Identifying Equatorial Pacific Sub-Seasonal Wind Event Impacts and Statistically Forecasting ENSO Ssta Development from Moored-Buoy and Scatterometer Winds

Accurate real-time knowledge of equatorial Pacific wind stress is critical for monitoring the state of the tropical Pacific and understanding sea surface temperature anomaly (SSTA) development driven by wind variability 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 (former TRITON array reduced to 1 moored-buoy) exceed the array’s minimal redundancy. Additional wind measurements are needed to adequately identify components of ENSO-associated wind variability and 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 based on 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 (2000-2017) achieves simulation accuracy improving upon that available from the moored-buoy array in the case that 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 basin-wide 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-forecasted, with skill comparable to state-of-the-art coupled-models, based on accurate knowledge of equatorial Pacific wind variability over spring or summer.