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...
Published in: | Journal of Marine Science and Engineering |
---|---|
Main Authors: | , , , |
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 |
id |
ftdoajarticles:oai:doaj.org/article:4c6e2a2a2c594f5293fc7f20f67984a6 |
---|---|
record_format |
openpolar |
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 |
container_start_page |
1361 |
_version_ |
1810433879395270656 |