1,071 research outputs found
The Emergence of El-Ni\~{n}o as an Autonomous Component in the Climate Network
We construct and analyze a climate network which represents the
interdependent structure of the climate in different geographical zones and
find that the network responds in a unique way to El-Ni\~{n}o events. Analyzing
the dynamics of the climate network shows that when El-Ni\~{n}o events begin,
the El-Ni\~{n}o basin partially loses its influence on its surroundings. After
typically three months, this influence is restored while the basin loses almost
all dependence on its surroundings and becomes \textit{autonomous}. The
formation of an autonomous basin is the missing link to understand the
seemingly contradicting phenomena of the afore--noticed weakening of the
interdependencies in the climate network during El-Ni\~{n}o and the known
impact of the anomalies inside the El-Ni\~{n}o basin on the global climate
system.Comment: 5 pages,10 figure
Efficient dynamical downscaling of general circulation models using continuous data assimilation
Continuous data assimilation (CDA) is successfully implemented for the first
time for efficient dynamical downscaling of a global atmospheric reanalysis. A
comparison of the performance of CDA with the standard grid and spectral
nudging techniques for representing long- and short-scale features in the
downscaled fields using the Weather Research and Forecast (WRF) model is
further presented and analyzed. The WRF model is configured at 25km horizontal
resolution and is driven by 250km initial and boundary conditions from
NCEP/NCAR reanalysis fields. Downscaling experiments are performed over a
one-month period in January, 2016. The similarity metric is used to evaluate
the performance of the downscaling methods for large and small scales.
Similarity results are compared for the outputs of the WRF model with different
downscaling techniques, NCEP reanalysis, and Final Analysis. Both spectral
nudging and CDA describe better the small-scale features compared to grid
nudging. The choice of the wave number is critical in spectral nudging;
increasing the number of retained frequencies generally produced better
small-scale features, but only up to a certain threshold after which its
solution gradually became closer to grid nudging. CDA maintains the balance of
the large- and small-scale features similar to that of the best simulation
achieved by the best spectral nudging configuration, without the need of a
spectral decomposition. The different downscaled atmospheric variables,
including rainfall distribution, with CDA is most consistent with the
observations. The Brier skill score values further indicate that the added
value of CDA is distributed over the entire model domain. The overall results
clearly suggest that CDA provides an efficient new approach for dynamical
downscaling by maintaining better balance between the global model and the
downscaled fields
Renormalization group theory for finite-size scaling in extreme statistics
We present a renormalization group (RG) approach to explain universal
features of extreme statistics, applied here to independent, identically
distributed variables. The outlines of the theory have been described in a
previous Letter, the main result being that finite-size shape corrections to
the limit distribution can be obtained from a linearization of the RG
transformation near a fixed point, leading to the computation of stable
perturbations as eigenfunctions. Here we show details of the RG theory which
exhibit remarkable similarities to the RG known in statistical physics. Besides
the fixed points explaining universality, and the least stable eigendirections
accounting for convergence rates and shape corrections, the similarities
include marginally stable perturbations which turn out to be generic for the
Fisher-Tippett-Gumbel class. Distribution functions containing unstable
perturbations are also considered. We find that, after a transitory divergence,
they return to the universal fixed line at the same or at a different point
depending on the type of perturbation.Comment: 15 pages, 8 figures, to appear in Phys. Rev.
Extreme value distributions and Renormalization Group
In the classical theorems of extreme value theory the limits of suitably
rescaled maxima of sequences of independent, identically distributed random
variables are studied. So far, only affine rescalings have been considered. We
show, however, that more general rescalings are natural and lead to new limit
distributions, apart from the Gumbel, Weibull, and Fr\'echet families. The
problem is approached using the language of Renormalization Group
transformations in the space of probability densities. The limit distributions
are fixed points of the transformation and the study of the differential around
them allows a local analysis of the domains of attraction and the computation
of finite-size corrections.Comment: 16 pages, 5 figures. Final versio
Modeling long-range memory with stationary Markovian processes
In this paper we give explicit examples of power-law correlated stationary
Markovian processes y(t) where the stationary pdf shows tails which are
gaussian or exponential. These processes are obtained by simply performing a
coordinate transformation of a specific power-law correlated additive process
x(t), already known in the literature, whose pdf shows power-law tails 1/x^a.
We give analytical and numerical evidence that although the new processes (i)
are Markovian and (ii) have gaussian or exponential tails their autocorrelation
function still shows a power-law decay =1/T^b where b grows with a
with a law which is compatible with b=a/2-c, where c is a numerical constant.
When a<2(1+c) the process y(t), although Markovian, is long-range correlated.
Our results help in clarifying that even in the context of Markovian processes
long-range dependencies are not necessarily associated to the occurrence of
extreme events. Moreover, our results can be relevant in the modeling of
complex systems with long memory. In fact, we provide simple processes
associated to Langevin equations thus showing that long-memory effects can be
modeled in the context of continuous time stationary Markovian processes.Comment: 5 figure
Maximal height statistics for 1/f^alpha signals
Numerical and analytical results are presented for the maximal relative
height distribution of stationary periodic Gaussian signals (one dimensional
interfaces) displaying a 1/f^alpha power spectrum. For 0<alpha<1 (regime of
decaying correlations), we observe that the mathematically established limiting
distribution (Fisher-Tippett-Gumbel distribution) is approached extremely
slowly as the sample size increases. The convergence is rapid for alpha>1
(regime of strong correlations) and a highly accurate picture gallery of
distribution functions can be constructed numerically. Analytical results can
be obtained in the limit alpha -> infinity and, for large alpha, by
perturbation expansion. Furthermore, using path integral techniques we derive a
trace formula for the distribution function, valid for alpha=2n even integer.
From the latter we extract the small argument asymptote of the distribution
function whose analytic continuation to arbitrary alpha > 1 is found to be in
agreement with simulations. Comparison of the extreme and roughness statistics
of the interfaces reveals similarities in both the small and large argument
asymptotes of the distribution functions.Comment: 17 pages, 8 figures, RevTex
The potential of historical spy-satellite imagery to support research in ecology and conservation
Remote sensing data are important for assessing ecological change, but their value is often restricted by their limited temporal coverage. Major historical events that affected the environment, such as those associated with colonial history, World War II, or the Green Revolution are not captured by modern remote sensing. In the present article, we highlight the potential of globally available black-and-white satellite photographs to expand ecological and conservation assessments back to the 1960s and to illuminate ecological concepts such as shifting baselines, time-lag responses, and legacy effects. This historical satellite photography can be used to monitor ecosystem extent and structure, species’ populations and habitats, and human pressures on the environment. Even though the data were declassified decades ago, their use in ecology and conservation remains limited. But recent advances in image processing and analysis can now unlock this research resource. We encourage the use of this opportunity to address important ecological and conservation questions
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Generalized early warning signals in multivariate and gridded data with an application to tropical cyclones
Tipping events in dynamical systems have been studied across many applications, often by measuring changes in variance or autocorrelation in a one-dimensional time series. In this paper, methods for detecting early warning signals of tipping events in multidimensional systems are reviewed and expanded. An analytical justification of the use of dimension-reduction by empirical orthogonal functions, in the context of early warning signals, is provided and the one-dimensional techniques are also extended to spatially separated time series over a 2D field. The challenge of predicting an approaching tropical cyclone by a tipping-point analysis of the sea-level pressure series is used as the primary example, and an analytical model of a moving cyclone is also developed in order to test predictions. We show that the one-dimensional power spectrum indicator may be used following dimension-reduction or over a 2D field. We also show the validity of our moving cyclone model with respect to tipping-point indicators.
Many dynamical systems experience sudden shifts in behavior, often referred to as tipping points or critical transitions. A volume of work is dedicated to detecting and predicting these critical transitions, often making use of generic early warning signal (EWS) indicators based on autocorrelation1,2
and increasing variance.3,4
Similar indicators based on other scaling properties of the time series, namely, detrended fluctuation analysis (DFA)5,6
and power spectrum scaling,7
have also been used. Other methods have estimated parameters to fit a model to the data, both for detecting critical transitions8–10
and for predicting future transitions dynamics
D-cycloserine-augmented one-session treatment of specific phobias in children and adolescents.
BACKGROUND: D-Cycloserine has potential to enhance exposure therapy outcomes. The current study presents a preliminary randomized, placebo-controlled double-blind pilot trial of DCS-augmented one-session treatment (OST) for youth (7-14 years) with specific phobia. A secondary aim of this pilot study was to explore the effects of youth age and within-session fear reduction as potential moderators of DCS outcomes in order to generate hypotheses for a larger trial. It was hypothesized that DCS would be associated with greater improvements than placebo, that children (7-10 years) would have greater benefits than adolescents (11-14 years), and that DCS effects would be stronger for participants with the greater within-session fear reduction during the OST. METHODS: Thirty-five children and adolescents were randomized to either OST combined with DCS (n = 17), or OST combined with placebo (PBO; n = 18) and assessed at 1 week, 1 month, and 3 month following treatment. RESULTS: There were no significant pre- to post-treatment or follow-up benefits of DCS relative to placebo. Secondary analyses of age indicated that relative to PBO, DCS was associated with greater improvements for children (but not adolescents) on measures of severity at 1-month follow-up. Children in the DCS condition also showed significantly greater improvement to 1 month on global functioning relative to other groups. Conversely, adolescents had significant post-treatment benefits in the PBO condition on symptom severity measures relative to DCS, and adolescents in the DCS condition had significantly poorer functioning at 3 months relative to all other groups. Finally, there was a trend for within-session fear reduction to be associated with moderating effects of DCS, whereby greater reduction in fear was associated with greater functioning at one-month follow-up for children who received DCS, relative to PBO. LIMITATIONS: The study sample was small and therefore conclusions are tentative and require replication. CONCLUSIONS: Age and within-session fear reduction may be important moderators of DCS-augmented one-session exposure therapy, which requires testing in a fully powered randomized controlled trial
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