Late-twentieth-century simulation of Arctic sea ice and ocean properties in the CCSM4
To establish how well the new Community Climate System model version 4 (CCSM4) simulates the properties of the Arctic sea ice and ocean, we here compare results from six CCSM4 20th century ensemble simulations with available data. We find that the CCSM4 simulations capture most of the important clim...
Published in: | Journal of Climate |
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Other Authors: | , , , , , , , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
2012
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Subjects: | |
Online Access: | http://nldr.library.ucar.edu/repository/collections/OSGC-000-000-007-670 https://doi.org/10.1175/JCLI-D-11-00201.1 |
Summary: | To establish how well the new Community Climate System model version 4 (CCSM4) simulates the properties of the Arctic sea ice and ocean, we here compare results from six CCSM4 20th century ensemble simulations with available data. We find that the CCSM4 simulations capture most of the important climatological features of the Arctic sea ice and ocean state well, among them the sea-ice thickness distribution, the fraction of multiyear sea ice, and the sea-ice edge. The strongest bias exists in the simulated spring to fall sea-ice motion field, the location of the Beaufort Gyre, and the temperature of the deep Arctic Ocean (below 250 m), which are caused by deficiencies in the simulation of the Arctic sea-level pressure field and the lack of deep-water formation on the Arctic shelves. The observed decrease in the sea-ice extent and of the multiyear ice cover is also well captured by the CCSM4. It is important to note, however, that the temporal evolution of the simulated Arctic sea-ice cover over the satellite era is strongly influenced by internal variability. For example, while one ensemble member shows an even larger decrease in the sea-ice extent over 1981 - 2005 than observed, two ensemble members show no statistically significant trend over he same period. It is therefore important to compare the observed sea-ice extent trend not just with the ensemble mean or a multi-model ensemble mean, but also with individual ensemble members, due to the strong imprint of internal variability on these relatively short trends. National Science Foundation (NSF): OPP-0902068, ARC-0909313 National Aeronautics and Space Administration (NASA): NNG04GO51G, NNG06GB26G |
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