EGU21-339
https://doi.org/10.5194/egusphere-egu21-339
EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

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

Jun Meng1,2
Jun Meng
  • 1Beijing University of Posts and Telecommunications (junmeng@bupt.edu.cn)
  • 2Potsdam Institute for Climate Impact Research, Germany (meng@pik-potsdam.de)

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=0.25C for the average of the individual datasets forecasts). For the recent two 2018 and 2019 El Niño events, our method forecasted weak El Niños with magnitudes of 1.11±0.23C and 0.69±0.25C, both within one root-mean-square error comparing to the observed magnitudes, i.e. 0.9C and 0.6C. 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.

How to cite: Meng, J.: Complexity-based approach for El Nino magnitude forecasting before the spring predictability barrier, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-339, https://doi.org/10.5194/egusphere-egu21-339, 2020.

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