Assessment of sea ice thickness simulations in the CMIP6 models with CICE components
Arctic sea ice plays a critical role in modulating our global climate system and the exchange of heat fluxes in the polar region, but its impact on climate varies across different sea ice thickness (SIT) categories. Compared to sea ice cover, the performance of ice models in simulating SIT has been...
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2023
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Online Access: | https://doi.org/10.3389/fmars.2023.1223772 https://doaj.org/article/a426aba3909f4020a963aac1d85925be |
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ftdoajarticles:oai:doaj.org/article:a426aba3909f4020a963aac1d85925be 2023-11-12T04:10:57+01:00 Assessment of sea ice thickness simulations in the CMIP6 models with CICE components Mengliu Xu Junde Li 2023-10-01T00:00:00Z https://doi.org/10.3389/fmars.2023.1223772 https://doaj.org/article/a426aba3909f4020a963aac1d85925be EN eng Frontiers Media S.A. https://www.frontiersin.org/articles/10.3389/fmars.2023.1223772/full https://doaj.org/toc/2296-7745 2296-7745 doi:10.3389/fmars.2023.1223772 https://doaj.org/article/a426aba3909f4020a963aac1d85925be Frontiers in Marine Science, Vol 10 (2023) sea ice thickness PIOMAS CICE MME CMIP6 Arctic Ocean Science Q General. Including nature conservation geographical distribution QH1-199.5 article 2023 ftdoajarticles https://doi.org/10.3389/fmars.2023.1223772 2023-10-22T00:41:09Z Arctic sea ice plays a critical role in modulating our global climate system and the exchange of heat fluxes in the polar region, but its impact on climate varies across different sea ice thickness (SIT) categories. Compared to sea ice cover, the performance of ice models in simulating SIT has been less evaluated, particularly in the sixth Coupled Model Intercomparison Project Phase (CMIP6). Here, we chose 12 CMIP6 models with the Community Ice Code model (CICE) components and compared their SIT simulations with the satellite observations and the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) model between 1980 and 2014. Our results show that the seasonal cycle of the PIOMAS SIT is consistent with satellite observations. Compared to the PIOMAS reanalysis, the multi-model ensemble mean (MME) well represents the sea ice extent in both the thin ice (<0.6 m) and thick ice (> 3.6 m). However, the MME SIT has larger biases in the Chukchi Sea, the Beaufort Sea, the central Arctic, and the Greenland Sea during winter and mainly in the central Arctic during summer. Both the MME and PIOMAS show decreasing trends in SIT over the entire Arctic Ocean in all seasons, but the interannual variability of SIT in MME is smaller than that in PIOMAS. Among the 12 CMIP6 models, the FIO-ESM-2.0 model shows the best simulation of the annual mean SIT, but the SAM0-UNICON and NESM3 models have the largest biases in the climatological mean SIT over the Arctic Ocean. We also demonstrate that the FIO-ESM-2.0 performs the best in the seasonal cycles of SIT. Our study suggests that more attention should be paid to the coupling of the CICE model with ocean and atmosphere models, which is vital to improving the SIT simulation in CMIP6 models and to better understanding the impact of Arctic sea ice on our climate system. Article in Journal/Newspaper Arctic Arctic Ocean Beaufort Sea Chukchi Chukchi Sea Greenland Greenland Sea Sea ice Directory of Open Access Journals: DOAJ Articles Arctic Arctic Ocean Chukchi Sea Greenland Frontiers in Marine Science 10 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
sea ice thickness PIOMAS CICE MME CMIP6 Arctic Ocean Science Q General. Including nature conservation geographical distribution QH1-199.5 |
spellingShingle |
sea ice thickness PIOMAS CICE MME CMIP6 Arctic Ocean Science Q General. Including nature conservation geographical distribution QH1-199.5 Mengliu Xu Junde Li Assessment of sea ice thickness simulations in the CMIP6 models with CICE components |
topic_facet |
sea ice thickness PIOMAS CICE MME CMIP6 Arctic Ocean Science Q General. Including nature conservation geographical distribution QH1-199.5 |
description |
Arctic sea ice plays a critical role in modulating our global climate system and the exchange of heat fluxes in the polar region, but its impact on climate varies across different sea ice thickness (SIT) categories. Compared to sea ice cover, the performance of ice models in simulating SIT has been less evaluated, particularly in the sixth Coupled Model Intercomparison Project Phase (CMIP6). Here, we chose 12 CMIP6 models with the Community Ice Code model (CICE) components and compared their SIT simulations with the satellite observations and the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) model between 1980 and 2014. Our results show that the seasonal cycle of the PIOMAS SIT is consistent with satellite observations. Compared to the PIOMAS reanalysis, the multi-model ensemble mean (MME) well represents the sea ice extent in both the thin ice (<0.6 m) and thick ice (> 3.6 m). However, the MME SIT has larger biases in the Chukchi Sea, the Beaufort Sea, the central Arctic, and the Greenland Sea during winter and mainly in the central Arctic during summer. Both the MME and PIOMAS show decreasing trends in SIT over the entire Arctic Ocean in all seasons, but the interannual variability of SIT in MME is smaller than that in PIOMAS. Among the 12 CMIP6 models, the FIO-ESM-2.0 model shows the best simulation of the annual mean SIT, but the SAM0-UNICON and NESM3 models have the largest biases in the climatological mean SIT over the Arctic Ocean. We also demonstrate that the FIO-ESM-2.0 performs the best in the seasonal cycles of SIT. Our study suggests that more attention should be paid to the coupling of the CICE model with ocean and atmosphere models, which is vital to improving the SIT simulation in CMIP6 models and to better understanding the impact of Arctic sea ice on our climate system. |
format |
Article in Journal/Newspaper |
author |
Mengliu Xu Junde Li |
author_facet |
Mengliu Xu Junde Li |
author_sort |
Mengliu Xu |
title |
Assessment of sea ice thickness simulations in the CMIP6 models with CICE components |
title_short |
Assessment of sea ice thickness simulations in the CMIP6 models with CICE components |
title_full |
Assessment of sea ice thickness simulations in the CMIP6 models with CICE components |
title_fullStr |
Assessment of sea ice thickness simulations in the CMIP6 models with CICE components |
title_full_unstemmed |
Assessment of sea ice thickness simulations in the CMIP6 models with CICE components |
title_sort |
assessment of sea ice thickness simulations in the cmip6 models with cice components |
publisher |
Frontiers Media S.A. |
publishDate |
2023 |
url |
https://doi.org/10.3389/fmars.2023.1223772 https://doaj.org/article/a426aba3909f4020a963aac1d85925be |
geographic |
Arctic Arctic Ocean Chukchi Sea Greenland |
geographic_facet |
Arctic Arctic Ocean Chukchi Sea Greenland |
genre |
Arctic Arctic Ocean Beaufort Sea Chukchi Chukchi Sea Greenland Greenland Sea Sea ice |
genre_facet |
Arctic Arctic Ocean Beaufort Sea Chukchi Chukchi Sea Greenland Greenland Sea Sea ice |
op_source |
Frontiers in Marine Science, Vol 10 (2023) |
op_relation |
https://www.frontiersin.org/articles/10.3389/fmars.2023.1223772/full https://doaj.org/toc/2296-7745 2296-7745 doi:10.3389/fmars.2023.1223772 https://doaj.org/article/a426aba3909f4020a963aac1d85925be |
op_doi |
https://doi.org/10.3389/fmars.2023.1223772 |
container_title |
Frontiers in Marine Science |
container_volume |
10 |
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1782330196663205888 |