Articles | Volume 17, issue 7
Atmos. Chem. Phys., 17, 4731–4749, 2017
https://doi.org/10.5194/acp-17-4731-2017
Atmos. Chem. Phys., 17, 4731–4749, 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 et al.

Related authors

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
Radiative effect and climate impacts of brown carbon with the Community Atmosphere Model (CAM5)
Hunter Brown, Xiaohong Liu, Yan Feng, Yiquan Jiang, Mingxuan Wu, Zheng Lu, Chenglai Wu, Shane Murphy, and Rudra Pokhrel
Atmos. Chem. Phys., 18, 17745–17768, https://doi.org/10.5194/acp-18-17745-2018,https://doi.org/10.5194/acp-18-17745-2018, 2018
Short summary

Related subject area

Subject: Clouds and Precipitation | Research Activity: Atmospheric Modelling | Altitude Range: Troposphere | Science Focus: Physics (physical properties and processes)
Sensitivity analysis of an aerosol-aware microphysics scheme in Weather Research and Forecasting (WRF) during case studies of fog in Namibia
Michael John Weston, Stuart John Piketh, Frédéric Burnet, Stephen Broccardo, Cyrielle Denjean, Thierry Bourrianne, and Paola Formenti
Atmos. Chem. Phys., 22, 10221–10245, https://doi.org/10.5194/acp-22-10221-2022,https://doi.org/10.5194/acp-22-10221-2022, 2022
Short summary
Do Arctic mixed-phase clouds sometimes dissipate due to insufficient aerosol? Evidence from comparisons between observations and idealized simulations
Lucas J. Sterzinger, Joseph Sedlar, Heather Guy, Ryan R. Neely III, and Adele L. Igel
Atmos. Chem. Phys., 22, 8973–8988, https://doi.org/10.5194/acp-22-8973-2022,https://doi.org/10.5194/acp-22-8973-2022, 2022
Short summary
Contrail formation within cirrus: ICON-LEM simulations of the impact of cirrus cloud properties on contrail formation
Pooja Verma and Ulrike Burkhardt
Atmos. Chem. Phys., 22, 8819–8842, https://doi.org/10.5194/acp-22-8819-2022,https://doi.org/10.5194/acp-22-8819-2022, 2022
Short summary
Impact of Holuhraun volcano aerosols on clouds in cloud-system-resolving simulations
Mahnoosh Haghighatnasab, Jan Kretzschmar, Karoline Block, and Johannes Quaas
Atmos. Chem. Phys., 22, 8457–8472, https://doi.org/10.5194/acp-22-8457-2022,https://doi.org/10.5194/acp-22-8457-2022, 2022
Short summary
Warm and moist air intrusions into the winter Arctic: a Lagrangian view on the near-surface energy budgets
Cheng You, Michael Tjernström, and Abhay Devasthale
Atmos. Chem. Phys., 22, 8037–8057, https://doi.org/10.5194/acp-22-8037-2022,https://doi.org/10.5194/acp-22-8037-2022, 2022
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