Interpretation of North Paciflc Variability as a Short and Long Memory Process
A major di–culty 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 flrst order autoregressive (AR(1)) model with a fractionally difierenced (FD) model as applied...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Text |
| Language: | English |
| Published: |
2001
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| Subjects: | |
| Online Access: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.532.4268 http://www.nrcse.washington.edu/pdf/trs65_memproc.pdf |
| Summary: | A major di–culty 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 flrst order autoregressive (AR(1)) model with a fractionally difierenced (FD) model as applied to the winter averaged sea level pressure time series for the Aleutian low (the North Paciflc (NP) index), and the Sitka winter air temperature record. Both models flt 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 flt 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 |
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