scholarly journals Oceanic El-Niño wave dynamics and climate networks

2016 ◽  
Vol 18 (3) ◽  
pp. 033021 ◽  
Author(s):  
Yang Wang ◽  
Avi Gozolchiani ◽  
Yosef Ashkenazy ◽  
Shlomo Havlin
1995 ◽  
Vol 8 (10) ◽  
pp. 2415-2439 ◽  
Author(s):  
Edwin K. Schneider ◽  
Bohua Huang ◽  
J. Shukla
Keyword(s):  
El Niño ◽  
El Nino ◽  

2008 ◽  
Vol 100 (22) ◽  
Author(s):  
K. Yamasaki ◽  
A. Gozolchiani ◽  
S. Havlin
Keyword(s):  
El Niño ◽  
El Nino ◽  

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
RUPALI SONONE ◽  
RUBY SAHA ◽  
NEELIMA GUPTE
Keyword(s):  
El Niño ◽  
El Nino ◽  
La Niña ◽  
La Nina ◽  

2009 ◽  
Vol 179 ◽  
pp. 178-188 ◽  
Author(s):  
Kazuko Yamasaki ◽  
Avi Gozolchiani ◽  
Shlomo Havlin

2013 ◽  
Vol 102 (4) ◽  
pp. 48003 ◽  
Author(s):  
E. A. Martin ◽  
M. Paczuski ◽  
J. Davidsen
Keyword(s):  
El Niño ◽  
El Nino ◽  

2014 ◽  
Vol 21 (3) ◽  
pp. 617-631 ◽  
Author(s):  
J. I. Deza ◽  
C. Masoller ◽  
M. Barreiro

Abstract. The fact that the climate on the earth is a highly complex dynamical system is well-known. In the last few decades great deal of effort has been focused on understanding how climate phenomena in one geographical region affects the climate of other regions. Complex networks are a powerful framework for identifying climate interdependencies. To further exploit the knowledge of the links uncovered via the network analysis (for, e.g., improvements in prediction), a good understanding of the physical mechanisms underlying these links is required. Here we focus on understanding the role of atmospheric variability, and construct climate networks representing internal and forced variability using the output of an ensemble of AGCM runs. A main strength of our work is that we construct the networks using MIOP (mutual information computed from ordinal patterns), which allows the separation of intraseasonal, intra-annual and interannual timescales. This gives further insight to the analysis of climatological data. The connectivity of these networks allows us to assess the influence of two main indices, NINO3.4 – one of the indices used to describe ENSO (El Niño–Southern oscillation) – and of the North Atlantic Oscillation (NAO), by calculating the networks from time series where these indices were linearly removed. A main result of our analysis is that the connectivity of the forced variability network is heavily affected by "El Niño": removing the NINO3.4 index yields a general loss of connectivity; even teleconnections between regions far away from the equatorial Pacific Ocean are lost, suggesting that these regions are not directly linked, but rather, are indirectly interconnected via El Niño, particularly at interannual timescales. On the contrary, on the internal variability network – independent of sea surface temperature (SST) forcing – the links connecting the Labrador Sea with the rest of the world are found to be significantly affected by NAO, with a maximum at intra-annual timescales. While the strongest non-local links found are those forced by the ocean, the presence of teleconnections due to internal atmospheric variability is also shown.


2018 ◽  
Vol 29 (04) ◽  
pp. 1850033 ◽  
Author(s):  
Juan Carlos Graciosa ◽  
Marissa Pastor

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.


2020 ◽  
Vol 55 (4) ◽  
pp. 1-14
Author(s):  
K. Legal ◽  
P. Plantin
Keyword(s):  
El Niño ◽  

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