Articles | Volume 26, issue 13
https://doi.org/10.5194/acp-26-9373-2026
© Author(s) 2026. 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-26-9373-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Jul 2026
Research article |
| 03 Jul 2026
| 03 Jul 2026
Susceptibility of marine warm clouds to aerosols in different monsoon periods over the South China Sea
Advanced Science & Technology of Space and Atmospheric Physics Group (ASAG), School of Atmospheric Sciences, Sun Yat-sen University, 519082 Zhuhai, China
Hailing Jia
SRON Space Research Organisation Netherlands, Leiden, the Netherlands
Yong Han
CORRESPONDING AUTHOR
Advanced Science & Technology of Space and Atmospheric Physics Group (ASAG), School of Atmospheric Sciences, Sun Yat-sen University, 519082 Zhuhai, China
Related authors
No articles found.
Hengqi Wang, Yiran Peng, Knut von Salzen, Husi Letu, Minqi Wang, Jiangnan Li, Hailing Jia, Huazhe Shang, Dabin Ji, Yuying Wang, Xin He, Luke Fraser-Leach, and Johannes Quaas
EGUsphere, https://doi.org/10.5194/egusphere-2026-6624, https://doi.org/10.5194/egusphere-2026-6624, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Aerosols act as seeds for cloud formation and influence Earth's climate. Building on the aerosol evaluation in Part 1, we further evaluated an improved aerosol-climate model against observations to assess its ability to simulate clouds, rainfall, radiation, and aerosol-cloud interactions. The new model better simulates key cloud particles and their responses to aerosols, improving our understanding of aerosol–cloud interactions and their effects on climate.
Jianqi Zhao, Xiaoyan Ma, Hailing Jia, Tong Yang, Peili Zou, and Yingying Ku
Atmos. Chem. Phys., 26, 15117–15138, https://doi.org/10.5194/acp-26-15117-2026, https://doi.org/10.5194/acp-26-15117-2026, 2026
Short summary
Short summary
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.
Ardit Arifi, Hailing Jia, Guangliang Fu, Athanasios Tsikerdekis, Nick Schutgens, Anne Kubin, Yusuf Bhatti, Ulrike Dusek, and Otto Hasekamp
EGUsphere, https://doi.org/10.5194/egusphere-2026-5361, https://doi.org/10.5194/egusphere-2026-5361, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Aerosols are tiny particles that affect climate by scattering and absorbing sunlight and altering clouds. Much of the uncertainty in their climate effect comes from poorly known emissions. Here we provide a new global estimate of aerosol emissions using PACE/SPEXone satellite observations of aerosol amount, size, and absorption. Combining these with a global aerosol climate model through data assimilation, we find a substantial update to current estimates, suggesting stronger aerosol cooling.
Hailing Jia, David Neubauer, Yusuf Bhatti, Duncan Watson-Parris, Philip Stier, Johannes Quaas, Daniel Partridge, Ardit Arifi, Anne Kubin, Athanasios Nenes, Muhammed Irfan, Ulas Im, Carl Svenhag, Nick Schutgens, Bastiaan van Diedenhoven, Sylvaine Ferrachat, Ulrike Lohmann, Ina Tegen, Alice Henkes, Guangliang Fu, and Otto Hasekamp
EGUsphere, https://doi.org/10.5194/egusphere-2026-3275, https://doi.org/10.5194/egusphere-2026-3275, 2026
Short summary
Short summary
Tiny aerosol particles affect Earth’s climate by interacting with clouds and sunlight, but their impact remains uncertain. Combining climate model simulations and emulators, we explored millions of model variants across different aerosol and cloud settings to identify the causes of the uncertainty. We found that aerosol emissions and cloud convection are key contributors. Our work suggests that better integration of existing observations could help reduce these uncertainties in the future.
Yusuf A. Bhatti, Duncan Watson-Parris, Leighton A. Regayre, Hailing Jia, David Neubauer, Ulas Im, Carl Svenhag, Nick Schutgens, Athanasios Tsikerdekis, Athanasios Nenes, Muhammed Irfan, Bastiaan van Diedenhoven, Ardit Arifi, Guangliang Fu, and Otto P. Hasekamp
Atmos. Chem. Phys., 26, 269–293, https://doi.org/10.5194/acp-26-269-2026, https://doi.org/10.5194/acp-26-269-2026, 2026
Short summary
Short summary
Aerosols (small airborne particles) impact Earth's climate, but their extent is unknown. By running climate model simulations and using machine learning to emulate millions of additional variants with different settings, we found that natural emissions like sea spray and sulfur are key sources of uncertainty in climate predictions. Our work shows that understanding these natural processes better can help improve climate models and make future climate projections more accurate.
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
Short summary
Short summary
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.
Hao Luo, Johannes Quaas, and Yong Han
Atmos. Chem. Phys., 23, 8169–8186, https://doi.org/10.5194/acp-23-8169-2023, https://doi.org/10.5194/acp-23-8169-2023, 2023
Short summary
Short summary
Clouds exhibit a wide range of vertical structures with varying microphysical and radiative properties. We show a global survey of spatial distribution, vertical extent and radiative effect of various classified cloud vertical structures using joint satellite observations from the new CCCM datasets during 2007–2010. Moreover, the long-term trends in CVSs are investigated based on different CMIP6 future scenarios to capture the cloud variations with different, increasing anthropogenic forcings.
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
Preprint archived
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Hailing Jia, Johannes Quaas, Edward Gryspeerdt, Christoph Böhm, and Odran Sourdeval
Atmos. Chem. Phys., 22, 7353–7372, https://doi.org/10.5194/acp-22-7353-2022, https://doi.org/10.5194/acp-22-7353-2022, 2022
Short summary
Short summary
Aerosol–cloud interaction is the most uncertain component of the anthropogenic forcing of the climate. By combining satellite and reanalysis data, we show that the strength of the Twomey effect (S) increases remarkably with vertical velocity. Both the confounding effect of aerosol–precipitation interaction and the lack of vertical co-location between aerosol and cloud are found to overestimate S, whereas the retrieval biases in aerosol and cloud appear to underestimate S.
Hao Luo, Li Dong, Yichen Chen, Yuefeng Zhao, Delong Zhao, Mengyu Huang, Deping Ding, Jiayuan Liao, Tian Ma, Maohai Hu, and Yong Han
Atmos. Chem. Phys., 22, 2507–2524, https://doi.org/10.5194/acp-22-2507-2022, https://doi.org/10.5194/acp-22-2507-2022, 2022
Short summary
Short summary
Aerosol–planetary boundary layer (PBL) interaction is a key mechanism for stabilizing the atmosphere and exacerbating surface air pollution. Using aircraft measurements and WRF-Chem simulations, we find that the aerosol–PBL interaction of different aerosols under contrasting synoptic patterns, PBL structures, and aerosol vertical distributions vary significantly. We attempt to determine which pollutants to target in different synoptic conditions to attain more precise air pollution control.
Cited articles
Ackerman, A. S., Kirkpatrick, M. P., Stevens, D. E., and Toon, O. B.: The impact of humidity above stratiform clouds on indirect aerosol climate forcing, Nature, 432, 1014–1017, https://doi.org/10.1038/nature03174, 2004.
Albrecht, B. A.: Aerosols, Cloud Microphysics, and Fractional Cloudiness, Science, 245, 1227–1230, 1989.
Bellouin, N., Quaas, J., Gryspeerdt, E., Kinne, S., Stier, P., Watson-Parris, D., Boucher, O., Carslaw, K. S., Christensen, M., Daniau, A.-L., Dufresne, J.-L., Feingold, G., Fiedler, S., Forster, P., Gettelman, A., Haywood, J. M., Lohmann, U., Malavelle, F., Mauritsen, T., McCoy, D. T., Myhre, G., Mülmenstädt, J., Neubauer, D., Possner, A., Rugenstein, M., Sato, Y., Schulz, M., Schwartz, S. E., Sourdeval, O., Storelvmo, T., Toll, V., Winker, D., and Stevens, B.: Bounding Global Aerosol Radiative Forcing of Climate Change, Rev. Geophys., 58, e2019RG000660, https://doi.org/10.1029/2019RG000660, 2020.
Chen, J. and Hu, Z.: Seasonal variability in spatial patterns of sea surface cold- and warm fronts over the continental shelf of the northern South China Sea, Front. Mar. Sci., 9, https://doi.org/10.3389/fmars.2022.1100772, 2023.
Chen, Y., Luo, T., Sun, G., Zhu, W., Liu, Q., Liu, Y., Jin, X., and Weng, N.: A Comprehensive Ensemble Model for Marine Atmospheric Boundary-Layer Prediction in Meteorologically Sparse and Complex Regions: A Case Study in the South China Sea, Remote Sens., 17, 2046, https://doi.org/10.3390/rs17122046, 2025.
Chen, Y.-C., Christensen, M. W., Stephens, G. L., and Seinfeld, J. H.: Satellite-based estimate of global aerosol–cloud radiative forcing by marine warm clouds, Nat. Geosci., 7, 643–646, https://doi.org/10.1038/ngeo2214, 2014.
Costantino, L. and Bréon, F.-M.: Aerosol indirect effect on warm clouds over South-East Atlantic, from co-located MODIS and CALIPSO observations, Atmos. Chem. Phys., 13, 69–88, https://doi.org/10.5194/acp-13-69-2013, 2013.
Dadashazar, H., Crosbie, E., Majdi, M. S., Panahi, M., Moghaddam, M. A., Behrangi, A., Brunke, M., Zeng, X., Jonsson, H. H., and Sorooshian, A.: Stratocumulus cloud clearings: statistics from satellites, reanalysis models, and airborne measurements, Atmos. Chem. Phys., 20, 4637–4665, https://doi.org/10.5194/acp-20-4637-2020, 2020.
Dagan, G., Koren, I., Altaratz, O., and Heiblum, R. H.: Time-dependent, non-monotonic response of warm convective cloud fields to changes in aerosol loading, Atmos. Chem. Phys., 17, 7435–7444, https://doi.org/10.5194/acp-17-7435-2017, 2017.
Dezfuli, A. K., Ichoku, C. M., Huffman, G. J., Mohr, K. I., Selker, J. S., Van De Giesen, N., Hochreutener, R., and Annor, F. O.: Validation of IMERG Precipitation in Africa, J. Hydrometeorol., 18, 2817–2825, https://doi.org/10.1175/JHM-D-17-0139.1, 2017.
Douglas, A. and L'Ecuyer, T.: Quantifying variations in shortwave aerosol–cloud–radiation interactions using local meteorology and cloud state constraints, Atmos. Chem. Phys., 19, 6251–6268, https://doi.org/10.5194/acp-19-6251-2019, 2019.
Douville, H., Raghavan, K., Renwick, J., Allan, R. P., Arias, P. A., Barlow, M., Cerezo-Mota, R., Cherchi, A., Gan, T. Y., and Gergis, J.: Climate Change 2021 – The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 1st edn., Cambridge University Press, https://doi.org/10.1017/9781009157896, 2023.
Durden, S. L.: Evaluation of IMERG Data over Open Ocean Using Observations of Tropical Cyclones, Remote Sens., 16, 2028, https://doi.org/10.3390/rs16112028, 2024.
Fan, J., Yuan, T., Comstock, J. M., Ghan, S., Khain, A., Leung, L. R., Li, Z., Martins, V. J., and Ovchinnikov, M.: Dominant role by vertical wind shear in regulating aerosol effects on deep convective clouds, J. Geophys. Res.-Atmos., 114, https://doi.org/10.1029/2009JD012352, 2009.
Fan, J., Wang, Y., Rosenfeld, D., and Liu, X.: Review of Aerosol–Cloud Interactions: Mechanisms, Significance, and Challenges, J. Atmos. Sci., 73, 4221–4252, https://doi.org/10.1175/JAS-D-16-0037.1, 2016.
Feingold, G., Remer, L. A., Ramaprasad, J., and Kaufman, Y. J.: Analysis of smoke impact on clouds in Brazilian biomass burning regions: An extension of Twomey's approach, J. Geophys. Res., 106, 22907–22922, https://doi.org/10.1029/2001JD000732, 2001.
Feingold, G., Furrer, R., Pilewskie, P., Remer, L. A., Min, Q., and Jonsson, H.: Aerosol indirect effect studies at Southern Great Plains during the May 2003 Intensive Operations Period, J. Geophys. Res.-Atmos., 111, https://doi.org/10.1029/2004JD005648, 2006.
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.
Grosvenor, D. P., Sourdeval, O., Zuidema, P., Ackerman, A., Alexandrov, M. D., Bennartz, R., Boers, R., Cairns, B., Chiu, J. C., Christensen, M., Deneke, H., Diamond, M., Feingold, G., Fridlind, A., Hünerbein, A., Knist, C., Kollias, P., Marshak, A., McCoy, D., Merk, D., Painemal, D., Rausch, J., Rosenfeld, D., Russchenberg, H., Seifert, P., Sinclair, K., Stier, P., van Diedenhoven, B., Wendisch, M., Werner, F., Wood, R., Zhang, Z., and Quaas, J.: Remote Sensing of Droplet Number Concentration in Warm Clouds: A Review of the Current State of Knowledge and Perspectives, Rev. Geophys., 56, 409–453, https://doi.org/10.1029/2017RG000593, 2018.
Gryspeerdt, E., Stier, P., White, B. A., and Kipling, Z.: Wet scavenging limits the detection of aerosol effects on precipitation, Atmos. Chem. Phys., 15, 7557–7570, https://doi.org/10.5194/acp-15-7557-2015, 2015.
Gryspeerdt, E., Goren, T., Sourdeval, O., Quaas, J., Mülmenstädt, J., Dipu, S., Unglaub, C., Gettelman, A., and Christensen, M.: Constraining the aerosol influence on cloud liquid water path, Atmos. Chem. Phys., 19, 5331–5347, https://doi.org/10.5194/acp-19-5331-2019, 2019.
Gryspeerdt, E., McCoy, D. T., Crosbie, E., Moore, R. H., Nott, G. J., Painemal, D., Small-Griswold, J., Sorooshian, A., and Ziemba, L.: The impact of sampling strategy on the cloud droplet number concentration estimated from satellite data, Atmos. Meas. Tech., 15, 3875–3892, https://doi.org/10.5194/amt-15-3875-2022, 2022a.
Gryspeerdt, E., McCoy, D., Crosbie, E., Moore, R. H., Nott, G. J., Painemal, D., Small-Griswold, J., Sorooshian, A., and Ziemba, L.: Cloud droplet number concentration, calculated from the MODIS (Moderate resolution imaging spectroradiometer) cloud optical properties retrieval and gridded using different sampling strategies, NERC EDS Centre for Environmental Data Analysis [data set], https://doi.org/10.5285/864a46cc65054008857ee5bb772a2a2b, 2022b.
Hayden, L. J. M., Tan, J., Bolvin, D. T., and Huffman, G. J.: Variations in the Diurnal Cycle of Precipitation and Its Changes with Distance from Shore over Two Contrasting Regions as Observed by IMERG, ERA5, and Spaceborne Ku Radar, J. Hydrometeorol., 24, 675–689, https://doi.org/10.1175/JHM-D-22-0154.1, 2023.
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.: The ERA5 global reanalysis, Q. J. Royal. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Huffman, G. J., Bolvin, D. T., Braithwaite, D., Hsu, K.-L., Joyce, R. J., Kidd, C., Nelkin, E. J., Sorooshian, S., Stocker, E. F., Tan, J., Wolff, D. B., and Xie, P.: Integrated Multi-satellite Retrievals for the Global Precipitation Measurement (GPM) Mission (IMERG), in: Satellite Precipitation Measurement, vol. 67, edited by: Levizzani, V., Kidd, C., Kirschbaum, D. B., Kummerow, C. D., Nakamura, K., and Turk, F. J., Springer International Publishing, Cham, 343–353, https://doi.org/10.1007/978-3-030-24568-9_19, 2020.
Huffman, G. J., Bolvin, D. T., Braithwaite, D., Hsu, K., Joyce, R., Kidd, C., Nelkin, E., Sorooshian, S., Tan, J., and Xie, P.: NASA Global Precipitation Measurement (GPM) Integrated Multi-Satellite Retrievals for GPM (IMERG) Version 07, Algorithm Theoretical Basis Document (ATBD) Version, 47, https://gpm.nasa.gov/resources/documents/imerg-v07-atbd (last access: 19 September 2025), 2023.
Jia, H. and Quaas, J.: Nonlinearity of the cloud response postpones climate penalty of mitigating air pollution in polluted regions, Nat. Clim. Chang., 13, 943–950, https://doi.org/10.1038/s41558-023-01775-5, 2023.
Jia, H., Ma, X., Quaas, J., Yin, Y., and Qiu, T.: Is positive correlation between cloud droplet effective radius and aerosol optical depth over land due to retrieval artifacts or real physical processes?, Atmos. Chem. Phys., 19, 8879–8896, https://doi.org/10.5194/acp-19-8879-2019, 2019.
Jia, H., Ma, X., Yu, F., and Quaas, J.: Significant underestimation of radiative forcing by aerosol–cloud interactions derived from satellite-based methods, Nat. Commun., 12, 3649, https://doi.org/10.1038/s41467-021-23888-1, 2021.
Jia, H., Quaas, J., Gryspeerdt, E., Böhm, C., and Sourdeval, O.: Addressing the difficulties in quantifying droplet number response to aerosol from satellite observations, Atmos. Chem. Phys., 22, 7353–7372, https://doi.org/10.5194/acp-22-7353-2022, 2022.
Jia, H., Hasekamp, O., and Quaas, J.: Revisiting Aerosol–Cloud Interactions From Weekly Cycles, Geophys. Res. Lett., 51, https://doi.org/10.1029/2024gl108266, 2024.
Jose, S., Nair, V. S., and Babu, S. S.: Anthropogenic emissions from South Asia reverses the aerosol indirect effect over the northern Indian Ocean, Sci. Rep., 10, 18360, https://doi.org/10.1038/s41598-020-74897-x, 2020.
Kim, B.-G., Schwartz, S. E., Miller, M. A., and Min, Q.: Effective radius of cloud droplets by ground-based remote sensing: Relationship to aerosol, J. Geophys. Res.-Atmos., 108, https://doi.org/10.1029/2003JD003721, 2003.
Klein, S. A. and Hartmann, D. L.: The Seasonal Cycle of Low Stratiform Clouds, J. Climate, 6, 1587–1606, https://doi.org/10.1175/1520-0442(1993)006<1587:TSCOLS>2.0.CO;2, 1993.
Lee, S. S. and Penner, J. E.: Dependence of aerosol–cloud interactions in stratocumulus clouds on liquid-water path, Atmos. Environ., 45, 6337–6346, https://doi.org/10.1016/j.atmosenv.2011.08.050, 2011.
Lee, T.-W. and Park, J. E.: Thermodynamic correlations between the sea surface temperature, water vapor content, and cloud fraction, using MODIS data, Theor. Appl. Climatol., 150, 1699–1706, https://doi.org/10.1007/s00704-022-04261-8, 2022.
Li, Y., Wang, B., Lee, S.-Y., Zhang, Z., Wang, Y., and Dong, W.: Micro-Pulse Lidar Cruising Measurements in Northern South China Sea, Remote Sens., 12, https://doi.org/10.3390/rs12101695, 2020.
Liu, J., Yu, J., Lin, C., He, M., Liu, H., Wang, W., and Min, M.: Near-real-time atmospheric and oceanic science products of Himawari-8 and Himawari-9 geostationary satellites over the South China Sea, Earth Syst. Sci. Data, 16, 4949–4969, https://doi.org/10.5194/essd-16-4949-2024, 2024.
Ma, P.-L., Rasch, P. J., Chepfer, H., Winker, D. M., and Ghan, S. J.: Observational constraint on cloud susceptibility weakened by aerosol retrieval limitations, Nat. Commun., 9, 2640, https://doi.org/10.1038/s41467-018-05028-4, 2018a.
Ma, X., Jia, H., Yu, F., and Quaas, J.: Opposite Aerosol Index-Cloud Droplet Effective Radius Correlations Over Major Industrial Regions and Their Adjacent Oceans, Geophys. Res. Lett., 45, 5771–5778, https://doi.org/10.1029/2018GL077562, 2018b.
Martin, D. W. and Howland, M. R.: Rainfall over the Arabian Sea during the onset of the 1979 monsoon, Nature, 300, 628–630, https://doi.org/10.1038/300628a0, 1982.
McComiskey, A., Feingold, G., Frisch, A. S., Turner, D. D., Miller, M. A., Chiu, J. C., Min, Q., and Ogren, J. A.: An assessment of aerosol-cloud interactions in marine stratus clouds based on surface remote sensing, J. Geophys. Res., 114, 2008JD011006, https://doi.org/10.1029/2008JD011006, 2009.
Miller, R. M., Rauber, R. M., Di Girolamo, L., Rilloraza, M., Fu, D., McFarquhar, G. M., Nesbitt, S. W., Ziemba, L. D., Woods, S., and Thornhill, K. L.: Influence of natural and anthropogenic aerosols on cloud base droplet size distributions in clouds over the South China Sea and West Pacific, Atmos. Chem. Phys., 23, 8959–8977, https://doi.org/10.5194/acp-23-8959-2023, 2023.
Minnis, P., Sun-Mack, S., Young, D. F., Heck, P. W., Garber, D. P., Chen, Y., Spangenberg, D. A., Arduini, R. F., Trepte, Q. Z., Smith, W. L., Ayers, J. K., Gibson, S. C., Miller, W. F., Hong, G., Chakrapani, V., Takano, Y., Liou, K.-N., Xie, Y., and Yang, P.: CERES Edition-2 Cloud Property Retrievals Using TRMM VIRS and Terra and Aqua MODIS Data – Part I: Algorithms, IEEE T. Geosci. Remote, 49, 4374–4400, https://doi.org/10.1109/TGRS.2011.2144601, 2011a.
Minnis, P., Sun-Mack, S., Chen, Y., Khaiyer, M. M., Yi, Y., Ayers, J. K., Brown, R. R., Dong, X., Gibson, S. C., Heck, P. W., Lin, B., Nordeen, M. L., Nguyen, L., Palikonda, R., Smith, W. L., Spangenberg, D. A., Trepte, Q. Z., and Xi, B.: CERES Edition-2 Cloud Property Retrievals Using TRMM VIRS and Terra and Aqua MODIS Data – Part II: Examples of Average Results and Comparisons With Other Data, IEEE T. Geosci. Remote, 49, 4401–4430, https://doi.org/10.1109/TGRS.2011.2144602, 2011b.
Minnis, P., Sun-Mack, S., Chen, Y., Chang, F.-L., Yost, C. R., Smith, W. L., Heck, P. W., Arduini, R. F., Bedka, S. T., Yi, Y., Hong, G., Jin, Z., Painemal, D., Palikonda, R., Scarino, B. R., Spangenberg, D. A., Smith, R. A., Trepte, Q. Z., Yang, P., and Xie, Y.: CERES MODIS Cloud Product Retrievals for Edition 4 – Part I: Algorithm Changes, IEEE T. Geosci. Remote, 59, 2744–2780, https://doi.org/10.1109/TGRS.2020.3008866, 2021.
Nakajima, T., Higurashi, A., Kawamoto, K., and Penner, J. E.: A possible correlation between satellite-derived cloud and aerosol microphysical parameters, Geophys. Res. Lett., 28, 1171–1174, https://doi.org/10.1029/2000GL012186, 2001.
Ou, H., Cai, M., Zhang, Y., Ni, X., Liang, B., Sun, Q., Mai, S., Sun, C., Zhou, S., Wang, H., Sun, J., and Zhao, J.: Measurement report: Cloud condensation nuclei (CCN) activity in the South China Sea from shipborne observations during the summer and winter of 2021 – seasonal variation and anthropogenic influence, Atmos. Chem. Phys., 25, 2495–2513, https://doi.org/10.5194/acp-25-2495-2025, 2025.
Painemal, D.: Global estimates of changes in shortwave low-cloud albedo and fluxes due to variations in cloud droplet number concentration derived from CERES-MODIS satellite sensors, Geophys. Res. Lett., 45, 9288–9296, https://doi.org/10.1029/2018GL078880, 2018.
Painemal, D., Chang, F.-L., Ferrare, R., Burton, S., Li, Z., Smith Jr., W. L., Minnis, P., Feng, Y., and Clayton, M.: Reducing uncertainties in satellite estimates of aerosol–cloud interactions over the subtropical ocean by integrating vertically resolved aerosol observations, Atmos. Chem. Phys., 20, 7167–7177, https://doi.org/10.5194/acp-20-7167-2020, 2020.
Peng, S., Zhu, Y., Huang, K., Ding, X., Shi, R., Wu, D., Feng, Y., and Wang, D.: Detecting the structure of marine atmospheric boundary layer over the Northern South China Sea by shipboard GPS sondes, Atmos. Sci. Lett., 17, 564–568, https://doi.org/10.1002/asl.693, 2016.
Qiu, Y., Zhao, C., Guo, J., and Li, J.: 8-Year ground-based observational analysis about the seasonal variation of the aerosol-cloud droplet effective radius relationship at SGP site, Atmos. Environ., 164, 139–146, https://doi.org/10.1016/j.atmosenv.2017.06.002, 2017.
Randles, C. A., da Silva, A. M., Buchard, V., Colarco, P. R., Darmenov, A., Govindaraju, R., Smirnov, A., Holben, B., Ferrare, R., Hair, J., Shinozuka, Y., and Flynn, C. J.: The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part I: System Description and Data Assimilation Evaluation, J. Climate, 30, 6823–6850, https://doi.org/10.1175/JCLI-D-16-0609.1, 2017.
Reynolds, R. W., Rayner, N. A., Smith, T. M., Stokes, D. C., and Wang, W.: An Improved In Situ and Satellite SST Analysis for Climate, J. Climate, 15, 1609–1625, https://doi.org/10.1175/1520-0442(2002)015<1609:AIISAS>2.0.CO;2, 2002.
Rosenfeld, D., Zhu, Y., Wang, M., Zheng, Y., Goren, T., and Yu, S.: Aerosol-driven droplet concentrations dominate coverage and water of oceanic low-level clouds, Science, 363, eaav0566, https://doi.org/10.1126/science.aav0566, 2019.
Saponaro, G., Kolmonen, P., Sogacheva, L., Rodriguez, E., Virtanen, T., and de Leeuw, G.: Estimates of the aerosol indirect effect over the Baltic Sea region derived from 12 years of MODIS observations, Atmos. Chem. Phys., 17, 3133–3143, https://doi.org/10.5194/acp-17-3133-2017, 2017.
Sato, Y., Goto, D., Michibata, T., Suzuki, K., Takemura, T., Tomita, H., and Nakajima, T.: Aerosol effects on cloud water amounts were successfully simulated by a global cloud-system resolving model, Nat. Commun., 9, 985, https://doi.org/10.1038/s41467-018-03379-6, 2018.
Seinfeld, J. H., Bretherton, C., Carslaw, K. S., Coe, H., DeMott, P. J., Dunlea, E. J., Feingold, G., Ghan, S., Guenther, A. B., Kahn, R., Kraucunas, I., Kreidenweis, S. M., Molina, M. J., Nenes, A., Penner, J. E., Prather, K. A., Ramanathan, V., Ramaswamy, V., Rasch, P. J., Ravishankara, A. R., Rosenfeld, D., Stephens, G., and Wood, R.: Improving our fundamental understanding of the role of aerosol-cloud interactions in the climate system, P. Natl. Acad. Sci. USA, 113, 5781–5790, https://doi.org/10.1073/pnas.1514043113, 2016.
Sorooshian, A., Anderson, B., Bauer, S. E., Braun, R. A., Cairns, B., Crosbie, E., Dadashazar, H., Diskin, G., Ferrare, R., Flagan, R. C., Hair, J., Hostetler, C., Jonsson, H. H., Kleb, M. M., Liu, H., MacDonald, A. B., McComiskey, A., Moore, R., Painemal, D., Russell, L. M., Seinfeld, J. H., Shook, M., Smith, W. L., Thornhill, K., Tselioudis, G., Wang, H., Zeng, X., Zhang, B., Ziemba, L., and Zuidema, P.: Aerosol–Cloud–Meteorology Interaction Airborne Field Investigations: Using Lessons Learned from the U.S. West Coast in the Design of ACTIVATE off the U.S. East Coast, B. Am. Meteor. Soc., 100, 1511–1528, https://doi.org/10.1175/BAMS-D-18-0100.1, 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, W., Loeb, N. G., Xu, K.-M., Schuster, G. L., and Eitzen, Z. A.: An estimate of aerosol indirect effect from satellite measurements with concurrent meteorological analysis, J. Geophys. Res.-Atmos., 115, https://doi.org/10.1029/2010JD013948, 2010.
Su, Y., Han, Y., Luo, H., Zhang, Y., Shao, S., Xie, X., Su, Y., Han, Y., Luo, H., Zhang, Y., Shao, S., and Xie, X.: Physical-Optical Properties of Marine Aerosols over the South China Sea: Shipboard Measurements and MERRA-2 Reanalysis, Remote Sens., 14, https://doi.org/10.3390/rs14102453, 2022.
Sun, Q., Liang, B., Cai, M., Zhang, Y., Ou, H., Ni, X., Sun, X., Han, B., Deng, X., Zhou, S., and Zhao, J.: Cruise observation of the marine atmosphere and ship emissions in South China Sea: Aerosol composition, sources, and the aging process, Environ. Pollut., 316, 120539, https://doi.org/10.1016/j.envpol.2022.120539, 2023.
Tan, J., Huffman, G. J., Bolvin, D. T., and Nelkin, E. J.: Diurnal Cycle of IMERG V06 Precipitation, Geophys. Res. Lett., 46, 13584–13592, https://doi.org/10.1029/2019GL085395, 2019a.
Tan, J., Huffman, G. J., Bolvin, D. T., and Nelkin, E. J.: IMERG V06: Changes to the Morphing Algorithm, J. Atmos. Ocean. Technol., 36, 2471–2482, https://doi.org/10.1175/JTECH-D-19-0114.1, 2019b.
Tu, Q., Zhao, Y., Guo, J., Cheng, C., Shi, L., Yan, Y., and Hao, Z.: Spatial and Temporal Variations of Aerosol Optical Thickness over the China Seas from Himawari-8, Remote Sens., 13, 5082, https://doi.org/10.3390/rs13245082, 2021.
Twomey, S.: Pollution and the planetary albedo, Atmos. Environ., 8, 1251–1256, https://doi.org/10.1016/0004-6981(74)90004-3, 1974.
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.
Wall, C. J., Norris, J. R., Possner, A., McCoy, D. T., McCoy, I. L., and Lutsko, N. J.: Assessing effective radiative forcing from aerosol–cloud interactions over the global ocean, P. Natl. Acad. Sci. USA, 119, e2210481119, https://doi.org/10.1073/pnas.2210481119, 2022.
Wall, C. J., Storelvmo, T., and Possner, A.: Global observations of aerosol indirect effects from marine liquid clouds, Atmos. Chem. Phys., 23, 13125–13141, https://doi.org/10.5194/acp-23-13125-2023, 2023.
Wang, B., LinHo, Zhang, Y., and Lu, M.-M.: Definition of South China Sea Monsoon Onset and Commencement of the East Asia Summer Monsoon, J. Climate, 17, 699–710, https://doi.org/10.1175/2932.1, 2004.
Wang, B., Huang, F., Wu, Z., Yang, J., Fu, X., and Kikuchi, K.: Multi-scale climate variability of the South China Sea monsoon: A review, Dynam. Atmos. Ocean., 47, 15–37, https://doi.org/10.1016/j.dynatmoce.2008.09.004, 2009.
Wang, F., Guo, J., Wu, Y., Zhang, X., Deng, M., Li, X., Zhang, J., and Zhao, J.: Satellite observed aerosol-induced variability in warm cloud properties under different meteorological conditions over eastern China, Atmos. Environ., 84, 122–132, https://doi.org/10.1016/j.atmosenv.2013.11.018, 2014.
Wang, J.-J., Li, X., and Carey, L. D.: Evolution, Structure, Cloud Microphysical, and Surface Rainfall Processes of Monsoon Convection during the South China Sea Monsoon Experiment, J. Atmos. Sci., 64, 360–380, https://doi.org/10.1175/JAS3852.1, 2007.
Wang, K.-Y., Sui, C.-H., Lu, M.-M., and Hong, J.-S.: Cold Surge Impacts on the Structure, Energy Budget, and Turbulence of the South China Sea Boundary Layer, Mon. Weather Rev., 152, 2129–2148, https://doi.org/10.1175/MWR-D-23-0238.1, 2 2024a.
Wang, S., Wang, Q., and Feingold, G.: Turbulence, Condensation, and Liquid Water Transport in Numerically Simulated Nonprecipitating Stratocumulus Clouds, J. Atmos. Sci., 60, 262–278, https://doi.org/10.1175/1520-0469(2003)060<0262:TCALWT>2.0.CO;2, 2003.
Wang, Y., Zhao, P., Xiao, H., and Zhang, P.: Aerosol effects on liquid cloud microphysical properties in south China: Land–ocean contrasts, Atmos. Pollut. Res., 15, 102032, https://doi.org/10.1016/j.apr.2023.102032, 2024b.
Wang, Y., Li, J., Fang, F., Zhang, P., He, J., Pöhlker, M. L., Henning, S., Tang, C., Jia, H., Wang, Y., Jian, B., Shi, J., and Huang, J.: In-situ observations reveal weak hygroscopicity in the Southern Tibetan Plateau: implications for aerosol activation and indirect effects, npj Clim. Atmos. Sci., 7, 77, https://doi.org/10.1038/s41612-024-00629-x, 2024c.
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, 2024d.
Watters, D. and Battaglia, A.: The Summertime Diurnal Cycle of Precipitation Derived from IMERG, Remote Sens., 11, 1781, https://doi.org/10.3390/rs11151781, 2019.
Watters, D., Battaglia, A., and Allan, R. P.: The Diurnal Cycle of Precipitation according to Multiple Decades of Global Satellite Observations, Three CMIP6 Models, and the ECMWF Reanalysis, J. Climate, 34, 5063–5080, https://doi.org/10.1175/JCLI-D-20-0966.1, 2021.
Wu, Z., Jiang, C., Conde, M., Chen, J., and Deng, B.: The long-term spatiotemporal variability of sea surface temperature in the northwest Pacific and China offshore, Ocean Sci., 16, 83–97, https://doi.org/10.5194/os-16-83-2020, 2020.
Xiao, H.-W., Xiao, H.-Y., Luo, L., Shen, C.-Y., Long, A.-M., Chen, L., Long, Z.-H., and Li, D.-N.: Atmospheric aerosol compositions over the South China Sea: temporal variability and source apportionment, Atmos. Chem. Phys., 17, 3199–3214, https://doi.org/10.5194/acp-17-3199-2017, 2017.
Yost, C. R., Minnis, P., Sun-Mack, S., Chen, Y., and Smith, W. L.: CERES MODIS Cloud Product Retrievals for Edition 4 – Part II: Comparisons to CloudSat and CALIPSO, IEEE T. Geosci. Remote, 59, 3695–3724, https://doi.org/10.1109/TGRS.2020.3015155, 2021.
Yuan, C.-S., Chuang, H.-L., Tseng, Y.-L., Li, T.-C., Soong, K.-Y., and Cheng, W.-H.: Long-range transport and source apportionment of marine fine particles in the Taiwan Strait and South China Sea Intersection: Spatiotemporal variations and chemical fingerprints, Atmos. Environ., 339, 120867, https://doi.org/10.1016/j.atmosenv.2024.120867, 2024.
Yuan, T., Li, Z., Zhang, R., and Fan, J.: Increase of cloud droplet size with aerosol optical depth: An observation and modeling study, J. Geophys. Res.-Atmos., 113, https://doi.org/10.1029/2007JD008632, 2008.
Zhang, C., Xu, H., Li, Z., Xie, Y., and Li, D.: Maritime Aerosol Optical and Microphysical Properties in the South China Sea Under Multi-source Influence, Sci. Rep., 9, 17796, https://doi.org/10.1038/s41598-019-54483-6, 2019.
Zhang, G. J., Ramanathan, V., and McPhaden, M. J.: Convection–Evaporation Feedback in the Equatorial Pacific, J. Climate, 8, 3040–3051, https://doi.org/10.1175/1520-0442(1995)008<3040:CEFITE>2.0.CO;2, 1995.
Zhang, Y. and Wang, K.: The Changing Morphology of Global Precipitation Systems during the Last Two Decades, B. Am. Meteorol. Soc., 105, E1861–E1880, https://doi.org/10.1175/BAMS-D-23-0106.1, 2024.
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.
Zheng, B., Qu, J., Huang, Y., Peng, D., Gu, D., Li, C., and Huang, R.: Evaluating the Seasonal Cycle of the South China Sea Monsoon in CMIP6 Models, J. Meteorol. Res., 39, 322–337, https://doi.org/10.1007/s13351-025-4170-x, 2025.
Zheng, H., Liu, M., Lohmann, R., Li, D., Vojta, S., Katz, S., Wang, W., Ke, H., Wang, C., and Cai, M.: Gaseous polycyclic aromatic hydrocarbons over the South China Sea: Implications for atmospheric transport under monsoon influences, Mar. Pollut. Bull., 191, 114982, https://doi.org/10.1016/j.marpolbul.2023.114982, 2023.
Zheng, X., Xi, B., Dong, X., Wu, P., Logan, T., and Wang, Y.: Environmental effects on aerosol–cloud interaction in non-precipitating marine boundary layer (MBL) clouds over the eastern North Atlantic, Atmos. Chem. Phys., 22, 335–354, https://doi.org/10.5194/acp-22-335-2022, 2022.
Zhu, S., Xiao, Z., Che, H., and Chen, Q.: Impact of aerosols on warm clouds over the Sichuan Basin, China in winter based on the MERRA-2 reanalysis dataset, Atmos. Pollut. Res., 13, 101342, https://doi.org/10.1016/j.apr.2022.101342, 2022.
Zhu, S., Li, Z., Chen, M., Wen, Y., Gao, S., Zhang, J., Wang, J., Nan, Y., Ferraro, S. C., Tsoodle, T. E., and Hong, Y.: How has the latest IMERG V07 improved the precipitation estimates and hydrologic utility over CONUS against IMERG V06?, J. Hydrol., 645, 132257, https://doi.org/10.1016/j.jhydrol.2024.132257, 2024.
Short summary
This study explores how tiny particles in the air influence non-raining warm clouds over the South China Sea in different monsoon periods. Using long-term satellite observations and reanalysis datasets, we show that cloud sensitivity to these particles changes with moisture and atmospheric stability. Dry and stable conditions make clouds more responsive, while moist environments weaken this effect. These results help improve how climate models describe cloud behavior.
This study explores how tiny particles in the air influence non-raining warm clouds over the...
Altmetrics
Final-revised paper
Preprint