Results 1  10
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25
Advanced Spectral Methods for Climatic Time Series
, 2001
"... The analysis of uni or multivariate time series provides crucial information to describe, understand, and predict climatic variability. The discovery and implementation of a number of novel methods for extracting useful information from time series has recently revitalized this classical eld of ..."
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Cited by 220 (32 self)
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The analysis of uni or multivariate time series provides crucial information to describe, understand, and predict climatic variability. The discovery and implementation of a number of novel methods for extracting useful information from time series has recently revitalized this classical eld of study. Considerable progress has also been made in interpreting the information so obtained in terms of dynamical systems theory.
Solving Problems with GCMs: General Circulation Models and Their Role in the Climate Modeling Hierarchy
 IN GENERAL CIRCULATION MODEL DEVELOPMENT: PAST, PRESENT AND FUTURE, EDITED BY
, 2000
"... We outline the familiar concept of a hierarchy of models for solving problems in climate dynamics. General circulation models (GCMs) occupy a special position at the apex of this hierarchy, and provide the main link between basic conceptsbest captured by very simple, "toy" modelsand ..."
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Cited by 47 (31 self)
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We outline the familiar concept of a hierarchy of models for solving problems in climate dynamics. General circulation models (GCMs) occupy a special position at the apex of this hierarchy, and provide the main link between basic conceptsbest captured by very simple, "toy" modelsand the incomplete and inaccurate observations of climate variability in space and time. We illustrate this role of GCMs in addressing the problems of climate variability on three time scales: intraseasonal, seasonaltointerannual, and interdecadal. The problems involved require the use of atmospheric, oceanic, and coupled oceanatmosphere GCMs. We emphasize the role of dynamical systems theory in communicating between the rungs of the modeling hierarchytoy models, intermediate ones, and GCMsand between modeling results and observations.
A Hierarchy of DataBased ENSO Models
, 2005
"... Global sea surface temperature (SST) evolution is analyzed by constructing predictive models that best describe the dataset’s statistics. These inverse models assume that the system’s variability is driven by spatially coherent, additive noise that is white in time and are constructed in the phase s ..."
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Cited by 28 (13 self)
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Global sea surface temperature (SST) evolution is analyzed by constructing predictive models that best describe the dataset’s statistics. These inverse models assume that the system’s variability is driven by spatially coherent, additive noise that is white in time and are constructed in the phase space of the dataset’s leading empirical orthogonal functions. Multiple linear regression has been widely used to obtain inverse stochastic models; it is generalized here in two ways. First, the dynamics is allowed to be nonlinear by using polynomial regression. Second, a multilevel extension of classic regression allows the additive noise to be correlated in time; to do so, the residual stochastic forcing at a given level is modeled as a function of variables at this level and the preceding ones. The number of variables, as well as the order of nonlinearity, is determined by optimizing model performance. The twolevel linear and quadratic models have a better El Niño–Southern Oscillation (ENSO) hindcast skill than their onelevel counterparts. Estimates of skewness and kurtosis of the models ’ simulated Niño3 index reveal that the quadratic model reproduces better the observed asymmetry between the positive El Niño and negative La Niña events. The benefits of the quadratic model are less clear in terms of its overall, crossvalidated hindcast skill; this model outperforms, however, the linear one in predicting the magnitude of extreme SST anomalies. Seasonal ENSO dependence is captured by incorporating additive, as well as multiplicative forcing with a 12month period into the first level of each model. The quasiquadrennial ENSO oscillatory mode is robustly simulated by all models. The “spring barrier ” of ENSO forecast skill is explained by Floquet and singular vector analysis, which show that the leading ENSO mode becomes strongly damped in summer, while nonnormal optimum growth has a strong peak in December.
DataAdaptive Wavelets and MultiScale SingularSpectrum Analysis
, 2000
"... Using multiscale ideas from wavelet analysis, we extend singularspectrum analysis (SSA) to the study of nonstationary time series, including the case where intermittency gives rise to the divergence of their variance. The wavelet transform resembles a local Fourier transform within a finite moving ..."
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Cited by 12 (0 self)
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Using multiscale ideas from wavelet analysis, we extend singularspectrum analysis (SSA) to the study of nonstationary time series, including the case where intermittency gives rise to the divergence of their variance. The wavelet transform resembles a local Fourier transform within a finite moving window whose width W , proportional to the major period of interest, is varied to explore a broad range of such periods. SSA, on the other hand, relies on the construction of the lagcorrelation matrix C on M lagged copies of the time series over a fixed window width W to detect the regular part of the variability in that window in terms of the minimal number of oscillatory components; here W = M#t with #t as the time step. The proposed multiscale SSA is a local SSA analysis within a movingwindowof width M # W # N , whereN is the length of the time series. Multiscale SSA varies W , while keeping a fixed W/M ratio, and uses the eigenvectors of the corresponding lagcorrelation matrix...
The Great Salt Lake: A barometer of low frequency climatic variability
 Water Resources Research
, 1995
"... inclusion in Reports by an authorized administrator of ..."
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Cited by 8 (5 self)
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inclusion in Reports by an authorized administrator of
A Multivariate FrequencyDomain Approach to Long Lead Climate Forecasting, Weather and Forecasting
, 1998
"... ABSTRACT Guided by the increasing awareness and detectability of spatiotemporally organized climatic variability at interannual and longer timescales, the authors motivate the paradigm of a climate system that exhibits excitations of quasioscillatory eigenmodes with characteristic timescales and l ..."
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Cited by 6 (4 self)
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ABSTRACT Guided by the increasing awareness and detectability of spatiotemporally organized climatic variability at interannual and longer timescales, the authors motivate the paradigm of a climate system that exhibits excitations of quasioscillatory eigenmodes with characteristic timescales and largescale spatial patterns of coherence. It is assumed that any such modes are superposed on a spatially and temporally autocorrelated stochastic noise background. Under such a paradigm, a previously described (Mann and Park) multivariate frequencydomain approach is promoted as a particularly effective means of spatiotemporal signal identification and reconstruction, and an associated forecasting methodology is introduced. This combined signal detection/forecasting scheme exhibits significantly greater skill than conventional forecasting approaches in the context of a synthetic example consistent with the adopted paradigm. The example application demonstrates statistically significant skill at 510yr lead times. Applications to operational longrange climatic forecasting are motivated and discussed.
Atmospheric flow indices and interannual Great Salt Lake variability
 J. Hydrol. Eng
, 1996
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Intraseasonal Variability
, 2004
"... c) High Frequency ISV..................................................................................................................................... 5 ..."
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Cited by 3 (0 self)
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c) High Frequency ISV..................................................................................................................................... 5
2000: Dataadaptive wavelets and multiscale SSA
 Physica D
"... Using multiscale ideas from wavelet analysis, we extend singularspectrum analysis (SSA) to the study of nonstationary time series of length N whose intermittency can give rise to the divergence of their variance. The wavelet transform is a kind of local Fourier transform within a finite moving win ..."
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Cited by 2 (1 self)
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Using multiscale ideas from wavelet analysis, we extend singularspectrum analysis (SSA) to the study of nonstationary time series of length N whose intermittency can give rise to the divergence of their variance. The wavelet transform is a kind of local Fourier transform within a finite moving window whose width W, proportional to the major period of interest, is varied to explore a broad range of such periods. SSA, on the other hand, relies on the construction of the lagcovariance matrix C on M lagged copies of the time series over a fixed window width W to detect the regular part of the variability in that window in terms of the minimal number of oscillatory components; here W = M∆t, with ∆t the time step. The proposed multiscale SSA is a local SSA analysis within a moving window of width M ≤ W ≤ N. Multiscale SSA varies W, while keeping a fixed W/M ratio, and uses the eigenvectors of the corresponding lagcovariance matrix CM as a dataadaptive wavelets; successive eigenvectors of CM correspond approximately to successive derivatives of the first mother wavelet in standard wavelet analysis. Multiscale SSA thus solves objectively the delicate problem of optimizing the analyzing wavelet in the timefrequency domain, by a suitable localization of the signal’s covariance matrix. We present several examples of application to synthetic signals with fractal or powerlaw behavior
Interannual prediction of the Paraná river
"... Abstract. Interannualtodecadal predictability of the Paraná river in South America is investigated by extracting nearcyclic components in summerseason streamflows at Corrientes over the period 1904–1997. It is found that oscillatory components with periods of about 2–5, 8 and 17 years are accomp ..."
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Abstract. Interannualtodecadal predictability of the Paraná river in South America is investigated by extracting nearcyclic components in summerseason streamflows at Corrientes over the period 1904–1997. It is found that oscillatory components with periods of about 2–5, 8 and 17 years are accompanied by statistically significant changes in monthly streamflow. Autoregressive predictive models are constructed for each component. Crossvalidated categorical hindcasts based on the 8yr predicted component are found to yield some skill up to four years in advance for belowaverage flows. A prediction based upon the 8 and 17yr components including data up to 1999 suggests increased probability of belowaverage flows until 2006. 1.