Interpretation of North Pacific variability as a short- and long-memory process

A major difficulty in investigating the nature of interdecadal variability of climatic time series is their shortness. An approach to this problem is through comparison of models. In this paper we contrast a first order autoregressive (AR(1)) model with a fractionally differenced (FD) model as appli...

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Main Authors: Donald B. Percival, James E. Overl, Harold O. Mofjeld
Other Authors: The Pennsylvania State University CiteSeerX Archives
Format: Text
Language:English
Published: 2001
Subjects:
Online Access:http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.495.2316
http://faculty.washington.edu/dbp/PDFFILES/iNPvslmp.pdf
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spelling ftciteseerx:oai:CiteSeerX.psu:10.1.1.495.2316 2023-05-15T13:15:05+02:00 Interpretation of North Pacific variability as a short- and long-memory process Donald B. Percival James E. Overl Harold O. Mofjeld The Pennsylvania State University CiteSeerX Archives 2001 application/pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.495.2316 http://faculty.washington.edu/dbp/PDFFILES/iNPvslmp.pdf en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.495.2316 http://faculty.washington.edu/dbp/PDFFILES/iNPvslmp.pdf Metadata may be used without restrictions as long as the oai identifier remains attached to it. http://faculty.washington.edu/dbp/PDFFILES/iNPvslmp.pdf text 2001 ftciteseerx 2016-01-08T08:44:06Z A major difficulty in investigating the nature of interdecadal variability of climatic time series is their shortness. An approach to this problem is through comparison of models. In this paper we contrast a first order autoregressive (AR(1)) model with a fractionally differenced (FD) model as applied to the winter averaged sea level pressure time series for the Aleutian low (the North Pacific (NP) index), and the Sitka winter air temperature record. Both models fit the same number of parameters. The AR(1) model is a ‘short memory ’ model in that it has a rapidly decaying autocovariance sequence, whereas an FD model exhibits ‘long memory ’ because its autocovariance sequence decays more slowly. Statistical tests cannot distinguish the superiority of one model over the other when fit with 100 NP or 146 Sitka data points. The FD model does equally well for short term prediction and has potentially important implications for long term behavior. In particular, the zero crossings of the FD model tend to be further apart, so they have more of a ‘regime’-like character; a quarter century interval between zero crossings is four times more likely with the FD than the AR(1) model. The long memory parameter δ for the FD model can be used as a characterization of regime-like behavior. The estimated δs for the NP index (spanning 100 years) and the Sitka time series (168 years) are virtually identical, and their size implies moderate long memory behavior. Although the NP index and the Sitka series have broadband low frequency variability and modest long memory behavior, temporal irregularities in their zero crossings are still prevalent. Comparison of the FD and AR(1) models indicates that regime-like behavior cannot be ruled out for North Pacific processes. 2 1. Text aleutian low Unknown Pacific
institution Open Polar
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description A major difficulty in investigating the nature of interdecadal variability of climatic time series is their shortness. An approach to this problem is through comparison of models. In this paper we contrast a first order autoregressive (AR(1)) model with a fractionally differenced (FD) model as applied to the winter averaged sea level pressure time series for the Aleutian low (the North Pacific (NP) index), and the Sitka winter air temperature record. Both models fit the same number of parameters. The AR(1) model is a ‘short memory ’ model in that it has a rapidly decaying autocovariance sequence, whereas an FD model exhibits ‘long memory ’ because its autocovariance sequence decays more slowly. Statistical tests cannot distinguish the superiority of one model over the other when fit with 100 NP or 146 Sitka data points. The FD model does equally well for short term prediction and has potentially important implications for long term behavior. In particular, the zero crossings of the FD model tend to be further apart, so they have more of a ‘regime’-like character; a quarter century interval between zero crossings is four times more likely with the FD than the AR(1) model. The long memory parameter δ for the FD model can be used as a characterization of regime-like behavior. The estimated δs for the NP index (spanning 100 years) and the Sitka time series (168 years) are virtually identical, and their size implies moderate long memory behavior. Although the NP index and the Sitka series have broadband low frequency variability and modest long memory behavior, temporal irregularities in their zero crossings are still prevalent. Comparison of the FD and AR(1) models indicates that regime-like behavior cannot be ruled out for North Pacific processes. 2 1.
author2 The Pennsylvania State University CiteSeerX Archives
format Text
author Donald B. Percival
James E. Overl
Harold O. Mofjeld
spellingShingle Donald B. Percival
James E. Overl
Harold O. Mofjeld
Interpretation of North Pacific variability as a short- and long-memory process
author_facet Donald B. Percival
James E. Overl
Harold O. Mofjeld
author_sort Donald B. Percival
title Interpretation of North Pacific variability as a short- and long-memory process
title_short Interpretation of North Pacific variability as a short- and long-memory process
title_full Interpretation of North Pacific variability as a short- and long-memory process
title_fullStr Interpretation of North Pacific variability as a short- and long-memory process
title_full_unstemmed Interpretation of North Pacific variability as a short- and long-memory process
title_sort interpretation of north pacific variability as a short- and long-memory process
publishDate 2001
url http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.495.2316
http://faculty.washington.edu/dbp/PDFFILES/iNPvslmp.pdf
geographic Pacific
geographic_facet Pacific
genre aleutian low
genre_facet aleutian low
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