Modeling cycles and interdependence in irregularly sampled geophysical time series
We show how an autoregressive Gaussian process model incorporating a time scale coefficient can be used to represent irregularly sampled geophysical time series. Selection of this coefficient, together with the order of autoregression, provides flexibility of the model appropriate to the structure o...
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ftulancaster:oai:eprints.lancs.ac.uk:162393 2023-08-27T04:06:02+02:00 Modeling cycles and interdependence in irregularly sampled geophysical time series Tunnicliffe Wilson, G. Haywood, J. Petherick, L. 2022-03-31 text https://eprints.lancs.ac.uk/id/eprint/162393/ https://eprints.lancs.ac.uk/id/eprint/162393/1/GeophysicalTimeSeriesModeling.pdf https://doi.org/10.1002/env.2708 en eng https://eprints.lancs.ac.uk/id/eprint/162393/1/GeophysicalTimeSeriesModeling.pdf Tunnicliffe Wilson, G. and Haywood, J. and Petherick, L. (2022) Modeling cycles and interdependence in irregularly sampled geophysical time series. Environmetrics, 33 (2). ISSN 1180-4009 creative_commons_attribution_noncommercial_4_0_international_license Journal Article PeerReviewed 2022 ftulancaster https://doi.org/10.1002/env.2708 2023-08-03T22:40:38Z We show how an autoregressive Gaussian process model incorporating a time scale coefficient can be used to represent irregularly sampled geophysical time series. Selection of this coefficient, together with the order of autoregression, provides flexibility of the model appropriate to the structure of the data. This leads to a valuable improvement in the identification of the periodicities within and dependence between such series, which arise frequently and are often acquired at some cost in time and effort. We carefully explain the modeling procedure and demonstrate its efficacy for identifying periodic behavior in the context of an application to dust flux measurements from lake sediments in a region of subtropical eastern Australia. The model is further applied to the measurements of atmospheric carbon dioxide concentrations and temperature obtained from Antarctic ice cores. The model identifies periods in the glacial-interglacial cycles of these series that are associated with astronomical forcing, determines that they are causally related, and, by application to current measurements, confirms the prediction of climate warming. © 2021 John Wiley & Sons Ltd. Article in Journal/Newspaper Antarc* Antarctic Lancaster University: Lancaster Eprints Antarctic Environmetrics 33 2 |
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Lancaster University: Lancaster Eprints |
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ftulancaster |
language |
English |
description |
We show how an autoregressive Gaussian process model incorporating a time scale coefficient can be used to represent irregularly sampled geophysical time series. Selection of this coefficient, together with the order of autoregression, provides flexibility of the model appropriate to the structure of the data. This leads to a valuable improvement in the identification of the periodicities within and dependence between such series, which arise frequently and are often acquired at some cost in time and effort. We carefully explain the modeling procedure and demonstrate its efficacy for identifying periodic behavior in the context of an application to dust flux measurements from lake sediments in a region of subtropical eastern Australia. The model is further applied to the measurements of atmospheric carbon dioxide concentrations and temperature obtained from Antarctic ice cores. The model identifies periods in the glacial-interglacial cycles of these series that are associated with astronomical forcing, determines that they are causally related, and, by application to current measurements, confirms the prediction of climate warming. © 2021 John Wiley & Sons Ltd. |
format |
Article in Journal/Newspaper |
author |
Tunnicliffe Wilson, G. Haywood, J. Petherick, L. |
spellingShingle |
Tunnicliffe Wilson, G. Haywood, J. Petherick, L. Modeling cycles and interdependence in irregularly sampled geophysical time series |
author_facet |
Tunnicliffe Wilson, G. Haywood, J. Petherick, L. |
author_sort |
Tunnicliffe Wilson, G. |
title |
Modeling cycles and interdependence in irregularly sampled geophysical time series |
title_short |
Modeling cycles and interdependence in irregularly sampled geophysical time series |
title_full |
Modeling cycles and interdependence in irregularly sampled geophysical time series |
title_fullStr |
Modeling cycles and interdependence in irregularly sampled geophysical time series |
title_full_unstemmed |
Modeling cycles and interdependence in irregularly sampled geophysical time series |
title_sort |
modeling cycles and interdependence in irregularly sampled geophysical time series |
publishDate |
2022 |
url |
https://eprints.lancs.ac.uk/id/eprint/162393/ https://eprints.lancs.ac.uk/id/eprint/162393/1/GeophysicalTimeSeriesModeling.pdf https://doi.org/10.1002/env.2708 |
geographic |
Antarctic |
geographic_facet |
Antarctic |
genre |
Antarc* Antarctic |
genre_facet |
Antarc* Antarctic |
op_relation |
https://eprints.lancs.ac.uk/id/eprint/162393/1/GeophysicalTimeSeriesModeling.pdf Tunnicliffe Wilson, G. and Haywood, J. and Petherick, L. (2022) Modeling cycles and interdependence in irregularly sampled geophysical time series. Environmetrics, 33 (2). ISSN 1180-4009 |
op_rights |
creative_commons_attribution_noncommercial_4_0_international_license |
op_doi |
https://doi.org/10.1002/env.2708 |
container_title |
Environmetrics |
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33 |
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2 |
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1775346775580737536 |