Percolation framework to describe El Ni\~no conditions

TL;DR

This paper introduces a percolation-based measure to analyze climate networks, which can serve as an early warning indicator for El Niño events, showing promise for improved forecasting.

Contribution

It develops a novel percolation framework and order parameter for climate networks, linking abrupt transitions to El Niño prediction and phase transition theory.

Findings

Order parameter transitions occur ~1 year before El Niño events

Method shows potential for reliable El Niño forecasting

Order parameter exhibits discontinuous features indicating phase transition behavior

Abstract

Complex networks have been used intensively to investigate the flow and dynamics of many natural systems including the climate system. Here, we develop a percolation based measure, the order parameter, to study and quantify climate networks. We find that abrupt transitions of the order parameter usually occur $\sim$1 year before El Ni\~{n}o ~ events, suggesting that they can be used as early warning precursors of El Ni\~{n}o. Using this method we analyze several reanalysis datasets and show the potential for good forecasting of El Ni\~{n}o. The percolation based order parameter exhibits discontinuous features, indicating possible relation to the first order phase transition mechanism.