Long-term projections of global, northern hemisphere, and arctic sea ice concentration using statistical and deep learning approaches

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dc.contributor.authorBilgili, Mehmet
dc.contributor.authorPinar, Engin
dc.contributor.authorMowla, Md. Najmul
dc.contributor.authorDurhasan, Tahir
dc.contributor.authorAksoy, Muhammed M.
dc.date.accessioned2026-02-27T07:33:14Z
dc.date.available2026-02-27T07:33:14Z
dc.date.issued2025
dc.description.abstractThe accelerating decline in sea ice concentration (SIC) poses significant challenges for global climate regulation, maritime navigation, and arctic ecosystem stability. This study develops and evaluates two advanced time-series forecasting models, seasonal autoregressive integrated moving average (SARIMA) and long short-term memory (LSTM) networks, to project SIC trends through 2050 across three spatial domains: the globe, the northern hemisphere, and the arctic. Utilizing the ERA5 reanalysis dataset (1970-2024) from the European center for medium-range weather forecasts (ECMWF), the models capture seasonal cycles and complex temporal dependencies to enable robust long-term projections. Comparative analysis demonstrates that SARIMA effectively models periodic fluctuations, while LSTM excels at learning nonlinear dependencies inherent in SIC dynamics. Performance metrics, including mean absolute percentage error (MAPE), root mean square error (RMSE), and correlation coefficient (R), confirm the high accuracy of both models, with SARIMA showing superior capability in representing structured seasonal patterns. Projections indicate a persistent decline in SIC, with arctic concentrations decreasing from 55.60% in 2023 to approximately 46.84% by 2050, underscoring the pronounced effects of arctic amplification. These results provide valuable insights for climate modeling, arctic policy formulation, and the development of adaptive navigation strategies in a rapidly changing polar environment.
dc.identifier.doi10.1016/j.jastp.2025.106634
dc.identifier.issn1364-6826
dc.identifier.issn1879-1824
dc.identifier.urihttp://dx.doi.org/10.1016/j.jastp.2025.106634
dc.identifier.urihttps://hdl.handle.net/20.500.14669/4510
dc.identifier.volume277
dc.identifier.wosWOS:001577931300001
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/openAccess
dc.snmzKA_20260302
dc.subjectSea ice concentration
dc.subjectSARIMA
dc.subjectLSTM
dc.subjectTime series forecasting
dc.subjectArctic amplification
dc.subjectClimate modeling
dc.titleLong-term projections of global, northern hemisphere, and arctic sea ice concentration using statistical and deep learning approaches
dc.typeArticle

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