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.
@article{arxiv.1010.1564,
title = {Inferring long memory processes in the climate network via ordinal pattern analysis},
author = {Marcelo Barreiro and Arturo C. Marti and Cristina Masoller},
journal= {arXiv preprint arXiv:1010.1564},
year = {2015}
}
Comments
10 pages, 13 figures Enlarged version, new sections and figures. Accepted in Chaos