Modeling the annual cycle of daily Antarctic sea ice extent
The total Antarctic sea ice extent (SIE) experiences a distinct annual cycle, peaking in September and troughing in March. In this paper we propose a mathematical and statistical decomposition of this temporal variation in SIE. Each component is interpretable and, when combined, give a complete pict...
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ftcopernicus:oai:publications.copernicus.org:tcd79800 2023-05-15T13:35:08+02:00 Modeling the annual cycle of daily Antarctic sea ice extent Handcock, Mark S. Raphael, Marilyn N. 2019-10-29 application/pdf https://doi.org/10.5194/tc-2019-203 https://www.the-cryosphere-discuss.net/tc-2019-203/ eng eng doi:10.5194/tc-2019-203 https://www.the-cryosphere-discuss.net/tc-2019-203/ eISSN: 1994-0424 Text 2019 ftcopernicus https://doi.org/10.5194/tc-2019-203 2019-12-24T09:48:17Z The total Antarctic sea ice extent (SIE) experiences a distinct annual cycle, peaking in September and troughing in March. In this paper we propose a mathematical and statistical decomposition of this temporal variation in SIE. Each component is interpretable and, when combined, give a complete picture of the variation of the sea ice. We consider time scales varying from the instantaneous, and not previously defined, to the multidecadal curvilinear trend, the longest. Because our representation is daily, these timescales of variability give precise information about the timing and rates of advance and retreat of the ice and may be used to diagnose physical contributors to variability in the sea ice. We define a number of annual cycles each capturing different components of variation, especially the yearly amplitude and phase that are major contributors to SIE variation. Using daily sea ice concentration data, we show that our proposed invariant annual cycle explains 29 % more of the variation of daily SIE than the traditional method. The proposed annual cycle that incorporates amplitude and phase variation explains 77 % more variation than the traditional method. The variation in phase explains more of the variability in SIE than the amplitude. Using our methodology, we show that the anomalous decay of sea ice in 2016 was associated largely with a change of phase rather than amplitude. We show that the long term trend in Antarctic sea ice extent is strongly curvilinear and the reported positive linear trend is small and dependent strongly on a positive trend that began around 2011 and continued until 2016. Text Antarc* Antarctic Sea ice Copernicus Publications: E-Journals Antarctic |
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Copernicus Publications: E-Journals |
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ftcopernicus |
language |
English |
description |
The total Antarctic sea ice extent (SIE) experiences a distinct annual cycle, peaking in September and troughing in March. In this paper we propose a mathematical and statistical decomposition of this temporal variation in SIE. Each component is interpretable and, when combined, give a complete picture of the variation of the sea ice. We consider time scales varying from the instantaneous, and not previously defined, to the multidecadal curvilinear trend, the longest. Because our representation is daily, these timescales of variability give precise information about the timing and rates of advance and retreat of the ice and may be used to diagnose physical contributors to variability in the sea ice. We define a number of annual cycles each capturing different components of variation, especially the yearly amplitude and phase that are major contributors to SIE variation. Using daily sea ice concentration data, we show that our proposed invariant annual cycle explains 29 % more of the variation of daily SIE than the traditional method. The proposed annual cycle that incorporates amplitude and phase variation explains 77 % more variation than the traditional method. The variation in phase explains more of the variability in SIE than the amplitude. Using our methodology, we show that the anomalous decay of sea ice in 2016 was associated largely with a change of phase rather than amplitude. We show that the long term trend in Antarctic sea ice extent is strongly curvilinear and the reported positive linear trend is small and dependent strongly on a positive trend that began around 2011 and continued until 2016. |
format |
Text |
author |
Handcock, Mark S. Raphael, Marilyn N. |
spellingShingle |
Handcock, Mark S. Raphael, Marilyn N. Modeling the annual cycle of daily Antarctic sea ice extent |
author_facet |
Handcock, Mark S. Raphael, Marilyn N. |
author_sort |
Handcock, Mark S. |
title |
Modeling the annual cycle of daily Antarctic sea ice extent |
title_short |
Modeling the annual cycle of daily Antarctic sea ice extent |
title_full |
Modeling the annual cycle of daily Antarctic sea ice extent |
title_fullStr |
Modeling the annual cycle of daily Antarctic sea ice extent |
title_full_unstemmed |
Modeling the annual cycle of daily Antarctic sea ice extent |
title_sort |
modeling the annual cycle of daily antarctic sea ice extent |
publishDate |
2019 |
url |
https://doi.org/10.5194/tc-2019-203 https://www.the-cryosphere-discuss.net/tc-2019-203/ |
geographic |
Antarctic |
geographic_facet |
Antarctic |
genre |
Antarc* Antarctic Sea ice |
genre_facet |
Antarc* Antarctic Sea ice |
op_source |
eISSN: 1994-0424 |
op_relation |
doi:10.5194/tc-2019-203 https://www.the-cryosphere-discuss.net/tc-2019-203/ |
op_doi |
https://doi.org/10.5194/tc-2019-203 |
_version_ |
1766061338751139840 |