Articles | Volume 24, issue 17
https://doi.org/10.5194/acp-24-10039-2024
© Author(s) 2024. 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-24-10039-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Microphysical processes involving the vapour phase dominate in simulated low-level Arctic clouds
Institute of Geophysics and Meteorology, University of Cologne, 50939 Cologne, Germany
Davide Ori
Institute of Geophysics and Meteorology, University of Cologne, 50939 Cologne, Germany
Vera Schemann
CORRESPONDING AUTHOR
Institute of Geophysics and Meteorology, University of Cologne, 50939 Cologne, Germany
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Leonie von Terzi, José Dias Neto, Davide Ori, Alexander Myagkov, and Stefan Kneifel
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Alexander Myagkov and Davide Ori
Atmos. Meas. Tech., 15, 1333–1354, https://doi.org/10.5194/amt-15-1333-2022, https://doi.org/10.5194/amt-15-1333-2022, 2022
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This study provides equations to characterize random errors of spectral polarimetric observations from cloud radars. The results can be used for a broad spectrum of applications. For instance, accurate error characterization is essential for advanced retrievals of microphysical properties of clouds and precipitation. Moreover, error characterization allows for the use of measurements from polarimetric cloud radars to potentially improve weather forecasts.
Hélène Bresson, Annette Rinke, Mario Mech, Daniel Reinert, Vera Schemann, Kerstin Ebell, Marion Maturilli, Carolina Viceto, Irina Gorodetskaya, and Susanne Crewell
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Markus Karrer, Axel Seifert, Davide Ori, and Stefan Kneifel
Atmos. Chem. Phys., 21, 17133–17166, https://doi.org/10.5194/acp-21-17133-2021, https://doi.org/10.5194/acp-21-17133-2021, 2021
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Earth Syst. Sci. Data, 13, 4067–4119, https://doi.org/10.5194/essd-13-4067-2021, https://doi.org/10.5194/essd-13-4067-2021, 2021
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The EUREC4A field campaign, designed to test hypothesized mechanisms by which clouds respond to warming and benchmark next-generation Earth-system models, is presented. EUREC4A comprised roughly 5 weeks of measurements in the downstream winter trades of the North Atlantic – eastward and southeastward of Barbados. It was the first campaign that attempted to characterize the full range of processes and scales influencing trade wind clouds.
Davide Ori, Leonie von Terzi, Markus Karrer, and Stefan Kneifel
Geosci. Model Dev., 14, 1511–1531, https://doi.org/10.5194/gmd-14-1511-2021, https://doi.org/10.5194/gmd-14-1511-2021, 2021
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Snowflakes have very complex shapes, and modeling their properties requires vast computing power. We produced a large number of realistic snowflakes and modeled their average properties by leveraging their fractal structure. Our approach allows modeling the properties of big ensembles of snowflakes, taking into account their natural variability, at a much lower cost. This enables the usage of remote sensing instruments, such as radars, to monitor the evolution of clouds and precipitation.
Kamil Mróz, Alessandro Battaglia, Stefan Kneifel, Leonie von Terzi, Markus Karrer, and Davide Ori
Atmos. Meas. Tech., 14, 511–529, https://doi.org/10.5194/amt-14-511-2021, https://doi.org/10.5194/amt-14-511-2021, 2021
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The article examines the relationship between the characteristics of rain and the properties of the ice cloud from which the rain originated. Our results confirm the widely accepted assumption that the mass flux through the melting zone is well preserved with an exception of extreme aggregation and riming conditions. Moreover, it is shown that the mean (mass-weighted) size of particles above and below the melting zone is strongly linked, with the former being on average larger.
Marek Jacob, Pavlos Kollias, Felix Ament, Vera Schemann, and Susanne Crewell
Geosci. Model Dev., 13, 5757–5777, https://doi.org/10.5194/gmd-13-5757-2020, https://doi.org/10.5194/gmd-13-5757-2020, 2020
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Jie Gong, Xiping Zeng, Dong L. Wu, S. Joseph Munchak, Xiaowen Li, Stefan Kneifel, Davide Ori, Liang Liao, and Donifan Barahona
Atmos. Chem. Phys., 20, 12633–12653, https://doi.org/10.5194/acp-20-12633-2020, https://doi.org/10.5194/acp-20-12633-2020, 2020
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Mario Mech, Maximilian Maahn, Stefan Kneifel, Davide Ori, Emiliano Orlandi, Pavlos Kollias, Vera Schemann, and Susanne Crewell
Geosci. Model Dev., 13, 4229–4251, https://doi.org/10.5194/gmd-13-4229-2020, https://doi.org/10.5194/gmd-13-4229-2020, 2020
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The Passive and Active Microwave TRAnsfer tool (PAMTRA) is a public domain software package written in Python and Fortran for the simulation of microwave remote sensing observations. PAMTRA models the interaction of radiation with gases, clouds, precipitation, and the surface using either in situ observations or model output as input parameters. The wide range of applications is demonstrated for passive (radiometer) and active (radar) instruments on ground, airborne, and satellite platforms.
Cited articles
Barrett, A. I. and Hoose, C.: Microphysical Pathways Active within Thunderstorms and Their Sensitivity to CCN Concentration and Wind Shear, J. Geophys. Res.-Atmos., 128, e2022JD036965, https://doi.org/10.1029/2022JD036965, 2023. a
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Cesana, G. and Chepfer, H.: How well do climate models simulate cloud vertical structure? A comparison between CALIPSO-GOCCP satellite observations and CMIP5 models, Geophys. Res. Lett., 39, L20803, https://doi.org/10.1029/2012GL053153, 2012. a
Chellini, G., Gierens, R., and Kneifel, S.: Ice Aggregation in Low-Level Mixed-Phase Clouds at a High Arctic Site: Enhanced by Dendritic Growth and Absent Close to the Melting Level, J. Geophys. Res.-Atmos., 127, e2022JD036860, https://doi.org/10.1029/2022JD036860, 2022. a
Curry, J. A. and Ebert, E. E.: Annual Cycle of Radiation Fluxes over the Arctic Ocean: Sensitivity to Cloud Optical Properties, J. Climate, 5, 1267–1280, https://doi.org/10.1175/1520-0442(1992)005<1267:ACORFO>2.0.CO;2, 1992. a
Dipankar, A., Stevens, B., Heinze, R., Moseley, C., Zängl, G., Giorgetta, M., and Brdar, S.: Large eddy simulation using the general circulation model ICON, J. Adv. Model. Earth Syst., 7, 963–986, https://doi.org/10.1002/2015MS000431, 2015. a, b, c
Ebell, K., Nomokonova, T., Maturilli, M., and Ritter, C.: Radiative Effect of Clouds at Ny-Ålesund, Svalbard, as Inferred from Ground-Based Remote Sensing Observations, J. Appl. Meteorol. Clim., 59, 3–22, https://doi.org/10.1175/JAMC-D-19-0080.1, 2020. a
Eirund, G. K., Possner, A., and Lohmann, U.: Response of Arctic mixed-phase clouds to aerosol perturbations under different surface forcings, Atmos. Chem. Phys., 19, 9847–9864, https://doi.org/10.5194/acp-19-9847-2019, 2019. a
Fan, J., Leung, L. R., Rosenfeld, D., and DeMott, P. J.: Effects of cloud condensation nuclei and ice nucleating particles on precipitation processes and supercooled liquid in mixed-phase orographic clouds, Atmos. Chem. Phys., 17, 1017–1035, https://doi.org/10.5194/acp-17-1017-2017, 2017. a, b, c
Gettelman, A., Morrison, H., Terai, C. R., and Wood, R.: Microphysical process rates and global aerosol–cloud interactions, Atmos. Chem. Phys., 13, 9855–9867, https://doi.org/10.5194/acp-13-9855-2013, 2013. a
Gierens, R., Kneifel, S., Shupe, M. D., Ebell, K., Maturilli, M., and Löhnert, U.: Low-level mixed-phase clouds in a complex Arctic environment, Atmos. Chem. Phys., 20, 3459–3481, https://doi.org/10.5194/acp-20-3459-2020, 2020. a, b
Goosse, H., Kay, J. E., Armour, K. C., Bodas-Salcedo, A., Chepfer, H., Docquier, D., Jonko, A., Kushner, P. J., Lecomte, O., Massonnet, F., Park, H.-S., Pithan, F., Svensson, G., and Vancoppenolle, M.: Quantifying climate feedbacks in polar regions, Nat. Commun., 9, 1919, https://doi.org/10.1038/s41467-018-04173-0, 2018. a
Heinze, R., Dipankar, A., Henken, C. C., Moseley, C., Sourdeval, O., Trömel, S., Xie, X., Adamidis, P., Ament, F., Baars, H., Barthlott, C., Behrendt, A., Blahak, U., Bley, S., Brdar, S., Brueck, M., Crewell, S., Deneke, H., Girolamo, P. D., Evaristo, R., Fischer, J., Frank, C., Friederichs, P., Göcke, T., Gorges, K., Hande, L., Hanke, M., Hansen, A., Hege, H., Hoose, C., Jahns, T., Kalthoff, N., Klocke, D., Kneifel, S., Knippertz, P., Kuhn, A., Laar, T. v., Macke, A., Maurer, V., Mayer, B., Meyer, C. I., Muppa, S. K., Neggers, R. A. J., Orlandi, E., Pantillon, F., Pospichal, B., Röber, N., Scheck, L., Seifert, A., Seifert, P., Senf, F., Siligam, P., Simmer, C., Steinke, S., Stevens, B., Wapler, K., Weniger, M., Wulfmeyer, V., Zängl, G., Zhang, D., and Quaas, J.: Large‐eddy simulations over Germany using ICON: a comprehensive evaluation, Q. J. Roy. Meteor. Soc., 143, 69–100, https://doi.org/10.1002/qj.2947, 2017. a
Huang, Y., Dong, X., Kay, J. E., Xi, B., and McIlhattan, E. A.: The climate response to increased cloud liquid water over the Arctic in CESM1: a sensitivity study of Wegener-Bergeron-Findeisen process, Clim. Dynam., 56, 3373–3394, https://doi.org/10.1007/s00382-021-05648-5, 2021. a
Kalesse, H., de Boer, G., Solomon, A., Oue, M., Ahlgrimm, M., Zhang, D., Shupe, M. D., Luke, E., and Protat, A.: Understanding Rapid Changes in Phase Partitioning between Cloud Liquid and Ice in Stratiform Mixed-Phase Clouds: An Arctic Case Study, Mon. Weather Rev., 144, 4805–4826, https://doi.org/10.1175/MWR-D-16-0155.1, 2016. a, b
Kay, J. E., L'Ecuyer, T., Chepfer, H., Loeb, N., Morrison, A., and Cesana, G.: Recent Advances in Arctic Cloud and Climate Research, Current Climate Change Reports, 2, 159–169, https://doi.org/10.1007/s40641-016-0051-9, 2016. a
Kiszler, T.: TracyMcBean/Kiszler_et_al_2023_microphysics: First revision (v2.0.0-alpha), Zenodo [code], https://doi.org/10.5281/zenodo.10945484, 2024. a
Kiszler, T., Schemann, V., and Ori, D.: ICON-LEM Ny-Ålesund low-level clouds polar night and polar day 2021/2022, Zenodo [data set], https://doi.org/10.5281/zenodo.10117706, 2023b. a, b
Komurcu, M., Storelvmo, T., Tan, I., Lohmann, U., Yun, Y., Penner, J. E., Wang, Y., Liu, X., and Takemura, T.: Intercomparison of the cloud water phase among global climate models, J. Geophys. Res.-Atmos., 119, 3372–3400, https://doi.org/10.1002/2013JD021119, 2014. a, b
Korolev, A., McFarquhar, G., Field, P. R., Franklin, C., Lawson, P., Wang, Z., Williams, E., Abel, S. J., Axisa, D., Borrmann, S., Crosier, J., Fugal, J., Krämer, M., Lohmann, U., Schlenczek, O., Schnaiter, M., and Wendisch, M.: Mixed-Phase Clouds: Progress and Challenges, Meteor. Mon., 58, 5.1–5.50, https://doi.org/10.1175/AMSMONOGRAPHS-D-17-0001.1, 2017. a
Korolev, A. V.: Rates of phase transformations in mixed-phase clouds, Q. J. Roy. Meteor. Soc., 134, 595–608, https://doi.org/10.1002/qj.230, 2008. a, b, c
Lasher-Trapp, S., Kumar, S., Moser, D. H., Blyth, A. M., French, J. R., Jackson, R. C., Leon, D. C., and Plummer, D. M.: On Different Microphysical Pathways to Convective Rainfall, J. Appl. Meteorol. Clim., 57, 2399–2417, https://doi.org/10.1175/JAMC-D-18-0041.1, 2018. a
McGraw, Z., Storelvmo, T., Polvani, L. M., Hofer, S., Shaw, J. K., and Gettelman, A.: On the Links Between Ice Nucleation, Cloud Phase, and Climate Sensitivity in CESM2, Geophys. Res. Lett., 50, e2023GL105053, https://doi.org/10.1029/2023GL105053, 2023. a
Middlemas, E. A., Kay, J. E., Medeiros, B. M., and Maroon, E. A.: Quantifying the Influence of Cloud Radiative Feedbacks on Arctic Surface Warming Using Cloud Locking in an Earth System Model, Geophys. Res. Lett., 47, e2020GL089207, https://doi.org/10.1029/2020GL089207, 2020. a
Mioche, G., Jourdan, O., Ceccaldi, M., and Delanoë, J.: Variability of mixed-phase clouds in the Arctic with a focus on the Svalbard region: a study based on spaceborne active remote sensing, Atmos. Chem. Phys., 15, 2445–2461, https://doi.org/10.5194/acp-15-2445-2015, 2015. a, b
Mitchell, J. F. B., Senior, C. A., and Ingram, W. J.: C02 and climate: a missing feedback?, Nature, 341, 132–134, https://doi.org/10.1038/341132a0, 1989. a, b
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 Syst., 12, e2019MS001689, https://doi.org/10.1029/2019MS001689, 2020. a
Nomokonova, T., Ebell, K., Löhnert, U., Maturilli, M., Ritter, C., and O'Connor, E.: Statistics on clouds and their relation to thermodynamic conditions at Ny-Ålesund using ground-based sensor synergy, Atmos. Chem. Phys., 19, 4105–4126, https://doi.org/10.5194/acp-19-4105-2019, 2019. a
Omanovic, N., Ferrachat, S., Fuchs, C., Henneberger, J., Miller, A. J., Ohneiser, K., Ramelli, F., Seifert, P., Spirig, R., Zhang, H., and Lohmann, U.: Evaluating the Wegener–Bergeron–Findeisen process in ICON in large-eddy mode with in situ observations from the CLOUDLAB project, Atmos. Chem. Phys., 24, 6825–6844, https://doi.org/10.5194/acp-24-6825-2024, 2024. a, b
Phillips, V. T. J., DeMott, P. J., and Andronache, C.: An Empirical Parameterization of Heterogeneous Ice Nucleation for Multiple Chemical Species of Aerosol, J. Atmos. Sci., 65, 2757–2783, https://doi.org/10.1175/2007JAS2546.1, 2008. a, b
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Schemann, V. and Ebell, K.: Simulation of mixed-phase clouds with the ICON large-eddy model in the complex Arctic environment around Ny-Ålesund, Atmos. Chem. Phys., 20, 475–485, https://doi.org/10.5194/acp-20-475-2020, 2020. a, b
Segal, Y. and Khain, A.: Dependence of droplet concentration on aerosol conditions in different cloud types: Application to droplet concentration parameterization of aerosol conditions, J. Geophys. Res.-Atmos., 111, D15204, https://doi.org/10.1029/2005JD006561, 2006. a, b
Seifert, A. and Beheng, K. D.: A two-moment cloud microphysics parameterization for mixed-phase clouds. Part 1: Model description, Meteorol. Atmos. Phys., 92, 45–66, https://doi.org/10.1007/s00703-005-0112-4, 2006. a, b, c
Shaw, J., McGraw, Z., Bruno, O., Storelvmo, T., and Hofer, S.: Using Satellite Observations to Evaluate Model Microphysical Representation of Arctic Mixed-Phase Clouds, Geophys. Res. Lett., 49, e2021GL096191, https://doi.org/10.1029/2021GL096191, 2022. a, b
Shupe, M. D. and Intrieri, J. M.: Cloud Radiative Forcing of the Arctic Surface: The Influence of Cloud Properties, Surface Albedo, and Solar Zenith Angle, J. Climate, 17, 616–628, https://doi.org/10.1175/1520-0442(2004)017<0616:CRFOTA>2.0.CO;2, 2004. a
Shupe, M. D., Kollias, P., Persson, P. O. G., and McFarquhar, G. M.: Vertical Motions in Arctic Mixed-Phase Stratiform Clouds, J. Atmos. Sci., 65, 1304–1322, https://doi.org/10.1175/2007JAS2479.1, 2008. a, b
Storelvmo, T. and Tan, I.: The Wegener-Bergeron-Findeisen process? Its discovery and vital importance for weather and climate, Meteorol. Z., 24, 455–461, https://doi.org/10.1127/metz/2015/0626, 2015. a
Storelvmo, T., Tan, I., and Korolev, A. V.: Cloud Phase Changes Induced by CO2 Warming – a Powerful yet Poorly Constrained Cloud-Climate Feedback, Curr. Clim. Change Rep., 1, 288–296, https://doi.org/10.1007/s40641-015-0026-2, 2015. a
Tan, I., Storelvmo, T., and Zelinka, M. D.: Observational constraints on mixed-phase clouds imply higher climate sensitivity, Science, 352, 224–227, https://doi.org/10.1126/science.aad5300, 2016. a
Zelinka, M. D., Myers, T. A., McCoy, D. T., Po-Chedley, S., Caldwell, P. M., Ceppi, P., Klein, S. A., and Taylor, K. E.: Causes of Higher Climate Sensitivity in CMIP6 Models, Geophys. Res. Lett., 47, e2019GL085782, https://doi.org/10.1029/2019GL085782, 2020. a, b
Zhang, M., Xie, S., Liu, X., Lin, W., Zhang, K., Ma, H.-Y., Zheng, X., and Zhang, Y.: Toward Understanding the Simulated Phase Partitioning of Arctic Single-Layer Mixed-Phase Clouds in E3SM, Earth and Space Science, 7, e2020EA001125, https://doi.org/10.1029/2020EA001125, 2020. a
Short summary
Microphysical processes impact the phase-partitioning of clouds. In this study we evaluate these processes while focusing on low-level Arctic clouds. To achieve this we used an extensive simulation set in combination with a new diagnostic tool. This study presents our findings on the relevance of these processes and their behaviour under different thermodynamic regimes.
Microphysical processes impact the phase-partitioning of clouds. In this study we evaluate these...
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