Abstract
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.
Highlights
The El Niño-Southern Oscillation (ENSO), the largest interannual signal in the climate system, is a typical coupled atmosphere-ocean phenomenon with time scales of approximately 2–7 years (Ren et al, 2020)
Clustering using traditional index types could not accurately describe the evolution of the entire event, and clustering analysis using the evolution of the anomalous sea temperature field (120°E-80°W, 30°S30°N) better fit the physical changes of the actual event; for such a coupled system, both the sea surface temperature anomaly (SSTA) field and the anomalous wind field showed a better fit with the observation
There was an obvious inconsistency in ocean-atmosphere coordination, and the strength of the coupled ocean-atmosphere system was weak; the cold SSTA in the equatorial eastern Pacific in spring did not develop even under the action of an easterly wind anomaly, leading to the occurrence of coldwarm-cold variation in the SSTA in the equatorial eastern Pacific
Summary
The El Niño-Southern Oscillation (ENSO), the largest interannual signal in the climate system, is a typical coupled atmosphere-ocean phenomenon with time scales of approximately 2–7 years (Ren et al, 2020). With the increase in computing power and the introduction of techniques such as data assimilation (Evensen, 2004; Zheng and Zhu, 2010), current climate models can realize the effective prediction of El Niño. ENSO prediction skills were lower in the 2000s than in the 1980s or 1990s (Barnston et al, 2012; Zheng et al, 2016), even with an increase in ocean observations, especially in the equatorial tropical Pacific (Kumar et al, 2015). Most current models have a faster decline across the boreal spring in the prediction skill of La Niña events than for El Niño events (Lopez and Kirtman 2014)
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