Articles | Volume 25, issue 22
https://doi.org/10.5194/acp-25-15765-2025
© Author(s) 2025. 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-25-15765-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Nov 2025
Research article |
| 18 Nov 2025
| 18 Nov 2025
Role of aerosol–cloud–radiation interactions in modulating summertime quasi-biweekly rainfall intensity over South China
Hongli Chen
State Key Laboratory of Climate System Prediction and Risk Management/Key Laboratory of Meteorological Disaster, Ministry of Education/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China
State Key Laboratory of Climate System Prediction and Risk Management/Key Laboratory of Meteorological Disaster, Ministry of Education/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China
Anbao Zhu
State Key Laboratory of Climate System Prediction and Risk Management/Key Laboratory of Meteorological Disaster, Ministry of Education/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China
Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing, China
Xiaoyan Ma
Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, China
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The 2022 Hunga Tonga eruption injected sulfur gases and massive water vapor into the stratosphere. With observations and simulations, we assessed effects on the tropical quasi-biennial oscillation (QBO). Pollutants lingered over a year; the water-rich eruption notably altered QBO winds. Cooling from excess vapor and particles, concentrated south of 15° N, reshaped upper circulation and drove downward wind anomalies modulating the QBO, with April–October 2022 cooling causing more wind shifts.
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We use a size-resolved aerosol-cloud model to analyze the effects of aerosol size and hygroscopicity in modulating marine liquid-phase cloud responses under the winter monsoon. Our findings highlight the environmental dependence of the impacts of aerosol physicochemical properties on aerosol-cloud interactions, their pronounced effects under specific conditions, and the relative contributions of aerosol size and hygroscopicity.
Anbao Zhu, Xin Huang, Haiming Xu, Jiechun Deng, Lian Xue, Zilin Wang, Ke Ding, Tianshuai Li, and Aijun Ding
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Every spring, East Asian dust travels across the North Pacific. We investigated its effect on the North Pacific storm track using decades of data and a numerical model. We found that dust absorbs sunlight and warms the middle atmosphere, altering temperature patterns. This shifts the zone favorable for storm development poleward. These results highlight that natural dust meaningfully influences large-scale atmospheric circulation and should be considered in regional climate assessments.
Peili Zou, Xiaoyan Ma, Rong Tian, Jianqi Zhao, Tong Yang, and Yingying Ku
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Jianqi Zhao, Xiaoyan Ma, Johannes Quaas, and Tong Yang
Atmos. Chem. Phys., 25, 17701–17723, https://doi.org/10.5194/acp-25-17701-2025, https://doi.org/10.5194/acp-25-17701-2025, 2025
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Jianqi Zhao, Xiaoyan Ma, and Johannes Quaas
EGUsphere, https://doi.org/10.5194/egusphere-2024-3662, https://doi.org/10.5194/egusphere-2024-3662, 2024
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We conduct a comparative analysis of aerosol-cloud responses in liquid-phase clouds under different aerosol and meteorological conditions based on simulations using the WRF-Chem-SBM model. Our findings highlight the different effects of aerosols on clouds and precipitation, as well as variations in the roles of aerosol and meteorological factors influencing aerosol-cloud interactions, in different environment.
Jianqi Zhao, Xiaoyan Ma, Johannes Quaas, and Hailing Jia
Atmos. Chem. Phys., 24, 9101–9118, https://doi.org/10.5194/acp-24-9101-2024, https://doi.org/10.5194/acp-24-9101-2024, 2024
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We explore aerosol–cloud interactions in liquid-phase clouds over eastern China and its adjacent ocean in winter based on the WRF-Chem–SBM model, which couples a spectral-bin microphysics scheme and an online aerosol module. Our study highlights the differences in aerosol–cloud interactions between land and ocean and between precipitation clouds and non-precipitation clouds, and it differentiates and quantifies their underlying mechanisms.
Tong Sha, Siyu Yang, Qingcai Chen, Liangqing Li, Xiaoyan Ma, Yan-Lin Zhang, Zhaozhong Feng, K. Folkert Boersma, and Jun Wang
Atmos. Chem. Phys., 24, 8441–8455, https://doi.org/10.5194/acp-24-8441-2024, https://doi.org/10.5194/acp-24-8441-2024, 2024
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Using an updated soil reactive nitrogen emission scheme in the Unified Inputs for Weather Research and Forecasting coupled with Chemistry (UI-WRF-Chem) model, we investigate the role of soil NO and HONO (Nr) emissions in air quality and temperature in North China. Contributions of soil Nr emissions to O3 and secondary pollutants are revealed, exceeding effects of soil NOx or HONO emission. Soil Nr emissions play an important role in mitigating O3 pollution and addressing climate change.
Kun Wang, Xiaoyan Ma, Rong Tian, and Fangqun Yu
Atmos. Chem. Phys., 23, 4091–4104, https://doi.org/10.5194/acp-23-4091-2023, https://doi.org/10.5194/acp-23-4091-2023, 2023
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From 12 March to 6 April 2016 in Beijing, there were 11 typical new particle formation days, 13 non-event days, and 2 undefined days. We first analyzed the favorable background of new particle formation in Beijing and then conducted the simulations using four nucleation schemes based on a global chemistry transport model (GEOS-Chem) to understand the nucleation mechanism.
Jianqi Zhao, Xiaoyan Ma, Johannes Quaas, and Hailing Jia
EGUsphere, https://doi.org/10.5194/egusphere-2023-331, https://doi.org/10.5194/egusphere-2023-331, 2023
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We improve the ability of WRF-Chem model to simulate aerosol-cloud physical and chemical processes by coupling a spectral-bin cloud microphysics scheme and online aerosol module, and consequently explore the aerosol-cloud interactions over eastern China and its adjacent ocean in boreal winter. Our study highlights the differences in aerosol-cloud interactions between land and ocean, precipitation clouds and non-precipitation clouds, and differentiates and quantifies their underlying mechanisms.
Anbao Zhu, Haiming Xu, Jiechun Deng, Jing Ma, and Shaofeng Hua
Atmos. Chem. Phys., 22, 15425–15447, https://doi.org/10.5194/acp-22-15425-2022, https://doi.org/10.5194/acp-22-15425-2022, 2022
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This study demonstrates the instant and delayed effects of biomass burning (BB) aerosols on precipitation over the Indochina Peninsula (ICP). The convection suppression due to the BB aerosol-induced stabilized atmosphere dominates over the favorable water-vapor condition induced by large-scale circulation responses, leading to an overall reduced precipitation in March, while the delayed effect promotes precipitation from early April to mid April due to the anomalous atmospheric circulations.
Yiwen Hu, Zengliang Zang, Xiaoyan Ma, Yi Li, Yanfei Liang, Wei You, Xiaobin Pan, and Zhijin Li
Atmos. Chem. Phys., 22, 13183–13200, https://doi.org/10.5194/acp-22-13183-2022, https://doi.org/10.5194/acp-22-13183-2022, 2022
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This study developed a four-dimensional variational assimilation (4DVAR) system based on WRF–Chem to optimise SO2 emissions. The 4DVAR system was applied to obtain the SO2 emissions during the early period of the COVID-19 pandemic over China. The results showed that the 4DVAR system effectively optimised emissions to describe the actual changes in SO2 emissions related to the COVID lockdown, and it can thus be used to improve the accuracy of forecasts.
Johannes Quaas, Hailing Jia, Chris Smith, Anna Lea Albright, Wenche Aas, Nicolas Bellouin, Olivier Boucher, Marie Doutriaux-Boucher, Piers M. Forster, Daniel Grosvenor, Stuart Jenkins, Zbigniew Klimont, Norman G. Loeb, Xiaoyan Ma, Vaishali Naik, Fabien Paulot, Philip Stier, Martin Wild, Gunnar Myhre, and Michael Schulz
Atmos. Chem. Phys., 22, 12221–12239, https://doi.org/10.5194/acp-22-12221-2022, https://doi.org/10.5194/acp-22-12221-2022, 2022
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Pollution particles cool climate and offset part of the global warming. However, they are washed out by rain and thus their effect responds quickly to changes in emissions. We show multiple datasets to demonstrate that aerosol emissions and their concentrations declined in many regions influenced by human emissions, as did the effects on clouds. Consequently, the cooling impact on the Earth energy budget became smaller. This change in trend implies a relative warming.
Cited articles
Abida, R., Addad, Y., Francis, D., Temimi, M., Nelli, N., Fonseca, R., Nesterov, O., and Bosc, E.: Evaluation of the Performance of the WRF Model in a Hyper-Arid Environment: A Sensitivity Study, Atmosphere, 13, 985, https://doi.org/10.3390/atmos13060985, 2022.
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, 2000.
Albrecht, B. A.: Aerosols, Cloud Microphysics, and Fractional Cloudiness, Science, 245, 1227–1230, https://doi.org/10.1126/science.245.4923.1227, 1989.
Argüeso, D., Hidalgo-Muñoz, J. M., Gámiz-Fortis, S. R., Esteban-Parra, M. J., and Castro-Díez, Y.: High-resolution projections of mean and extreme precipitation over Spain using the WRF model (2070–2099 versus 1970–1999), J. Geophys. Res.-Atmos., 117, https://doi.org/10.1029/2011JD017399, 2012.
Arya, V. B., Surendran, S., and Rajendran, K.: On the build-up of dust aerosols and possible indirect effect during Indian summer monsoon break spells using recent satellite observations of aerosols and cloud properties, J. Earth Syst. Sci., 130, 42, https://doi.org/10.1007/s12040-020-01526-6, 2021.
Austin, R. T., Heymsfield, A. J., and Stephens, G. L.: Retrieval of ice cloud microphysical parameters using the CloudSat millimeter-wave radar and temperature, J. Geophys. Res.-Atmos., 114, https://doi.org/10.1029/2008JD010049, 2009.
Baklanov, A., Schlünzen, K., Suppan, P., Baldasano, J., Brunner, D., Aksoyoglu, S., Carmichael, G., Douros, J., Flemming, J., Forkel, R., Galmarini, S., Gauss, M., Grell, G., Hirtl, M., Joffre, S., Jorba, O., Kaas, E., Kaasik, M., Kallos, G., Kong, X., Korsholm, U., Kurganskiy, A., Kushta, J., Lohmann, U., Mahura, A., Manders-Groot, A., Maurizi, A., Moussiopoulos, N., Rao, S. T., Savage, N., Seigneur, C., Sokhi, R. S., Solazzo, E., Solomos, S., Sørensen, B., Tsegas, G., Vignati, E., Vogel, B., and Zhang, Y.: Online coupled regional meteorology chemistry models in Europe: current status and prospects, Atmos. Chem. Phys., 14, 317–398, https://doi.org/10.5194/acp-14-317-2014, 2014.
Bhattacharya, A., Chakraborty, A., and Venugopal, V.: Role of aerosols in modulating cloud properties during active–break cycle of Indian summer monsoon, Clim. Dynam., 49, 2131–2145, https://doi.org/10.1007/s00382-016-3437-4, 2017.
Bollasina, M. A., Ming, Y., and Ramaswamy, V.: Anthropogenic Aerosols and the Weakening of the South Asian Summer Monsoon, Science, 334, 502–505, https://doi.org/10.1126/science.1204994, 2011.
Caldwell, P., Chin, H.-N. S., Bader, D. C., and Bala, G.: Evaluation of a WRF dynamical downscaling simulation over California, Climatic Change, 95, 499–521, https://doi.org/10.1007/s10584-009-9583-5, 2009.
Chen, F., Yang, Y., Yu, L., Li, Y., Liu, W., Liu, Y., and Lolli, S.: Distinct effects of fine and coarse aerosols on microphysical processes of shallow-precipitation systems in summer over southern China, Atmos. Chem. Phys., 25, 1587–1601, https://doi.org/10.5194/acp-25-1587-2025, 2025.
Chen, G., Yang, J., Bao, Q., and Wang, W.-C.: Intraseasonal responses of the East Asia summer rainfall to anthropogenic aerosol climate forcing, Clim. Dynam., 51, 3985–3998, https://doi.org/10.1007/s00382-017-3691-0, 2018.
Chen, H., Hsu, P.-C., and Hu, S.: Role of Quasi-Biweekly Cloud-Radiative Feedback in Modulating and Simulating Extreme Rainfall Intensity Over Asian Monsoon Regions, Geophys. Res. Lett., 51, e2024GL111671, https://doi.org/10.1029/2024GL111671, 2024.
Chen, M., Shi, W., Xie, P., Silva, V. B. S., Kousky, V. E., Wayne Higgins, R., and Janowiak, J. E.: Assessing objective techniques for gauge-based analyses of global daily precipitation, J. Geophys. Res.-Atmos., 113, https://doi.org/10.1029/2007JD009132, 2008.
Chen, Y., Yang, K., Zhou, D., Qin, J., and Guo, X.: Improving the Noah Land Surface Model in Arid Regions with an Appropriate Parameterization of the Thermal Roughness Length, J. Hydrol., 11, 995–1006, https://doi.org/10.1175/2010JHM1185.1, 2010.
Chyi, D., Wang, X., Yu, X., and Zhang, J.: Synoptic-Scale Analysis on Development and Maintenance of the 19–21 July 2021 Extreme Heavy Rainfall in Henan, Central China, J. Meteorol. Res., 37, 174–191, https://doi.org/10.1007/s13351-023-2914-z, 2023.
Dave, P., Bhushan, M., and Venkataraman, C.: Aerosols cause intraseasonal short-term suppression of Indian monsoon rainfall, Sci. Rep., 7, 17347, https://doi.org/10.1038/s41598-017-17599-1, 2017.
Debnath, S., Govardhan, G., Saha, S. K., Hazra, A., Pohkrel, S., Jena, C., Kumar, R., and Ghude, S. D.: Impact of dust aerosols on the Indian Summer Monsoon Rainfall on intra-seasonal time-scale, Atmos. Environ., 305, 119802, https://doi.org/10.1016/j.atmosenv.2023.119802, 2023.
Duchon, C. E.: Lanczos Filtering in One and Two Dimensions, J. Appl. Meteorol. Clim., 18, 1016–1022, https://doi.org/10.1175/1520-0450(1979)018<1016:LFIOAT>2.0.CO;2, 1979.
Fan, J., Zhang, R., Tao, W.-K., and Mohr, K. I.: Effects of aerosol optical properties on deep convective clouds and radiative forcing, J. Geophys. Res.-Atmos., 113, https://doi.org/10.1029/2007JD009257, 2008.
Fan, J., Rosenfeld, D., Yang, Y., Zhao, C., Leung, L. R., and Li, Z.: Substantial contribution of anthropogenic air pollution to catastrophic floods in Southwest China, Geophys. Res. Lett., 42, 6066–6075, https://doi.org/10.1002/2015GL064479, 2015.
Fan, J., Rosenfeld, D., Zhang, Y., Giangrande, S. E., Li, Z., Machado, L. A. T., Martin, S. T., Yang, Y., Wang, J., Artaxo, P., Barbosa, H. M. J., Braga, R. C., Comstock, J. M., Feng, Z., Gao, W., Gomes, H. B., Mei, F., Pöhlker, C., Pöhlker, M. L., Pöschl, U., and de Souza, R. A. F.: Substantial convection and precipitation enhancements by ultrafine aerosol particles, Science, 359, 411–418, https://doi.org/10.1126/science.aan8461, 2018.
Fan, J., Zhang, Y., Li, Z., Yan, H., Prabhakaran, T., Rosenfeld, D., and Khain, A.: Unveiling Aerosol Impacts on Deep Convective Clouds: Scientific Concept, Modeling, Observational Analysis, and Future Direction, J. Geophys. Res.-Atmos., 130, e2024JD041931, https://doi.org/10.1029/2024JD041931, 2025.
Fast, J. D., Gustafson Jr., W. I., Easter, R. C., Zaveri, R. A., Barnard, J. C., Chapman, E. G., Grell, G. A., and Peckham, S. E.: Evolution of ozone, particulates, and aerosol direct radiative forcing in the vicinity of Houston using a fully coupled meteorology-chemistry-aerosol model, J. Geophys. Res.-Atmos., 111, https://doi.org/10.1029/2005JD006721, 2006.
Fu, Z., Hsu, P.-C., and Liu, F.: Factors Regulating the Multidecadal Changes in MJO Amplitude over the Twentieth Century, J. Climate, 33, 9513–9529, https://doi.org/10.1175/JCLI-D-20-0111.1, 2020.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
Ginoux, P., Chin, M., Tegen, I., Prospero, J. M., Holben, B., Dubovik, O., and Lin, S.-J.: Sources and distributions of dust aerosols simulated with the GOCART model, J. Geophys. Res.-Atmos., 106, 20255–20273, https://doi.org/10.1029/2000JD000053, 2001.
GMAO (Global Modeling and Assimilation Office): MERRA-2 tavg1_2d_aer_Nx: 2d,1-Hourly, Time-averaged, Single-Level, Assimilation, Aerosol Diagnostics V5.12.4, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/KLICLTZ8EM9D, 2015a.
GMAO (Global Modeling and Assimilation Office): MERRA-2 tavg1_2d_rad_Nx: 2d,1-Hourly, Time-Averaged, Single-Level, Assimilation, Radiation Diagnostics V5.12.4, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/Q9QMY5PBNV1T, 2015b.
GMAO (Global Modeling and Assimilation Office): MERRA-2 inst3_3d_asm_Np: 3d,3-Hourly, Instantaneous, Pressure-Level, Assimilation, Assimilated Meteorological Fields V5.12.4, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/QBZ6MG944HW0, 2015c.
Grell, G. A. and Freitas, S. R.: A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling, Atmos. Chem. Phys., 14, 5233–5250, https://doi.org/10.5194/acp-14-5233-2014, 2014.
Grell, G. A., Peckham, S. E., Schmitz, R., McKeen, S. A., Frost, G., Skamarock, W. C., and Eder, B.: Fully coupled “online” chemistry within the WRF model, Atmos. Environ., 39, 6957–6975, https://doi.org/10.1016/j.atmosenv.2005.04.027, 2005.
Guenther, A. B., Jiang, X., Heald, C. L., Sakulyanontvittaya, T., Duhl, T., Emmons, L. K., and Wang, X.: The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions, Geosci. Model Dev., 5, 1471–1492, https://doi.org/10.5194/gmd-5-1471-2012, 2012.
Guo, J., Deng, M., Lee, S. S., Wang, F., Li, Z., Zhai, P., Liu, H., Lv, W., Yao, W., and Li, X.: Delaying precipitation and lightning by air pollution over the Pearl River Delta. Part I: Observational analyses, J. Geophys. Res.-Atmos., 121, 6472–6488, https://doi.org/10.1002/2015JD023257, 2016.
Guo, J., Luo, Y., Yang, J., Furtado, K., and Lei, H.: Effects of anthropogenic and sea salt aerosols on a heavy rainfall event during the early-summer rainy season over coastal Southern China, Atmos. Res., 265, 105923, https://doi.org/10.1016/j.atmosres.2021.105923, 2022.
Guo, X., Fu, D., Guo, X., and Zhang, C.: A case study of aerosol impacts on summer convective clouds and precipitation over northern China, Atmos. Res., 142, 142–157, https://doi.org/10.1016/j.atmosres.2013.10.006, 2014.
Hazra, A., Goswami, B. N., and Chen, J.-P.: Role of Interactions between Aerosol Radiative Effect, Dynamics, and Cloud Microphysics on Transitions of Monsoon Intraseasonal Oscillations, J. Atmos. Sci., 70, 2073–2087, https://doi.org/10.1175/JAS-D-12-0179.1, 2013.
He, J., Glotfelty, T., Yahya, K., Alapaty, K., and Yu, S.: Does temperature nudging overwhelm aerosol radiative effects in regional integrated climate models?, Atmos. Environ., 154, 42–52, https://doi.org/10.1016/j.atmosenv.2017.01.040, 2017.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J. N.: ERA5 hourly data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2023a.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023b.
Hong, S.-Y., Noh, Y., and Dudhia, J.: A New Vertical Diffusion Package with an Explicit Treatment of Entrainment Processes, Mon. Weather Rev., 134, 2318–2341, https://doi.org/10.1175/MWR3199.1, 2006.
Hsu, P.-C., Lee, J.-Y., and Ha, K.-J.: Influence of boreal summer intraseasonal oscillation on rainfall extremes in southern China, Int. J. Climatol., 36, 1403–1412, https://doi.org/10.1002/joc.4433, 2016.
Huang, R.-J., Zhang, Y., Bozzetti, C., Ho, K.-F., Cao, J.-J., Han, Y., Daellenbach, K. R., Slowik, J. G., Platt, S. M., Canonaco, F., Zotter, P., Wolf, R., Pieber, S. M., Bruns, E. A., Crippa, M., Ciarelli, G., Piazzalunga, A., Schwikowski, M., Abbaszade, G., Schnelle-Kreis, J., Zimmermann, R., An, Z., Szidat, S., Baltensperger, U., Haddad, I. E., and Prévôt, A. S. H.: High secondary aerosol contribution to particulate pollution during haze events in China, Nature, 514, 218–222, https://doi.org/10.1038/nature13774, 2014.
Huang, X. and Ding, A.: Aerosol as a critical factor causing forecast biases of air temperature in global numerical weather prediction models, Sci. Bull., 66, 1917–1924, https://doi.org/10.1016/j.scib.2021.05.009, 2021.
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A., and Collins, W. D.: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models, J. Geophys. Res.-Atmos., 113, https://doi.org/10.1029/2008JD009944, 2008.
Jia, H., Hasekamp, O., and Quaas, J.: Revisiting Aerosol–Cloud Interactions From Weekly Cycles, Geophys. Res. Lett., 51, e2024GL108266, https://doi.org/10.1029/2024GL108266, 2024.
Koren, I., Kaufman, Y. J., Remer, L. A., and Martins, J. V.: Measurement of the Effect of Amazon Smoke on Inhibition of Cloud Formation, Science, 303, 1342–1345, https://doi.org/10.1126/science.1089424, 2004.
Lee, K. H., Li, Z., Wong, M. S., Xin, J., Wang, Y., Hao, W.-M., and Zhao, F.: Aerosol single scattering albedo estimated across China from a combination of ground and satellite measurements, J. Geophys. Res.-Atmos., 112, https://doi.org/10.1029/2007JD009077, 2007.
Lee, S. S., Guo, J., and Li, Z.: Delaying precipitation by air pollution over the Pearl River Delta: 2. Model simulations, J. Geophys. Res.-Atmos., 121, 11739–11760, https://doi.org/10.1002/2015JD024362, 2016.
Levy, R. C., Mattoo, S., Munchak, L. A., Remer, L. A., Sayer, A. M., Patadia, F., and Hsu, N. C.: The Collection 6 MODIS aerosol products over land and ocean, Atmos. Meas. Tech., 6, 2989–3034, https://doi.org/10.5194/amt-6-2989-2013, 2013.
Li, M., Liu, H., Geng, G., Hong, C., Liu, F., Song, Y., Tong, D., Zheng, B., Cui, H., Man, H., Zhang, Q., and He, K.: Anthropogenic emission inventories in China: a review, Natl. Sci. Rev., 4, 834–866, https://doi.org/10.1093/nsr/nwx150, 2017a.
Li, M., Zhang, Q., Kurokawa, J.-I., Woo, J.-H., He, K., Lu, Z., Ohara, T., Song, Y., Streets, D. G., Carmichael, G. R., Cheng, Y., Hong, C., Huo, H., Jiang, X., Kang, S., Liu, F., Su, H., and Zheng, B.: MIX: a mosaic Asian anthropogenic emission inventory under the international collaboration framework of the MICS-Asia and HTAP, Atmos. Chem. Phys., 17, 935–963, https://doi.org/10.5194/acp-17-935-2017, 2017b.
Li, Z., Niu, F., Fan, J., Liu, Y., Rosenfeld, D., and Ding, Y.: Long-term impacts of aerosols on the vertical development of clouds and precipitation, Nat. Geosci., 4, 888–894, https://doi.org/10.1038/ngeo1313, 2011.
Li, Z., Lau, W. K.-M., Ramanathan, V., Wu, G., Ding, Y., Manoj, M. G., Liu, J., Qian, Y., Li, J., Zhou, T., Fan, J., Rosenfeld, D., Ming, Y., Wang, Y., Huang, J., Wang, B., Xu, X., Lee, S.-S., Cribb, M., Zhang, F., Yang, X., Zhao, C., Takemura, T., Wang, K., Xia, X., Yin, Y., Zhang, H., Guo, J., Zhai, P. M., Sugimoto, N., Babu, S. S., and Brasseur, G. P.: Aerosol and monsoon climate interactions over Asia, Rev. Geophy., 54, 866–929, https://doi.org/10.1002/2015RG000500, 2016.
Li, Z., Wang, Y., Guo, J., Zhao, C., Cribb, M. C., Dong, X., Fan, J., Gong, D., Huang, J., Jiang, M., Jiang, Y., Lee, S.-S., Li, H., Li, J., Liu, J., Qian, Y., Rosenfeld, D., Shan, S., Sun, Y., Wang, H., Xin, J., Yan, X., Yang, X., Yang, X.-Q., Zhang, F., and Zheng, Y.: East Asian Study of Tropospheric Aerosols and their Impact on Regional Clouds, Precipitation, and Climate (EAST-AIRCPC), J. Geophys. Res.-Atmos., 124, 13026–13054, https://doi.org/10.1029/2019JD030758, 2019.
Liebmann, B. and Smith, C. A.: Description of a complete (interpolated) outgoing longwave radiation dataset, Bull. Am. Meteorol. Soc., 77, 1275–1277, 1996.
Lin, Z. and Chen, G.: The Role of Anthropogenic Forcings on the Regional Climate of Summertime Diurnal Variations over North China, J. Climate, 36, 4491–4509, https://doi.org/10.1175/JCLI-D-22-0498.1, 2023.
Liu, Z., Ming, Y., Zhao, C., Lau, N. C., Guo, J., Bollasina, M., and Yim, S. H. L.: Contribution of local and remote anthropogenic aerosols to a record-breaking torrential rainfall event in Guangdong Province, China, Atmos. Chem. Phys., 20, 223–241, https://doi.org/10.5194/acp-20-223-2020, 2020.
Manoj, M. G., Devara, P. C. S., Safai, P. D., and Goswami, B. N.: Absorbing aerosols facilitate transition of Indian monsoon breaks to active spells, Clim. Dynam., 37, 2181–2198, https://doi.org/10.1007/s00382-010-0971-3, 2011.
Morrison, H., Thompson, G., and Tatarskii, V.: Impact of Cloud Microphysics on the Development of Trailing Stratiform Precipitation in a Simulated Squall Line: Comparison of One- and Two-Moment Schemes, Mon. Weather Rev., 137, 991–1007, https://doi.org/10.1175/2008MWR2556.1, 2009.
Mudelsee, M.: Bootstrap confidence intervals, in: Climate Time Series Analysis, Atmospheric and Oceanographic Sciences Library, Vol. 51, Springer, Cham, Germany, 61–104, https://doi.org/10.1007/978-3-319-04450-7, 2014.
NASA (National Aeronautics and Space Administration): CERES and GEO-Enhanced TOA, Within-Atmosphere and Surface Fluxes, Clouds and Aerosols Daily Terra-Aqua-NOAA20 Edition4B, NASA Langley Atmospheric Science Data Center DAAC [data set], https://doi.org/10.5067/Terra-Aqua-NOAA20/CERES/SYN1degDay_L3.004B, 2017.
NCEP (National Centers for Environmental Prediction), NWS (National Weather Service), NOAA, and DOC (U.S. Department of Commerce): NCEP FNL Operational Model Global Tropospheric Analyses, continuing from July 1999, National Science Foundation National Center for Atmospheric Research [data set], https://doi.org/10.5065/D6M043C6, 2000.
NOAA (National Oceanic and Atmospheric Administration): Climate Prediction Center (CPC) Global Unified Gauge-Based Analysis of Daily Precipitation data, NOAA [data set], https://psl.noaa.gov/data/gridded/data.cpc.globalprecip.html, last access: 20 September 2025a.
NOAA (National Oceanic and Atmospheric Administration): NOAA Interpolated Outgoing Longwave Radiation (OLR) data, NOAA [data set], https://psl.noaa.gov/data/gridded/data.olrcdr.interp.html, last access: 20 September 2025b.
Pahlow, M., Parlange, M. B., and Porté-Agel, F.: On Monin–Obukhov Similarity In The Stable Atmospheric Boundary Layer, Bound.-Lay. Meteorol., 99, 225–248, https://doi.org/10.1023/A:1018909000098, 2001.
Platnick, S., Hubanks, P., Meyer, K., and King, M. D.: MODIS Atmosphere L3 Daily Product, NASA MODIS Adaptive Processing System, Goddard Space Flight Center [data set], https://doi.org/10.5067/MODIS/MOD08_D3.061, 2015.
Platnick, S., Meyer, K. G., King, M. D., Wind, G., Amarasinghe, N., Marchant, B., Arnold, G. T., Zhang, Z., Hubanks, P. A., Holz, R. E., Yang, P., Ridgway, W. L., and Riedi, J.: The MODIS Cloud Optical and Microphysical Products: Collection 6 Updates and Examples from Terra and Aqua, IEEE T. Geosci. Remote, 55, 502–525, https://doi.org/10.1109/TGRS.2016.2610522, 2017.
Reid, J. S., Lagrosas, N. D., Jonsson, H. H., Reid, E. A., Sessions, W. R., Simpas, J. B., Uy, S. N., Boyd, T. J., Atwood, S. A., Blake, D. R., Campbell, J. R., Cliff, S. S., Holben, B. N., Holz, R. E., Hyer, E. J., Lynch, P., Meinardi, S., Posselt, D. J., Richardson, K. A., Salinas, S. V., Smirnov, A., Wang, Q., Yu, L., and Zhang, J.: Observations of the temporal variability in aerosol properties and their relationships to meteorology in the summer monsoonal South China Sea/East Sea: the scale-dependent role of monsoonal flows, the Madden–Julian Oscillation, tropical cyclones, squall lines and cold pools, Atmos. Chem. Phys., 15, 1745–1768, https://doi.org/10.5194/acp-15-1745-2015, 2015.
Rosenfeld, D., Lohmann, U., Raga, G. B., O'Dowd, C. D., Kulmala, M., Fuzzi, S., Reissell, A., and Andreae, M. O.: Flood or Drought: How Do Aerosols Affect Precipitation?, Science, 321, 1309–1313, https://doi.org/10.1126/science.1160606, 2008.
Rutan, D. A., Kato, S., Doelling, D. R., Rose, F. G., Nguyen, L. T., Caldwell, T. E., and Loeb, N. G.: CERES Synoptic Product: Methodology and Validation of Surface Radiant Flux, J. Atmos. Oceanic Technol., 32, 1121–1143, https://doi.org/10.1175/JTECH-D-14-00165.1, 2015.
Shao, T., Liu, Y., Wang, R., Zhu, Q., Tan, Z., and Luo, R.: Role of anthropogenic aerosols in affecting different-grade precipitation over eastern China: A case study, Sci. Total Environ., 807, 150886, https://doi.org/10.1016/j.scitotenv.2021.150886, 2022.
Singh, C., Ganguly, D., and Sharma, P.: Impact of West Asia, Tibetan Plateau and local dust emissions on intra-seasonal oscillations of the South Asian monsoon rainfall, Clim. Dynam., 53, 6569–6593, https://doi.org/10.1007/s00382-019-04944-5, 2019.
Stier, P., van den Heever, S. C., Christensen, M. W., Gryspeerdt, E., Dagan, G., Saleeby, S. M., Bollasina, M., Donner, L., Emanuel, K., Ekman, A. M. L., Feingold, G., Field, P., Forster, P., Haywood, J., Kahn, R., Koren, I., Kummerow, C., L'Ecuyer, T., Lohmann, U., Ming, Y., Myhre, G., Quaas, J., Rosenfeld, D., Samset, B., Seifert, A., Stephens, G., and Tao, W.-K.: Multifaceted aerosol effects on precipitation, Nat. Geosci., 17, 719–732, https://doi.org/10.1038/s41561-024-01482-6, 2024.
Su, Y., Zhao, C., Wang, Y., and Ma, Z.: Spatiotemporal Variations of Precipitation in China Using Surface Gauge Observations from 1961 to 2016, Atmosphere, 11, 303, https://doi.org/10.3390/atmos11030303, 2020.
Sun, Y. and Zhao, C.: Distinct impacts on precipitation by aerosol radiative effect over three different megacity regions of eastern China, Atmos. Chem. Phys., 21, 16555–16574, https://doi.org/10.5194/acp-21-16555-2021, 2021.
Surendran, S., Ajay Anand, K. V., Ravindran, S., and Rajendran, K.: Exacerbation of Indian Summer Monsoon Breaks by the Indirect Effect of Regional Dust Aerosols, Geophys. Res. Lett., 49, e2022GL101106, https://doi.org/10.1029/2022GL101106, 2022.
Tao, W.-K., Chen, J.-P., Li, Z., Wang, C., and Zhang, C.: Impact of aerosols on convective clouds and precipitation, Rev. Geophy., 50, https://doi.org/10.1029/2011RG000369, 2012.
Tian, B., Waliser, D. E., Kahn, R. A., and Wong, S.: Modulation of Atlantic aerosols by the Madden-Julian Oscillation, J. Geophys. Res.-Atmos., 116, https://doi.org/10.1029/2010JD015201, 2011.
Tsinghua University, CEADs (China Carbon Emission Accounts and Datasets) team, and CAEP (Chinese Academy of Environmental Planning of the Ministry of Ecology and Environment): Multi-resolution Emission Inventory for China and MIX-Asia dataset, MEIC Data Platform [data set], http://meicmodel.org.cn/, last access: 20 September 2025.
Twomey, S.: The Influence of Pollution on the Shortwave Albedo of Clouds, J. Atmos. Sci., 34, 1149–1152, https://doi.org/10.1175/1520-0469(1977)034<1149:TIOPOT>2.0.CO;2, 1977.
UCAR (University Corporation for Atmospheric Research): WRF Source Codes and Graphics Software Downloads, UCAR [code], https://www2.mmm.ucar.edu/wrf/users/download/get_source.html, last access: 20 September 2025a.
UCAR (University Corporation for Atmospheric Research): Fire Emission Factors and Emission Inventories, UCAR [data set], https://www.acom.ucar.edu/Data/fire/, last access: 20 September 2025b.
Vinoj, V., Rasch, P. J., Wang, H., Yoon, J.-H., Ma, P.-L., Landu, K., and Singh, B.: Short-term modulation of Indian summer monsoon rainfall by West Asian dust, Nat. Geosci., 7, 308–313, https://doi.org/10.1038/ngeo2107, 2014.
Wang, L., Zhang, Y., Wang, K., Zheng, B., Zhang, Q., and Wei, W.: Application of Weather Research and Forecasting Model with Chemistry (WRF/Chem) over northern China: Sensitivity study, comparative evaluation, and policy implications, Atmos. Environ., 124, 337–350, https://doi.org/10.1016/j.atmosenv.2014.12.052, 2016.
Wang, Y., Wan, Q., Meng, W., Liao, F., Tan, H., and Zhang, R.: Long-term impacts of aerosols on precipitation and lightning over the Pearl River Delta megacity area in China, Atmos. Chem. Phys., 11, 12421–12436, https://doi.org/10.5194/acp-11-12421-2011, 2011.
Wang, Z., Xue, L., Liu, J., Ding, K., Lou, S., Ding, A., Wang, J., and Huang, X.: Roles of Atmospheric Aerosols in Extreme Meteorological Events: a Systematic Review, Curr. Pollut. Rep., 8, 177–188, https://doi.org/10.1007/s40726-022-00216-9, 2022.
Wei, J., Mao, Q., Shan, Y., Jin, Q., Yang, Y., and Chen, H.: Spring biomass burning in Indochina enhances summer Yangtze River Valley rainfall through land–atmosphere interactions, npj Clim. Atmos. Sci., 6, 183, https://doi.org/10.1038/s41612-023-00514-z, 2023.
Wei, S., Hsu, P.-C., and Xie, J.: Effects of Intraseasonal Oscillation on Timing and Subseasonal Predictability of Mei-yu Onset over the Yangtze River Basin, J. Climate, 37, 2277–2295, https://doi.org/10.1175/JCLI-D-23-0504.1, 2024.
Wiedinmyer, C., Akagi, S. K., Yokelson, R. J., Emmons, L. K., Al-Saadi, J. A., Orlando, J. J., and Soja, A. J.: The Fire INventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning, Geosci. Model Dev., 4, 625–641, https://doi.org/10.5194/gmd-4-625-2011, 2011.
Xiao, H., Liu, X., Li, H., Yue, Q., Feng, L., and Qu, J.: Extent of aerosol effect on the precipitation of squall lines: A case study in South China, Atmos. Res., 292, 106886, https://doi.org/10.1016/j.atmosres.2023.106886, 2023a.
Xiao, Z., Yu, Y., Miao, Y., Zhu, S., He, H., Wang, Y., and Che, H.: Impact of Aerosols on Convective System Over the North China Plain: A Numerical Case Study in Autumn, J. Geophys. Res.-Atmos., 128, e2022JD037465, https://doi.org/10.1029/2022JD037465, 2023b.
Xie, J., Hsu, P.-C., Lee, J.-Y., Wang, L., and Turner, A. G.: Tropical intraseasonal oscillations as key driver and source of predictability for the 2022 Pakistan record-breaking rainfall event, npj Clim. Atmos. Sci., 7, 256, https://doi.org/10.1038/s41612-024-00809-9, 2024.
Yanai, M., Esbensen, S., and Chu, J.-H.: Determination of Bulk Properties of Tropical Cloud Clusters from Large-Scale Heat and Moisture Budgets, J. Atmos. Sci., 30, 611–627, https://doi.org/10.1175/1520-0469(1973)030<0611:DOBPOT>2.0.CO;2, 1973.
Yang, X. and Li, Z.: Increases in thunderstorm activity and relationships with air pollution in southeast China, J. Geophys. Res.-Atmos., 119, 1835–1844, https://doi.org/10.1002/2013JD021224, 2014.
Yang, X., Li, Z., Liu, L., Zhou, L., Cribb, M., and Zhang, F.: Distinct weekly cycles of thunderstorms and a potential connection with aerosol type in China, Geophys. Res. Lett., 43, 8760–8768, https://doi.org/10.1002/2016GL070375, 2016.
Yang, X., Zhou, L., Zhao, C., and Yang, J.: Impact of aerosols on tropical cyclone-induced precipitation over the mainland of China, Climatic Change, 148, 173–185, https://doi.org/10.1007/s10584-018-2175-5, 2018.
Yu, Y. and Ginoux, P.: Assessing the contribution of the ENSO and MJO to Australian dust activity based on satellite- and ground-based observations, Atmos. Chem. Phys., 21, 8511–8530, https://doi.org/10.5194/acp-21-8511-2021, 2021.
Yun, Y., Zhang, D.-L., Gao, W., Yin, J., Zhao, C., Li, J., Guo, J., and Liu, H.: Spatiotemporal Variations of the Effects of Aerosols on Clouds and Precipitation in an Extreme-Rain-Producing MCS in South China, J. Geophys. Res.-Atmos., 129, e2023JD040014, https://doi.org/10.1029/2023JD040014, 2024.
Zaveri, R. A. and Peters, L. K.: A new lumped structure photochemical mechanism for large-scale applications, J. Geophys. Res.-Atmos., 104, 30387–30415, https://doi.org/10.1029/1999JD900876, 1999.
Zaveri, R. A., Easter, R. C., Fast, J. D., and Peters, L. K.: Model for Simulating Aerosol Interactions and Chemistry (MOSAIC), J. Geophys. Res.-Atmos., 113, https://doi.org/10.1029/2007JD008782, 2008.
Zhang, L., Fu, T.-M., Tian, H., Ma, Y., Chen, J.-P., Tsai, T.-C., Tsai, I.-C., Meng, Z., and Yang, X.: Anthropogenic Aerosols Significantly Reduce Mesoscale Convective System Occurrences and Precipitation Over Southern China in April, Geophys. Res. Lett., 47, e2019GL086204, https://doi.org/10.1029/2019GL086204, 2020.
Zhang, Y., Wen, X. Y., and Jang, C. J.: Simulating chemistry–aerosol–cloud–radiation–climate feedbacks over the continental U.S. using the online-coupled Weather Research Forecasting Model with chemistry (WRF/Chem), Atmos. Environ., 44, 3568–3582, https://doi.org/10.1016/j.atmosenv.2010.05.056, 2010.
Zhang, Y., Gao, Y., Guo, L., and Zhang, M.: Numerical analysis of aerosol direct and indirect effects on an extreme rainfall event over Beijing in July 2016, Atmos. Res., 264, 105871, https://doi.org/10.1016/j.atmosres.2021.105871, 2021.
Zhao, C., Yang, Y., Chi, Y., Sun, Y., Zhao, X., Letu, H., and Xia, Y.: Recent progress in cloud physics and associated radiative effects in China from 2016 to 2022, Atmos. Res., 293, 106899, https://doi.org/10.1016/j.atmosres.2023.106899, 2023.
Zhao, C., Sun, Y., Yang, J., Li, J., Zhou, Y., Yang, Y., Fan, H., and Zhao, X.: Observational evidence and mechanisms of aerosol effects on precipitation, Sci. Bull., 69, 1569–1580, https://doi.org/10.1016/j.scib.2024.03.014, 2024.
Zhao, J., Ma, X., Wu, S., and Sha, T.: Dust emission and transport in Northwest China: WRF-Chem simulation and comparisons with multi-sensor observations, Atmos. Res., 241, 104978, https://doi.org/10.1016/j.atmosres.2020.104978, 2020.
Zhou, S., Yang, J., Wang, W.-C., Zhao, C., Gong, D., and Shi, P.: An observational study of the effects of aerosols on diurnal variation of heavy rainfall and associated clouds over Beijing–Tianjin–Hebei, Atmos. Chem. Phys., 20, 5211–5229, https://doi.org/10.5194/acp-20-5211-2020, 2020.
Zhu, A., Xu, H., Deng, J., Ma, J., and Hua, S.: Instant and delayed effects of March biomass burning aerosols over the Indochina Peninsula , Atmos. Chem. Phys., 22, 15425–15447, https://doi.org/10.5194/acp-22-15425-2022, 2022.
Zhu, H., Li, R., Yang, S., Zhao, C., Jiang, Z., and Huang, C.: The impacts of dust aerosol and convective available potential energy on precipitation vertical structure in southeastern China as seen from multisource observations, Atmos. Chem. Phys., 23, 2421–2437, https://doi.org/10.5194/acp-23-2421-2023, 2023.
Zhu, H., Yang, S., Zhao, H., Wang, Y., and Li, R.: Complex interplay of sulfate aerosols and meteorology conditions on precipitation and latent heat vertical structure, npj Clim. Atmos. Sci., 7, 191, https://doi.org/10.1038/s41612-024-00743-w, 2024.
Short summary
Previous studies mainly examined aerosol effects on short-term rainfall variations. Here we show, using observations and model experiments, that aerosols also intensify rainfall variability on two- to four-week timescales over South China primarily through cloud-microphysical processes, while radiative effects play a secondary role, thereby amplifying prolonged heavy rainfall events.
Previous studies mainly examined aerosol effects on short-term rainfall variations. Here we...
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