COMPARATIVE STUDY OF DYNAMICAL CRITICAL SCALING IN THE SPACE STORM INDEX VERSUS SOLAR WIND FLUCTUATIONS James Wanliss, Presbyterian College, Clinton, SC.

Slides:



Advertisements
Similar presentations
1 McGill University Department of Civil Engineering and Applied Mechanics Montreal, Quebec, Canada.
Advertisements

Jiangfeng Wei with support from Paul Dirmeyer, Zhichang Guo, and Li Zhang Center for Ocean-Land-Atmosphere Studies Maryland, USA.
Objectives (BPS chapter 24)
Crackling Noise JPS, Shekhawat, Papanikolaou, Bierbaum, …, Dahmen, Myers, Durin, Zapperi Jan March Oct Dec Month Magnitude 7 8 Hiroshimas Earthquakes:
4. PREFERENTIAL ATTACHMENT The rich gets richer. Empirical evidences Many large networks are scale free The degree distribution has a power-law behavior.
Auroral Complexity Mervyn Freeman Thanks to: Nick Watkins, Steve Morley, and many others.
Criticality and disturbance in spatial ecological systems Mercedes Pascual & Frederic Guichard TREE, February 2005 dominant spatial scaledominant patch.
Correlation 2 Computations, and the best fitting line.
A Confidence Limit for Hilbert Spectrum Through stoppage criteria.
THE PROCESS OF SCIENCE. Assumptions  Nature is real, understandable, knowable through observation  Nature is orderly and uniform  Measurements yield.
Zhaohua Wu and N. E. Huang:
Self Organized Criticality Benjamin Good March 21, 2008.
Long-Term Ambient Noise Statistics in the Gulf of Mexico Mark A. Snyder & Peter A. Orlin Naval Oceanographic Office Stennis Space Center, MS Anthony I.
Monte Carlo Simulation of Ising Model and Phase Transition Studies
Study of magnetic helicity in solar active regions: For a better understanding of solar flares Sung-Hong Park Center for Solar-Terrestrial Research New.
CHAPTER 6 Statistical Analysis of Experimental Data
Chap.3 A Tour through Critical Phenomena Youjin Deng
STOCHASTIC GEOMETRY AND RANDOM GRAPHS FOR THE ANALYSIS AND DESIGN OF WIRELESS NETWORKS Haenggi et al EE 360 : 19 th February 2014.
What Exists? The nature of existence. Dictionary definition (Merriam-Webster) To exist: To have real being whether material or spiritual. Being: The quality.
Motor Control Theories
A self organized critical model of a highly correlated model of a highly correlated flow-driven turbulent magnetosphere L. F. Morales 1, W.W. Liu 1,2,
The Erdös-Rényi models
Inference for regression - Simple linear regression
Monte Carlo Simulation of Ising Model and Phase Transition Studies By Gelman Evgenii.
Downscaling in time. Aim is to make a probabilistic description of weather for next season –How often is it likely to rain, when is the rainy season likely.
Free energies and phase transitions. Condition for phase coexistence in a one-component system:
Claudio Castellano CNR-INFM Statistical Mechanics and Complexity and
Theory of wind-driven sea by V.E. Zakharov S. Badulin A.Dyachenko V.Geogdjaev N.Ivenskykh A.Korotkevich A.Pushkarev In collaboration with:
Analysis of the quiet-time statistics of edge electrostatic fluxes measured in tokamaks and stellarators B. Ph. Van Milligen 1, R. Sánchez 2, D. E. Newman.
Study Questions: 1) Define biology and science.. Study Questions: 1)Define biology and science. - Biology: The scientific study of living systems - Science:
How and why things crackle We expect that there ought to be a simple, underlying reason that earthquakes occur on all different sizes. The very small earthquake.
BMME 560 & BME 590I Medical Imaging: X-ray, CT, and Nuclear Methods Introductory Topics Part 2.
Interacting Earthquake Fault Systems: Cellular Automata and beyond... D. Weatherley QUAKES & AccESS 3 rd ACES Working Group Meeting Brisbane, Aust. 5 th.
Arrangement of Electrons. Spectroscopy and the Bohr atom (1913) Spectroscopy, the study of the light emitted or absorbed by substances, has made a significant.
Statistical properties of southward IMF and its geomagnetic effectiveness X. Zhang, M. B. Moldwin Department of Atmospheric, Oceanic, and Space Sciences,
Geo479/579: Geostatistics Ch4. Spatial Description.
Solar cycle dependence of EMIC wave frequencies Marc Lessard, Carol Weaver, Erik Lindgren 1 Mark Engebretson University of New HampshireAugsburg College.
Lecture V Probability theory. Lecture questions Classical definition of probability Frequency probability Discrete variable and probability distribution.
Janine Bolliger 1, Julien C. Sprott 2, David J. Mladenoff 1 1 Department of Forest Ecology & Management, University of Wisconsin-Madison 2 Department of.
Stepwise change in time series Radical change of between alternative stable states (attractors) Resistance, resilience Reversibility, trajectory of change.
Chapter 8: Simple Linear Regression Yang Zhenlin.
Long time correlation due to high-dimensional chaos in globally coupled tent map system Tsuyoshi Chawanya Department of Pure and Applied Mathematics, Graduate.
BASIC STATISTICAL CONCEPTS Statistical Moments & Probability Density Functions Ocean is not “stationary” “Stationary” - statistical properties remain constant.
Intermittency Analysis and Spatial Dependence of Magnetic Field Disturbances in the Fast Solar Wind Sunny W. Y. Tam 1 and Ya-Hui Yang 2 1 Institute of.
1 Satellite Meeting of STATPHYS 22(KIAS) Bak-Sneppen Evolution models on Random and Scale-free Networks I. Introduction II. Random Neighbor Model III.
ECE-7000: Nonlinear Dynamical Systems 2. Linear tools and general considerations 2.1 Stationarity and sampling - In principle, the more a scientific measurement.
1 Heart rate variability: challenge for both experiment and modelling I. Khovanov, N. Khovanova, P. McClintock, A. Stefanovska Physics Department, Lancaster.
Percolation Percolation is a purely geometric problem which exhibits a phase transition consider a 2 dimensional lattice where the sites are occupied with.
ABSTRACT Disturbances in the magnetosphere caused by the input of energy from the solar wind enhance the magnetospheric currents and it carries a variation.
Thermodynamics, fluctuations, and response for systems out of equilibrium Shin-ichi Sasa (University of Tokyo) 2007/11/05 in collaboration with T.S. Komatsu,
ESS 261 Lecture April 28, 2008 Marissa Vogt. Overview  “Probabilistic forecasting of geomagnetic indices using solar wind air mass analysis” by McPherron.
Effects of January 2010 stratospheric sudden warming in the low-latitude ionosphere L. Goncharenko, A. Coster, W. Rideout, MIT Haystack Observatory, USA.
CORRELATION-REGULATION ANALYSIS Томский политехнический университет.
Computational Physics (Lecture 10) PHY4370. Simulation Details To simulate Ising models First step is to choose a lattice. For example, we can us SC,
How can we measure turbulent microscales in the Interstellar Medium? Steven R. Spangler, University of Iowa.
Gravity Wave Turbulence in Wave Tanks S Lukaschuk 1, S Nazarenko 2 1 Fluid Dynamics Laboratory, University of Hull 2 Mathematics Institute, University.
Ryan Woodard (Univ. of Alaska - Fairbanks)
Computational Physics (Lecture 10)
A Physicist’s View of SOC Models
The Probability Distribution of Extreme Geomagnetic Events in the Auroral Zone R.S. Weigel Space Weather Laboratory Department of Computational and Data.
METHOD TEST PREP EDUCATIONAL SERIES
Team Coordinator: Markus J. Aschwanden
Self-organized criticality of landscape patterning
Process Capability.
The spectral evolution of impulsive solar X-ray flares
Singular Behavior of Slow Dynamics of Single Excitable Cells
CHAPTER – 1.2 UNCERTAINTIES IN MEASUREMENTS.
Biointelligence Laboratory, Seoul National University
Volume 32, Issue 1, Pages (October 2001)
CHAPTER – 1.2 UNCERTAINTIES IN MEASUREMENTS.
Presentation transcript:

COMPARATIVE STUDY OF DYNAMICAL CRITICAL SCALING IN THE SPACE STORM INDEX VERSUS SOLAR WIND FLUCTUATIONS James Wanliss, Presbyterian College, Clinton, SC Thanks: Vadim Uritsky, James Weygand Isradynamics, 13 April Ein Bokek, Israel

1. Statistical Physics Concepts In thermodynamics (statistical mechanics), a phase transition or phase change is the transformation of a thermodynamic system from one phase to another. The distinguishing characteristic of a phase transition is an abrupt sudden change in one or more physical properties, in particular the heat capacity, with a small change in a thermodynamic variable such as the temperature. Non-equilibrium thermodynamics is a branch of thermodynamics concerned with studying time- dependent thermodynamic systems. Open system

Definition: Self-Organized Behavior Spontaneous change in the internal organization of the system. Change appears not to be guided or managed by natural sources

Definition: Critical Behaviour In standard critical phenomena, there is a control parameter which an experimenter can vary to obtain this radical change in behaviour. In the case of melting, the control parameter is temperature. Self-organized critical phenomenon, by contrast, is exhibited by driven systems which reach a critical state by their intrinsic dynamics, independently of the value of any control parameter. If system is critical, results should be robust irrespective of activity levels.

In a physical system the time interval between two "events" is called a waiting-time, for instance, the time interval of a certain activity. This can give information on whether storms are independent events, and provides a test for models for storm statistics.  Burst lifetime is the time, T, of a burst of activity. Total duration is given by θ. Standard Poisson waiting-time distributions (‘used is good as new’) 2. Testing for Self-Organized or Critical Behavior: Waiting times Wanliss and Weygand, GRL, 2007

Intermittent behaviour, with long-range dependence. Low-Latitudes: SYM-H Fluctuations

Burst lifetimes of SYM-H, є, VB s ( ) Power-law slope over several orders of magnitude Doesn’t vary for different thresholds (SOC-like) Scaling properties of the low-latitude magnetosphere, whose output is recorded by SYM-H, is not purely a direct response to the scale-free properties of the solar wind! SYM-HVB s ε ± ± ± ± ± ± 0.08

Demonstrate that the temporal dynamics of SYM-H perturbations exhibit non-trivial power-law relations. The avalanche dynamics are described in terms of the ensemble averaged number of active sites as a function of delay time from the start of each excitation in the ensemble, and the probability that an avalanche survives this time interval, For a system near a critical point, As well, for every avalanche with lifetime T there is a relationship between the lifetime and size of the avalanche, S, viz. 3. Testing for Self-Organised or Critical Behavior: Dynamic Critical Scaling Wanliss and Uritsky, JGR, 2010

 Size vs Lifetime (S vs. T) shows a power law dependence, as does θ vs T. A very slight break in slope occurs near 10,000 seconds.  Slope for S vs T for whole interval gives a slope of 1.705±0.022  SYM-H fit for τ<2 hours gives  η=0.263±0.008; δ=0.416±0.004  Thus 1+η+δ=1.679±0.063 (i.) Spreading critical exponents

 THEORY  t T =1.40±0.04; t S =1.18±0.03. MEASUREMENT (ii.) Avalanche critical exponents

4.Testing for Self-Organized or Critical Behavior: (iii.) Power Spectra In addition to the above results, if the bursty dynamics is due to a critical avalanching process, the exponent ( ) should allow one to predict the power-law slope β of the Fourier power spectrum describing the average burst shape. It has been shown for < 2 (which is the case for SYM-H bursts and sandpile avalanching models) the integral relating P(f) with the avalanche size probability distribution and the conditional energy spectrum of avalanches of a given size is dominated by a frequency dependent upper cut-off, yielding the simple scaling relation

To verify this relation, we performed two semi-independent statistical tests. In the first test, we put together SYM-H bursts with T < 240 min in their natural order by eliminating the quiet periods separating the bursts. The resulting time series is analogous to the time dependence of the number of topplings in an avalanching model studied under infinitely slow driving conditions In the 2 nd test, we overlapped bursts by randomizing their starting times and taking their sum for each time step. The time series obtained mimics the dynamics of “running” sandpiles with slow but continuous driving, producing no interference between concurrent avalanches.

In both tests, the power spectra have a distinct power- law region for frequencies above (240 minutes) -1 with the exponent β being statistically indistinguishable from the exponent as predicted for critical avalanching systems.

To make sure that the obtained spectra characterize correlations within bursts we also checked the power spectrum of the lifetime dynamics as represented by consecutive T values. The spectrum of the lifetimes has a clear white noise shape indicating that the interburst correlations have no significant effect on the burst shape, in agreement with the behavior of the described class of critical avalanching models.

6. Summary Burst lifetime distribution functions yield clear power-law exponents of the lifetime probability distributions. Since SYM- H scaling was remarkably robust, irrespective of solar cycle, it could be that the solar wind almost never acts as a direct driver for the SYM-H scaling. Tests on ensemble averaged dynamics of the bursts of activity in the SYM-H index demonstrated scale-free behavior, and strong correlation between the size S and lifetime T of the activity bursts. These scaling behaviors were consistent with theoretical predictions from nonequilibrium systems near criticality. Our results show what could be the first quantitative evidence for the same universality class as directed percolation in a natural system. Similar scaling behavior is NOT observed in solar wind fluctuations.

Summary (2) The second level of results is our demonstration of the possibility that the multiscale dynamics of the ring current system is a result of its cooperative behavior governed by a specific statistical principle. We associate this dynamics with nonlinear interactions of spatially distributed degrees of freedom (e.g., current filaments) keeping the system in the vicinity of a global critical point. The results can also be used for validating existing and future ring current models in terms of their ability to correctly represent the cross ‐ scale coupling effects in this system.

References J. A. Wanliss and J. M. Weygand (2007), Power law burst lifetime distribution of the SYM ‐ H index, Geophys. Res. Lett., 34, L04107, doi: /2006GL Wanliss, J., and V. Uritsky (2010), Understanding bursty behavior in midlatitude geomagnetic activity, J. Geophys. Res., 115, A03215, doi: /2009JA