Articles | Volume 19, issue 20
https://doi.org/10.5194/gmd-19-10185-2026
© Author(s) 2026. 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-19-10185-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Stochastic homogeneous freezing of supercooled droplets in mixed-phase clouds in particle-based idealised microphysics framework (exemplified with PySDM v3.0)
Tim Lüttmer
CORRESPONDING AUTHOR
Institute for Atmospheric Physics, Johannes Gutenberg University Mainz, Mainz, Germany
Sylwester Arabas
Faculty of Physics and Applied Computer Science, AGH University of Krakow, Kraków, Poland
Peter Spichtinger
Institute for Atmospheric Physics, Johannes Gutenberg University Mainz, Mainz, Germany
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This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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Moist air rising rapidly in cyclones produces ice clouds. Uncertain assumptions about ice-nucleating particles strongly affect ice formation. Ice formed by freezing of droplets and ice formed directly from vapor mix as they fall.
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We investigate ice formation pathways in idealized convective clouds using a novel microphysics scheme that distinguishes between five ice classes each with their own unique formation mechanism. Ice crystals from rime splintering form the lowermost layer of ice crystals around the updraft core. The majority of ice crystals in the anvil of the convective cloud stems from frozen droplets. Ice stemming from homogeneous and deposition nucleation was only relevant in the overshoot.
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Moist air rising rapidly in cyclones produces ice clouds. Uncertain assumptions about ice-nucleating particles strongly affect ice formation. Ice formed by freezing of droplets and ice formed directly from vapor mix as they fall.
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The paper focuses on the use of Python and its package ecosystem for modeling the dynamics of atmospheric aerosol and cloud droplets. We highlight that concise code and high-level abstractions (incl. programmatic expression of physical units) can be used without performance tradeoffs. The paper features two cloud-microphysics classics: an adiabatic parcel model and a Monte-Carlo coagulation solver. The presented code constitutes a single Jupyter notebook that can be executed "in the cloud".
Wenhan Tang, Sylwester Arabas, Jeffrey H. Curtis, Daniel A. Knopf, Matthew West, and Nicole Riemer
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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.
Philipp Reutter and Peter Spichtinger
Atmos. Chem. Phys., 25, 16303–16314, https://doi.org/10.5194/acp-25-16303-2025, https://doi.org/10.5194/acp-25-16303-2025, 2025
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We present a new technique to determine the tropopause based on the gradient of relative humidity over ice. This approach captures the character of the tropopause remarkably well, both in individual vertical profiles and in long-term averages, providing a consistent and physically meaningful representation of the transition between the troposphere and the stratosphere.
Alena Kosareva, Stamen Dolaptchiev, Peter Spichtinger, and Ulrich Achatz
Geosci. Model Dev., 18, 6117–6133, https://doi.org/10.5194/gmd-18-6117-2025, https://doi.org/10.5194/gmd-18-6117-2025, 2025
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This study improves how we predict ice formation in clouds by accounting for variable ice sizes and different weather conditions. Using simulations, we developed a more accurate method that works efficiently, making it suitable for application in weather and climate prediction models. The new approach is numerically verified and provides precise predictions of ice formation events and reliable estimates of key parameters.
Tim Lüttmer, Annette Miltenberger, and Peter Spichtinger
Atmos. Chem. Phys., 25, 10245–10265, https://doi.org/10.5194/acp-25-10245-2025, https://doi.org/10.5194/acp-25-10245-2025, 2025
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We investigate ice formation pathways in a warm conveyor belt case study. We employ a multi-phase microphysics scheme that distinguishes between ice from different nucleation processes. Ice crystals in the cirrus outflow mostly stem from in situ formation. Hence, they were formed directly from the vapor phase. Sedimentational redistribution modulates cirrus properties and leads to disagreement between cirrus origin classifications based on thermodynamic history and nucleation processes.
Tim Lüttmer, Peter Spichtinger, and Axel Seifert
Atmos. Chem. Phys., 25, 4505–4529, https://doi.org/10.5194/acp-25-4505-2025, https://doi.org/10.5194/acp-25-4505-2025, 2025
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We investigate ice formation pathways in idealized convective clouds using a novel microphysics scheme that distinguishes between five ice classes each with their own unique formation mechanism. Ice crystals from rime splintering form the lowermost layer of ice crystals around the updraft core. The majority of ice crystals in the anvil of the convective cloud stems from frozen droplets. Ice stemming from homogeneous and deposition nucleation was only relevant in the overshoot.
Daniel Köhler, Philipp Reutter, and Peter Spichtinger
Atmos. Chem. Phys., 24, 10055–10072, https://doi.org/10.5194/acp-24-10055-2024, https://doi.org/10.5194/acp-24-10055-2024, 2024
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In this work, the influence of humidity on the properties of the tropopause is studied. The tropopause is the interface between the troposphere and the stratosphere and represents a barrier for the transport of air masses between the troposphere and the stratosphere. We consider not only the tropopause itself, but also a layer around it called the tropopause inversion layer (TIL). It is shown that the moister the underlying atmosphere is, the more this layer acts as a barrier.
Emily de Jong, John Ben Mackay, Oleksii Bulenok, Anna Jaruga, and Sylwester Arabas
Geosci. Model Dev., 16, 4193–4211, https://doi.org/10.5194/gmd-16-4193-2023, https://doi.org/10.5194/gmd-16-4193-2023, 2023
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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.
Peter Spichtinger, Patrik Marschalik, and Manuel Baumgartner
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We investigate the impact of the homogeneous nucleation rate on nucleation events in cirrus. As long as the slope of the rate is represented sufficiently well, the resulting ice crystal number concentrations are not crucially affected. Even a change in the prefactor over orders of magnitude does not change the results. However, the maximum supersaturation during nucleation events shows strong changes. This quantity should be used for diagnostics instead of the popular nucleation threshold.
Michael A. Olesik, Jakub Banaśkiewicz, Piotr Bartman, Manuel Baumgartner, Simon Unterstrasser, and Sylwester Arabas
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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.
Stefan Niebler, Annette Miltenberger, Bertil Schmidt, and Peter Spichtinger
Weather Clim. Dynam., 3, 113–137, https://doi.org/10.5194/wcd-3-113-2022, https://doi.org/10.5194/wcd-3-113-2022, 2022
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We use machine learning to create a network that detects and classifies four types of synoptic-scale weather fronts from ERA5 atmospheric reanalysis data. We present an application of our method, showing its use case in a scientific context. Additionally, our results show that multiple sources of training data are necessary to perform well on different regions, implying differences within those regions. Qualitative evaluation shows that the results are physically plausible.
Manuel Baumgartner, Christian Rolf, Jens-Uwe Grooß, Julia Schneider, Tobias Schorr, Ottmar Möhler, Peter Spichtinger, and Martina Krämer
Atmos. Chem. Phys., 22, 65–91, https://doi.org/10.5194/acp-22-65-2022, https://doi.org/10.5194/acp-22-65-2022, 2022
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An important mechanism for the appearance of ice particles in the upper troposphere at low temperatures is homogeneous nucleation. This process is commonly described by the
Koop line, predicting the humidity at freezing. However, laboratory measurements suggest that the freezing humidities are above the Koop line, motivating the present study to investigate the influence of different physical parameterizations on the homogeneous freezing with the help of a detailed numerical model.
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Short summary
Homogeneous freezing describes the spontaneous freezing of supercooled droplets, with the probability depending on temperature, droplet size, and elapsed time. Droplets freeze across a range of temperatures rather than all at once, and faster updrafts and larger droplets lead to freezing at warmer temperatures. Once some droplets freeze, the growing ice crystals deplete the surrounding vapour, causing the remaining droplets to evaporate. This affects the number of ice crystals in the cloud.
Homogeneous freezing describes the spontaneous freezing of supercooled droplets, with the...