Sola Salvatierra, YolandaPagès Ticó, Marcel2025-09-162025-09-162025-06https://hdl.handle.net/2445/223188Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2025, Tutora: Yolanda Sola SalvatierraThis 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 strategies6 p.application/pdfengcc-by-nc-nd (c) Pagès, 2025http://creativecommons.org/licenses/by-nc-nd/3.0/es/Canvi climàticEstadística bayesianaTreballs de fi de grauClimatic changeBayesian statistical decisionBachelor's thesesinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/openAccess