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

Related authors

General circulation models simulate negative liquid water path–droplet number correlations, but anthropogenic aerosols still increase simulated liquid water path
Johannes Mülmenstädt, Edward Gryspeerdt, Sudhakar Dipu, Johannes Quaas, Andrew S. Ackerman, Ann M. Fridlind, Florian Tornow, Susanne E. Bauer, Andrew Gettelman, Yi Ming, Youtong Zheng, Po-Lun Ma, Hailong Wang, Kai Zhang, Matthew W. Christensen, Adam C. Varble, L. Ruby Leung, Xiaohong Liu, David Neubauer, Daniel G. Partridge, Philip Stier, and Toshihiko Takemura
Atmos. Chem. Phys., 24, 7331–7345, https://doi.org/10.5194/acp-24-7331-2024,https://doi.org/10.5194/acp-24-7331-2024, 2024
Short summary
Biomass Burning Emissions Analysis Based on MODIS AOD and AeroCom Multi-Model Simulations
Mariya Petrenko, Ralph Kahn, Mian Chin, Susanne E. Bauer, Tommi Bergman, Huisheng Bian, Gabriele Curci, Ben Johnson, Johannes Kaiser, Zak Kipling, Harri Kokkola, Xiaohong Liu, Keren Mezuman, Tero Mielonen, Gunnar Myhre, Xiaohua Pan, Anna Protonotariou, Samuel Remy, Ragnhild Bieltvedt Skeie, Philip Stier, Toshihiko Takemura, Kostas Tsigaridis, Hailong Wang, Duncan Watson-Parris, and Kai Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2024-1487,https://doi.org/10.5194/egusphere-2024-1487, 2024
Short summary
Impacts of spatial heterogeneity of anthropogenic aerosol emissions in a regionally refined global aerosol–climate model
Taufiq Hassan, Kai Zhang, Jianfeng Li, Balwinder Singh, Shixuan Zhang, Hailong Wang, and Po-Lun Ma
Geosci. Model Dev., 17, 3507–3532, https://doi.org/10.5194/gmd-17-3507-2024,https://doi.org/10.5194/gmd-17-3507-2024, 2024
Short summary
The 4DEnVar-based weakly coupled land data assimilation system for E3SM version 2
Pengfei Shi, L. Ruby Leung, Bin Wang, Kai Zhang, Samson M. Hagos, and Shixuan Zhang
Geosci. Model Dev., 17, 3025–3040, https://doi.org/10.5194/gmd-17-3025-2024,https://doi.org/10.5194/gmd-17-3025-2024, 2024
Short summary
Can GCMs represent cloud adjustments to aerosol–cloud interactions?
Johannes Mülmenstädt, Andrew S. Ackerman, Ann M. Fridlind, Meng Huang, Po-Lun Ma, Naser Mahfouz, Susanne E. Bauer, Susannah M. Burrows, Matthew W. Christensen, Sudhakar Dipu, Andrew Gettelman, L. Ruby Leung, Florian Tornow, Johannes Quaas, Adam C. Varble, Hailong Wang, Kai Zhang, and Youtong Zheng
EGUsphere, https://doi.org/10.5194/egusphere-2024-778,https://doi.org/10.5194/egusphere-2024-778, 2024
Short summary

Related subject area

Atmospheric sciences
The CHIMERE chemistry-transport model v2023r1
Laurent Menut, Arineh Cholakian, Romain Pennel, Guillaume Siour, Sylvain Mailler, Myrto Valari, Lya Lugon, and Yann Meurdesoif
Geosci. Model Dev., 17, 5431–5457, https://doi.org/10.5194/gmd-17-5431-2024,https://doi.org/10.5194/gmd-17-5431-2024, 2024
Short summary
tobac v1.5: introducing fast 3D tracking, splits and mergers, and other enhancements for identifying and analysing meteorological phenomena
G. Alexander Sokolowsky, Sean W. Freeman, William K. Jones, Julia Kukulies, Fabian Senf, Peter J. Marinescu, Max Heikenfeld, Kelcy N. Brunner, Eric C. Bruning, Scott M. Collis, Robert C. Jackson, Gabrielle R. Leung, Nils Pfeifer, Bhupendra A. Raut, Stephen M. Saleeby, Philip Stier, and Susan C. van den Heever
Geosci. Model Dev., 17, 5309–5330, https://doi.org/10.5194/gmd-17-5309-2024,https://doi.org/10.5194/gmd-17-5309-2024, 2024
Short summary
Merged Observatory Data Files (MODFs): an integrated observational data product supporting process-oriented investigations and diagnostics
Taneil Uttal, Leslie M. Hartten, Siri Jodha Khalsa, Barbara Casati, Gunilla Svensson, Jonathan Day, Jareth Holt, Elena Akish, Sara Morris, Ewan O'Connor, Roberta Pirazzini, Laura X. Huang, Robert Crawford, Zen Mariani, Øystein Godøy, Johanna A. K. Tjernström, Giri Prakash, Nicki Hickmon, Marion Maturilli, and Christopher J. Cox
Geosci. Model Dev., 17, 5225–5247, https://doi.org/10.5194/gmd-17-5225-2024,https://doi.org/10.5194/gmd-17-5225-2024, 2024
Short summary
Simulation of marine stratocumulus using the super-droplet method: numerical convergence and comparison to a double-moment bulk scheme using SCALE-SDM 5.2.6-2.3.1
Chongzhi Yin, Shin-ichiro Shima, Lulin Xue, and Chunsong Lu
Geosci. Model Dev., 17, 5167–5189, https://doi.org/10.5194/gmd-17-5167-2024,https://doi.org/10.5194/gmd-17-5167-2024, 2024
Short summary
WRF-Comfort: simulating microscale variability in outdoor heat stress at the city scale with a mesoscale model
Alberto Martilli, Negin Nazarian, E. Scott Krayenhoff, Jacob Lachapelle, Jiachen Lu, Esther Rivas, Alejandro Rodriguez-Sanchez, Beatriz Sanchez, and José Luis Santiago
Geosci. Model Dev., 17, 5023–5039, https://doi.org/10.5194/gmd-17-5023-2024,https://doi.org/10.5194/gmd-17-5023-2024, 2024
Short summary

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.
Download
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.