Distinguishing the effects of internal and forced atmospheric variability in climate networks

The fact that the Earth climate is a highly complex dynamical system is well-known. In the last few decades a lot 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 identifyi...

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Main Authors: Deza, J. Ignacio, Masoller, Cristina, Barreiro, Marcelo
Format: Text
Language:unknown
Published: arXiv 2013
Subjects:
Online Access:https://dx.doi.org/10.48550/arxiv.1311.3089
https://arxiv.org/abs/1311.3089
id ftdatacite:10.48550/arxiv.1311.3089
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spelling ftdatacite:10.48550/arxiv.1311.3089 2023-05-15T17:35:14+02:00 Distinguishing the effects of internal and forced atmospheric variability in climate networks Deza, J. Ignacio Masoller, Cristina Barreiro, Marcelo 2013 https://dx.doi.org/10.48550/arxiv.1311.3089 https://arxiv.org/abs/1311.3089 unknown arXiv https://dx.doi.org/10.5194/npg-21-617-2014 arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Chaotic Dynamics nlin.CD Atmospheric and Oceanic Physics physics.ao-ph FOS Physical sciences article-journal Article ScholarlyArticle Text 2013 ftdatacite https://doi.org/10.48550/arxiv.1311.3089 https://doi.org/10.5194/npg-21-617-2014 2022-04-01T13:13:00Z The fact that the Earth climate is a highly complex dynamical system is well-known. In the last few decades a lot 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 in understanding the role of atmospheric variability, and construct climate networks representing internal and forced variability. In the connectivity of these networks we assess the influence of two main indices, NINO3.4 and the North Atlantic Oscillation (NAO), by calculating the networks from time-series where these indices were linearly removed. We find 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 on interannual time scales. On the contrary, in the internal variability network (independent of sea surface temperature forcing) we find that the links are significantly affected by NAO with a maximum in intra-annual time scales. While the strongest non-local links found are those forced by the ocean, we show that there are also strong teleconnections due to internal atmospheric variability. : 14 pages, submitted Text North Atlantic North Atlantic oscillation DataCite Metadata Store (German National Library of Science and Technology) Pacific
institution Open Polar
collection DataCite Metadata Store (German National Library of Science and Technology)
op_collection_id ftdatacite
language unknown
topic Chaotic Dynamics nlin.CD
Atmospheric and Oceanic Physics physics.ao-ph
FOS Physical sciences
spellingShingle Chaotic Dynamics nlin.CD
Atmospheric and Oceanic Physics physics.ao-ph
FOS Physical sciences
Deza, J. Ignacio
Masoller, Cristina
Barreiro, Marcelo
Distinguishing the effects of internal and forced atmospheric variability in climate networks
topic_facet Chaotic Dynamics nlin.CD
Atmospheric and Oceanic Physics physics.ao-ph
FOS Physical sciences
description The fact that the Earth climate is a highly complex dynamical system is well-known. In the last few decades a lot 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 in understanding the role of atmospheric variability, and construct climate networks representing internal and forced variability. In the connectivity of these networks we assess the influence of two main indices, NINO3.4 and the North Atlantic Oscillation (NAO), by calculating the networks from time-series where these indices were linearly removed. We find 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 on interannual time scales. On the contrary, in the internal variability network (independent of sea surface temperature forcing) we find that the links are significantly affected by NAO with a maximum in intra-annual time scales. While the strongest non-local links found are those forced by the ocean, we show that there are also strong teleconnections due to internal atmospheric variability. : 14 pages, submitted
format Text
author Deza, J. Ignacio
Masoller, Cristina
Barreiro, Marcelo
author_facet Deza, J. Ignacio
Masoller, Cristina
Barreiro, Marcelo
author_sort Deza, J. Ignacio
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 arXiv
publishDate 2013
url https://dx.doi.org/10.48550/arxiv.1311.3089
https://arxiv.org/abs/1311.3089
geographic Pacific
geographic_facet Pacific
genre North Atlantic
North Atlantic oscillation
genre_facet North Atlantic
North Atlantic oscillation
op_relation https://dx.doi.org/10.5194/npg-21-617-2014
op_rights arXiv.org perpetual, non-exclusive license
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
op_doi https://doi.org/10.48550/arxiv.1311.3089
https://doi.org/10.5194/npg-21-617-2014
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