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.Comment: 10 pages, 13 figures Enlarged version, new sections and figures.
  Accepted in Chao

Barreiro, Marcelo

Marti, Arturo C.

Masoller, Cristina

Chaos An Interdisciplinary Journal of Nonlinear Science

English

arXiv

We analyze climatological data from a complex networks perspective, using techniques of nonlinear time series

symbolic analysis. Specifically, we employ ordinal patterns and binary representations to analyze monthly averaged surface air temperature (SAT) anomalies. By computing the mutual information of the time series in

regular grid points covering the Earth’s surface and then performing global thresholding, we construct climate networks

that uncover short-term memory processes, as well as long ones (5–6 yr). Our results suggest that the time variability of the SAT anomalies is determined by patterns of oscillatory behavior that repeat from time to

time with a periodicity related to intraseasonal variations and to El Niño on seasonal to interannual time scales. The present work is located at the triple intersection of three highly active interdisciplinary research fields in

nonlinear science: symbolic methods for nonlinear time series analysis, network theory, and non linear processes in the earth climate. While a lot of effort is being done in order to improve our understanding of natural complex systems, with many different methods for mapping time

series to network representations being investigated and employed in complex systems such as the human brain, our work is the first one aimed at characterizing the

global climate network in terms of oscillatory patterns that tend to repeat from time to time, with various time

scales. By mapping these processes into a global network, using ordinal patterns and binary representations, we find that the structure of the network changes drastically

at different time scales.Peer Reviewe

Marti, Arturo

Masoller Alonso, Cristina

UPCommons

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

We analyze climatological data from a complex networks perspective, using techniques of nonlinear time series
symbolic analysis. Specifically, we employ ordinal patterns and binary representations to analyze monthly averaged surface air temperature (SAT) anomalies. By computing the mutual information of the time series in
regular grid points covering the Earth’s surface and then performing global thresholding, we construct climate networks
that uncover short-term memory processes, as well as long ones (5–6 yr). Our results suggest that the time variability of the SAT anomalies is determined by patterns of oscillatory behavior that repeat from time to
time with a periodicity related to intraseasonal variations and to El Niño on seasonal to interannual time scales. The present work is located at the triple intersection of three highly active interdisciplinary research fields in
nonlinear science: symbolic methods for nonlinear time series analysis, network theory, and non linear processes in the earth climate. While a lot of effort is being done in order to improve our understanding of natural complex systems, with many different methods for mapping time
series to network representations being investigated and employed in complex systems such as the human brain, our work is the first one aimed at characterizing the
global climate network in terms of oscillatory patterns that tend to repeat from time to time, with various time
scales. By mapping these processes into a global network, using ordinal patterns and binary representations, we find that the structure of the network changes drastically
at different time scales.Peer ReviewedPostprint (published version

UPCommons. Portal del coneixement obert de la UPC

We use ordinal patterns and symbolic analysis to construct global climate networks and uncover long- and short-term memory processes. Data analyzed are 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 Niño on seasonal-to-interannual time scales

Martí, Arturo

LAReferencia - Red Federada de Repositorios Institucionales de Publicaciones Científicas Latinoamericanas

Marcelo Barreiro

Arturo C. Marti

Cristina Masoller

Crossref

https://www.colibri.udelar.edu.uy/jspui/bitstream/20.500.12008/34197/1/1010.1564v3.pdf

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

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UPCommons

UPCommons. Portal del coneixement obert de la UPC

LAReferencia - Red Federada de Repositorios Institucionales de Publicaciones Científicas Latinoamericanas

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