Editorial Type:
Article
Article Type:
Research Article

Empirically Derived Markov Models and Prediction of Tropical Pacific Sea Surface Temperature Anomalies

Scot D. Johnson Department of Atmospheric Sciences, University of Washington, Seattle, Washington

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David S. Battisti Department of Atmospheric Sciences, University of Washington, Seattle, Washington

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E. S. Sarachik Department of Atmospheric Sciences, University of Washington, Seattle, Washington

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Print Publication:
01 Jan 2000
DOI:
https://doi.org/10.1175/1520-0442(2000)013<0003:EDMMAP>2.0.CO;2
Page(s):
3–17
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Abstract

Empirical dynamical modeling (EDM) is employed to determine if ENSO forecasting skill using monthly mean SST data can be enhanced by including subsurface temperature anomaly data. The Niño 3.4 index is forecast first using an EDM constructed from the principal component time series corresponding to EOFs of SST anomaly maps of the central and eastern tropical Pacific (32°N–32°S, 120°E–70°W) for the period 1965–93. Cross validation is applied to minimize the artificial skill of the forecasts, which are made over the same 29-yr period. The forecasting is then repeated with the inclusion of principal components of heat content of the upper 300 m over the northern tropical Pacific (30°N–0°, 120°E–72°W).

The forecast skill using SST alone and SST plus subsurface temperature is compared for lead times ranging between 3 and 12 months. The EDM, which includes the subsurface information, forecasts with greater skill at all lead times; particularly important is the second principal component of the heat content, which appears to contribute information on the transition phase between warm and cold ENSO events. The apparent improvement by including subsurface information, although robust, does not appear to be statistically significant. However, the temporal and spatial coverage of the subsurface data is limited, so this study probably underestimates the usefulness of including subsurface temperature data in efforts to predict ENSO. Finally, cross-validated forecasts using a Markov model that includes an annual cycle are shown to be less skillful than forecasts using a seasonally invariant Markov model. The reason for this appears to be that dividing the data yields an insufficient database to derive an accurate Markov model.

Corresponding author: Scot Johnson, JISAO, University of Washington, Box 354235, Seattle WA 98195-4235.

Abstract

Empirical dynamical modeling (EDM) is employed to determine if ENSO forecasting skill using monthly mean SST data can be enhanced by including subsurface temperature anomaly data. The Niño 3.4 index is forecast first using an EDM constructed from the principal component time series corresponding to EOFs of SST anomaly maps of the central and eastern tropical Pacific (32°N–32°S, 120°E–70°W) for the period 1965–93. Cross validation is applied to minimize the artificial skill of the forecasts, which are made over the same 29-yr period. The forecasting is then repeated with the inclusion of principal components of heat content of the upper 300 m over the northern tropical Pacific (30°N–0°, 120°E–72°W).

The forecast skill using SST alone and SST plus subsurface temperature is compared for lead times ranging between 3 and 12 months. The EDM, which includes the subsurface information, forecasts with greater skill at all lead times; particularly important is the second principal component of the heat content, which appears to contribute information on the transition phase between warm and cold ENSO events. The apparent improvement by including subsurface information, although robust, does not appear to be statistically significant. However, the temporal and spatial coverage of the subsurface data is limited, so this study probably underestimates the usefulness of including subsurface temperature data in efforts to predict ENSO. Finally, cross-validated forecasts using a Markov model that includes an annual cycle are shown to be less skillful than forecasts using a seasonally invariant Markov model. The reason for this appears to be that dividing the data yields an insufficient database to derive an accurate Markov model.

Corresponding author: Scot Johnson, JISAO, University of Washington, Box 354235, Seattle WA 98195-4235.

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