Forecasts of Near-Global Sea Surface Temperatures Using Canonical Correlation Analysis

Abstract

The skill of global-scale sea surface temperature forecasts using a statistically based linear forecasting technique is investigated. Canonical variates are used to make monthly sea surface temperature anomaly forecasts using evolutionary and steady-state features of antecedent sea surface temperatures as predictors. Levels of forecast skill are investigated over several months' lead time by comparing the model performance with a simple forecast strategy involving the persistence of sea surface temperature anomalies. Forecast skill is investigated over an independent test period of 18 yr (1982/83?1999/2000), for which the model training period was updated after every 3 yr. Forecasts for the equatorial Pacific Ocean are a significant improvement over a strategy of random guessing, and outscore forecasts of persisted anomalies beyond lead times of about one season during the development stages of the El Niño?Southern Oscillation phenomenon, but only outscore forecasts of persisted anomalies beyond 6 months' lead time during its most intense phase. Model predictions of the tropical Indian Ocean outscore persistence during the second half of the boreal winter, that is, from about December or January, with maximum skill during the March?May spring season, but poor skill during the autumn months from September to November. Some loss in predictability of the equatorial Pacific and Indian Oceans is evident during the early and mid-1990s, but forecasts appear to have improved in the last few years. The tropical Atlantic Ocean forecast skill has generally been poor. There is little evidence of forecast skill over the midlatitudes in any of the oceans. However, during the spring months significant skill has been found over the Indian Ocean as far south as 20°S and over the southern North Atlantic as far north as 30°N, both of which outscore persistence beyond a lead time of less than about one season.