资源环境科技发展态势分析平台

The El Nino 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 Nino 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 Nino and the previous calendar year's SysSampEn (complexity). We show that this correlation allows us to forecast the magnitude of an El Nino with a prediction horizon of 1 y and high accuracy (i.e., root-mean-square error = 0.23 degrees C for the average of the individual datasets forecasts). For the 2018 El Nino event, our method forecasted a weak El Nino with a magnitude of 1.11 +/- 0.23 degrees C. Our framework presented here not only facilitates long-term forecasting of the El Nino magnitude but can potentially also be used as a measure for the complexity of other natural or engineering complex systems.