The Nino-3.4 Prediction Skill of Empirically Adjusted Wind Power

Wind power, defined as the energy received by the ocean from wind, has been identified as a potentially viable precursor of ENSO. The correlation between tropical Pacific wind power anomalies and eastern equatorial Pacific sea surface temperature anomalies can be enhanced over a range of lead times by applying an empirical adjusted framework that accounts for both the underlying climatological state upon which a wind power perturbation acts and the directionality of wind anomalies. Linear regression is used to assess the seasonal prediction skill of adjusted wind power in comparison to unadjusted, as well as the conventional ENSO predictors wind stress and warm water volume. The forecast skill of each regression model is evaluated in a 1800-yr preindustrial climate simulation (CESM-LENS), as well as 23 years of observations. The simulation results show that each predictor's effectiveness varies considerably with the sample record, providing a measure of the uncertainty involved in evaluating prediction skill based on the short observational record. The influence of climatological biases is however a demonstrable concern for results from the simulated climate system. Despite the short record, the observational analysis indicates that adjusted wind power skill is comparable to the conventional dynamical predictors and notably is significantly more predictable than unadjusted wind power when initialized in the summer. Moreover, the adjusted framework results in a reduction of error when evaluating wind power associated with wind bursts, reinforcing previous findings that the adjusted framework is particularly useful for capturing the ENSO response to westerly wind bursts.

Automated summary

Wind power has been identified as a potentially viable precursor of ENSO, and the adjusted framework significantly enhances the seasonal prediction skill, making it comparable to conventional dynamical predictors.