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  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. (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 (PNAS), 117, 1, 177-183.
https://doi.org/10.1073/pnas.1917007117

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???ViewItemFull_lblCiteItemAs???: https://publications.pik-potsdam.de/pubman/item/item_23419 ???ViewItemFull_lblCiteItemVersionAs???: https://publications.pik-potsdam.de/pubman/item/item_23419_5
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 ???ViewItemFull_lblCreators???:
Meng, Jun1, ???ENUM_CREATORROLE_AUTHOR???           
Fan, Jingfang1, ???ENUM_CREATORROLE_AUTHOR???           
Ludescher, Josef1, ???ENUM_CREATORROLE_AUTHOR???           
Agarwal, Ankit1, ???ENUM_CREATORROLE_AUTHOR???           
Chen, X.2, ???ENUM_CREATORROLE_AUTHOR???
Bunde, A.2, ???ENUM_CREATORROLE_AUTHOR???
Kurths, Jürgen1, ???ENUM_CREATORROLE_AUTHOR???           
Schellnhuber, Hans Joachim1, ???ENUM_CREATORROLE_AUTHOR???                 
???ViewItemFull_lblAffiliations???:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

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 ???ViewItemFull_lblAbstract???: 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.

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 ???ViewItemFull_lblDates???: 2020
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 ???ViewItemFull_lblIdentifiers???: ???ENUM_IDENTIFIERTYPE_DOI???: 10.1073/pnas.1917007117
???ENUM_IDENTIFIERTYPE_PIKDOMAIN???: RD4 - Complexity Science
???ENUM_IDENTIFIERTYPE_PIKDOMAIN???: RD1 - Earth System Analysis
???ENUM_IDENTIFIERTYPE_PIKDOMAIN???: Director Emeritus / Executive Staff / Science & Society
???ENUM_IDENTIFIERTYPE_EDOC???: 8652
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???ENUM_IDENTIFIERTYPE_ORGANISATIONALK???: RD1 - Earth System Analysis
???ENUM_IDENTIFIERTYPE_ORGANISATIONALK???: RD4 - Complexity Science
???ENUM_IDENTIFIERTYPE_ORGANISATIONALK???: Director Emeritus Schellnhuber
???ENUM_IDENTIFIERTYPE_WORKINGGROUP???: Terrestrial Safe Operating Space
???ENUM_IDENTIFIERTYPE_WORKINGGROUP???: Network- and machine-learning-based prediction of extreme events
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???ViewItemFull_lblSourceTitle???: Proceedings of the National Academy of Sciences of the United States of America (PNAS)
???ViewItemFull_lblSourceGenre???: ???ENUM_GENRE_JOURNAL???, SCI, Scopus, p3
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???ENUM_IDENTIFIERTYPE_PUBLISHER???: National Academy of Sciences (NAS)