Articles | Volume 9, issue 2
https://doi.org/10.5194/gmd-9-607-2016
https://doi.org/10.5194/gmd-9-607-2016
Development and technical paper
 | 
12 Feb 2016
Development and technical paper |  | 12 Feb 2016

Quantifying the impact of sub-grid surface wind variability on sea salt and dust emissions in CAM5

Kai Zhang, Chun Zhao, Hui Wan, Yun Qian, Richard C. Easter, Steven J. Ghan, Koichi Sakaguchi, and Xiaohong Liu

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Cited articles

Arakawa, A., Jung, J.-H., and Wu, C.-M.: Toward unification of the multiscale modeling of the atmosphere, Atmos. Chem. Phys., 11, 3731–3742, https://doi.org/10.5194/acp-11-3731-2011, 2011.
Banta, R. M., Pichugina, Y. L., and Brewer, W. A.: Turbulent velocity-variance profiles in the stable boundary layer generated by a nocturnal low-level jet, J. Atmos. Sci., 63, 2700–2719, https://doi.org/10.1175/JAS3776.1, 2006.
Bretherton, C. S. and Park, S.: A new moist turbulence parameterization in the community atmosphere model, J. Climate, 22, 3422–3448, https://doi.org/10.1175/2008JCLI2556.1, 2009.
Cakmur, R. V., Miller, R. L., and Torres, O.: Incorporating the effect of small-scale circulations upon dust emission in an atmospheric general circulation model, J. Geophys. Res.-Atmos., 109, 7201, https://doi.org/10.1029/2003JD004067, 2004.
Capps, S. B. and Zender, C. S.: Observed and CAM3 GCM sea surface wind speed distributions: characterization, comparison, and bias reduction, J. Climate, 21, 6569, https://doi.org/10.1175/2008JCLI2374.1, 2008.
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Short summary
A sub-grid treatment based on Weibull distribution is introduced to CAM5 to take into account the impact of unresolved variability of surface wind speed on sea salt and dust emissions. Simulations show that sub-grid wind variability has relatively small impacts on the global mean sea salt emissions, but considerable influence on dust emissions. Dry convective eddies and mesoscale flows associated with complex topography are the major causes of dust emission enhancement.