Improved Predictability of the Indian Ocean Dipole Using Seasonally Modulated ENSO Forcing Forecasts
Improved Predictability of the Indian Ocean Dipole Using Seasonally Modulated ENSO Forcing Forecasts
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DOI：
10.1029/2019gl084196
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发表时间：
2019-08
影响因子：
5.2
通讯作者：
Sen Zhao;F. Jin;M. Stuecker
中科院分区：
文献类型：
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作者：
Sen Zhao;F. Jin;M. Stuecker
尽管季节预报系统最近取得了进展，但在动力模型和经验模型中，印度洋偶极（IOD）的预测技能通常仍然局限于一个季节或更短的提前时间。在这里，我们开发了一个简单的随机动力学模型（SDM）来预测IOD使用季节调制的厄尔尼诺-南方涛动（ENSO）强迫与季节调制的印度洋耦合海洋-大气反馈。SDM，无论是观测或预测厄尔尼诺/南方涛动强迫，一般表现出更高的技能和更长的提前时间预测IOD事件比业务气候预报系统版本2和尺度相互作用实验前沿系统。这些改进主要来自于对ENSO相关IOD事件的更好预测和减少误报。这些结果证实了我们的假设，即业务IOD的可预测性超过持久性在很大程度上是由ENSO的可预测性和系统的信噪比控制。因此，未来可能对模式进行的ENSO改进应转化为更熟练的IOD预测。
Despite recent progress in seasonal forecast systems, the predictive skill for the Indian Ocean Dipole (IOD) remains typically limited to a lead time of one season or less in both dynamical and empirical models. Here we develop a simple stochastic‐dynamical model (SDM) to predict the IOD using seasonally modulated El Niño–Southern Oscillation (ENSO) forcing together with a seasonally modulated Indian Ocean coupled ocean‐atmosphere feedback. The SDM, with either observed or forecasted ENSO forcing, exhibits generally higher skill and longer lead times for predicting IOD events than the operational Climate Forecast System version 2 and the Scale Interaction Experiment–Frontier system. The improvements mainly originate from better prediction of ENSO‐dependent IOD events and from reducing false alarms. These results affirm our hypothesis that operational IOD predictability beyond persistence is largely controlled by ENSO predictability and the signal‐to‐noise ratio of the system. Therefore, potential future ENSO improvements in models should translate to more skillful IOD predictions.