectra for the AERONET (aerosol inversions) stations Davos and Mauna Loa for the year 2010 and Izana for the year 2009 daily averages. The general systems theory model for aerosol size distribution is scale free and is derived directly from atmospheric eddy dynamical concepts. At present empirical models such as the log normal distribution with arbitrary constants for the size distribution of atmospheric suspended particulates are used for quantitative estimation of earth-atmosphere radiation budget related to climate warming/cooling trends. The universal aerosol size spectrum will have applications in computations of radiation balance of earth-atmosphere system in climate models.

Section: Probability Distribution Of Fractal Fluctuations In Atmosphesupporting

confidence: 89%

Scale-Free Universal Spectrum for Atmospheric Aerosol Size Distribution for Davos, Mauna Loa and Izana

Int. J. Bifurcation Chaos

2013

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“…eddy fluctuations. Spectra of time series of meteorological parameters when plotted as cumulative percentage contribution to total variance versus t have been shown to follow the model predicted universal spectrum (Selvam, 1987;1990;Selvam and Radhamani, 1995;Selvam and Joshi, 1995;Selvam et al, 1996;Selvam and Fadnavis, 1998;1999;Joshi and Selvam, 1999) which is identified as a signature of quantumlike chaos.…”

Section: Model Predictionsmentioning

confidence: 82%

“…Computed model solutions are therefore mere mathematical artifacts of the universal process of round-off error growth in iterative computations. Selvam (1993) has shown that the computed domain is the successive cumulative integration of round-off error domains analogous to the formation of large eddy domains as envelopes enclosing turbulent eddy fluctuation domains such as in atmospheric flows (Selvam, 1990;Selvam, Pethkar, and Kulkarni, 1992;Selvam, and Joshi, 1995;Selvam et al, 1996). Computed solutions, therefore qualitatively resemble real world dynamical systems such as atmospheric flows with manifestation of self-organized criticality.…”

Section: Introductionmentioning

confidence: 95%

Self-organized Criticality: A Signature of Quantum-like Chaos in Atmospheric Flows

Springer Atmospheric Sciences

2017

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 spacetime scales ranging from turbulence (centimetres-seconds) to climate (kilometres-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 mainstream computation and gives unrealistic solutions in numerical weather prediction (NWP) and climate models, which incorporate thousands of iterative computations in longterm numerical integration schemes. An alternative non-deterministic cell dynamical system model for atmospheric flows developed by the author 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 climatic regimes. Universal spectrum for natural climate variability rules out linear trends. Man-made greenhouse gas related atmospheric warming would result in intensification of natural climate variability, seen immediately in high frequency fluctuations such as QBO and ENSO and even shorter timescales. Model concepts and results of analyses are discussed with reference to possible prediction of climate change. Model concepts, if correct, rule out unambiguously, linear trends in climate. Climate change will only be manifested as increase or decrease in the natural variability. However, more stringent tests of model concepts and predictions are required before applications to such an important issue as climate change. The cell dynamical system model for atmospheric flows is a general systems theory applicable in general to all dynamical systems in other fields of science, such as, number theory, biology, physics and botany.

“…Realistic modeling of atmospheric flows therefore requires alternative concepts for fluid flows and robust computational techniques which do not require round-off error prone calculus-based longterm numerical integration schemes. In this paper, a recently developed non-deterministic cell dynamical system model for atmospheric flows [5][6][7][8] is summarized.…”

Section: Introductionmentioning

confidence: 99%

Identification of Self-Organized Criticality in Atmospheric Low Frequency Variability

Joshi

1999

Fractals

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Atmospheric flows exhibit long-range spatiotemporal correlations manifested as self-similar fractal geometry to the global cloud cover pattern concomitant with inverse power law form f B .Such non-local connections 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. A recently developed cell dynamical model for atmospheric flows predicts the observed self-organized criticality as a direct consequence of quantumlike mechanics governing flow dynamics. The model predictions are in agreement with continuous periodogram power spectral analyses of two-day mean TOGA temperature time-series. The application of model concepts for prediction of atmospheric low frequency variability is discussed. * Email: rrjcpt@tropmet.ernet.in 421 Fractals 1999.07:421-425. Downloaded from www.worldscientific.com by MONASH UNIVERSITY on 04/11/15. For personal use only.