Revisiting Antarctic sea-ice decadal variability since 1980

Sea-ice extent (SIE) satellite data has been stored and studied over the past four decades. Antarctica’s SIE has recently attained both its maximum (2014) and its minimum (2017) values, which raised questions regarding how SIE variability is evolving. Here we discuss a new approach to improve our un...

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Published in:Polar Science
Format: Article in Journal/Newspaper
Language:English
Published: 2022
Subjects:
Online Access:https://nipr.repo.nii.ac.jp/?action=repository_uri&item_id=16900
http://id.nii.ac.jp/1291/00016769/
id ftnipr:oai:nipr.repo.nii.ac.jp:00016900
record_format openpolar
spelling ftnipr:oai:nipr.repo.nii.ac.jp:00016900 2023-05-15T13:49:00+02:00 Revisiting Antarctic sea-ice decadal variability since 1980 2022-03 https://nipr.repo.nii.ac.jp/?action=repository_uri&item_id=16900 http://id.nii.ac.jp/1291/00016769/ en eng https://doi.org/10.1016/j.polar.2021.100743 https://nipr.repo.nii.ac.jp/?action=repository_uri&item_id=16900 http://id.nii.ac.jp/1291/00016769/ Polar Science, 31, 100743(2022-03) 18739652 Antarctic sea ice extent Probability density function SIE variability Journal Article 2022 ftnipr https://doi.org/10.1016/j.polar.2021.100743 2022-12-03T19:43:26Z Sea-ice extent (SIE) satellite data has been stored and studied over the past four decades. Antarctica’s SIE has recently attained both its maximum (2014) and its minimum (2017) values, which raised questions regarding how SIE variability is evolving. Here we discuss a new approach to improve our understanding of SIE variability for the 1982–1993 and 2006–2017 periods. Using a probability density function and examining the mean and standard deviation of SIE distributions as a function of seasonality. Results show that the whole Southern Ocean presented both mean and standard deviation growth in all seasons. The largest SIE distribution difference for Southern Ocean between 1982–1993 and 2006–2017 periods was observed in July, August and September (JAS), during which all individual Sea sectors presented growth of the SIE mean. Not only different Sea sectors showed different variations, but the SIE data distributions from the same Sea sector yielded different changes with differing seasons. January, February and March (JFM) together with April, May and June were the seasons with the largest SIE decadal variability for Weddell, Indian, Amundsen and West Pacific Sea Sectors, while the Ross Sea showed greater SIE variability during JAS and October November, December. This methodology showed consistent results with the traditional SIE trend calculation and introduced a new quantification index for evaluating the differences between two distributions not necessarily connected in time. Article in Journal/Newspaper Antarc* Antarctic Polar Science Polar Science Ross Sea Sea ice Southern Ocean National Institute of Polar Research Repository, Japan Antarctic Southern Ocean Ross Sea Pacific Indian Weddell Polar Science 31 100743
institution Open Polar
collection National Institute of Polar Research Repository, Japan
op_collection_id ftnipr
language English
topic Antarctic sea ice extent
Probability density function
SIE variability
spellingShingle Antarctic sea ice extent
Probability density function
SIE variability
Revisiting Antarctic sea-ice decadal variability since 1980
topic_facet Antarctic sea ice extent
Probability density function
SIE variability
description Sea-ice extent (SIE) satellite data has been stored and studied over the past four decades. Antarctica’s SIE has recently attained both its maximum (2014) and its minimum (2017) values, which raised questions regarding how SIE variability is evolving. Here we discuss a new approach to improve our understanding of SIE variability for the 1982–1993 and 2006–2017 periods. Using a probability density function and examining the mean and standard deviation of SIE distributions as a function of seasonality. Results show that the whole Southern Ocean presented both mean and standard deviation growth in all seasons. The largest SIE distribution difference for Southern Ocean between 1982–1993 and 2006–2017 periods was observed in July, August and September (JAS), during which all individual Sea sectors presented growth of the SIE mean. Not only different Sea sectors showed different variations, but the SIE data distributions from the same Sea sector yielded different changes with differing seasons. January, February and March (JFM) together with April, May and June were the seasons with the largest SIE decadal variability for Weddell, Indian, Amundsen and West Pacific Sea Sectors, while the Ross Sea showed greater SIE variability during JAS and October November, December. This methodology showed consistent results with the traditional SIE trend calculation and introduced a new quantification index for evaluating the differences between two distributions not necessarily connected in time.
format Article in Journal/Newspaper
title Revisiting Antarctic sea-ice decadal variability since 1980
title_short Revisiting Antarctic sea-ice decadal variability since 1980
title_full Revisiting Antarctic sea-ice decadal variability since 1980
title_fullStr Revisiting Antarctic sea-ice decadal variability since 1980
title_full_unstemmed Revisiting Antarctic sea-ice decadal variability since 1980
title_sort revisiting antarctic sea-ice decadal variability since 1980
publishDate 2022
url https://nipr.repo.nii.ac.jp/?action=repository_uri&item_id=16900
http://id.nii.ac.jp/1291/00016769/
geographic Antarctic
Southern Ocean
Ross Sea
Pacific
Indian
Weddell
geographic_facet Antarctic
Southern Ocean
Ross Sea
Pacific
Indian
Weddell
genre Antarc*
Antarctic
Polar Science
Polar Science
Ross Sea
Sea ice
Southern Ocean
genre_facet Antarc*
Antarctic
Polar Science
Polar Science
Ross Sea
Sea ice
Southern Ocean
op_relation https://doi.org/10.1016/j.polar.2021.100743
https://nipr.repo.nii.ac.jp/?action=repository_uri&item_id=16900
http://id.nii.ac.jp/1291/00016769/
Polar Science, 31, 100743(2022-03)
18739652
op_doi https://doi.org/10.1016/j.polar.2021.100743
container_title Polar Science
container_volume 31
container_start_page 100743
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