Articles | Volume 17, issue 7
https://doi.org/10.5194/acp-17-4731-2017
https://doi.org/10.5194/acp-17-4731-2017
Research article
 | 
11 Apr 2017
Research article |  | 11 Apr 2017

Direct comparisons of ice cloud macro- and microphysical properties simulated by the Community Atmosphere Model version 5 with HIPPO aircraft observations

Chenglai Wu, Xiaohong Liu, Minghui Diao, Kai Zhang, Andrew Gettelman, Zheng Lu, Joyce E. Penner, and Zhaohui Lin

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Cited articles

Abdul-Razzak, H. and Ghan, S. J.: A parameterization of aerosol activation, 2. Multiple aerosol types, J. Geophys. Res.-Atmos., 105, 6837–6844, 2000.
Bardeen, C. G., Gettelman, A., Jensen, E. J., Heymsfield, A., Conley, A. J., Delanoë, J., Deng, M., and Toon, O. B.: Improved cirrus simulations in a GCM using CARMA sectional microphysics, J. Geophys. Res., 118, 11679–11697, https://doi.org/10.1002/2013JD020193, 2013.
Bodas-Salcedo, A., Webb, M. J., Bony, S., Chepfer, H., Dufresne, J.-L., Klein, S. A., Zhang, Y., Marchand, R., Haynes, J. M., Pincus, R., and John, V. O.: COSP: A satellite simulation software for model assessment, Bull. Amer. Meteor. Soc., 92, 1023–1043, 2011.
Bogenschutz, P. A., Gettelman, A., Morrison, H., Larson, V. E., Craig, C., and Schanen, D. P.: Higher-Order Turbulence Closure and Its Impact on Climate Simulations in the Community Atmosphere Model, J. Clim., 26, 9655–9676, https://doi.org/10.1175/JCLI-D-13-00075.1, 2013.
Boucher, O., Randall, D., Artaxo, P., Bretherton, C., Feingold, G., Forster, P., Kerminen, V.-M., Kondo, Y., Liao, H., Lohmann, U., Rasch, P., Satheesh, S. K., Sherwood, S., Stevens, B., and Zhang, X. Y.: Clouds and Aerosols, in: Climate Change 2013: The Physical Science Basis, Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535, 571–657, 2013.
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This study utilizes a novel approach to directly compare the CAM5-simulated cloud macro- and microphysics with the collocated HIPPO observations for the period of 2009 to 2011. The model cannot capture the large spatial variabilities of observed RH, which is responsible for much of the model missing low-level warm clouds. A large portion of the RH bias results from the discrepancy in water vapor. The model underestimates the observed number concentration and ice water content.
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