Articles | Volume 26, issue 14
https://doi.org/10.5194/acp-26-10071-2026
© 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-26-10071-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Aerosol-deep convection interaction based on joint cell-thermal tracking in Large Eddy Simulations during the TRACER campaign
Daniel Hernandez-Deckers
CORRESPONDING AUTHOR
Departamento de Geociencias, Universidad Nacional de Colombia, Bogotá D.C., Colombia
Toshihisa Matsui
NASA Goddard Space Flight Center Code 612, Greenbelt, MD, USA
Earth System Science Interdisciplanary Center – ESSIC, University of Maryland, College Park, MD, USA
Takamichi Iguchi
NASA Goddard Space Flight Center Code 612, Greenbelt, MD, USA
Earth System Science Interdisciplanary Center – ESSIC, University of Maryland, College Park, MD, USA
Kelcy Brunner
National Wind Institute, Texas Tech University, Lubbock, TX, USA
Eric Bruning
Department of Geosciences, Texas Tech University, Lubbock, TX, USA
Marcus van Lier-Walqui
NASA Goddard Institute for Space Studies, New York, NY, USA
Center for Climate System Research (CCSR), The Earth Institute, Columbia University, New York, NY, USA
Edward R. Mansell
NOAA National Severe Storms Laboratory, Norman, OK, USA
Tamanna Subba
Environmental Science and Technologies Department, Brookhaven National Laboratory, Upton, NY, USA
Chongai Kuang
Environmental Science and Technologies Department, Brookhaven National Laboratory, Upton, NY, USA
Michael P. Jensen
Environmental Science and Technologies Department, Brookhaven National Laboratory, Upton, NY, USA
Scott Braun
NASA Goddard Space Flight Center Code 612, Greenbelt, MD, USA
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Cited articles
Abbott, T. H. and Cronin, T. W.: Aerosol invigoration of atmospheric convection through increases in humidity, Science, 371, 83–85, https://doi.org/10.1126/science.abc5181, 2021. a, b, c, d
Abdul-Razzak, H. and Ghan, S. J.: A parameterization of aerosol activation: 2. Multiple aerosol types, J. Geophys. Res., 105, 6837–6844, https://doi.org/10.1029/1999JD901161, 2000. a
Ackerman, A. S., Toon, O. B., Stevens, D. E., Heymsfield, A. J., Ramanathan, V., and Welton, E. J.: Reduction of Tropical Cloudiness by Soot, Science, 288, 1042–1047, https://doi.org/10.1126/science.288.5468.1042, 2000. a
Albrecht, B. A.: Aerosols, Cloud Microphysics, and Fractional Cloudiness, Science, 245, 1227–1230, https://doi.org/10.1126/science.245.4923.1227, 1989. a
Andreae, M. O., Rosenfeld, D., Artaxo, P., Costa, A. A., Frank, G. P., Longo, K. M., and Silva-Dias, M. A. F.: Smoking Rain Clouds over the Amazon, Science, 303, 1337–1342, https://doi.org/10.1126/science.1092779, 2004. a, b
Arakawa, A. and Schubert, W. H.: Interaction of a Cumulus Cloud Ensemble with the Large-Scale Environment, Part I, J. Atmos. Sci., 31, 674–701, https://doi.org/10.1175/1520-0469(1974)031<0674:IOACCE>2.0.CO;2, 1974. a
Birch, C. E., Parker, D. J., O'Leary, A., Marsham, J. H., Taylor, C. M., Harris, P. P., and Lister, G. M. S.: Impact of soil moisture and convectively generated waves on the initiation of a West African mesoscale convective system, Q. J. Roy. Meteor. Soc., 139, 1712–1730, https://doi.org/10.1002/qj.2062, 2013. a
Blyth, A. M., Lasher-Trapp, S. G., and Cooper, W. A.: A study of thermals in cumulus clouds, Q. J. Roy. Meteor. Soc., 131, 1171–1190, https://doi.org/10.1256/qj.03.180, 2005. a, b
Bruning, E. C., Brunner, K. N., van Lier-Walqui, M., Logan, T., and Matsui, T.: Lightning and Radar Measures of Mixed-Phase Updraft Variability in Tracked Storms during the TRACER Field Campaign in Houston, Texas, Mon. Weather Rev., 152, 2753–2769, https://doi.org/10.1175/MWR-D-24-0060.1, 2024. a, b
Chen, B., Thompson, S. A., Matthews, B. H., Sharma, M., Li, R., Nowotarski, C. J., Rapp, A. D., and Brooks, S. D.: A new technique to retrieve aerosol vertical profiles using micropulse lidar and ground-based aerosol measurements, Atmos. Meas. Tech., 18, 5841–5859, https://doi.org/10.5194/amt-18-5841-2025, 2025. a
Chen, F., Kusaka, H., Bornstein, R., Ching, J., Grimmond, C. S. B., Grossman-Clarke, S., Loridan, T., Manning, K. W., Martilli, A., Miao, S., Sailor, D., Salamanca, F. P., Taha, H., Tewari, M., Wang, X., Wyszogrodzki, A. A., and Zhang, C.: The integrated WRF/urban modelling system: development, evaluation, and applications to urban environmental problems, Int. J. Climatol., 31, 273–288, https://doi.org/10.1002/joc.2158, 2011. a
Choi, Y. and Lee, Y.-H.: Urban Effect on Sea-Breeze-Initiated Rainfall: A Case Study for Seoul Metropolitan Area, Atmosphere, 12, https://doi.org/10.3390/atmos12111483, 2021. a
Chou, M.-D. and Suarez, M. J.: A Solar Radiation Parameterization for Atmospheric Studies, Tech. Rep. NASA/TM-1999-104606, Vol. 15, NASA Goddard Space Flight Center, Greenbelt, Maryland, https://ntrs.nasa.gov/api/citations/19990060930/downloads/19990060930.pdf (last access: 14 July 2026), 1999. a
Chou, M.-D., Suarez, M. J., Liang, X.-Z., and Yah, M. M.-H.: A Thermal Infrared Radiation Parameterization for Atmospheric Studies, Tech. Rep. NASA/TM-2001-104606, Vol. 19, NASA Goddard Space Flight Center, Greenbelt, Maryland, https://gmao.gsfc.nasa.gov/media/publications/zbly36ziNFDFbmYmvhQeVqPhUo/104606vol19.pdf (last access: 14 July 2026), 2001. a
Damiani, R., Vali, G., and Haimov, S.: The Structure of Thermals in Cumulus from Airborne Dual-Doppler Radar Observations, J. Atmos. Sci., 63, 1432–1450, https://doi.org/10.1175/JAS3701.1, 2006. a, b
DeMott, P. J., Prenni, A. J., Liu, X., Kreidenweis, S. M., Petters, M. D., Twohy, C. H., Richardson, M. S., Eidhammer, T., and Rogers, D. C.: Predicting global atmospheric ice nuclei distributions and their impacts on climate, P. Natl. Acad. Sci. USA, 107, 11217–11222, https://doi.org/10.1073/pnas.0910818107, 2010. a, b
Dudhia, J.: Overview of WRF Physics: Boundary Layer and Turbulence [PDF], NCAR/MMM2, National Center for Atmospheric Research, https://www2.mmm.ucar.edu/wrf/users/tutorial/presentation_pdfs/202101/dudhia_physics_pbl_turbulence.pdf (last access: 9 April 2026), 2021. a
Emanuel, K.: Atmospheric Convection, Oxford University Press, New York, ISBN 978-0-19-506630-2, 1994. a
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., 130, e2024JD041931, https://doi.org/10.1029/2024JD041931, 2025. a, b, c
Fierro, A. O., Mansell, E. R., MacGorman, D. R., and Ziegler, C. L.: The Implementation of an Explicit Charging and Discharge Lightning Scheme within the WRF-ARW Model: Benchmark Simulations of a Continental Squall Line, a Tropical Cyclone, and a Winter Storm, Mon. Weather Rev., 141, 2390–2415, https://doi.org/10.1175/MWR-D-12-00278.1, 2013. a
Fridlind, A. M., Li, X., Wu, D., van Lier-Walqui, M., Ackerman, A. S., Tao, W.-K., McFarquhar, G. M., Wu, W., Dong, X., Wang, J., Ryzhkov, A., Zhang, P., Poellot, M. R., Neumann, A., and Tomlinson, J. M.: Derivation of aerosol profiles for MC3E convection studies and use in simulations of the 20 May squall line case, Atmos. Chem. Phys., 17, 5947–5972, https://doi.org/10.5194/acp-17-5947-2017, 2017. a
Friedl, M., McIver, D., Hodges, J., Zhang, X., Muchoney, D., Strahler, A., Woodcock, C., Gopal, S., Schneider, A., Cooper, A., Baccini, A., Gao, F., and Schaaf, C.: Global land cover mapping from MODIS: algorithms and early results, Remote Sens. Environ., 83, 287–302, https://doi.org/10.1016/S0034-4257(02)00078-0, 2002. a
Givati, A. and Rosenfeld, D.: Quantifying Precipitation Suppression Due to Air Pollution, J. Appl. Meteorol., 43, 1038–1056, https://doi.org/10.1175/1520-0450(2004)043<1038:QPSDTA>2.0.CO;2, 2004. a, b
Grabowski, W. W. and Morrison, H.: Do Ultrafine Cloud Condensation Nuclei Invigorate Deep Convection?, J. Atmos. Sci., 77, 2567–2583, https://doi.org/10.1175/JAS-D-20-0012.1, 2020. a
Grant, L. D. and van den Heever, S. C.: Cold Pool and Precipitation Responses to Aerosol Loading: Modulation by Dry Layers, J. Atmos. Sci., 72, 1398–1408, https://doi.org/10.1175/JAS-D-14-0260.1, 2015. a
Gu, J.-F., Stephen Plant, R., Holloway, C. E., and Muetzelfeldt, M. R.: Pressure Drag for Shallow Cumulus Clouds: From Thermals to the Cloud Ensemble, Geophys. Res. Lett., 47, e2020GL090460, https://doi.org/10.1029/2020GL090460, 2020. a, b
Harkema, S. S., Mansell, E. R., Fierro, A. O., Carey, L. D., Schultz, C. J., Matsui, T., and Berndt, E. B.: Explicitly Resolving Lightning and Electrification Processes From the 10–12 April 2019 Thundersnow Outbreak, J. Geophys. Res., 129, e2023JD039987, https://doi.org/10.1029/2023JD039987, 2024. a
Heikenfeld, M., Marinescu, P. J., Christensen, M., Watson-Parris, D., Senf, F., van den Heever, S. C., and Stier, P.: tobac 1.2: towards a flexible framework for tracking and analysis of clouds in diverse datasets, Geosci. Model Dev., 12, 4551–4570, https://doi.org/10.5194/gmd-12-4551-2019, 2019. a
Hernandez-Deckers, D. and Sherwood, S. C.: On the Role of Entrainment in the Fate of Cumulus Thermals, J. Atmos. Sci., 75, 3911–3924, https://doi.org/10.1175/JAS-D-18-0077.1, 2018. a, b, c
Hernandez-Deckers, D., Matsui, T., and Fridlind, A. M.: Updraft dynamics and microphysics: on the added value of the cumulus thermal reference frame in simulations of aerosol–deep convection interactions, Atmos. Chem. Phys., 22, 711–724, https://doi.org/10.5194/acp-22-711-2022, 2022. a, b
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Hirt, M., Craig, G. C., Schäfer, S. A. K., Savre, J., and Heinze, R.: Cold-pool-driven convective initiation: using causal graph analysis to determine what convection-permitting models are missing, Q. J. Roy. Meteor. Soc., 146, 2205–2227, https://doi.org/10.1002/qj.3788, 2020. a
Hu, J., Rosenfeld, D., Ryzhkov, A., Zrnic, D., Williams, E., Zhang, P., Snyder, J. C., Zhang, R., and Weitz, R.: Polarimetric Radar Convective Cell Tracking Reveals Large Sensitivity of Cloud Precipitation and Electrification Properties to CCN, J. Geophys. Res., 124, 12194–12205, https://doi.org/10.1029/2019JD030857, 2019. a, b, c
Hu, Q.: A Cumulus Parameterization Based on a Cloud Model of Intermittently Rising Thermals, J. Atmos. Sci., 54, 2292–2307, https://doi.org/10.1175/1520-0469(1997)054<2292:ACPBOA>2.0.CO;2, 1997. a
Igel, A. L. and van den Heever, S. C.: Invigoration or Enervation of Convective Clouds by Aerosols?, Geophys. Res. Lett., 48, e2021GL093804, https://doi.org/10.1029/2021GL093804, 2021. a
Iguchi, T., Rutledge, S. A., Tao, W.-K., Matsui, T., Dolan, B., Lang, S. E., and Barnum, J.: Impacts of Aerosol and Environmental Conditions on Maritime and Continental Deep Convective Systems Using a Bin Microphysical Model, J. Geophys. Res., 125, e2019JD030952, https://doi.org/10.1029/2019JD030952, 2020. a, b
Jensen, M. P., Flynn, J. H., Judd, L. M., Kollias, P., Kuang, C., Mcfarquhar, G., Nadkarni, R., Powers, H., and Sullivan, J.: A Succession of Cloud, Precipitation, Aerosol, and Air Quality Field Experiments in the Coastal Urban Environment, B. Am. Meteorol. Soc., 103, 103–105, https://doi.org/10.1175/BAMS-D-21-0104.1, 2022. a
Jensen, M. P., Flynn, J. H., Gonzalez-Cruz, J. E., Judd, L. M., Kollias, P., Kuang, C., McFarquhar, G. M., Powers, H., Ramamurthy, P., Sullivan, J., Aiken, A. C., Alvarez, S. L., Argay, P., Argrow, B., Bell, T. M., Boyer, D., Brooks, S. D., Bruning, E. C., Brunner, K., Butterworth, B., Calmer, R., Cappa, C. D., Chakrabarty, R. K., Chandrasekar, V., Chao, C.-Y., Chen, B., China, S., Collins, D. R., Collis, S. M., Crowell, S., Porto, R. D., de Boer, G., Deng, M., Dexheimer, D., Drager, A. J., Du, X., Dubey, M. K., Dzambo, A. M., Etten-Bohm, M., Fan, J., Farley, R., Feng, Y.-C., Feng, Y., Fenn, M., Ferrare, R. E., Flusche, S., Fridlind, A. M., Galewsky, J., Gamarro, H., Gardner, S., Ghate, V. P., Giangrande, S. E., Griffin, R. J., Griggs, T., Gronoff, G. P., Grover, M., Gaugenti, M., Guo, F., Gupta, S., Hu, J., Huang, Y., Jackson, R. C., Hair, J. W., Johnson, K. L., Kasparoglu, S., Klein, P., Kotsakis, A. E., Kumar, J., Kumjian, M. R., Lamer, K., Lappin, F. M., Lei, Z., Li, J., Li, R., Li, Y., Logan, T., Lombardo, K., Luke, E. P., Mages, Z., Matthews, A. A., Matthews, B. H., Mayol-Bracero, O., Matsui, T., McKeown, K. E., Mehra, M., Mei, F., Meskidzhe, N., Nguyen, C., Nielsen, E. R., Nowotarski, C. J., Oaks, D., Oktem, R., Oue, M., Park, J. M., Partida, N., Patil, S., Pena, J. C., Petters, M. D., Phoenix, D. B., Puthserry, J. V., Rapp, A. D., Romps, D. M., Roots, M., Rosenfeld, D., Saleeby, S. M., Savala, P., Sedlacek, A. J., Sharma, M., Sheesley, R., Shingler, T. J., Shrestha, S., Singh, A., Smith, E. N., Smith, J. N., Smith, S., Snyder, J., Spicer, E., Spinei, E., Spychala, M., Steir, P., Storm, M. R., Subba, T., Treserras, B. P., Trojanowski, R., Theisen, A., Thompson, S. A., Twigg, L., Uin, J., Ulinski, A. R., van den Heever, S., van Lier-Walqui, M., Varble, A. C., Wagner, T. J., Wakeen, J., Wales, N. A., Walter, P. J., Wang, D., Wang, J., Wood, L., Wang, Y., Wolde, M., Yoon, S., Young, M. H., Zawadowicz, M. A., Zhang, Q., Zhou, A., Zhu, Z., and Zhu, Z.: Studying Aerosol, Clouds, and Air Quality in the Coastal Urban Environment of Southeastern Texas, B. Am. Meteorol. Soc., https://doi.org/10.1175/BAMS-D-23-0331.1, 2025. a, b
Khain, A. P., BenMoshe, N., and Pokrovsky, A.: Factors Determining the Impact of Aerosols on Surface Precipitation from Clouds: An Attempt at Classification, J. Atmos. Sci., 65, 1721–1748, https://doi.org/10.1175/2007JAS2515.1, 2008. a
Koontz, A., Uin, J., Andrews, E., Enekwizu, O., Hayes, C., and Salwen, C.: Cloud Condensation Nuclei Particle Counter (AOSCCN2COLASPECTRA), 2021-10-01 to 2022-09-02, ARM Mobile Facility (HOU), Houston, TX, AMF1 (main site for TRACER) (M1), https://doi.org/10.5439/1323896, 2021. a
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, https://doi.org/10.1029/2005GL023187, 2005. a
Kusaka, H. and Kimura, F.: Coupling a Single-Layer Urban Canopy Model with a Simple Atmospheric Model: Impact on Urban Heat Island Simulation for an Idealized Case, J. Meteorol. Soc. Jpn., 82, 67–80, https://doi.org/10.2151/jmsj.82.67, 2004. a
Kusaka, H., Kondo, H., Kikegawa, Y., and Kimura, F.: A Simple Single-Layer Urban Canopy Model For Atmospheric Models: Comparison With Multi-Layer And Slab Models, Bound.-Lay. Meteorol., 101, 329–358, https://doi.org/10.1023/A:1019207923078, 2001. a
Li, Z., Niu, F., Fan, J., Liu, Y., Rosenfeld, D., and Ding, Y.: Long-term impacts of aerosols on the vertical development of clouds and precipitation, Nat. Geosci., 4, 888–894, https://doi.org/10.1038/ngeo1313, 2011. a
Lilly, D. K.: Stratified Turbulence and the Mesoscale Variability of the Atmosphere, J. Atmos. Sci., 40, 749–761, https://doi.org/10.1175/1520-0469(1983)040<0749:STATMV>2.0.CO;2, 1983. a
Lin, J. C., Matsui, T., Pielke Sr., R. A., and Kummerow, C.: Effects of biomass-burning-derived aerosols on precipitation and clouds in the Amazon Basin: a satellite-based empirical study, J. Geophys. Res., 111, https://doi.org/10.1029/2005JD006884, 2006. a
Mages, Z., Kollias, P., Treserras, B. P., Borque, P., and Oue, M.: Shallow cloud variability in Houston, Texas, during the ESCAPE and TRACER field experiments, Atmos. Chem. Phys., 25, 6025–6045, https://doi.org/10.5194/acp-25-6025-2025, 2025. a
Mansell, E. R., MacGorman, D. R., Ziegler, C. L., and Straka, J. M.: Charge structure and lightning sensitivity in a simulated multicell thunderstorm, J. Geophys. Res., 110, https://doi.org/10.1029/2004JD005287, 2005. a
Mansell, E. R., Ziegler, C. L., and Bruning, E. C.: Simulated Electrification of a Small Thunderstorm with Two-Moment Bulk Microphysics, J. Atmos. Sci., 67, 171–194, https://doi.org/10.1175/2009JAS2965.1, 2010. a
Marinescu, P. J., van den Heever, S. C., Heikenfeld, M., Barrett, A. I., Barthlott, C., Hoose, C., Fan, J., Fridlind, A. M., Matsui, T., Miltenberger, A. K., Stier, P., Vie, B., White, B. A., and Zhang, Y.: Impacts of Varying Concentrations of Cloud Condensation Nuclei on Deep Convective Cloud Updrafts—A Multimodel Assessment, J. Atmos. Sci., 78, 1147–1172, https://doi.org/10.1175/JAS-D-20-0200.1, 2021. a, b, c, d, e
Matsui, T., Dolan, B., Iguchi, T., Rutledge, S. A., Tao, W.-K., and Lang, S.: Polarimetric Radar Characteristics of Simulated and Observed Intense Convective Cores for a Midlatitude Continental and Tropical Maritime Environment, J. Hydrometeorol., 21, 501–517, https://doi.org/10.1175/JHM-D-19-0185.1, 2020a. a
Matsui, T., Zhang, S. Q., Lang, S. E., Tao, W.-K., Ichoku, C., and Peters-Lidard, C. D.: Impact of radiation frequency, precipitation radiative forcing, and radiation column aggregation on convection-permitting West African monsoon simulations, Clim. Dynam., 55, 193–213, https://doi.org/10.1007/s00382-018-4187-2, 2020b. a
Matsui, T., Wolff, D. B., Lang, S., Mohr, K., Zhang, M., Xie, S., Tang, S., Saleeby, S. M., Posselt, D. J., Braun, S. A., Chern, J.-D., Dolan, B., Pippitt, J. L., and Loftus, A. M.: Systematic Validation of Ensemble Cloud-Process Simulations Using Polarimetric Radar Observations and Simulator Over the NASA Wallops Flight Facility, J. Geophys. Res., 128, e2022JD038134, https://doi.org/10.1029/2022JD038134, 2023. a
Matsui, T., Hernandez-Deckers, D., Giangrande, S. E., Biscaro, T. S., Fridlind, A., and Braun, S.: A thermal-driven graupel generation process to explain dry-season convective vigor over the Amazon, Atmos. Chem. Phys., 24, 10793–10814, https://doi.org/10.5194/acp-24-10793-2024, 2024. a, b
Monin, A. and Obukhov, A.: Basic laws of turbulent mixing in the ground surface layer, Tr. Geophiz. Inst. Akad. Nauk. SSSR, 151, 163–187, 1954. a
Morrison, H. and Peters, J. M.: Theoretical Expressions for the Ascent Rate of Moist Deep Convective Thermals, J. Atmos. Sci., 75, 1699–1719, https://doi.org/10.1175/jas-d-17-0295.1, 2018. a
Morrison, H., Peters, J. M., Varble, A. C., Hannah, W. M., and Giangrande, S. E.: Thermal Chains and Entrainment in Cumulus Updrafts. Part I: Theoretical Description, J. Atmos. Sci., 77, 3637–3660, https://doi.org/10.1175/JAS-D-19-0243.1, 2020a. a
Morrison, H., van Lier-Walqui, M., Fridlind, A. M., Grabowski, W. W., Harrington, J. Y., Hoose, C., Korolev, A., Kumjian, M. R., Milbrandt, J. A., Pawlowska, H., Posselt, D. J., Prat, O. P., Reimel, K. J., Shima, S.-I., van Diedenhoven, B., and Xue, L.: Confronting the Challenge of Modeling Cloud and Precipitation Microphysics, J. Adv. Model. Earth Sy., 12, e2019MS001689, https://doi.org/10.1029/2019MS001689, 2020b. a
Morrison, H., Jeevanjee, N., Lecoanet, D., and Peters, J. M.: What Controls the Entrainment Rate of Dry Buoyant Thermals with Varying Initial Aspect Ratio?, J. Atmos. Sci., 80, 2711–2728, https://doi.org/10.1175/JAS-D-23-0063.1, 2023. a
Nakanishi, M. and Niino, H.: An Improved Mellor–Yamada Level-3 Model: Its Numerical Stability and Application to a Regional Prediction of Advection Fog, Bound.-Lay. Meteorol., 119, 397–407, https://doi.org/10.1007/s10546-005-9030-8, 2006. a
Nakanishi, M. and Niino, H.: Development of an Improved Turbulence Closure Model for the Atmospheric Boundary Layer, J. Meteorol. Soc. Jpn., 87, 895–912, https://doi.org/10.2151/jmsj.87.895, 2009. a
Niu, G.-Y., Yang, Z.-L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements, J. Geophys. Res., 116, https://doi.org/10.1029/2010JD015139, 2011. a
Öktem, R., Romps, D. M., and Varble, A. C.: No Warm-Phase Invigoration of Convection Detected during GoAmazon, J. Atmos. Sci., 80, 2345–2364, https://doi.org/10.1175/JAS-D-22-0241.1, 2023. a
Peters, J. M., Morrison, H., Varble, A. C., Hannah, W. M., and Giangrande, S. E.: Thermal Chains and Entrainment in Cumulus Updrafts. Part II: Analysis of Idealized Simulations, J. Atmos. Sci., 77, 3661–3681, https://doi.org/10.1175/JAS-D-19-0244.1, 2020. a, b, c
Peters-Lidard, C. D., Kemp, E. M., Matsui, T., Santanello, J. A., Kumar, S. V., Jacob, J. P., Clune, T., Tao, W.-K., Chin, M., Hou, A., Case, J. L., Kim, D., Kim, K.-M., Lau, W., Liu, Y., Shi, J., Starr, D., Tan, Q., Tao, Z., Zaitchik, B. F., Zavodsky, B., Zhang, S. Q., and Zupanski, M.: Integrated modeling of aerosol, cloud, precipitation and land processes at satellite-resolved scales, Environ. Modell. Softw., 67, 149–159, https://doi.org/10.1016/j.envsoft.2015.01.007, 2015. a
Raymond, D. J. and Herman, M. J.: Convective quasi-equilibrium reconsidered, J. Adv. Model. Earth Sy., 3, https://doi.org/10.1029/2011MS000079, 2011. a
Romps, D. M. and Charn, A. B.: Sticky Thermals: Evidence for a Dominant Balance between Buoyancy and Drag in Cloud Updrafts, J. Atmos. Sci., 72, 2890–2901, https://doi.org/10.1175/JAS-D-15-0042.1, 2015. a
Romps, D. M., Öktem, R., Endo, S., and Vogelmann, A. M.: On the Life Cycle of a Shallow Cumulus Cloud: Is It a Bubble or Plume, Active or Forced?, J. Atmos. Sci., 78, 2823–2833, https://doi.org/10.1175/JAS-D-20-0361.1, 2021. a, b
Rosenfeld, D., Lohmann, U., Raga, G. B., O'Dowd, C. D., Kulmala, M., Fuzzi, S., Reissell, A., and Andreae, M. O.: Flood or Drought: How Do Aerosols Affect Precipitation?, Science, 321, 1309–1313, https://doi.org/10.1126/science.1160606, 2008. a, b, c
Saleeby, S. M., van den Heever, S. C., Marinescu, P. J., Oue, M., Barrett, A. I., Barthlott, C., Cherian, R., Fan, J., Fridlind, A. M., Heikenfeld, M., Hoose, C., Matsui, T., Miltenberger, A. K., Quaas, J., Shpund, J., Stier, P., Vie, B., White, B. A., and Zhang, Y.: Model Intercomparison of the Impacts of Varying Cloud Droplet–Nucleating Aerosols on the Life Cycle and Microphysics of Isolated Deep Convection, J. Atmos. Sci., 82, 2197–2217, https://doi.org/10.1175/JAS-D-24-0181.1, 2025. a, b, c, d
Salinas, V., Bruning, E. C., and Mansell, E. R.: Examining the Kinematic Structures within which Lightning Flashes Are Initiated Using a Cloud-Resolving Model, J. Atmos. Sci., 79, 513–530, https://doi.org/10.1175/JAS-D-21-0132.1, 2022. a
Saunders, P. M.: An observational study of Cumulus, J. Atmos. Sci., 18, 451–467, https://doi.org/10.1175/1520-0469(1961)018<0451:AOSOC>2.0.CO;2, 1961. a
Scorer, R. S. and Ludlam, F. H.: Bubble theory of penetrative convection, Q. J. Roy. Meteor. Soc., 79, 94–103, https://doi.org/10.1002/qj.49707933908, 1953. a
Seinfeld, J. and Pandis, S.: Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, John Wiley & Sons, New York, ISBN 978-0-471-72017-1, 2006. a
Shepherd, J. M. and Burian, S. J.: Detection of Urban-Induced Rainfall Anomalies in a Major Coastal City, Earth Interact., 7, 1–17, https://doi.org/10.1175/1087-3562(2003)007<0001:DOUIRA>2.0.CO;2, 2003. a
Sherwood, S. C., Hernández-Deckers, D., Colin, M., and Robinson, F.: Slippery Thermals and the Cumulus Entrainment Paradox, J. Atmos. Sci., 70, 2426–2442, https://doi.org/10.1175/JAS-D-12-0220.1, 2013. a, b, c, d
Singh, A. and Kuang, C.: Scanning Mobility Particle Sizer (SMPS) Instrument Handbook, Tech. Rep. DOE/SC-ARM-TR-147, U.S. Department of Energy, Atmospheric Radiation Measurement user facility, Richland, Washington, https://doi.org/10.2172/1245993, 2024. a
Singh, A., Kuang, C., Howie, J., Salwen, C., and Hayes, C.: Scanning mobility particle sizer (AOSSMPS), 2021-10-01 to 2022-10-01, ARM Mobile Facility (HOU), Houston, TX; AMF1 (main site for TRACER) (M1), Atmospheric Radiation Measurement (ARM) user facility [data set], https://doi.org/10.5439/1476898, 2022. a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda, M. G., Huang, X.-Y., Wang, W., and Powers, J. G.: A Description of the Advanced Research WRF Version 3, NCAR Technical Note NCAR/TN-475+STR, June 2008, Mesoscale and Microscale Meteorology Division. National Center for Atmospheric Research, Boulder, https://doi.org/10.5065/D68S4MVH, 2008. a
Sokolowsky, G. A., Freeman, S. W., Jones, W. K., Kukulies, J., Senf, F., Marinescu, P. J., Heikenfeld, M., Brunner, K. N., Bruning, E. C., Collis, S. M., Jackson, R. C., Leung, G. R., Pfeifer, N., Raut, B. A., Saleeby, S. M., Stier, P., and van den Heever, S. C.: tobac v1.5: introducing fast 3D tracking, splits and mergers, and other enhancements for identifying and analysing meteorological phenomena, Geosci. Model Dev., 17, 5309–5330, https://doi.org/10.5194/gmd-17-5309-2024, 2024. a, b
Stanford, M. W., Fridlind, A. M., Ackerman, A. S., van Diedenhoven, B., Xiao, Q., Wang, J., Matsui, T., Hernandez-Deckers, D., and Lawson, P.: Warm-phase microphysical evolution in large-eddy simulations of tropical cumulus congestus: evaluating drop size distribution evolution using polarimetry retrievals, in situ measurements, and a thermal-based framework, Atmos. Chem. Phys., 25, 11199–11231, https://doi.org/10.5194/acp-25-11199-2025, 2025. a
Stephens, G. L., Shiro, K. A., Hakuba, M. Z., Takahashi, H., Pilewskie, J. A., Andrews, T., Stubenrauch, C. J., and Wu, L.: Tropical Deep Convection, Cloud Feedbacks and Climate Sensitivity, Surv. Geophys., 45, 1903–1931, https://doi.org/10.1007/s10712-024-09831-1, 2024. a
Tao, W.-K., Li, X., Khain, A., Matsui, T., Lang, S., and Simpson, J.: Role of atmospheric aerosol concentration on deep convective precipitation: Cloud-resolving model simulations, J. Geophys. Res., 112, https://doi.org/10.1029/2007JD008728, 2007. a
Tao, W.-K., Chen, J.-P., Li, Z., Wang, C., and Zhang, C.: Impact of aerosols on convective clouds and precipitation, Rev. Geophys., 50, https://doi.org/10.1029/2011RG000369, 2012. a
Thompson, S. A., Peña, T. A., Lei, Z., Chen, B., Sharma, M., Matthews, B. H., Li, R., Nowotarski, C. J., Rapp, A. D., and Brooks, S. D.: Understanding aerosol properties in convective outflows during TRACER, Aerosol Sci. Tech., 60, 858–879, https://doi.org/10.1080/02786826.2025.2608352, 2026. a
Twomey, S.: Pollution and the planetary albedo, Atmos. Environ., 8, 1251–1256, https://doi.org/10.1016/0004-6981(74)90004-3, 1974. a
Uin, J., Aiken, A. C., Dubey, M. K., Kuang, C., Pekour, M., Salwen, C., Sedlacek, A. J., Senum, G., Smith, S., Wang, J., Watson, T. B., and Springston, S. R.: Atmospheric Radiation Measurement (ARM) Aerosol Observing Systems (AOS) for Surface-Based In Situ Atmospheric Aerosol and Trace Gas Measurements, J. Atmos. Ocean. Tech., 36, 2429–2447, https://doi.org/10.1175/JTECH-D-19-0077.1, 2019. a
Varble, A. C., Igel, A. L., Morrison, H., Grabowski, W. W., and Lebo, Z. J.: Opinion: A critical evaluation of the evidence for aerosol invigoration of deep convection, Atmos. Chem. Phys., 23, 13791–13808, https://doi.org/10.5194/acp-23-13791-2023, 2023. a, b
Vraciu, C. V., Kruse, I. L., and Haerter, J. O.: The Role of Passive Cloud Volumes in the Transition From Shallow to Deep Atmospheric Convection, Geophys. Res. Lett., 50, e2023GL105996, https://doi.org/10.1029/2023GL105996, 2023. a
Vraciu, C. V., Savre, J., and Colin, M.: The Rapid Transition From Shallow to Precipitating Convection as a Predator–Prey Process, J. Adv. Model. Earth Sy., 17, e2024MS004630, https://doi.org/10.1029/2024MS004630, 2025. a
Wang, D., Jensen, M. P., Taylor, D., Kowalski, G., Hogan, M., Wittemann, B. M., Rakotoarivony, A., Giangrande, S. E., and Park, J. M.: Linking Synoptic Patterns to Cloud Properties and Local Circulations Over Southeastern Texas, J. Geophys. Res., 127, e2021JD035920, https://doi.org/10.1029/2021JD035920, 2022. a, b
Wang, D., Melvin, E. C., Smith, N., Jensen, M. P., Gupta, S., Abdullah-Smoot, A., Pszeniczny, N., and Hahn, T.: TRACER Perspectives on Gulf-Breeze and Bay-Breeze Circulations and Coastal Convection, Mon. Weather Rev., 152, 2207–2228, https://doi.org/10.1175/MWR-D-23-0292.1, 2024. a
Wang, D., Kobrosly, R., Zhang, T., Subba, T., van den Heever, S., Gupta, S., and Jensen, M.: Aerosol impacts on isolated deep convection: findings from TRACER, Atmos. Chem. Phys., 25, 9295–9314, https://doi.org/10.5194/acp-25-9295-2025, 2025. a
Xue, H. and Feingold, G.: Large-Eddy Simulations of Trade Wind Cumuli: Investigation of Aerosol Indirect Effects, J. Atmos. Sci., 63, 1605–1622, https://doi.org/10.1175/JAS3706.1, 2006. a, b
Yang, Z.-L., Niu, G.-Y., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Longuevergne, L., Manning, K., Niyogi, D., Tewari, M., and Xia, Y.: The community Noah land surface model with multiparameterization options (Noah-MP): 2. Evaluation over global river basins, J. Geophys. Res., 116, https://doi.org/10.1029/2010JD015140, 2011. a
Yano, J.-I.: Basic convective element: bubble or plume? A historical review, Atmos. Chem. Phys., 14, 7019–7030, https://doi.org/10.5194/acp-14-7019-2014, 2014. a, b
Yin, J., Pan, Z., Mao, F., Rosenfeld, D., Zang, L., Chen, J., and Gong, J.: Large effects of fine and coarse aerosols on tropical deep convective systems throughout their lifecycle, NPJ Clim. Atmos. Sci., 7, https://doi.org/10.1038/s41612-024-00739-6, 2024. a, b, c
Short summary
Aerosols from air pollution affect weather and climate in various ways. Uncertainties remain on their interactions with clouds, in particular via microphysics (processes related to phase-changes of water that generate rain and lightning). We investigate this with high resolution simulations, focusing on cumulus thermals (the rising bubbles in clouds). We describe the thermals’ roles in these interactions, and identify related mesoscale feedback that enhance convection under polluted conditions.
Aerosols from air pollution affect weather and climate in various ways. Uncertainties remain on...
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