Mapping atmospheric waves and unveiling phase coherent structures in a global surface air temperature reanalysis...

Area of Science:

• * Earth System Science
• * Climate Dynamics
• * Signal Processing

Background:

• * Analyzing complex systems requires identifying genuine interactions within empirical signals.
• * Surface air temperature (SAT) data presents a rich dataset for exploring global climate dynamics.
• * Traditional methods often struggle to disentangle processes acting on multiple time scales.

Purpose of the Study:

• * To apply Hilbert analysis to daily surface air temperature (SAT) data.
• * To investigate the phase, instantaneous frequency, and amplitude dynamics of SAT seasonal cycles globally.
• * To differentiate climatic processes and track atmospheric waves by analyzing signal dynamics.

Main Methods:

• * Hilbert analysis applied to a global daily surface air temperature (SAT) dataset.
• * Extraction of phase, instantaneous frequency, and amplitude information from SAT seasonal cycles.
• * Comparative analysis of dynamics across different geographical zones.

Main Results:

• * Coherent seasonality observed across large geographical regions based on phase dynamics.
• * Wave patterns with alternating positive and negative correlations identified in instantaneous frequencies.
• * Large-scale structures detected in amplitude dynamics, influenced by both short and long time-scale processes.

Conclusions:

• * Hilbert analysis effectively disentangles climatic processes and tracks planetary atmospheric waves.
• * Instantaneous frequency dynamics are primarily governed by fast processes, while amplitude is affected by both fast and slow processes.
• * This approach offers a general strategy for uncovering interactions in complex oscillatory signals across various time scales.