Interdecadal Changes in the ENSO–Monsoon System

Torrence, Christopher; Webster, Peter J.

doi:10.1175/1520-0442(1999)012<2679:icitem>2.0.co;2

Cited by 1,691 publications

(1,189 citation statements)

References 49 publications

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“…One promising future avenue to overcome the choice of a fixed window length is the use of the wavelet transform, which would allow to conveniently focus on particular frequencies (scales). In particular, the wavelet transform coherence (WTC) has been suggested as one alternative to estimate instantaneous "correlation coefficients" at different frequency bands, with a window length adjusted to the frequency content of the signal (Chang and Glover, 2010;Hutchison et al, 2013a;Torrence and Webster, 1999). In particular, the signal at each wavelet scale has been band-pass filtered with a high-pass cut-off according to the rule of the thumb, and, consequently, temporal variations in the scale-dependent correlations are limited to the same frequency.…”

Section: Resultsmentioning

confidence: 99%

On spurious and real fluctuations of dynamic functional connectivity during rest

2015

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Functional brain networks reconfigure spontaneously during rest. Such network dynamics can be studied by dynamic functional connectivity (dynFC); i.e., sliding-window correlations between regional brain activity. Key parameters-such as window length and cut-off frequencies for filtering-are not yet systematically studied. In this letter we provide the fundamental theory from signal processing to address these parameter choices when estimating and interpreting dynFC. We guide the reader through several illustrative cases, both simple analytical models and experimental fMRI BOLD data. First, we show how spurious fluctuations in dynFC can arise due to the estimation method when the window length is shorter than the largest wavelength present in both signals, even for deterministic signals with a fixed relationship. Second, we study how real fluctuations of dynFC can be explained using a frequency-based view, which is particularly instructive for signals with multiple frequency components such as fMRI BOLD, demonstrating that fluctuations in sliding-window correlation emerge by interaction between frequency components similar to the phenomenon of beat frequencies. We conclude with practical guidelines for the choice and impact of the window length.

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Section: Resultsmentioning

confidence: 99%

On spurious and real fluctuations of dynamic functional connectivity during rest

2015

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“…To characterise and detect the rhythms between two random processes, cross correlation and cross spectral methods can be employed. Correlation detection based on wavelet transforms has been used in wind engineering [8] and climatic and oceanic research [36]. In recent years in connection with neurophysiological applications, cross spectrum and coherence analysis have been used to analyse spike activity [25,15].…”

Section: Introductionmentioning

confidence: 99%

Detecting time-dependent coherence between non-stationary electrophysiological signals—A combined statistical and time–frequency approach

Zhan

Halliday

Jiang

et al. 2006

Journal of Neuroscience Methods

112

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Various time-frequency methods have been used to study the time-varying properties of non-stationary neurophysiological signals. In the present study, a time-frequency coherence using continuous wavelet transform (CWT) together with its confidence intervals are proposed to evaluate the correlation between two non-stationary processes. A systematic comparison between approaches using CWT and short-time Fourier transform (STFT) is carried out. Simulated data are generated to test the performance of these methods when estimating time-frequency based coherence. Surprisingly and in contrast to the common belief, the coherence estimation based upon CWT does not always supersede STFT. We suggest that a combination of STFT and CWT would be most suitable for analysing non-stationary neural data. In both frequency and time domains, methods to test whether there are two coherent signals presented in recorded data are presented. Our approach is then applied to the electroencephalogram (EEG) and surface electromyogram (EMG) during wrist movements in healthy subjects and the local field potential (LFP) and surface EMG during resting tremor in patients with Parkinson's disease. A software package including all results presented in the current paper is available at

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“…The interaction between ENSO and the SASM is complicated (Yasunari, 1990;Torrence and Webster, 1999) and has been known since the pioneering work of Sir Gilbert Walker (Walker, 1924). The influence of ENSO on the SASM was explained by Sikka (1980) and Shukla (1987) as being caused by a change in the Walker circulation and impacts on the regional Hadley circulation associated with the SASM Goswami, 1998).…”

Section: Introductionmentioning

confidence: 99%

Relationship between the tropical Pacific and Indian Ocean sea‐surface temperature and monthly precipitation over the central highlands, Vietnam

Nguyen

Uvo

Rosbjerg

2007

Intl Journal of Climatology

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Abstract:The relationship between monthly sea-surface temperature (SST) in the tropical Pacific and the Indian Ocean and monthly precipitation over the Vietnamese central highlands (VCH) has been investigated by means of singular value decomposition. The seasonal variation of SST plays a critical role in the onset of the monsoon season and convective rain band movement in the inter-tropical convergence zone.The relationships between precipitation and SST in both oceans vary significantly through the rainy season. In April, ENSO is strongly correlated with precipitation over the VCH, while the Indian Ocean SST only shows a significant correlation with precipitation in the northern VCH. In May, there is no significant relationship between precipitation and SST in either of the oceans. In June, precipitation over the VCH is negatively correlated with the northern Indian Ocean and the southern Pacific SST. Through July to September, no significant relationships were found between the Indian Ocean SST and precipitation patterns. The equatorial central to the eastern Pacific SST, in turn, is positively correlated with precipitation in a small area from the north to the south of the VCH. In October, precipitation over the VCH is strongly related to ENSO and positively correlated with the equatorial eastern Indian Ocean SST. For November, the north-western Pacific as well as the equatorial eastern Indian Ocean SST is positively and strongly correlated with precipitation over the VCH.Lag-time analyses demonstrate a potential link between the Pacific SST and monthly precipitation patterns through the rainy season from one to three months in advance, and between the Indian Ocean SST and monthly precipitation patterns in October and November from one to two months in advance. The analysed results provide a strong basis for a predictive scheme, but further analysis of skill levels needs to be developed.

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Interdecadal Changes in the ENSO–Monsoon System

Cited by 1,691 publications

References 49 publications

On spurious and real fluctuations of dynamic functional connectivity during rest

On spurious and real fluctuations of dynamic functional connectivity during rest

Detecting time-dependent coherence between non-stationary electrophysiological signals—A combined statistical and time–frequency approach

Relationship between the tropical Pacific and Indian Ocean sea‐surface temperature and monthly precipitation over the central highlands, Vietnam

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