EGU2020-2378
https://doi.org/10.5194/egusphere-egu2020-2378
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

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

Jun Meng1, Jingfang Fan1, Josef Ludescher1, Ankit Agarwala1, Xiaosong Chen2, Armin Bunde3, Juergen Kurths1, and Hans Joachim Schellnhuber1
Jun Meng et al.
  • 1Potsdam Institute for Climate Impact Research, Germany (meng@pik-potsdam.de)
  • 2School of Systems Science, Beijing Normal University
  • 3University of Giessen

The El Niño Southern Oscillation (ENSO) is one of the most prominent interannual climate phenomena. An 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” (SPB) remains a great challenge for long (over 6-month) lead-time forecasting. To overcome this barrier, here we develop an analysis tool, the 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 to forecast the magnitude of an El Niño with a prediction horizon of 1 year 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 forecasts a weak El Niño with a magnitude of 1.11±0.23°C.  Our framework presented here not only facilitates a 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.

How to cite: Meng, J., Fan, J., Ludescher, J., Agarwala, A., Chen, X., Bunde, A., Kurths, J., and Schellnhuber, H. J.: Complexity-based approach for El Niño magnitude forecasting before the spring predictability barrier, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2378, https://doi.org/10.5194/egusphere-egu2020-2378, 2020

This abstract will not be presented.