35 research outputs found
Climate dynamics and fluid mechanics: Natural variability and related uncertainties
The purpose of this review-and-research paper is twofold: (i) to review the
role played in climate dynamics by fluid-dynamical models; and (ii) to
contribute to the understanding and reduction of the uncertainties in future
climate-change projections. To illustrate the first point, we focus on the
large-scale, wind-driven flow of the mid-latitude oceans which contribute in a
crucial way to Earth's climate, and to changes therein. We study the
low-frequency variability (LFV) of the wind-driven, double-gyre circulation in
mid-latitude ocean basins, via the bifurcation sequence that leads from steady
states through periodic solutions and on to the chaotic, irregular flows
documented in the observations. This sequence involves local, pitchfork and
Hopf bifurcations, as well as global, homoclinic ones. The natural climate
variability induced by the LFV of the ocean circulation is but one of the
causes of uncertainties in climate projections. Another major cause of such
uncertainties could reside in the structural instability in the topological
sense, of the equations governing climate dynamics, including but not
restricted to those of atmospheric and ocean dynamics. We propose a novel
approach to understand, and possibly reduce, these uncertainties, based on the
concepts and methods of random dynamical systems theory. As a very first step,
we study the effect of noise on the topological classes of the Arnol'd family
of circle maps, a paradigmatic model of frequency locking as occurring in the
nonlinear interactions between the El Nino-Southern Oscillations (ENSO) and the
seasonal cycle. It is shown that the maps' fine-grained resonant landscape is
smoothed by the noise, thus permitting their coarse-grained classification.
This result is consistent with stabilizing effects of stochastic
parametrization obtained in modeling of ENSO phenomenon via some general
circulation models.Comment: Invited survey paper for Special Issue on The Euler Equations: 250
Years On, in Physica D: Nonlinear phenomen
Climate dynamics and fluid mechanics: Natural variability and related uncertainties
The purpose of this review-and-research paper is twofold: (i) to review the role played in climate dynamics by fluid-dynamical models; and (ii) to contribute to the understanding and reduction of the uncertainties in future climate-change projections. To illustrate the first point, we review recent theoretical advances in studying the wind-driven circulation of the oceans. In doing so, we concentrate on the large-scale, wind-driven flow of the mid-latitude oceans, which is dominated by the presence of a larger, anticyclonic and a smaller, cyclonic gyre. The two gyres share the eastward extension of western boundary currents, such as the Gulf Stream or Kuroshio, and are induced by the shear in the winds that cross the respective ocean basins. The boundary currents and eastward jets carry substantial amounts of heat and momentum, and thus contribute in a crucial way to Earth's climate, and to changes therein. Changes in this double-gyre circulation occur from year to year and decade to decade. We study this low-frequency variability of the wind-driven, double-gyre circulation in mid-latitude ocean basins, via the bifurcation sequence that leads from steady states through periodic solutions and on to the chaotic, irregular flows documented in the observations. This sequence involves local, pitchfork and Hopf bifurcations, as well as global, homoclinic ones. The natural climate variability induced by the low-frequency variability of the ocean circulation is but one of the causes of uncertainties in climate projections. The range of these uncertainties has barely decreased, or even increased, over the last three decades. Another major cause of such uncertainties could reside in the structural instability---in the classical, topological sense---of the equations governing climate dynamics, including but not restricted to those of atmospheric and ocean dynamics. We propose a novel approach to understand, and possibly reduce, these uncertainties, based on the concepts and methods of random dynamical systems theory. The idea is to compare the climate simulations of distinct general circulation models (GCMs) used in climate projections, by applying stochastic-conjugacy methods and thus perform a stochastic classification of GCM families. This approach is particularly appropriate given recent interest in stochastic parametrization of subgrid-scale processes in GCMs. As a very first step in this direction, we study the behavior of the Arnol'd family of circle maps in the presence of noise. The maps' fine-grained resonant landscape is smoothed by the noise, thus permitting their coarse-grained classification
Reduction of dimension for nonlinear dynamical systems
We consider reduction of dimension for nonlinear dynamical systems. We
demonstrate that in some cases, one can reduce a nonlinear system of equations
into a single equation for one of the state variables, and this can be useful
for computing the solution when using a variety of analytical approaches. In
the case where this reduction is possible, we employ differential elimination
to obtain the reduced system. While analytical, the approach is algorithmic,
and is implemented in symbolic software such as {\sc MAPLE} or {\sc SageMath}.
In other cases, the reduction cannot be performed strictly in terms of
differential operators, and one obtains integro-differential operators, which
may still be useful. In either case, one can use the reduced equation to both
approximate solutions for the state variables and perform chaos diagnostics
more efficiently than could be done for the original higher-dimensional system,
as well as to construct Lyapunov functions which help in the large-time study
of the state variables. A number of chaotic and hyperchaotic dynamical systems
are used as examples in order to motivate the approach.Comment: 16 pages, no figure
Predictability: a way to characterize Complexity
Different aspects of the predictability problem in dynamical systems are
reviewed. The deep relation among Lyapunov exponents, Kolmogorov-Sinai entropy,
Shannon entropy and algorithmic complexity is discussed. In particular, we
emphasize how a characterization of the unpredictability of a system gives a
measure of its complexity. Adopting this point of view, we review some
developments in the characterization of the predictability of systems showing
different kind of complexity: from low-dimensional systems to high-dimensional
ones with spatio-temporal chaos and to fully developed turbulence. A special
attention is devoted to finite-time and finite-resolution effects on
predictability, which can be accounted with suitable generalization of the
standard indicators. The problems involved in systems with intrinsic randomness
is discussed, with emphasis on the important problems of distinguishing chaos
from noise and of modeling the system. The characterization of irregular
behavior in systems with discrete phase space is also considered.Comment: 142 Latex pgs. 41 included eps figures, submitted to Physics Reports.
Related information at this http://axtnt2.phys.uniroma1.i
Synchronisation in dynamically coupled maps
The central aim of this thesis is to better understand the dynamics of a set of dynamically
coupled map systems previously introduced by Ito & Kaneko in a series of papers (Phys.
Rev. Lett. 88 (2002), no. 2, 028701 and Phys. Rev. E 67 (2003), no. 4, 046226). The current
work extends Ito & Kanekoâs studies to clarify the changes in macrodynamics induced
by the differences in microdynamics between the two systems. A third system is also
introduced that has a minor change to the microdynamics from nonlinear to linear output
function in the externally coupled system.
The dynamics of these three dynamically-coupled maps is also compared with their
simplified systems with static coupling. The previous studies of these dynamically-coupled
maps showed a partitioning of the parameter space into regions of different macrodynamics.
Here, an in-depth study is presented of the behaviour of the systems as they cross the
boundary between one region and another. The behaviour across this boundary is shown to
be much more complicated than suggested in the previous studies.
These three systems of dynamically-coupled maps all differ in the form of their microscopic
couplings, yet two of the systems are shown to produce similar macrodynamics,
whereas the third differs dramatically by almost any measure of the macrodynamics.
The time it takes for the systems to synchronise, both the dynamically-coupled and
static-coupled systems, is investigated. It is shown that the introduction of dynamicalcouplings
stops the systems from synchronising quasi-instantaneously. Details of potential
consequences of this in the field of neuroscience are discussed.
A brief study of the effect of driving the systems with external stimuli is presented.
The different microscopic coupling forms cause different responses to the external stimuli.
Some of the responses are similar to that observed by the visual cortex area of the brain
The physics of climate variability and climate change
The climate system is a forced, dissipative, nonlinear, complex and heterogeneous system that is out of thermodynamic equilibrium. The system exhibits natural variability on many scales of motion, in time as well as space, and it is subject to various external forcings, natural as well as anthropogenic. This paper reviews the observational evidence on climate phenomena and the governing equations of planetary-scale flow, as well as presenting the key concept of a hierarchy of models as used in the climate sciences. Recent advances in the application of dynamical systems theory, on the one hand, and of nonequilibrium statistical physics, on the other, are brought together for the first time and shown to complement each other in helping understand and predict the system's behavior. These complementary points of view permit a self-consistent handling of subgrid-scale phenomena as stochastic processes, as well as a unified handling of natural climate variability and forced climate change, along with a treatment of the crucial issues of climate sensitivity, response, and predictability