Inferring long memory processes in the climate network via ordinal pattern analysis

TL;DR

This paper employs ordinal pattern analysis to construct climate networks from surface air temperature data, revealing oscillatory behaviors and memory processes across multiple time scales.

Contribution

It introduces a novel application of symbolic ordinal pattern analysis to infer long and short-term memory in climate networks.

Findings

Identifies oscillatory patterns related to intraseasonal oscillations.

Detects seasonal-to-interannual variability linked to El Niño.

Reveals that SAT variability is governed by recurring oscillatory patterns.

Abstract

We use ordinal patterns and symbolic analysis to construct global climate networks and uncover long and short term memory processes. The data analyzed is the monthly averaged surface air temperature (SAT field) and the results suggest that the time variability of the SAT field is determined by patterns of oscillatory behavior that repeat from time to time, with a periodicity related to intraseasonal oscillations and to El Ni\~{n}o on seasonal-to-interannual time scales.