2006: Attribution and impacts of upper-ocean biases in CCSM3
The largest and potentially most important ocean near-surface biases are examined in the Community Climate System Model coupled simulation of present-day conditions. They are attributed to problems in the component models of the ocean or atmosphere, or both. Tropical biases in sea surface salinity (...
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| Format: | Text |
| Language: | English |
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| Online Access: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.671.2875 http://clivar.org/sites/default/files/documents/wgomd/Large_Danabasoglu_2006.pdf |
| Summary: | The largest and potentially most important ocean near-surface biases are examined in the Community Climate System Model coupled simulation of present-day conditions. They are attributed to problems in the component models of the ocean or atmosphere, or both. Tropical biases in sea surface salinity (SSS) are associated with precipitation errors, with the most striking being a band of excess rainfall across the South Pacific at about 8°S. Cooler-than-observed equatorial Pacific sea surface temperature (SST) is necessary to control a potentially catastrophic positive feedback, involving precipitation along the equator. The strength of the wind-driven gyres and interbasin exchange is in reasonable agreement with observations, despite the generally too strong near-surface winds. However, the winds drive far too much transport through Drake Passage [190 Sv (1 Sv 106 m3 s1)], but with little effect on SST and SSS. Problems with the width, separation, and location of western boundary currents and their extensions create large correlated SST and SSS biases in midlatitudes. Ocean model deficiencies are suspected because similar signals are seen in uncoupled ocean solutions, but there is no evidence of serious remote impacts. The seasonal cycles of SST and winds in the equatorial Pacific are not well represented, and numerical experiments suggest that these problems are initiated by the coupling of either or both wind components. The largest mean SST biases |
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