Analysis of the structure of climate networks under El Niño and La Niña conditions

Juan Carlos Graciosa () and Marissa Pastor ()
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Juan Carlos Graciosa: Physics Department, University of San Carlos, Nasipit, Talamban Cebu City 6000, Philippines
Marissa Pastor: Physics Department, University of San Carlos, Nasipit, Talamban Cebu City 6000, Philippines

International Journal of Modern Physics C (IJMPC), 2018, vol. 29, issue 04, 1-14

Abstract: The El Niño-Southern Oscillation (ENSO) is the most important driver of natural climate variability and is characterized by anomalies in the sea surface temperatures (SST) over the tropical Pacific ocean. It has three phases: neutral, a warming phase or El Niño, and a cooling phase called La Niña. In this research, we modeled the climate under the three phases as a network and characterized its properties. We utilized the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) daily surface temperature reanalysis data from January 1950 to December 2016. A network associated to a month was created using the temperature spanning from the previous month to the succeeding month, for a total of three months worth of data for each network. Each site of the included data was a potential node in the network and the existence of links were determined by the strength of their relationship, which was based on mutual information. Interestingly, we found that climate networks exhibit small-world properties and these are found to be more prominent from October to April, coinciding with observations that El Niño occurrences peak from December to March. During these months, the temperature of a relatively large part of the Pacific ocean and its surrounding areas increase and the anomaly values become synchronized. This synchronization in the temperature anomalies forms links around the Pacific, increasing the clustering in the region and in effect, that of the entire network.

Keywords: El Niño-Southern Oscillation; climate networks; small-world networks; mutual information (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1142/S012918311850033X

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