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DOI | 10.1073/pnas.1917007117 |
Complexity-based approach for El Nino magnitude forecasting before the spring predictability barrier | |
Meng, Jun1; Fan, Jingfang1,2; Ludescher, Josef1; Agarwal, Ankit1,3,4; Chen, Xiaosong2; Bunde, Armin5; Kurths, Juergen1,6; Schellnhuber, Hans Joachim1 | |
2020-01-07 | |
发表期刊 | PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
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ISSN | 0027-8424 |
出版年 | 2020 |
卷号 | 117期号:1页码:177-183 |
文章类型 | Article |
语种 | 英语 |
国家 | Germany; Peoples R China; India |
英文摘要 | The El Nino Southern Oscillation (ENSO) is one of the most prominent interannual climate phenomena. Early and reliable ENSO forecasting remains a crucial goal, due to its serious implications for economy, society, and ecosystem. Despite the development of various dynamical and statistical prediction models in the recent decades, the "spring predictability barrier" remains a great challenge for long-lead-time (over 6 mo) forecasting. To overcome this barrier, here we develop an analysis tool, System Sample Entropy (SysSampEn), to measure the complexity (disorder) of the system composed of temperature anomaly time series in the Nino 3.4 region. When applying this tool to several near-surface air temperature and sea surface temperature datasets, we find that in all datasets a strong positive correlation exists between the magnitude of El Nino and the previous calendar year's SysSampEn (complexity). We show that this correlation allows us to forecast the magnitude of an El Nino with a prediction horizon of 1 y and high accuracy (i.e., root-mean-square error = 0.23 degrees C for the average of the individual datasets forecasts). For the 2018 El Nino event, our method forecasted a weak El Nino with a magnitude of 1.11 +/- 0.23 degrees C. Our framework presented here not only facilitates long-term forecasting of the El Nino magnitude but can potentially also be used as a measure for the complexity of other natural or engineering complex systems. |
英文关键词 | ENSO system complexity entropy spring barrier forecasting |
领域 | 地球科学 ; 气候变化 ; 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000506001200032 |
WOS关键词 | OCEAN RECHARGE PARADIGM ; APPROXIMATE ENTROPY ; ENSO ; CLIMATE ; PREDICTION ; MONSOON ; MODEL ; ASSIMILATION ; SYSTEM ; SKILL |
WOS类目 | Multidisciplinary Sciences |
WOS研究方向 | Science & Technology - Other Topics |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/249665 |
专题 | 地球科学 |
作者单位 | 1.Potsdam Inst Climate Impact Res, D-14412 Potsdam, Germany; 2.Beijing Normal Univ, Sch Syst Sci, Beijing 100875, Peoples R China; 3.Indian Inst Technol Roorkee, Dept Hydrol, Roorkee 247667, Uttar Pradesh, India; 4.GFZ German Res Ctr Geosci, Sect Hydrol 44, D-14473 Potsdam, Germany; 5.Justus Liebig Univ Giessen, Inst Theoret Phys, D-35392 Giessen, Germany; 6.Humboldt Univ, Dept Phys, D-10099 Berlin, Germany |
推荐引用方式 GB/T 7714 | Meng, Jun,Fan, Jingfang,Ludescher, Josef,et al. Complexity-based approach for El Nino magnitude forecasting before the spring predictability barrier[J]. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA,2020,117(1):177-183. |
APA | Meng, Jun.,Fan, Jingfang.,Ludescher, Josef.,Agarwal, Ankit.,Chen, Xiaosong.,...&Schellnhuber, Hans Joachim.(2020).Complexity-based approach for El Nino magnitude forecasting before the spring predictability barrier.PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA,117(1),177-183. |
MLA | Meng, Jun,et al."Complexity-based approach for El Nino magnitude forecasting before the spring predictability barrier".PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 117.1(2020):177-183. |
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