[Climate Predictability] On the “spring predictability barrier” for strong El Nino events as derived from an intermediate coupled model ensemble prediction system

The “spring predictability barrier” (SPB) is a significant phenomenon in model predictions of the El Nino-Southern Oscillation (ENSO), which means that the prediction skill shows a sharp drop during boreal spring, regardless of the start month of the predictions. For the strong El Nino event occurred in 2015, although the intermediate coupled model ensemble prediction system (ICM EPS) successfully predicted its onset, notable prediction uncertainties still exist, particularly for long lead times. Several interesting questions are therefore raised: Is a significant SPB generated by the ICM EPS for 2015/16 El Nino predictions? If so, is it initial errors or model errors that play a more important role in causing the SPB phenomenon?

Recently, Qi Qianqian (Institute of Atmospheric Physics), Prof. Duan Wansuo (Institute of Atmospheric Physics), Prof. Zheng Fei (Institute of Atmospheric Physics), and Prof. Tang Youmin (Second Institute of Oceanology) explored the SPB phenomenon in model predictions of ICM EPS and demonstrated that the SPB of predictions for 2015/16 El Nino event is mainly caused by model errors, which present a similar pattern with the most sensitive nonlinear forcing singular vector (NFSV)-tendency errors reported by Duan et al. (2016). These results suggest that the forecast skill of the ICM EPS for strong El Nino events could be greatly enhanced by using the NFSV-like tendency error to correct the model.

Figure 1 The tendency error pattern that emphasizes the prominent characteristic of the tendency errors that yield a significant SPB and, in particular, the largest prediction error for the 2015/16 El Nino event.

This study is published in Science China Earth Sciences (doi: 10.1007/s11430-017-9087-2).

Reference: Qi Qianqian, Wansuo Duan, Fei Zheng and Youmin Tang, 2017: On the “spring predictability barrier” for strong El Nino events as derived from an intermediate coupled model ensemble prediction system. Science China Earth Sciences, Vol.60 No.9:1614–1631. doi: 10.1007/s11430-017-9087-2.

Contact: Duan Wansuo, duanws@lasg.iap.ac.cn