Chaotic Hamiltonian dynamics of surface air temperature on daily to intraseasonal time scales

TL;DR

This study reveals that surface air temperature fluctuations across various locations exhibit Hamiltonian distributed chaos, allowing for predictable dynamics up to a fundamental period, with implications for climate variability understanding.

Contribution

It demonstrates the universal Hamiltonian chaos behavior in surface air temperature data across diverse climates and links it to practical predictability.

Findings

Power spectra show universal Hamiltonian chaos behavior.

Predictability extends up to the fundamental period of chaos.

Surface temperature dynamics are linked to distributed chaos theory.

Abstract

The surface air temperature daily records at the land-based locations with different climate conditions (from Arctic to Patagonia) have been studied on the daily to intraseasonal time scales (low frequency annual and seasonal variations have been removed by subtracting a wavelet regression from the daily records). It is shown that the power spectra of the daily time series exhibit a universal behaviour corresponding to the Hamiltonian distributed chaos. Global average temperature fluctuations (land-based data) and the tropical Pacific sea surface temperature fluctuations (El Ni\~no/La Ni\~na phenomenon) have been also considered in this context. It is shown that the practical smooth predictability for the surface air temperature dynamics is possible at least up to the fundamental (pumping) period of the distributed chaos.