globalchange  > 气候变化与战略
DOI: 10.1073/pnas.1917007117
论文题名:
Complexity-based approach for El Niño magnitude forecasting before the spring predictability barrier
作者: Meng J.; Fan J.; Ludescher J.; Agarwal A.; Chen X.; Bunde A.; Kurths J.; Schellnhuber H.J.
刊名: Proceedings of the National Academy of Sciences of the United States of America
ISSN: 0027-8424
出版年: 2020
卷: 117, 期:1
起始页码: 177
结束页码: 183
语种: 英语
英文关键词: ENSO ; Entropy ; Forecasting ; Spring barrier ; System complexity
Scopus关键词: air temperature ; article ; El Nino ; entropy ; forecasting ; prediction ; sea surface temperature ; spring ; time series analysis
英文摘要: The El Niño 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 Niño 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 Niño and the previous calendar year’s SysSampEn (complexity). We show that this correlation allows us to forecast the magnitude of an El Niño with a prediction horizon of 1 y and high accuracy (i.e., root-mean-square error = 0.23◦ C for the average of the individual datasets forecasts). For the 2018 El Niño event, our method forecasted a weak El Niño with a magnitude of 1.11 ± 0.23◦ C. Our framework presented here not only facilitates long-term forecasting of the El Niño magnitude but can potentially also be used as a measure for the complexity of other natural or engineering complex systems. © 2020 National Academy of Sciences. All rights reserved.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/164369
Appears in Collections:气候变化与战略

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作者单位: Meng, J., Potsdam Institute for Climate Impact Research, Potsdam, 14412, Germany; Fan, J., Potsdam Institute for Climate Impact Research, Potsdam, 14412, Germany, School of Systems Science, Beijing Normal University, Beijing, 100875, China; Ludescher, J., Potsdam Institute for Climate Impact Research, Potsdam, 14412, Germany; Agarwal, A., Potsdam Institute for Climate Impact Research, Potsdam, 14412, Germany, Department of Hydrology, Indian Institute of Technology Roorkee, Roorkee, 247667, India, Hydrology, GFZ German Research Centre for Geosciences, Potsdam, 14473, Germany; Chen, X., School of Systems Science, Beijing Normal University, Beijing, 100875, China; Bunde, A., Institut für Theoretische Physik, Justus-Liebig-Universität Giessen, Giessen, 35392, Germany; Kurths, J., Potsdam Institute for Climate Impact Research, Potsdam, 14412, Germany, Department of Physics, Humboldt University, Berlin, 10099, Germany; Schellnhuber, H.J., Potsdam Institute for Climate Impact Research, Potsdam, 14412, Germany

Recommended Citation:
Meng J.,Fan J.,Ludescher J.,et al. Complexity-based approach for El Niño magnitude forecasting before the spring predictability barrier[J]. Proceedings of the National Academy of Sciences of the United States of America,2020-01-01,117(1)
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