Synchronization and causality across time scales in El Niño Southern Oscillation. Issue 1 (December 2018) - Jajcay, Nikola; Kravtsov, Sergey; Sugihara, George; Tsonis, Anastasios; Paluš, Milan
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. Climate: Synchronization of different variability modes gives rise to extreme El Niño events Strong El Niño and La Niña events arise from the interaction and synchronization between El Niño Southern Oscillation (ENSO) cycles that operate on different time scales. The warm, El Niño, and cold, La Niña, phases of ENSO are irregular and occur every 2 to 7 years, governed by the interaction between the annual, biannual and interannual cycles. Milan Palus, from the Czech Academy of Sciences in Prague, and colleagues apply statistical diagnostics from information theory to ENSO data to detect the causal interactions between these three variability modes that lead to extreme El Niño/La Niña event. They find a particularly important role for the biannual cycle in these extreme events. The authors suggest that this statistical framework could also be used for inferring cross-scale interactions in neuronal networks in the human brain and other complex systems.