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Frontal Scale Air–Sea Interaction in High-Resolution Coupled Climate Models

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https://ir.library.oregonstate.edu/concern/articles/c247dt750

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Abstract
  • The emerging picture of frontal scale air–sea interaction derived from high-resolution satellite observations of surface winds and sea surface temperature (SST) provides a unique opportunity to test the fidelity of high-resolution coupled climate simulations. Initial analysis of the output of a suite of Community Climate System Model (CCSM) experiments indicates that characteristics of frontal scale ocean–atmosphere interaction, such as the positive correlation between SST and surface wind stress, are realistically captured only when the ocean component is eddy resolving. The strength of the coupling between SST and surface stress is weaker than observed, however, as has been found previously for numerical weather prediction models and other coupled climate models. The results are similar when the atmospheric component model grid resolution is doubled from 0.5° to 0.25°, an indication that shortcomings in the representation of subgrid scale atmospheric planetary boundary layer processes, rather than resolved scale processes, are responsible for the weakness of the coupling. In the coupled model solutions the response to mesoscale SST features is strongest in the atmospheric boundary layer, but there is a deeper reaching response of the atmospheric circulation apparent in free tropospheric clouds. This simulated response is shown to be consistent with satellite estimates of the relationship between mesoscale SST and all-sky albedo.
  • Keywords: Mesoscale systems, Coupled models, Sea surface temperature, Wind stress, Airndashsea interaction, Albedo
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  • Bryan, Frank O., Robert Tomas, John M. Dennis, Dudley B. Chelton, Norman G. Loeb, Julie L. McClean, 2010: Frontal Scale Air–Sea Interaction in High-Resolution Coupled Climate Models. Journal of Climate, 23, 6277–6291.
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  • 23
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  • 23
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  • Computing time for the experiments described in this paper was provided by the Department of Energy as part of the Multiprogrammatic and Institutional Computing Initiative at Lawrence Livermore National Laboratory (LLNL), under the Office of Science (BER), U.S. Department of Energy, through Cooperative Agreement DE-FC02-97ER62402 at the National Energy Supercomputing Center, and by NCAR. FOB, RT, and JD are supported through National Science Foundation Cooperative Grant NSF01, which funds NCAR. JD was additionally supported by NSF Grants OCI-0749206 and OCE-0825754 and the Department of Energy, through CCPP Program Grant DE-PS02- 07ER07-06.DBC was supported by NASA Grant NAS5- 32965 for funding of Ocean Vector Winds Science Team activities. JLM was supported by the DOE, CCPP Program Grant DE-FG02-05ER64119, and an LLNL subcontract.
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