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    Rights statement: This is the peer reviewed version of the following article:Beaulieu, C, Killick, R, Ireland, D, Norwood, B. Considering long‐memory when testing for changepoints in surface temperature: A classification approach based on the time‐varying spectrum. Environmetrics. 2019; e2568. https://doi.org/10.1002/env.2568 which has been published in final form at https://onlinelibrary.wiley.com/doi/10.1002/env.2568 This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.

    Accepted author manuscript, 7.49 MB, PDF document

    Embargo ends: 22/04/20

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

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Considering long-memory when testing for changepoints in surface temperature: a classification approach based on the time-varying spectrum

Research output: Contribution to journalJournal article

Published
Article numbere2568
<mark>Journal publication date</mark>1/02/2020
<mark>Journal</mark>Environmetrics
Issue number1
Volume31
Number of pages14
Publication statusPublished
Early online date22/04/19
Original languageEnglish

Abstract

Changepoint models are increasingly used to represent changes in the rate of warming in surface temperature records. On the opposite hand, a large body of literature has suggested long‐memory processes to characterize long‐term behavior in surface temperatures. While these two model representations provide different insights into the underlying mechanisms, they share similar spectrum properties that create “ambiguity” and challenge distinguishing between the two classes of models. This study aims to compare the two representations to explain temporal changes and variability in surface temperatures. To address this question, we extend a recently developed time‐varying spectral procedure and assess its accuracy through a synthetic series mimicking observed global monthly surface temperatures. We vary the length of the synthetic series to determine the number of observations needed to be able to accurately distinguish between changepoints and long‐memory models. We apply the approach to two gridded surface temperature data sets. Our findings unveil regions in the oceans where long‐memory is prevalent. These results imply that the presence of long‐memory in monthly sea surface temperatures may impact the significance of trends, and special attention should be given to the choice of model representing memory (short versus long) when assessing long‐term changes.

Bibliographic note

This is the peer reviewed version of the following article:Beaulieu, C, Killick, R, Ireland, D, Norwood, B. Considering long‐memory when testing for changepoints in surface temperature: A classification approach based on the time‐varying spectrum. Environmetrics. 2019; e2568. https://doi.org/10.1002/env.2568 which has been published in final form at https://onlinelibrary.wiley.com/doi/10.1002/env.2568 This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.