Season-dependent predictability and error growth dynamics for La Niña predictions

Abstract

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.

Optimally growing initial errors of El Niño events in the CESM

“…For La Niña events and neutral conditions, the predictability problems related to initial errors are also important. Hu and Duan (2016) and Hu et al (2019) found that there exist similarities between optimal precursory perturbations superimposed on the neutral states and most likely to evolve into ENSO events, and OGEs associated with ENSO events in the CESM, and emphasized that the off-equatorial regions around 10°N in the central Pacific may also be the important error resources for La Niña predictions. These results are equally obtained by the EOF-based ensemble method and need to be further studied by the optimization method.…”

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

“…One possible reason for this development may be that its cold SSTA in the eastern equatorial Pacific was obviously stronger than the worst prediction members in January 2020 (Figure 6). Furthermore, we analyzed the Hovmoller diagrams (Figure 7), which are profile diagrams first proposed by Hovmöller in 1949 to reflect the temporal variation of atmospheric variables (Hovmöller, 1949) and are now widely used in the analysis of zonal or meridional variation characteristics of ENSO events (Feng et al, 2015;Lian et al, 2017;Lian and Chen, 2021;Hu et al, 2019;Zheng et al, 2021). At the starting time of the forecast (January 2020), the most obvious differences between the worst prediction members and the best prediction members in the four variables (Taux, Tauy, SSTA, and Z20) were the cold SSTA located in the southeast Pacific.…”

“…Moreover, ENSO prediction skills were lower in the 2000s than in the 1980s or 1990s (Barnston et al, 2012;, even with an increase in ocean observations, especially in the equatorial tropical Pacific (Kumar et al, 2015). In addition, due to asymmetric ENSO features, the predictability between El Niño and La Niña events is distinctively different (Hu et al, 2019). First, the successful prediction of La Niña has not received the same attention as that of El Niño (Barnston et al, 2012).…”

ENSO‐GTC: ENSO Deep Learning Forecast Model With a Global Spatial‐Temporal Teleconnection Coupler

“…As for the iterative strategy, we make long-term forecasts with different forecast start calendar months and calculate the monthly correlation skills of Niño3 and Niño4 indexes for every lead month respectively as suggested in Hou et al (2019), Hu et al (2019). The results are shown in Figure 11.…”