Distinguishing the effects of internal and forced atmospheric variability in climate networks
The fact that the climate on the earth is a highly complex dynamical system is well-known. In the last few decades great deal of effort has been focused on understanding how climate phenomena in one geographical region affects the climate of other regions. Complex networks are a powerful framework f...
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ftdoajarticles:oai:doaj.org/article:6603ca9e175d4fbca9a5cc9d85903aa6 2023-05-15T17:06:13+02:00 Distinguishing the effects of internal and forced atmospheric variability in climate networks J. I. Deza C. Masoller M. Barreiro 2014-05-01T00:00:00Z https://doi.org/10.5194/npg-21-617-2014 https://doaj.org/article/6603ca9e175d4fbca9a5cc9d85903aa6 EN eng Copernicus Publications http://www.nonlin-processes-geophys.net/21/617/2014/npg-21-617-2014.pdf https://doaj.org/toc/1023-5809 https://doaj.org/toc/1607-7946 1023-5809 1607-7946 doi:10.5194/npg-21-617-2014 https://doaj.org/article/6603ca9e175d4fbca9a5cc9d85903aa6 Nonlinear Processes in Geophysics, Vol 21, Iss 3, Pp 617-631 (2014) Science Q Physics QC1-999 Geophysics. Cosmic physics QC801-809 article 2014 ftdoajarticles https://doi.org/10.5194/npg-21-617-2014 2022-12-31T02:47:11Z The fact that the climate on the earth is a highly complex dynamical system is well-known. In the last few decades great deal of effort has been focused on understanding how climate phenomena in one geographical region affects the climate of other regions. Complex networks are a powerful framework for identifying climate interdependencies. To further exploit the knowledge of the links uncovered via the network analysis (for, e.g., improvements in prediction), a good understanding of the physical mechanisms underlying these links is required. Here we focus on understanding the role of atmospheric variability, and construct climate networks representing internal and forced variability using the output of an ensemble of AGCM runs. A main strength of our work is that we construct the networks using MIOP (mutual information computed from ordinal patterns), which allows the separation of intraseasonal, intra-annual and interannual timescales. This gives further insight to the analysis of climatological data. The connectivity of these networks allows us to assess the influence of two main indices, NINO3.4 – one of the indices used to describe ENSO (El Niño–Southern oscillation) – and of the North Atlantic Oscillation (NAO), by calculating the networks from time series where these indices were linearly removed. A main result of our analysis is that the connectivity of the forced variability network is heavily affected by "El Niño": removing the NINO3.4 index yields a general loss of connectivity; even teleconnections between regions far away from the equatorial Pacific Ocean are lost, suggesting that these regions are not directly linked, but rather, are indirectly interconnected via El Niño, particularly at interannual timescales. On the contrary, on the internal variability network – independent of sea surface temperature (SST) forcing – the links connecting the Labrador Sea with the rest of the world are found to be significantly affected by NAO, with a maximum at intra-annual timescales. While the strongest ... Article in Journal/Newspaper Labrador Sea North Atlantic North Atlantic oscillation Directory of Open Access Journals: DOAJ Articles Pacific Nonlinear Processes in Geophysics 21 3 617 631 |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
Science Q Physics QC1-999 Geophysics. Cosmic physics QC801-809 |
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Science Q Physics QC1-999 Geophysics. Cosmic physics QC801-809 J. I. Deza C. Masoller M. Barreiro Distinguishing the effects of internal and forced atmospheric variability in climate networks |
topic_facet |
Science Q Physics QC1-999 Geophysics. Cosmic physics QC801-809 |
description |
The fact that the climate on the earth is a highly complex dynamical system is well-known. In the last few decades great deal of effort has been focused on understanding how climate phenomena in one geographical region affects the climate of other regions. Complex networks are a powerful framework for identifying climate interdependencies. To further exploit the knowledge of the links uncovered via the network analysis (for, e.g., improvements in prediction), a good understanding of the physical mechanisms underlying these links is required. Here we focus on understanding the role of atmospheric variability, and construct climate networks representing internal and forced variability using the output of an ensemble of AGCM runs. A main strength of our work is that we construct the networks using MIOP (mutual information computed from ordinal patterns), which allows the separation of intraseasonal, intra-annual and interannual timescales. This gives further insight to the analysis of climatological data. The connectivity of these networks allows us to assess the influence of two main indices, NINO3.4 – one of the indices used to describe ENSO (El Niño–Southern oscillation) – and of the North Atlantic Oscillation (NAO), by calculating the networks from time series where these indices were linearly removed. A main result of our analysis is that the connectivity of the forced variability network is heavily affected by "El Niño": removing the NINO3.4 index yields a general loss of connectivity; even teleconnections between regions far away from the equatorial Pacific Ocean are lost, suggesting that these regions are not directly linked, but rather, are indirectly interconnected via El Niño, particularly at interannual timescales. On the contrary, on the internal variability network – independent of sea surface temperature (SST) forcing – the links connecting the Labrador Sea with the rest of the world are found to be significantly affected by NAO, with a maximum at intra-annual timescales. While the strongest ... |
format |
Article in Journal/Newspaper |
author |
J. I. Deza C. Masoller M. Barreiro |
author_facet |
J. I. Deza C. Masoller M. Barreiro |
author_sort |
J. I. Deza |
title |
Distinguishing the effects of internal and forced atmospheric variability in climate networks |
title_short |
Distinguishing the effects of internal and forced atmospheric variability in climate networks |
title_full |
Distinguishing the effects of internal and forced atmospheric variability in climate networks |
title_fullStr |
Distinguishing the effects of internal and forced atmospheric variability in climate networks |
title_full_unstemmed |
Distinguishing the effects of internal and forced atmospheric variability in climate networks |
title_sort |
distinguishing the effects of internal and forced atmospheric variability in climate networks |
publisher |
Copernicus Publications |
publishDate |
2014 |
url |
https://doi.org/10.5194/npg-21-617-2014 https://doaj.org/article/6603ca9e175d4fbca9a5cc9d85903aa6 |
geographic |
Pacific |
geographic_facet |
Pacific |
genre |
Labrador Sea North Atlantic North Atlantic oscillation |
genre_facet |
Labrador Sea North Atlantic North Atlantic oscillation |
op_source |
Nonlinear Processes in Geophysics, Vol 21, Iss 3, Pp 617-631 (2014) |
op_relation |
http://www.nonlin-processes-geophys.net/21/617/2014/npg-21-617-2014.pdf https://doaj.org/toc/1023-5809 https://doaj.org/toc/1607-7946 1023-5809 1607-7946 doi:10.5194/npg-21-617-2014 https://doaj.org/article/6603ca9e175d4fbca9a5cc9d85903aa6 |
op_doi |
https://doi.org/10.5194/npg-21-617-2014 |
container_title |
Nonlinear Processes in Geophysics |
container_volume |
21 |
container_issue |
3 |
container_start_page |
617 |
op_container_end_page |
631 |
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1766061260595527680 |