Analysis of Air Mean Temperature Anomalies by Using Horizontal Visibility Graphs

Area of Science:

• Climate Science
• Complex Systems Analysis
• Statistical Physics

Background:

• Recent decades show significant global warming trends.
• Climate variability research increasingly uses complex networks to analyze spatial relationships in climate data.
• Understanding how climate change impacts network properties of temperature data is crucial.

Purpose of the Study:

• To investigate changes in the topological properties of climate networks over several years.
• To apply the horizontal visibility graph (HVG) approach to analyze daily mean temperature anomalies.
• To determine if network structures are affected by rising global temperatures.

Main Methods:

• Utilized a 60-year dataset of daily mean temperature anomalies from stations across the Iberian Peninsula.
• Applied the horizontal visibility graph (HVG) method to transform time series data into complex networks.
• Analyzed key network metrics: average degree, degree distribution exponent, and global clustering coefficient.

Main Results:

• While annual mean temperature anomalies show a clear upward trend, the analyzed network topological properties (average degree, degree distribution exponent, global clustering coefficient) exhibit no significant trends.
• Results indicate that complex network structures and nonlinear features, such as weak correlations, remain stable despite increasing global temperatures.
• Consistent behavior was observed across different geographical locations within the Iberian Peninsula.

Conclusions:

• Complex network structures derived from temperature anomaly time series are robust to rising global temperatures.
• Network parameters effectively describe the intrinsic nature of climate signals, independent of long-term warming trends.
• Climate system's nonlinear dynamics, as represented by network topology, appear resilient to current climate change impacts.