Articles | Volume 21, issue 18
https://doi.org/10.5194/acp-21-13997-2021
© Author(s) 2021. 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-21-13997-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Supersaturation, buoyancy, and deep convection dynamics
Wojciech W. Grabowski
CORRESPONDING AUTHOR
Mesoscale and Microscale Meteorology Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USA
Hugh Morrison
Mesoscale and Microscale Meteorology Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USA
Related authors
Wojciech W. Grabowski and Hanna Pawlowska
Atmos. Chem. Phys., 25, 5273–5285, https://doi.org/10.5194/acp-25-5273-2025, https://doi.org/10.5194/acp-25-5273-2025, 2025
Short summary
Short summary
A simple diagram to depict cloud droplets' formation via the activation of cloud condensation nuclei (CCN) as well as their subsequent growth and evaporation is presented.
Damian K. Wójcik, Michał Z. Ziemiański, and Wojciech W. Grabowski
EGUsphere, https://doi.org/10.5194/egusphere-2025-1017, https://doi.org/10.5194/egusphere-2025-1017, 2025
Short summary
Short summary
Representation of severe convection is a challenge for the numerical weather prediction models. We show that an explicit stochastic convection initiation scheme allows numerical representation of the isolated bow echo of severe social impact, showing its cold-pool-driven dynamics, formation of the rear inflow jet and strong surface winds. In moist convection context, we polemize with the idea of horizontal sizes of model perturbations being no less than the effective model’s resolution.
Adam C. Varble, Adele L. Igel, Hugh Morrison, Wojciech W. Grabowski, and Zachary J. Lebo
Atmos. Chem. Phys., 23, 13791–13808, https://doi.org/10.5194/acp-23-13791-2023, https://doi.org/10.5194/acp-23-13791-2023, 2023
Short summary
Short summary
As atmospheric particles called aerosols increase in number, the number of droplets in clouds tends to increase, which has been theorized to increase storm intensity. We critically evaluate the evidence for this theory, showing that flaws and limitations of previous studies coupled with unaddressed cloud process complexities draw it into question. We provide recommendations for future observations and modeling to overcome current uncertainties.
Istvan Geresdi, Lulin Xue, Sisi Chen, Youssef Wehbe, Roelof Bruintjes, Jared A. Lee, Roy M. Rasmussen, Wojciech W. Grabowski, Noemi Sarkadi, and Sarah A. Tessendorf
Atmos. Chem. Phys., 21, 16143–16159, https://doi.org/10.5194/acp-21-16143-2021, https://doi.org/10.5194/acp-21-16143-2021, 2021
Short summary
Short summary
By releasing soluble aerosols into the convective clouds, cloud seeding potentially enhances rainfall. The seeding impacts are hard to quantify with observations only. Numerical models that represent the detailed physics of aerosols, cloud and rain formation are used to investigate the seeding impacts on rain enhancement under different natural aerosol backgrounds and using different seeding materials. Our results indicate that seeding may enhance rainfall under certain conditions.
Wojciech W. Grabowski and Lois Thomas
Atmos. Chem. Phys., 21, 4059–4077, https://doi.org/10.5194/acp-21-4059-2021, https://doi.org/10.5194/acp-21-4059-2021, 2021
Short summary
Short summary
This paper presents a modeling study that investigates the impact of cloud turbulence on the diffusional growth of cloud droplets and compares modeling results to analytic solutions published in the past. The focus is on comparing the two microphysics modeling methodologies – the Eulerian bin microphysics and Lagrangian particle-based microphysics – and exposing their limitations.
Wojciech W. Grabowski and Hanna Pawlowska
Atmos. Chem. Phys., 25, 5273–5285, https://doi.org/10.5194/acp-25-5273-2025, https://doi.org/10.5194/acp-25-5273-2025, 2025
Short summary
Short summary
A simple diagram to depict cloud droplets' formation via the activation of cloud condensation nuclei (CCN) as well as their subsequent growth and evaporation is presented.
Damian K. Wójcik, Michał Z. Ziemiański, and Wojciech W. Grabowski
EGUsphere, https://doi.org/10.5194/egusphere-2025-1017, https://doi.org/10.5194/egusphere-2025-1017, 2025
Short summary
Short summary
Representation of severe convection is a challenge for the numerical weather prediction models. We show that an explicit stochastic convection initiation scheme allows numerical representation of the isolated bow echo of severe social impact, showing its cold-pool-driven dynamics, formation of the rear inflow jet and strong surface winds. In moist convection context, we polemize with the idea of horizontal sizes of model perturbations being no less than the effective model’s resolution.
Trude Eidhammer, Andrew Gettelman, Katherine Thayer-Calder, Duncan Watson-Parris, Gregory Elsaesser, Hugh Morrison, Marcus van Lier-Walqui, Ci Song, and Daniel McCoy
Geosci. Model Dev., 17, 7835–7853, https://doi.org/10.5194/gmd-17-7835-2024, https://doi.org/10.5194/gmd-17-7835-2024, 2024
Short summary
Short summary
We describe a dataset where 45 parameters related to cloud processes in the Community Earth System Model version 2 (CESM2) Community Atmosphere Model version 6 (CAM6) are perturbed. Three sets of perturbed parameter ensembles (263 members) were created: current climate, preindustrial aerosol loading and future climate with sea surface temperature increased by 4 K.
Adam C. Varble, Adele L. Igel, Hugh Morrison, Wojciech W. Grabowski, and Zachary J. Lebo
Atmos. Chem. Phys., 23, 13791–13808, https://doi.org/10.5194/acp-23-13791-2023, https://doi.org/10.5194/acp-23-13791-2023, 2023
Short summary
Short summary
As atmospheric particles called aerosols increase in number, the number of droplets in clouds tends to increase, which has been theorized to increase storm intensity. We critically evaluate the evidence for this theory, showing that flaws and limitations of previous studies coupled with unaddressed cloud process complexities draw it into question. We provide recommendations for future observations and modeling to overcome current uncertainties.
Andrew Gettelman, Hugh Morrison, Trude Eidhammer, Katherine Thayer-Calder, Jian Sun, Richard Forbes, Zachary McGraw, Jiang Zhu, Trude Storelvmo, and John Dennis
Geosci. Model Dev., 16, 1735–1754, https://doi.org/10.5194/gmd-16-1735-2023, https://doi.org/10.5194/gmd-16-1735-2023, 2023
Short summary
Short summary
Clouds are a critical part of weather and climate prediction. In this work, we document updates and corrections to the description of clouds used in several Earth system models. These updates include the ability to run the scheme on graphics processing units (GPUs), changes to the numerical description of precipitation, and a correction to the ice number. There are big improvements in the computational performance that can be achieved with GPU acceleration.
Zhipeng Qu, Alexei Korolev, Jason A. Milbrandt, Ivan Heckman, Yongjie Huang, Greg M. McFarquhar, Hugh Morrison, Mengistu Wolde, and Cuong Nguyen
Atmos. Chem. Phys., 22, 12287–12310, https://doi.org/10.5194/acp-22-12287-2022, https://doi.org/10.5194/acp-22-12287-2022, 2022
Short summary
Short summary
Secondary ice production (SIP) is an important physical phenomenon that results in an increase in the cloud ice particle concentration and can have a significant impact on the evolution of clouds. Here, idealized simulations of a tropical convective system were conducted. Agreement between the simulations and observations highlights the impacts of SIP on the maintenance of tropical convection in nature and the importance of including the modelling of SIP in numerical weather prediction models.
Po-Lun Ma, Bryce E. Harrop, Vincent E. Larson, Richard B. Neale, Andrew Gettelman, Hugh Morrison, Hailong Wang, Kai Zhang, Stephen A. Klein, Mark D. Zelinka, Yuying Zhang, Yun Qian, Jin-Ho Yoon, Christopher R. Jones, Meng Huang, Sheng-Lun Tai, Balwinder Singh, Peter A. Bogenschutz, Xue Zheng, Wuyin Lin, Johannes Quaas, Hélène Chepfer, Michael A. Brunke, Xubin Zeng, Johannes Mülmenstädt, Samson Hagos, Zhibo Zhang, Hua Song, Xiaohong Liu, Michael S. Pritchard, Hui Wan, Jingyu Wang, Qi Tang, Peter M. Caldwell, Jiwen Fan, Larry K. Berg, Jerome D. Fast, Mark A. Taylor, Jean-Christophe Golaz, Shaocheng Xie, Philip J. Rasch, and L. Ruby Leung
Geosci. Model Dev., 15, 2881–2916, https://doi.org/10.5194/gmd-15-2881-2022, https://doi.org/10.5194/gmd-15-2881-2022, 2022
Short summary
Short summary
An alternative set of parameters for E3SM Atmospheric Model version 1 has been developed based on a tuning strategy that focuses on clouds. When clouds in every regime are improved, other aspects of the model are also improved, even though they are not the direct targets for calibration. The recalibrated model shows a lower sensitivity to anthropogenic aerosols and surface warming, suggesting potential improvements to the simulated climate in the past and future.
Yongjie Huang, Wei Wu, Greg M. McFarquhar, Ming Xue, Hugh Morrison, Jason Milbrandt, Alexei V. Korolev, Yachao Hu, Zhipeng Qu, Mengistu Wolde, Cuong Nguyen, Alfons Schwarzenboeck, and Ivan Heckman
Atmos. Chem. Phys., 22, 2365–2384, https://doi.org/10.5194/acp-22-2365-2022, https://doi.org/10.5194/acp-22-2365-2022, 2022
Short summary
Short summary
Numerous small ice crystals in tropical convective storms are difficult to detect and could be potentially hazardous for commercial aircraft. Previous numerical simulations failed to reproduce this phenomenon and hypothesized that key microphysical processes are still lacking in current models to realistically simulate the phenomenon. This study uses numerical experiments to confirm the dominant role of secondary ice production in the formation of these large numbers of small ice crystals.
Istvan Geresdi, Lulin Xue, Sisi Chen, Youssef Wehbe, Roelof Bruintjes, Jared A. Lee, Roy M. Rasmussen, Wojciech W. Grabowski, Noemi Sarkadi, and Sarah A. Tessendorf
Atmos. Chem. Phys., 21, 16143–16159, https://doi.org/10.5194/acp-21-16143-2021, https://doi.org/10.5194/acp-21-16143-2021, 2021
Short summary
Short summary
By releasing soluble aerosols into the convective clouds, cloud seeding potentially enhances rainfall. The seeding impacts are hard to quantify with observations only. Numerical models that represent the detailed physics of aerosols, cloud and rain formation are used to investigate the seeding impacts on rain enhancement under different natural aerosol backgrounds and using different seeding materials. Our results indicate that seeding may enhance rainfall under certain conditions.
Yongjie Huang, Wei Wu, Greg M. McFarquhar, Xuguang Wang, Hugh Morrison, Alexander Ryzhkov, Yachao Hu, Mengistu Wolde, Cuong Nguyen, Alfons Schwarzenboeck, Jason Milbrandt, Alexei V. Korolev, and Ivan Heckman
Atmos. Chem. Phys., 21, 6919–6944, https://doi.org/10.5194/acp-21-6919-2021, https://doi.org/10.5194/acp-21-6919-2021, 2021
Short summary
Short summary
Numerous small ice crystals in the tropical convective storms are difficult to detect and could be potentially hazardous for commercial aircraft. This study evaluated the numerical models against the airborne observations and investigated the potential cloud processes that could lead to the production of these large numbers of small ice crystals. It is found that key microphysical processes are still lacking or misrepresented in current numerical models to realistically simulate the phenomenon.
Wojciech W. Grabowski and Lois Thomas
Atmos. Chem. Phys., 21, 4059–4077, https://doi.org/10.5194/acp-21-4059-2021, https://doi.org/10.5194/acp-21-4059-2021, 2021
Short summary
Short summary
This paper presents a modeling study that investigates the impact of cloud turbulence on the diffusional growth of cloud droplets and compares modeling results to analytic solutions published in the past. The focus is on comparing the two microphysics modeling methodologies – the Eulerian bin microphysics and Lagrangian particle-based microphysics – and exposing their limitations.
Georgia Sotiropoulou, Étienne Vignon, Gillian Young, Hugh Morrison, Sebastian J. O'Shea, Thomas Lachlan-Cope, Alexis Berne, and Athanasios Nenes
Atmos. Chem. Phys., 21, 755–771, https://doi.org/10.5194/acp-21-755-2021, https://doi.org/10.5194/acp-21-755-2021, 2021
Short summary
Short summary
Summer clouds have a significant impact on the radiation budget of the Antarctic surface and thus on ice-shelf melting. However, these are poorly represented in climate models due to errors in their microphysical structure, including the number of ice crystals that they contain. We show that breakup from ice particle collisions can substantially magnify the ice crystal number concentration with significant implications for surface radiation. This process is currently missing in climate models.
Cited articles
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.
Betts, A. K.:
Non-precipitating cumulus convection and its parameterization,
Q. J. Roy. Meteor. Soc.,
99, 178–196, 1973.
Böing, S. J., H. J. J. Jonker, H. J. J., Siebesma, A. P., and Grabowski, W. W.:
Influence of the subcloud layer on the development of a deep convective ensemble,
J. Atmos. Sci.,
69, 2682–2698, https://doi.org/10.1175/JAS-D-11-0317.1, 2012.
Clark, T. L.:
Numerical simulations with a three dimensional cloud model: Lateral boundary condition experiments and multicellular severe storm simulations,
J. Atmos. Sci.,
36, 2191–2215, 1979.
Cotton, W. R. and Walko, R.:
Examination of aerosol-induced convective invigoration using idealized simulations,
J. Atmos. Sci.,
78, 287–298, 2021.
Dawe, J. T. and Austin, P. H.: Direct entrainment and detrainment rate distributions of individual shallow cumulus clouds in an LES, Atmos. Chem. Phys., 13, 7795–7811, https://doi.org/10.5194/acp-13-7795-2013, 2013.
Del Genio, A. D. and Wu, J.:
The role of entrainment in the diurnal cycle of continental convection,
J. Climate,
23, 2722–2738, 2010.
De Rooy, W. C. and Siebesma, A. P.:
Analytic expressions for entrainment and detrainment in cumulus convection,
Q. J. Roy. Meteor. Soc.,
136, 1216–1227, 2010.
De Rooy, W. C., Bechtold, P., Froehlich, K., Hohenegger, C., Jonker, H., Mironov, D., Siebesma, A. P., Teixeira, J., and Yano, J.-I.:
Entrainment and detrainment in cumulus convection: an overview,
Q. J. Roy. Meteor. Soc.,
139, 1–19, 2013.
Emanuel, K. A.:
Atmospheric Convection,
Oxford University Press, New York, 580 pp, 1994.
Fan, J., Rosenfeld, D., Zhang, Y., Giangrande, S. E., Li, Z., Machado, L. A. T., Martin, S. T., Yang, Y., Wang, J., Artaxo, P., Barbosa, H. M. J., Braga, R. C., Comstock, J. M., Feng, Z., Gao, W., Gomes, H. B., Mei, F., Pöhlker, C., Pöhlker, M. L., Pöschl, U., and de Souza, R. A. F.:
Substantial convection and precipitation enhancements by ultrafine aerosol particles,
Science,
359, 411–418, https://doi.org/10.1126/science.aan8461, 2018.
Fan, J. and Khain, A.:
Comments on “Do ultrafine cloud condensation nuclei invigorate deep convection?”,
J. Atmos. Sci.,
78, 329–339, https://doi.org/10.1175/JAS-D-20-0218.1, 2021.
Feingold, G., Kreindenweis, S. M., Stevens, B., and Cotton, W. R.:
Numerical simulations of stratocumulus processing of cloud condensation nuclei through collision-coalescence,
J. Geophys. Res.,
101, 21391–21402, 1996.
Fridlind, A. M., Ackerman, A. S., Chaboureau, J.-P., Fan, J., Grabowski, W. W., Hill, A. A., Jones, T. R., Khaiyer, M. M., Liu, G., Minnis, P., Morrison, H., Nguyen, L., Park, S., Petch, J. C., Pinty, J.-P., Schumacher, C., Shipway, B. J., Varble, A. C., Wu, X., Xie, S., and Zhang, M.:
A comparison of TWP-ICE observational data with cloud-resolving model results,
J. Geophys. Res.,
117, D05204, https://doi.org/10.1029/2011JD016595, 2012.
Grabowski, W. W.:
Cumulus entrainment, fine-scale mixing and buoyancy reversal,
Q. J. Roy. Meteor. Soc.,
119, 935–956, 1993.
Grabowski, W. W.:
A parameterization of cloud microphysics for long-term cloud-resolving modeling of tropical convection,
Atmos. Res.,
52, 17–41, 1999.
Grabowski, W. W.:
Untangling microphysical impacts on deep convection applying a novel modeling methodology,
J. Atmos. Sci.,
72, 2446–2464, 2015.
Grabowski, W. W.: Separating physical impacts from natural variability using piggybacking technique, Adv. Geosci., 49, 105–111, https://doi.org/10.5194/adgeo-49-105-2019, 2019.
Grabowski, W. W.: Buoyancy in Deep Convection Simulations, Version 1.0, UCAR/NCAR – DASH Repository [data set], https://doi.org/10.5065/hqt3-1h72, 2021.
Grabowski, W. W. and Jarecka, D.:
Modeling condensation in shallow nonprecipitating convection,
J. Atmos. Sci.,
72, 4661–4679, 2015.
Grabowski, W. W. and Morrison, H.:
Untangling microphysical impacts on deep convection applying a novel modeling methodology. Part II: Double-moment microphysics,
J. Atmos. Sci.,
73, 3749–3770, 2016.
Grabowski W. W. and Morrison, H.:
Modeling condensation in deep convection,
J. Atmos. Sci.,
74, 2247–2267, 2017.
Grabowski W. W. and Morrison, H.:
Do ultrafine cloud condensation nuclei invigorate deep convection?,
J. Atmos. Sci.,
77, 2567–2582, 2020.
Grabowski W. W. and Morrison, H.:
Reply to Fan and Khain comments on Grabowski and Morrison 2020 paper “Do ultrafine cloud condensation nuclei invigorate deep convection?”,
J. Atmos. Sci.,
78, 341–350, 2021.
Grabowski, W. W. and Prein, A. F.:
Separating dynamic and thermodynamic impacts of climate change on daytime convective development over land,
J. Climate,
32, 5213–5234, 2019.
Grabowski, W. W. and Smolarkiewicz, P. K.:
A multiscale anelastic model for meteorological research,
Mon. Weather Rev.,
130, 939–956, 2002.
Grabowski, W. W., Bechtold, P., Cheng, A., Forbes, R. Halliwell, C, Khairoutdinov, M., Lang, S., Nasuno, T., Petch, J., Tao, W.-K., Wong, R., Wu, X., and Xu, K.-M.:
Daytime convective development over land: a model intercomparison based on LBA observations,
Q. J. Roy. Meteor. Soc.,
132, 317–344, 2006.
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.
Khain, A. and Lynn, B.:
Simulation of a supercell storm in clean and dirty atmosphere using weather research and forecast model with spectral bin microphysics,
J. Geophys. Res.,
114, D19209, https://doi.org/10.1029/2009JD011827, 2009.
Khain, A., Pokrovsky, A., Pinsky, M., Seifert, A., and Phillips, V.:
Simulation of effects of atmospheric aerosols on deep turbulent convective clouds using a spectral microphysics mixed-phase cumulus cloud model. Part I: Model descrip- tion and possible applications,
J. Atmos. Sci.,
61, 2963–2982, https://doi.org/10.1175/Jas-3350.1, 2004.
Khairoutdinov, M. and Randall, D.:
High-resolution simulation of shallow-to-deep convection transition over land,
J. Atmos. Sci.,
63, 3421–3436, 2006.
Klemp J. B. and Wilhelmson R. B.:
The simulation of three-dimensional convective storm dynamics,
J. Atmos. Sci.,
35, 1070–1096, 1978.
Kogan, Y. L.:
The simulation of a convective cloud in a 3-D model with explicit microphysics. Part I: Model description and sensitivity experiments,
J. Atmos. Sci.,
48, 1160–1189, 1991.
Kuang, Z. and Bretherton, C. S.:
A mass-flux scheme view of high-resolution simulation of a transition from shallow to deep cumulus convection,
J. Atmos. Sci.,
63, 1895–1909, 2006.
Kurowski, M. J., Grabowski, W. W., and Smolarkiewicz, P. K.:
Towards multiscale simulation of moist flows with soundproof equations,
J. Atmos. Sci.,
70, 3995–4011, 2013.
Kurowski, M. J., Grabowski, W. W., and Smolarkiewicz, P. K.:
Anelastic and compressible simulation of moist deep convection,
J. Atmos. Sci.,
71, 3767–3787, 2014.
Kurowski, M. J., Grabowski, W. W., and Smolarkiewicz, P. K.:
Anelastic and compressible simulation of moist dynamics at planetary scales,
J. Atmos. Sci.,
72, 3975–3995, 2015.
Kurowski, M. J., Suselj, K., Grabowski, W. W., and Teixeira, J.:
Shallow-to-deep transition of continental moist convection: cold pools, surface fluxes, and mesoscale organization,
J. Atmos. Sci.,
75, 4071–4090, 2018.
Kurowski, M. J., Suselj, K., and Grabowski, W. W.:
Is shallow convection sensitive to environmental heterogeneities?,
Geophys. Res. Lett.,
46, 1785–1793, 2018.
Lebo, Z. J. and Seinfeld, J. H.: A continuous spectral aerosol-droplet microphysics model, Atmos. Chem. Phys., 11, 12297–12316, https://doi.org/10.5194/acp-11-12297-2011, 2011.
Lebo, Z. J., Morrison, H., and Seinfeld, J. H.: Are simulated aerosol-induced effects on deep convective clouds strongly dependent on saturation adjustment?, Atmos. Chem. Phys., 12, 9941–9964, https://doi.org/10.5194/acp-12-9941-2012, 2012.
Lipps, F. B. and Hemler, R. S.:
A scale analysis of deep moist convection and some related numerical calculations,
J. Atmos. Sci.,
39, 2192–2210, 1982.
Morrison, H.:
An analytic description of the structure and evolution of growing deep cumulus updrafts,
J. Atmos. Sci.,
74, 809–834, 2017.
Morrison, H. and Grabowski, W. W.:
Comparison of bulk and bin warm rain microphysics models using a kinematic framework,
J. Atmos. Sci.,
64, 2839–2861, 2007.
Morrison, H. and Grabowski, W. W.:
Modeling supersaturation and subgrid-scale mixing with two-moment bulk warm microphysics,
J. Atmos. Sci.,
65, 792–812, 2008a.
Morrison, H. and Grabowski, W. W.:
A novel approach for representing ice micro- physics in models: description and tests using a kinematic framework,
J. Atmos. Sci.,
65, 1528–1548, 2008b.
Morrison, H., Curry, J. A., and Khvorostyanov,
V. I.: A new double-moment microphysics parameterization for application in cloud and climate
models. Part I: Description, J. Atmos. Sci., 62, 1665–1677, 2005.
Morrison, H., Peters, J. M., Chandrakar, K. K., and Sherwood, S. C.:
Influences of environmental relative humidity and horizontal scale of sub-cloud ascent on deep convective initiation,
J. Atmos. Sci.,
submitted, 2021.
Morton, B.:
Buoyant plumes in a moist atmosphere,
J. Fluid Mech.,
2, 127–144, https://doi.org/10.1017/S0022112057000038, 1957.
Mrowiec, A. A., Rio,, C., Fridlind, A. M., Ackerman, A. S., Del Genio, A. D., Pauluis, O. M., Varble, A. C., and Fan, J.:
Analysis of cloud-resolving simulations of a tropical mesoscale convective system observed during TWP-ICE: Vertical fluxes and draft properties in convective and stratiform regions,
J. Geophys. Res.,
117, D19201, https://doi.org/10.1029/2012JD017759, 2012.
Ogura, Y.:
The evolution of a moist convective element in a shallow, conditionally unstable atmosphere: A numerical calculation,
J. Atmos. Sci.,
20, 407–424, 1963.
Orville, H. D.:
A numerical study of the initiation of cumulus clouds over mountainous terrain,
J. Atmos. Sci.,
22, 684–699, 1965.
Peters, J. M., Morrison, H., Nowotarski, C. J., and Mulholland, J. P.:
A formula for the maximum vertical velocity in supercell updrafts,
J. Atmos. Sci.,
77, 3747–3757, 2020.
Politovich, M. K. and Cooper, W. A.:
Variability of supersaturation in cumulus clouds,
J. Atmos. Sci.,
45, 1651–1664, https://doi.org/10.1175/1520-0469(1988)045,1651:VOTSIC.2.0.CO;2, 1988.
Prabha, T. V., Khain, A., Maheshkumar, R. S., Pandithurai, G., Kulkarni, J. R., Konwar, M., and Goswami, B. N.:
Microphysics of premonsoon and monsoon clouds as seen from in situ measurements during the Cloud Aerosol Interaction and Precipitation Enhancement Experiment (CAIPEEX),
J. Atmos. Sci.,
68, 1882–1901, 2011.
Pruppacher, H. R. and Klett, J. D.: Microphysics of clouds and
precipitation, Kluwer, Dodrecht/Boston/London, 954 pp., 1997.
Romps, D. M.:
A direct measure of entrainment,
J. Atmos. Sci.,
67, 1908–1927, 2010.
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.
Shima, S., Sato, Y., Hashimoto, A., and Misumi, R.: Predicting the morphology of ice particles in deep convection using the super-droplet method: development and evaluation of SCALE-SDM 0.2.5-2.2.0, -2.2.1, and -2.2.2, Geosci. Model Dev., 13, 4107–4157, https://doi.org/10.5194/gmd-13-4107-2020, 2020.
Soong, S. and Ogura, Y.:
A comparison between axisymmetric and slab-symmetric cumulus cloud models,
J. Atmos. Sci.,
30, 879–893, 1973.
Stommel, H.:
Entrainment of air into a cumulus cloud,
J. Atmos. Sci.,
4, 91–94, https://doi.org/10.1175/1520-0469(1947)004<0091:EOAIAC>2.0.CO;2, 1947.
Squires, P.:
The growth of cloud drops by condensation. 1. General characteristics,
Aust. J. Sci. Res.,
5, 59–86, https://doi.org/10.1071/CH9520059, 1952.
Thompson, G., Rasmussen, R. M., and
Manning, K.: Explicit forecasts of winter precipitation using an improved bulk microphysics
scheme. Part I: Description and sensitivity analysis, Mon. Weather Rev., 132, 519–542, https://doi.org/10.1175/1520-0493(2004)132<0519:EFOWPU>2.0.CO;2, 2004.
Varble, A., Zipser, E. J., Fridlind, A. M., Zhu, P., Ackerman, A. S., Chaboureau, J.-P., Collis, S., Fan, J., Hill, A., and Shipway, B.:
Evaluation of cloud-resolving and limited area model intercomparison simulations using TWP-ICE observations: 1. Deep convective updraft properties,
J. Geophys. Res.-Atmos.,
119, 13891–13918, https://doi.org/10.1002/2013JD021371, 2014.
Warner, J.:
The water content of cumuliform cloud,
Tellus,
7, 449–457, 1955.
Warner, J.: On steady-state one-dimensional models of
cumulus convection, J. Atmos. Sci., 27, 1035–1040, 1970.
Wu, C. M., Stevens, B., and Arakawa, A.:
What controls the transition from shallow to deep convection?,
J. Atmos. Sci.,
66, 1793–1806, https://doi.org/10.1175/2008JAS2945.1, 2009.
Zhang, Y., Fan, J., Li, Z., and Rosenfeld, D.: Impacts of cloud microphysics parameterizations on simulated aerosol–cloud interactions for deep convective clouds over Houston, Atmos. Chem. Phys., 21, 2363–2381, https://doi.org/10.5194/acp-21-2363-2021, 2021.
Short summary
The paper provides a discussion of key elements of moist convective dynamics: cloud buoyancy, latent heating, precipitation, and entrainment. The motivation comes from recent discussions concerning differences in convective dynamics in polluted and pristine environments.
The paper provides a discussion of key elements of moist convective dynamics: cloud buoyancy,...
Altmetrics
Final-revised paper
Preprint