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

Area of Science:

• Climate Science
• Complex Systems Analysis
• Time Series Forecasting

Background:

• The El Niño Southern Oscillation (ENSO) is a major climate pattern with significant socioeconomic and ecological impacts.
• Accurate long-lead-time ENSO forecasting is challenged by the "spring predictability barrier."

Purpose of the Study:

• To develop a novel method for improving long-lead-time ENSO magnitude forecasting.
• To assess the relationship between system complexity and El Niño event intensity.

Main Methods:

• Introduction of System Sample Entropy (SysSampEn) to quantify the complexity of temperature anomaly time series in the Niño 3.4 region.
• Analysis of near-surface air and sea surface temperature datasets to identify correlations between complexity and El Niño magnitude.

Main Results:

• A significant positive correlation was found between the previous year's SysSampEn (complexity) and the magnitude of subsequent El Niño events.
• The developed method achieved accurate forecasting of El Niño magnitude with a 1-year prediction horizon and a root-mean-square error of 0.23° C.
• Successfully forecasted the 2018 El Niño event as weak (1.11±0.23° C).

Conclusions:

• System complexity, measured by SysSampEn, offers a valuable tool for overcoming the ENSO spring predictability barrier.
• The framework provides a new approach for long-term ENSO magnitude prediction.
• The SysSampEn method has potential applications in assessing the complexity of other natural and engineered systems.