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

TL;DR

This paper introduces a complexity-based analysis tool, SysSampEn, to forecast El Nino magnitude up to one year in advance by correlating system disorder with El Nino strength, overcoming the spring predictability barrier.

Contribution

The paper develops a novel complexity measure, SysSampEn, for long-term El Nino forecasting, demonstrating high accuracy and potential application to other complex systems.

Findings

Strong correlation between system complexity and El Nino magnitude.

Forecast accuracy with RMSE of 0.23°C for a one-year horizon.

Method successfully predicted the 2018 El Nino as weak.

Abstract

The El Nino 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 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 to forecast the magnitude of an El Nino with a prediction horizon of 1 year and high accuracy (i.e., Root Mean Square Error $=0.23^\circ C$ for the average of the individual datasets forecasts). For the on-going 2018 El Nino event, our method forecasts a weak El Nino with a magnitude of $1.11\pm 0.23^\circ C$. Our framework presented here not only facilitates a 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.