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

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
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Fire–precipitation interactions amplify the quasi-biennial variability in fires over southern Mexico and Central America
Yawen Liu, Yun Qian, Philip J. Rasch, Kai Zhang, Lai-yung Ruby Leung, Yuhang Wang, Minghuai Wang, Hailong Wang, Xin Huang, and Xiu-Qun Yang
Atmos. Chem. Phys., 24, 3115–3128, https://doi.org/10.5194/acp-24-3115-2024,https://doi.org/10.5194/acp-24-3115-2024, 2024
Short summary
Representing the effects of giant aerosol in droplet nucleation in E3SMv2
Yu Yao, Po-Lun Ma, Yi Qin, Matthew W. Christensen, Hui Wan, Kai Zhang, Balwinder Singh, Meng Huang, and Mikhail Ovchinnikov
EGUsphere, https://doi.org/10.5194/egusphere-2024-523,https://doi.org/10.5194/egusphere-2024-523, 2024
Short summary
Numerical coupling of aerosol emissions, dry removal, and turbulent mixing in the E3SM Atmosphere Model version 1 (EAMv1) – Part 1: Dust budget analyses and the impacts of a revised coupling scheme
Hui Wan, Kai Zhang, Christopher J. Vogl, Carol S. Woodward, Richard C. Easter, Philip J. Rasch, Yan Feng, and Hailong Wang
Geosci. Model Dev., 17, 1387–1407, https://doi.org/10.5194/gmd-17-1387-2024,https://doi.org/10.5194/gmd-17-1387-2024, 2024
Short summary
Assessing the sensitivity of aerosol mass budget and effective radiative forcing to horizontal grid spacing in E3SMv1 using a regional refinement approach
Jianfeng Li, Kai Zhang, Taufiq Hassan, Shixuan Zhang, Po-Lun Ma, Balwinder Singh, Qiyang Yan, and Huilin Huang
Geosci. Model Dev., 17, 1327–1347, https://doi.org/10.5194/gmd-17-1327-2024,https://doi.org/10.5194/gmd-17-1327-2024, 2024
Short summary

Related subject area

Atmospheric sciences
MEXPLORER 1.0.0 – a mechanism explorer for analysis and visualization of chemical reaction pathways based on graph theory
Rolf Sander
Geosci. Model Dev., 17, 2419–2425, https://doi.org/10.5194/gmd-17-2419-2024,https://doi.org/10.5194/gmd-17-2419-2024, 2024
Short summary
Advances and prospects of deep learning for medium-range extreme weather forecasting
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 2347–2358, https://doi.org/10.5194/gmd-17-2347-2024,https://doi.org/10.5194/gmd-17-2347-2024, 2024
Short summary
An overview of the Western United States Dynamically Downscaled Dataset (WUS-D3)
Stefan Rahimi, Lei Huang, Jesse Norris, Alex Hall, Naomi Goldenson, Will Krantz, Benjamin Bass, Chad Thackeray, Henry Lin, Di Chen, Eli Dennis, Ethan Collins, Zachary J. Lebo, Emily Slinskey, Sara Graves, Surabhi Biyani, Bowen Wang, Stephen Cropper, and the UCLA Center for Climate Science Team
Geosci. Model Dev., 17, 2265–2286, https://doi.org/10.5194/gmd-17-2265-2024,https://doi.org/10.5194/gmd-17-2265-2024, 2024
Short summary
cloudbandPy 1.0: an automated algorithm for the detection of tropical–extratropical cloud bands
Romain Pilon and Daniela I. V. Domeisen
Geosci. Model Dev., 17, 2247–2264, https://doi.org/10.5194/gmd-17-2247-2024,https://doi.org/10.5194/gmd-17-2247-2024, 2024
Short summary
PyRTlib: an educational Python-based library for non-scattering atmospheric microwave radiative transfer computations
Salvatore Larosa, Domenico Cimini, Donatello Gallucci, Saverio Teodosio Nilo, and Filomena Romano
Geosci. Model Dev., 17, 2053–2076, https://doi.org/10.5194/gmd-17-2053-2024,https://doi.org/10.5194/gmd-17-2053-2024, 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.