Analysis of Arctic Sea Ice Concentration Anomalies Using Spatiotemporal Clustering

The dynamic changes of sea ice exhibit spatial clustering, and this clustering has characteristics extending from its origin, through its development, and to its dissipation. Current research on sea ice change primarily focuses on spatiotemporal variation trends and remote correlation analysis, and...

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Published in:Journal of Marine Science and Engineering
Main Authors: Yongheng Li, Yawen He, Yanhua Liu, Feng Jin
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2024
Subjects:
Online Access:https://doi.org/10.3390/jmse12081361
https://doaj.org/article/4c6e2a2a2c594f5293fc7f20f67984a6
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spelling ftdoajarticles:oai:doaj.org/article:4c6e2a2a2c594f5293fc7f20f67984a6 2024-09-15T17:57:43+00:00 Analysis of Arctic Sea Ice Concentration Anomalies Using Spatiotemporal Clustering Yongheng Li Yawen He Yanhua Liu Feng Jin 2024-08-01T00:00:00Z https://doi.org/10.3390/jmse12081361 https://doaj.org/article/4c6e2a2a2c594f5293fc7f20f67984a6 EN eng MDPI AG https://www.mdpi.com/2077-1312/12/8/1361 https://doaj.org/toc/2077-1312 doi:10.3390/jmse12081361 2077-1312 https://doaj.org/article/4c6e2a2a2c594f5293fc7f20f67984a6 Journal of Marine Science and Engineering, Vol 12, Iss 8, p 1361 (2024) spatiotemporal clustering Arctic sea ice concentration anomalies spatiotemporal analysis Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581 article 2024 ftdoajarticles https://doi.org/10.3390/jmse12081361 2024-09-02T15:34:38Z The dynamic changes of sea ice exhibit spatial clustering, and this clustering has characteristics extending from its origin, through its development, and to its dissipation. Current research on sea ice change primarily focuses on spatiotemporal variation trends and remote correlation analysis, and lacks an analysis of spatiotemporal evolution characteristics. This study utilized monthly sea ice concentration (SIC) data from the National Snow and Ice Data Center (NSIDC) for the period from 1979 to 2022, utilizing classical spatiotemporal clustering algorithms to analyze the clustering patterns and evolutionary characteristics of SIC anomalies in key Arctic regions. The results revealed that the central-western region of the Barents Sea was a critical area where SIC anomaly evolutionary behaviors were concentrated and persisted for longer durations. The relationship between the intensity and duration of SIC anomaly events was nonlinear. A positive correlation was observed for shorter durations, while a negative correlation was noted for longer durations. Anomalies predominantly occurred in December, with complex evolution happening in April and May of the following year, and concluded in July. Evolutionary state transitions mainly occurred in the Barents Sea. These transitions included shifts from the origin state in the northwestern margin to the dissipation state in the central-north Barents Sea, from the origin state in the central-north to the dissipation state in the central-south, and from the origin state in the northeastern to the dissipation state in the central-south Barents Sea and southeastern Kara Sea. Various evolutionary states were observed in the same area on the southwest edge of the Barents Sea. These findings provide insights into the evolutionary mechanism of sea ice anomalies. Article in Journal/Newspaper Barents Sea Kara Sea National Snow and Ice Data Center Sea ice Directory of Open Access Journals: DOAJ Articles Journal of Marine Science and Engineering 12 8 1361
institution Open Polar
collection Directory of Open Access Journals: DOAJ Articles
op_collection_id ftdoajarticles
language English
topic spatiotemporal clustering
Arctic
sea ice concentration anomalies
spatiotemporal analysis
Naval architecture. Shipbuilding. Marine engineering
VM1-989
Oceanography
GC1-1581
spellingShingle spatiotemporal clustering
Arctic
sea ice concentration anomalies
spatiotemporal analysis
Naval architecture. Shipbuilding. Marine engineering
VM1-989
Oceanography
GC1-1581
Yongheng Li
Yawen He
Yanhua Liu
Feng Jin
Analysis of Arctic Sea Ice Concentration Anomalies Using Spatiotemporal Clustering
topic_facet spatiotemporal clustering
Arctic
sea ice concentration anomalies
spatiotemporal analysis
Naval architecture. Shipbuilding. Marine engineering
VM1-989
Oceanography
GC1-1581
description The dynamic changes of sea ice exhibit spatial clustering, and this clustering has characteristics extending from its origin, through its development, and to its dissipation. Current research on sea ice change primarily focuses on spatiotemporal variation trends and remote correlation analysis, and lacks an analysis of spatiotemporal evolution characteristics. This study utilized monthly sea ice concentration (SIC) data from the National Snow and Ice Data Center (NSIDC) for the period from 1979 to 2022, utilizing classical spatiotemporal clustering algorithms to analyze the clustering patterns and evolutionary characteristics of SIC anomalies in key Arctic regions. The results revealed that the central-western region of the Barents Sea was a critical area where SIC anomaly evolutionary behaviors were concentrated and persisted for longer durations. The relationship between the intensity and duration of SIC anomaly events was nonlinear. A positive correlation was observed for shorter durations, while a negative correlation was noted for longer durations. Anomalies predominantly occurred in December, with complex evolution happening in April and May of the following year, and concluded in July. Evolutionary state transitions mainly occurred in the Barents Sea. These transitions included shifts from the origin state in the northwestern margin to the dissipation state in the central-north Barents Sea, from the origin state in the central-north to the dissipation state in the central-south, and from the origin state in the northeastern to the dissipation state in the central-south Barents Sea and southeastern Kara Sea. Various evolutionary states were observed in the same area on the southwest edge of the Barents Sea. These findings provide insights into the evolutionary mechanism of sea ice anomalies.
format Article in Journal/Newspaper
author Yongheng Li
Yawen He
Yanhua Liu
Feng Jin
author_facet Yongheng Li
Yawen He
Yanhua Liu
Feng Jin
author_sort Yongheng Li
title Analysis of Arctic Sea Ice Concentration Anomalies Using Spatiotemporal Clustering
title_short Analysis of Arctic Sea Ice Concentration Anomalies Using Spatiotemporal Clustering
title_full Analysis of Arctic Sea Ice Concentration Anomalies Using Spatiotemporal Clustering
title_fullStr Analysis of Arctic Sea Ice Concentration Anomalies Using Spatiotemporal Clustering
title_full_unstemmed Analysis of Arctic Sea Ice Concentration Anomalies Using Spatiotemporal Clustering
title_sort analysis of arctic sea ice concentration anomalies using spatiotemporal clustering
publisher MDPI AG
publishDate 2024
url https://doi.org/10.3390/jmse12081361
https://doaj.org/article/4c6e2a2a2c594f5293fc7f20f67984a6
genre Barents Sea
Kara Sea
National Snow and Ice Data Center
Sea ice
genre_facet Barents Sea
Kara Sea
National Snow and Ice Data Center
Sea ice
op_source Journal of Marine Science and Engineering, Vol 12, Iss 8, p 1361 (2024)
op_relation https://www.mdpi.com/2077-1312/12/8/1361
https://doaj.org/toc/2077-1312
doi:10.3390/jmse12081361
2077-1312
https://doaj.org/article/4c6e2a2a2c594f5293fc7f20f67984a6
op_doi https://doi.org/10.3390/jmse12081361
container_title Journal of Marine Science and Engineering
container_volume 12
container_issue 8
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