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

Philip G. Sansom, Daniel B. Williamson and David B. Stephenson

Journal of the Royal Statistical Society Series C, 2019, vol. 68, issue 5, 1259-1280

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 auto‐regressive 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 methodology proposed 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 interannual variance in the winter (December–January–February) mean level is attributable to an unobserved process. Skilful forecasts for the winter (December–January–February) mean are possible from the beginning of December.

Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1111/rssc.12354

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssc:v:68:y:2019:i:5:p:1259-1280

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-9876

Access Statistics for this article

Journal of the Royal Statistical Society Series C is currently edited by R. Chandler and P. W. F. Smith

More articles in Journal of the Royal Statistical Society Series C from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().