• DOI:10.1073/pnas.1701214114
• Corpus ID: 21185854

Network analysis reveals strongly localized impacts of El Niño

@article{Fan2017NetworkAR,
  title={Network analysis reveals strongly localized impacts of El Ni{\~n}o},
  author={Jingfang Fan and Jun Meng and Yosef Ashkenazy and Shlomo Havlin and Hans Joachim Schellnhuber},
  journal={Proceedings of the National Academy of Sciences},
  year={2017},
  volume={114},
  pages={7543 - 7548}
}

• Jingfang Fan, Jun Meng, +2 authors H. Schellnhuber
• Published 3 July 2017
• Environmental Science
• Proceedings of the National Academy of Sciences

Significance El Niño, one of the strongest climatic phenomena on interannual time scales, affects the climate system and is associated with natural disasters and serious social conflicts. Here, using network theory, we construct a directed and weighted climate network to study the global impacts of El Niño and La Niña. The constructed climate network enables the identification of the regions that are most drastically affected by specific El Niño/La Niña events. Our analysis indicates that the effect of the El Niño basin on worldwide regions is more localized and stronger during El Niño events compared with normal times. Climatic conditions influence the culture and economy of societies and the performance of economies. Specifically, El Niño as an extreme climate event is known to have notable effects on health, agriculture, industry, and conflict. Here, we construct directed and weighted climate networks based on near-surface air temperature to investigate the global impacts of El Niño and La Niña. We find that regions that are characterized by higher positive/negative network “in”-weighted links are exhibiting stronger correlations with the El Niño basin and are warmer/cooler during El Niño/La Niña periods. In contrast to non-El Niño periods, these stronger in-weighted activities are found to be concentrated in very localized areas, whereas a large fraction of the globe is not influenced by the events. The regions of localized activity vary from one El Niño (La Niña) event to another; still, some El Niño (La Niña) events are more similar to each other. We quantify this similarity using network community structure. The results and methodology reported here may be used to improve the understanding and prediction of El Niño/La Niña events and also may be applied in the investigation of other climate variables.