Diagnosing and Predicting ENSO SSTA Development from Moored-Buoy and Scatterometer Winds
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Diagnosing and Predicting ENSO SSTA Development from Moored-Buoy and Scatterometer Winds

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  • Journal Title:
    Journal of Climate
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    Accurate real-time knowledge of equatorial Pacific wind stress is critical for monitoring the state of the tropical Pacific Ocean and understanding sea surface temperature anomaly (SSTA) development associated with El Niño–Southern Oscillation (ENSO) events. The tropical Pacific moored-buoy array has been shown to adequately provide this knowledge when operating as designed. Ocean model simulation of equatorial Pacific SSTA by moored-buoy winds reveals that recent western Pacific buoy losses exceed the array’s minimal redundancy. Additional wind measurements are needed to adequately simulate ENSO-related SSTA development when large portions of the moored-buoy array have been lost or decommissioned. Prospects for obtaining this supplemental wind information in real time are evaluated from simulations of central equatorial Pacific SSTA development during 2017 and end-of-year Niño-3.4 conditions during the previous 25 years. Results show that filling multiple-buoy-dropout gaps with winds from a pair of scatterometers (2000–17) achieves simulation accuracy improving upon that available from the moored-buoy array in the case in which large portions of the array are out. Forcing with the reanalysis-product winds most commonly used in recent ENSO studies or the scatterometer measurements (without the buoy winds) degrades simulation accuracy. The utility of having accurate basinwide wind stress information is demonstrated in an examination of the role that easterly weather-scale wind events played in driving the unexpected development of La Niña in 2017 and by showing that wintertime Niño-3.4 conditions can be statistically forecast, with skill comparable to state-of-the-art coupled models, on the basis of accurate knowledge of equatorial Pacific wind variability over spring or summer.
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    Journal of Climate, 32(24), 8755-8770
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