Capacity of a set of CMIP6 models to simulate Arctic sea ice drift
Evaluating CMIP6 model performance helps to improve the prediction of future changes in Arctic sea ice. We analyze the seasonal cycles, distribution, and evolution of sea ice in different regions from 1979 to 2014. We compare the output from selected CMIP6 models with reference data for sea ice moti...
Main Authors: | , , |
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Other Authors: | , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Cambridge University Press
2024
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Online Access: | http://hdl.handle.net/10138/586593 |
_version_ | 1821509099286691840 |
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author | Zhang, Xinfang Haapala, Jari Uotila, Petteri |
author2 | Ilmatieteen laitos Finnish Meteorological Institute orcid:0000-0002-1203-6436 |
author_facet | Zhang, Xinfang Haapala, Jari Uotila, Petteri |
author_sort | Zhang, Xinfang |
collection | HELDA – University of Helsinki Open Repository |
description | Evaluating CMIP6 model performance helps to improve the prediction of future changes in Arctic sea ice. We analyze the seasonal cycles, distribution, and evolution of sea ice in different regions from 1979 to 2014. We compare the output from selected CMIP6 models with reference data for sea ice motion. We also discuss the correlations between sea ice motion(SIM) and sea ice thickness (SIT) in reference data, and how CMIP6 models explain them. We select EC-Earth3, ACCESS-CM2, BCC-CSM2-MR, MPI-ESM1-2-HR, and NorESM2-LM for CMIP6 study. We compare outputs with reference data: Sea ice extent (SIE) from NSIDC; SIT from PIOMAS; and SIM from the IABP buoy data. Analytical techniques include Theil-Sen and Ordinary least squares (OLS) regression. Most selected CMIP6 models have seasonal cycles of SIM lagging behind IABP observations by 1-2 month and overestimate central Arctic SIM magnitude, with MPI-ESM1-2-HR having the highest discrepancy and NorESM2-LM lowest. The models show better simulation of SIM in the ice melting season than in the growing season. Models perform worse at capturing regional differences in SIM evolution and are overly conservative when simulating the increasing trend in ice motion, especially in coastal Arctic seas during summer. There is significant negative correlation between SIT and SIM in October. |
format | Article in Journal/Newspaper |
genre | Annals of Glaciology Arctic Arktinen alue Sea ice |
genre_facet | Annals of Glaciology Arctic Arktinen alue Sea ice |
geographic | Arctic |
geographic_facet | Arctic |
id | ftunivhelsihelda:oai:helda.helsinki.fi:10138/586593 |
institution | Open Polar |
language | English |
op_collection_id | ftunivhelsihelda |
op_relation | Annals of glaciology 10.1017/aog.2024.25 0260-3055 1727-5644 106776 http://hdl.handle.net/10138/586593 |
op_rights | CC BY 4.0 |
publishDate | 2024 |
publisher | Cambridge University Press |
record_format | openpolar |
spelling | ftunivhelsihelda:oai:helda.helsinki.fi:10138/586593 2025-01-16T18:59:41+00:00 Capacity of a set of CMIP6 models to simulate Arctic sea ice drift Zhang, Xinfang Haapala, Jari Uotila, Petteri Ilmatieteen laitos Finnish Meteorological Institute orcid:0000-0002-1203-6436 2024-10-07T09:25:09Z 1-19 application/pdf http://hdl.handle.net/10138/586593 en eng Cambridge University Press Annals of glaciology 10.1017/aog.2024.25 0260-3055 1727-5644 106776 http://hdl.handle.net/10138/586593 CC BY 4.0 ice sea ice arctic region simulation conservation of the seas seas ice cover mathematical models forecasts seasons jää merijää arktinen alue simulointi meriensuojelu meret jääpeite matemaattiset mallit ennusteet vuodenajat A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä A1 Journal article (refereed), original research publishedVersion 2024 ftunivhelsihelda 2024-10-10T00:14:43Z Evaluating CMIP6 model performance helps to improve the prediction of future changes in Arctic sea ice. We analyze the seasonal cycles, distribution, and evolution of sea ice in different regions from 1979 to 2014. We compare the output from selected CMIP6 models with reference data for sea ice motion. We also discuss the correlations between sea ice motion(SIM) and sea ice thickness (SIT) in reference data, and how CMIP6 models explain them. We select EC-Earth3, ACCESS-CM2, BCC-CSM2-MR, MPI-ESM1-2-HR, and NorESM2-LM for CMIP6 study. We compare outputs with reference data: Sea ice extent (SIE) from NSIDC; SIT from PIOMAS; and SIM from the IABP buoy data. Analytical techniques include Theil-Sen and Ordinary least squares (OLS) regression. Most selected CMIP6 models have seasonal cycles of SIM lagging behind IABP observations by 1-2 month and overestimate central Arctic SIM magnitude, with MPI-ESM1-2-HR having the highest discrepancy and NorESM2-LM lowest. The models show better simulation of SIM in the ice melting season than in the growing season. Models perform worse at capturing regional differences in SIM evolution and are overly conservative when simulating the increasing trend in ice motion, especially in coastal Arctic seas during summer. There is significant negative correlation between SIT and SIM in October. Article in Journal/Newspaper Annals of Glaciology Arctic Arktinen alue Sea ice HELDA – University of Helsinki Open Repository Arctic |
spellingShingle | ice sea ice arctic region simulation conservation of the seas seas ice cover mathematical models forecasts seasons jää merijää arktinen alue simulointi meriensuojelu meret jääpeite matemaattiset mallit ennusteet vuodenajat Zhang, Xinfang Haapala, Jari Uotila, Petteri Capacity of a set of CMIP6 models to simulate Arctic sea ice drift |
title | Capacity of a set of CMIP6 models to simulate Arctic sea ice drift |
title_full | Capacity of a set of CMIP6 models to simulate Arctic sea ice drift |
title_fullStr | Capacity of a set of CMIP6 models to simulate Arctic sea ice drift |
title_full_unstemmed | Capacity of a set of CMIP6 models to simulate Arctic sea ice drift |
title_short | Capacity of a set of CMIP6 models to simulate Arctic sea ice drift |
title_sort | capacity of a set of cmip6 models to simulate arctic sea ice drift |
topic | ice sea ice arctic region simulation conservation of the seas seas ice cover mathematical models forecasts seasons jää merijää arktinen alue simulointi meriensuojelu meret jääpeite matemaattiset mallit ennusteet vuodenajat |
topic_facet | ice sea ice arctic region simulation conservation of the seas seas ice cover mathematical models forecasts seasons jää merijää arktinen alue simulointi meriensuojelu meret jääpeite matemaattiset mallit ennusteet vuodenajat |
url | http://hdl.handle.net/10138/586593 |