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| Role of Parameter Errors in the Spring Predictability Barrier for ENSO Events in the Zebiak–Cane Model | |
| 作者姓名： | YU Liang MU Mu Yanshan YU |
| 基金项目： | jointly sponsored by the National Nature Scientific Foundation of China (Grant Nos.41230420 and 41006015);the National Basic Research Program of China (Grant No.2012CB417404);the Basic Research Program of Science and Technology Projects of Qingdao (Grant No11-1-4-95-jch) |
| 摘 要： | ABSTRACT The impact of both initial and parameter errors on the spring predictability barrier （SPB） is investigated using the Zebiak Cane model （ZC model）. Previous studies have shown that initial errors contribute more to the SPB than parameter errors in the ZC model. Although parameter errors themselves are less important, there is a possibility that nonlinear interactions can occur between the two types of errors, leading to larger prediction errors compared with those induced by initial errors alone. In this case, the impact of parameter errors cannot be overlooked. In the present paper, the optimal combination of these two types of errors i.e., conditional nonlinear optimal perturbation （CNOP） errors] is calculated to investigate whether this optimal error combination may cause a more notable SPB phenomenon than that caused by initial errors alone. Using the CNOP approach, the CNOP errors and CNOP-I errors （optimal errors when only initial errors are considered） are calculated and then three aspects of error growth are compared： （1） the tendency of the seasonal error growth; （2） the prediction error of the sea surface temperature anomaly; and （3） the pattern of error growth. All three aspects show that the CNOP errors do not cause a more significant SPB than the CNOP-I errors. Therefore, this result suggests that we could improve the prediction of the E1 Nifio during spring by simply focusing on reducing the initial errors in this model. |
| 关 键 词： | ENSO事件 甘蔗 预报 非线性相互作用 参数误差 模型参数 预测误差 海表温度异常 |
Role of parameter errors in the spring predictability barrier for ENSO events in the Zebiak-Cane model |
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| YU Liang,MU Mu,Yanshan,YU.Role of parameter errors in the spring predictability barrier for ENSO events in the Zebiak-Cane model[J].Advances in Atmospheric Sciences,2014,31(3):647-656. | |
| Authors: | Liang Yu Mu Mu Yanshan Yu |
| Institution: | 1. Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, 266071, China 2. University of Chinese Academy of Sciences, Beijing, 100049, China 3. School of Mathematical Sciences, Monash University, Clayton, Victoria, 3800, Australia |
| Abstract: | The impact of both initial and parameter errors on the spring predictability barrier (SPB) is investigated using the Zebiak-Cane model (ZC model). Previous studies have shown that initial errors contribute more to the SPB than parameter errors in the ZC model. Although parameter errors themselves are less important, there is a possibility that nonlinear interactions can occur between the two types of errors, leading to larger prediction errors compared with those induced by initial errors alone. In this case, the impact of parameter errors cannot be overlooked. In the present paper, the optimal combination of these two types of errors i.e., conditional nonlinear optimal perturbation (CNOP) errors] is calculated to investigate whether this optimal error combination may cause a more notable SPB phenomenon than that caused by initial errors alone. Using the CNOP approach, the CNOP errors and CNOP-I errors (optimal errors when only initial errors are considered) are calculated and then three aspects of error growth are compared: (1) the tendency of the seasonal error growth; (2) the prediction error of the sea surface temperature anomaly; and (3) the pattern of error growth. All three aspects show that the CNOP errors do not cause a more significant SPB than the CNOP-I errors. Therefore, this result suggests that we could improve the prediction of the El Niño during spring by simply focusing on reducing the initial errors in this model. |
| Keywords: | ENSO predictability spring predictability barrier initial errors parameter errors error growth |
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