Impact of the ice thickness distribution discretization on the sea ice concentration variability in the NEMO3.6–LIM3 global ocean–sea ice model
This study assesses the impact of different sea ice thickness distribution (ITD) discretizations on the sea ice concentration (SIC) variability in ocean stand-alone NEMO3.6–LIM3 simulations. Three ITD discretizations with different numbers of sea ice thickness categories and boundaries are evaluated...
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ftdoajarticles:oai:doaj.org/article:e9478f40a1cd43298059bf1a192fc442 2023-05-15T13:43:27+02:00 Impact of the ice thickness distribution discretization on the sea ice concentration variability in the NEMO3.6–LIM3 global ocean–sea ice model E. Moreno-Chamarro P. Ortega F. Massonnet 2020-10-01T00:00:00Z https://doi.org/10.5194/gmd-13-4773-2020 https://doaj.org/article/e9478f40a1cd43298059bf1a192fc442 EN eng Copernicus Publications https://gmd.copernicus.org/articles/13/4773/2020/gmd-13-4773-2020.pdf https://doaj.org/toc/1991-959X https://doaj.org/toc/1991-9603 doi:10.5194/gmd-13-4773-2020 1991-959X 1991-9603 https://doaj.org/article/e9478f40a1cd43298059bf1a192fc442 Geoscientific Model Development, Vol 13, Pp 4773-4787 (2020) Geology QE1-996.5 article 2020 ftdoajarticles https://doi.org/10.5194/gmd-13-4773-2020 2023-01-08T01:32:09Z This study assesses the impact of different sea ice thickness distribution (ITD) discretizations on the sea ice concentration (SIC) variability in ocean stand-alone NEMO3.6–LIM3 simulations. Three ITD discretizations with different numbers of sea ice thickness categories and boundaries are evaluated against three different satellite products (hereafter referred to as “data”). Typical model and data interannual SIC variability is characterized by K -means clustering both in the Arctic and Antarctica between 1979 and 2014. We focus on two seasons, winter (January–March) and summer (August–October), in which correlation coefficients across clusters in individual months are largest. In the Arctic, clusters are computed before and after detrending the series with a second-degree polynomial to separate interannual from longer-term variability. The analysis shows that, before detrending, winter clusters reflect the SIC response to large-scale atmospheric variability at both poles, while summer clusters capture the negative and positive trends in Arctic and Antarctic SIC, respectively. After detrending, Arctic clusters reflect the SIC response to interannual atmospheric variability predominantly. The cluster analysis is complemented with a model–data comparison of the sea ice extent and SIC anomaly patterns. The single-category discretization shows the worst model–data agreement in the Arctic summer before detrending, related to a misrepresentation of the long-term melting trend. Similarly, increasing the number of thin categories reduces model–data agreement in the Arctic, due to a poor representation of the summer melting trend and an overly large winter sea ice volume associated with a net increase in basal ice growth. In contrast, more thin categories improve model realism in Antarctica, and more thick ones improve it in central Arctic regions with very thick ice. In all the analyses we nonetheless identify no optimal discretization. Our results thus suggest that no clear benefit in the representation of SIC ... Article in Journal/Newspaper Antarc* Antarctic Antarctica Arctic Sea ice Directory of Open Access Journals: DOAJ Articles Antarctic Arctic Geoscientific Model Development 13 10 4773 4787 |
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Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Geology QE1-996.5 |
spellingShingle |
Geology QE1-996.5 E. Moreno-Chamarro P. Ortega F. Massonnet Impact of the ice thickness distribution discretization on the sea ice concentration variability in the NEMO3.6–LIM3 global ocean–sea ice model |
topic_facet |
Geology QE1-996.5 |
description |
This study assesses the impact of different sea ice thickness distribution (ITD) discretizations on the sea ice concentration (SIC) variability in ocean stand-alone NEMO3.6–LIM3 simulations. Three ITD discretizations with different numbers of sea ice thickness categories and boundaries are evaluated against three different satellite products (hereafter referred to as “data”). Typical model and data interannual SIC variability is characterized by K -means clustering both in the Arctic and Antarctica between 1979 and 2014. We focus on two seasons, winter (January–March) and summer (August–October), in which correlation coefficients across clusters in individual months are largest. In the Arctic, clusters are computed before and after detrending the series with a second-degree polynomial to separate interannual from longer-term variability. The analysis shows that, before detrending, winter clusters reflect the SIC response to large-scale atmospheric variability at both poles, while summer clusters capture the negative and positive trends in Arctic and Antarctic SIC, respectively. After detrending, Arctic clusters reflect the SIC response to interannual atmospheric variability predominantly. The cluster analysis is complemented with a model–data comparison of the sea ice extent and SIC anomaly patterns. The single-category discretization shows the worst model–data agreement in the Arctic summer before detrending, related to a misrepresentation of the long-term melting trend. Similarly, increasing the number of thin categories reduces model–data agreement in the Arctic, due to a poor representation of the summer melting trend and an overly large winter sea ice volume associated with a net increase in basal ice growth. In contrast, more thin categories improve model realism in Antarctica, and more thick ones improve it in central Arctic regions with very thick ice. In all the analyses we nonetheless identify no optimal discretization. Our results thus suggest that no clear benefit in the representation of SIC ... |
format |
Article in Journal/Newspaper |
author |
E. Moreno-Chamarro P. Ortega F. Massonnet |
author_facet |
E. Moreno-Chamarro P. Ortega F. Massonnet |
author_sort |
E. Moreno-Chamarro |
title |
Impact of the ice thickness distribution discretization on the sea ice concentration variability in the NEMO3.6–LIM3 global ocean–sea ice model |
title_short |
Impact of the ice thickness distribution discretization on the sea ice concentration variability in the NEMO3.6–LIM3 global ocean–sea ice model |
title_full |
Impact of the ice thickness distribution discretization on the sea ice concentration variability in the NEMO3.6–LIM3 global ocean–sea ice model |
title_fullStr |
Impact of the ice thickness distribution discretization on the sea ice concentration variability in the NEMO3.6–LIM3 global ocean–sea ice model |
title_full_unstemmed |
Impact of the ice thickness distribution discretization on the sea ice concentration variability in the NEMO3.6–LIM3 global ocean–sea ice model |
title_sort |
impact of the ice thickness distribution discretization on the sea ice concentration variability in the nemo3.6–lim3 global ocean–sea ice model |
publisher |
Copernicus Publications |
publishDate |
2020 |
url |
https://doi.org/10.5194/gmd-13-4773-2020 https://doaj.org/article/e9478f40a1cd43298059bf1a192fc442 |
geographic |
Antarctic Arctic |
geographic_facet |
Antarctic Arctic |
genre |
Antarc* Antarctic Antarctica Arctic Sea ice |
genre_facet |
Antarc* Antarctic Antarctica Arctic Sea ice |
op_source |
Geoscientific Model Development, Vol 13, Pp 4773-4787 (2020) |
op_relation |
https://gmd.copernicus.org/articles/13/4773/2020/gmd-13-4773-2020.pdf https://doaj.org/toc/1991-959X https://doaj.org/toc/1991-9603 doi:10.5194/gmd-13-4773-2020 1991-959X 1991-9603 https://doaj.org/article/e9478f40a1cd43298059bf1a192fc442 |
op_doi |
https://doi.org/10.5194/gmd-13-4773-2020 |
container_title |
Geoscientific Model Development |
container_volume |
13 |
container_issue |
10 |
container_start_page |
4773 |
op_container_end_page |
4787 |
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