Percolation Phase Transition of Surface Air Temperature Networks: A new test bed for El Niño/La Niña simulations

Abstract

In this work, we studied the air-sea interaction over the tropical central eastern Pacific from a new perspective, climate network. The surface air temperatures over the tropical Pacific were constructed as a network, and the nodes within this network were linked if they have a similar temporal varying pattern. Using three different reanalysis datasets, we verified the percolation phase transition. That is, when the influences of El Niño/La Niña are strong enough to isolate more than 48% of the nodes, the network may abruptly be divided into many small pieces, indicating a change of the network state. This phenomenon was reproduced successfully by a coupled general circulation model, Flexible Global Ocean-Atmosphere-Land System Model Spectral Version 2, but another model, Flexible Global Ocean-Atmosphere-Land System Model Grid-point Version 2, failed. As both models have the same oceanic component, but are with different atmospheric components, the improperly used atmospheric component should be responsible for the missing of the percolation phase transition. Considering that this new phenomenon is only recently noticed, current state-of-the-art models may ignore this process and induce unrealistic simulations. Accordingly, percolation phase transition is proposed as a new test bed, which deserves more attention in the future.

On the Impacts of El Niño Events: A New Monitoring Approach Using Complex Network Analysis

“…While in the El Niño group (Figure a), the is divided into two parts that one is above 0.6 and the other one drops abruptly to a lower level (below 0.6) as long as the is larger than a critical point ( ). These results are in line with previous works (Hua et al, ; Lu et al, ), indicating that phase transitions in the SAT network indeed exist when the impacts from the underlying SSTA are strong enough.…”

“…To understand why the phase transition in 2015/2016 is different from those in 1982/1983 and 1997/1998, it is straightforward to look into the SAT network and study the node vulnerability (Hua et al, ; Lu et al, , ). is a quantity that measures how vulnerable a node is when the network is influenced.…”

“…Recent works have shown that the climate network method has advantages in revealing the structures of the climate systems (Donges et al, ; Radebach et al, ; Tsonis et al, ), predicting major climate events (Ludescher et al, , ; Boers et al, ), and estimating climate impacts (Fan et al, ; Yamasaki et al, ). Particularly, a recent work studied the phase transition phenomenon in the surface air temperature (SAT) network over the tropical Pacific and deduced that only when the SAT network collapses under the influences of the underlying sea surface temperature anomalies (SSTAs), the impacts of El Niño can be significantly transported to remote regions (Hua et al, ; Lu et al, , ). This means that the phase transition in the SAT network might be related to the remote impacts of El Niño.…”

Evaluation of ENSO simulations in CMIP5 models: A new perspective based on percolation phase transition in complex networks

“…For example, the electrical power networks in the real world can be damaged by malicious attacks or natural disasters, which may further result in abrupt power blackouts 35 , 36 . In climate science, influences of ENSO on its upper surface air temperature (SAT) network have been recently studied from the perspective of percolation 37 – 39 . It is found that, as long as the fraction of isolated nodes (nodes with no links with any other node of the network) in the SAT network is higher than a threshold P c = 0.48, the SAT network will abruptly be divided into many small isolated clusters, indicating a conversion of the network state 37 , 38 .…”

“…In climate science, influences of ENSO on its upper surface air temperature (SAT) network have been recently studied from the perspective of percolation 37 – 39 . It is found that, as long as the fraction of isolated nodes (nodes with no links with any other node of the network) in the SAT network is higher than a threshold P c = 0.48, the SAT network will abruptly be divided into many small isolated clusters, indicating a conversion of the network state 37 , 38 . This abrupt percolation phase transition means that the anomalous SST warming/cooling in the tropical central eastern Pacific has significantly changed the SAT field, which may further transport the influences of ENSO to remote regions via an atmospheric bridge.…”