State space models for non‐stationary intermittently coupled systems: an application to the North Atlantic oscillation
This is the final version. Available on open access from Wiley via the DOI in this record Data availability: The data that are analysed in the paper and the programs that were used to analyse them can be obtained from https://rss.onlinelibrary.wiley.com/hub/journal/14679876/seriescdatasets We develo...
| Published in: | Journal of the Royal Statistical Society Series C: Applied Statistics |
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| Main Authors: | , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Wiley / Royal Statistical Society
2019
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10871/37092 https://doi.org/10.1111/rssc.12354 |
| Summary: | This is the final version. Available on open access from Wiley via the DOI in this record Data availability: The data that are analysed in the paper and the programs that were used to analyse them can be obtained from https://rss.onlinelibrary.wiley.com/hub/journal/14679876/seriescdatasets 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. Natural Environment Research Council (NERC) |
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