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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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 |
_version_ |
1766250398541152256 |