ENSO forecasts near the spring predictability barrier and possible reasons for the recently reduced predictability
Journal of Climate
American Meteorological Society
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Lai,, Herzog, M., & Graf, H. (2018). ENSO forecasts near the spring predictability barrier and possible reasons for the recently reduced predictability. Journal of Climate, 31 (2)https://doi.org/10.1175/JCLI-D-17-0180.1
A cross-validated statistical model has been developed to produce hindcasts for the 1980-2016 Nov-Dec-Jan (NDJ, assumed El Niño peak) mean Niño3.4 sea surface temperature anomalies (SSTA). A linear combination of two parameters is sufficient to successfully predict the peak SSTA: (1) the 130oE-180o, 5oN-5oS, 5m-250m oceanic potential temperature anomalies in February, and (2) the 140oE-160oW, 5oN-5oS cumulative zonal wind anomalies (ZWA), integrated from November (one year before) up to the prediction month. Our model is simple but comparable to, or even outperforms, many NOAA Climate Prediction Centre's statistical models during the boreal spring predictability barrier. In contrast to most statistical models, the predictand Niño3.4 SSTA is not used as a predictor. The explained variance between observed and predicted NDJ Niño3.4 SSTA at a lead-time of 8 months is 57% using five years for cross-validation, 63% in full hindcast mode. Predictive skill is lower after 2000 when the mean climate state is more La Niña-like due to stronger equatorial easterly ZWA. Strengthened Pacific subtropical highs are observed, with weaker westerly ZWA that emerge at a later time during El Niño. The Western Pacific is more recharged, with stronger upwelling over the Eastern Pacific. The resulting strong zonal subsurface temperature gradient provides a high potential for Kelvin waves being triggered without strong westerly ZWA. However, the persistent easterly ZWA lead to more Central Pacific-like El Niños. These are more difficult to predict because the contribution of the thermocline feedback is reduced. Overall, we find that the importance of the recharge state for ENSO prediction has increased after 2000, contradicting some previous studies.
El Nino, ENSO, Statistical forecasting, Oscillations
Partial funding for this research was provided by the Engineering and Physical Sciences Research Council (EPSRC) as part of the Low Carbon Climate-Responsive Heating and Cooling of Cities (LoHCool) project (EP/N009797/1). Andy W.-C. Lai would also like to acknowledge the support from James Lai, Grace Chang, and Joey Cheng.
Royal Society (IE121259)
Embargo Lift Date
External DOI: https://doi.org/10.1175/JCLI-D-17-0180.1
This record's URL: https://www.repository.cam.ac.uk/handle/1810/273575