气候变化领域集成服务门户(CCPortal): Complexity-based approach for El Niño magnitude forecasting before the spring predictability barrier

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DOI	10.1073/pnas.1917007117
	Complexity-based approach for El Niño magnitude forecasting before the spring predictability barrier
	Meng J.; Fan J.; Ludescher J.; Agarwal A.; Chen X.; Bunde A.; Kurths J.; Schellnhuber H.J.
发表日期	2020
ISSN	0027-8424
起始页码	177
结束页码	183
卷号	117 期号:1
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.
英文关键词	ENSO; Entropy; Forecasting; Spring barrier; System complexity
语种	英语
scopus关键词	air temperature; article; El Nino; entropy; forecasting; prediction; sea surface temperature; spring; time series analysis
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/161094
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
推荐引用方式 GB/T 7714	Meng J.,Fan J.,Ludescher J.,et al. Complexity-based approach for El Niño magnitude forecasting before the spring predictability barrier[J],2020,117(1).
APA	Meng J..,Fan J..,Ludescher J..,Agarwal A..,Chen X..,...&Schellnhuber H.J..(2020).Complexity-based approach for El Niño magnitude forecasting before the spring predictability barrier.Proceedings of the National Academy of Sciences of the United States of America,117(1).
MLA	Meng J.,et al."Complexity-based approach for El Niño magnitude forecasting before the spring predictability barrier".Proceedings of the National Academy of Sciences of the United States of America 117.1(2020).