Oscillatory Climate Modes in the Indian Monsoon, North Atlantic, and Tropical Pacific

Abstract

This paper explores the three-way interactions between the Indian monsoon, the North 7 Atlantic and the Tropical Pacific. Four climate records were analyzed: the monsoon rainfall 8 in two Indian regions, the Southern Oscillation Index for the Tropical Pacific, and the NAO 9 index for the North Atlantic. The individual records exhibit highly significant oscillatory 10 modes with spectral peaks at 7-8 yr and in the quasi-biennial and quasi-quadrennial bands. 11The interactions between the three regions were investigated in the light of the synchro- 15A key result is that the 7-8-yr and 2.7-yr oscillatory modes in all three regions are 16 completely synchronized, and the energy ratio analysis suggests that the NAO induces these 17 modes in the other two regions. Both these modes in the NAO appear to be connected to bands. This approach should be applicable also to ocean modes of variability and to the 26 problems of air-sea interaction.

Pathogens trigger top-down climate forcing on ecosystem dynamics

“…Such a 7-8-year oscillation of temperatures in England has previously been reported (Plaut et al 1995), and probably reflects corresponding oscillations in sea level pressures and sea surface temperatures over the North Atlantic basin (Feliks et al 2004;Feliks et al 2010;Feliks et al 2013). Examination of the chain of causalities through time lags suggests that these thermal oscillations intially propagated into the Windermere ecosystem through a modulation of juvenile survival in perch and pike.…”

Cross-Scale Interactions and Information Transfer

“…The time delay η is chosen as the first minimum of the auto-mutual information I x 1 (t); x 1 (t + η) following the Fraser-Swinney recipe [33]. I x(t); y(t + τ )|y(t) with the 1-dimensional condition, in the direction of coupling (full red line) and I y(t); x(t + τ )|x(t) in the direction without coupling (dashed blue line); (c) the conditional mutual information I x(t); y(t + τ )|y(t), y(t − η), y(t − 2η) with the 3-dimensional condition, in the direction of coupling (full red line) and I y(t); x(t + τ )|x(t), x(t − η), x(t − 2η) in the direction without coupling (dashed blue line); for the unidirectionally coupled Rössler systems (13) and (14), as functions of the coupling strength ǫ. …”

“…The chaotic solutions of the Rössler systems (13) and (14) oscillate in a relatively narrow frequency band (Figure 3a, black line), therefore it is possible to apply the Hilbert transform directly to the components x 1 and y 1 in order to obtain the instantaneous phases φ 1 and φ 2 of the driving system (13) and the driven system (14), respectively. Then the direction of coupling can be inferred using the conditional mutual information I φ 1 (t); φ 2 (t + τ )|φ 2 (t) and I φ 2 (t); φ 1 (t + τ )|φ 1 (t) already with the one-dimensional condition (Figure 3b) since the phase represents a simplified one-dimensional description of the Rössler system's oscillations.…”

“…Even in such a case, however, the conditioning dimension necessary for the phase φ(t) is typically lower than that required for the oscillatory component s(t). The conditional mutual information I φ 1 (t); φ 2 (t+τ )|φ 2 (t) with the 1-dimensional condition, in the direction of coupling (red line) and I φ 2 (t); φ 1 (t + τ )|φ 1 (t) in the direction without coupling (blue line); (c) the conditional mutual information I φ 1 (t); φ 2 (t + τ )|φ 2 (t), φ 2 (t − η), φ 2 (t − 2η) with the 3-dimensional condition, in the direction of coupling (red line) and I φ 2 (t); φ 1 (t + τ )|φ 1 (t), φ 1 (t − η), φ 1 (t − 2η) in the direction without coupling (blue line); for the unidirectionally coupled Rössler systems (13) and (14), as functions of the coupling strength ǫ. …”

“…Synchronization and related phenomena have been observed in diverse physical, biological and social systems. Examples include cardio-respiratory interactions [2,3], synchronization of neural signals on various levels of organization of brain tissues [4][5][6][7], or coherent variability in the Earth atmosphere and climate [8][9][10][11][12][13]. In such systems it is not only important to detect interactions and possible synchronized states, but also to distinguish mutual interactions from a directional coupling, i.e., to identify drive-response relationships between the systems studied.…”