Climate model attractors: chaos, quasi-regularity and sensitivity to small perturbations of external forcing

International audienceIn this paper we discuss some theoretical results obtained for climate models (theorems for the existence of global attractors and inertial manifolds, estimates of attractor dimension and Lyapunov exponents, symmetry property of Lyapunov spectrum). We define the conditions for "quasi-regular behaviour" of a climate system. Under these conditions, the system behaviour is subject to the Kraichnan fluctuation-dissipation relation. This fact allows us to solve the problem of determining a system's sensitivity to small perturbations to an external forcing. The applicability of the above approach to the analysis of the climate system sensitivity is verified numerically with the example of the two-layer quasi-geostrophic atmospheric model

Spin Orientation of Holes in Quantum Wells

This paper reviews the spin orientation of spin-3/2 holes in quantum wells. We discuss the Zeeman and Rashba spin splitting in hole systems that are qualitatively different from their counterparts in electron systems. We show how a systematic understanding of the unusual spin-dependent phenomena in hole systems can be gained using a multipole expansion of the spin density matrix. As an example we discuss spin precession in hole systems that can give rise to an alternating spin polarization. Finally, we discuss the qualitatively different regimes of hole spin polarization decay in clean and dirty samples.Comment: 14 pages, 8 figure

Spin relaxation in low-dimensional systems

We review some of the newest findings on the spin dynamics of carriers and excitons in GaAs/GaAlAs quantum wells. In intrinsic wells, where the optical properties are dominated by excitonic effects, we show that exciton-exciton interaction produces a breaking of the spin degeneracy in two-dimensional semiconductors. In doped wells, the two spin components of an optically created two-dimensional electron gas are well described by Fermi-Dirac distributions with a common temperature but different chemical potentials. The rate of the spin depolarization of the electron gas is found to be independent of the mean electron kinetic energy but accelerated by thermal spreading of the carriers.Comment: 1 PDF file, 13 eps figures, Proceedings of the 1998 International Workshop on Nanophysics and Electronics (NPE-98)- Lecce (Italy

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

Estimating the Fractal Dimension, K_2-entropy, and the Predictability of the Atmosphere

The series of mean daily temperature of air recorded over a period of 215 years is used for analysing the dimensionality and the predictability of the atmospheric system. The total number of data points of the series is 78527. Other 37 versions of the original series are generated, including ``seasonally adjusted'' data, a smoothed series, series without annual course, etc. Modified methods of Grassberger and Procaccia are applied. A procedure for selection of the ``meaningful'' scaling region is proposed. Several scaling regions are revealed in the ln C(r) versus ln r diagram. The first one in the range of larger ln r has a gradual slope and the second one in the range of intermediate ln r has a fast slope. Other two regions are settled in the range of small ln r. The results lead us to claim that the series arises from the activity of at least two subsystems. The first subsystem is low-dimensional (d_f=1.6) and it possesses the potential predictability of several weeks. We suggest that this subsystem is connected with seasonal variability of weather. The second subsystem is high-dimensional (d_f>17) and its error-doubling time is about 4-7 days. It is found that the predictability differs in dependence on season. The predictability time for summer, winter and the entire year (T_2 approx. 4.7 days) is longer than for transition-seasons (T_2 approx. 4.0 days for spring, T_2 approx. 3.6 days for autumn). The role of random noise and the number of data points are discussed. It is shown that a 15-year-long daily temperature series is not sufficient for reliable estimations based on Grassberger and Procaccia algorithms.Comment: 27 pages (LaTex version 2.09) and 15 figures as .ps files, e-mail: [email protected]

Cloud feedback in atmospheric general circulation models: An update

Six years ago, we compared the climate sensitivity of 19 atmospheric general circulation models and found a roughly threefold variation among the models; most of this variation was attributed to differences in the models' depictions of cloud feedback. In an update of this comparison, current models showed considerably smaller differences in net cloud feedback, with most producing modest values. There are, however, substantial differences in the feedback components, indicating that the models still have physical disagreements