Statistical and Dynamical Properties of Covariant Lyapunov Vectors in a Coupled Atmosphere-Ocean Model - Multiscale Effects, Geometric Degeneracy, and Error Dynamics

TL;DR

This paper analyzes the complex behavior of covariant Lyapunov vectors in a simplified coupled atmosphere-ocean model, revealing multiscale effects, geometric degeneracy, and implications for predictability and data assimilation.

Contribution

It introduces a detailed study of CLVs in a multiscale chaotic model, highlighting the role of near-zero LEs and geometric degeneracy in error dynamics and predictability.

Findings

Presence of two positive, sixteen negative, and eighteen near-zero Lyapunov exponents.

Near-zero LEs caused by multiscale separation lead to degenerate CLVs and complex error growth.

Robust large deviations laws for finite-time LEs enable long-term predictability assessment.

Abstract

We study a simplified coupled atmosphere-ocean model using the formalism of covariant Lyapunov vectors (CLVs), which link physically-based directions of perturbations to growth/decay rates. The model is obtained via a severe truncation of quasi-geostrophic equations for the two fluids, and includes a simple yet physically meaningful representation of their dynamical/thermodynamical coupling. The model has 36 degrees of freedom, and the parameters are chosen so that a chaotic behaviour is observed. One finds two positive Lyapunov exponents (LEs), sixteen negative LEs, and eighteen near-zero LEs. The presence of many near-zero LEs results from the vast time-scale separation between the characteristic time scales of the two fluids, and leads to nontrivial error growth properties in the tangent space spanned by the corresponding CLVs, which are geometrically very degenerate. Such CLVs correspond to two different classes of ocean/atmosphere coupled modes. The tangent space spanned by the CLVs corresponding to the positive and negative LEs has, instead, a non-pathological behaviour, and one can construct robust large deviations laws for the finite time LEs, thus providing a universal model for assessing predictability on long to ultra-long scales along such directions. It is somewhat surprising to find that the tangent space of the unstable manifold has strong projection on both atmospheric and oceanic components. Our results underline the difficulties in using hyperbolicity as a conceptual framework for multiscale chaotic dynamical systems, whereas the framework of partial hyperbolicity seems better suited, possibly indicating an alternative definition for the chaotic hypothesis. Our results suggest the need for accurate analysis of error dynamics on different time scales and domains and for a careful set-up of assimilation schemes when looking at coupled atmosphere-ocean models.