ENSO Predictions in an Intermediate Coupled Model Influenced by Removing Initial Condition Errors in Sensitive Areas: A Target Observation Perspective
文献类型：期刊论文
| 作者 | Tao, Ling-Jiang1,2 ; Gao, Chuan1,3; Zhang, Rong-Hua1,2,3
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| 刊名 | ADVANCES IN ATMOSPHERIC SCIENCES
 |
| 出版日期 | 2018-07-01 |
| 卷号 | 35期号:7页码:853-867 |
| 关键词 | El Nino prediction initial condition errors target observations |
| ISSN号 | 0256-1530 |
| DOI | 10.1007/s00376-017-7138-7 |
| 通讯作者 | Zhang, Rong-Hua(rzhang@qdio.ac.cn) |
| 英文摘要 | Previous studies indicate that ENSO predictions are particularly sensitive to the initial conditions in some key areas (socalled "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 Nio prediction skill is examined. The results indicate that the initial states in the central and eastern equatorial Pacific are important to improve El NiEeno 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. |
| 资助项目 | Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19060102] ; National Natural Science Foundation of China[41475101] ; National Natural Science Foundation of China[41690122] ; National Natural Science Foundation of China[41690120] ; National Natural Science Foundation of China[41421005] ; National Programme on Global Change and Air-Sea Interaction Interaction[GASI-IPOVAI-06] ; National Programme on Global Change and Air-Sea Interaction Interaction[GASI-IPOVAI-01-01] ; Taishan Scholarship |
| WOS研究方向 | Meteorology & Atmospheric Sciences |
| 语种 | 英语 |
| WOS记录号 | WOS:000432691200009 |
| 出版者 | SCIENCE PRESS |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/159189]  |
| 专题 | 海洋研究所_海洋环流与波动重点实验室 |
| 通讯作者 | Zhang, Rong-Hua |
| 作者单位 | 1.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100029, Peoples R China 3.Qingdao Natl Lab Marine Sci & Technol, Qingdao 266237, Peoples R China |
| 推荐引用方式 GB/T 7714 | Tao, Ling-Jiang,Gao, Chuan,Zhang, Rong-Hua. ENSO Predictions in an Intermediate Coupled Model Influenced by Removing Initial Condition Errors in Sensitive Areas: A Target Observation Perspective[J]. ADVANCES IN ATMOSPHERIC SCIENCES,2018,35(7):853-867. |
| APA | Tao, Ling-Jiang,Gao, Chuan,&Zhang, Rong-Hua.(2018).ENSO Predictions in an Intermediate Coupled Model Influenced by Removing Initial Condition Errors in Sensitive Areas: A Target Observation Perspective.ADVANCES IN ATMOSPHERIC SCIENCES,35(7),853-867. |
| MLA | Tao, Ling-Jiang,et al."ENSO Predictions in an Intermediate Coupled Model Influenced by Removing Initial Condition Errors in Sensitive Areas: A Target Observation Perspective".ADVANCES IN ATMOSPHERIC SCIENCES 35.7(2018):853-867. |
入库方式： OAI收割
来源：海洋研究所
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