Distinguishing the effects of internal and forced atmospheric variability in climate networks
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.
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 |
dc.format.mimetype.es.fl_str_mv | application/pdf |
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 |
id | COLIBRI_b4f34416f6b770594262d985c135de69 |
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 |
instacron_str | Universidad de la República |
institution | Universidad de la República |
instname_str | Universidad de la República |
language | eng |
language_invalid_str_mv | en |
network_acronym_str | COLIBRI |
network_name_str | COLIBRI |
oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/34221 |
publishDate | 2014 |
reponame_str | COLIBRI |
repository.mail.fl_str_mv | mabel.seroubian@seciu.edu.uy |
repository.name.fl_str_mv | COLIBRI - Universidad de la República |
repository_id_str | 4771 |
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 |