Improved El Ni\~no-Forecasting by Cooperativity Detection

TL;DR

This paper presents a novel network-based method for El Niño forecasting that detects emerging teleconnections, extending the reliable prediction window from six to twelve months with high accuracy.

Contribution

It introduces a new approach using network methods to identify cooperative teleconnection modes, significantly improving early warning times for El Niño events.

Findings

Achieves over 0.5 hit rate in predictions.

False-alarm rate maintained below 0.1.

Doubles the early-warning period from six to twelve months.

Abstract

Although anomalous episodical warming of the eastern equatorial Pacific, dubbed El Ni\~no by Peruvian fishermen, has major (and occasionally devastating) impacts around the globe, robust forecasting is still limited to about six months ahead. A significant extension of the pre-warming time would be instrumental for avoiding some of the worst damages such as harvest failures in developing countries. Here we introduce a novel avenue towards El Ni\~no-prediction based on network methods inspecting emerging teleconnections. Our approach starts from the evidence that a large-scale cooperative mode - linking the El Ni\~no-basin (equatorial Pacific corridor) and the rest of the ocean - builds up in the calendar year before the warming event. On this basis, we can develop an efficient 12 months-forecasting scheme, i.e., achieve some doubling of the early-warning period. Our method is based on high-quality observational data as available since 1950 and yields hit rates above 0.5, while false-alarm rates are below 0.1.