Seasonal sea-level forecasts by canonical correlation analysis - an operational scheme for the US-affiliated Pacific Islands

摘要

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 Nino-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 Nino and high sea level during La Nina 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 (C) 2007 Royal Meteorological Society.