1,538 research outputs found
Comparing modern and Pleistocene ENSO-like influences in NW Argentina using nonlinear time series analysis methods
Higher variability in rainfall and river discharge could be of major
importance in landslide generation in the north-western Argentine Andes. Annual
layered (varved) deposits of a landslide dammed lake in the Santa Maria Basin
(26 deg S, 66 deg W) with an age of 30,000 14C years provide an archive of
precipitation variability during this time. The comparison of these data with
present-day rainfall observations tests the hypothesis that increased rainfall
variability played a major role in landslide generation. A potential cause of
such variability is the El Nino/Southern Oscillation (ENSO). The causal link
between ENSO and local rainfall is quantified by using a new method of
nonlinear data analysis, the quantitative analysis of cross recurrence plots
(CRP). This method seeks similarities in the dynamics of two different
processes, such as an ocean-atmosphere oscillation and local rainfall. Our
analysis reveals significant similarities in the statistics of both modern and
palaeo-precipitation data. The similarities in the data suggest that an
ENSO-like influence on local rainfall was present at around 30,000 14C years
ago. Increased rainfall, which was inferred from a lake balance modeling in a
previous study, together with ENSO-like cyclicities could help to explain the
clustering of landslides at around 30,000 14C years ago.Comment: 11 pages, 9 figure
Coping with dating errors in causality estimation
We consider the problem of estimating causal influences between observed processes from time series possibly corrupted by errors in the time variable (dating errors) which are typical in palaeoclimatology, planetary science and astrophysics. "Causality ratio" based on the Wiener-Granger causality is proposed and studied for a paradigmatic class of model systems to reveal conditions under which it correctly indicates directionality of unidirectional coupling. It is argued that in the case of a priori known directionality, the causality ratio allows a characterization of dating errors and observational noise. Finally, we apply the developed approach to palaeoclimatic data and quantify the influence of solar activity on tropical Atlantic climate dynamics over the last two millennia. A stronger solar influence in the first millennium A.D. is inferred. The results also suggest a dating error of about 20 years in the solar proxy time series over the same period
Power-laws in recurrence networks from dynamical systems
Recurrence networks are a novel tool of nonlinear time series analysis
allowing the characterisation of higher-order geometric properties of complex
dynamical systems based on recurrences in phase space, which are a fundamental
concept in classical mechanics. In this Letter, we demonstrate that recurrence
networks obtained from various deterministic model systems as well as
experimental data naturally display power-law degree distributions with scaling
exponents that can be derived exclusively from the systems' invariant
densities. For one-dimensional maps, we show analytically that is not
related to the fractal dimension. For continuous systems, we find two distinct
types of behaviour: power-laws with an exponent depending on a
suitable notion of local dimension, and such with fixed .Comment: 6 pages, 7 figure
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Identifying causal gateways and mediators in complex spatio-temporal systems
Spatial structures and directionalities in Monsoonal precipitation over South Asia
Precipitation during the monsoon season over the Indian subcontinent occurs in form of enormously complex spatiotemporal patterns due to the underlying dynamics of atmospheric circulation and varying topography. Employing methods from nonlinear time series analysis, we study spatial structures of the rainfall field during the summer monsoon and identify principle regions where the dynamics of monsoonal rainfall is more coherent or homogenous. Moreover, we estimate the time delay patterns of rain events. Here we present an analysis of two separate high resolution gridded data sets of daily rainfall covering the Indian subcontinent. Using the method of event synchronization (ES), we estimate regions where heavy rain events during monsoon happen in some lag synchronised form. Further using the delay behaviour of rainfall events, we estimate the directionalities related to the progress of such type of rainfall events. The Active (break) phase of a monsoon is characterised by an increase(decrease) of rainfall over certain regions of the Indian subcontinent. We show that our method is able to identify regions of such coherent rainfall activity
Comparison of correlation analysis techniques for irregularly sampled time series
Geoscientific measurements often provide time series with irregular time sampling, requiring either data reconstruction (interpolation) or sophisticated methods to handle irregular sampling. We compare the linear interpolation technique and different approaches for analyzing the correlation functions and persistence of irregularly sampled time series, as Lomb-Scargle Fourier transformation and kernel-based methods. In a thorough benchmark test we investigate the performance of these techniques. <br><br> All methods have comparable root mean square errors (RMSEs) for low skewness of the inter-observation time distribution. For high skewness, very irregular data, interpolation bias and RMSE increase strongly. We find a 40 % lower RMSE for the lag-1 autocorrelation function (ACF) for the Gaussian kernel method vs. the linear interpolation scheme,in the analysis of highly irregular time series. For the cross correlation function (CCF) the RMSE is then lower by 60 %. The application of the Lomb-Scargle technique gave results comparable to the kernel methods for the univariate, but poorer results in the bivariate case. Especially the high-frequency components of the signal, where classical methods show a strong bias in ACF and CCF magnitude, are preserved when using the kernel methods. <br><br> We illustrate the performances of interpolation vs. Gaussian kernel method by applying both to paleo-data from four locations, reflecting late Holocene Asian monsoon variability as derived from speleothem &delta;<sup>18</sup>O measurements. Cross correlation results are similar for both methods, which we attribute to the long time scales of the common variability. The persistence time (memory) is strongly overestimated when using the standard, interpolation-based, approach. Hence, the Gaussian kernel is a reliable and more robust estimator with significant advantages compared to other techniques and suitable for large scale application to paleo-data
Extended Recurrence Plot Analysis and its Application to ERP Data
We present new measures of complexity and their application to event related
potential data. The new measures base on structures of recurrence plots and
makes the identification of chaos-chaos transitions possible. The application
of these measures to data from single-trials of the Oddball experiment can
identify laminar states therein. This offers a new way of analyzing
event-related activity on a single-trial basis.Comment: 21 pages, 8 figures; article for the workshop ''Analyzing and
Modelling Event-Related Brain Potentials: Cognitive and Neural Approaches``
at November 29 - December 01, 2001 in Potsdam, German
Mutations in the PH Domain of DNM1 are associated with a nonepileptic phenotype characterized by developmental delay and neurobehavioral abnormalities
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