Articles | Volume 19, issue 16
https://doi.org/10.5194/acp-19-10717-2019
© Author(s) 2019. 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-19-10717-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Core and margin in warm convective clouds – Part 1: Core types and evolution during a cloud's lifetime
Reuven H. Heiblum
Department of Earth and Planetary Sciences, Weizmann Institute of
Science, Rehovot, Israel
Lital Pinto
Department of Earth and Planetary Sciences, Weizmann Institute of
Science, Rehovot, Israel
Orit Altaratz
Department of Earth and Planetary Sciences, Weizmann Institute of
Science, Rehovot, Israel
Guy Dagan
Department of Earth and Planetary Sciences, Weizmann Institute of
Science, Rehovot, Israel
now at: Atmospheric, Oceanic and Planetary Physics, Department of
Physics, University of Oxford, Oxford, UK
Department of Earth and Planetary Sciences, Weizmann Institute of
Science, Rehovot, Israel
Related authors
No articles found.
Huan Liu, Ilan Koren, Orit Altaratz, and Shutian Mu
EGUsphere, https://doi.org/10.5194/egusphere-2025-2574, https://doi.org/10.5194/egusphere-2025-2574, 2025
Short summary
Short summary
Clouds play a crucial role in Earth's climate by reflecting sunlight and trapping heat. Understanding how clouds respond to global warming (cloud feedback) is essential for climate change. However, the natural climate variability, like ENSO, can distort these estimates. Relying on long-term reanalysis data and simulations, this study finds that ENSO with a typical periodicity of 2–7 years can introduce a significant bias on cloud feedback estimates on even decadal to century time scales.
Suf Lorian and Guy Dagan
Atmos. Chem. Phys., 24, 9323–9338, https://doi.org/10.5194/acp-24-9323-2024, https://doi.org/10.5194/acp-24-9323-2024, 2024
Short summary
Short summary
We examine the combined effect of aerosols and sea surface temperature (SST) on clouds under equilibrium conditions in cloud-resolving radiative–convective equilibrium simulations. We demonstrate that the aerosol–cloud interaction's effect on top-of-atmosphere energy gain strongly depends on the underlying SST, while the shortwave part of the spectrum is significantly more sensitive to SST. Furthermore, increasing aerosols influences upper-troposphere stability and thus anvil cloud fraction.
Manuel Santos Gutiérrez, Mickaël David Chekroun, and Ilan Koren
EGUsphere, https://doi.org/10.48550/arXiv.2405.11545, https://doi.org/10.48550/arXiv.2405.11545, 2024
Preprint withdrawn
Short summary
Short summary
This letter explores a novel approach for the formation of cloud droplets in rising adiabatic air parcels. Our approach combines microphysical equations accounting for moisture, updrafts and concentration of aerosols. Our analysis reveals three regimes: A) Low moisture and high concentration can hinder activation; B) Droplets can activate and stabilize above critical sizes, and C) sparse clouds can have droplets exhibiting activation and deactivation cycles.
Huan Liu, Ilan Koren, Orit Altaratz, and Mickaël D. Chekroun
Atmos. Chem. Phys., 23, 6559–6569, https://doi.org/10.5194/acp-23-6559-2023, https://doi.org/10.5194/acp-23-6559-2023, 2023
Short summary
Short summary
Clouds' responses to global warming contribute the largest uncertainty in climate prediction. Here, we analyze 42 years of global cloud cover in reanalysis data and show a decreasing trend over most continents and an increasing trend over the tropical and subtropical oceans. A reduction in near-surface relative humidity can explain the decreasing trend in cloud cover over land. Our results suggest potential stress on the terrestrial water cycle, associated with global warming.
Elisa T. Sena, Ilan Koren, Orit Altaratz, and Alexander B. Kostinski
Atmos. Chem. Phys., 22, 16111–16122, https://doi.org/10.5194/acp-22-16111-2022, https://doi.org/10.5194/acp-22-16111-2022, 2022
Short summary
Short summary
We used record-breaking statistics together with spatial information to create record-breaking SST maps. The maps reveal warming patterns in the overwhelming majority of the ocean and coherent islands of cooling, where low records occur more frequently than high ones. Some of these cooling spots are well known; however, a surprising elliptical area in the Southern Ocean is observed as well. Similar analyses can be performed on other key climatological variables to explore their trend patterns.
Guy Dagan
Atmos. Chem. Phys., 22, 15767–15775, https://doi.org/10.5194/acp-22-15767-2022, https://doi.org/10.5194/acp-22-15767-2022, 2022
Short summary
Short summary
Using idealized simulations we demonstrate that the equilibrium climate sensitivity (ECS), i.e. the increase in surface temperature under equilibrium conditions due to doubling of the CO2 concentration, increases with the aerosol concentration. The ECS increase is explained by a faster increase in precipitation efficiency with warming under high aerosol concentrations, which more efficiently depletes the water from the cloud and thus is manifested as an increase in the cloud feedback parameter.
Matthew W. Christensen, Andrew Gettelman, Jan Cermak, Guy Dagan, Michael Diamond, Alyson Douglas, Graham Feingold, Franziska Glassmeier, Tom Goren, Daniel P. Grosvenor, Edward Gryspeerdt, Ralph Kahn, Zhanqing Li, Po-Lun Ma, Florent Malavelle, Isabel L. McCoy, Daniel T. McCoy, Greg McFarquhar, Johannes Mülmenstädt, Sandip Pal, Anna Possner, Adam Povey, Johannes Quaas, Daniel Rosenfeld, Anja Schmidt, Roland Schrödner, Armin Sorooshian, Philip Stier, Velle Toll, Duncan Watson-Parris, Robert Wood, Mingxi Yang, and Tianle Yuan
Atmos. Chem. Phys., 22, 641–674, https://doi.org/10.5194/acp-22-641-2022, https://doi.org/10.5194/acp-22-641-2022, 2022
Short summary
Short summary
Trace gases and aerosols (tiny airborne particles) are released from a variety of point sources around the globe. Examples include volcanoes, industrial chimneys, forest fires, and ship stacks. These sources provide opportunistic experiments with which to quantify the role of aerosols in modifying cloud properties. We review the current state of understanding on the influence of aerosol on climate built from the wide range of natural and anthropogenic laboratories investigated in recent decades.
Eshkol Eytan, Ilan Koren, Orit Altaratz, Mark Pinsky, and Alexander Khain
Atmos. Chem. Phys., 21, 16203–16217, https://doi.org/10.5194/acp-21-16203-2021, https://doi.org/10.5194/acp-21-16203-2021, 2021
Short summary
Short summary
Describing cloud mixing processes is among the most challenging fronts in cloud physics. Therefore, the adiabatic fraction (AF) that serves as a mixing measure is a valuable metric. We use high-resolution (10 m) simulations of single clouds with a passive tracer to test the skill of different methods used to derive AF. We highlight a method that is insensitive to the available cloud samples and allows considering microphysical effects on AF estimations in different environmental conditions.
Tom Dror, Mickaël D. Chekroun, Orit Altaratz, and Ilan Koren
Atmos. Chem. Phys., 21, 12261–12272, https://doi.org/10.5194/acp-21-12261-2021, https://doi.org/10.5194/acp-21-12261-2021, 2021
Short summary
Short summary
A part of continental shallow convective cumulus (Cu) was shown to share properties such as organization and formation over vegetated areas, thus named green Cu. Mechanisms behind the formed patterns are not understood. We use different metrics and an empirical orthogonal function (EOF) to decompose the dataset and quantify organization factors (cloud streets and gravity waves). We show that clouds form a highly organized grid structure over hundreds of kilometers at the field lifetime.
Tom Dror, J. Michel Flores, Orit Altaratz, Guy Dagan, Zev Levin, Assaf Vardi, and Ilan Koren
Atmos. Chem. Phys., 20, 15297–15306, https://doi.org/10.5194/acp-20-15297-2020, https://doi.org/10.5194/acp-20-15297-2020, 2020
Short summary
Short summary
We used in situ aerosol measurements over the Atlantic, Caribbean, and Pacific to initialize a cloud model and study the impact of aerosol concentration and sizes on warm clouds. We show that high aerosol concentration increases cloud mass and reduces surface rain when giant particles (diameter > 9 µm) are present. The large aerosols changed the timing and magnitude of internal cloud processes and resulted in an enhanced evaporation below cloud base and dramatically reduced surface rain.
Cited articles
Ackerman, B.: Buoyancy and precipitation in tropical cumuli, J. Meteorol.,
13, 302–310, https://doi.org/10.1175/1520-0469(1956)013<0302:BAPITC>2.0.CO;2, 1956.
Altaratz, O., Koren, I., Reisin, T., Kostinski, A., Feingold, G., Levin, Z., and Yin, Y.: Aerosols' influence on the interplay between condensation, evaporation and rain in warm cumulus cloud, Atmos. Chem. Phys., 8, 15–24, https://doi.org/10.5194/acp-8-15-2008, 2008.
Betts, A. K.: Non-precipitating cumulus convection and its parameterization,
Q. J. Roy. Meteor. Soc., 99, 178–196, https://doi.org/10.1002/qj.49709941915, 1973.
Burnet, F. and Brenguier, J.-L.: The onset of precipitation in warm cumulus
clouds: An observational case-study, Q. J. Roy. Meteor. Soc., 136, 374–381,
https://doi.org/10.1002/qj.552, 2010.
Craven, J. P., Jewell, R. E., and Brooks, H. E.: Comparison between Observed
Convective Cloud-Base Heights and Lifting Condensation Level for Two
Different Lifted Parcels, Weather Forecast., 17, 885–890,
https://doi.org/10.1175/1520-0434(2002)017<0885:CBOCCB>2.0.CO;2,
2002.
Dagan, G., Koren, I., and Altaratz, O.: Competition between core and periphery-based processes in warm convective clouds – from invigoration to suppression, Atmos. Chem. Phys., 15, 2749–2760, https://doi.org/10.5194/acp-15-2749-2015, 2015.
Dawe, J. T. and Austin, P. H.: The influence of the cloud shell on tracer
budget measurements of LES cloud entrainment, J. Atmos. Sci., 68,
2909–2920, https://doi.org/10.1175/2011JAS3658.1, 2011.
Dawe, J. T. and Austin, P. H.: Statistical analysis of an LES shallow cumulus cloud ensemble using a cloud tracking algorithm, Atmos. Chem. Phys., 12, 1101–1119, https://doi.org/10.5194/acp-12-1101-2012, 2012.
de Roode, S. R.: Thermodynamics of cumulus clouds, Física de la Tierra,
Vol. 19, Universidad Complutense de Madrid, 2007.
de Roode, S. R. and Bretherton, C. S.: Mass-Flux Budgets of Shallow Cumulus
Clouds, J. Atmos. Sci., 60, 137–151,
https://doi.org/10.1175/1520-0469(2003)060<0137:MFBOSC>2.0.CO;2,
2003.
de Roode, S. R., Siebesma, A. P., Jonker, H. J. J., and de Voogd, Y.:
Parameterization of the vertical velocity equation for shallow cumulus
clouds, Mon. Weather Rev., 140, 2424–2436, https://doi.org/10.1175/MWR-D-11-00277.1,
2012.
de Rooy, W. C. and Siebesma, A. P.: A simple parameterization for
detrainment in shallow cumulus, Mon. Weather Rev., 136, 560–576,
https://doi.org/10.1175/2007MWR2201.1, 2008.
Derbyshire, S. H., Maidens, A. V., Milton, S. F., Stratton, R. A., and
Willett, M. R.: Adaptive detrainment in a convective parametrization, Q. J.
Roy. Meteor. Soc., 137, 1856–1871, https://doi.org/10.1002/qj.875, 2011.
Emanuel, K. A.: A Scheme for Representing Cumulus Convection in Large-Scale
Models, J. Atmos. Sci., 48, 2313–2329,
https://doi.org/10.1175/1520-0469(1991)048<2313:ASFRCC>2.0.CO;2,
1991.
Feingold, G., Tzivion, S., and Levin, Z.: Evolution of raindrop spectra. part
I: solution to the stochastic collection/breakup equation using the method
of moments, J. Atmos. Sci., 45, 3387–3399,
https://doi.org/10.1175/1520-0469(1988)045<3387:EORSPI>2.0.CO;2,
1988.
Feingold, G., Levin, Z., and Tzivion, S.: The evolution of raindrop spectra.
part III: downdraft generation in an axisymmetrical rainshaft model, J.
Atmos. Sci., 48, 315–330, https://doi.org/10.1175/1520-0469(1991)048<0315:TEORSP>2.0.CO;2, 1991.
Garstang, M. and Betts, A. K.: A review of the tropical boundary layer and
cumulus convection: structure, parameterization, and modeling, B. Am.
Meteorol. Soc., 55, 1195–1205, https://doi.org/10.1175/1520-0477(1974)055<1195:AROTTB>2.0.CO;2, 1974.
Grabowski, W. W. and Jarecka, D.: Modeling condensation in shallow
nonprecipitating convection, J. Atmos. Sci., 72, 4661–4679,
https://doi.org/10.1175/JAS-D-15-0091.1, 2015.
Grant, A. L. M. and Lock, A. P.: The turbulent kinetic energy budget for
shallow cumulus convection, Q. J. Roy. Meteorol. Soc., 130, 401–422,
https://doi.org/10.1256/qj.03.50, 2004.
Gregory, D. and Rowntree, P. R.: A Mass Flux Convection Scheme with
Representation of Cloud Ensemble Characteristics and Stability-Dependent
Closure, Mon. Weather Rev., 118, 1483–1506,
https://doi.org/10.1175/1520-0493(1990)118<1483:AMFCSW>2.0.CO;2,
1990.
Hannah, W. M.: Entrainment versus Dilution in Tropical Deep Convection, J.
Atmos. Sci., 74, 3725–3747, https://doi.org/10.1175/JAS-D-16-0169.1, 2017.
Heiblum, R. H., Altaratz, O., Koren, I., Feingold, G., Kostinski, A. B.,
Khain, A. P., Ovchinnikov, M., Fredj, E., Dagan, G., Pinto, L., Yaish, R.,
and Chen, Q.: Characterization of cumulus cloud fields using trajectories in
the center of gravity versus water mass phase space: 1. Cloud tracking and
phase space description, J. Geophys. Res.-Atmos., 121, 6336–6355,
https://doi.org/10.1002/2015JD024186, 2016a.
Heiblum, R. H., Altaratz, O., Koren, I., Feingold, G., Kostinski, A. B.,
Khain, A. P., Ovchinnikov, M., Fredj, E., Dagan, G., Pinto, L., Yaish, R.,
and Chen, Q.: Characterization of cumulus cloud fields using trajectories in
the center of gravity versus water mass phase space: 2. Aerosol effects on
warm convective clouds, J. Geophys. Res.-Atmos., 121, 6356–6373,
https://doi.org/10.1002/2015JD024193, 2016b.
Heiblum, R. H., Pinto, L., Altaratz, O., Dagan, G., and Koren, I.:
Core and margin in warm convective clouds – Part 2: Aerosol effects on core properties, Atmos. Chem. Phys., 19, 10739–10755, https://doi.org/10.5194/acp-19-10739-2019, 2019.
Hernandez-Deckers, D. and Sherwood, S. C.: A numerical investigation of
cumulus thermals, J. Atmos. Sci., 73, 4117–4136,
https://doi.org/10.1175/JAS-D-15-0385.1, 2016.
Heus, T. and Jonker, H. J. J.: Subsiding Shells around Shallow Cumulus
Clouds, J. Atmos. Sci., 65, 1003–1018, https://doi.org/10.1175/2007JAS2322.1, 2008.
Heus, T. and Seifert, A.: Automated tracking of shallow cumulus clouds in large domain, long duration large eddy simulations, Geosci. Model Dev., 6, 1261–1273, https://doi.org/10.5194/gmd-6-1261-2013, 2013.
Heus, T., Pols, C. F. J., Jonker, H. J. J., Van den Akker, H. E. A., and
Lenschow, D. H.: Observational validation of the compensating mass flux through
the shell around cumulus clouds, Q. J. Roy. Meteor. Soc., 135, 101–112,
https://doi.org/10.1002/qj.358, 2009a.
Heus, T., Jonker, H. J. J., Van den Akker, H. E. A., Griffith, E. J.,
Koutek, M., and Post, F. H.: A statistical approach to the life cycle
analysis of cumulus clouds selected in a virtual reality environment, J.
Geophys. Res., 114, D06208, https://doi.org/10.1029/2008JD010917, 2009b.
Holland, J. Z. and Rasmusson, E. M.: Measurements of the Atmospheric Mass,
Energy, and Momentum Budgets Over a 500-Kilometer Square of Tropical Ocean,
Mon. Weather Rev., 101, 44–55, https://doi.org/10.1175/1520-0493(1973)101<0044:MOTAME>2.3.CO;2, 1973.
IPCC: Climate Change 2013: The Physical Science Basis. Working Group I
Contribution to the Fifth Assessment Report of the IPCC, Cambridge Univ.
Press, New York, 2013.
Jaenicke, R.: 9.3.1 Physical properties, in: Physical and chemical properties
of the air, edited by: Fischer, G., 405–420, Springer-Verlag,
Berlin/Heidelberg, 1988.
Jiang, H. and Feingold, G.: Effect of aerosol on warm convective clouds:
Aerosol-cloud-surface flux feedbacks in a new coupled large eddy model, J.
Geophys. Res., 111, D01202, https://doi.org/10.1029/2005JD006138, 2006.
Jonker, H. J. J., Heus, T., and Sullivan, P. P.: A refined view of vertical
mass transport by cumulus convection, Geophys. Res. Lett., 35, L07810,
https://doi.org/10.1029/2007GL032606, 2008.
Kain, J. S. and Fritsch, J. M.: A One-Dimensional Entraining/Detraining
Plume Model and Its Application in Convective Parameterization, J. Atmos.
Sci., 47, 2784–2802, https://doi.org/10.1175/1520-0469(1990)047<2784:AODEPM>2.0.CO;2, 1990.
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 Description and Possible Applications, J. Atmos. Sci., 61,
2963–2982, https://doi.org/10.1175/JAS-3350.1, 2004.
Khain, A. P., Beheng, K. D., Heymsfield, A., Korolev, A., Krichak, S. O.,
Levin, Z., Pinsky, M., Phillips, V., Prabhakaran, T., Teller, A., van den
Heever, S. C., and Yano, J. I.: Representation of microphysical processes in
cloud-resolving models: Spectral (bin) microphysics versus bulk
parameterization, Rev. Geophys., 53, 247–322, https://doi.org/10.1002/2014RG000468,
2015.
Khairoutdinov, M. F. and Randall, D. A.: Cloud resolving modeling of the ARM
summer 1997 IOP: model formulation, results, uncertainties, and
sensitivities, J. Atmos. Sci., 60, 607–625,
https://doi.org/10.1175/1520-0469(2003)060<0607:CRMOTA>2.0.CO;2,
2003.
Khairoutdinov, M. F., Krueger, S. K., Moeng, C.-H., Bogenschutz, P. A., and
Randall, D. A.: Large-eddy simulation of maritime deep tropical convection,
J. Adv. Model. Earth Sy., 2, 15, https://doi.org/10.3894/JAMES.2009.1.15, 2009.
Korolev, A., Khain, A., Pinsky, M., and French, J.: Theoretical study of mixing in liquid clouds – Part 1: Classical concepts, Atmos. Chem. Phys., 16, 9235–9254, https://doi.org/10.5194/acp-16-9235-2016, 2016.
Kumar, V. V., Jakob, C., Protat, A., Williams, C. R., and May, P. T.:
Mass-Flux Characteristics of Tropical Cumulus Clouds from Wind Profiler
Observations at Darwin, Australia, J. Atmos. Sci., 72, 1837–1855,
https://doi.org/10.1175/JAS-D-14-0259.1, 2015.
Lebo, Z. J. and Seinfeld, J. H.: Theoretical basis for convective invigoration due to increased aerosol concentration, Atmos. Chem. Phys., 11, 5407–5429, https://doi.org/10.5194/acp-11-5407-2011, 2011.
Lehmann, K., Siebert, H., and Shaw, R. A.: Homogeneous and inhomogeneous
mixing in cumulus clouds: Dependence on local turbulence structure, J. Atmos. Sci., 66, 3641–3659, 2009.
Malkus, J. S.: On the structure of the trade wind moist layer, Woods Hole
Oceanographic Institution, Woods Hole, MA, 1957.
Meerkötter, R. and Bugliaro, L.: Diurnal evolution of cloud base heights in convective cloud fields from MSG/SEVIRI data, Atmos. Chem. Phys., 9, 1767–1778, https://doi.org/10.5194/acp-9-1767-2009, 2009.
Morrison, H.: On the robustness of aerosol effects on an idealized supercell storm simulated with a cloud system-resolving model, Atmos. Chem. Phys., 12, 7689–7705, https://doi.org/10.5194/acp-12-7689-2012, 2012.
Morrison, H.: Impacts of updraft size and dimensionality on the perturbation
pressure and vertical velocity in cumulus convection. part I: simple,
generalized analytic solutions, J. Atmos. Sci., 73, 1441–1454,
https://doi.org/10.1175/JAS-D-15-0040.1, 2016a.
Morrison, H.: Impacts of updraft size and dimensionality on the perturbation
pressure and vertical velocity in cumulus convection. part II: comparison of
theoretical and numerical solutions and fully dynamical simulations, J.
Atmos. Sci., 73, 1455–1480, https://doi.org/10.1175/JAS-D-15-0041.1, 2016b.
Morrison, H.: An analytic description of the structure and evolution of
growing deep cumulus updrafts, J. Atmos. Sci., 74, 809–834,
https://doi.org/10.1175/JAS-D-16-0234.1, 2017.
Neggers, R. A. J., Stevens, B., and Neelin, J. D.: Variance scaling in
shallow-cumulus-topped mixed layers, Q. J Roy. Meteor. Soc., 133,
1629–1641, https://doi.org/10.1002/qj.105, 2007.
Paluch, I. R.: The entrainment mechanism in colorado cumuli, J. Atmos. Sci.,
36, 2467–2478, https://doi.org/10.1175/1520-0469(1979)036<2467:TEMICC>2.0.CO;2, 1979.
Peters, J. M.: The Impact of Effective Buoyancy and Dynamic Pressure Forcing
on Vertical Velocities within Two-Dimensional Updrafts, J. Atmos. Sci.,
73, 4531–4551, https://doi.org/10.1175/JAS-D-16-0016.1, 2016.
Pinsky, M., Mazin, I. P., Korolev, A., and Khain, A.: Supersaturation and
diffusional droplet growth in liquid clouds, J. Atmos. Sci., 70,
2778–2793, https://doi.org/10.1175/JAS-D-12-077.1, 2013.
Reisin, T., Levin, Z., and Tzivion, S.: Rain Production in Convective Clouds
As Simulated in an Axisymmetric Model with Detailed Microphysics. Part I:
Description of the Model, J. Atmos. Sci., 53, 497–519,
https://doi.org/10.1175/1520-0469(1996)053<0497:RPICCA>2.0.CO;2,
1996.
Rennó, N. O. and Ingersoll, A. P.: Natural convection as a heat engine:
A theory for CAPE, J. Atmos. Sci., 53, 572–585,
https://doi.org/10.1175/1520-0469(1996)053<0572:NCAAHE>2.0.CO;2,
1996.
Reutter, P., Su, H., Trentmann, J., Simmel, M., Rose, D., Gunthe, S. S., Wernli, H., Andreae, M. O., and Pöschl, U.: Aerosol- and updraft-limited regimes of cloud droplet formation: influence of particle number, size and hygroscopicity on the activation of cloud condensation nuclei (CCN), Atmos. Chem. Phys., 9, 7067–7080, https://doi.org/10.5194/acp-9-7067-2009, 2009.
Rodts, S. M. A., Duynkerke, P. G. and Jonker, H. J. J.: Size Distributions
and Dynamical Properties of Shallow Cumulus Clouds from Aircraft
Observations and Satellite Data, J. Atmos. Sci., 60, 1895–1912,
https://doi.org/10.1175/1520-0469(2003)060<1895:SDADPO>2.0.CO;2,
2003.
Rogers, R. R. and Yau, M. K.: A Short Course in Cloud Physics, Butterworth
Heinemann, Burlington, MA, 1989.
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.
Seigel, R. B.: Shallow Cumulus Mixing and Subcloud-Layer Responses to
Variations in Aerosol Loading, J. Atmos. Sci., 71, 2581–2603,
https://doi.org/10.1175/JAS-D-13-0352.1, 2014.
Seiki, T. and Nakajima, T.: Aerosol effects of the condensation process on a
convective cloud simulation, J. Atmos. Sci., 71, 833–853,
https://doi.org/10.1175/JAS-D-12-0195.1, 2014.
Siebesma, A. P. and Cuijpers, J. W. M.: Evaluation of parametric assumptions
for shallow cumulus convection, J. Atmos. Sci., 52, 650–666,
https://doi.org/10.1175/1520-0469(1995)052<0650:EOPAFS>2.0.CO;2,
1995.
Siebesma, A. P., Bretherton, C. S., Brown, A., Chlond, A., Cuxart, J.,
Duynkerke, P. G., Jiang, H., Khairoutdinov, M., Lewellen, D., Moeng, C.-H.,
Sanchez, E., Stevens, B., and Stevens, D. E.: A large eddy simulation
intercomparison study of shallow cumulus convection, J. Atmos. Sci., 60,
1201–1219, https://doi.org/10.1175/1520-0469(2003)60<1201:ALESIS>2.0.CO;2, 2003.
Sinkevich, A. A. and Lawson, R. P.: A survey of temperature measurements in
convective clouds, J. Appl. Meteorol., 44, 1133–1145,
https://doi.org/10.1175/JAM2247.1, 2005.
Taylor, G. R. and Baker, M. B.: Entrainment and detrainment in cumulus
clouds, J. Atmos. Sci., 48, 112–121,
https://doi.org/10.1175/1520-0469(1991)048<0112:EADICC>2.0.CO;2,
1991.
Trenberth, K. E., Fasullo, J. T., and Kiehl, J.: Earth's global energy
budget, B. Am. Meteorol. Soc., 90, 311–323,
https://doi.org/10.1175/2008BAMS2634.1, 2009.
Tzivion, S., Feingold, G., and Levin, Z.: The evolution of raindrop spectra.
part II: collisional collection/breakup and evaporation in a rainshaft, J.
Atmos. Sci., 46, 3312–3328, https://doi.org/10.1175/1520-0469(1989)046<3312:TEORSP>2.0.CO;2, 1989.
Tzivion, S., Reisin, T., and Levin, Z.: Numerical simulation of hygroscopic
seeding in a convective cloud, J. Appl. Meteorol., 33, 252–267,
https://doi.org/10.1175/1520-0450(1994)033<0252:NSOHSI>2.0.CO;2,
1994.
Wang, Y., Geerts, B., and French, J.: Dynamics of the cumulus cloud margin:
an observational study, J. Atmos. Sci., 66, 3660–3677,
https://doi.org/10.1175/2009JAS3129.1, 2009.
Wei, D., Blyth, A. M., and Raymond, D. J.: Buoyancy of convective clouds in
TOGA COARE, J. Atmos. Sci., 55, 3381–3391, 1998.
Williams, E. and Stanfill, S.: The physical origin of the land–ocean
contrast in lightning activity, CR Phys., 3, 1277–1292,
https://doi.org/10.1016/S1631-0705(02)01407-X, 2002.
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.
Yano, J.-I., Chaboureau, J.-P., and Guichard, F.: A generalization of CAPE
into potential-energy convertibility, Q. J. Roy. Meteor. Soc., 131,
861–875, https://doi.org/10.1256/qj.03.188, 2005.
Zhang, Y., Klein, S. A., Fan, J., Chandra, A. S., Kollias, P., Xie, S., and
Tang, S.: Large-Eddy Simulation of Shallow Cumulus over Land: A Composite
Case Based on ARM Long-Term Observations at Its Southern Great Plains Site,
J. Atmos. Sci., 74, 3229–3251, https://doi.org/10.1175/JAS-D-16-0317.1, 2017.
Short summary
It is useful to divide a cloud into two regions: core and margin. Three parameters used to define a core are compared: buoyancy (B), relative humidity (RH), and vertical velocity (W). Using theoretical arguments and simulations, we show that during most of a cloud's lifetime, the cores are subsets of one another: Bcore ⊆ RHcore ⊆ Wcore. Moreover, the core–shell cloud model applies to all core definitions. Our findings can serve as a benchmark in the partition the core and margin.
It is useful to divide a cloud into two regions: core and margin. Three parameters used to...
Altmetrics
Final-revised paper
Preprint