Spatial Regionalization of the Arctic Ocean Based on Ocean Physical Property

• Published in: Remote sensing (Basel, Switzerland) Vol. 17; no. 6; p. 1065
• Main Authors: Yoon, Joo-Eun, Park, Jinku, Kim, Hyun-Cheol
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2025
• Subjects: Algorithms; Analysis; Arctic Ocean; Arctic zone; Atmosphere; Climate change; Climatic changes; Cluster analysis; Clustering; Complex systems; Datasets; Environmental impact; Global climate; Heterogeneity; Homogeneity; Marine environment; Meteorological satellites; Ocean; Ocean circulation; Ocean currents; ocean physical property; Optimization; Performance evaluation; Physical characteristics; Physical properties; Regional analysis; Regions; Salinity; Sea currents; Sea ice; Sea surface temperature; spatial clustering; Spatial heterogeneity; Variables; Vector quantization; Barents Sea; United States; Pacific Ocean; Arctic Ocean; Arctic region
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs17061065

The Arctic Ocean has a uniquely complex system associated with tightly coupled ocean–ice–atmosphere–land interactions. The Arctic Ocean is considered to be highly susceptible to global climate change, with the potential for dramatic environmental impacts at both regional and global scales, and its spatial differences particularly have been exacerbated. A comprehensive understanding of Arctic Ocean environmental responses to climate change thus requires classifying the Arctic Ocean into subregions that describe spatial homogeneity of the clusters and heterogeneity between clusters based on ocean physical properties and implementing the regional-scale analysis. In this study, utilizing the long-term optimum interpolation sea surface temperature (SST) datasets for the period 1982–2023, which is one of the essential indicators of physical processes, we applied the K-means clustering algorithm to generate subregions of the Arctic Ocean, reflecting distinct physical characteristics. Using the variance ratio criterion, the optimal number of subregions for spatial clustering was 12. Employing methods such as information mapping and pairwise multi-comparison analysis, we found that the 12 subregions of the Arctic Ocean well represent spatial heterogeneity and homogeneity of physical properties, including sea ice concentration, surface ocean currents, SST, and sea surface salinity. Spatial patterns in SST changes also matched well with the boundaries of clustered subregions. The newly identified physical subregions of the Arctic Ocean will contribute to a more comprehensive understanding of the Arctic Ocean’s environmental response to accelerating climate change.