An improved sea level forecasting scheme for hazards management in the US-affiliated Pacific Islands

Authors

  • Md. Rashed Chowdhury,

    Corresponding author
    1. Pacific ENSO Applications Climate Center, Joint Institute for Marine and Atmospheric Research, University of Hawaii at Manoa, Honolulu, HI, USA
    • Correspondence to: Dr Md. R. Chowdhury, Principal Research Scientist, Pacific ENSO Applications Climate Center, University of Hawaii at Manoa, 2525 Correa Road, HIG 350, Honolulu, HI 96822, USA. E-mail: rashed@hawaii.edu

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  • Pao-Shin Chu,

    1. Department of Meteorology, School of Ocean and Earth Science and Technology (SOEST), University of Hawaii at Manoa, Honolulu, HI, USA
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  • Charles Chip Guard

    1. National Oceanic and Atmospheric Administration, National Weather Service, Weather Forecast Office, Guam, USA
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ABSTRACT

This study describes an improved seasonal sea level forecasting scheme by the Pacific ENSO Applications Climate Center (PEAC). Since 2005, an operational sea level forecasting scheme (3–5 months in advance) for the US-affiliated Pacific Islands (USAPI) has been instrumental (http://www.prh.noaa.gov/peac/sea-level.php). 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 these forecasts on seasonal time scales. The current SST-based canonical correlations analysis (CCA) hindcast forecasts have been found to be skillful. However, the skill gradually decreases as the lead-time increases. This has motivated us to revisit the forecasting scheme at PEAC. In contrast to previous endeavours which relied only on SSTs, we now incorporate both trade winds and SSTs for modulating sea level variability on seasonal time scales.

The average forecasts for zero to three seasons' lead-times are found to be 0.647, 0.598, and 0.625 for combined SST and the zonal component of the trade wind (U), SST, and wind (U), respectively. It is therefore revealed that the combined SST-wind-based forecasts are more skillful than the SST or wind-based forecasts alone. It is particularly more efficient on longer time scales for most of the stations (e.g. 10–25% improvement on two to three seasons' lead-times). The improvements of these forecasts have enabled the capability of our clients in the USAPI region to develop a more efficient long-term response plan for hazard management.

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