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

Abstract

In this study, we investigate probabilistic predictability for the El Niño-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.