Articles | Volume 24, issue 23
https://doi.org/10.5194/acp-24-13525-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-24-13525-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Dec 2024
Research article |
| 09 Dec 2024
| 09 Dec 2024
Numerical simulation of aerosol concentration effects on cloud droplet size spectrum evolutions of warm stratiform clouds in Jiangxi, China
Yi Li
China Meteorological Administration Aerosol–Cloud and Precipitation Key Laboratory, Nanjing University of Information Science and Technology, Nanjing 210044, China
College of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, China
China Meteorological Administration Aerosol–Cloud and Precipitation Key Laboratory, Nanjing University of Information Science and Technology, Nanjing 210044, China
College of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, China
Hengjia Cai
China Meteorological Administration Aerosol–Cloud and Precipitation Key Laboratory, Nanjing University of Information Science and Technology, Nanjing 210044, China
College of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, China
Related authors
Yi Li and Xiaoli Liu
EGUsphere, https://doi.org/10.5194/egusphere-2024-1592, https://doi.org/10.5194/egusphere-2024-1592, 2024
Preprint withdrawn
Short summary
Short summary
We studied how coarse-mode aerosols affect macro and micro characteristics in Jiangxi, China using a numerical model. Results indicate that higher coarse-mode aerosol concentrations (Ncm) accelerate cloud development. Yet, the response of cloud microphysics to Ncm changes is not linear, depending on the combined effects of updraft strength and cloud droplet activation. This research improves our understanding of coarse-mode aerosols' impact on cloud formation and weather.
Jingwen Zhang, Xiaoli Liu, and Zhenya An
EGUsphere, https://doi.org/10.5194/egusphere-2025-3632, https://doi.org/10.5194/egusphere-2025-3632, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Fog affects human activities and regional climate, yet our understanding of it remains limited. This study analyzes 27 fog events in Nanjing, revealing that droplet size distributions evolve from single to multiple peaks during fog lifecycles. A new segmented fitting significantly improves the estimation of key microphysical and radiative parameters. These results improve our understanding of fog microphysics and droplet size distribution parameterizations in weather and climate models.
Zhuoxuan Shen, Xiaoli Liu, Yan Li, Jingyuan Xiong, Kerui Min, Hengjia Cai, and Nan Wang
EGUsphere, https://doi.org/10.5194/egusphere-2025-3034, https://doi.org/10.5194/egusphere-2025-3034, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
This research combines aircraft observations and numerical simulations, particularly using the McSnow model, to track ice particle growth and evolution in stratocumulus clouds. Our findings show that the maximum rime density in convective regions reaches 0.5 to 0.55 g cm-3, leading to a thicker melting layer (400 to 500 m thicker than in stratiform regions). The study also identifies key relationships between spectral parameters, offering insights into cloud microphysical parameterization.
Yi Li and Xiaoli Liu
EGUsphere, https://doi.org/10.5194/egusphere-2024-1592, https://doi.org/10.5194/egusphere-2024-1592, 2024
Preprint withdrawn
Short summary
Short summary
We studied how coarse-mode aerosols affect macro and micro characteristics in Jiangxi, China using a numerical model. Results indicate that higher coarse-mode aerosol concentrations (Ncm) accelerate cloud development. Yet, the response of cloud microphysics to Ncm changes is not linear, depending on the combined effects of updraft strength and cloud droplet activation. This research improves our understanding of coarse-mode aerosols' impact on cloud formation and weather.
Zhenya An and Xiaoli Liu
EGUsphere, https://doi.org/10.5194/egusphere-2023-2516, https://doi.org/10.5194/egusphere-2023-2516, 2023
Preprint archived
Short summary
Short summary
The study of warm fog helps to provide some theoretical support for cloud microphysics observational studies and scientific understanding. In this study, the microphysical characteristics of fog were explored using data sampled by the instrument FM-120 and WPS-1000XP, and it was found that the relationship between the microphysical quantities of fog is strongly influenced by the collision process, and the size of the fog droplets in turn affects the collision process between the droplets.
Cited articles
Anil Kumar, V., Pandithurai, G., Leena, P. P., Dani, K. K., Murugavel, P., Sonbawne, S. M., Patil, R. D., and Maheskumar, R. S.: Investigation of aerosol indirect effects on monsoon clouds using ground-based measurements over a high-altitude site in Western Ghats, Atmos. Chem. Phys., 16, 8423–8430, https://doi.org/10.5194/acp-16-8423-2016, 2016.
Cecchini, M. A., Machado, L. A. T., Andreae, M. O., Martin, S. T., Albrecht, R. I., Artaxo, P., Barbosa, H. M. J., Borrmann, S., Fütterer, D., Jurkat, T., Mahnke, C., Minikin, A., Molleker, S., Pöhlker, M. L., Pöschl, U., Rosenfeld, D., Voigt, C., Weinzierl, B., and Wendisch, M.: Sensitivities of Amazonian clouds to aerosols and updraft speed, Atmos. Chem. Phys., 17, 10037–10050, https://doi.org/10.5194/acp-17-10037-2017, 2017.
Chandrakar, K. K., Cantrell, W., Chang, K., Ciochetto, D., Niedermeier, D., Ovchinnikov, M., Shaw, R. A., and Yang, F.: Aerosol indirect effect from turbulence-induced broadening of cloud-droplet size distributions, P. Natl. Acad. Sci. USA, 113, 14243–14248, 2016.
Chandrakar, K. K., Cantrell, W., and Shaw, R. A.: Influence of turbulent fluctuations on cloud droplet size dispersion and aerosol indirect effects, J. Atmos. Sci., 75, 3191–3209, 2018.
Chen, J., Liu, Y., Zhang, M., and Peng, Y.: New understanding and quantification of the regime dependence of aerosol-cloud interaction for studying aerosol indirect effects, Geophys. Res. Lett., 43, 1780–1787, 2016.
Copernicus: ERA5 hourly data on pressure levels from 1940 to present, Climate Data Store [data set], https://doi.org/10.24381/cds.bd0915c6, 2018.
Deng, Z., Zhao, C., Zhang, Q., Huang, M., and Ma, X.: Statistical analysis of microphysical properties and the parameterization of effective radius of warm clouds in Beijing area, Atmos. Res., 93, 888–896, 2009.
Desai, N., Glienke, S., Fugal, J., and Shaw, R.: Search for microphysical signatures of stochastic condensation in marine boundary layer clouds using airborne digital holography, J. Geophys. Res.-Atmos., 124, 2739–2752, 2019.
Fan, J., Leung, L. R., Li, Z., Morrison, H., Chen, H., Zhou, Y., Qian, Y., and Wang, Y.: Aerosol impacts on clouds and precipitation in eastern China: Results from bin and bulk microphysics, J. Geophys. Res.-Atmos., 117, D00K34, https://doi.org/10.1029/2010JD015257, 2012.
Flossmann, A. I. and Wobrock, W.: A review of our understanding of the aerosol–cloud interaction from the perspective of a bin resolved cloud scale modelling, Atmos. Res., 97, 478–497, 2010.
Grosvenor, D. P., Sourdeval, O., Zuidema, P., Ackerman, A., Alexandrov, M. D., Bennartz, R., Boers, R., Cairns, B., Chiu, J. C., and Christensen, M.: Remote sensing of droplet number concentration in warm clouds: A review of the current state of knowledge and perspectives, Rev. Geophys., 56, 409–453, 2018.
Han, B., Fan, J., Varble, A., Morrison, H., Williams, C. R., Chen, B., Dong, X., Giangrande, S. E., Khain, A., and Mansell, E.: Cloud-resolving model intercomparison of an MC3E squall line case: Part II. Stratiform precipitation properties, J. Geophys. Res.-Atmos., 124, 1090–1117, 2019.
Ilotoviz, E., Khain, A. P., Benmoshe, N., Phillips, V. T., and Ryzhkov, A. V.: Effect of aerosols on freezing drops, hail, and precipitation in a midlatitude storm, J. Atmos. Sci., 73, 109–144, 2016.
Jensen, J. B. and Nugent, A. D.: Condensational growth of drops formed on giant sea-salt aerosol particles, J. Atmos. Sci., 74, 679–697, 2017.
Jin, Y., Niu, S., and LÜ, J.: Study of the Microphysical Structural Characteristics and Cloud–Rain Autoconversion Threshold Function of Stratiform Warm Clouds in Jiangxi [J], Chin. J. Atmos. Sci., 45, 981–993, 2021.
Kant, S., Panda, J., and Gautam, R.: A seasonal analysis of aerosol-cloud-radiation interaction over Indian region during 2000–2017, Atmos. Environ., 201, 212–222, 2019.
Khain, A., Ovtchinnikov, M., Pinsky, M., Pokrovsky, A., and Krugliak, H.: Notes on the state-of-the-art numerical modeling of cloud microphysics, Atmos. Res., 55, 159–224, 2000.
Khain, A., Rosenfeld, D., and Pokrovsky, A.: Aerosol impact on the dynamics and microphysics of deep convective clouds, Q. J. Roy. Meteor. Soc., 131, 2639–2663, 2005.
Khain, A. and Lynn, B.: Simulation of a supercell storm in clean and dirty atmosphere using weather research and forecast model with spectral bin microphysics, J. Geophys. Res.-Atmos., 114, D19209, https://doi.org/10.1029/2009JD011827, 2009.
Khain, A. P. and Sednev, I.: Simulation of precipitation formation in the Eastern Mediterranean coastal zone using a spectral microphysics cloud ensemble model, Atmos. Res., 43, 77–110, 1996.
Kovačević, N.: Hail suppression effectiveness for varying solubility of natural aerosols in water, Meteorol. Atmos. Phys., 131, 585–599, 2019.
Kumar, B., Bera, S., Prabha, T. V., and Grabowski, W. W.: Cloud-edge mixing: Direct numerical simulation and observations in Indian Monsoon clouds, J. Adv. Model. Earth Syst., 9, 332–353, 2017.
Lau, K. and Wu, H.: Warm rain processes over tropical oceans and climate implications, Geophys. Res. Lett., 30, 2290, https://doi.org/10.1029/2003GL018567, 2003.
Lerach, D. G. and Cotton, W. R.: Simulating southwestern US desert dust influences on supercell thunderstorms, Atmos. Res., 204, 78–93, 2018.
Li, Y., Liu, X., Cai, H.: Satellite, Aerosol, and Numerical Simulation Dataset in Jiangxi, China [DS/OL], V2, Science Data Bank [data set], https://doi.org/10.57760/sciencedb.11210, 2023.
Liu, F., Mao, F., Rosenfeld, D., Pan, Z., Zang, L., Zhu, Y., Yin, J., and Gong, W.: Opposing comparable large effects of fine aerosols and coarse sea spray on marine warm clouds, Commun. Earth Environ., 3, 232, https://doi.org/10.1038/s43247-022-00562-y, 2022.
Liu, G., Shao, H., Coakley Jr, J. A., Curry, J. A., Haggerty, J. A., and Tschudi, M. A.: Retrieval of cloud droplet size from visible and microwave radiometric measurements during INDOEX: Implication to aerosols' indirect radiative effect, J. Geophys. Res.-Atmos., 108, AAC 2-1–AAC 2-10, 2003.
Liu, M., Li, L., Xu, L., Yu, J., and Liu, P.: Operation and maintenance of RPG-HATPRO multi-channel ground based microwave radiometer, Anal. Instrum., 89–92, https://doi.org/10.3936/j.issn.1001-232x.2014.05.018, 2014 (in Chinese).
Liu, Y. and Daum, P. H.: Indirect warming effect from dispersion forcing, Nature, 419, 580–581, 2002.
Liu, Y., Daum, P. H., and McGraw, R. L.: Size truncation effect, threshold behavior, and a new type of autoconversion parameterization, Geophys. Res. Lett., 32, L11811, https://doi.org/10.1029/2005GL022636, 2005.
Liu, Y., Daum, P. H., McGraw, R., and Miller, M.: Generalized threshold function accounting for effect of relative dispersion on threshold behavior of autoconversion process, Geophys. Res. Lett., 33, L11804, https://doi.org/10.1029/2005GL025500, 2006.
Liu, Y., Daum, P. H., Guo, H., and Peng, Y.: Dispersion bias, dispersion effect, and the aerosol–cloud conundrum, Environ. Res. Lett., 3, 045021, https://doi.org/10.1088/1748-9326/3/4/045021, 2008.
Liu, Y., Zhu, Q., Hua, S., Alam, K., Dai, T., and Cheng, Y.: Tibetan Plateau driven impact of Taklimakan dust on northern rainfall, Atmos. Environ., 234, 117583, https://doi.org/10.1016/j.atmosenv.2020.117583, 2020.
Lu, C., Liu, Y., Niu, S., and Vogelmann, A. M.: Observed impacts of vertical velocity on cloud microphysics and implications for aerosol indirect effects, Geophys. Res. Lett., 39, L21808, https://doi.org/10.1029/2012GL053599, 2012.
Lu, C., Niu, S., Liu, Y., and Vogelmann, A. M.: Empirical relationship between entrainment rate and microphysics in cumulus clouds, Geophys. Res. Lett., 40, 2333–2338, 2013.
Lu, C., Liu, Y., Yum, S. S., Chen, J., Zhu, L., Gao, S., Yin, Y., Jia, X., and Wang, Y.: Reconciling contrasting relationships between relative dispersion and volume-mean radius of cloud droplet size distributions, J. Geophys. Res.-Atmos., 125, e2019JD031868, https://doi.org/10.1029/2019JD031868, 2020.
Lu, C. and Xu, X.: Advances in the studies of cloud entrainment and mixing process, Torrent. Rain Disast., 40, 271–279, 2021 (in Chinese).
Ma, J., Chen, Y., Wang, W., Yan, P., Liu, H., Yang, S., Hu, Z., and Lelieveld, J.: Strong air pollution causes widespread haze-clouds over China, J. Geophys. Res.-Atmos., 115, D18204, https://doi.org/10.1029/2009JD013065, 2010.
Morrison, H., Witte, M., Bryan, G. H., Harrington, J. Y., and Lebo, Z. J.: Broadening of modeled cloud droplet spectra using bin microphysics in an Eulerian spatial domain, J. Atmos. Sci., 75, 4005–4030, 2018.
Pandithurai, G., Dipu, S., Prabha, T. V., Maheskumar, R., Kulkarni, J., and Goswami, B.: Aerosol effect on droplet spectral dispersion in warm continental cumuli, J. Geophys. Res.-Atmos., 117, D16202, https://doi.org/10.1029/2011JD016532, 2012.
Peng, Y., Lohmann, U., Leaitch, R., and Kulmala, M.: An investigation into the aerosol dispersion effect through the activation process in marine stratus clouds, J. Geophys. Res.-Atmos., 112, D11117, https://doi.org/10.1029/2006JD007401, 2007.
Pinsky, M. and Khain, A.: Theoretical analysis of the entrainment–mixing process at cloud boundaries. Part I: Droplet size distributions and humidity within the interface zone, J. Atmos. Sci., 75, 2049–2064, 2018.
Platnick, S., Ackerman, S., King, M., Meyer, K., Menzel, W., Holz, R., Baum, B., and Yang, P.: MODIS atmosphere L2 cloud product (06_L2), NASA MODIS Adaptive Processing System, Goddard Space Flight Center, 2, https://doi.org/10.5067/MODIS/MOD06_L2.061, 2015.
Prabha, T. V., Patade, S., Pandithurai, G., Khain, A., Axisa, D., Pradeep-Kumar, P., Maheshkumar, R., Kulkarni, J., and Goswami, B.: Spectral width of premonsoon and monsoon clouds over Indo-Gangetic valley, J. Geophys. Res.-Atmos., 117, D20205, https://doi.org/10.1029/2011JD016837, 2012.
Qian, Y., Gong, D., Fan, J., Leung, L. R., Bennartz, R., Chen, D., and Wang, W.: Heavy pollution suppresses light rain in China: Observations and modeling, J. Geophys. Res.-Atmos., 114, D00K02, https://doi.org/10.1029/2008JD011575, 2009.
Rosenfeld, D., Rudich, Y., and Lahav, R.: Desert dust suppressing precipitation: A possible desertification feedback loop, P. Natl. Acad. Sci. USA, 98, 5975–5980, 2001.
Rotstayn, L. D. and Liu, Y.: Sensitivity of the first indirect aerosol effect to an increase of cloud droplet spectral dispersion with droplet number concentration, J. Climate, 16, 3476–3481, 2003.
Rotstayn, L. D. and Liu, Y.: Cloud droplet spectral dispersion and the indirect aerosol effect: Comparison of two treatments in a GCM, Geophys. Res. Lett., 36, L10801, https://doi.org/10.1029/2009GL038216, 2009.
Seifert, A., Nuijens, L., and Stevens, B.: Turbulence effects on warm-rain autoconversion in precipitating shallow convection, Q. J. Roy. Meteor. Soc., 136, 1753–1762, 2010.
Shpund, J., Khain, A., Lynn, B., Fan, J., Han, B., Ryzhkov, A., Snyder, J., Dudhia, J., and Gill, D.: Simulating a mesoscale convective system using WRF with a new spectral bin microphysics: 1: Hail vs graupel, J. Geophys. Res.-Atmos., 124, 14072–14101, 2019.
Tas, E., Koren, I., and Altaratz, O.: On the sensitivity of droplet size relative dispersion to warm cumulus cloud evolution, Geophys. Res. Lett., 39, L13807, https://doi.org/10.1029/2012GL052157, 2012.
Tas, E., Teller, A., Altaratz, O., Axisa, D., Bruintjes, R., Levin, Z., and Koren, I.: The relative dispersion of cloud droplets: its robustness with respect to key cloud properties, Atmos. Chem. Phys., 15, 2009–2017, https://doi.org/10.5194/acp-15-2009-2015, 2015.
Wang, F., Li, Z., Zhao, D., Ma, X., Gao, Y., Sheng, J., Tian, P., and Cribb, M.: An airborne study of the aerosol effect on the dispersion of cloud droplets in a drizzling marine stratocumulus cloud over eastern China, Atmos. Res., 265, 105885, https://doi.org/10.1016/j.atmosres.2021.105885, 2022.
Wang, F. and Lu, C.: Advances of Theoretical, Observational, and Numerical Studies on Relative Dispersion of Cloud Droplet Spectra, Plateau Meteorol., 42, 809–820, 2023 (in Chinese).
Wang, X., Xue, H., Fang, W., and Zheng, G.: A study of shallow cumulus cloud droplet dispersion by large eddy simulations, Acta Meteorol. Sin., 25, 166–175, 2011.
Wang, Y., Niu, S., Lu, C., Liu, Y., Chen, J., and Yang, W.: An observational study on cloud spectral width in North China, Atmosphere, 10, 109, https://doi.org/10.3390/atmos10030109, 2019.
Wang, Y., Lu, C., Niu, S., Lv, J., Jia, X., Xu, X., Xue, Y., Zhu, L., and Yan, S.: Diverse dispersion effects and parameterization of relative dispersion in urban fog in eastern China, J. Geophys. Res.-Atmos., 128, e2022JD037514, https://doi.org/10.1029/2022JD037514, 2023.
Wang, Y., Jia, H., Zhang, P., Fang, F., Li, J., Zhu, L., Wang, Y., Wang, T., and Li, J.: Sensitivity of cloud microphysics to aerosol is highly associated with cloud water content: Implications for indirect radiative forcing, Atmos. Res., 309, 107552, https://doi.org/10.1016/j.atmosres.2024.107552, 2024.
Wehbe, Y., Temimi, M., and Adler, R. F.: Enhancing precipitation estimates through the fusion of weather radar, satellite retrievals, and surface parameters, Remote Sensing, 12, 1342, https://doi.org/10.3390/rs12081342, 2020.
Xie, X., Liu, X., and Wang, Z.: Review of influence of cloud droplet spectral dispersion on aerosol indirect effects, J. Earth Environ., 6, 127–134, 2015 (in Chinese).
Xie, X., Zhang, H., Liu, X., Peng, Y., and Liu, Y.: Sensitivity study of cloud parameterizations with relative dispersion in CAM5.1: impacts on aerosol indirect effects, Atmos. Chem. Phys., 17, 5877–5892, https://doi.org/10.5194/acp-17-5877-2017, 2017.
Yang, F., Lu, H., Yang, K., He, J., Wang, W., Wright, J. S., Li, C., Han, M., and Li, Y.: Evaluation of multiple forcing data sets for precipitation and shortwave radiation over major land areas of China, Hydrol. Earth Syst. Sci., 21, 5805–5821, https://doi.org/10.5194/hess-21-5805-2017, 2017.
Yang, S., Zhang, Y., Yu, X., Lu, C., and Li, Y.: Effects of aerosol number concentration and updraft velocity on relative dispersion during the collision–coalescence growth stage of warm clouds, Atmosphere, 14, 828, https://doi.org/10.3390/atmos14050828, 2023.
Yin, Y., Levin, Z., Reisin, T. G., and Tzivion, S.: The effects of giant cloud condensation nuclei on the development of precipitation in convective clouds – A numerical study, Atmos. Res., 53, 91–116, 2000.
Yum, S. S. and Hudson, J. G.: Adiabatic predictions and observations of cloud droplet spectral broadness, Atmos. Res., 73, 203–223, 2005.
Zhao, C., Tie, X., Brasseur, G., Noone, K. J., Nakajima, T., Zhang, Q., Zhang, R., Huang, M., Duan, Y., and Li, G.: Aircraft measurements of cloud droplet spectral dispersion and implications for indirect aerosol radiative forcing, Geophys. Res. Lett., 33, L16809, https://doi.org/10.1029/2006GL026653, 2006.
Zheng, X., Yang, Y., Yuan, Y., Cao, Y., and Gao, J.: Comparison of macro-and microphysical properties in precipitating and non-Precipitating clouds over Central-Eastern China during warm season, Remote Sens., 14, 152, https://doi.org/10.3390/rs14010152, 2021.
Zhu, L., Lu, C., Gao, S., and Yum, S. S.: Spectral Width of Cloud Droplet Spectra and Its Impact Factors in Marine Stratocumulus, Chin. J. Atmos. Sci., 44, 575–590, 2020.
Short summary
The influence of different aerosol modes on cloud processes remains controversial. We modified the aerosol spectra and concentrations to simulate a warm stratiform cloud process in Jiangxi, China, using the WRF-SBM scheme. Research shows that different aerosol spectra have diverse effects on cloud droplet spectra, cloud development, and the correlation between dispersion (ε) and cloud physics quantities. Compared to cloud droplet concentration, ε is more sensitive to the volume radius.
The influence of different aerosol modes on cloud processes remains controversial. We modified...
Altmetrics
Final-revised paper
Preprint