SSA-MEM NINO3 Forecast

Last updated: 3/11/09

Singular spectrum analysis (SSA: Vautard and Ghil 1989) and the maximum entropy method (MEM: Penland et al. 1991) are combined to produce long-lead forecasts of sea-surface temperature (SST) anomalies, averaged over the Niño-3 area, and of the Southern Oscillation Index (SOI). The forecast is for up to one year ahead based on data from January 1950 through February 2009. This forecast follows up on earlier forecasts using combined SSA-MEM methodology for the SOI index by C. Keppenne and M. Ghil, starting in the March 1992 issue of the Center for Ocean-Land-Atmosphere Studies's Experimental Long-Lead Forecast Bulletin (ELLFB), on those of N. Jiang, M. Ghil and J. D. Neelin for Niño-3 SST anomalies, starting from March 1995, and on those of A. Saunders, M. Ghil and J. D. Neelin from September 1997. Detailed information on the forecast method is given by Keppenne and Ghil (1992) and in the March 1995 issue of ELLFB (also Jiang et al. 1995). Briefly, the time series is filtered by SSA so that only the statistically significant low-frequency components are retained, specifically the quasi-quadrennial (QQ) and the quasi-biennial (QB) components of ENSO variability (Rasmusson et al. 1990; Keppenne and Ghil 1992; Jiang et al. 1995). Next, MEM is applied to advance these components in time. The extended components are then used in the SSA-reconstruction to produce the forecast values.

Figure 1 shows the method's Nino-3 SSTA forecasts for lead time of 6 months, from 1990 to 2006. 
The forecast for each point utilizes only the appropriate part of the record that precedes the initial forecast time.

The current SSA-MEM forecast for Nino-3 SSTA (Fig. 2) is for a return to a near neutral conditions in the short term.The forecast SOI index (Fig. 3) is, in general, consistent with the SSTA forecast.

Figure 1 shows observed area-averaged Nino 3 SSTAs and it's forecast for lead time of 6 months since 1990, using the SSA-MEM scheme. The forecast for each point utilizes only the appropriate part of the record that precedes the initial forecast time. The solid blue line gives the observed SSTAs; the solid red line is the forecast; and the magenta lines are situated each at a distance of one standard deviation from the SSA-MEM forecasts. The standard deviation value is based on forecast verification.

Fig. 2. Forecast Nino-3 SSTAs for the next 12 months using the SSA-MEM scheme. The red line is  Nino-3 SSTAs, data-adaptively filtered by SSA,  and it's prediction;  the solid blue line is raw SST data. The magenta line indicate forecast error bars (see caption to Fig. 1). 

Fig.3. SSA-MEM forecast of the SOI for the next 12 months.The red line is the SSA-filtered observed SOI index and it's prediction, the blue line is the raw data. The magenta lines are forecast error bars.

References

Ghil, M., and N. Jiang, 1998: Recent forecast skill for the El Nino/Southern
Oscillation. Geophys. Res. Lett.,25, 171-174.

Ghil, M., M. R. Allen, M. D. Dettinger, K. Ide, D. Kondrashov, M. E. Mann,
A. W. Robertson, A. Saunders, Y. Tian, F. Varadi, and P. Yiou, 2002:
Advanced spectral methods for climatic time series, Rev. Geophys.,
40(1), pp. 3.1-3.41, 10.1029/2000RG000092.

Jiang, N., D. Neelin and M. Ghil, 1995: Quasi-quadrennial and quasi-biennial
variability in the equatorial Pacific. Clim. Dyn., 12, 101-112.

Keppenne, C.L. and M. Ghil, 1992: Adaptive filtering and prediction of the
Southern Oscillation Index. J. Geophys. Res, 97,20449-20454.

Penland, C., M. Ghil and K. M. Weickmann, 1991: Adaptive filtering and
maximum entropy spectra, with application to changes in atmospheric angular
momentum. J. Geophys. Res., 96, 22, 659-22, 671.

Vautard, R., and M. Ghil, 1989: Singular spectrum analysis in nonlinear
dynamics with applications to paleoclimatic time series. Physica D, 35,
395-424.