Initial Error-induced Optimal Perturbations in ENSO Predictions, as Derived from an Intermediate Coupled Model

• Published in: Advances in atmospheric sciences 2017-06, Vol.34 (6), p.791-803
• Main Authors: Tao, Ling-Jiang, Zhang, Rong-Hua, Gao, Chuan
• Format: Article
• Language: English
• Subjects: Atmospheric Sciences; Data collection; Earth and Environmental Science; Earth Sciences; El Nino; ENSO事件; ENSO预测; Geophysics/Geodesy; La Nina; Marine; Meteorology; Original Paper; Sea surface temperature; Seasonal variations; Southern Oscillation; 季节变化; 扰动; 最大误差; 温度异常; 耦合模型; 预报技巧
• ISSN: 0256-1530; 1861-9533
• DOI: 10.1007/s00376-017-6266-4

The initial errors constitute one of the main limiting factors in the ability to predict the E1 Nino-Southem Oscillation （ENSO） in ocean-atmosphere coupled models. The conditional nonlinear optimal perturbation （CNOP） approach was em- ployed to study the largest initial error growth in the E1 Nino predictions of an intermediate coupled model （ICM）. The optimal initial errors （as represented by CNOPs） in sea surface temperature anomalies （SSTAs） and sea level anomalies （SLAs） were obtained with seasonal variation. The CNOP-induced perturbations, which tend to evolve into the La Nifia mode, were found to have the same dynamics as ENSO itself. This indicates that, if CNOP-type errors are present in the initial conditions used to make a prediction of E1 Nino, the E1 Nino event tends to be under-predicted. In particular, compared with other seasonal CNOPs, the CNOPs in winter can induce the largest error growth, which gives rise to an ENSO amplitude that is hardly ever predicted accurately. Additionally, it was found that the CNOP-induced perturbations exhibit a strong spring predictability barrier （SPB） phenomenon for ENSO prediction. These results offer a way to enhance ICM prediction skill and, particularly, weaken the SPB phenomenon by filtering the CNOP-type errors in the initial state. The characteristic distributions of the CNOPs derived from the ICM also provide useful information for targeted observations through data assimilation. Given the fact that the derived CNOPs are season-dependent, it is suggested that seasonally varying targeted observations should be implemented to accurately predict ENSO events.