717 research outputs found
Improved linear response for stochastically driven systems
The recently developed short-time linear response algorithm, which predicts
the average response of a nonlinear chaotic system with forcing and dissipation
to small external perturbation, generally yields high precision of the response
prediction, although suffers from numerical instability for long response times
due to positive Lyapunov exponents. However, in the case of stochastically
driven dynamics, one typically resorts to the classical fluctuation-dissipation
formula, which has the drawback of explicitly requiring the probability density
of the statistical state together with its derivative for computation, which
might not be available with sufficient precision in the case of complex
dynamics (usually a Gaussian approximation is used). Here we adapt the
short-time linear response formula for stochastically driven dynamics, and
observe that, for short and moderate response times before numerical
instability develops, it is generally superior to the classical formula with
Gaussian approximation for both the additive and multiplicative stochastic
forcing. Additionally, a suitable blending with classical formula for longer
response times eliminates numerical instability and provides an improved
response prediction even for long response times
A simple closure approximation for slow dynamics of a multiscale system: nonlinear and multiplicative coupling
Multiscale dynamics are ubiquitous in applications of modern science. Because
of time scale separation between relatively small set of slowly evolving
variables and (typically) much larger set of rapidly changing variables, direct
numerical simulations of such systems often require relatively small time
discretization step to resolve fast dynamics, which, in turn, increases
computational expense. As a result, it became a popular approach in
applications to develop a closed approximate model for slow variables alone,
which both effectively reduces the dimension of the phase space of dynamics, as
well as allows for a longer time discretization step. In this work we develop a
new method for approximate reduced model, based on the linear
fluctuation-dissipation theorem applied to statistical states of the fast
variables. The method is suitable for situations with quadratically nonlinear
and multiplicative coupling. We show that, with complex quadratically nonlinear
and multiplicative coupling in both slow and fast variables, this method
produces comparable statistics to what is exhibited by an original multiscale
model. In contrast, it is observed that the results from the simplified closed
model with a constant coupling term parameterization are consistently less
precise
Multi-level Dynamical Systems: Connecting the Ruelle Response Theory and the Mori-Zwanzig Approach
In this paper we consider the problem of deriving approximate autonomous
dynamics for a number of variables of a dynamical system, which are weakly
coupled to the remaining variables. In a previous paper we have used the Ruelle
response theory on such a weakly coupled system to construct a surrogate
dynamics, such that the expectation value of any observable agrees, up to
second order in the coupling strength, to its expectation evaluated on the full
dynamics. We show here that such surrogate dynamics agree up to second order to
an expansion of the Mori-Zwanzig projected dynamics. This implies that the
parametrizations of unresolved processes suited for prediction and for the
representation of long term statistical properties are closely related, if one
takes into account, in addition to the widely adopted stochastic forcing, the
often neglected memory effects.Comment: 14 pages, 1 figur
On spurious detection of linear response and misuse of the fluctuation–dissipation theorem in finite time series
Using a sensitive statistical test we determine whether or not one can detect the breakdown of linear response given observations of deterministic dynamical systems. A goodness-of-fit statistics is developed for a linear statistical model of the observations, based on results for central limit theorems for deterministic dynamical systems, and used to detect linear response breakdown. We apply the method to discrete maps which do not obey linear response and show that the successful detection of breakdown depends on the length of the time series, the magnitude of the perturbation and on the choice of the observable.
We find that in order to reliably reject the assumption of linear response for typical observables sufficiently large data sets are needed. Even for simple systems such as the logistic map, one needs of the order of observations to reliably detect the breakdown with a confidence level of ; if less observations are available one may be falsely led to conclude that linear response theory is valid. The amount of data required is larger the smaller the applied perturbation. For judiciously chosen observables the necessary amount of data can be drastically reduced, but requires detailed a priori knowledge about the invariant measure which is typically not available for complex dynamical systems.
Furthermore we explore the use of the fluctuation–dissipation theorem (FDT) in cases with limited data length or coarse-graining of observations. The FDT, if applied naively to a system without linear response, is shown to be very sensitive to the details of the sampling method, resulting in erroneous predictions of the response
Predicting climate change using response theory: global averages and spatial patterns
The provision of accurate methods for predicting the climate response to anthropogenic and natural forcings is a key contemporary scientific challenge. Using a simplified and efficient open-source general circulation model of the atmosphere featuring O(105105) degrees of freedom, we show how it is possible to approach such a problem using nonequilibrium statistical mechanics. Response theory allows one to practically compute the time-dependent measure supported on the pullback attractor of the climate system, whose dynamics is non-autonomous as a result of time-dependent forcings. We propose a simple yet efficient method for predicting—at any lead time and in an ensemble sense—the change in climate properties resulting from increase in the concentration of CO22 using test perturbation model runs. We assess strengths and limitations of the response theory in predicting the changes in the globally averaged values of surface temperature and of the yearly total precipitation, as well as in their spatial patterns. The quality of the predictions obtained for the surface temperature fields is rather good, while in the case of precipitation a good skill is observed only for the global average. We also show how it is possible to define accurately concepts like the inertia of the climate system or to predict when climate change is detectable given a scenario of forcing. Our analysis can be extended for dealing with more complex portfolios of forcings and can be adapted to treat, in principle, any climate observable. Our conclusion is that climate change is indeed a problem that can be effectively seen through a statistical mechanical lens, and that there is great potential for optimizing the current coordinated modelling exercises run for the preparation of the subsequent reports of the Intergovernmental Panel for Climate Change
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