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

Related authors

Regional to global distributions, trends, and drivers of biogenic volatile organic compound emission from 2001 to 2020
Hao Wang, Xiaohong Liu, Chenglai Wu, and Guangxing Lin
Atmos. Chem. Phys., 24, 3309–3328, https://doi.org/10.5194/acp-24-3309-2024,https://doi.org/10.5194/acp-24-3309-2024, 2024
Short summary
Understanding processes that control dust spatial distributions with global climate models and satellite observations
Mingxuan Wu, Xiaohong Liu, Hongbin Yu, Hailong Wang, Yang Shi, Kang Yang, Anton Darmenov, Chenglai Wu, Zhien Wang, Tao Luo, Yan Feng, and Ziming Ke
Atmos. Chem. Phys., 20, 13835–13855, https://doi.org/10.5194/acp-20-13835-2020,https://doi.org/10.5194/acp-20-13835-2020, 2020
Short summary
The global dust cycle and uncertainty in CMIP5 (Coupled Model Intercomparison Project phase 5) models
Chenglai Wu, Zhaohui Lin, and Xiaohong Liu
Atmos. Chem. Phys., 20, 10401–10425, https://doi.org/10.5194/acp-20-10401-2020,https://doi.org/10.5194/acp-20-10401-2020, 2020
Short summary
Quantifying snow darkening and atmospheric radiative effects of black carbon and dust on the South Asian monsoon and hydrological cycle: experiments using variable-resolution CESM
Stefan Rahimi, Xiaohong Liu, Chenglai Wu, William K. Lau, Hunter Brown, Mingxuan Wu, and Yun Qian
Atmos. Chem. Phys., 19, 12025–12049, https://doi.org/10.5194/acp-19-12025-2019,https://doi.org/10.5194/acp-19-12025-2019, 2019
Short summary
CAM6 simulation of mean and extreme precipitation over Asia: sensitivity to upgraded physical parameterizations and higher horizontal resolution
Lei Lin, Andrew Gettelman, Yangyang Xu, Chenglai Wu, Zhili Wang, Nan Rosenbloom, Susan C. Bates, and Wenjie Dong
Geosci. Model Dev., 12, 3773–3793, https://doi.org/10.5194/gmd-12-3773-2019,https://doi.org/10.5194/gmd-12-3773-2019, 2019
Short summary

Related subject area

Subject: Clouds and Precipitation | Research Activity: Atmospheric Modelling and Data Analysis | Altitude Range: Troposphere | Science Focus: Physics (physical properties and processes)
The presence of clouds lowers climate sensitivity in the MPI-ESM1.2 climate model
Andrea Mosso, Thomas Hocking, and Thorsten Mauritsen
Atmos. Chem. Phys., 24, 12793–12806, https://doi.org/10.5194/acp-24-12793-2024,https://doi.org/10.5194/acp-24-12793-2024, 2024
Short summary
Diurnal variation in an amplified canopy urban heat island during heat wave periods in the megacity of Beijing: roles of mountain–valley breeze and urban morphology
Tao Shi, Yuanjian Yang, Ping Qi, and Simone Lolli
Atmos. Chem. Phys., 24, 12807–12822, https://doi.org/10.5194/acp-24-12807-2024,https://doi.org/10.5194/acp-24-12807-2024, 2024
Short summary
Diurnal evolution of non-precipitating marine stratocumuli in a large-eddy simulation ensemble
Yao-Sheng Chen, Jianhao Zhang, Fabian Hoffmann, Takanobu Yamaguchi, Franziska Glassmeier, Xiaoli Zhou, and Graham Feingold
Atmos. Chem. Phys., 24, 12661–12685, https://doi.org/10.5194/acp-24-12661-2024,https://doi.org/10.5194/acp-24-12661-2024, 2024
Short summary
High ice water content in tropical mesoscale convective systems (a conceptual model)
Alexei Korolev, Zhipeng Qu, Jason Milbrandt, Ivan Heckman, Mélissa Cholette, Mengistu Wolde, Cuong Nguyen, Greg M. McFarquhar, Paul Lawson, and Ann M. Fridlind
Atmos. Chem. Phys., 24, 11849–11881, https://doi.org/10.5194/acp-24-11849-2024,https://doi.org/10.5194/acp-24-11849-2024, 2024
Short summary
Evolution of cloud droplet temperature and lifetime in spatiotemporally varying subsaturated environments with implications for ice nucleation at cloud edges
Puja Roy, Robert M. Rauber, and Larry Di Girolamo
Atmos. Chem. Phys., 24, 11653–11678, https://doi.org/10.5194/acp-24-11653-2024,https://doi.org/10.5194/acp-24-11653-2024, 2024
Short summary

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.
Download
Short summary
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.
Altmetrics
Final-revised paper
Preprint