Causal dependencies and Shannon entropy budget -- Analysis of a reduced order atmospheric model

TL;DR

This paper analyzes the information entropy budget and transfer in a chaotic reduced-order atmospheric model, highlighting the roles of nonlinear advection, orography, and surface friction in uncertainty generation.

Contribution

It introduces an extended method for estimating nonlinear information transfer and provides detailed insights into the entropy dynamics of atmospheric chaos.

Findings

Nonlinear advection dominates entropy transfer.

Orography and surface friction significantly influence uncertainty.

Co-variability often drives information transfer for certain variables.

Abstract

The information entropy budget and the rate of information transfer between variables is studied in the context of a nonlinear reduced-order atmospheric model. The key ingredients of the dynamics are present in this model, namely the baroclinic and barotropic instabilities, the instability related to the presence of an orography, the dissipation related to the surface friction, and the large-scale meridional imbalance of energy. For the parameter chosen, the solutions of this system display a chaotic dynamics reminiscent of the large-scale atmospheric dynamics in the extra-tropics. The detailed information entropy budget analysis of this system reveals that the linear rotation terms plays a minor role in the generation of uncertainties as compared to the orography and the surface friction. Additionally, the dominant contribution comes from the nonlinear advection terms, and their decomposition in synergetic (co-variability) and single (impact of each single variable on the target one) components reveals that for some variables the co-variability dominates the information transfer. The estimation of the rate of information transfer based on time series is also discussed, and an extension of the Liang's approach to nonlinear observables, is proposed.