Analysis of dynamical statistical characteristics of temperature correlation networks of 1—30d scales
Year: 2008
Type: article
Abstract: Based on reanalysis data of National Center of Environment Prediction of America， temperature correlation networks of different time scales are constructed and their dynamical statistical properties are also analyzed. Results show that temperature correlation networks of 1 and 30d belong to small world and semi-globally coupled network， respectively. The time scale condition is 11—12d for networks to shift from one type to the other. For 1 d scale， the total number of links at each geographic location is semi-symmetrical between north and south hemisphere， the equatorial nodes have a larger number of connections in three regions: the equatorial Indian ocean， equatorial east Pacific ocean and equatorialeast Atlantic ocean. Sub-system networks of both mid-latitude and low-latitude belong to the small world network， while the accumulative degree distribution of low-latitude follows the exponentical and Gaussian distributions and that of mid-latitude follows the semi-Gaussian distribution. The range of correlation condition value for temperature network belonging to small world network is 0.2—0.9. Meanwhile for the 30 d scale， ocean nodes show a larger number of connections than the land nodes. The ocean and the land sub-system networks belong to the semi-globally coupled and the small world networks， respectively. The range of correlation condition value for temperature network belonging to semi-globally coupled network is 0—0.6.
Source: Acta Physica Sinica
DOI: 10.7498/aps.57.5351
Authors: Gong Zhi-Qiang, Zhou Lei, Zhi Rong, Feng Guo-Lin, (1)国家气候中心，中国气象局气候研究开放实验室，北京 100081；兰州大学大气科学学院，兰州 730000; (2)扬州大学物理科学与技术学院，扬州 225002; (3)扬州大学物理科学与技术学院，扬州 225002；国家气候中心，中国气象局气候研究开放实验室，北京 100081; (4)中国科学院大气物理研究所东亚区域气候-环境重点实验室，北京 100029；国家气候中心，中国气象局气候研究开放实验室，北京 100081
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
SDG: Climate action
Open Access status: hybrid
OpenAlex ID: W770099552