Distillation of CNN Ensemble Results for Enhanced Long-Term Prediction of the ENSO Phenomenon

ABSTRACT: The accurate long - term forecasting of the El Ni n o Southern Oscillation (ENSO) is still one of the biggest challenges in climate science . While it is true that short - to medium - range performance has been improved significantly using the advances in deep learning, statistical dynamical hybrids, most operational systems still use the simple mean of all ensemble members, implicitly assuming equal skill across members . In this study, w e demonstrate, through a strictly a - posteriori evaluation, for any large enough ensemble of ENSO forecasts, there is a subset of members whose skill is substantially higher than that of the ensemble mean. Using a s tate - of - the - art ENSO forecast system cross - validated against the 1986 - 2017 observed Ni no 3.4 index, we identify two Top - 5 subsets one ranked on lowest Root Mean Square Error (RMSE) and another on highest Pearson correlation. Generally across all leads, these outstanding members show higher correlation and lower RMSE, with the advantage rising enormously with lead time. Whereas at sho rt leads (1 month) raises the mean correlation by about +0.02 (+1.7%) and lowers the RMSE by around 0.14 C or by 23.3% compared to the All - 40 mean, at extreme leads (23 months) the correlation is raised by +0.43 (+172%) and RMSE by 0.18 C or by 22.5% de crease. The enhancements are largest during crucial ENSO transition periods such as SON and DJF, when accurate amplitude and phase forecasting is of greatest socio - economic benefit, and furthermore season - dependent e.g., mid - year months such as JJA and MJJ have incredibly large RMSE reductions. This study provides a solid foundation for further investigations to identify reliable clues for detecting high - quality ensemble members, thereby enhancing forecasting skill. Introduction Long - lead prediction of the El Niño Southern Oscillation (ENSO) is among the most significant and scientifically challenging problems of climate research. ENSO is a coupled ocean atmosphere phenomenon comprising quasi - periodic variations of sea surface temperature (SST) anomalies in the equatorial Pacific with widespread impacts on global weather patterns, hydrology, agriculture, ecosystems, and socio - economic activities [21,23] . Successful prediction at lead times exceeding one year has particular significance for water resources management planning, disaster preparedness, agricultural planning, and climate - sensitive economic practice [24,25] . Howe ver, the inherent nonlinearity of ocean atmosphere interaction, the sensitivity to initial conditions, and the complex web of teleconnections controlling ENSO variability make the forecast skill decline very quickly with lead time.