Seasonal sea‐level forecasts by canonical correlation analysis—an operational scheme for the U.S.‐affiliated Pacific Islands

Abstract

The objective of this study is to develop an operational canonical correlation analysis (CCA) statistical model for sea‐level forecasts in the U. S.‐affiliated Pacific Islands (USAPI) with lead times of several months or longer. The El Niño‐Southern Oscillation (ENSO) climate cycle and the sea‐surface temperatures (SSTs) in the tropical Pacific Ocean are taken as the primary factors in modulating sea‐level variability on the seasonal time scales. Observations revealed that the sea‐level variations in the USAPI are sensitive to ENSO cycle with low sea level during El Niño and high sea level during La Niña events. The correlation between the sea‐level variability and the fluctuations of tropical Pacific SSTs has been found to be strong. The cross‐validated results indicated that the SST‐based CCA model is potentially useful in predicting seasonal sea‐level variations in the USAPI. For all target seasons at 1‐ and 2‐season lead times, the average correlation skill has been found to be 0.50 or better. Based on this operational CCA model, the real‐time forecasts for seasonal sea‐level variations (i.e. deviations with respect to climatology) are published at the official web site of Pacific ENSO Applications Center (PEAC) ( http://lumahai.soest.hawaii.edu/Enso/peu/update.html ) for planning and decision options regarding hazard management in the USAPI. Copyright © 2007 Royal Meteorological Society

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