Structural Origins and Real-Time Drivers of Intermittency

TL;DR

This paper investigates the local causes and real-time indicators of regime changes in intermittent systems across various fields, aiming to improve prediction of these transitions by identifying common precursors and mechanisms.

Contribution

It introduces a local, real-time perspective on intermittency, identifying universal indicators and mechanisms of regime changes across diverse dynamical systems.

Findings

Correlation between Lyapunov vector alignment and regime change

Discovered crisis-induced intermittency in Lorenz 96

Detected spatially global intermittency in Kuramoto-Shivanshinki

Abstract

In general terms, intermittency is the property for which time evolving systems alternate among two or more different regimes. Predicting the instance when the regime switch will occur is extremely challenging, often practically impossible. Intermittent processes include turbulence, convection, precipitation patterns, as well as several in plasma physics, medicine, neuroscience, and economics. Traditionally, focus has been on global statistical indicators, e.g. the average frequency of regime changes under fixed conditions, or how these vary as a function of the system's parameters. We add a local perspective: we study the causes and drivers of the regime changes in real time, with the ultimate goal of predicting them. Using five different systems, of various complexities, we identify indicators and precursors of regime transitions that are common across the different intermittency mechanisms and dynamical models. For all the systems and intermittency types under study, we find a correlation between the alignment of some Lyapunov vectors and the concomitant, or aftermath, regime change. We discovered peculiar behaviors in the Lorenz 96 and in the Kuramoto-Shivanshinki models. In Lorenz 96 we identified crisis-induced intermittency with laminar intermissions, while in the Kuramoto-Shivanshinki we detected a spatially global intermittency which follows the scaling of type-I intermittency. The identification of general mechanisms driving intermittent behaviors, and in particular the unearthing of indicators spotting the regime change, pave the way to designing prediction tools in more realistic scenarios. These include turbulent geophysical fluids, rainfall patterns, or atmospheric deep convection.