Articles | Volume 26, issue 9
https://doi.org/10.5194/acp-26-6351-2026
© 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-26-6351-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Spatial-scale dependence of aerosol indirect effects over land in eastern China: a comparative analysis
Yuqin Liu
State Key Laboratory of Regional and Urban Ecology, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
Fujian Key Laboratory of Digital Technology for Territorial Space Analysis and Simulation, Fuzhou 350108, China
Tao Lin
CORRESPONDING AUTHOR
State Key Laboratory of Regional and Urban Ecology, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
Fujian Key Laboratory of Digital Technology for Territorial Space Analysis and Simulation, Fuzhou 350108, China
Jiahua Zhang
Key Laboratory of Digital Earth Sciences, The Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
CMA Earth System Modeling and Prediction Centre (CEMC), Beijing 100081, China
Meixia Lin
State Key Laboratory of Regional and Urban Ecology, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
Fujian Key Laboratory of Digital Technology for Territorial Space Analysis and Simulation, Fuzhou 350108, China
Yuan Chen
State Key Laboratory of Regional and Urban Ecology, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
Fujian Key Laboratory of Digital Technology for Territorial Space Analysis and Simulation, Fuzhou 350108, China
Yiyi Huang
State Key Laboratory of Regional and Urban Ecology, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
Fujian Key Laboratory of Digital Technology for Territorial Space Analysis and Simulation, Fuzhou 350108, China
Hongkai Geng
State Key Laboratory of Regional and Urban Ecology, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
Fujian Key Laboratory of Digital Technology for Territorial Space Analysis and Simulation, Fuzhou 350108, China
Xin Cao
State Key Laboratory of Regional and Urban Ecology, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
Fujian Key Laboratory of Digital Technology for Territorial Space Analysis and Simulation, Fuzhou 350108, China
Royal Netherlands Meteorological Institute (KNMI), R&D Satellite Observations, 3730 AE De Bilt, the Netherlands
State Key Laboratory of Remote Sensing and Digital Earth & Key Laboratory of Satellite Remote Sensing of Ministry of Ecology and Environment, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Related authors
Yuqin Liu, Tao Lin, Jiahua Zhang, Fu Wang, Yiyi Huang, Xian Wu, Hong Ye, Guoqin Zhang, Xin Cao, and Gerrit de Leeuw
Atmos. Chem. Phys., 24, 4651–4673, https://doi.org/10.5194/acp-24-4651-2024, https://doi.org/10.5194/acp-24-4651-2024, 2024
Short summary
Short summary
A new method, the geographical detector method (GDM), has been applied to satellite data, in addition to commonly used statistical methods, to study the sensitivity of cloud properties to aerosol over China. Different constraints for aerosol and cloud liquid water path apply over polluted and clean areas. The GDM shows that cloud parameters are more sensitive to combinations of parameters than to individual parameters, but confounding effects due to co-variation of parameters cannot be excluded.
Lamei Shi, Jiahua Zhang, Da Zhang, Jingwen Wang, Xianglei Meng, Yuqin Liu, and Fengmei Yao
Atmos. Chem. Phys., 22, 11255–11274, https://doi.org/10.5194/acp-22-11255-2022, https://doi.org/10.5194/acp-22-11255-2022, 2022
Short summary
Short summary
Dust impacts climate and human life. Analyzing the interdecadal change in dust activity and its influence factors is crucial for disaster mitigation. Based on a linear regression method, this study revealed the interdecadal variability of relationships between ENSO and dust over northwestern South Asia from 1982 to 2014 and analyzed the effects of atmospheric factors on this interdecadal variability. The result sheds new light on numerical simulation involving the interdecadal variation of dust.
Yuqin Liu, Tao Lin, Juan Hong, Yonghong Wang, Lamei Shi, Yiyi Huang, Xian Wu, Hao Zhou, Jiahua Zhang, and Gerrit de Leeuw
Atmos. Chem. Phys., 21, 12331–12358, https://doi.org/10.5194/acp-21-12331-2021, https://doi.org/10.5194/acp-21-12331-2021, 2021
Short summary
Short summary
The four-dimensional variation of aerosol properties over the BTH, YRD and PRD (east China) were investigated using satellite observations from 2007 to 2020. Distinct differences between the aerosol optical depth and vertical distribution of the occurrence of aerosol types over these regions depend on season, aerosol loading and meteorological conditions. Day–night differences between the vertical distribution of aerosol types suggest effects of boundary layer dynamics and aerosol transport.
Zhe Ji, Zhengqiang Li, Gerrit de Leeuw, Zihan Zhang, Yan Ma, Zheng Shi, Cheng Fan, and Qian Yao
Atmos. Meas. Tech., 18, 5783–5803, https://doi.org/10.5194/amt-18-5783-2025, https://doi.org/10.5194/amt-18-5783-2025, 2025
Short summary
Short summary
A global AOD product from Particulate Observing Scanning Polarimeter (POSP) has been proposed and validated using Aerosol Robotic Network (AERONET). Results show a high accuracy, with correlation coefficients (R) of 0.914, a root mean square error (RMSE) of 0.085, outperforming Moderate Resolution Imaging Spectroradiometer (MODIS). Error analysis reveals seasonal variation with lower accuracy in autumn/winter, and increased uncertainty with lower Normalized Difference Vegetation Index NDVI.
Cheng Fan, Gerrit de Leeuw, Xiaoxi Yan, Jiantao Dong, Hanqing Kang, Chengwei Fang, Zhengqiang Li, and Ying Zhang
Atmos. Chem. Phys., 25, 11951–11973, https://doi.org/10.5194/acp-25-11951-2025, https://doi.org/10.5194/acp-25-11951-2025, 2025
Short summary
Short summary
This study describes the analysis of time series of the MODIS-derived aerosol optical depth (AOD) over China between 2010 and 2024. Emission reduction policies were effective with respect to reducing the AOD until 2018. Thereafter, the overall reduction until the end of the study was very small due to unfavorable meteorological factors cancelling favorable anthropogenic effects and resulting in an AOD increase during extended periods. The variations over different areas in China are discussed.
Ying Zhang, Yuanyuan Wei, Gerrit de Leeuw, Ouyang Liu, Yu Chen, Yang Lv, Yuanxun Zhang, and Zhengqiang Li
Atmos. Chem. Phys., 25, 10643–10660, https://doi.org/10.5194/acp-25-10643-2025, https://doi.org/10.5194/acp-25-10643-2025, 2025
Short summary
Short summary
Nitrogen dioxide (NO2) is a major pollutant that, at high concentrations, may affect human health. We evaluated the remote sensing column NO2 in relation to near-surface concentrations throughout the day and found that the prohibition of vertical transport in the morning and the mixing in the afternoon resulted in different relations between the near-surface (NS) and total column NO2 concentrations. These different relationships have consequences for the use of satellite remote sensing to estimate NS NO2 concentrations.
Yuqin Liu, Tao Lin, Jiahua Zhang, Fu Wang, Yiyi Huang, Xian Wu, Hong Ye, Guoqin Zhang, Xin Cao, and Gerrit de Leeuw
Atmos. Chem. Phys., 24, 4651–4673, https://doi.org/10.5194/acp-24-4651-2024, https://doi.org/10.5194/acp-24-4651-2024, 2024
Short summary
Short summary
A new method, the geographical detector method (GDM), has been applied to satellite data, in addition to commonly used statistical methods, to study the sensitivity of cloud properties to aerosol over China. Different constraints for aerosol and cloud liquid water path apply over polluted and clean areas. The GDM shows that cloud parameters are more sensitive to combinations of parameters than to individual parameters, but confounding effects due to co-variation of parameters cannot be excluded.
Ouyang Liu, Zhengqiang Li, Yangyan Lin, Cheng Fan, Ying Zhang, Kaitao Li, Peng Zhang, Yuanyuan Wei, Tianzeng Chen, Jiantao Dong, and Gerrit de Leeuw
Atmos. Meas. Tech., 17, 377–395, https://doi.org/10.5194/amt-17-377-2024, https://doi.org/10.5194/amt-17-377-2024, 2024
Short summary
Short summary
Nitrogen dioxide (NO2) is a trace gas which is important for atmospheric chemistry and may affect human health. To understand processes leading to harmful concentrations, it is important to monitor NO2 concentrations near the surface and higher up. To this end, a Pandora instrument has been installed in Beijing. An overview of the first year of data shows the large variability on diurnal to seasonal timescales and how this is affected by wind speed and direction and chemistry.
Lamei Shi, Jiahua Zhang, Da Zhang, Jingwen Wang, Xianglei Meng, Yuqin Liu, and Fengmei Yao
Atmos. Chem. Phys., 22, 11255–11274, https://doi.org/10.5194/acp-22-11255-2022, https://doi.org/10.5194/acp-22-11255-2022, 2022
Short summary
Short summary
Dust impacts climate and human life. Analyzing the interdecadal change in dust activity and its influence factors is crucial for disaster mitigation. Based on a linear regression method, this study revealed the interdecadal variability of relationships between ENSO and dust over northwestern South Asia from 1982 to 2014 and analyzed the effects of atmospheric factors on this interdecadal variability. The result sheds new light on numerical simulation involving the interdecadal variation of dust.
Hanqing Kang, Bin Zhu, Gerrit de Leeuw, Bu Yu, Ronald J. van der A, and Wen Lu
Atmos. Chem. Phys., 22, 10623–10634, https://doi.org/10.5194/acp-22-10623-2022, https://doi.org/10.5194/acp-22-10623-2022, 2022
Short summary
Short summary
This study quantified the contribution of each urban-induced meteorological effect (temperature, humidity, and circulation) to aerosol concentration. We found that the urban heat island (UHI) circulation dominates the UHI effects on aerosol. The UHI circulation transports aerosol and its precursor gases from the warmer lower boundary layer to the colder lower free troposphere and promotes the secondary formation of ammonium nitrate aerosol in the cold atmosphere.
Boming Liu, Jianping Guo, Wei Gong, Yong Zhang, Lijuan Shi, Yingying Ma, Jian Li, Xiaoran Guo, Ad Stoffelen, Gerrit de Leeuw, and Xiaofeng Xu
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-26, https://doi.org/10.5194/amt-2022-26, 2022
Publication in AMT not foreseen
Short summary
Short summary
Aeolus is the first satellite mission to directly observe wind profile information on a global scale. However, Aeolus wind products over China were thus far not evaluated by in-situ comparison. This work is the comparison of wind speed on a large scale between the Aeolus, ERA5 and RS , shedding important light on the data application of Aeolus wind products.
Yuqin Liu, Tao Lin, Juan Hong, Yonghong Wang, Lamei Shi, Yiyi Huang, Xian Wu, Hao Zhou, Jiahua Zhang, and Gerrit de Leeuw
Atmos. Chem. Phys., 21, 12331–12358, https://doi.org/10.5194/acp-21-12331-2021, https://doi.org/10.5194/acp-21-12331-2021, 2021
Short summary
Short summary
The four-dimensional variation of aerosol properties over the BTH, YRD and PRD (east China) were investigated using satellite observations from 2007 to 2020. Distinct differences between the aerosol optical depth and vertical distribution of the occurrence of aerosol types over these regions depend on season, aerosol loading and meteorological conditions. Day–night differences between the vertical distribution of aerosol types suggest effects of boundary layer dynamics and aerosol transport.
Cheng Fan, Zhengqiang Li, Ying Li, Jiantao Dong, Ronald van der A, and Gerrit de Leeuw
Atmos. Chem. Phys., 21, 7723–7748, https://doi.org/10.5194/acp-21-7723-2021, https://doi.org/10.5194/acp-21-7723-2021, 2021
Short summary
Short summary
Emission control policy in China has resulted in the decrease of nitrogen dioxide concentrations, which however leveled off and stabilized in recent years, as shown from satellite data. The effects of the further emission reduction during the COVID-19 lockdown in 2020 resulted in an initial improvement of air quality, which, however, was offset by chemical and meteorological effects. The study shows the regional dependence over east China, and results have a wider application than China only.
Cited articles
Albrecht, B. A.: Aerosols, cloud microphysics, and fractional cloudiness, Science, 245, 1227–1230, https://doi.org/10.1126/science.245.4923.1227, 1989.
Altaratz, O., Koren, I., Remer, L. A., and Hirsch, E.: Review: Cloud invigoration by aerosols – Coupling between microphysics and dynamics, Atmos. Res., 140–141, 38–60, https://doi.org/10.1016/j.atmosres.2014.01.009, 2014.
Anderson, T. L., Charlson, R. J., Winker, D. M., Ogren, J. A., and Holmén, K.: Mesoscale Variations of Tropospheric Aerosols, J. Atmos. Sci., 60, https://doi.org/10.1175/1520-0469(2003)060<0119:MVOTA>2.0.CO;2, 2003.
Andreae, M. O.: Correlation between cloud condensation nuclei concentration and aerosol optical thickness in remote and polluted regions, Atmos. Chem. Phys., 9, 543–556, https://doi.org/10.5194/acp-9-543-2009, 2009.
Baum, B. A., Paul Menzel, W., Frey, R. A., and Tobin, D.: MODIS cloud-top property refinement for Collection 6, J. Appl. Meteorol. Clim., 51, 1145–1163, https://doi.org/10.1175/JAMC-D-11-0203.1, 2012.
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., Foster, P., Gettelman, A., Haywood, J. M., Lohmann, U., Malavelle, F., Mauritsen, T., McCoy, D. T., 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.
Bender, F. A. M., Frey, L., McCoy, D. T., Grosvenor, D. P., and Mohrmann, J. K.: Assessment of aerosol–cloud–radiation correlations in satellite observations, climate models and reanalysis, Clim. Dynam., 52, 4371–4392, https://doi.org/10.1007/s00382-018-4384-z, 2019.
Boucher, O. and Quaas, J.: Water vapour affects both rain and aerosol optical depth, Nat. Geosci., 6, 4–5, https://doi.org/10.1038/ngeo1692, 2012.
Bréon, F. M., Tanré, D., and Generoso, S.: Aerosol effect on cloud droplet size monitored from satellite, Science, 295, 834–838, https://doi.org/10.1126/science.1066434, 2002.
Bulgin, C. E., Palmer, P. I., Thomas, G. E., Arnold, C. P. G., Campmany, E., Carboni, E., Grainger, R. G., Poulsen, C., Siddans, R., and Lawrence, B. N.: Regional and seasonal variations of the Twomey indirect effect as observed by the ATSR-2 satellite instrument, Geophys. Res. Lett., 35, https://doi.org/10.1029/2007GL031394, 2008.
Cai, H., Yang, Y., and Chen, Q.: Distribution Characteristics of Cloud Types and Cloud Phases over China and Their Relationship with Cloud Temperature, Remote Sens., 14, https://doi.org/10.3390/rs14215601, 2022.
Chen, G., Wang, W.-C., and Chen, J.-P.: Aerosol–stratocumulus–radiation interactions over southeast Pacific, J. Atmos. Sci., 72, 2612–2621, https://doi.org/10.1175/JAS-D-14-0319.1, 2015.
Christensen, M. W., Chen, Y.-C., and Stephens, G. L.: Aerosol indirect effect dictated by liquid clouds, J. Geophys. Res., 121, 14636–14650, https://doi.org/10.1002/2016JD025245, 2016.
Christensen, M. W., Neubauer, D., Poulsen, C. A., Thomas, G. E., McGarragh, G. R., Povey, A. C., Proud, S. R., and Grainger, R. G.: Unveiling aerosol–cloud interactions – Part 1: Cloud contamination in satellite products enhances the aerosol indirect forcing estimate, Atmos. Chem. Phys., 17, 13151–13164, https://doi.org/10.5194/acp-17-13151-2017, 2017.
Costantino, L. and Bréon, F. M.: Analysis of aerosol-cloud interaction from multi-sensor satellite observations, Geophys. Res. Lett., 37, L11801, https://doi.org/10.1029/2009GL041828, 2010.
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.
Dagan, G., Yeheskel, N., and Williams, A. I. L.: Radiative forcing from aerosol–cloud interactions enhanced by large-scale circulation adjustments, Nat. Geosci., 16, 1092–1098, https://doi.org/10.1038/s41561-023-01319-8, 2023.
de Leeuw, G., van der A, R., Bai, J., Xue, Y., Varotsos, C., Li, Z., Fan, C., Chen, X., Christodoulakis, I., Ding, J., Hou, X., Kouremadas, G., Li, D., Wang, J., Zara, M., Zhang, K., and Zhang, Y.: Air Quality over China, Remote Sens., 13, 3542, https://doi.org/10.3390/rs13173542, 2021.
de Leeuw, G., Fan, C., Li, Z., Dong, J., Li, Y., Ou, Y., and Zhu, S.: Spatiotemporal variation and provincial scale differences of the AOD across China during 2000–2021, Atmos. Pollut. Res., 13, 101359, https://doi.org/10.1016/j.apr.2022.101359, 2022.
de Leeuw, G., Kang, H., Fan, C., Li, Z., Fang, C., and Zhang, Y.: Meteorological and anthropogenic contributions to changes in the Aerosol Optical Depth (AOD) over China during the last decade, Atmos. Environ., 301, 119676, https://doi.org/10.1016/j.atmosenv.2023.119676, 2023.
Ekman, A. M. L., Nygren, E., Pérez, A. B., Schwarz, M., Svensson, G., and Bellouin, N.: Influence of horizontal resolution and complexity of aerosol–cloud interactions on marine stratocumulus and stratocumulus-to-cumulus transition in HadGEM3-GC3.1, Q. J. Roy. Meteor. Soc., 149, 2049–2066, https://doi.org/10.1002/qj.4494, 2023.
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.
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.
Feingold, G.: Modeling of the first indirect effect: analysis of measurement requirements, Geophys. Res. Lett., 30, 1997, https://doi.org/10.1029/2003GL017967, 2003.
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., Goren, T., and Yamaguchi, T.: Quantifying albedo susceptibility biases in shallow clouds, Atmos. Chem. Phys., 22, 3303–3319, https://doi.org/10.5194/acp-22-3303-2022, 2022.
Grandey, B. S. and Stier, P.: A critical look at spatial scale choices in satellite-based aerosol indirect effect studies, Atmos. Chem. Phys., 10, 11459–11470, https://doi.org/10.5194/acp-10-11459-2010, 2010.
Gryspeerdt, E., Stier, P., and Partridge, D. G.: Satellite observations of cloud regime development: the role of aerosol processes, Atmos. Chem. Phys., 14, 1141–1158, https://doi.org/10.5194/acp-14-1141-2014, 2014.
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, 2022.
Gryspeerdt, E., Povey, A. C., Grainger, R. G., Hasekamp, O., Hsu, N. C., Mulcahy, J. P., Sayer, A. M., and Sorooshian, A.: Uncertainty in aerosol–cloud radiative forcing is driven by clean conditions, Atmos. Chem. Phys., 23, 4115–4122, https://doi.org/10.5194/acp-23-4115-2023, 2023.
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., Diedenhoven, B. V., 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.
Han, X., Zhao, B., Lin, Y., Chen, Q., Shi, H., Jiang, Z., Fan, X., Wang, J., Liou, L. N., and Gu, Y.: Type-dependent impact of aerosols on precipitation associated with deep convective cloud over East Asia, J. Geophys. Res.-Atmos., 127, e2021JD036127, https://doi.org/10.1029/2021JD036127, 2022.
Hassan, T., Zhang, K., Li, J., Singh, B., Zhang, S., Wang, H., and Ma, P.-L.: Impacts of spatial heterogeneity of anthropogenic aerosol emissions in a regionally refined global aerosol–climate model, Geosci. Model Dev., 17, 3507–3532, https://doi.org/10.5194/gmd-17-3507-2024, 2024.
Huang, R.-J., Zhang, Y. L., 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., Zimmerman, 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.
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., 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.
Jones, T. A., Christopher, S. A., and Quaas, J.: A six year satellite-based assessment of the regional variations in aerosol indirect effects, Atmos. Chem. Phys., 9, 4091–4114, https://doi.org/10.5194/acp-9-4091-2009, 2009.
Koren, I., Kaufman, Y. J., Rosenfeld, D., Remer, L. A., and Rudich, Y.: Aerosol invigoration and restructuring of Atlantic convective clouds, Geophys. Res. Lett., 32, L14828, https://doi.org/10.1029/2005GL023187, 2005.
Kaufman, Y. J. and Fraser, R. S.: The effect of smoke particles on clouds and climate forcing, Science, 277, 1636–1639, https://doi.org/10.1126/science.277.5332.1636, 1997.
Kaufman, Y. J., Remer, L., Tanré, D., Li, R., Kleidman, R., Mattoo, S., Levy, R., Eck, T., Holben, B., Ichoku, C., Martins, J., and Koren, I.: A critical examination of the residual cloud contamination and diurnal sampling effects on MODIS estimates of aerosol over ocean, IEEE T. Geosci. Remote, 43, 2886–2897, https://doi.org/10.1109/TGRS.2005.858430, 2005.
King, M. D., Tsay, S. C., Platnick, S. E., Wang, M., and Liou, K. N.: Cloud Retrieval Algorithms for MODIS: Optical Thickness, Effective Particle Radius, and Thermodynamic Phase, MODIS Algorithm Theoretical Basis Document, http://eospso.nasa.gov/sites/default/files/atbd/atbd_mod05.pdf (last access: 6 May 2026), 1997.
King, M. D., Menzel, W. P., Kaufman, Y. J., Tanré, D., Gao, B. C., Platnick, S., Ackerman, S. A., Remer, L. A., Pincus, R., and Hubanks, P. A.: Cloud and aerosol properties, precipitable water, and profiles of temperature and water vapor from MODIS, IEEE T. Geosci. Remote, 41, 442–458, https://doi.org/10.1109/TGRS.2002.808226, 2003.
Lebsock, M., Morrison, H., and Gettelman, A.: Microphysical implications of cloud-precipitation covariance derived from satellite remote sensing, J. Geophys. Res.-Atmos., 118, 6521–6533, https://doi.org/10.1002/jgrd.50347, 2013.
Levy, R. C., Remer, L. A., Kleidman, R. G., Mattoo, S., Ichoku, C., Kahn, R., and Eck, T. F.: Global evaluation of the Collection 5 MODIS dark-target aerosol products over land, Atmos. Chem. Phys., 10, 10399–10420, https://doi.org/10.5194/acp-10-10399-2010, 2010.
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.
Lee, H.-H., Zheng, X., Qiu, S., and Wang, Y.: Numerical case study of the aerosol–cloud interactions in warm boundary layer clouds over the eastern North Atlantic with an interactive chemistry module, Atmos. Chem. Phys., 25, 6069–6091, https://doi.org/10.5194/acp-25-6069-2025, 2025.
Lee, S. S., Donner, L. J., and Phillips, V. T. J.: Impacts of aerosol chemical composition on microphysics and precipitation in deep convection, Atmos. Res., 94, 220–237, https://doi.org/10.1016/j.atmosres.2009.05.015, 2009.
Leung, G. R., Saleeby, S. M., Sokolowsky, G. A., Freeman, S. W., and van den Heever, S. C.: Aerosol–cloud impacts on aerosol detrainment and rainout in shallow maritime tropical clouds, Atmos. Chem. Phys., 23, 5263–5278, https://doi.org/10.5194/acp-23-5263-2023, 2023.
Li, G. H., Wang, Y., and Zhang, R. Y.: Implementation of a two-moment bulk microphysics scheme to the WRF model to investigate aerosol-cloud interaction, J. Geophys. Res.-Atmos., 113, D15, https://doi.org/10.1029/2007JD009361, 2008.
Li, Y., Liu, X., and Cai, H.: Numerical simulation of aerosol concentration effects on cloud droplet size spectrum evolutions of warm stratiform clouds in Jiangxi, China, Atmos. Chem. Phys., 24, 13525–13540, https://doi.org/10.5194/acp-24-13525-2024, 2024.
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. Geophys., 54, 866–929, https://doi.org/10.1002/2015RG000500, 2016.
Liu, Q., Duan, S. Y., He, Q. S., Chen, Y. H., Zhang, H., Cheng, N. X., Huang, Y. W., Chen, B., Zhan, Q. Y., and Li, J. Z.: The variability of warm cloud droplet radius induced by aerosols and water vapor in Shanghai from MODIS observations, Atmos. Res., 253, 105470, https://doi.org/10.1016/j.atmosres.2021.105470, 2021.
Liu, Q., Shen, X., Li, L., Sun, J., Liu, Z., Zhu, W., Zhong, J., Zhang, Y., Hu, X., Liu, S., Che, H., and Zhang, X.: Impacts of Aerosol Chemical Composition on Cloud Condensation Nuclei (CCN) Activity during Wintertime in Beijing, China, Remote Sens., 15, 4119, https://doi.org/10.3390/rs15174119, 2023.
Liu, T. Q., Liu, Q., Chen, Y. H., Wang, W. C., Zhang, H., Li, D., and Sheng, J.: Effect of aerosols on the macro- and micro-physical properties of warm clouds in the Beijing-Tianjin-Heibei region, Sci. Total Environ., 720, 137618, https://doi.org/10.1016/j.scitotenv.2020.137618, 2020.
Liu, Y., de Leeuw, G., Kerminen, V.-M., Zhang, J., Zhou, P., Nie, W., Qi, X., Hong, J., Wang, Y., Ding, A., Guo, H., Krüger, O., Kulmala, M., and Petäjä, T.: Analysis of aerosol effects on warm clouds over the Yangtze River Delta from multi-sensor satellite observations, Atmos. Chem. Phys., 17, 5623–5641, https://doi.org/10.5194/acp-17-5623-2017, 2017.
Liu, Y., Zhang, J., Zhou, P., Lin, T., Hong, J., Shi, L., Yao, F., Wu, J., Guo, H., and de Leeuw, G.: Satellite-based estimate of the variability of warm cloud properties associated with aerosol and meteorological conditions, Atmos. Chem. Phys., 18, 18187–18202, https://doi.org/10.5194/acp-18-18187-2018, 2018.
Liu, Y., Lin, T., Zhang, J., Wang, F., Huang, Y., Wu, X., Ye, H., Zhang, G., Cao, X., and de Leeuw, G.: Opposite effects of aerosols and meteorological parameters on warm clouds in two contrasting regions over eastern China, Atmos. Chem. Phys., 24, 4651–4673, https://doi.org/10.5194/acp-24-4651-2024, 2024.
Liu, Z., Vaughan, M., Winker, D., Kittaka, C., Getzewich, B., Kuehn, R., Omar, A., Powell, K., Trepte, C., and Hostetler, C.: The CALIPSO lidar cloud and aerosol discrimination: Version 2 algorithm and initial assessment of performance, J. Atmos. Ocean. Tech., 26, 1198–1213, https://doi.org/10.1175/2009JTECHA1229.1, 2009.
Ma, P.-L., Rasch, P. J., Wang, M., Wang, H., Ghan, S. J., Easter, R. C., GustafsonJr, W. I., Liu, X., Zhang, Y., and Ma, H.-Y.: How does increasing horizontal resolution in a global climate model improve the simulation of aerosol-cloud interactions?, Geophys. Res. Lett., 42, 5058–5065, https://doi.org/10.1002/2015GL064183, 2015.
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, 2018.
Marchant, B., Platnick, S., Meyer, K., Arnold, G. T., and Riedi, J.: MODIS Collection 6 shortwave-derived cloud phase classification algorithm and comparisons with CALIOP, Atmos. Meas. Tech., 9, 1587–1599, https://doi.org/10.5194/amt-9-1587-2016, 2016.
Matheson, M. A., Coakley Jr., J. A., and Tahnk, W. R.: Aerosol and cloud property from relationships for summer stratiform clouds in the northeastern Atlantic from advanced very high resolution radiometer observations, J. Geophys. Res., 110, D24204, https://doi.org/10.1029/2005JD006165, 2005.
McComiskey, A. and Feingold, G.: The scale problem in quantifying aerosol indirect effects, Atmos. Chem. Phys., 12, 1031–1049, https://doi.org/10.5194/acp-12-1031-2012, 2012.
McComiskey, A., Feingold, G., Frisch, A. S., Turner, D. D., Miller, M. A., Chiu, J. C., Min, Q., and Ogren, J. A.: Anassessment of aerosol-cloud interactions in marine stratus clouds based on surface remote sensing, J. Geophys. Res., 114, D09203, https://doi.org/10.1029/2008JD011006, 2009.
Meskhidze, N. and Nenes, A.: Effects of ocean ecosystem on marine aerosol-cloud interaction, Adv. Meteorol, https://doi.org/10.1155/2010/239808, 2010.
Michibata, T., Kawamoto, K., and Takemura, T.: The effects of aerosols on water cloud microphysics and macrophysics based on satellite-retrieved data over East Asia and the North Pacific, Atmos. Chem. Phys., 14, 11935–11948, https://doi.org/10.5194/acp-14-11935-2014, 2014.
Mohebalhojeh, M., Frederick, S., Riemer, N., and West, M.: A Metric for Quantifying Spatial Heterogeneity in Gridded Atmospheric Fields, Earth Space Sci., https://doi.org/10.22541/essoar.176805046.69348251/v1, 2026.
Murray-Watson, R. J. and Gryspeerdt, E.: Stability-dependent increases in liquid water with droplet number in the Arctic, Atmos. Chem. Phys., 22, 5743–5756, https://doi.org/10.5194/acp-22-5743-2022, 2022.
Platnick, S., Meyer, K. G., King, M. D., Wind, G., Amarasinghe, N., Marchant, B., Arnold, G. T., Zhang, Z., Hubanks, P. A., Ridgway, W. L., and Riedi, J.: MODIS Cloud optical properties: User guide for the Collection 6/6.1 level-2 MOD06/MYD06 product and associated level-3 datasets, v1.1, NASA, https://atmosphere-imager.gsfc.nasa.gov/sites/default/files/ModAtmo/MODISCloudOpticalPropertyUserGuideFinal_v1.1_1.pdf (last access: 7 May 2026), July 2018.
Proestakis, E., Amiridis, V., Marinou, E., Georgoulias, A. K., Solomos, S., Kazadzis, S., Chimot, J., Che, H., Alexandri, G., Binietoglou, I., Daskalopoulou, V., Kourtidis, K. A., de Leeuw, G., and van der A, R. J.: Nine-year spatial and temporal evolution of desert dust aerosols over South and East Asia as revealed by CALIOP, Atmos. Chem. Phys., 18, 1337–1362, https://doi.org/10.5194/acp-18-1337-2018, 2018.
Possner, A., Zubler, E. M., Lohmann, U., and Schär, C.: The resolution dependence of cloud effects and ship-induced aerosol-cloud interactions in marine stratocumulus, J. Geophys. Res.-Atmos., 121, 4810–4829, https://doi.org/10.1002/2015JD024685, 2016.
Pandey, S. K., Vinoj, V., and Panwar, A.: The short-term variability of aerosols and their impact on cloud properties and radiative effect over the Indo-Gangetic Plain, Atmos. Pollut. Res., 11, 630–638, https://doi.org/10.1016/j.apr.2019.12.017, 2020.
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 Sens., 55, 502–525, https://doi.org/10.1109/TGRS.2016.2610522, 2017.
Quaas, J., Boucher, O., and Lohmann, U.: Constraining the total aerosol indirect effect in the LMDZ and ECHAM4 GCMs using MODIS satellite data, Atmos. Chem. Phys., 6, 947–955, https://doi.org/10.5194/acp-6-947-2006, 2006.
Quaas, J., Boucher, O., Bellouin, N., and Kinne, S.: Satellite-based estimate of the direct and indirect aerosol climate forcing, J. Geophys. Res., 113, D05204, https://doi.org/10.1029/2007JD008962, 2008.
Quaas, J., Ming, Y., Menon, S., Takemura, T., Wang, M., Penner, J. E., Gettelman, A., Lohmann, U., Bellouin, N., Boucher, O., Sayer, A. M., Thomas, G. E., McComiskey, A., Feingold, G., Hoose, C., Kristjánsson, J. E., Liu, X., Balkanski, Y., Donner, L. J., Ginoux, P. A., Stier, P., Grandey, B., Feichter, J., Sednev, I., Bauer, S. E., Koch, D., Grainger, R. G., Kirkevåg, A., Iversen, T., Seland, Ø., Easter, R., Ghan, S. J., Rasch, P. J., Morrison, H., Lamarque, J.-F., Iacono, M. J., Kinne, S., and Schulz, M.: Aerosol indirect effects – general circulation model intercomparison and evaluation with satellite data, Atmos. Chem. Phys., 9, 8697–8717, https://doi.org/10.5194/acp-9-8697-2009, 2009.
Quaas, J., Stevens, B., Stier, P., and Lohmann, U.: Interpreting the cloud cover – aerosol optical depth relationship found in satellite data using a general circulation model, Atmos. Chem. Phys., 10, 6129–6135, https://doi.org/10.5194/acp-10-6129-2010, 2010.
Rao, S. and Dey, S.: Consistent signal of aerosol indirect and semi-direct effect on water clouds in the oceanic regions adjacent to the Indian subcontinent, Atmos. Res., 232, 104677, https://doi.org/10.1016/j.atmosres.2019.104677, 2020.
Remer, L. A., Kaufman, Y. J., Tanre, D., Mattoo, S., Chu, D. A., Martins, J. V., Li, R. R., Ichoku, C., Levy, R. C., Kleidman, R. G., Eck, T. F., Vermote, E., and Holben, B. N.: The MODIS aerosol algorithm, products, and validation, J. Atmos. Sci., 62, 947–973, https://doi.org/10.1175/JAS3385.1, 2005.
Rosenfeld, D., Zhu, Y. N., Wang, M. H., Zheng, Y. T., Goren, T., and Yu, S. C.: Aerosol-driven droplet concentrations dominate coverage and water of oceanic low-level clouds, Science, 363, eaav0566, https://doi.org/10.1126/science.aav0566, 2019.
Sarna, K. and Russchenberg, H. W. J.: Ground-based remote sensing scheme for monitoring aerosol–cloud interactions, Atmos. Meas. Tech., 9, 1039–1050, https://doi.org/10.5194/amt-9-1039-2016, 2016.
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.
Stephens, G., Vane, D. G., Boain, R. J., Mace, G. G., Sassen, K., Wang, Z., Illingworth, A. J., O'Connor, E. J., Rossow, W. B., Durden, S. L., Miller, S. D., Austin, R. T., Benedetti, A., and Mitrescu, C.: The CloudSat Science Team: The CloudSat mission and the A-Train, B. Am. Meteor. Soc., 83, 1771–1790, https://doi.org/10.1175/BAMS-83-12-1771, 2002.
Sourdeval, O., C.-Labonnote, L., Baran, A. J., Mülmenstädt, J., and Brogniez, G.: A methodology for simultaneous retrieval of ice and liquid water cloud properties. Part 2: Near-global retrievals and evaluation against A-Train products, Q. J. Roy. Meteor. Soc., 142, 3063–3081, https://doi.org/10.1002/qj.2889, 2016.
Sundström, A.-M., Kolmonen, P., Sogacheva, L., and de Leeuw, D.: Aerosol retrievals over China with the AATSR dual view algorithm, Remote Sens. Environ., 116, 189–198, https://doi.org/10.1016/j.rse.2011.04.041, 2012.
Tang, J., Wang, P., Mickley, L. J., Xia, X., Liao, H., Yue, X., Sun, L., and Xia, J.: Positive relationship between liquid cloud droplet effective radius and aerosol optical depth over Eastern China from satellite data, Atmos. Environ., 84, 244–253, https://doi.org/10.1016/j.atmosenv.2013.08.024, 2014.
Tao, W. K., Chen, J. P., Li, Z., Wang, C. E., and Zhang, C. D.: Impact of aerosols on convective clouds and precipitation, Rev. Geophys., 50, RG2001, https://doi.org/10.1029/2011RG000369, 2012.
Twomey, S.: Pollution and the planetary albedo, Atmos. Environ., 41, 120–125, 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.
Várnai, T. and Marshak, A.: MODIS observations of enhanced clear sky reflectance near clouds, Geophys. Res. Lett., 36, L06807, https://doi.org/10.1029/2008GL037089, 2009.
Wang, F., Guo, J., Zhang, J., Wu, Y., Zhang, X., Deng, M., and Li, X.: 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.
Winker, D. M., Hunt, W. H., and McGill, M. J.: Initial performance assessment of CALIOP, Geophys. Res. Lett., 34, L19803, https://doi.org/10.1029/2007GL030135, 2007.
Winker, D. M., Vaughan, M. A., Omar, A., Hu, Y., Powell, K. A., Liu, Z. Y., Hunt, W. H., and Young, S. A.: Overview of the CALIPSO Mission and CALIOP Data Processing Algorithms, J. Atmos. Ocean. Tech., 26, 2310–2323, https://doi.org/10.1175/2009JTECHA1281.1, 2009.
Winker, D. M., Pelon, J., Coakley Jr., J. A., Ackerman, S. A., Charlson, R. J., Colarco, P. R., Flamant, P., Fu, Q., Hoff, R. M., Kittaka, C., Kubar, T. L., Le Treut, H., Mccormick, M. P., Mégie, G., Poole, L., Powell, K., Trepte, C., Vaughan, M. A., and Wielicki, B. A.: The CALIPSO Mission, B. Am. Meteor. Soc., 91, 1211–1230, https://doi.org/10.1175/2010BAMS3009.1, 2010.
Wang, F., Guo, J., Zhang, J., Huang, J., Min, M., Chen, T., Liu, H., Deng, M., and Li, X.: Multi-sensor quantification of aerosol-induced variability in warm clouds over eastern China, Atmos. Environ., 113, 1–9, https://doi.org/10.1016/j.atmosenv.2015.04.063, 2015.
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., 113, D04201, https://doi.org/10.1029/2007JD008632, 2008.
Zhang, L., Li, J., Li, J., Li, R., Zhang, W., Lei, M., Lv, Q., and Jian, B.: Studying the impacts of meteorological factors on distribution of cloud horizontal scales based on active satellite, J. Geophys. Res.-Atmos., 129, e2024JD041844, https://doi.org/10.1029/2024JD041844, 2024.
Zhang, Q., Meng, J., Quan, J., Gao, Y., Zhao, D., Chen, P., and He, H.: Impact of aerosol composition on cloud condensation nuclei activity, Atmos. Chem. Phys., 12, 3783–3790, https://doi.org/10.5194/acp-12-3783-2012, 2012.
Zhao, J., Ma, X., Quaas, J., and Yang, T.: How meteorological conditions influence aerosol-cloud interactions under different pollution regimes, Atmos. Chem. Phys., 25, 17701–17723, https://doi.org/10.5194/acp-25-17701-2025, 2025.
Zheng, B., Tong, D., Li, M., Liu, F., Hong, C., Geng, G., Li, H., Li, X., Peng, L., Qi, J., Yan, L., Zhang, Y., Zhao, H., Zheng, Y., He, K., and Zhang, Q.: Trends in China's anthropogenic emissions since 2010 as the consequence of clean air actions, Atmos. Chem. Phys., 18, 14095–14111, https://doi.org/10.5194/acp-18-14095-2018, 2018.
Zheng, X., Xi, B., Dong, X., Logan, T., Wang, Y., and Wu, P.: Investigation of aerosol–cloud interactions under different absorptive aerosol regimes using Atmospheric Radiation Measurement (ARM) southern Great Plains (SGP) ground-based measurements, Atmos. Chem. Phys., 20, 3483–3501, https://doi.org/10.5194/acp-20-3483-2020, 2020.
Zheng, X., Dong, X., Xi, B., Logan, T., and Wang, Y.: Distinctive aerosol–cloud–precipitation interactions in marine boundary layer clouds from the ACE-ENA and SOCRATES aircraft field campaigns, Atmos. Chem. Phys., 24, 10323–10347, https://doi.org/10.5194/acp-24-10323-2024, 2024.
Short summary
This study reveals how air pollution affects cloud properties in eastern China using satellite data from 2008–2022. We find CER/LWP (cloud droplet effective radius/liquid water path) relationship exhibits three regimes, modulated by aerosol concentration. The Twomey effect is confirmed, and its sensitivity shows significant spatial-scale dependence. Surprisingly, cleaner air after 2015 make clouds less sensitive to pollution's effects. The optimal buffer sizes show notable variations for the study area in the range from 6°×6° to 10°×10°.
This study reveals how air pollution affects cloud properties in eastern China using satellite...
Altmetrics
Final-revised paper
Preprint