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By superimposing initial sea temperature disturbances in neutral years, we determine the precursory disturbances that are most likely to evolve into El Niño and La Niña events using an Earth System Model. These precursory disturbances for El Niño and La Niña events are deemed optimal precursory disturbances because they are more likely to trigger strong ENSO events. Specifically, the optimal precursory disturbance for El Niño exhibits negative sea surface temperature anomalies (SSTAs) in the central‐eastern equatorial Pacific. Additionally, the subsurface temperature component exhibits negative anomalies in the upper layers of the eastern equatorial Pacific and positive anomalies in the lower layers of the western equatorial Pacific. The optimal precursory disturbance for La Niña is almost opposite to that of El Niño. The optimal precursory disturbances show that both El Niño and La Niña originate from precursory signals in the subsurface layers of the western equatorial Pacific and in the surface layers of the eastern equatorial Pacific. We find that the optimal precursory disturbances for El Niño and La Niña are particularly similar to the optimally growing initial errors associated with El Niño prediction that have been presented in previous studies. The optimally growing initial errors show that the optimal precursor source areas represent the sensitive areas for target observations associated with ENSO prediction. Combining the optimal precursory disturbances and the optimally growing initial errors for ENSO, we infer that additional observations in these sensitive areas can reduce initial errors and be used to detect precursory signals, thereby improving ENSO predictions.

The "spring predictability barrier" (SPB) is a well-known characteristic of ENSO prediction, which has been widely studied for El Niño events. However, due to the nonlinearity of the coupled ocean-atmosphere system and the asymmetries between El Niño and La Niña, it is worthy to investigate the SPB for La Niña events and reveal their differences with El Niño. This study investigates the season-dependent predictability of sea surface temperature (SST) for La Niña events by exploring initial error growth in a perfect model scenario within the Community Earth System Model. The results show that for the prediction through the spring season, the prediction errors caused by initial errors have a season-dependent evolution and induce an SPB for La Niña events. Two types of initial errors that often yield the SPB phenomenon are identified: the first are type-1 initial errors showing positive SST errors in the central-eastern equatorial Pacific accompanied by a large positive error in the upper layers of the eastern equatorial Pacific. The second are type-2 errors presenting an SST pattern with positive errors in the southeastern equatorial Pacific and a west-east dipole pattern in the subsurface ocean. The type-1 errors exhibit an evolving mode similar to the growth phase of an El Niño-like event, while the type-2 initially experience a La Niña-like decay and then a transition to the growth phase of an El Niño-like event. Both types of initial errors cause positive prediction errors for Niño3 SST and under-predict the corresponding La Niña events. The resultant prediction errors of type-1 errors are owing to the growth of the initial errors in the upper layers of the eastern equatorial Pacific. For the type-2 errors, the prediction errors originate from the initial errors in the subsurface layers of the western equatorial Pacific. These two regions may represent the sensitive areas of targeted observation for La Niña prediction. In addition, the type-2 errors in the equatorial regions are enlarged by the recharge process from 10°N in the central Pacific during the eastward propagation. Therefore, the off-equatorial regions around 10°N in the central Pacific may represent another sensitive area of La Niña prediction. Additional observations may be prioritized in these identified sensitive areas to better predict La Niña events.

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The role of interdecadal wind stress variability in the genesis of ENSO diversity is examined by using an intermediate coupled model (ICM) in the tropical Pacific; two types of experiments are performed, one with the original ICM, and the other with interdecadal wind stress (τ 𝑖𝑛𝑡𝑒𝑟𝑑𝑒 ) effect is explicitly represented. The τ 𝑖𝑛𝑡𝑒𝑟𝑑𝑒 component is derived from NCEP/NCAR reanalysis dataset as follows. First, the ensemble empirical mode decomposition (EEMD) is used to extract the interdecadal component of wind stress anomalies on about a 10-40yr timescale. Next, an idealized interdecadal cycle of τ 𝑖𝑛𝑡𝑒𝑟𝑑𝑒 is reconstructed by a principal oscillation pattern (POP) analysis based on the EEMD-extracted interdecadal wind component. A 110-yr model integration is then performed by explicitly incorporating the reconstructed τ 𝑖𝑛𝑡𝑒𝑟𝑑𝑒 cycle into the ICM. Compared with the regular interannual oscillation in the original ICM, the simulated ENSO events become highly irregular with decadal variations in the amplitude and asymmetry when the τ 𝑖𝑛𝑡𝑒𝑟𝑑𝑒 effect is included. Especially, the model reproduces different types of El Niño and La Niña events with different spatial distribution and temporal evolution of SST anomalies, namely Eastern-Pacific (EP) and Central-Pacific (CP) types. Further attribution analyses are performed to understand the modulating effects of τ 𝑖𝑛𝑡𝑒𝑟𝑑𝑒 in the tropical Pacific using the ocean component of the ICM, forced by the added τ 𝑖𝑛𝑡𝑒𝑟𝑑𝑒 effect. Two different roles of the Interdecadal Pacific Oscillation (IPO) in modulating different El Niño and La Niña events are illustrated. On the one hand, the IPO favors for the emergence of the EP type of El Niño and La Niña events, in association with the initial anomaly signals 3 occurring in the eastern equatorial Pacific, which are absent in the original ICM. On the other hand, the IPO tends to shift the El Niño/La Niña events to an CP type, with which the SST anomalies propagate eastward along the equator but cannot extend into the eastern boundary. This simple modeling study highlights the significant contributions of interdecadal wind variability to the genesis of ENSO irregularity and diversity theoretically.

<p>The “spring predictability barrier” (SPB) is a well-known characteristic of ENSO prediction, which has been widely studied for El Niño events. However, due to the nonlinearity of the coupled ocean–atmosphere system and the asymmetries between El Niño and La Niña, it is worthy to investigate the SPB for La Niña events and reveal their differences with El Niño. This study investigates the season-dependent predictability of sea surface temperature (SST) for La Niña events by exploring initial error growth in a perfect model scenario within the Community Earth System Model. The results show that for the prediction through the spring season, the prediction errors caused by initial errors have a season-dependent evolution and induce an SPB for La Niña events. Two types of initial errors that often yield the SPB phenomenon are identified: the first are type-1 initial errors showing positive SST errors in the central-eastern equatorial Pacific accompanied by a large positive error in the upper layers of the eastern equatorial Pacific. The second are type-2 errors presenting an SST pattern with positive errors in the southeastern equatorial Pacific and a west–east dipole pattern in the subsurface ocean. The type-1 errors exhibit an evolving mode similar to the growth phase of an El Niño-like event, while the type-2 initially experience a La Niña-like decay and then a transition to the growth phase of an El Niño-like event. Both types of initial errors cause positive prediction errors for Niño3 SST and under-predict the corresponding La Niña events. The resultant prediction errors of type-1 errors are owing to the growth of the initial errors in the upper layers of the eastern equatorial Pacific. For the type-2 errors, the prediction errors originate from the initial errors in the subsurface layers of the western equatorial Pacific. These two regions may represent the sensitive areas of targeted observation for La Niña prediction. In addition, the type-2 errors in the equatorial regions are enlarged by the recharge process from 10°N in the central Pacific during the eastward propagation. Therefore, the off-equatorial regions around 10°N in the central Pacific may represent another sensitive area of La Niña prediction. Additional observations may be prioritized in these identified sensitive areas to better predict La Niña events.</p>