Interpretation of Aerosol Effects on Precipitation Susceptibility in Warm Clouds Inferred from Satellite Measurements and Model Evaluation over Northeast Asia

Shin-Young Park aDepartment of Atmospheric Sciences, Pusan National University, Busan, South Korea

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Cheol-Hee Kim aDepartment of Atmospheric Sciences, Pusan National University, Busan, South Korea

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Abstract

Precipitation susceptibility (So), a parameter of aerosol–cloud–precipitation interaction over Northeast Asia during the Korea–United States Air Quality (KORUS-AQ) campaign, was analyzed using the Clouds from Advanced Very High-Resolution Radiometer Extended (CLAVR-x) satellite data and WRF-Chem model. As Northeast Asia is one of the areas with the highest aerosol emissions, this study is expected to explore more elaborate aerosol–cloud linkages. Our results obtained from satellite data showed that So increased as the atmospheric condition became stable and humid, and the shift of the water conversion process to precipitation occurred in the LWP range of 300–500 g m−2. The So exhibited a maximum value of 0.61 at an LWP of 350 g m−2, where the dominance of the cloud water conversion process changed from autoconversion to accretion. In the aerosol–cloud relation, the susceptibility of the cloud-drop effective radius showed a negative response to the cloud droplet number concentration (Nd) regardless of the environmental conditions, whereas the LWP versus Nd relationship was highly dependent on the meteorological conditions. The WRF-Chem produced higher So values than those of the satellite data by factors of 2.4–3.3; the simulated results exhibited differences in shape, range, and amplitude. The overestimation of So was mainly due to the high precipitation rate under low-LWP conditions as compared to the satellite observations. This result is associated with the initiation and intensity of precipitation, considering both autoconversion and accretion. Our modeling results were verified during KORUS-AQ, which implied that the aerosol–cloud relationship might be elucidated by improved microphysical parameterization schemes based on more detailed measurements such as aircraft-based observations.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Cheol-Hee Kim, chkim2@pusan.ac.kr

Abstract

Precipitation susceptibility (So), a parameter of aerosol–cloud–precipitation interaction over Northeast Asia during the Korea–United States Air Quality (KORUS-AQ) campaign, was analyzed using the Clouds from Advanced Very High-Resolution Radiometer Extended (CLAVR-x) satellite data and WRF-Chem model. As Northeast Asia is one of the areas with the highest aerosol emissions, this study is expected to explore more elaborate aerosol–cloud linkages. Our results obtained from satellite data showed that So increased as the atmospheric condition became stable and humid, and the shift of the water conversion process to precipitation occurred in the LWP range of 300–500 g m−2. The So exhibited a maximum value of 0.61 at an LWP of 350 g m−2, where the dominance of the cloud water conversion process changed from autoconversion to accretion. In the aerosol–cloud relation, the susceptibility of the cloud-drop effective radius showed a negative response to the cloud droplet number concentration (Nd) regardless of the environmental conditions, whereas the LWP versus Nd relationship was highly dependent on the meteorological conditions. The WRF-Chem produced higher So values than those of the satellite data by factors of 2.4–3.3; the simulated results exhibited differences in shape, range, and amplitude. The overestimation of So was mainly due to the high precipitation rate under low-LWP conditions as compared to the satellite observations. This result is associated with the initiation and intensity of precipitation, considering both autoconversion and accretion. Our modeling results were verified during KORUS-AQ, which implied that the aerosol–cloud relationship might be elucidated by improved microphysical parameterization schemes based on more detailed measurements such as aircraft-based observations.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Cheol-Hee Kim, chkim2@pusan.ac.kr
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  • Ackermann, I. J., H. Hass, M. Memmsheimer, A. Ebel, F. S. Binkowski, and U. Shankar, 1998: Modal aerosol dynamics model for Europe: Development and first applications. Atmos. Environ., 32, 29812999, https://doi.org/10.1016/S1352-2310(98)00006-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ahmadov, R., and Coauthors, 2012: A volatility basis set model for summertime secondary organic aerosols over the eastern United States in 2006. J. Geophys. Res., 117, D06301, https://doi.org/10.1029/2011JD016831.

    • Search Google Scholar
    • Export Citation
  • Albrecht, B. A., 1989: Aerosols, cloud microphysics, and fractional cloudiness. Science, 245, 12271230, https://doi.org/10.1126/science.245.4923.1227.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bai, H., C. Gong, M. Wang, Z. Zhang, and T. L’Ecuyer, 2018: Estimating precipitation susceptibility in warm marine clouds using multi-sensor aerosol and cloud products from A-Train satellites. Atmos. Chem. Phys., 18, 17631783, https://doi.org/10.5194/acp-18-1763-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Benas, N., J. F. Meirink, K.-G. Karlsson, M. Stengel, and P. Stammes, 2020: Satellite observations of aerosols and clouds over southern China from 2006 to 2015: Analysis of changes and possible interaction mechanisms. Atmos. Chem. Phys., 20, 457474, https://doi.org/10.5194/acp-20-457-2020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bennartz, R., 2007: Global assessment of marine boundary layer cloud droplet number concentration from satellite. J. Geophys. Res., 112, D02201, https://doi.org/10.1029/2006JD007547.

    • Search Google Scholar
    • Export Citation
  • Bessho, K., and Coauthors, 2016: An introduction to Himawari-8/9—Japan’s new-generation geostationary meteorological satellites. J. Meteor. Soc. Japan, 94, 151183, https://doi.org/10.2151/jmsj.2016-009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chapman, E. G., W. I. Gustafson Jr., R. C. Easter, J. C. Barnard, S. J. Ghan, M. S. Pekour, and J. D. Fast, 2009: Coupling aerosol-cloud-radiative processes in the WRF-Chem model: Investigating the radiative impact of elevated point sources. Atmos. Chem. Phys., 9, 945964, https://doi.org/10.5194/acp-9-945-2009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, F., and J. Dudhia, 2001: Coupling an advanced land-surface/hydrology model with the Penn State/NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Mon. Wea. Rev., 129, 569585, https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, Y.-C., M. W. Christensen, G. L. Stephens, and J. H. Seinfeld, 2014: Satellite-based estimate of global aerosol–cloud radiative forcing by marine warm clouds. Nat. Geosci., 7, 643646, https://doi.org/10.1038/ngeo2214.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Choi, J., and Coauthors, 2019: Impacts of local vs. trans-boundary emissions from different sectors on PM2.5 exposure in South Korea during the KORUS-AQ campaign. Atmos. Environ., 203, 196205, https://doi.org/10.1016/j.atmosenv.2019.02.008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dadashazar, H., and Coauthors, 2017: Relationships between giant sea salt particles and clouds inferred from aircraft physicochemical data. J. Geophys. Res. Atmos., 122, 34213434, https://doi.org/10.1002/2016JD026019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DeCarlo, P. F., and Coauthors, 2006: Field-deployable, high-resolution, time-of-flight aerosol mass spectrometer. Anal. Chem., 78, 82818289, https://doi.org/10.1021/ac061249n.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Duong, H. T., A. Sorooshian, and G. Feingold, 2011: Investigating potential biases in observed and modeled metrics of aerosol-cloud-precipitation interactions. Atmos. Chem. Phys., 11, 40274037, https://doi.org/10.5194/acp-11-4027-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Emmons, L. K., and Coauthors, 2010: Description and evaluation of the Model for Ozone and Related chemical Tracers, version 4 (MOZART-4). Geosci. Model Dev., 3, 4367, https://doi.org/10.5194/gmd-3-43-2010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fan, C., M. Wang, D. Rosenfeld, Y. Zhu, J. Liu, and B. Chen, 2020: Strong precipitation suppression by aerosols in marine low clouds. Geophys. Res. Lett., 47, e2019GL086207, https://doi.org/10.1029/2019GL086207.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fast, J. D., W. I. Gustafson Jr., R. C. Easter, R. A. Zaveri, J. C. Barnard, E. G. Chapman, G. A. Grell, and S. E. Peckham, 2006: 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., 111, D21305, https://doi.org/10.1029/2005JD006721.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feingold, G., and H. Siebert, 2009: Cloud-aerosol interactions from the micro to the cloud scale. Clouds in the Perturbed Climate System: Their Relationship to Energy Balance, Atmospheric Dynamics, and Precipitation, J. Heintzenberg and R. J. Charlson, Eds., MIT Press, 319–338.

    • Crossref
    • Export Citation
  • Feingold, G., A. McComiskey, D. Rosenfeld, and A. Sorooshian, 2013: On the relationship between cloud contact time and precipitation susceptibility to aerosol. J. Geophys. Res. Atmos., 118, 10 54410 554, https://doi.org/10.1002/jgrd.50819.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gao, Y., M. Zhang, Z. Liu, L. Wang, P. Wang, X. Xia, M. Tao, and L. Zhu, 2015: Modeling the feedback between aerosol and meteorological variables in the atmospheric boundary layer during a severe fog-haze event over the North China Plain. Atmos. Chem. Phys., 15, 42794295, https://doi.org/10.5194/acp-15-4279-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gettelman, A., H. Morrison, C. R. Terai, and R. Wood, 2013: Microphysical process rates and global aerosol–cloud interactions. Atmos. Chem. Phys., 13, 98559867, https://doi.org/10.5194/acp-13-9855-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gettelman, A., H. Morrison, S. Santos, P. Bogenschutz, and P. M. Caldwell, 2015: Advanced two-moment bulk microphysics for global models. Part II: Global model solutions and aerosol–cloud interactions. J. Climate, 28, 12881307, https://doi.org/10.1175/JCLI-D-14-00103.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ghan, S., and Coauthors, 2016: Challenges in constraining anthropogenic aerosol effects on cloud radiative forcing using present-day spatiotemporal variability. Proc. Natl. Acad. Sci. USA, 113, 58045811, https://doi.org/10.1073/pnas.1514036113.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grell, G. A., S. E. Peckham, R. Schmitz, S. A. McKeen, G. Frost, W. C. Skamarock, and B. Eder, 2005: Fully coupled “online” chemistry within the WRF Model. Atmos. Environ., 39, 69576975, https://doi.org/10.1016/j.atmosenv.2005.04.027.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Griffith, S. M., W.-S. Huang, C.-C. Lin, Y.-C. Chen, K.-E. Chang, T.-H. Lin, S.-H. Wang, and N.-H. Li, 2020: Long-range air pollution transport in East Asia during the first week of the COVID-19 lockdown in China. Sci. Total Environ., 741, 140214, https://doi.org/10.1016/j.scitotenv.2020.140214.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guenther, A., T. Karl, P. Harley, C. Wiedinmyer, P. I. Palmer, and C. Geron, 2006: Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature). Atmos. Chem. Phys., 6, 31813210, https://doi.org/10.5194/acp-6-3181-2006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Haywood, J., and O. Boucher, 2000: Estimates of the direct and indirect radiative forcing due to tropospheric aerosols: A review. Rev. Geophys., 38, 513543, https://doi.org/10.1029/1999RG000078.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Heidinger, A., 2013: ABI cloud height: Version 3.0. NOAA/NESDIS/STAR Algorithm Theoretical Basis Doc., 79 pp., https://www.star.nesdis.noaa.gov/goesr/docs/ATBD/Cloud_Height.pdf.

  • Heidinger, A., A. Walther, D. Botambekov, W. C. Straka, and S. Wanzong, 2014: The Clouds from AVHRR Extended user’s guide. Version 5.4.1. CIMSS Doc., 60 pp., https://cimss.ssec.wisc.edu/clavrx/clavr_page_files/clavrx_users_guide_v5.4.1.pdf.

  • Hillger, D., and Coauthors, 2013: First-light imagery from Suomi NPP VIIRS. Bull. Amer. Meteor. Soc., 94, 10191029, https://doi.org/10.1175/BAMS-D-12-00097.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hong, S.-Y., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 23182341, https://doi.org/10.1175/MWR3199.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Iacono, M. J., J. S. Delamere, E. J. Mlawer, M. W. Shephard, S. A. Clough, and W. D. Collins, 2008: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res., 113, D13103, https://doi.org/10.1029/2008JD009944.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiang, H., G. Feingold, and A. Sorooshian, 2010: Effect of aerosol on the susceptibility and efficiency of precipitation in warm trade cumulus clouds. J. Atmos. Sci., 67, 35253540, https://doi.org/10.1175/2010JAS3484.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiménez, P. A., J. Dudhia, J. F. González-Rouco, J. Navarro, J. P. Montávez, and E. García-Bustamante, 2012: A revised scheme for the WRF surface layer formulation. Mon. Wea. Rev., 140, 898918, https://doi.org/10.1175/MWR-D-11-00056.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jing, X., K. Suzuki, and T. Michibata, 2019: The key role of warm rain parameterization in determining the aerosol indirect effect in a global climate model. J. Climate, 32, 44094430, https://doi.org/10.1175/jcli-d-18-0789.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jo, H.-Y., and C.-H. Kim, 2013: Identification of long-range transported haze phenomena and their meteorological features over Northeast Asia. J. Appl. Meteor. Climatol., 52, 13181328, https://doi.org/10.1175/JAMC-D-11-0235.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jung, E., B. A. Albrecht, A. Sorooshian, P. Zuidema, and H. H. Jonsson, 2016: Precipitation susceptibility in marine stratocumulus and shallow cumulus from airborne measurements. Atmos. Chem. Phys., 16, 11 39511 413, https://doi.org/10.5194/acp-16-11395-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kasoar, M., D. Shawki, and A. Voulgarakis, 2018: Similar spatial patterns of global climate response to aerosols from different regions. npj Climate Atmos. Sci., 1, 12, https://doi.org/10.1038/s41612-018-0022-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Khairoutdinov, M., and Y. Kogan, 2000: A new cloud physics parameterization in a large-eddy simulation model of marine stratocumulus. Mon. Wea. Rev., 128, 229243, https://doi.org/10.1175/1520-0493(2000)128<0229:ANCPPI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, H. J., Q. Zhang, and J. Heo, 2018: Influence of intense secondary aerosol formation and long-range transport on aerosol chemistry and properties in the Seoul metropolitan area during spring time: Results from KORUS-AQ. Atmos. Chem. Phys., 18, 71497168, https://doi.org/10.5194/acp-18-7149-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, N., M. Park, S. S. Yum, J. S. Park, H. J. Shin, and J. Y. Ahn, 2018: Impact of urban aerosol properties on cloud condensation nuclei (CCN) activity during the KORUS-AQ field campaign. Atmos. Environ., 185, 221236, https://doi.org/10.1016/j.atmosenv.2018.05.019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klein, S. A., and D. L. Hartmann, 1993: The seasonal cycle of low stratiform clouds. J. Climate, 6, 15871606, https://doi.org/10.1175/1520-0442(1993)006<1587:TSCOLS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • L’Ecuyer, T. S., W. Berg, J. Haynes, M. Lebsock, and T. Takemura, 2009: Global observations of aerosol impacts on precipitation occurrence in warm maritime clouds. J. Geophys. Res., 114, D09211, https://doi.org/10.1029/2008JD011273.

    • Search Google Scholar
    • Export Citation
  • Lee, H.-J., H.-Y. Jo, S.-Y. Park, Y.-J. Jo, W. Jeon, J.-Y. Ahn, and C.-H. Kim, 2019: A case study of the transport/transformation of air pollutants over the Yellow Sea during the MAPS 2015 campaign. J. Geophys. Res. Atmos., 124, 65326553, https://doi.org/10.1029/2018JD029751.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, T., and Coauthors, 2015: Characterization of aerosol composition, concentrations, and sources at Baengnyeong Island, Korea using an aerosol mass spectrometer. Atmos. Environ., 120, 297306, https://doi.org/10.1016/j.atmosenv.2015.08.038.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leena, P. P., V. Anilkumar, N. Sravanthi, R. Patil, K. Chakravarty, S. K. Saha, and G. Pandithurai, 2018: On the precipitation susceptibility of monsoon clouds to aerosols using high-altitude ground-based observations over Western Ghats, India. Atmos. Environ., 185, 128136, https://doi.org/10.1016/j.atmosenv.2018.05.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, M., and Coauthors, 2014: Mapping Asian anthropogenic emissions of non-methane volatile organic compounds to multiple chemical mechanisms. Atmos. Chem. Phys., 14, 56175638, https://doi.org/10.5194/acp-14-5617-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, M., and Coauthors, 2017: MIX: A mosaic Asian anthropogenic emission inventory under the international collaboration framework of the MICS-Asia and HTAP. Atmos. Chem. Phys., 17, 935963, https://doi.org/10.5194/acp-17-935-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lu, M.-L., and J. H. Seinfeld, 2006: Effect of aerosol number concentration on cloud droplet dispersion: A large-eddy simulation study and implications for aerosol indirect forcing. J. Geophys. Res., 111, D02207, https://doi.org/10.1029/2005JD006419.

    • Search Google Scholar
    • Export Citation
  • Lu, M.-L., A. Sorooshian, H. H. Jonsson, G. Feingold, R. C. Flagan, and J. H. Seinfeld, 2009: Marine stratocumulus aerosol-cloud relationships in the MASE-II experiment: Precipitation susceptibility in eastern Pacific marine stratocumulus. J. Geophys. Res., 114, D24203, https://doi.org/10.1029/2009JD012774.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ma, P.-L., P. J. Rasch, H. Chepfer, D. M. Winker, and S. J. Ghan, 2018: Observational constraint on cloud susceptibility weakened by aerosol retrieval limitations. Nat. Commun., 9, 2640, https://doi.org/10.1038/s41467-018-05028-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Madronich, S., 1987: Photodissociation in the atmosphere: 1. Actinic flux and the effects of ground reflections and clouds. J. Geophys. Res., 92, 97409752, https://doi.org/10.1029/JD092iD08p09740.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Michibata, T., K. Suzuki, Y. Sato, and T. Takemura, 2016: The source of discrepancies in aerosol-cloud-precipitation interactions between GCM and A-Train retrievals. Atmos. Chem. Phys., 16, 15 41315 424, https://doi.org/10.5194/acp-16-15413-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Michibata, T., K. Suzuki, M. Sekiguchi, and T. Takemura, 2019: Prognostic precipitation in the MIROC6-SPRINTARS GCM: Description and evaluation against satellite observations. J. Adv. Model. Earth Syst., 11, 839860, https://doi.org/10.1029/2018MS001596.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morrison, H., G. Thompson, and V. Tatarskii, 2009: Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall line: Comparison of one- and two-moment schemes. Mon. Wea. Rev., 137, 9911007, https://doi.org/10.1175/2008MWR2556.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neubauer, D., M. W. Christensen, C. A. Poulsen, and U. Lohmann, 2017: Unveiling aerosol–cloud interactions—Part II: Minimising the effects of aerosol swelling and wet scavenging in ECHAM6-HAM2 for comparison to satellite data. Atmos. Chem. Phys., 17, 13 16513 185, https://doi.org/10.5194/acp-17-13165-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ohara, T., H. Akimoto, J. Kurokawa, N. Horii, K. Yamaji, X. Yan, and T. Hayasaka, 2007: An Asian emission inventory of anthropogenic emission sources for the period 1980–2020. Atmos. Chem. Phys., 7, 44194444, https://doi.org/10.5194/acp-7-4419-2007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Park, M., S. S. Yum, N. Kim, B. E. Anderson, A. Beyersdorf, and K. L. Thornhill, 2020: On the submicron aerosol distributions and CCN activity in and around the Korean Peninsula measured onboard the NASA DC-8 research aircraft during the KORUS-AQ field campaign. Atmos. Res., 243, 105004, https://doi.org/10.1016/j.atmosres.2020.105004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Park, S., G.-H. Yu, and S. Lee, 2018: Optical absorption characteristics of brown carbon aerosols during the KORUS-AQ campaign at an urban site. Atmos. Res., 203, 1627, https://doi.org/10.1016/j.atmosres.2017.12.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Park, S.-Y., H.-J. Lee, J.-E. Kang, T. Lee, and C.-H. Kim, 2018: Aerosol radiative effects on mesoscale cloud–precipitation variables over Northeast Asia during the MAPS-Seoul 2015 campaign. Atmos. Environ., 172, 109123, https://doi.org/10.1016/j.atmosenv.2017.10.044.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peterson, D. A., and Coauthors, 2019: Meteorology influencing springtime air quality, pollution transport, and visibility in Korea. Elem. Sci. Anthropocene, 7, 57, https://doi.org/10.1525/elementa.395.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pincus, R., and M. B. Baker, 1994: Effect of precipitation on the albedo susceptibility of clouds in the marine boundary layer. Nature, 372, 250252, https://doi.org/10.1038/372250a0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pincus, R., H. W. Barker, and J.-J. Morcrette, 2003: A fast, flexible, approximate technique for computing radiative transfer in inhomogeneous cloud fields. J. Geophys. Res., 108, 4376, https://doi.org/10.1029/2002JD003322.

    • Search Google Scholar
    • Export Citation
  • Quaas, J., O. Boucher, and U. Lohmann, 2006: Constraining the total aerosol indirect effect in the LMDZ and ECHAM4 GCMs using MODIS satellite data. Atmos. Chem. Phys., 6, 947955, https://doi.org/10.5194/acp-6-947-2006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rosenfeld, D., U. Lohmann, G. B. Raga, C. D. O’Dowd, M. Kulmala, S. Fuzzi, A. Reissell, and M. O. Andreae, 2008: Flood or drought: How do aerosols affect precipitation? Science, 321, 13091313, https://doi.org/10.1126/science.1160606.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rosenfeld, D., S. Sherwood, R. Wood, and L. Donner, 2014: Climate effects of aerosol-cloud interactions. Science, 343, 379380, https://doi.org/10.1126/science.1247490.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp., https://doi.org/10.5065/D68S4MVH.

    • Crossref
    • Export Citation
  • Sorooshian, A., G. Feingold, M. D. Lebsock, H. Jiang, and G. L. Stephens, 2009: On the precipitation susceptibility of clouds to aerosol perturbations. Geophys. Res. Lett., 36, L13803, https://doi.org/10.1029/2009GL038993.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sorooshian, A., G. Feingold, M. D. Lebsock, H. Jiang, and G. L. Stephens, 2010: Deconstructing the precipitation susceptibility construct: Improving methodology for aerosol-cloud precipitation studies. J. Geophys. Res., 115, D17201, https://doi.org/10.1029/2009JD013426.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., 1978: Radiation profiles in extended water clouds. II: Parameterization schemes. J. Atmos. Sci., 35, 21232132, https://doi.org/10.1175/1520-0469(1978)035<2123:RPIEWC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stevens, B., and G. Feingold, 2009: Untangling aerosol effects on clouds and precipitation in a buffered system. Nature, 461, 607613, https://doi.org/10.1038/nature08281.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stockwell, W. R., F. Kirchner, M. Kuhn, and S. Seefeld, 1997: A new mechanism for regional atmospheric chemistry modeling. J. Geophys. Res., 102, 25 84725 879, https://doi.org/10.1029/97JD00849.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Szczodrak, M., P. H. Austin, and P. B. Krummel, 2001: Variability of optical depth and effective radius in marine stratocumulus clouds. J. Atmos. Sci., 58, 29122926, https://doi.org/10.1175/1520-0469(2001)058<2912:VOODAE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Terai, C. R., R. Wood, and T. L. Kubar, 2015: Satellite estimates of precipitation susceptibility in low-level marine stratiform clouds. J. Geophys. Res. Atmos., 120, 88788889, https://doi.org/10.1002/2015JD023319.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Terai, C. R., M. S. Pritchard, P. Blossey, and C. S. Bretherton, 2020: The impact of resolving subkilometer processes on aerosol-cloud interactions of low-level clouds in global model simulations. J. Adv. Model. Earth Syst., 12, e2020MS002274, https://doi.org/10.1029/2020MS002274.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tuccella, P., G. Curci, G. A. Grell, G. Visconti, S. Crumeyrolle, A. Schwarzenboeck, and A. A. Mensah, 2015: A new chemistry option in WRF-Chem v. 3.4 for the simulation of direct and indirect aerosol effects using VBS: Evaluation against IMPACT-EUCAARI data. Geosci. Model Dev., 8, 27492776, https://doi.org/10.5194/gmd-8-2749-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Twomey, S., 1977: The influence of pollution on the shortwave albedo of clouds. J. Atmos. Sci., 34, 11491152, https://doi.org/10.1175/1520-0469(1977)034<1149:TIOPOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Walther, A., and A. K. Heidinger, 2012: Implementation of the daytime cloud optical and microphysical properties algorithm (DCOMP) in PATMOS-x. J. Appl. Meteor. Climatol., 51, 13711390, https://doi.org/10.1175/JAMC-D-11-0108.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, M., and Coauthors, 2012: Constraining cloud lifetime effects of aerosols using A-Train satellite observations. Geophys. Res. Lett., 39, L15709, https://doi.org/10.1029/2012GL052204.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Woo, J.-H., and Coauthors, 2012: Development of an anthropogenic emissions processing system for Asia using SMOKE. Atmos. Environ., 58, 513, https://doi.org/10.1016/j.atmosenv.2011.10.042.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wood, R., and D. L. Hartmann, 2006: Spatial variability of liquid water path in marine low cloud: The importance of mesoscale cellular convection. J. Climate, 19, 17481764, https://doi.org/10.1175/JCLI3702.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, Q., and Coauthors, 2011: Assessing regional scale predictions of aerosols, marine stratocumulus, and their interactions during VOCALS-REx using WRF-Chem. Atmos. Chem. Phys., 11, 11 95111 975, https://doi.org/10.5194/acp-11-11951-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, B., Y. Wang, and J. Hao, 2015: Simulating aerosol-radiation-cloud feedbacks on meteorology and air quality over eastern China under severe haze conditions in winter. Atmos. Chem. Phys., 15, 23872404, https://doi.org/10.5194/acp-15-2387-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, S., and Coauthors, 2016: On the characteristics of aerosol indirect effect based on dynamic regimes in global climate models. Atmos. Chem. Phys., 16, 27652783, https://doi.org/10.5194/acp-16-2765-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, Y., X.-Y. Wen, and C. Jang, 2010: Simulating chemistry-aerosol-cloud-radiation-climate feedbacks over the continental US using the online-coupled Weather Research Forecasting Model with chemistry (WRF/Chem). Atmos. Environ., 44, 35683582, https://doi.org/10.1016/j.atmosenv.2010.05.056.

    • Crossref
    • Search Google Scholar
    • Export Citation
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