Suppression of chaos at slow variables by rapidly mixing fast dynamics through linear energy-preserving coupling

Chaotic multiscale dynamical systems are common in many areas of science, one of the examples being the interaction of the low-frequency dynamics in the atmosphere with the fast turbulent weather dynamics. One of the key questions about chaotic multiscale systems is how the fast dynamics affects chaos at the slow variables, and, therefore, impacts uncertainty and predictability of the slow dynamics. Here we demonstrate that the linear slow-fast coupling with the total energy conservation property promotes the suppression of chaos at the slow variables through the rapid mixing at the fast variables, both theoretically and through numerical simulations. A suitable mathematical framework is developed, connecting the slow dynamics on the tangent subspaces to the infinite-time linear response of the mean state to a constant external forcing at the fast variables. Additionally, it is shown that the uncoupled dynamics for the slow variables may remain chaotic while the complete multiscale system loses chaos and becomes completely predictable at the slow variables through increasing chaos and turbulence at the fast variables. This result contradicts the common sense intuition, where, naturally, one would think that coupling a slow weakly chaotic system with another much faster and much stronger mixing system would result in general increase of chaos at the slow variables.

[1]  D. Ruelle,et al.  Ergodic theory of chaos and strange attractors , 1985 .

[2]  Eric Vanden Eijnden Numerical techniques for multi-scale dynamical systems with stochastic effects , 2003 .

[3]  R. Abramov Linear response for slow variables of deterministic or stochastic dynamics with time scale separation , 2009 .

[4]  国田 寛 Stochastic flows and stochastic differential equations , 1990 .

[5]  E. Vanden-Eijnden,et al.  Analysis of multiscale methods for stochastic differential equations , 2005 .

[6]  H. Herzel Chaotic Evolution and Strange Attractors , 1991 .

[7]  E. Lorenz Predictability of Weather and Climate: Predictability – a problem partly solved , 2006 .

[8]  Eric Vanden-Eijnden,et al.  Subgrid-Scale Parameterization with Conditional Markov Chains , 2008 .

[9]  Vanden Eijnden E,et al.  Models for stochastic climate prediction. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[10]  R. Abramov Improved linear response for stochastically driven systems , 2010, 1001.2343.

[11]  Hans Crauel,et al.  Random attractors , 1997 .

[12]  Rafail V. Abramov,et al.  Short-time linear response with reduced-rank tangent map , 2009 .

[13]  R. Mazo On the theory of brownian motion , 1973 .

[14]  S. Sharma,et al.  The Fokker-Planck Equation , 2010 .

[15]  Andrew J. Majda,et al.  A mathematical framework for stochastic climate models , 2001 .

[16]  David Ruelle,et al.  General linear response formula in statistical mechanics, and the fluctuation-dissipation theorem far from equilibrium☆ , 1998 .

[17]  Lai-Sang Young,et al.  What Are SRB Measures, and Which Dynamical Systems Have Them? , 2002 .

[18]  Andrew J. Majda,et al.  New Approximations and Tests of Linear Fluctuation-Response for Chaotic Nonlinear Forced-Dissipative Dynamical Systems , 2008, J. Nonlinear Sci..

[19]  Frank M. Selten,et al.  An Efficient Description of the Dynamics of Barotropic Flow , 1995 .

[20]  Andrew J. Majda,et al.  Low-Frequency Climate Response of Quasigeostrophic Wind-Driven Ocean Circulation , 2012 .

[21]  Eric Vanden-Eijnden,et al.  A computational strategy for multiscale systems with applications to Lorenz 96 model , 2004 .

[22]  Eric Vanden-Eijnden,et al.  NUMERICAL TECHNIQUES FOR MULTI-SCALE DYNAMICAL SYSTEMS WITH STOCHASTIC EFFECTS ⁄ , 2003 .

[23]  C. Franzke Dynamics of Low-Frequency Variability: Barotropic Mode , 2002 .

[24]  임규호,et al.  Optimal sites for supplementary weather observations , 2011 .

[25]  V. Volosov,et al.  AVERAGING IN SYSTEMS OF ORDINARY DIFFERENTIAL EQUATIONS , 1962 .

[26]  Andrew J. Majda,et al.  Blended response algorithms for linear fluctuation-dissipation for complex nonlinear dynamical systems , 2007 .

[27]  M. J. Grote,et al.  Dynamic Mean Flow and Small-Scale Interaction through Topographic Stress , 1999 .

[28]  David Ruelle,et al.  A MEASURE ASSOCIATED WITH AXIOM-A ATTRACTORS. , 1976 .

[29]  Andrew J. Majda,et al.  A New Algorithm for Low-Frequency Climate Response , 2009 .

[30]  Andrew J. Majda,et al.  Quantifying Uncertainty for Non-Gaussian Ensembles in Complex Systems , 2005, SIAM J. Sci. Comput..

[31]  Andrew J. Majda,et al.  Systematic Strategies for Stochastic Mode Reduction in Climate , 2003 .

[32]  Tomás Caraballo,et al.  Pullback attractors for asymptotically compact non-autonomous dynamical systems , 2006 .

[33]  Andrew J. Majda,et al.  Information theory and stochastics for multiscale nonlinear systems , 2005 .