Articles | Volume 16, issue 14
https://doi.org/10.5194/gmd-16-4193-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-16-4193-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Breakups are complicated: an efficient representation of collisional breakup in the superdroplet method
Department of Mechanical and Civil Engineering, California Institute of Technology, Pasadena, CA, USA
John Ben Mackay
Scripps Institution of Oceanography, San Diego, CA, USA
formerly at: Department of Environmental Science and Engineering, California Institute of Technology, Pasadena, CA, USA
Oleksii Bulenok
Faculty of Mathematics and Computer Science, Jagiellonian University, Kraków, Poland
Anna Jaruga
Department of Environmental Science and Engineering, California Institute of Technology, Pasadena, CA, USA
Sylwester Arabas
Faculty of Physics and Applied Computer Science, AGH University of Krakow, Kraków, Poland
formerly at: Department of Atmospheric Sciences, University of Illinois Urbana-Champaign, Urbana, IL, USA
formerly at: Faculty of Mathematics and Computer Science, Jagiellonian University, Kraków, Poland
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EGUsphere, https://doi.org/10.5194/egusphere-2025-4326, https://doi.org/10.5194/egusphere-2025-4326, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We studied how aerosol particles help form ice in clouds. Using new theory and detailed computer simulations, we found that the way different materials are mixed within these particles has a strong impact on how much ice forms. When ice-forming material is spread across all particles, more droplets freeze than when it is only in a few. This result means that to better predict clouds and climate, models need to account for how particle materials are mixed.
Michael A. Olesik, Jakub Banaśkiewicz, Piotr Bartman, Manuel Baumgartner, Simon Unterstrasser, and Sylwester Arabas
Geosci. Model Dev., 15, 3879–3899, https://doi.org/10.5194/gmd-15-3879-2022, https://doi.org/10.5194/gmd-15-3879-2022, 2022
Short summary
Short summary
In systems such as atmospheric clouds, droplets undergo growth through condensation of vapor. The broadness of the resultant size spectrum of droplets influences precipitation likelihood and the radiative properties of clouds. One of the inherent limitations of simulations of the problem is the so-called numerical diffusion causing overestimation of the spectrum width, hence the term numerical broadening. In the paper, we take a closer look at one of the algorithms used in this context: MPDATA.
Cited articles
Andrejczuk, M., Reisner, J. M., Henson, B., Dubey, M. K., and Jeffery, C. A.:
The potential impacts of pollution on a nondrizzling stratus deck: Does
aerosol number matter more than type?, J. Geophys. Res.-Atmos., 113, D19204, https://doi.org/10.1029/2007JD009445, 2008. a
Andrejczuk, M., Grabowski, W. W., Reisner, J., and Gadian, A.: Cloud-aerosol
interactions for boundary layer stratocumulus in the Lagrangian Cloud
Model, J. Geophys. Res.-Atmos., 115, D22214,
https://doi.org/10.1029/2010JD014248, 2010. a
Arabas, S., Jaruga, A., Pawlowska, H., and Grabowski, W. W.: libcloudph++ 1.0: a single-moment bulk, double-moment bulk, and particle-based warm-rain microphysics library in C++, Geosci. Model Dev., 8, 1677–1707, https://doi.org/10.5194/gmd-8-1677-2015, 2015. a
Arabas, S., Bartman, P., de Jong, E., Singer, C., Olesik, M. A., Mackay, B.,
Bulenok, O., Azimi, S., Górski, K., Jaruga, A., Piasecki, B., and Badger,
C.: atmos-cloud-sim-uj/PySDM: PySDM v2.12, Zenodo [code],
https://doi.org/10.5281/zenodo.7037182, 2022. a
Arabas, S., Azimi, S., Bartman, P., Bulenok, O., de Jong, E., Derlatka, K., Dula, I., Górski, K., Jaruga, A., Łazarski, G., Mackay, J. B., Olesik, M., Piasecki, B., Singer, C. E., Talar, A., and Ward, R. X.: PySDM (v2.20), Zenodo [code], https://doi.org/10.5281/zenodo.7851352, 2023a. a
Arabas, S., Singer, C., de Jong, E., Azimi, S., Bulenok, O., Bartman, P., Dula, I., Mackay, B., Jaruga, A., and Tang, W.: open-atmos/PySDM-examples: PySDM-examples v2.20 (v2.20), Zenodo [code], https://doi.org/10.5281/zenodo.7851288, 2023b. a
Arenberg, D.: Turbulence As The Major Factor in the Growth of Cloud
Drops, B. Am. Meteorol. Soc., 20, 444–448,
https://doi.org/10.1175/1520-0477-20.10.444, 1939. a
Barros, A. P., Prat, O. P., Shrestha, P., Testik, F. Y., and Bliven, L. F.:
Revisiting Low and List (1982): Evaluation of Raindrop Collision
Parameterizations Using Laboratory Observations and Modeling,
J. Atmos. Sci., 65, 2983–2993,
https://doi.org/10.1175/2008JAS2630.1, 2008. a
Bartman, P., Banaśkiewicz, J., Drenda, S., Manna, M., Olesik, M. A., Rozwoda,
P., Sadowski, M., and Arabas, S.: PyMPDATA v1: Numba-accelerated
implementation of MPDATA with examples in Python, Julia and Matlab,
J. Open Source Softw., 7, 3896, https://doi.org/10.21105/joss.03896,
2022a. a
Bartman, P., Bulenok, O., Górski, K., Jaruga, A., Łazarski, G., Olesik,
M. A., Piasecki, B., Singer, C. E., Talar, A., and Arabas, S.: PySDM v1:
particle-based cloud modeling package for warm-rain microphysics and aqueous
chemistry, J. Open Source Softw., 7, 3219,
https://doi.org/10.21105/joss.03219, 2022b. a
Beard, K. V. and Ochs, H. T.: Collisions between Small Precipitation
Drops. Part II: Formulas for Coalescence, Temporary
Coalescence, and Satellites, J. Atmos. Sci., 52,
3977–3996, https://doi.org/10.1175/1520-0469(1995)052<3977:CBSPDP>2.0.CO;2, 1995. a, b
Berry, E. X.: Cloud Droplet Growth by Collection, J. Atmos.
Sci., 24, 688–701, https://doi.org/10.1175/1520-0469(1967)024<0688:CDGBC>2.0.CO;2,
1967. a
Bieli, M., Dunbar, O. R. A., de Jong, E. K., Jaruga, A., Schneider, T., and
Bischoff, T.: An Efficient Bayesian Approach to Learning Droplet
Collision Kernels: Proof of Concept Using “Cloudy”, a New
n-Moment Bulk Microphysics Scheme, J. Adv. Model.
Earth Sy., 14, e2022MS002994, https://doi.org/10.1029/2022MS002994, 2022. a
Blatz, P. and Tobolsky, A.: Note on the kinetics of systems manifesting
simultaneous polymerization-depolymerization phenomena, J. Phys. Chem., 49, 77–80,
https://doi.org/10.1021/j150440a004, 1945. a
Bringi, V., Seifert, A., Wu, W., Thurai, M., Huang, G.-J., and Siewert, C.:
Hurricane Dorian Outer Rain Band Observations and 1D Particle
Model Simulations: A Case Study, Atmosphere, 11, 879,
https://doi.org/10.3390/atmos11080879, 2020. a
Chandrakar, K. K., Grabowski, W. W., Morrison, H., and Bryan, G. H.: Impact of
Entrainment Mixing and Turbulent Fluctuations on Droplet Size
Distributions in a Cumulus Cloud: An Investigation Using
Lagrangian Microphysics with a Subgrid-Scale Model, J.
Atmos. Sci., 78, 2983–3005, https://doi.org/10.1175/JAS-D-20-0281.1, 2021. a
de Jong, E. K., Singer, C. E., Azimi, S., Bartman, P., Bulenok, O., Derlatka,
K., Dula, I., Jaruga, A., Mackay, J. B., Ward, R. X., and Arabas, S.: New
developments in PySDM and PySDM-examples v2: collisional breakup, immersion
freezing, dry aerosol initialization, and adaptive time-stepping, J.
Open Source Softw., 8, 4968, https://doi.org/10.21105/joss.04968, 2023. a, b
Dziekan, P., Waruszewski, M., and Pawlowska, H.: University of Warsaw Lagrangian Cloud Model (UWLCM) 1.0: a modern large-eddy simulation tool for warm cloud modeling with Lagrangian microphysics, Geosci. Model Dev., 12, 2587–2606, https://doi.org/10.5194/gmd-12-2587-2019, 2019. a
Feingold, G., Cotton, W. R., Kreidenweis, S. M., and Davis, J. T.: The Impact
of Giant Cloud Condensation Nuclei on Drizzle Formation in
Stratocumulus: Implications for Cloud Radiative Properties, J. Atmos. Sci., 56, 4100–4117,
https://doi.org/10.1175/1520-0469(1999)056<4100:TIOGCC>2.0.CO;2, 1999. a
Grabowski, W. W.: Comparison of Eulerian Bin and Lagrangian
Particle-Based Schemes in Simulations of Pi Chamber Dynamics
and Microphysics, J. Atmos. Sci., 77, 1151–1165,
https://doi.org/10.1175/JAS-D-19-0216.1, 2020. a
Grabowski, W. W., Morrison, H., Shima, S.-I., Abade, G. C., Dziekan, P., and
Pawlowska, H.: Modeling of Cloud Microphysics: Can We Do Better?,
B. Am. Meteorol. Soc., 100, 655–672,
https://doi.org/10.1175/BAMS-D-18-0005.1, 2019. a
Gunn, R. and Kinzer, G. D.: The Terminal Velocity of Fall for Water
Droplets in Stagnant Air, J. Atmos. Sci., 6,
243–248, https://doi.org/10.1175/1520-0469(1949)006<0243:TTVOFF>2.0.CO;2, 1949. a, b
Hallett, J. and Mossop, S. C.: Production of secondary ice particles during the
riming process, Nature, 249, 26–28, https://doi.org/10.1038/249026a0, 1974. a, b
Hansen, K.: Abundance Distributions; Large Scale Features, in: Statistical Physics of Nanoparticles in the Gas Phase. Springer Series on Atomic, Optical, and Plasma Physics, Springer, Cham, vol. 73,
https://doi.org/10.1007/978-3-319-90062-9_8, 2018. a
Harris-Hobbs, R. L. and Cooper, W. A.: Field Evidence Supporting
Quantitative Predictions of Secondary Ice Production Rates,
J. Atmos. Sci., 44, 1071–1082,
https://doi.org/10.1175/1520-0469(1987)044<1071:FESQPO>2.0.CO;2, 1987. a, b
Hoffmann, F.: On the limits of Köhler activation theory: how do collision and coalescence affect the activation of aerosols?, Atmos. Chem. Phys., 17, 8343–8356, https://doi.org/10.5194/acp-17-8343-2017, 2017. a
James, R. L., Phillips, V. T. J., and Connolly, P. J.: Secondary ice production during the break-up of freezing water drops on impact with ice particles, Atmos. Chem. Phys., 21, 18519–18530, https://doi.org/10.5194/acp-21-18519-2021, 2021. a, b
Jensen, E. and Pfister, L.: Transport and freeze-drying in the tropical
tropopause layer, J. Geophys. Res.-Atmos., 109, D02207,
https://doi.org/10.1029/2003JD004022, 2004. a
Jokulsdottir, T. and Archer, D.: A stochastic, Lagrangian model of sinking biogenic aggregates in the ocean (SLAMS 1.0): model formulation, validation and sensitivity, Geosci. Model Dev., 9, 1455–1476, https://doi.org/10.5194/gmd-9-1455-2016, 2016. a
Kamra, A. K., Bhalwankar, R. V., and Sathe, A. B.: Spontaneous breakup of
charged and uncharged water drops freely suspended in a wind tunnel, J. Geophys. Res.-Atmos., 96, 17159–17168,
https://doi.org/10.1029/91JD01475, 1991. a
Kotalczyk, G., Devi, J., and Kruis, F. E.: A time-driven constant-number
Monte Carlo method for the GPU-simulation of particle breakage based on
weighted simulation particles, Powder Technol., 317, 417–429,
https://doi.org/10.1016/j.powtec.2017.05.002, 2017. a
Lee, K. and Matsoukas, T.: Simultaneous coagulation and break-up using
constant-N Monte Carlo, Powder Technol., 110, 82–89,
https://doi.org/10.1016/S0032-5910(99)00270-3, 2000. a
Low, T. B. and List, R.: Collision, Coalescence and Breakup of Raindrops.
Part I: Experimentally Established Coalescence Efficiencies and
Fragment Size Distributions in Breakup, J. Atmos.
Sci., 39, 1591–1606,
https://doi.org/10.1175/1520-0469(1982)039<1591:CCABOR>2.0.CO;2, 1982. a, b
McFarquhar, G. M.: A New Representation of Collision-Induced Breakup
of Raindrops and Its Implications for the Shapes of Raindrop Size
Distributions, J. Atmos. Sci., 61, 777–794,
https://doi.org/10.1175/1520-0469(2004)061<0777:ANROCB>2.0.CO;2, 2004. a, b
Morrison, H., Kumjian, M. R., Martinkus, C. P., Prat, O. P., and van
Lier-Walqui, M.: A General N-Moment Normalization Method for
Deriving Raindrop Size Distribution Scaling Relationships,
J. Appl. Meteorol. Clim., 58, 247–267,
https://doi.org/10.1175/JAMC-D-18-0060.1, 2019. a
Morrison, H., Lier‐Walqui, M. V., 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.,
Diedenhoven, B. v., 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, 2020. a
Paoli, R., Hélie, J., and Poinsot, T.: Contrail formation in aircraft wakes,
J. Fluid Mech., 502, 361–373, https://doi.org/10.1017/S0022112003007808,
2004. a
Phillips, V. T. J., Yano, J.-I., and Khain, A.: Ice Multiplication by
Breakup in Ice–Ice Collisions. Part I: Theoretical
Formulation, J. Atmos. Sci., 74, 1705–1719,
https://doi.org/10.1175/JAS-D-16-0224.1, 2017. a
Riechelmann, T., Noh, Y., and Raasch, S.: A new method for large-eddy
simulations of clouds with Lagrangian droplets including the effects of
turbulent collision, New J. Phys., 14, 065008,
https://doi.org/10.1088/1367-2630/14/6/065008, 2012. a
Schlottke, J., Straub, W., Beheng, K. D., Gomaa, H., and Weigand, B.: Numerical
Investigation of Collision-Induced Breakup of Raindrops. Part
I: Methodology and Dependencies on Collision Energy and
Eccentricity, J. Atmos. Sci., 67, 557–575,
https://doi.org/10.1175/2009JAS3174.1, 2010. a, b
Seifert, A. and Rasp, S.: Potential and Limitations of Machine Learning
for Modeling Warm-Rain Cloud Microphysical Processes, J.
Adv. Model. Earth Sy., 12, e2020MS002301,
https://doi.org/10.1029/2020MS002301, 2020. a
Seifert, A., Khain, A., Blahak, U., and Beheng, K. D.: Possible Effects of
Collisional Breakup on Mixed-Phase Deep Convection Simulated by
a Spectral (Bin) Cloud Model, J. Atmos. Sci.,
62, 1917–1931, https://doi.org/10.1175/JAS3432.1, 2005. a, b
Shima, S., Kusano, K., Kawano, A., Sugiyama, T., and Kawahara, S.: The
super-droplet method for the numerical simulation of clouds and
precipitation: a particle-based and probabilistic microphysics model coupled
with a non-hydrostatic model, Q. J. Roy. Meteor.
Soc., 135, 1307–1320, https://doi.org/10.1002/qj.441, 2009. a, b, c, d, e, f, g, h, i, j
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. a
Shipway, B. J. and Hill, A. A.: Diagnosis of systematic differences between
multiple parametrizations of warm rain microphysics using a kinematic
framework, Q. J. Roy. Meteor. Soc., 138,
2196–2211, https://doi.org/10.1002/qj.1913, 2012. a
Shirgaonkar, A. and Lele, S.: Large Eddy Simulation of Early Stage
Contrails: Effect of Atmospheric Properties, in: 44th AIAA
Aerospace Sciences Meeting and Exhibit, Aerospace Sciences
Meetings, American Institute of Aeronautics and Astronautics,
https://arc.aiaa.org/doi/10.2514/6.2006-1414 (last access: 21 April 2023), 2006. a
Straub, W., Beheng, K. D., Seifert, A., Schlottke, J., and Weigand, B.:
Numerical Investigation of Collision-Induced Breakup of Raindrops.
Part II: Parameterizations of Coalescence Efficiencies and
Fragment Size Distributions, J. Atmos. Sci., 67,
576–588, https://doi.org/10.1175/2009JAS3175.1, 2010. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s
Sölch, I. and Kärcher, B.: A large-eddy model for cirrus clouds with explicit
aerosol and ice microphysics and Lagrangian ice particle tracking,
Q. J. Roy. Meteor. Soc., 136, 2074–2093,
https://doi.org/10.1002/qj.689, 2010. a
Testik, F. Y. and Rahman, M. K.: First in situ observations of binary raindrop
collisions, Geophys. Res. Lett., 44, 1175–1181,
https://doi.org/10.1002/2017GL072516, 2017. a
Wood, R., Irons, S., and Jonas, P. R.: How Important Is the Spectral
Ripening Effect in Stratiform Boundary Layer Clouds? Studies
Using Simple Trajectory Analysis, J. Atmos.
Sci., 59, 2681–2693,
https://doi.org/10.1175/1520-0469(2002)059<2681:HIITSR>2.0.CO;2, 2002.
a
Yin, Y., Levin, Z., Reisin, T. G., and Tzivion, S.: The effects of giant cloud
condensation nuclei on the development of precipitation in convective clouds
– a numerical study, Atmos. Res., 53, 91–116,
https://doi.org/10.1016/S0169-8095(99)00046-0, 2000. a
Zhao, X. and Liu, X.: Primary and secondary ice production: interactions and their relative importance, Atmos. Chem. Phys., 22, 2585–2600, https://doi.org/10.5194/acp-22-2585-2022, 2022. a
Short summary
In clouds, collisional breakup occurs when two colliding droplets splinter into new, smaller fragments. Particle-based modeling approaches often do not represent breakup because of the computational demands of creating new droplets. We present a particle-based breakup method that preserves the computational efficiency of these methods. In a series of simple demonstrations, we show that this representation alters cloud processes in reasonable and expected ways.
In clouds, collisional breakup occurs when two colliding droplets splinter into new, smaller...