Probabilistic Prediction of ENSO Over the Past 137 Years Using the CESM Model

In this study, we investigate probabilistic predictability for the El Nino-Southern Oscillation (ENSO) by assessing both actual prediction skill and potential predictability using a long-term retrospective forecast from a complicated coupled general circulation model (CGCM). Our results indicate that above and below normal events are more predictable than neutral events. The probabilistic prediction skill suffers prominent Spring Predictability Barrier and undergoes notable interdecadal variation. For the above and below normal events, the lowest probabilistic prediction skills appear during 1920-1940 and the higher prediction skills occur after the 1960s. The seasonal and interdecadal variability of the probabilistic prediction skill stems mainly from the variability of the ENSO signal intensity. There is much room for improvement for the predictability of all three categories of ENSO events. At least an additional 1 or 2 months of skillful probabilistic predictions can be expected to progress in the future. To our knowledge, this is the first study to use a CGCM to evaluate probabilistic predictability for ENSO at various time scales. Plain Language Summary The predictability of El Nino-Southern Oscillation (ENSO) has important implications for seasonal climate predictions. The chaotic nature of ENSO means that probabilistic prediction is required to quantitatively capture forecast uncertainty. Most current studies of probabilistic ENSO prediction are based on hindcasts from complicated coupled general circulation models (CGCMs) that cover the past three or four decades. It is difficult to fully understand the variability of probabilistic ENSO predictability on inter-decadal (or longer) timescales. In this study, we firstly assess probabilistic predictability for the ENSO by assessing both actual and potential probabilistic prediction skills using a long-term retrospective forecast from a CGCM. Robust conclusions are derived and deepen the understanding of probabilistic predictability for the ENSO.

Automated summary

This study investigates the probabilistic predictability of the El Nino-Southern Oscillation (ENSO) using a long-term retrospective forecast from a complicated coupled general circulation model (CGCM). The results suggest that above and below normal events are more predictable than neutral events. The predictability of ENSO shows notable seasonal and interdecadal variation due to the variability of the ENSO signal intensity.