Time Series Clustering of Sea Surface Temperature in the Mediterranean and Black Sea Marine System

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dc.authoridTAGIL, Sermin/0000-0001-9496-6823
dc.authoridDanacioglu, Sevki/0000-0003-1118-352X
dc.authoridYurtseven, Nami/0000-0001-7345-9988
dc.contributor.authorTagil, Sermin
dc.contributor.authorDanacioglu, Sevki
dc.contributor.authorYurtseven, Nami
dc.date.accessioned2025-03-20T09:51:25Z
dc.date.available2025-03-20T09:51:25Z
dc.date.issued2024
dc.departmentİzmir Bakırçay Üniversitesi
dc.description.abstractSea surface temperature (SST) is a significant climatic variable that affects the climate of the Earth. Monitoring a location's SST pattern is useful for several research areas, including weather forecasting and climate change. In this study, the emerging hot spot and cold spot patterns of SST in the Mediterranean and Black Sea Marine System (MBMS) were examined, the spatial distribution characteristics and temporal changes of SST in the sub-basins were analysed, and future predictions were made. A distinctive aspect of the research lies in the introduction of novel techniques, specifically the application of space time cube and evolving hot spot analysis, for visualising and evaluating SST in the MBMS. This approach sets the study apart by pioneering the utilisation of these methods in this particular context. In the examined region, SST demonstrates a decreasing trend from east to west and from south to north. The forecast suggests that this spatial distribution pattern will persist in 2033, further accentuated by the intensification of the warming effect. Nine different time series clusters are defined within this distribution pattern. Although it changes seasonally, the prevailing statistically significant hot spots in the study area are primarily characterised by new hot spots, intensifying hot spots, sporadic hot spots and oscillating hot spots. The trends of hot and cold spot clusters, along with SST values, were assessed for all sub-basins in the MBMS. Conversely, the observed clustering category among statistically significant cold spots is identified as persistent cold spots, diminishing cold spots, sporadic cold spots, oscillating cold spots and historical cold spots. The spatiotemporal analysis in this research has provided notable insights, offering a spatial context to the previously explored temporal trends of SST in the MBMS.
dc.identifier.doi10.1002/joc.8687
dc.identifier.endpage6099
dc.identifier.issn0899-8418
dc.identifier.issn1097-0088
dc.identifier.issue16
dc.identifier.scopus2-s2.0-85209352780
dc.identifier.scopusqualityQ1
dc.identifier.startpage6083
dc.identifier.urihttps://doi.org/10.1002/joc.8687
dc.identifier.urihttps://hdl.handle.net/20.500.14034/2536
dc.identifier.volume44
dc.identifier.wosWOS:001357003700001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofInternational Journal of Climatology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250319
dc.subjectcurve fit forecast
dc.subjectemerging hot spot analysis
dc.subjectMediterranean and Black Sea Marine System
dc.subjectsea surface temperature
dc.subjectspace time cube
dc.subjectthree-dimensional model
dc.subjecttime-enabled multidimensional raster
dc.titleTime Series Clustering of Sea Surface Temperature in the Mediterranean and Black Sea Marine System
dc.typeArticle

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