Articles | Volume 15, issue 1
https://doi.org/10.5194/gmd-15-199-2022
https://doi.org/10.5194/gmd-15-199-2022
Model evaluation paper
 | 
12 Jan 2022
Model evaluation paper |  | 12 Jan 2022

The sensitivity of simulated aerosol climatic impact to domain size using regional model (WRF-Chem v3.6)

Xiaodong Wang, Chun Zhao, Mingyue Xu, Qiuyan Du, Jianqiu Zheng, Yun Bi, Shengfu Lin, and Yali Luo

Related authors

Toward a learnable Artificial Intelligence Model for Aerosol Chemistry and Interactions (AIMACI) based on the Multi-Head Self-Attention algorithm
Zihan Xia, Chun Zhao, Zining Yang, Qiuyan Du, Jiawang Feng, Chen Jin, Jun Shi, and Hong An
Atmos. Chem. Phys., 25, 6197–6218, https://doi.org/10.5194/acp-25-6197-2025,https://doi.org/10.5194/acp-25-6197-2025, 2025
Short summary
WRF-Chem simulations of snow nitrate and other physicochemical properties in northern China
Xia Wang, Tao Che, Xueyin Ruan, Shanna Yue, Jing Wang, Chun Zhao, and Lei Geng
Geosci. Model Dev., 18, 651–670, https://doi.org/10.5194/gmd-18-651-2025,https://doi.org/10.5194/gmd-18-651-2025, 2025
Short summary
Amending the algorithm of aerosol–radiation interactions in WRF-Chem (v4.4)
Jiawang Feng, Chun Zhao, Qiuyan Du, Zining Yang, and Chen Jin
Geosci. Model Dev., 18, 585–603, https://doi.org/10.5194/gmd-18-585-2025,https://doi.org/10.5194/gmd-18-585-2025, 2025
Short summary
Aerosol impacts on regional climate: chaotic or physical effect?
Jiawang Feng, Chun Zhao, Jun Gu, Gudongze Li, Mingyue Xu, Shengfu Lin, and Jie Feng
EGUsphere, https://doi.org/10.5194/egusphere-2024-4037,https://doi.org/10.5194/egusphere-2024-4037, 2025
Short summary
Comprehensive evaluation of iAMAS (v1.0) in simulating Antarctic meteorological fields with observations and reanalysis
Qike Yang, Chun Zhao, Jiawang Feng, Gudongze Li, Jun Gu, Zihan Xia, Mingyue Xu, and Zining Yang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-229,https://doi.org/10.5194/gmd-2024-229, 2025
Revised manuscript accepted for GMD
Short summary

Related subject area

Atmospheric sciences
Low-level jets in the North and Baltic seas: mesoscale model sensitivity and climatology using WRF V4.2.1
Bjarke T. E. Olsen, Andrea N. Hahmann, Nicolas G. Alonso-de-Linaje, Mark Žagar, and Martin Dörenkämper
Geosci. Model Dev., 18, 4499–4533, https://doi.org/10.5194/gmd-18-4499-2025,https://doi.org/10.5194/gmd-18-4499-2025, 2025
Short summary
SynRad v1.0: a radar forward operator to simulate synthetic weather radar observations from volcanic ash clouds
Vishnu Nair, Anujah Mohanathan, Michael Herzog, David G. Macfarlane, and Duncan A. Robertson
Geosci. Model Dev., 18, 4417–4432, https://doi.org/10.5194/gmd-18-4417-2025,https://doi.org/10.5194/gmd-18-4417-2025, 2025
Short summary
Chempath 1.0: an open-source pathway analysis program for photochemical models
Daniel Garduno Ruiz, Colin Goldblatt, and Anne-Sofie Ahm
Geosci. Model Dev., 18, 4433–4454, https://doi.org/10.5194/gmd-18-4433-2025,https://doi.org/10.5194/gmd-18-4433-2025, 2025
Short summary
PALACE v1.0: Paranal Airglow Line And Continuum Emission model
Stefan Noll, Carsten Schmidt, Patrick Hannawald, Wolfgang Kausch, and Stefan Kimeswenger
Geosci. Model Dev., 18, 4353–4398, https://doi.org/10.5194/gmd-18-4353-2025,https://doi.org/10.5194/gmd-18-4353-2025, 2025
Short summary
Atmospheric moisture tracking with WAM2layers v3
Peter Kalverla, Imme Benedict, Chris Weijenborg, and Ruud J. van der Ent
Geosci. Model Dev., 18, 4335–4352, https://doi.org/10.5194/gmd-18-4335-2025,https://doi.org/10.5194/gmd-18-4335-2025, 2025
Short summary

Cited articles

Ackerman, A. S., Toon, O. B., Stevens, D. E., Heymsfield, A. J., Ramanathan, V., and Welton, E. J.: Reduction of tropical cloudiness by soot, Science, 288, 1042–1047, https://doi.org/10.1126/science.288.5468.1042, 2018. 
Albrecht, B. A.: Aerosols, Cloud Microphysics, and Fractional Cloudiness, Science, 245, 1227–1230, https://doi.org/10.1126/science.245.4923.1227, 1989. 
An, Z. S., Wu, G. X., Li, J. P., Sun, Y. B., Liu, Y. M., Zhou, W. J., Cai, Y., Duan, A., Li, L., Mao, J., Cheng, H., Shi, Z., Tan, L, Yan, H., Ao, H., Chang, H., and Feng, J.: Global Monsoon Dynamics and Climate Change, Annu. Rev. Earth Pl. Sc., 43, 29–77, https://doi.org/10.1146/annurev-earth-060313-054623, 2015. 
An, Z., Huang, R.-J., Zhang, R., Tie, X., Li, G., Cao, J., Zhou, W., Shi, Z., Han, Y., and Gu, Z.: Severe haze in northern China: A synergy of anthropogenic emissions and atmospheric processes, P. Natl. Acad. Sci. USA, 116, 8657–8666, https://doi.org/10.1073/pnas.1900125116, 2019. 
Bhaskaran, B., Ramachandran, A., Jones, R., and Moufouma-Okia, W.: Regional climate model applications on sub-regional scales over the Indian monsoon region: The role of domain size on downscaling uncertainty, J. Geophys. Res.-Atmos., 117, D10113, https://doi.org/10.1029/2012jd017956, 2012. 
Download
Short summary
Regional models are widely used to investigate aerosol climatic impacts. However, there are few studies examining the sensitivities of modeling results to regional domain size. In this study, the regional model is used to study the aerosol impacts on the East Asian summer monsoon system and focus on the modeling sensitivities to domain size. This study highlights the important impacts of domain size on regional modeling results of aerosol climatic impacts, which may not be limited to East Asia.
Share