University of Washington - Applied Physics Lab
A major difficulty in investigating the nature of atmospheric circulation changes over the North Pacific is the shortness of historical time series. An approach to this problem is through comparison of models. In this talk we contrast two stochastic models and a 'signal plus noise' model for the winter averaged sea level pressure time series for the Aleutian low (the North Pacific (NP) index) and for air temperatures from Sitka, Alaska. The two stochastic models are a first order autoregressive (AR(1)) model and a fractionally differenced (FD) model. 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. The 'signal plus noise' model consists of a square wave oscillation (SWO) picked out using matching pursuit. The dictionary of candidate signals for the matching pursuit was constructed based upon descriptions for the NP index recently suggested by Minobe (1999). All three models formally involve the same number of parameters. Statistical tests cannot distinguish the superiority of any one model over the other two, but the three models can have quite different statistical implications. In particular, the zero crossings of the FD model tend to be further apart than those for the AR(1) model but lack a predominant characteristic length, whereas those for the SWO model have a 'regime'-like character with lengths consistent with the presumed period of the oscillations. We present some ideas for combining the three models to give an overall characterization of atmospheric circulation changes.
(This is ongoing joint work with Jim Overland and Hal Mofjeld, Pacific Marine Environmental Laboratory, National Oceanic and Atmospheric Administration, Seattle, Washington, USA.)