Extreme Value Analysis in dynamical systems: two case studies

We give here a brief summary of classical Extreme Value Theory for random variables, followed by that for deterministic dynamical systems, which is a rapidly developing area of research. Here we would like to contribute to that by conducting a numerical analysis designed to show particular features of extreme value statistics in dynamical systems, and also to explore the validity of the theory. We find that formulae that link the extreme value statistics with geometrical properties of the attractor hold typically for high-dimensional systems – whether a so-called geometric distance observable or a physical observable is concerned. In very low-dimensional settings, however, the fractality of the attractor prevents the system from having an extreme value law, which might well render the evaluation of extreme value statistics meaningless and so ill-suited for application.

[1]  E. Ott Chaos in Dynamical Systems: Contents , 1993 .

[2]  G. Nicolis,et al.  Extreme events in deterministic dynamical systems. , 2006, Physical review letters.

[3]  H. Kantz,et al.  Nonlinear time series analysis , 1997 .

[4]  M. Cross,et al.  Pattern formation outside of equilibrium , 1993 .

[5]  George Haiman,et al.  Extreme values of the tent map process , 2003 .

[6]  Katharina Burger Counterexamples In Probability And Statistics , 2016 .

[7]  Valerio Lucarini,et al.  Towards a General Theory of Extremes for Observables of Chaotic Dynamical Systems , 2013, Journal of statistical physics.

[8]  Catherine Nicolis,et al.  Extreme value distributions in chaotic dynamics , 1995 .

[9]  P. Grassberger,et al.  Characterization of experimental (noisy) strange attractors , 1984 .

[10]  Valerio Lucarini,et al.  Equivalence of Non-equilibrium Ensembles and Representation of Friction in Turbulent Flows: The Lorenz 96 Model , 2014, Journal of Statistical Physics.

[11]  Christopher B. Field,et al.  Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Index , 2012 .

[12]  M. Ivette Gomes,et al.  Penultimate limiting forms in extreme value theory , 1984 .

[13]  Giorgio Turchetti,et al.  Numerical Convergence of the Block-Maxima Approach to the Generalized Extreme Value Distribution , 2011, 1103.0889.

[14]  S. Vannitsem,et al.  Statistical properties of the temperature maxima in an intermediate order Quasi-Geostrophic model , 2007 .

[15]  Tamás Tél,et al.  Chaotic Dynamics: Introductory concepts , 2006 .

[16]  Giorgio Turchetti,et al.  Extreme value statistics for dynamical systems with noise , 2012, 1208.5582.

[17]  Tamas Bodai,et al.  Predictability of threshold exceedances in dynamical systems , 2014, 1408.1001.

[18]  Tamás Bódai,et al.  Probabilistic Concepts in a Changing Climate: A Snapshot Attractor Picture , 2015 .

[19]  J. Hüsler Extremes and related properties of random sequences and processes , 1984 .

[20]  B. Gnedenko Sur La Distribution Limite Du Terme Maximum D'Une Serie Aleatoire , 1943 .

[21]  A. Karimi,et al.  Extensive chaos in the Lorenz-96 model. , 2009, Chaos.

[22]  Giorgio Turchetti,et al.  Extreme value theory for singular measures. , 2012, Chaos.

[23]  R. Fisher,et al.  Limiting forms of the frequency distribution of the largest or smallest member of a sample , 1928, Mathematical Proceedings of the Cambridge Philosophical Society.

[24]  Ana Cristina Moreira Freitas,et al.  Hitting time statistics and extreme value theory , 2008, 0804.2887.

[25]  Swinney,et al.  Strange attractors in weakly turbulent Couette-Taylor flow. , 1987, Physical review. A, General physics.

[26]  Lennaert van Veen Baroclinic Flow and the Lorenz-84 Model , 2003, Int. J. Bifurc. Chaos.

[27]  Valerio Lucarini,et al.  Extremes and Recurrence in Dynamical Systems , 2016, 1605.07006.

[28]  Alef E. Sterk,et al.  Extreme value laws in dynamical systems under physical observables , 2011, 1107.5673.

[29]  Edward N. Lorenz,et al.  Irregularity: a fundamental property of the atmosphere* , 1984 .

[30]  Tamás Bódai,et al.  Fractal snapshot components in chaos induced by strong noise. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[31]  Judith Berner,et al.  Stochastic climate theory and modeling , 2015 .

[32]  Tamás Bódai,et al.  Driving a conceptual model climate by different processes: snapshot attractors and extreme events. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[33]  Pierre Collet,et al.  Statistics of closest return for some non-uniformly hyperbolic systems , 1999, Ergodic Theory and Dynamical Systems.

[34]  Jorge Milhazes Freitas,et al.  On the link between dependence and independence in extreme value theory for dynamical systems , 2008 .

[35]  Henk A. Dijkstra Nonlinear Physical Oceanography , 2010 .

[36]  Eric P. Smith,et al.  An Introduction to Statistical Modeling of Extreme Values , 2002, Technometrics.

[37]  G. Nicolis,et al.  Extreme events in multivariate deterministic systems. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.