Enhanced ENSO Prediction via Augmentation of Multimodel Ensembles with Initial Thermocline Perturbations

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O'Kane, Terry; Squire, Dougie ORCID ID icon; Sandery, Paul; Kitsios, Vassili; Matear, Richard; Moore, Thomas ORCID ID icon; Risbey, James ORCID ID icon; Watterson, Ian ORCID ID icon


2020-03-15


Journal Article


Journal of Climate


33


March2020


6


2281-2293


Recent studies have shown that regardless of model configuration, skill in predicting El Niño–Southern Oscillation (ENSO), in terms of target month and forecast lead time, remains largely dependent on the temporal characteristics of the boreal spring predictability barrier. Continuing the 2019 study by O’Kane et al., we compare multiyear ensemble ENSO forecasts from the Climate Analysis Forecast Ensemble (CAFE) to ensemble forecasts from state-of-the-art dynamical coupled models in the North American Multimodel Ensemble (NMME) project. The CAFE initial perturbations are targeted such that they are specific to tropical Pacific thermocline variability. With respect to individual NMME forecasts and multimodel ensemble averages, the CAFE forecasts reveal improvements in skill when predicting ENSO at lead times greater than 6 months, in particular when predictability is most strongly limited by the boreal spring barrier. Initial forecast perturbations generated exclusively as disturbances in the equatorial Pacific thermocline are shown to im- prove the forecast skill at longer lead times in terms of anomaly correlation and the random walk sign test. Our results indicate that augmenting current initialization methods with initial perturbations targeting in- stabilities specific to the tropical Pacific thermocline may improve long-range ENSO prediction.


American Meteorological Society


ENSO seasonal prediction


Earth Sciences not elsewhere classified


https://doi.org/10.1175/JCLI-D-19-0444.1


EP202558


Journal article - Refereed


English


O'Kane, Terry; Squire, Dougie; Sandery, Paul; Kitsios, Vassili; Matear, Richard; Moore, Thomas; Risbey, James; Watterson, Ian. Enhanced ENSO Prediction via Augmentation of Multimodel Ensembles with Initial Thermocline Perturbations. Journal of Climate. 2020; 33(March2020 6):2281-2293. https://doi.org/10.1175/JCLI-D-19-0444.1



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