DOI: 10.1002/env.2568
论文题名: Considering long-memory when testing for changepoints in surface temperature: A classification approach based on the time-varying spectrum
作者: Beaulieu C. ; Killick R. ; Ireland D. ; Norwood B.
刊名: Environmetrics
ISSN: 11804009
出版年: 2020
卷: 31, 期: 1 语种: 英语
英文关键词: changepoints
; long-memory
; short-memory
; surface temperature
; wavelet
Scopus关键词: model validation
; sea surface temperature
; spectral analysis
; spectrum
; temporal variation
; wavelet
英文摘要: 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. © 2019 John Wiley & Sons, Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/159866
Appears in Collections: 气候变化与战略
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作者单位: Ocean Sciences Department, University of California, Santa Cruz, Santa Cruz, CA, United States; Department of Mathematics and Statistics, Lancaster University, Lancaster, United Kingdom
Recommended Citation:
Beaulieu C.,Killick R.,Ireland D.,et al. Considering long-memory when testing for changepoints in surface temperature: A classification approach based on the time-varying spectrum[J]. Environmetrics,2020-01-01,31(1)