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...

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Bibliographic Details
Main Authors: Zhang, Xinfang, Haapala, Jari, Uotila, Petteri
Other Authors: Ilmatieteen laitos, Finnish Meteorological Institute, orcid:0000-0002-1203-6436
Format: Article in Journal/Newspaper
Language:English
Published: Cambridge University Press 2024
Subjects:
Online Access:http://hdl.handle.net/10138/586593
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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