Nonlinear Time Series Analysis in the Geosciences: Applications in Climatology, Geodynamics and Solar-Terrestrial Physics

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Reik V. Donner, Susana M. Barbosa
Springer Science & Business Media, Aug 18, 2008 - Science - 390 pages
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The enormous progress over the last decades in our understanding of the mechanisms behind the complex system “Earth” is to a large extent based on the availability of enlarged data sets and sophisticated methods for their analysis. Univariate as well as multivariate time series are a particular class of such data which are of special importance for studying the dynamical p- cesses in complex systems. Time series analysis theory and applications in geo- and astrophysics have always been mutually stimulating, starting with classical (linear) problems like the proper estimation of power spectra, which hasbeenputforwardbyUdnyYule(studyingthefeaturesofsunspotactivity) and, later, by John Tukey. In the second half of the 20th century, more and more evidence has been accumulated that most processes in nature are intrinsically non-linear and thus cannot be su?ciently studied by linear statistical methods. With mat- matical developments in the ?elds of dynamic system’s theory, exempli?ed by Edward Lorenz’s pioneering work, and fractal theory, starting with the early fractal concepts inferred by Harold Edwin Hurst from the analysis of geoph- ical time series,nonlinear methods became available for time seriesanalysis as well. Over the last decades, these methods have attracted an increasing int- est in various branches of the earth sciences. The world’s leading associations of geoscientists, the American Geophysical Union (AGU) and the European Geosciences Union (EGU) have reacted to these trends with the formation of special nonlinear focus groups and topical sections, which are actively present at the corresponding annual assemblies.
 

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Contents

Subsampling Methodology for the Analysis of Nonlinear Atmospheric Time Series
3
Global Patterns of Nonlinearity in Real and GCMSimulated Atmospheric Data
17
Prediction of Extreme Events
35
An Overview
60
Automatic Parameter Estimation in a Mesoscale Model Without Ensembles
81
Towards Robust Nonlinear Multivariate Analysis by Neural Network Methods
96
Complexity of SpatioTemporal Correlations in Japanese Air Temperature Records
125
Characterising LongTerm Variability
157
Applications to Internal Gravity Waves with Comparisons to Linear Tidal Data
223
Crustal Deformation Models and TimeFrequency Analysis of GPS Data from Deception Island Volcano South Shetland Islands Antarctica
245
Describing Seismic Pattern Dynamics by Means of Ising Cellular Automata
273
Template Analysis of the Hide Skeldon Acheson Dynamo
292
The Case of the Derivative Nonlinear Schrodinger Equation
311
Common Cycles in Atmospheric Geomagnetic and Solar Data
327
Phase Coherence Analysis of DecadalScale Sunspot Activity on Both Solar Hemispheres
354
Index
387

A New Empirical Global Ocean Tide and Mean Sea Level Model Based on Jason1 Satellite Altimetry Observations
174

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