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Jul.  2018

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# ENSO Predictions in an Intermediate Coupled Model Influenced by Removing Initial Condition Errors in Sensitive Areas: A Target Observation Perspective

• Previous studies indicate that ENSO predictions are particularly sensitive to the initial conditions in some key areas (so-called "sensitive areas"). And yet, few studies have quantified improvements in prediction skill in the context of an optimal observing system. In this study, the impact on prediction skill is explored using an intermediate coupled model in which errors in initial conditions formed to make ENSO predictions are removed in certain areas. Based on ideal observing system simulation experiments, the importance of various observational networks on improvement of El Niño prediction skill is examined. The results indicate that the initial states in the central and eastern equatorial Pacific are important to improve El Niño prediction skill effectively. When removing the initial condition errors in the central equatorial Pacific, ENSO prediction errors can be reduced by 25%. Furthermore, combinations of various subregions are considered to demonstrate the efficiency on ENSO prediction skill. Particularly, seasonally varying observational networks are suggested to improve the prediction skill more effectively. For example, in addition to observing in the central equatorial Pacific and its north throughout the year, increasing observations in the eastern equatorial Pacific during April to October is crucially important, which can improve the prediction accuracy by 62%. These results also demonstrate the effectiveness of the conditional nonlinear optimal perturbation approach on detecting sensitive areas for target observations.
摘要: 已有研究表明ENSO预报技巧取决于某一特定区域(也就是所谓的敏感区)内的初始场准确性. 但是, 很少有研究定量分析所设计的最优观测网对预报技巧的改善程度. 本文基于一个中等复杂程度的海气耦合模式, 通过去除不同区域内的初始误差, 探讨了其对预报技巧的影响. 基于理想观测模拟试验, 考察了不同观测网对预报技巧的改善程度. 结果表明, 位于中东太平洋的初始信号对预报技巧影响最大. 当去除位于赤道中太平洋的初始误差, ENSO预报误差下降25%. 同时, 也考察了多个区域加密观测对预报技巧的影响. 特别地, 文中指出随季节变化的观测网能够更有利于ENSO预报技巧提升. 例如除全年在赤道中太平洋及其北部布置观测外, 若在每年的4至10月份继续增加东太平洋的观测, 能够是预报技巧提升62%左右. 以上结果也充分证实了基于条件非线性最优扰动方法确定观测敏感区的有效性.

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## Manuscript History

Manuscript received: 26 May 2017
Manuscript revised: 30 August 2017
Manuscript accepted: 19 December 2017
###### 通讯作者: 陈斌, bchen63@163.com
• 1.

沈阳化工大学材料科学与工程学院 沈阳 110142

## ENSO Predictions in an Intermediate Coupled Model Influenced by Removing Initial Condition Errors in Sensitive Areas: A Target Observation Perspective

• 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 100029, China
• 3. Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China

Abstract: Previous studies indicate that ENSO predictions are particularly sensitive to the initial conditions in some key areas (so-called "sensitive areas"). And yet, few studies have quantified improvements in prediction skill in the context of an optimal observing system. In this study, the impact on prediction skill is explored using an intermediate coupled model in which errors in initial conditions formed to make ENSO predictions are removed in certain areas. Based on ideal observing system simulation experiments, the importance of various observational networks on improvement of El Niño prediction skill is examined. The results indicate that the initial states in the central and eastern equatorial Pacific are important to improve El Niño prediction skill effectively. When removing the initial condition errors in the central equatorial Pacific, ENSO prediction errors can be reduced by 25%. Furthermore, combinations of various subregions are considered to demonstrate the efficiency on ENSO prediction skill. Particularly, seasonally varying observational networks are suggested to improve the prediction skill more effectively. For example, in addition to observing in the central equatorial Pacific and its north throughout the year, increasing observations in the eastern equatorial Pacific during April to October is crucially important, which can improve the prediction accuracy by 62%. These results also demonstrate the effectiveness of the conditional nonlinear optimal perturbation approach on detecting sensitive areas for target observations.

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