Forecasting near-surface air temperature via SARIMA and LSTM: A regional time-series study

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dc.contributor.authorAksoy, Muhammed M.
dc.contributor.authorMowla, Najmul
dc.contributor.authorBilgili, Mehmet
dc.contributor.authorPinar, Engin
dc.contributor.authorDurhasan, Tahir
dc.contributor.authorAsadi, Davood
dc.date.accessioned2026-02-27T07:33:14Z
dc.date.available2026-02-27T07:33:14Z
dc.date.issued2025
dc.description.abstractAccurate modeling of near-surface air temperature (AT) trends is critical for assessing global and regional climate risks, particularly in light of the intensifying warming signals observed across the northern hemisphere and the tropics. This study proposes a robust and computationally efficient framework for forecasting near-surface AT across the global, the northern hemisphere, the southern hemisphere, and the tropics using two complementary time-series modeling techniques: seasonal autoregressive integrated moving average (SARIMA) and long short-term memory (LSTM) networks. The models are trained to capture both structured seasonal patterns and nonlinear temporal dynamics by leveraging the ERA5 reanalysis dataset (1970-2024) and incorporating preprocessing steps such as detrending and Z-score normalization. SARIMA consistently outperformed LSTM across most domains, particularly in the global region, achieving lower RMSE (0.0967 degrees C) and higher correlation (R = 0.9975), reflecting its superior capacity for linear and seasonal signal extraction. Quantitatively, SARIMA demonstrates 5%-10% lower RMSE and slightly higher correlation than LSTM across all domains, underscoring the statistical significance of its performance advantage. Projected near-surface AT anomalies by 2050 reveal a marked warming trend, with the SARIMA model estimating a global anomaly of +1.078 degrees C and a northern hemisphere anomaly of +1.474 degrees C, closely aligning with IPCC-reported trajectories and exceeding CMIP5 RCP4.5 projections. The findings underscore SARIMA's reliability for short-to mid-term near-surface AT forecasting and LSTM's potential for future hybrid modeling schemes. This work fills a critical methodological gap by integrating statistical rigor with scalable deep learning, offering enhanced fidelity for regional climate adaptation planning.
dc.identifier.doi10.1016/j.jastp.2025.106604
dc.identifier.issn1364-6826
dc.identifier.issn1879-1824
dc.identifier.urihttp://dx.doi.org/10.1016/j.jastp.2025.106604
dc.identifier.urihttps://hdl.handle.net/20.500.14669/4507
dc.identifier.volume275
dc.identifier.wosWOS:001560667200001
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofJournal of Atmospheric and Solar-Terrestrial Physics
dc.relation.publicationcategoryMakale - Uluslararas� Hakemli Dergi - Kurum ��retim Eleman�
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20260302
dc.subjectAir temperature forecasting
dc.subjectSARIMA
dc.subjectLSTM
dc.subjectClimate change projection
dc.titleForecasting near-surface air temperature via SARIMA and LSTM: A regional time-series study
dc.typeArticle

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