scholarly journals Early warning of the Pacific Decadal Oscillation phase transition using complex network analysis

2021 ◽  
Author(s):  
Zhenghui Lu ◽  
Naiming Yuan ◽  
Qing Yang ◽  
Zhuguo Ma ◽  
Jürgen Kurths
Keyword(s):  
Phase Transition ◽  
Network Analysis ◽  
Complex Network ◽  
Pacific Decadal Oscillation ◽  
Early Warning ◽  
Oscillation Phase ◽  
Complex Network Analysis ◽  
The Pacific ◽  
Pacific Decadal Oscillation Phase

Detecting early warning signal of the Pacific Decadal Oscillation phase  transition using complex network analysis 

2021 ◽  
Author(s):  
Zhenghui Lu ◽  
Naiming Yuan ◽  
Qing Yang ◽  
Zhuguo Ma ◽  
Juergen Kurths
Keyword(s):  
Phase Transition ◽  
Network Analysis ◽  
Pacific Decadal Oscillation ◽  
Early Warning ◽  
Heat Content ◽  
Warning Signal ◽  
Oscillation Phase ◽  
The Pacific ◽  
Pacific Decadal Oscillation Phase ◽  
Efficient Prediction

<p><span>Obtaining an efficient prediction of the Pacific Decadal Oscillation (PDO) phase transition </span><span>is a worldwide challenge. Here, we employed the climate network analysis to uncover early </span><span>warning signals prior to a PDO phase transition. This way an examination of cooperative </span><span>behavior in the PDO region revealed an enhanced signal that propagated from the western </span><span>Pacific to the northwest coast of North America. The detection of this signal corresponds </span><span>very well to the time when the upper ocean heat content in the off-equatorial northwestern </span><span>tropical Pacific reaches a threshold, in which case a PDO phase transition may be expected </span><span>with the arising of the next El Niño/La Niña event. The objectively detected early warning </span><span>signal successfully forewarned all the six PDO phase transitions from the 1890s to 2000s, and </span><span>also underpinned the possible PDO phase transition around 2015, which may be triggered </span><span>by the strong El Niño event in 2015-2016.</span></p>

Early warning of the Pacific Decadal Oscillation phase transition using complex network analysis

2020 ◽  
Author(s):  
Zhenghui Lu ◽  
Naiming Yuan ◽  
Zhuguo Ma ◽  
Qing Yang ◽  
Juergen Kurths
Keyword(s):  
Phase Transition ◽  
Network Analysis ◽  
Pacific Decadal Oscillation ◽  
Early Warning ◽  
Global Climate ◽  
Kuroshio Extension ◽  
Human Society ◽  
Decadal Climate Variability ◽  
Oscillation Phase ◽  
The Pacific

<p>The different phases of the Pacific Decadal Oscillation (PDO) are a primary source of internal decadal climate variability which have distinct impacts on global climate and human society. However, obtaining a reliable prediction of the PDO phase transition is still challenging. Here, we employed the new technique of climate network analysis to uncover early warning signals prior to a PDO phase transition. An examination of cooperative behaviors in the PDO region revealed an enhanced signal that propagated from the western Pacific, passed through the Kuroshio extension (KE) and the subtropical oceanic frontal (STF) regions, and finally reached the northwest coast of the Americas. This signal captured all six of the PDO phase transitions from the 1890s to 2000s, with a warning time of 6.5±2.3 years in advance. It also underpinned the possible PDO phase transition at years around 2015, which may be triggered by the strong El Niño in 2014-2016.</p>

scholarly journals Detecting Tourism Typologies of Regional Destinations Based on Their Spatio-Temporal and Socioeconomic Performance: A Correlation-Based Complex Network Approach for the Case of Greece

2021 ◽  
Vol 2 (1) ◽  
pp. 113-139
Author(s):  
Dimitrios Tsiotas ◽  
Thomas Krabokoukis ◽  
Serafeim Polyzos
Keyword(s):  
Network Analysis ◽  
Complex Network ◽  
Community Detection ◽  
Optimization Algorithm ◽  
Spatial Dimension ◽  
Economic Dimension ◽  
Complex Network Analysis ◽  
Time Period ◽  
Time Patterns ◽  
Spatio Temporal

Within the context that tourism-seasonality is a composite phenomenon described by temporal, geographical, and socio-economic aspects, this article develops a multilevel method for studying time patterns of tourism-seasonality in conjunction with its spatial dimension and socio-economic dimension. The study aims to classify the temporal patterns of seasonality into regional groups and to configure distinguishable seasonal profiles facilitating tourism policy and development. The study applies a multilevel pattern recognition approach incorporating time-series assessment, correlation, and complex network analysis based on community detection with the use of the modularity optimization algorithm, on data of overnight-stays recorded for the time-period 1998–2018. The analysis reveals four groups of seasonality, which are described by distinct seasonal, geographical, and socio-economic profiles. Overall, the analysis supports multidisciplinary and synthetic research in the modeling of tourism research and promotes complex network analysis in the study of socio-economic systems, by providing insights into the physical conceptualization that the community detection based on the modularity optimization algorithm can enjoy to the real-world applications.

Complex Network Analysis of Wire-Mesh Sensor Measurements for Characterizing Vertical Gas–Liquid Two-Phase Flows

2020 ◽  
Vol 67 (6) ◽  
pp. 1134-1138 ◽  
Author(s):  
Zhongke Gao ◽  
Hongtao Wang ◽  
Weidong Dang ◽  
Yongqiang Li ◽  
Xiaolin Hong ◽  
...  
Keyword(s):  
Network Analysis ◽  
Complex Network ◽  
Wire Mesh ◽  
Two Phase Flows ◽  
Two Phase ◽  
Complex Network Analysis

Combining K-means method and complex network analysis to evaluate city mobility

2016 ◽  
Author(s):  
Emerson Luiz Chiesse da Silva ◽  
Marcelo De Oliveira Rosa ◽  
Keiko Veronica Ono Fonseca ◽  
Ricardo Luders ◽  
Nadia Puchaslki Kozievitch
Keyword(s):  
Network Analysis ◽  
Complex Network ◽  
Complex Network Analysis

scholarly journals Structure and patterns of the international rare earths trade: A complex network analysis

2018 ◽  
Vol 55 ◽  
pp. 133-142 ◽  
Author(s):  
Wenyu Hou ◽  
Huifang Liu ◽  
Hui Wang ◽  
Fengyang Wu
Keyword(s):  
Network Analysis ◽  
Complex Network ◽  
Rare Earths ◽  
Complex Network Analysis
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