Synchronization and causality across time scales in El Ni\xf1o Southern Oscillation

Nikola Jajcay,George Sugihara,Milan Paluš,Anastasios A Tsonis,Sergey Kravtsov

doi:10.1038/s41612-018-0043-7

Abstract

Statistical inference of causal interactions and synchronization between dynamical phenomena evolving on different temporal scales is of vital importance for better understanding and prediction of natural complex systems such as the Earth’s climate. This article introduces and applies information theory diagnostics to phase and amplitude time series of different oscillatory components of observed data that characterizes El Niño/Southern Oscillation. A suite of significant interactions between processes operating on different time scales is detected and shown to be important for emergence of extreme events. The mechanisms of these nonlinear interactions are further studied in conceptual low-order and state-of-the-art dynamical, as well as statistical climate models. Observed and simulated interactions exhibit substantial discrepancies, whose understanding may be the key to an improved prediction of ENSO. Moreover, the statistical framework applied here is suitable for inference of cross-scale interactions in human brain dynamics and other complex systems.

Highlights

A better understanding of dynamics in complex systems, such as the Earth’s climate or human brain, is one of the key challenges for contemporary science and society
The quasi-biennial (QB) modes, which tend to peak in winter, are synchronized with the annual cycle (AC) as well as with the so-called combination tones (CT; periods approximately 9 and 14 months)
The LF modes with periods 5–6 yr and QB modes with periods 2–3 year exhibit phase synchronization. These results reconfirm an important role of the annual cycle in ENSO dynamics, with strong ENSO events peaking in boreal winter,[10,27] and point to the link between QB and LF modes, which may be responsible for extreme ENSO events[14,15,16]; our synchronization analysis brings out known ENSO properties consistent with previous research

Summary

Introduction

A better understanding of dynamics in complex systems, such as the Earth’s climate or human brain, is one of the key challenges for contemporary science and society. Data-driven approaches to detection and recognition of relationships between subsystems in complex systems have recently become an area of active study in a range of scientific fields. Thinking about the climate system as of a complex network of interacting subsystems[1] presents a new paradigm that brings out new data analysis methods helping to detect, describe and predict atmospheric phenomena.[2] A crucial step in constructing climate networks is the inference of network links between climate subsystems.[3] Directed causal links determine which subsystems influence other subsystems, and their identification would uncover the drivers of atmospheric phenomena. A succinct formalized description of causal relationships and synchronizations in complex systems could significantly improve our understanding of such systems, which would in turn promote the development of better schemes to predict their evolution. We investigate complex, multiple time-scale interactions in the El Niño/Southern Oscillation system in the equatorial Pacific

Methods

Results

Conclusion