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dc.contributor.authorDeza, Juan Ignacio
dc.contributor.authorMasoller Alonso, Cristina
dc.contributor.authorBarreiro, Marcelo
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Física i Enginyeria Nuclear
dc.date.accessioned2015-03-17T15:37:46Z
dc.date.available2015-03-17T15:37:46Z
dc.date.created2014-05-26
dc.date.issued2014-05-26
dc.identifier.citationDeza, J.Ignacio; Masoller, C.; Barreiro, M. Distinguishing the effects of internal and forced atmospheric variability in climate networks. "Nonlinear processes in geophysics", 26 Maig 2014, vol. 21, núm. 3, p. 617-631.
dc.identifier.issn1023-5809
dc.identifier.urihttp://hdl.handle.net/2117/26768
dc.description.abstractThe 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
dc.format.extent15 p.
dc.language.isoeng
dc.publisherUniversity of Thechnology and Agriculture Nicholas Copernicus University
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Enginyeria agroalimentària::Ciències de la terra i de la vida::Climatologia i meteorologia
dc.subject.lcshClimatic changes
dc.subject.otherAnnual variation
dc.subject.otherAtmospheric dynamics
dc.subject.otherClimate forcing
dc.subject.otherClimate prediction
dc.subject.otherClimate variation
dc.subject.otherEl Nino-Southern Oscillation
dc.subject.otherEnsemble forecasting
dc.subject.otherNetwork analysis
dc.subject.otherNorth Atlantic Oscillation
dc.subject.otherSea surface temperature
dc.subject.otherSeasonal variation
dc.subject.otherTeleconnection
dc.subject.otherTimescale
dc.titleDistinguishing the effects of internal and forced atmospheric variability in climate networks
dc.typeArticle
dc.subject.lemacCanvis climàtics
dc.subject.lemacClimatologia
dc.subject.lemacAnalisi de xarxes (Planificació)
dc.contributor.groupUniversitat Politècnica de Catalunya. DONLL - Dinàmica no Lineal, Òptica no Lineal i Làsers
dc.identifier.doi10.5194/npg-21-617-2014
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://www.nonlin-processes-geophys.net/21/617/2014/npg-21-617-2014.html
dc.rights.accessOpen Access
local.identifier.drac14939332
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/FP7/289447/EU/Learning about Interacting Networks in Climate/LINC
local.citation.authorDeza, J.Ignacio; Masoller, C.; Barreiro, M.
local.citation.publicationNameNonlinear processes in geophysics
local.citation.volume21
local.citation.number3
local.citation.startingPage617
local.citation.endingPage631


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