State space models for non-stationary intermittently coupled systems: an application to the North Atlantic Oscillation

TL;DR

This paper introduces Bayesian state space models to analyze non-stationary, intermittently coupled climate systems, specifically applied to the North Atlantic Oscillation, enabling better understanding and forecasting of winter climate variability.

Contribution

It develops novel Bayesian state space methods with intervention techniques and latent autoregressive components for modeling intermittent coupling effects in climate data.

Findings

Approximately 70% of winter variance is due to an unobserved process.

Forecasts of winter mean are skillful from early December.

Efficient filtering and smoothing methods are derived for the models.

Abstract

We develop Bayesian state space methods for modelling changes to the mean level or temporal correlation structure of an observed time series due to intermittent coupling with an unobserved process. Novel intervention methods are proposed to model the effect of repeated coupling as a single dynamic process. Latent time-varying autoregressive components are developed to model changes in the temporal correlation structure. Efficient filtering and smoothing methods are derived for the resulting class of models. We propose methods for quantifying the component of variance attributable to an unobserved process, the effect during individual coupling events, and the potential for skilful forecasts. The proposed methodology is applied to the study of winter-time variability in the dominant pattern of climate variation in the northern hemisphere, the North Atlantic Oscillation. Around 70% of the inter-annual variance in the winter (Dec-Jan-Feb) mean level is attributable to an unobserved process. Skilful forecasts for winter (Dec-Jan-Feb) mean are possible from the beginning of December.