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

Deza, J. Ignacio - Masoller, Cristina - Barreiro, Marcelo

Resumen:

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, intraannual 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 intraannual timescales. While the strongest non-local links found are those forced by the ocean, the presence of teleconnections due to internal atmospheric variability is also shown.


Detalles Bibliográficos
2014
Inglés
Universidad de la República
COLIBRI
https://hdl.handle.net/20.500.12008/34221
Acceso abierto
Licencia Creative Commons Atribución (CC - By 4.0)
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author Deza, J. Ignacio
author2 Masoller, Cristina
Barreiro, Marcelo
author2_role author
author
author_facet Deza, J. Ignacio
Masoller, Cristina
Barreiro, Marcelo
author_role author
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collection COLIBRI
dc.contributor.filiacion.none.fl_str_mv Deza J. Ignacio, Universitat Politècnica de Catalunya
Masoller Cristina, Universitat Politècnica de Catalunya
Barreiro Marcelo, Universidad de la República (Uruguay). Facultad de Ciencias. Instituto de Física.
dc.creator.none.fl_str_mv Deza, J. Ignacio
Masoller, Cristina
Barreiro, Marcelo
dc.date.accessioned.none.fl_str_mv 2022-10-17T17:37:48Z
dc.date.available.none.fl_str_mv 2022-10-17T17:37:48Z
dc.date.issued.none.fl_str_mv 2014
dc.description.abstract.none.fl_txt_mv 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, intraannual 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 intraannual timescales. While the strongest non-local links found are those forced by the ocean, the presence of teleconnections due to internal atmospheric variability is also shown.
dc.format.extent.es.fl_str_mv 15 h
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dc.identifier.citation.es.fl_str_mv Deza, J, Masoller, C y Barreiro, M. "Distinguishing the effects of internal and forced atmospheric variability in climate networks". Nonlinear Processes in Geophysics. [en línea] 2014, 21(3): 617–631. 15 h.
dc.identifier.doi.none.fl_str_mv 10.5194/npg-21-617-2014
dc.identifier.issn.none.fl_str_mv 1607-7946
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12008/34221
dc.language.iso.none.fl_str_mv en
eng
dc.publisher.es.fl_str_mv European Geosciences Union
dc.relation.ispartof.es.fl_str_mv Nonlinear Processes in Geophysics, 2014, 21(3): 617–631
dc.rights.license.none.fl_str_mv Licencia Creative Commons Atribución (CC - By 4.0)
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
dc.source.none.fl_str_mv reponame:COLIBRI
instname:Universidad de la República
instacron:Universidad de la República
dc.title.none.fl_str_mv Distinguishing the effects of internal and forced atmospheric variability in climate networks
dc.type.es.fl_str_mv Artículo
dc.type.none.fl_str_mv info:eu-repo/semantics/article
dc.type.version.none.fl_str_mv info:eu-repo/semantics/publishedVersion
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, intraannual 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 intraannual timescales. While the strongest non-local links found are those forced by the ocean, the presence of teleconnections due to internal atmospheric variability is also shown.
eu_rights_str_mv openAccess
format article
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identifier_str_mv Deza, J, Masoller, C y Barreiro, M. "Distinguishing the effects of internal and forced atmospheric variability in climate networks". Nonlinear Processes in Geophysics. [en línea] 2014, 21(3): 617–631. 15 h.
1607-7946
10.5194/npg-21-617-2014
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publishDate 2014
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repository.mail.fl_str_mv mabel.seroubian@seciu.edu.uy
repository.name.fl_str_mv COLIBRI - Universidad de la República
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rights_invalid_str_mv Licencia Creative Commons Atribución (CC - By 4.0)
spelling Deza J. Ignacio, Universitat Politècnica de CatalunyaMasoller Cristina, Universitat Politècnica de CatalunyaBarreiro Marcelo, Universidad de la República (Uruguay). Facultad de Ciencias. Instituto de Física.2022-10-17T17:37:48Z2022-10-17T17:37:48Z2014Deza, J, Masoller, C y Barreiro, M. "Distinguishing the effects of internal and forced atmospheric variability in climate networks". Nonlinear Processes in Geophysics. [en línea] 2014, 21(3): 617–631. 15 h.1607-7946https://hdl.handle.net/20.500.12008/3422110.5194/npg-21-617-2014The 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, intraannual 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 intraannual timescales. While the strongest non-local links found are those forced by the ocean, the presence of teleconnections due to internal atmospheric variability is also shown.Submitted by Faget Cecilia (lfaget@fcien.edu.uy) on 2022-10-17T17:22:06Z No. of bitstreams: 2 license_rdf: 19875 bytes, checksum: 9fdbed07f52437945402c4e70fa4773e (MD5) 10.5194npg-21-617-2014.pdf: 3985711 bytes, checksum: ef6497cab98b94f800c87897d434176b (MD5)Approved for entry into archive by Faget Cecilia (lfaget@fcien.edu.uy) on 2022-10-17T17:32:56Z (GMT) No. of bitstreams: 2 license_rdf: 19875 bytes, checksum: 9fdbed07f52437945402c4e70fa4773e (MD5) 10.5194npg-21-617-2014.pdf: 3985711 bytes, checksum: ef6497cab98b94f800c87897d434176b (MD5)Made available in DSpace by Luna Fabiana (fabiana.luna@seciu.edu.uy) on 2022-10-17T17:37:48Z (GMT). No. of bitstreams: 2 license_rdf: 19875 bytes, checksum: 9fdbed07f52437945402c4e70fa4773e (MD5) 10.5194npg-21-617-2014.pdf: 3985711 bytes, checksum: ef6497cab98b94f800c87897d434176b (MD5) Previous issue date: 201415 happlication/pdfenengEuropean Geosciences UnionNonlinear Processes in Geophysics, 2014, 21(3): 617–631Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad de la República.(Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución (CC - By 4.0)Distinguishing the effects of internal and forced atmospheric variability in climate networksArtículoinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaDeza, J. 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- Universidad de la Repúblicafalse
spellingShingle Distinguishing the effects of internal and forced atmospheric variability in climate networks
Deza, J. Ignacio
status_str publishedVersion
title 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_short 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
url https://hdl.handle.net/20.500.12008/34221