Articles | Volume 23, issue 18
https://doi.org/10.5194/acp-23-10713-2023
https://doi.org/10.5194/acp-23-10713-2023
Research article
 | 
27 Sep 2023
Research article |  | 27 Sep 2023

Substantially positive contributions of new particle formation to cloud condensation nuclei under low supersaturation in China based on numerical model improvements

Chupeng Zhang, Shangfei Hai, Yang Gao, Yuhang Wang, Shaoqing Zhang, Lifang Sheng, Bin Zhao, Shuxiao Wang, Jingkun Jiang, Xin Huang, Xiaojing Shen, Junying Sun, Aura Lupascu, Manish Shrivastava, Jerome D. Fast, Wenxuan Cheng, Xiuwen Guo, Ming Chu, Nan Ma, Juan Hong, Qiaoqiao Wang, Xiaohong Yao, and Huiwang Gao

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Cited articles

Arghavani, S., Rose, C., Banson, S., Lupascu, A., Gouhier, M., Sellegri, K., and Planche, C.: The Effect of Using a New Parameterization of Nucleation in the WRF-Chem Model on New Particle Formation in a Passive Volcanic Plume, Atmosphere, 13, 15, https://doi.org/10.3390/atmos13010015, 2022. 
Buchholz, R. R., Emmons, L. K., and Tilmes, S.: The CESM2 Development Team, CESM2.1/CAM-Chem Instantaneous Output for Boundary Conditions, UCAR/NCAR – Atmospheric Chemistry Observations and Modeling Laboratory, Lat: 10 to 70, Lon: 80 to 150, February 2017–March 2017, https://doi.org/10.5065/NMP7-EP60, 2019. 
Bzdek, B., Zordan, C., Luther, G., and Johnston, M.: Nanoparticle Chemical Composition During New Particle Formation, Aerosol Sci. Technol., 45, 1041–1048, https://doi.org/10.1080/02786826.2011.580392, 2011. 
Bzdek, B. R., Zordan, C. A., Pennington, M. R., Luther III, G. W., and Johnston, M. V.: Quantitative Assessment of the Sulfuric Acid Contribution to New Particle Growth, Environ. Sci. Technol., 46, 4365–4373, https://doi.org/10.1021/es204556c, 2012. 
Cai, C., Zhang, X., Wang, K., Zhang, Y., Wang, L., Zhang, Q., Duan, F., He, K., and Yu, S.-C.: Incorporation of new particle formation and early growth treatments into WRF/Chem: Model improvement, evaluation, and impacts of anthropogenic aerosols over East Asia, Atmos. Environ., 124, 262–284, https://doi.org/10.1016/j.atmosenv.2015.05.046, 2016. 
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Short summary
New particle formation is an important source of atmospheric particles, exerting critical influences on global climate. Numerical models are vital tools to understanding atmospheric particle evolution, which, however, suffer from large biases in simulating particle numbers. Here we improve the model chemical processes governing particle sizes and compositions. The improved model reveals substantial contributions of newly formed particles to climate through effects on cloud condensation nuclei.
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