Spatial Regionalization of the Arctic Ocean Based on Ocean Physical Property
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 s...
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MDPI AG
2025-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/6/1065 |
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| author | Joo-Eun Yoon Jinku Park Hyun-Cheol Kim |
| author_facet | Joo-Eun Yoon Jinku Park Hyun-Cheol Kim |
| author_sort | Joo-Eun Yoon |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3fcbb67c7ae547e9bb10a539bb823a89 |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-3fcbb67c7ae547e9bb10a539bb823a892025-08-20T02:43:02ZengMDPI AGRemote Sensing2072-42922025-03-01176106510.3390/rs17061065Spatial Regionalization of the Arctic Ocean Based on Ocean Physical PropertyJoo-Eun Yoon0Jinku Park1Hyun-Cheol Kim2Center of Remote Sensing & GIS, Korea Polar Research Institute, Incheon 21990, Republic of KoreaCenter of Remote Sensing & GIS, Korea Polar Research Institute, Incheon 21990, Republic of KoreaCenter of Remote Sensing & GIS, Korea Polar Research Institute, Incheon 21990, Republic of KoreaThe 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.https://www.mdpi.com/2072-4292/17/6/1065Arctic Oceansea surface temperatureocean physical propertyspatial clustering |
| spellingShingle | Joo-Eun Yoon Jinku Park Hyun-Cheol Kim Spatial Regionalization of the Arctic Ocean Based on Ocean Physical Property Remote Sensing Arctic Ocean sea surface temperature ocean physical property spatial clustering |
| title | Spatial Regionalization of the Arctic Ocean Based on Ocean Physical Property |
| title_full | Spatial Regionalization of the Arctic Ocean Based on Ocean Physical Property |
| title_fullStr | Spatial Regionalization of the Arctic Ocean Based on Ocean Physical Property |
| title_full_unstemmed | Spatial Regionalization of the Arctic Ocean Based on Ocean Physical Property |
| title_short | Spatial Regionalization of the Arctic Ocean Based on Ocean Physical Property |
| title_sort | spatial regionalization of the arctic ocean based on ocean physical property |
| topic | Arctic Ocean sea surface temperature ocean physical property spatial clustering |
| url | https://www.mdpi.com/2072-4292/17/6/1065 |
| work_keys_str_mv | AT jooeunyoon spatialregionalizationofthearcticoceanbasedonoceanphysicalproperty AT jinkupark spatialregionalizationofthearcticoceanbasedonoceanphysicalproperty AT hyuncheolkim spatialregionalizationofthearcticoceanbasedonoceanphysicalproperty |