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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.

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

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Considering long-memory when testing for changepoints in surface temperature : a classification approach based on the time-varying spectrum. / Beaulieu, Claudie; Killick, Rebecca Claire; Ireland, David; Norwood, Ben.

In: Environmetrics, Vol. 31, No. 1, e2568, 01.02.2020.

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@article{8f8d7fc06cb044e28c33a0a3ec987b56,
title = "Considering long-memory when testing for changepoints in surface temperature: a classification approach based on the time-varying spectrum",
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.",
keywords = "changepoints, long‐memory, short‐memory, surface temperature, wavelet",
author = "Claudie Beaulieu and Killick, {Rebecca Claire} and David Ireland and Ben Norwood",
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.",
year = "2020",
month = feb,
day = "1",
doi = "10.1002/env.2568",
language = "English",
volume = "31",
journal = "Environmetrics",
issn = "1180-4009",
publisher = "John Wiley and Sons Ltd",
number = "1",

}

RIS

TY - JOUR

T1 - Considering long-memory when testing for changepoints in surface temperature

T2 - a classification approach based on the time-varying spectrum

AU - Beaulieu, Claudie

AU - Killick, Rebecca Claire

AU - Ireland, David

AU - Norwood, Ben

N1 - 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.

PY - 2020/2/1

Y1 - 2020/2/1

N2 - 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.

AB - 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.

KW - changepoints

KW - long‐memory

KW - short‐memory

KW - surface temperature

KW - wavelet

U2 - 10.1002/env.2568

DO - 10.1002/env.2568

M3 - Journal article

VL - 31

JO - Environmetrics

JF - Environmetrics

SN - 1180-4009

IS - 1

M1 - e2568

ER -