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dc.contributor.authorZappala, Dario
dc.contributor.authorBarreiro Parrillo, Marcelo
dc.contributor.authorMasoller Alonso, Cristina
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Física Computacional i Aplicada
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Física
dc.date.accessioned2020-03-05T16:42:59Z
dc.date.available2021-01-24T01:35:29Z
dc.date.issued2020-01-23
dc.identifier.citationZappala, D.; Barreiro Parrillo, M.; Masoller, C. Mapping atmospheric waves and unveiling phase coherent structures in a global surface air temperature reanalysis dataset. "Chaos : an interdisciplinary journal of nonlinear science", 23 Gener 2020, vol. 30, núm. 1, p. 011103:1-011103:7.
dc.identifier.issn1054-1500
dc.identifier.urihttp://hdl.handle.net/2117/179313
dc.description.abstractIn the analysis of empirical signals, detecting correlations that capture genuine interactions between the elements of a complex system is a challenging task with applications across disciplines. Here, we analyze a global dataset of surface air temperature (SAT) with daily resolution. Hilbert analysis is used to obtain phase, instantaneous frequency, and amplitude information of SAT seasonal cycles in different geographical zones. The analysis of the phase dynamics reveals large regions with coherent seasonality. The analysis of the instantaneous frequencies uncovers clean wave patterns formed by alternating regions of negative and positive correlations. In contrast, the analysis of the amplitude dynamics uncovers wave patterns with additional large-scale structures. These structures are interpreted as due to the fact that the amplitude dynamics is affected by processes that act in long and short time scales, while the dynamics of the instantaneous frequency is mainly governed by fast processes. Therefore, Hilbert analysis allows us to disentangle climatic processes and to track planetary atmospheric waves. Our results are relevant for the analysis of complex oscillatory signals because they offer a general strategy for uncovering interactions that act at different time scales. In our “big data” times, extracting useful information from complex signals is an important challenge with applications across disciplines. Due to the presence of multiple time scales, climatological signals are particularly challenging to analyze. Here, we present a technique based on the Hilbert transform (HT) that, when applied to time series of surface air temperature (SAT) (with daily resolution, covering the last 30 years), unveils clear wave patterns that are interpreted as due to Rossby waves (these are atmospheric waves that propagate across our planet and have a major influence on weather). We also show that the patterns uncovered by analyzing anomaly times series include additional structures which likely appear due to climatic phenomena that have long time scales.
dc.language.isoeng
dc.publisherInstitute of Physics (IOP)
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::Física
dc.subject.lcshSignal processing
dc.subject.lcshEarth temperature
dc.subject.otherSignal processing
dc.subject.otherAtmospheric dynamics
dc.titleMapping atmospheric waves and unveiling phase coherent structures in a global surface air temperature reanalysis dataset
dc.typeArticle
dc.subject.lemacTractament del senyal
dc.subject.lemacGeotèrmia
dc.contributor.groupUniversitat Politècnica de Catalunya. DONLL - Dinàmica no Lineal, Òptica no Lineal i Làsers
dc.identifier.doi10.1063/1.5140620
dc.relation.publisherversionhttps://aip.scitation.org/doi/10.1063/1.5140620
dc.rights.accessOpen Access
local.identifier.drac27211737
dc.description.versionPostprint (published version)
local.citation.authorZappala, D.; Barreiro Parrillo, Marcelo; Masoller, C.
local.citation.publicationNameChaos : an interdisciplinary journal of nonlinear science
local.citation.volume30
local.citation.number1
local.citation.startingPage011103:1
local.citation.endingPage011103:7


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Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain