Probabilistic Concepts in a Changing Climate: A Snapshot Attractor Picture

AbstractThe authors argue that the concept of snapshot attractors and of their natural probability distributions are the only available tools by means of which mathematically sound statements can be made about averages, variances, etc., for a given time instant in a changing climate. A basic advantage of the snapshot approach, which relies on the use of an ensemble, is that the natural distribution and thus any statistics based on it are independent of the particular ensemble used, provided it is initiated in the past earlier than a convergence time. To illustrate these concepts, a tutorial presentation is given within the framework of a low-order model in which the temperature contrast parameter over a hemisphere decreases linearly in time. Furthermore, the averages and variances obtained from the snapshot attractor approach are demonstrated to strongly differ from the traditional 30-yr temporal averages and variances taken along single realizations. The authors also claim that internal variability can b...

[1]  Chen,et al.  Transition to chaos for random dynamical systems. , 1990, Physical review letters.

[2]  José A. Langa,et al.  Attractors for infinite-dimensional non-autonomous dynamical systems , 2012 .

[3]  C. DaCamara,et al.  Multistability, phase diagrams, and intransitivity in the Lorenz-84 low-order atmospheric circulation model. , 2008, Chaos.

[4]  V. Araújo Random Dynamical Systems , 2006, math/0608162.

[5]  Mw Hirsch,et al.  Chaos In Dynamical Systems , 2016 .

[6]  Celso Grebogi,et al.  Reactive particles in random flows. , 2004, Physical review letters.

[7]  George R. Sell,et al.  Nonautonomous differential equations and topological dynamics. II. Limiting equations , 1967 .

[8]  Michael Ghil,et al.  Stochastic climate dynamics: Random attractors and time-dependent invariant measures , 2011 .

[9]  Cristina Masoller,et al.  Regular and chaotic behavior in the new Lorenz system , 1992 .

[10]  Michel Crucifix,et al.  Why could ice ages be unpredictable , 2013, 1302.1492.

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

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

[13]  Thomas M. Antonsen,et al.  Modeling fractal entrainment sets of tracers advected by chaotic temporally irregular fluid flows using random maps , 1997 .

[14]  Paul J. Roebber,et al.  Climate variability in a low-order coupled atmosphere-ocean model , 1995 .

[15]  P. Imkeller,et al.  Stochastic climate models , 2001 .

[16]  Michael Ghil,et al.  A mathematical theory of climate sensitivity or, How to deal with both anthropogenic forcing and natural variability? , 2015 .

[17]  George R. Sell,et al.  NONAUTONOMOUS DIFFERENTIAL EQUATIONS AND TOPOLOGICAL DYNAMICS. I. THE BASIC THEORY , 1967 .

[18]  C. Nicolis,et al.  Short-range predict-ability of the atmosphere: mechanism for superexponential error growth , 1995 .

[19]  H. Crauel,et al.  Attractors for random dynamical systems , 1994 .

[20]  K. Hasselmann Stochastic climate models Part I. Theory , 1976 .

[21]  M. Ghil,et al.  Interdecadal oscillations and the warming trend in global temperature time series , 1991, Nature.

[22]  Tomas Bohr,et al.  Fractak tracer distributions in turbulent field theories , 1997, chao-dyn/9709008.

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

[24]  C. Deser,et al.  Uncertainty in climate change projections: the role of internal variability , 2012, Climate Dynamics.

[25]  Cristina Masoller,et al.  Characterization of strange attractors of lorenz model of general circulation of the atmosphere , 1995 .

[26]  Corinne Le Quéré,et al.  Climate Change 2013: The Physical Science Basis , 2013 .

[27]  Roger A. Pielke,et al.  Long-term variability of climate , 1994 .

[28]  Tamás Bódai,et al.  Annual variability in a conceptual climate model: snapshot attractors, hysteresis in extreme events, and climate sensitivity. , 2012, Chaos.

[29]  Edward Ott,et al.  Particles Floating on a Moving Fluid: A Dynamically Comprehensible Physical Fractal , 1993, Science.

[30]  Pierre Gaspard,et al.  Chaos, Scattering and Statistical Mechanics , 1998 .

[31]  Lai-Sang Young,et al.  Dimension formula for random transformations , 1988 .

[32]  Edward N. Lorenz,et al.  Can chaos and intransitivity lead to interannual variability , 1990 .

[33]  Michael Ghil,et al.  Climate dynamics and fluid mechanics: Natural variability and related uncertainties , 2008, 1006.2864.

[34]  E. Hawkins,et al.  The Potential to Narrow Uncertainty in Regional Climate Predictions , 2009 .

[35]  Andrey Shilnikov,et al.  Bifurcation and predictability analysis of a low-order atmospheric circulation model , 1995 .

[36]  J. Holton An introduction to dynamic meteorology , 2004 .

[37]  Tamás Bódai,et al.  A chaotically driven model climate: extreme events and snapshot attractors , 2011 .

[38]  Didier Paillard,et al.  From atmosphere, to climate, to Earth system science , 2008 .

[39]  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.

[40]  Jianping Huang,et al.  Evolution of land surface air temperature trend , 2014 .

[41]  Karl E. Taylor,et al.  An overview of CMIP5 and the experiment design , 2012 .

[42]  C. Franzke Warming trends: Nonlinear climate change , 2014 .

[43]  C. Deser,et al.  Communication of the role of natural variability in future North American climate , 2012 .

[44]  Grebogi,et al.  Multifractal properties of snapshot attractors of random maps. , 1990, Physical review. A, Atomic, molecular, and optical physics.

[45]  Tamás Tél,et al.  Advection in chaotically time-dependent open flows , 1998 .

[46]  Stefano Pierini,et al.  Stochastic tipping points in climate dynamics. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.