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The 2020/21 La Niña was not well predicted by most climate models when it started in early-mid 2020. This paper adopted an El Niño-Southern Oscillation (ENSO) ensemble prediction system to evaluate the key physical processes in the development of this cold event by performing a clustering analysis of 100 ensemble member predictions 1 year in advance. The abilities of two clustering approaches were first examined in regard to capturing the development of the 2020/21 La Niña event. One approach was index clustering, which adopted only the 12-month Niño3.4 indices in 2020 as an indicator, and the other was pattern clustering through contrasting the evolution of sea surface temperature (SST) anomalies over the tropical Pacific in 2020 for clustering. Pattern clustering surpasses index clustering in better describing the evolution over the off-equatorial and equatorial regions during the 2020/21 La Niña. Consequently, based on the pattern clustering approach, a comparison of the selected most (five best) and least (five worst) representative ensemble members illustrated that the predominance of anomalous southeasterly winds over the central equatorial Pacific in spring 2020 played a crucial role in initiating the moderate La Niña event in 2020/21, by preventing the development of westerly winds over the warm pool. Moreover, the inherent spring predictability barrier (SPB) was still a major challenge for improving the prediction skill of the 2020/21 La Niña event when the prediction occurred across the spring season.

Key Processes on Triggering the Moderate 2020/21 La Niña Event as Depicted by the Clustering Approach