Impacts of Arctic sea ice on cold season atmospheric variability and trends estimated from observations and a multimodel large ensemble

dc.contributor.author Liang, Yu-Chiao
dc.contributor.author Frankignoul, Claude
dc.contributor.author Kwon, Young-Oh
dc.contributor.author Gastineau, Guillaume
dc.contributor.author Manzini, Elisa
dc.contributor.author Danabasoglu, Gokhan
dc.contributor.author Suo, Lingling
dc.contributor.author Yeager, Stephen G.
dc.contributor.author Gao, Yongqi
dc.contributor.author Attema, Jisk J.
dc.contributor.author Cherchi, Annalisa
dc.contributor.author Ghosh, Rohit
dc.contributor.author Matei, Daniela
dc.contributor.author Mecking, Jennifer V.
dc.contributor.author Tian, Tian
dc.contributor.author Zhang, Ying
dc.date.accessioned 2022-06-06T13:52:36Z
dc.date.available 2022-06-06T13:52:36Z
dc.date.issued 2021-09-24
dc.description Author Posting. © American Meteorological Society, 2021. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Liang, Y.-C., Frankignoul, C., Kwon, Y.-O., Gastineau, G., Manzini, E., Danabasoglu, G., Suo, L., Yeager, S., Gao, Y., Attema, J. J., Cherchi, A., Ghosh, R., Matei, D., Mecking, J., Tian, T., & Zhang, Y. Impacts of Arctic sea ice on cold season atmospheric variability and trends estimated from observations and a multimodel large ensemble. Journal of Climate, 34(20), (2021): 8419–8443, https://doi.org/10.1175/JCLI-D-20-0578.s1. en_US
dc.description.abstract To examine the atmospheric responses to Arctic sea ice variability in the Northern Hemisphere cold season (from October to the following March), this study uses a coordinated set of large-ensemble experiments of nine atmospheric general circulation models (AGCMs) forced with observed daily varying sea ice, sea surface temperature, and radiative forcings prescribed during the 1979–2014 period, together with a parallel set of experiments where Arctic sea ice is substituted by its climatology. The simulations of the former set reproduce the near-surface temperature trends in reanalysis data, with similar amplitude, and their multimodel ensemble mean (MMEM) shows decreasing sea level pressure over much of the polar cap and Eurasia in boreal autumn. The MMEM difference between the two experiments allows isolating the effects of Arctic sea ice loss, which explain a large portion of the Arctic warming trends in the lower troposphere and drive a small but statistically significant weakening of the wintertime Arctic Oscillation. The observed interannual covariability between sea ice extent in the Barents–Kara Seas and lagged atmospheric circulation is distinguished from the effects of confounding factors based on multiple regression, and quantitatively compared to the covariability in MMEMs. The interannual sea ice decline followed by a negative North Atlantic Oscillation–like anomaly found in observations is also seen in the MMEM differences, with consistent spatial structure but much smaller amplitude. This result suggests that the sea ice impacts on trends and interannual atmospheric variability simulated by AGCMs could be underestimated, but caution is needed because internal atmospheric variability may have affected the observed relationship. en_US
dc.description.sponsorship We acknowledge support by the Blue-Action Project (the European Union’s Horizon 2020 research and innovation programme, #727852, http://www.blue-action.eu/index.php?id=3498). The WHOI–NCAR group was supported by the U.S. National Science Foundation (NSF) Office of Polar Programs Grants 1736738 and 1737377. Their computing and data storage resources, including the Cheyenne supercomputer (doi:10.5065/D6RX99HX), were provided by the Computational and Information Systems Laboratory at NCAR. NCAR is a major facility sponsored by the U.S. NSF under Cooperative Agreement No. 1852977. Guillaume Gastineau was granted access to the HPC resources of TGCC under the allocations A5-017403 and A7-017403 made by GENCI. The SST and SIC data were downloaded from the U.K. Met Office Hadley Centre Observations Datasets (http://www.metoffice.gov.uk/hadobs/hadisst). The work by NLeSC was carried out on the Dutch national e-infrastructure with the support of SURF Cooperative. The simulations of IAP AGCM were supported by the National Key R&D Program of China 2017YFE0111800. The NorESM2-CAM6 simulations were performed on resources provided by UNINETT Sigma2–the National Infrastructure for High Performance Computing and Data Storage in Norway (nn2343k, NS9015K). en_US
dc.identifier.citation Liang, Y.-C., Frankignoul, C., Kwon, Y.-O., Gastineau, G., Manzini, E., Danabasoglu, G., Suo, L., Yeager, S., Gao, Y., Attema, J. J., Cherchi, A., Ghosh, R., Matei, D., Mecking, J., Tian, T., & Zhang, Y. (2021). Impacts of Arctic sea ice on cold season atmospheric variability and trends estimated from observations and a multimodel large ensemble. Journal of Climate, 34(20), 8419–8443. en_US
dc.identifier.doi 10.1175/JCLI-D-20-0578.s1
dc.identifier.uri https://hdl.handle.net/1912/28966
dc.publisher American Meteorological Society en_US
dc.relation.uri https://doi.org/10.1175/JCLI-D-20-0578.s1
dc.subject Arctic en_US
dc.subject Sea ice en_US
dc.subject Atmospheric circulation en_US
dc.subject Climate models en_US
dc.title Impacts of Arctic sea ice on cold season atmospheric variability and trends estimated from observations and a multimodel large ensemble en_US
dc.type Article en_US
dspace.entity.type Publication
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