Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/223188
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dc.contributor.advisorSola Salvatierra, Yolanda-
dc.contributor.authorPagès Ticó, Marcel-
dc.date.accessioned2025-09-16T13:19:10Z-
dc.date.available2025-09-16T13:19:10Z-
dc.date.issued2025-06-
dc.identifier.urihttps://hdl.handle.net/2445/223188-
dc.descriptionTreballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2025, Tutora: Yolanda Sola Salvatierraca
dc.description.abstractThis work analyses global surface temperature anomalies from 1880 to 2024 using the GISS database. ARIMA and Bayesian Structural Time Series models are employed to make predictions. Both approaches reveal a strong warming trend predicting, respectively, an average annual increase of 0.021 K and 0.031 K until 2060. Higher warming over land and, especially in the Bayesian model, Arctic amplification are forecasted. These findings align with IPCC projections and underscore the urgency of climate mitigation strategiesca
dc.format.extent6 p.-
dc.format.mimetypeapplication/pdf-
dc.language.isoengca
dc.rightscc-by-nc-nd (c) Pagès, 2025-
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.sourceTreballs Finals de Grau (TFG) - Física-
dc.subject.classificationCanvi climàticcat
dc.subject.classificationEstadística bayesianacat
dc.subject.classificationTreballs de fi de graucat
dc.subject.otherClimatic changeeng
dc.subject.otherBayesian statistical decisioneng
dc.subject.otherBachelor's theseseng
dc.titleSurface temperature analysis: variability and trendseng
dc.typeinfo:eu-repo/semantics/bachelorThesisca
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca
Appears in Collections:Treballs Finals de Grau (TFG) - Física

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