Relationship between optimal precursory disturbances and optimally growing initial errors associated with ENSO events: Implications to target observations for ENSO prediction

Abstract

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.

Towards optimal observational array for dealing with challenges of El Niño-Southern Oscillation predictions due to diversities of El Niño

“…Specifically, with the start months changing from January through April and July to October, the regions of large errors tend to move westward. The regions of large errors for OGEs are shown to be the sensitive area for target observations, i.e., the area that the additional observations should be preferentially deployed Hu and Duan 2016;Tian and Duan 2016;Mu et al 2014;Yu et al 2012). Therefore, the result indicates that the sensitive areas for targeting observations associated with two types of El Niño events are dependent of the start months.…”

“…Previous studies showed that the region with large values of the OGEs represent the sensitive area for target observation associated with El Niño predictions Hu and Duan 2016;Tian and Duan 2016;Mu et al 2014;Yu et al 2012); and the initial errors in these sensitive areas make greatest contribution to the prediction error and the prediction Fig. 1 The mean of the SSTAs during the mature phase for three EP-El Niño events (1982/1983, 1986/1987 and 1997/1998) and five CP-El Niño events (1991/1992, 1994/1995, 2002/2003, 2004/2005 (Fig.…”

“…Generally speaking, these additional observations would be assimilated by a data assimilation system to provide the numerical model a more reliable initial state. The idea of the target observation has been applied to some weather and climate events forecasting, such as Fronts and Atlantic Storm-Track Experiment (FASTEX; Synder 1996), North Pacific Experiment (NORPEX; Langland et al 1999), tropical cyclone (TC; Qin et al 2013;Qin and Mu 2012;Zhou and Mu 2011), Kuroshio large meander (KLM; Wang et al 2013), India Ocean Dipole (IOD; Feng et al 2014), ENSO Hu and Duan 2016), etc.…”

“…Correspondingly, Mu et al (2014) proposed an idea that the ENSO forecast skill can be greatly improved by assimilating observations in some key areas that locate in the abovementioned initial errors of special structure and are covered by large errors. Such idea is related to target observations Duan and Hu 2016;Hu and Duan 2016).…”

“…They argued that the eastern equatorial Pacific is the sensitive areas for targeting observations associated with EP-El Niño forecasting. Considering that the Zebiak-Cane model is an intermediate ENSO model and does not have enough description of the subsurface layers, Duan and Hu (2016) and Hu and Duan (2016) further adopted a complex GCM and showed that the subsurface layer of western equatorial Pacific represent another sensitive area for El Niño forecasting. However, most of these above studies only focus on EP-El Niño events because most models do not have the ability to simulate two types of El Niño events well Kug et al 2012).…”