A hidden semi-Markov model for characterising regime shifts in ocean density variability
This is the author accepted manuscript. The final version is available from Wiley via the DOI in this record Societally important decadal predictions of temperature and precipitation over Europe are largely affected by variability in the North Atlantic Ocean. Within this region, the Labrador Sea is...
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Online Access: | http://hdl.handle.net/10871/38089 https://doi.org/10.1111/rssc.12373 |
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ftunivexeter:oai:ore.exeter.ac.uk:10871/38089 2024-09-09T19:50:43+00:00 A hidden semi-Markov model for characterising regime shifts in ocean density variability Economou, T Menary, M 2019 http://hdl.handle.net/10871/38089 https://doi.org/10.1111/rssc.12373 en eng Wiley / Royal Statistical Society Vol. 68 (5), pp. 1529-1553 doi:10.1111/rssc.12373 http://hdl.handle.net/10871/38089 0035-9254 Journal of the Royal Statistical Society: Series C © 2019 Royal Statistical Society 2020-08-26 Under embargo until 26 August 2020 in compliance with publisher policy http://www.rioxx.net/licenses/all-rights-reserved Reversible jump Bayesian Markov chain Monte Carlo methods Hidden Markov model Forward algorithm Adaptive Metropolis sampling Article 2019 ftunivexeter https://doi.org/10.1111/rssc.12373 2024-07-29T03:24:16Z This is the author accepted manuscript. The final version is available from Wiley via the DOI in this record Societally important decadal predictions of temperature and precipitation over Europe are largely affected by variability in the North Atlantic Ocean. Within this region, the Labrador Sea is of particular importance due its link between surface-driven density variability and the Atlantic Meridional Overturning Circulation (AMOC). Using physical justifications, we propose a statistical model to describe the temporal variability of ocean density in terms of salinity-driven and temperature-driven density. This is a hidden semi-Markov model that allows for either a salinity-driven or a temperature-driven ocean density regime, such that the persistence in each regime is governed probabilistically by a semiMarkov chain. The model is fitted in the Bayesian framework, and a reversible MCMC algorithm is proposed to deal with a single-regime scenario. The model is first applied to a reanalysis data set, where model checking measures are also proposed. Then it is applied to data from 43 climate models to investigate whether and how ocean density variability differs between them and also the reanalysis data. Parameter estimates relating to the mean holding time for each regime are used to establish a link between regime behaviour and the AMOC. Article in Journal/Newspaper Labrador Sea North Atlantic University of Exeter: Open Research Exeter (ORE) Journal of the Royal Statistical Society: Series C (Applied Statistics) 68 5 1529 1553 |
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Open Polar |
collection |
University of Exeter: Open Research Exeter (ORE) |
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ftunivexeter |
language |
English |
topic |
Reversible jump Bayesian Markov chain Monte Carlo methods Hidden Markov model Forward algorithm Adaptive Metropolis sampling |
spellingShingle |
Reversible jump Bayesian Markov chain Monte Carlo methods Hidden Markov model Forward algorithm Adaptive Metropolis sampling Economou, T Menary, M A hidden semi-Markov model for characterising regime shifts in ocean density variability |
topic_facet |
Reversible jump Bayesian Markov chain Monte Carlo methods Hidden Markov model Forward algorithm Adaptive Metropolis sampling |
description |
This is the author accepted manuscript. The final version is available from Wiley via the DOI in this record Societally important decadal predictions of temperature and precipitation over Europe are largely affected by variability in the North Atlantic Ocean. Within this region, the Labrador Sea is of particular importance due its link between surface-driven density variability and the Atlantic Meridional Overturning Circulation (AMOC). Using physical justifications, we propose a statistical model to describe the temporal variability of ocean density in terms of salinity-driven and temperature-driven density. This is a hidden semi-Markov model that allows for either a salinity-driven or a temperature-driven ocean density regime, such that the persistence in each regime is governed probabilistically by a semiMarkov chain. The model is fitted in the Bayesian framework, and a reversible MCMC algorithm is proposed to deal with a single-regime scenario. The model is first applied to a reanalysis data set, where model checking measures are also proposed. Then it is applied to data from 43 climate models to investigate whether and how ocean density variability differs between them and also the reanalysis data. Parameter estimates relating to the mean holding time for each regime are used to establish a link between regime behaviour and the AMOC. |
format |
Article in Journal/Newspaper |
author |
Economou, T Menary, M |
author_facet |
Economou, T Menary, M |
author_sort |
Economou, T |
title |
A hidden semi-Markov model for characterising regime shifts in ocean density variability |
title_short |
A hidden semi-Markov model for characterising regime shifts in ocean density variability |
title_full |
A hidden semi-Markov model for characterising regime shifts in ocean density variability |
title_fullStr |
A hidden semi-Markov model for characterising regime shifts in ocean density variability |
title_full_unstemmed |
A hidden semi-Markov model for characterising regime shifts in ocean density variability |
title_sort |
hidden semi-markov model for characterising regime shifts in ocean density variability |
publisher |
Wiley / Royal Statistical Society |
publishDate |
2019 |
url |
http://hdl.handle.net/10871/38089 https://doi.org/10.1111/rssc.12373 |
genre |
Labrador Sea North Atlantic |
genre_facet |
Labrador Sea North Atlantic |
op_relation |
Vol. 68 (5), pp. 1529-1553 doi:10.1111/rssc.12373 http://hdl.handle.net/10871/38089 0035-9254 Journal of the Royal Statistical Society: Series C |
op_rights |
© 2019 Royal Statistical Society 2020-08-26 Under embargo until 26 August 2020 in compliance with publisher policy http://www.rioxx.net/licenses/all-rights-reserved |
op_doi |
https://doi.org/10.1111/rssc.12373 |
container_title |
Journal of the Royal Statistical Society: Series C (Applied Statistics) |
container_volume |
68 |
container_issue |
5 |
container_start_page |
1529 |
op_container_end_page |
1553 |
_version_ |
1809919997636509696 |