A Study of the Time Lags of the Indian Ocean Dipole and Rainfall Over Thailand by Using the Cross Wavelet Analysis

Complex systems are composed of mutually interacting components and the output values of these components usually exhibit long-range cross-correlations. Using wavelet analysis, we propose a method of characterizing the joint multifractal nature of these long-range cross correlations, a method we call multifractal cross wavelet analysis (MFXWT). We assess the performance of the MFXWT method by performing extensive numerical experiments on the dual binomial measures with multifractal cross correlations and the bivariate fractional Brownian motions (bFBMs) with monofractal cross correlations. For binomial multifractal measures, we find the empirical joint multifractality of MFXWT to be in approximate agreement with the theoretical formula. For bFBMs, MFXWT may provide spurious multifractality because of the wide spanning range of the multifractal spectrum. We also apply the MFXWT method to stock market indices, and in pairs of index returns and volatilities we find an intriguing joint multifractal behavior. The tests on surrogate series also reveal that the cross correlation behavior, particularly the cross correlation with zero lag, is the main origin of cross multifractality.

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Wavelet analysis of the magnetotail response to solar wind fluctuations during HILDCCA events

10.5194/angeo-2019-70 ◽

2019 ◽

Author(s):

Adriane Marques de Souza Franco ◽

Ezequiel Echer ◽

Mauricio José Alves Bolzan

Keyword(s):

Solar Wind ◽

Wavelet Transform ◽

Wavelet Analysis ◽

High Intensity ◽

Geomagnetic Field ◽

Long Duration ◽

Analysis Technique ◽

The Cross ◽

Cross Wavelet Analysis ◽

Cross Wavelet

Abstract. In this work a study of the effects of the High-Intensity Long-Duration Continuous AE activity events (HILDCAAs) in the magnetotail was conducted. The aim of this study was to search the main frequencies during HILDCAAs in the Bx component of the geomagnetic field, as well as at the main frequencies which the magnetotail responds to the solar wind during these events. In order to conduct this analysis the wavelet transform was employed in 9 HILDCAA events that occurred after the Cluster mission (2000) and coincided with the Cluster crossing through the tail of the magnetosphere from 2003 to 2007. Altogether, 25 most energetic periods was observed, which 76 % are ≤ 4 hours. The cross wavelet analysis technique was also used for the development of this study. It was applied to data of the Bz-IMF component and the Bx geomagnetic component, searching to obtain the periods in that had the highest correlation between these two series. To obtain these periods is important to identify frequencies on which the coupling of energy is stronger, as well the modulation of the magnetotail by the solar wind during HILDCAA events. The majority of correlation periods between the Bz (IMF) and Bx component of the geomagnetic field observed also were ≤ 4 hours, with 62.9 % of the periods. Thus the magnetotail responds stronger to IMF fluctuations during HILDCCAS at 2–4 hours scales, which are typical substorm periods.

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ON THE CROSS WAVELET ANALYSIS OF DUFFING OSCILLATOR

Journal of Sound and Vibration ◽

10.1006/jsvi.1999.2420 ◽

1999 ◽

Vol 228 (1) ◽

pp. 199-210 ◽

Cited By ~ 17

Author(s):

A. KYPRIANOU ◽

W.J. STASZEWSKI

Keyword(s):

Wavelet Analysis ◽

Duffing Oscillator ◽

The Cross ◽

Cross Wavelet Analysis ◽

Cross Wavelet

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CORRELATION AND COHERENCE ANALYSIS OF SEA SURFACE TEMPERATURE (SST) DISTRIBUTED BY THE SURFACE WIND IN WEST SUMATERA WATERS

Jurnal Meteorologi dan Geofisika ◽

10.31172/jmg.v19i2.572 ◽

2019 ◽

Vol 19 (2) ◽

pp. 81

Author(s):

Ulung Jantama Wisha ◽

Rahaden Bagas Hatmaja ◽

Ivonne Milichristi Radjawane ◽

Try Al Tanto

Keyword(s):

Wind Speed ◽

Indian Ocean ◽

Indian Ocean Dipole ◽

Surface Wind ◽

Surface Wind Speed ◽

Sea Surface ◽

Wavelet Coherence ◽

Dipole Mode ◽

The Indian Ocean ◽

Cross Wavelet

West Sumatera Waters have a tremendous dynamic in ocean characteristics. It directly faces the Indian Ocean exactly located below the equator. Consequently, West Sumatera waters are influenced by the tropical climatic factors such as monsoons, climate variability, and the Indian Ocean Dipole (IOD), controlling sea surface temperature (SST) fluctuation in the Indian Ocean. This study aims to review the correlation and coherence of SST distributed by surface wind in the West Sumatera waters. Wavelet method (cross wavelet transforms and wavelet coherence) was used to analyze the correlation and coherency between SST and surface wind. The annual variation of SST for 365 days period is the strongest event throughout the year caused by either monsoon or the changes of wind speed in the surface. Otherwise, the strongest intra-seasonal SST variation of 35 - 60 days observed from December 2012 to March 2013. The highest surface wind speed occurs in the southern and western waters. During the positive dipole mode in October 2015, the surface wind speed is slightly high resulting in the SST declination. Nevertheless, during the negative dipole mode in July 2016, the condition is inversely proportional. The surface wind plays a role in the SST distribution of 35 - 60 days period (intra-seasonal variability). Besides, surface wind with 6 months period (semi-annual variability) influences the SST distribution, identified only in the southern waters and the Indian Ocean regions. These conditions predicted as the influence of monsoon. Sumatera Barat merupakan wilayah perairan yang stategis dimana secara langsung berhadapan dengan Samudera Hindia dan tepat berada pada dibawah Garis Katulistiwa. Oleh karena itu, Perairan Sumatera Barat dipengaruhi oleh faktor-faktor iklim tropis seperti monsun dan variabilitas iklim, sangat terkait dengan Indian Ocean Dipole (IOD) yang mengendalikan fluktuasi suhu permukaan laut (SPL) di Samudera Hindia. Tujuan dari penelitian ini adalah menelaah korelasi dan koherensi antara parameter SPL dan komponen kecepatan angin di perairan Sumatera Barat. Metode wavelet (cross wavelet transform dan wavelet coherence) digunakan untuk menganalisa korelasi dan koherensi dari kedua parameter yang diuji. Variasi tahunan dari SPL pada periode 365 hari merupakan kejadian terkuat sepanjang tahun yang disebabkan oleh monsun atau perubahan pengaruh angin dipermukaan. Sebaliknya, variasi musiman terkuat dari SPL pada periode 35-60 hari ditemukan terjadi pada bulan Desember 2012 hingga Maret 2013. Kecepatan angin tertinggi terjadi di perairan selatan dan barat. Selama dipole mode positif pada bulan Oktober 2015, kecepatan angin permukaan sedikit meningkat yang mengakibatkan penurunan suhu perairan. Namun, selama dipole mode negatif pada bulan Juli 2016, kondisinya berbanding terbalik. Angin permukaan memainkan peran pada peningkatan distribusi suhu permukaan laut pada periode 35-60 hari (variabilistas musiman). Selain itu, angin permukaan dengan periode 6 bulan (tengah tahunan) sangat mempengaruhi distribusi suhu yang teridentifikasi pada wilayah selatan dan Samudera Hindia. Kondisi tersebut diperkirakan sebagai pengaruh dari monsun.

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Nitrous oxide fluxes over establishing biofuel crops: Characterization of temporal variability using the cross‐wavelet analysis

GCB Bioenergy ◽

10.1111/gcbb.12728 ◽

2020 ◽

Vol 12 (9) ◽

pp. 756-770

Author(s):

Marcelo Zeri ◽

Wendy H. Yang ◽

Gisleine Cunha‐Zeri ◽

Christy D. Gibson ◽

Carl J. Bernacchi

Keyword(s):

Nitrous Oxide ◽

Wavelet Analysis ◽

Temporal Variability ◽

Biofuel Crops ◽

The Cross ◽

Cross Wavelet Analysis ◽

Cross Wavelet

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Analysis of Drought-Sensitive Areas and Evolution Patterns through Statistical Simulations of the Indian Ocean Dipole Mode

Water ◽

10.3390/w11061302 ◽

2019 ◽

Vol 11 (6) ◽

pp. 1302 ◽

Cited By ~ 2

Author(s):

Qing-Gang Gao ◽

Vonevilay Sombutmounvong ◽

Lihua Xiong ◽

Joo-Heon Lee ◽

Jong-Suk Kim

Keyword(s):

Indian Ocean ◽

Indian Ocean Dipole ◽

Diagnostic Techniques ◽

Eastern Coast ◽

Indochina Peninsula ◽

Indian Ocean Dipole Mode ◽

Sensitive Areas ◽

The Indian Ocean ◽

Long Term Trend

In this study, we investigated extreme droughts in the Indochina peninsula and their relationship with the Indian Ocean Dipole (IOD) mode. Areas most vulnerable to drought were analyzed via statistical simulations of the IOD based on historical observations. Results of the long-term trend analysis indicate that areas with increasing spring (March–May) rainfall are mainly distributed along the eastern coast (Vietnam) and the northwestern portions of the Indochina Peninsula (ICP), while Central and Northern Laos and Northern Cambodia have witnessed a reduction in spring rainfall over the past few decades. This trend is similar to that of extreme drought. During positive IOD years, the frequency of extreme droughts was reduced throughout Vietnam and in the southwestern parts of China, while increased drought was observed in Cambodia, Central Laos, and along the coastline adjacent to the Myanmar Sea. Results for negative IOD years were similar to changes observed for positive IOD years; however, the eastern and northern parts of the ICP experienced reduced droughts. In addition, the results of the statistical simulations proposed in this study successfully simulate drought-sensitive areas and evolution patterns of various IOD changes. The results of this study can help improve diagnostic techniques for extreme droughts in the ICP.

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Influence of the Indian Ocean Dipole on tree-ring δ18O of monsoonal Southeast Tibet

Climatic Change ◽

10.1007/s10584-016-1663-8 ◽

2016 ◽

Vol 137 (1-2) ◽

pp. 217-230 ◽

Cited By ~ 14

Author(s):

Philipp Hochreuther ◽

Jakob Wernicke ◽

Jussi Grießinger ◽

Thomas Mölg ◽

Haifeng Zhu ◽

...

Keyword(s):

Indian Ocean ◽

Tree Ring ◽

Indian Ocean Dipole ◽

The Indian Ocean ◽

Southeast Tibet

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Two Independent Triggers for the Indian Ocean Dipole/Zonal Mode in a Coupled GCM

Journal of Climate ◽

10.1175/jcli3478.1 ◽

2005 ◽

Vol 18 (17) ◽

pp. 3428-3449 ◽

Cited By ~ 114

Author(s):

Albert S. Fischer ◽

Pascal Terray ◽

Eric Guilyardi ◽

Silvio Gualdi ◽

Pascale Delecluse

Keyword(s):

Indian Ocean ◽

El Niño ◽

Indian Ocean Dipole ◽

El Nino ◽

Tropical Indian Ocean ◽

Sea Surface ◽

Dynamical Response ◽

Thermocline Depth ◽

Second Phase ◽

The Indian Ocean

Abstract The question of whether and how tropical Indian Ocean dipole or zonal mode (IOZM) interannual variability is independent of El Niño–Southern Oscillation (ENSO) variability in the Pacific is addressed in a comparison of twin 200-yr runs of a coupled climate model. The first is a reference simulation, and the second has ENSO-scale variability suppressed with a constraint on the tropical Pacific wind stress. The IOZM can exist in the model without ENSO, and the composite evolution of the main anomalies in the Indian Ocean in the two simulations is virtually identical. Its growth depends on a positive feedback between anomalous equatorial easterly winds, upwelling equatorial and coastal Kelvin waves reducing the thermocline depth and sea surface temperature off the coast of Sumatra, and the atmospheric dynamical response to the subsequently reduced convection. Two IOZM triggers in the boreal spring are found. The first is an anomalous Hadley circulation over the eastern tropical Indian Ocean and Maritime Continent, with an early northward penetration of the Southern Hemisphere southeasterly trades. This situation grows out of cooler sea surface temperatures in the southeastern tropical Indian Ocean left behind by a reinforcement of the late austral summer winds. The second trigger is a consequence of a zonal shift in the center of convection associated with a developing El Niño, a Walker cell anomaly. The first trigger is the only one present in the constrained simulation and is similar to the evolution of anomalies in 1994, when the IOZM occurred in the absence of a Pacific El Niño state. The presence of these two triggers—the first independent of ENSO and the second phase locking the IOZM to El Niño—allows an understanding of both the existence of IOZM events when Pacific conditions are neutral and the significant correlation between the IOZM and El Niño.

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