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Journal Article

Complexity-based approach for El Niño magnitude forecasting before the spring predictability barrier

Authors
/persons/resource/jun.meng

Meng,  Jun
Potsdam Institute for Climate Impact Research;

/persons/resource/Jingfang.Fan

Fan,  Jingfang
Potsdam Institute for Climate Impact Research;

/persons/resource/Josef.Ludescher

Ludescher,  Josef
Potsdam Institute for Climate Impact Research;

/persons/resource/agarwal

Agarwal,  Ankit
Potsdam Institute for Climate Impact Research;

Chen,  X.
External Organizations;

Bunde,  A.
External Organizations;

/persons/resource/Juergen.Kurths

Kurths,  Jürgen
Potsdam Institute for Climate Impact Research;

/persons/resource/emdir

Schellnhuber,  Hans Joachim
Potsdam Institute for Climate Impact Research;

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Fulltext (public)

23419oa.pdf
(Publisher version), 2MB

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Citation

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


Cite as: https://publications.pik-potsdam.de/pubman/item/item_23419
Abstract
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.