Articles | Volume 23, issue 22
https://doi.org/10.5194/acp-23-14077-2023
© Author(s) 2023. 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-23-14077-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Nov 2023
Research article |
| 14 Nov 2023
| 14 Nov 2023
Sensitivity of cloud-phase distribution to cloud microphysics and thermodynamics in simulated deep convective clouds and SEVIRI retrievals
Department Troposphere Research, Institute of Meteorology and Climate Research (IMK-TRO), Karlsruhe Institute of Technology, Karlsruhe, Germany
State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China
Department Troposphere Research, Institute of Meteorology and Climate Research (IMK-TRO), Karlsruhe Institute of Technology, Karlsruhe, Germany
Martin Stengel
Deutscher Wetterdienst (DWD), Offenbach, Germany
Quentin Coopman
Department of Atmospheric and Oceanic Sciences, McGill University, Montréal, Canada
now at: Univ. Lille, CNRS, UMR 8518 Laboratoire d'Optique Atmosphérique (LOA), Lille, France
Andrew Barrett
Department Troposphere Research, Institute of Meteorology and Climate Research (IMK-TRO), Karlsruhe Institute of Technology, Karlsruhe, Germany
Related authors
Binbin Wang, Yaoming Ma, Zeyong Hu, Weiqiang Ma, Xuelong Chen, Cunbo Han, Zhipeng Xie, Yuyang Wang, Maoshan Li, Bin Ma, Xingdong Shi, Weimo Li, and Zhengling Cai
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-195, https://doi.org/10.5194/essd-2025-195, 2025
Preprint under review for ESSD
Short summary
Short summary
This study reveals distinct patterns in water, heat, and carbon exchange over the Tibetan Plateau. Heat transfer peaks in spring, while water vapor release is highest in summer. Most stations act as carbon sinks, but one in a forested valley is a carbon source, likely due to vegetation loss and human activity. The findings highlight the strong connections between water, heat, and carbon fluxes, offering valuable insights into climate change and weather forecasting.
Cunbo Han, Yaoming Ma, Weiqiang Ma, Fanglin Sun, Yunshuai Zhang, Wei Hu, Hanying Xu, Chunhui Duan, and Zhenhua Xi
EGUsphere, https://doi.org/10.5194/egusphere-2024-1963, https://doi.org/10.5194/egusphere-2024-1963, 2024
Preprint archived
Short summary
Short summary
Wind speed spectra analysis is very important for understanding boundary layer turbulence characteristics, atmospheric numerical model development, and wind energy assessment. However, wind speed spectra studies in mountainous areas are extremely scarce. In this study, using a 15-year time series of wind speed observed by a PBL tower and eddy-covariance tower at a site on the north slope of Mt. Everest, we investigated the characteristics of wind speed and wind speed spectrum.
Ling Yuan, Xuelong Chen, Yaoming Ma, Cunbo Han, Binbin Wang, and Weiqiang Ma
Earth Syst. Sci. Data, 16, 775–801, https://doi.org/10.5194/essd-16-775-2024, https://doi.org/10.5194/essd-16-775-2024, 2024
Short summary
Short summary
Accurately monitoring and understanding the spatial–temporal variability of evapotranspiration (ET) components over the Tibetan Plateau (TP) remains difficult. Here, 37 years (1982–2018) of monthly ET component data for the TP was produced, and the data are consistent with measurements. The annual average ET for the TP was about 0.93 (± 0.037) × 103 Gt yr−1. The rate of increase of the ET was around 0.96 mm yr−1. The increase in the ET can be explained by warming and wetting of the climate.
Cunbo Han, Yaoming Ma, Binbin Wang, Lei Zhong, Weiqiang Ma, Xuelong Chen, and Zhongbo Su
Earth Syst. Sci. Data, 13, 3513–3524, https://doi.org/10.5194/essd-13-3513-2021, https://doi.org/10.5194/essd-13-3513-2021, 2021
Short summary
Short summary
Actual terrestrial evapotranspiration (ETa) is a key parameter controlling the land–atmosphere interaction processes and water cycle. However, the spatial distribution and temporal changes in ETa over the Tibetan Plateau (TP) remain very uncertain. Here we estimate the multiyear (2001–2018) monthly ETa and its spatial distribution on the TP by a combination of meteorological data and satellite products. Results have been validated at six eddy-covariance monitoring sites and show high accuracy.
Yaoming Ma, Zeyong Hu, Zhipeng Xie, Weiqiang Ma, Binbin Wang, Xuelong Chen, Maoshan Li, Lei Zhong, Fanglin Sun, Lianglei Gu, Cunbo Han, Lang Zhang, Xin Liu, Zhangwei Ding, Genhou Sun, Shujin Wang, Yongjie Wang, and Zhongyan Wang
Earth Syst. Sci. Data, 12, 2937–2957, https://doi.org/10.5194/essd-12-2937-2020, https://doi.org/10.5194/essd-12-2937-2020, 2020
Short summary
Short summary
In comparison with other terrestrial regions of the world, meteorological observations are scarce over the Tibetan Plateau.
This has limited our understanding of the mechanisms underlying complex interactions between the different earth spheres with heterogeneous land surface conditions.
The release of this continuous and long-term dataset with high temporal resolution is expected to facilitate broad multidisciplinary communities in understanding key processes on the
Third Pole of the world.
Peggy Achtert, Torsten Seelig, Gabriella Wallentin, Luisa Ickes, Matthew D. Shupe, Corinna Hoose, and Matthias Tesche
EGUsphere, https://doi.org/10.5194/egusphere-2025-3529, https://doi.org/10.5194/egusphere-2025-3529, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We quantify the occurrence of single- and multi-layer clouds in the Arctic based on combining soundings with cloud-radar observations. We also assess the rate of ice-crystal seeding in multi-layer cloud systems as this is an important initiator of glaciation in super-cooled liquid cloud layers. We find an abundance of multi-layer clouds in the Arctic with seeding in about half to two thirds of cases in which the gap between upper and lower layers ranges between 100 and 1000 m.
Lina Lucas, Christian Barthlott, Corinna Hoose, and Peter Knippertz
EGUsphere, https://doi.org/10.5194/egusphere-2025-3069, https://doi.org/10.5194/egusphere-2025-3069, 2025
Short summary
Short summary
We studied how climate change and cleaner air could affect severe storms in Central Europe. Using high-resolution weather simulations of past supercell storms under warmer and less polluted conditions, we found that storms may become more intense, with heavier rainfall and larger hailstones. These changes suggest an increased risk of damage in the future. Our findings help improve understanding of how extreme storms may evolve in a changing climate.
Nikos Benas, Jan Fokke Meirink, Rob Roebeling, and Martin Stengel
Atmos. Chem. Phys., 25, 6957–6973, https://doi.org/10.5194/acp-25-6957-2025, https://doi.org/10.5194/acp-25-6957-2025, 2025
Short summary
Short summary
This study examines how ship emissions affect clouds over a shipping corridor in the southeastern Atlantic. Using satellite data from 2004 to 2023, we find that ship emissions increase the number of cloud droplets while reducing their size and slightly decrease cloud water content. Effects on seasonal and daily patterns vary based on regional factors. The impact of emissions weakened after stricter regulations were implemented in 2020.
Gabriella Wallentin, Annika Oertel, Luisa Ickes, Peggy Achtert, Matthias Tesche, and Corinna Hoose
Atmos. Chem. Phys., 25, 6607–6631, https://doi.org/10.5194/acp-25-6607-2025, https://doi.org/10.5194/acp-25-6607-2025, 2025
Short summary
Short summary
Multilayer clouds are common in the Arctic but remain underrepresented. We use an atmospheric model to simulate multilayer cloud cases from the Arctic expedition MOSAiC 2019/2020. We find that it is complex to accurately model these cloud layers due to the lack of correct temperature profiles. The model also struggles to capture the observed cloud phase and the relative concentration of cloud droplets and cloud ice. We constrain our model to measured aerosols to mitigate this issue.
Binbin Wang, Yaoming Ma, Zeyong Hu, Weiqiang Ma, Xuelong Chen, Cunbo Han, Zhipeng Xie, Yuyang Wang, Maoshan Li, Bin Ma, Xingdong Shi, Weimo Li, and Zhengling Cai
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-195, https://doi.org/10.5194/essd-2025-195, 2025
Preprint under review for ESSD
Short summary
Short summary
This study reveals distinct patterns in water, heat, and carbon exchange over the Tibetan Plateau. Heat transfer peaks in spring, while water vapor release is highest in summer. Most stations act as carbon sinks, but one in a forested valley is a carbon source, likely due to vegetation loss and human activity. The findings highlight the strong connections between water, heat, and carbon fluxes, offering valuable insights into climate change and weather forecasting.
Konstantinos Rizos, Emmanouil Proestakis, Thanasis Georgiou, Antonis Gkikas, Eleni Marinou, Peristera Paschou, Kalliopi Artemis Voudouri, Athanasios Tsikerdekis, David Donovan, Gerd-Jan van Zadelhoff, Angela Benedetti, Holger Baars, Athena Augusta Floutsi, Nikos Benas, Martin Stengel, Christian Retscher, Edward Malina, and Vassilis Amiridis
EGUsphere, https://doi.org/10.5194/egusphere-2025-1175, https://doi.org/10.5194/egusphere-2025-1175, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
The Aeolus satellite's lidar system had limitations in detecting certain atmospheric layers and distinguishing between aerosol and cloud types. To improve accuracy, a new dust detection product was developed. By combining data from various sources and validating it with ground-based measurements, this enhanced product performs better than the original. It helps improve dust transport models and weather predictions, making it a valuable tool for atmospheric monitoring and forecasting.
Lisa Janina Muth, Sascha Bierbauer, Corinna Hoose, Bernhard Vogel, Heike Vogel, and Gholam Ali Hoshyaripour
EGUsphere, https://doi.org/10.5194/egusphere-2025-402, https://doi.org/10.5194/egusphere-2025-402, 2025
Short summary
Short summary
Our study explores how intense wildfires created thunderstorm-like clouds that can affect weather and climate globally. Using simulations with high resolution, we found that fire heat and moisture help form these clouds, lifting particles high into the atmosphere. This process is crucial for understanding how fires impact the environment. Despite some differences with observational data, our findings align well over time, showing the importance of fire-induced heat in cloud formation.
Cunbo Han, Yaoming Ma, Weiqiang Ma, Fanglin Sun, Yunshuai Zhang, Wei Hu, Hanying Xu, Chunhui Duan, and Zhenhua Xi
EGUsphere, https://doi.org/10.5194/egusphere-2024-1963, https://doi.org/10.5194/egusphere-2024-1963, 2024
Preprint archived
Short summary
Short summary
Wind speed spectra analysis is very important for understanding boundary layer turbulence characteristics, atmospheric numerical model development, and wind energy assessment. However, wind speed spectra studies in mountainous areas are extremely scarce. In this study, using a 15-year time series of wind speed observed by a PBL tower and eddy-covariance tower at a site on the north slope of Mt. Everest, we investigated the characteristics of wind speed and wind speed spectrum.
Barbara Dietel, Odran Sourdeval, and Corinna Hoose
Atmos. Chem. Phys., 24, 7359–7383, https://doi.org/10.5194/acp-24-7359-2024, https://doi.org/10.5194/acp-24-7359-2024, 2024
Short summary
Short summary
Uncertainty with respect to cloud phases over the Southern Ocean and Arctic marine regions leads to large uncertainties in the radiation budget of weather and climate models. This study investigates the phases of low-base and mid-base clouds using satellite-based remote sensing data. A comprehensive analysis of the correlation of cloud phase with various parameters, such as temperature, aerosols, sea ice, vertical and horizontal cloud extent, and cloud radiative effect, is presented.
Arndt Kaps, Axel Lauer, Rémi Kazeroni, Martin Stengel, and Veronika Eyring
Earth Syst. Sci. Data, 16, 3001–3016, https://doi.org/10.5194/essd-16-3001-2024, https://doi.org/10.5194/essd-16-3001-2024, 2024
Short summary
Short summary
CCClim displays observations of clouds in terms of cloud classes that have been in use for a long time. CCClim is a machine-learning-powered product based on multiple existing observational products from different satellites. We show that the cloud classes in CCClim are physically meaningful and can be used to study cloud characteristics in more detail. The goal of this is to make real-world clouds more easily understandable to eventually improve the simulation of clouds in climate models.
William K. Jones, Martin Stengel, and Philip Stier
Atmos. Chem. Phys., 24, 5165–5180, https://doi.org/10.5194/acp-24-5165-2024, https://doi.org/10.5194/acp-24-5165-2024, 2024
Short summary
Short summary
Storm clouds cover large areas of the tropics. These clouds both reflect incoming sunlight and trap heat from the atmosphere below, regulating the temperature of the tropics. Over land, storm clouds occur in the late afternoon and evening and so exist both during the daytime and at night. Changes in this timing could upset the balance of the respective cooling and heating effects of these clouds. We find that isolated storms have a larger effect on this balance than their small size suggests.
Behrooz Keshtgar, Aiko Voigt, Bernhard Mayer, and Corinna Hoose
Atmos. Chem. Phys., 24, 4751–4769, https://doi.org/10.5194/acp-24-4751-2024, https://doi.org/10.5194/acp-24-4751-2024, 2024
Short summary
Short summary
Cloud-radiative heating (CRH) affects extratropical cyclones but is uncertain in weather and climate models. We provide a framework to quantify uncertainties in CRH within an extratropical cyclone due to four factors and show that the parameterization of ice optical properties contributes significantly to uncertainty in CRH. We also argue that ice optical properties, by affecting CRH on spatial scales of 100 km, are relevant for the large-scale dynamics of extratropical cyclones.
Ling Yuan, Xuelong Chen, Yaoming Ma, Cunbo Han, Binbin Wang, and Weiqiang Ma
Earth Syst. Sci. Data, 16, 775–801, https://doi.org/10.5194/essd-16-775-2024, https://doi.org/10.5194/essd-16-775-2024, 2024
Short summary
Short summary
Accurately monitoring and understanding the spatial–temporal variability of evapotranspiration (ET) components over the Tibetan Plateau (TP) remains difficult. Here, 37 years (1982–2018) of monthly ET component data for the TP was produced, and the data are consistent with measurements. The annual average ET for the TP was about 0.93 (± 0.037) × 103 Gt yr−1. The rate of increase of the ET was around 0.96 mm yr−1. The increase in the ET can be explained by warming and wetting of the climate.
Hyunju Jung, Peter Knippertz, Yvonne Ruckstuhl, Robert Redl, Tijana Janjic, and Corinna Hoose
Weather Clim. Dynam., 4, 1111–1134, https://doi.org/10.5194/wcd-4-1111-2023, https://doi.org/10.5194/wcd-4-1111-2023, 2023
Short summary
Short summary
A narrow rainfall belt in the tropics is an important feature for large-scale circulations and the global water cycle. The accurate simulation of this rainfall feature has been a long-standing problem, with the reasons behind that unclear. We present a novel diagnostic tool that allows us to disentangle processes important for rainfall, which changes due to modifications in model. Using our diagnostic tool, one can potentially identify sources of uncertainty in weather and climate models.
Nikos Benas, Irina Solodovnik, Martin Stengel, Imke Hüser, Karl-Göran Karlsson, Nina Håkansson, Erik Johansson, Salomon Eliasson, Marc Schröder, Rainer Hollmann, and Jan Fokke Meirink
Earth Syst. Sci. Data, 15, 5153–5170, https://doi.org/10.5194/essd-15-5153-2023, https://doi.org/10.5194/essd-15-5153-2023, 2023
Short summary
Short summary
This paper describes CLAAS-3, the third edition of the Cloud property dAtAset using SEVIRI, which was created based on observations from geostationary Meteosat satellites. CLAAS-3 cloud properties are evaluated using a variety of reference datasets, with very good overall results. The demonstrated quality of CLAAS-3 ensures its usefulness in a wide range of applications, including studies of local- to continental-scale cloud processes and evaluation of climate models.
Karl-Göran Karlsson, Martin Stengel, Jan Fokke Meirink, Aku Riihelä, Jörg Trentmann, Tom Akkermans, Diana Stein, Abhay Devasthale, Salomon Eliasson, Erik Johansson, Nina Håkansson, Irina Solodovnik, Nikos Benas, Nicolas Clerbaux, Nathalie Selbach, Marc Schröder, and Rainer Hollmann
Earth Syst. Sci. Data, 15, 4901–4926, https://doi.org/10.5194/essd-15-4901-2023, https://doi.org/10.5194/essd-15-4901-2023, 2023
Short summary
Short summary
This paper presents a global climate data record on cloud parameters, radiation at the surface and at the top of atmosphere, and surface albedo. The temporal coverage is 1979–2020 (42 years) and the data record is also continuously updated until present time. Thus, more than four decades of climate parameters are provided. Based on CLARA-A3, studies on distribution of clouds and radiation parameters can be made and, especially, investigations of climate trends and evaluation of climate models.
Annika Oertel, Annette K. Miltenberger, Christian M. Grams, and Corinna Hoose
Atmos. Chem. Phys., 23, 8553–8581, https://doi.org/10.5194/acp-23-8553-2023, https://doi.org/10.5194/acp-23-8553-2023, 2023
Short summary
Short summary
Warm conveyor belts (WCBs) are cloud- and precipitation-producing airstreams in extratropical cyclones that are important for the large-scale flow and cloud radiative forcing. We analyze cloud formation processes during WCB ascent in a two-moment microphysics scheme. Quantification of individual diabatic heating rates shows the importance of condensation, vapor deposition, rain evaporation, melting, and cloud-top radiative cooling for total heating and WCB-related potential vorticity structure.
Kameswara S. Vinjamuri, Marco Vountas, Luca Lelli, Martin Stengel, Matthew D. Shupe, Kerstin Ebell, and John P. Burrows
Atmos. Meas. Tech., 16, 2903–2918, https://doi.org/10.5194/amt-16-2903-2023, https://doi.org/10.5194/amt-16-2903-2023, 2023
Short summary
Short summary
Clouds play an important role in Arctic amplification. Cloud data from ground-based sites are valuable but cannot represent the whole Arctic. Therefore the use of satellite products is a measure to cover the entire Arctic. However, the quality of such cloud measurements from space is not well known. The paper discusses the differences and commonalities between satellite and ground-based measurements. We conclude that the satellite dataset, with a few exceptions, can be used in the Arctic.
Julia Thomas, Andrew Barrett, and Corinna Hoose
Atmos. Chem. Phys., 23, 1987–2002, https://doi.org/10.5194/acp-23-1987-2023, https://doi.org/10.5194/acp-23-1987-2023, 2023
Short summary
Short summary
We study the sensitivity of rain formation processes during a heavy-rainfall event over mountains to changes in temperature and pollution. Total rainfall increases by 2 % K−1, and a 6 % K−1 increase is found at the highest altitudes, caused by a mixed-phase seeder–feeder mechanism (frozen cloud particles melt and grow further as they fall through a liquid cloud layer). In a cleaner atmosphere this process is enhanced. Thus the risk of severe rainfall in mountains may increase in the future.
Behrooz Keshtgar, Aiko Voigt, Corinna Hoose, Michael Riemer, and Bernhard Mayer
Weather Clim. Dynam., 4, 115–132, https://doi.org/10.5194/wcd-4-115-2023, https://doi.org/10.5194/wcd-4-115-2023, 2023
Short summary
Short summary
Forecasting extratropical cyclones is challenging due to many physical factors influencing their behavior. One such factor is the impact of heating and cooling of the atmosphere by the interaction between clouds and radiation. In this study, we show that cloud-radiative heating (CRH) increases the intensity of an idealized cyclone and affects its predictability. We find that CRH affects the cyclone mostly via increasing latent heat release and subsequent changes in the synoptic circulation.
Julia Bruckert, Gholam Ali Hoshyaripour, Ákos Horváth, Lukas O. Muser, Fred J. Prata, Corinna Hoose, and Bernhard Vogel
Atmos. Chem. Phys., 22, 3535–3552, https://doi.org/10.5194/acp-22-3535-2022, https://doi.org/10.5194/acp-22-3535-2022, 2022
Short summary
Short summary
Volcanic emissions endanger aviation and public health and also influence weather and climate. Forecasting the volcanic-plume dispersion is therefore a critical yet sophisticated task. Here, we show that explicit treatment of volcanic-plume dynamics and eruption source parameters significantly improves volcanic-plume dispersion forecasts. We further demonstrate the lofting of the SO2 due to a heating of volcanic particles by sunlight with major implications for volcanic aerosol research.
Cunbo Han, Yaoming Ma, Binbin Wang, Lei Zhong, Weiqiang Ma, Xuelong Chen, and Zhongbo Su
Earth Syst. Sci. Data, 13, 3513–3524, https://doi.org/10.5194/essd-13-3513-2021, https://doi.org/10.5194/essd-13-3513-2021, 2021
Short summary
Short summary
Actual terrestrial evapotranspiration (ETa) is a key parameter controlling the land–atmosphere interaction processes and water cycle. However, the spatial distribution and temporal changes in ETa over the Tibetan Plateau (TP) remain very uncertain. Here we estimate the multiyear (2001–2018) monthly ETa and its spatial distribution on the TP by a combination of meteorological data and satellite products. Results have been validated at six eddy-covariance monitoring sites and show high accuracy.
Yaoming Ma, Zeyong Hu, Zhipeng Xie, Weiqiang Ma, Binbin Wang, Xuelong Chen, Maoshan Li, Lei Zhong, Fanglin Sun, Lianglei Gu, Cunbo Han, Lang Zhang, Xin Liu, Zhangwei Ding, Genhou Sun, Shujin Wang, Yongjie Wang, and Zhongyan Wang
Earth Syst. Sci. Data, 12, 2937–2957, https://doi.org/10.5194/essd-12-2937-2020, https://doi.org/10.5194/essd-12-2937-2020, 2020
Short summary
Short summary
In comparison with other terrestrial regions of the world, meteorological observations are scarce over the Tibetan Plateau.
This has limited our understanding of the mechanisms underlying complex interactions between the different earth spheres with heterogeneous land surface conditions.
The release of this continuous and long-term dataset with high temporal resolution is expected to facilitate broad multidisciplinary communities in understanding key processes on the
Third Pole of the world.
Caroline A. Poulsen, Gregory R. McGarragh, Gareth E. Thomas, Martin Stengel, Matthew W. Christensen, Adam C. Povey, Simon R. Proud, Elisa Carboni, Rainer Hollmann, and Roy G. Grainger
Earth Syst. Sci. Data, 12, 2121–2135, https://doi.org/10.5194/essd-12-2121-2020, https://doi.org/10.5194/essd-12-2121-2020, 2020
Short summary
Short summary
We have created a satellite cloud and radiation climatology from the ATSR-2 and AATSR on board ERS-2 and Envisat, respectively, which spans the period 1995–2012. The data set was created using a combination of optimal estimation and neural net techniques. The data set was created as part of the ESA Climate Change Initiative program. The data set has been compared with active CALIOP lidar measurements and compared with MAC-LWP AND CERES-EBAF measurements and is shown to have good performance.
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.
Barthlott, C. and Hoose, C.: Aerosol effects on clouds and precipitation over central Europe in different weather regimes, J. Atmos. Sci., 75, 4247–4264, https://doi.org/10.1175/JAS-D-18-0110.1, 2018.
Benas, N., Finkensieper, S., Stengel, M., van Zadelhoff, G. J., Hanschmann, T., Hollmann, R., and Meirink, J. F.: The MSG-SEVIRI-based cloud property data record CLAAS-2, Earth Syst. Sci. Data, 9, 415–434, https://doi.org/10.5194/essd-9-415-2017, 2017.
Bruno, O., Hoose, C., Storelvmo, T., Coopman, Q., and Stengel, M.: Exploring the cloud top phase partitioning in different cloud types using active and passive satellite sensors, Geophys. Res. Lett., 48, e2020GL089863, https://doi.org/10.1029/2020GL089863, 2021.
Coopman, Q., Hoose, C., and Stengel, M.: Analysis of the thermodynamic phase transition of tracked convective clouds based on geostationary satellite observations, J. Geophys. Res.-Atmos., 125, e2019JD032146, https://doi.org/10.1029/2019JD032146, 2020.
Coopman, Q., Hoose, C., and Stengel, M.: Analyzing the thermodynamic phase partitioning of mixed phase clouds over the southern ocean using passive satellite observations, Geophys. Res. Lett., 48, e2021GL093225, https://doi.org/10.1029/2021GL093225, 2021.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J. J., Park, B. K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J. N., and Vitart, F.: The ERA-Interim reanalysis: configuration and performance of the data assimilation system, Q. J. Roy. Meteorol. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011.
Deng, X., Xue, H., and Meng, Z.: The effect of ice nuclei on a deep convective cloud in South China, Atmos. Res., 206, 1–12, https://doi.org/10.1016/j.atmosres.2018.02.013, 2018.
Derrien, M. and Le Gléau, H.: MSG/SEVIRI cloud mask and type from SAFNWC, Int. J. Remote Sens., 26, 4707–4732, https://doi.org/10.1080/01431160500166128, 2005.
Fan, J., Comstock, J. M., and Ovchinnikov, M.: The cloud condensation nuclei and ice nuclei effects on tropical anvil characteristics and water vapor of the tropical tropopause layer, Environ. Res. Lett., 5, 044005, https://doi.org/10.1088/1748-9326/5/4/044005, 2010.
Fan, J., Wang, Y., Rosenfeld, D., and Liu, X.: Review of aerosol–cloud interactions: Mechanisms, significance, and challenges, J. Atmos. Sci., 73, 4221–4252, https://doi.org/10.1175/JAS-D-16-0037.1, 2016.
Field, P. R., Hogan, R. J., Brown, P. R. A., Illingworth, A. J., Choularton, T. W., Kaye, P. H., Hirst, E., and Greenaway, R.: Simultaneous radar and aircraft observations of mixed-phase cloud at the 100 m scale, Q. J. Roy. Meteorol. Soc., 130, 1877–1904, https://doi.org/10.1256/qj.03.102, 2004.
Gassmann, A. and Herzog, H.-J.: Towards a consistent numerical compressible non-hydrostatic model using generalized Hamiltonian tools, Q. J. Roy. Meteorol. Soc., 134, 1597–1613, https://doi.org/10.1002/qj.297, 2008.
Geiss, S., Scheck, L., de Lozar, A., and Weissmann, M.: Understanding the model representation of clouds based on visible and infrared satellite observations, Atmos. Chem. Phys., 21, 12273–12290, https://doi.org/10.5194/acp-21-12273-2021, 2021.
Grabowski, W. W.: Untangling microphysical impacts on deep convection applying a novel modeling methodology, J. Atmos. Sci., 72, 2446–2464, https://doi.org/10.1175/JAS-D-14-0307.1, 2015.
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.
Hande, L. B., Engler, C., Hoose, C., and Tegen, I.: Seasonal variability of Saharan desert dust and ice nucleating particles over Europe, Atmos. Chem. Phys., 15, 4389–4397, https://doi.org/10.5194/acp-15-4389-2015, 2015.
Hande, L. B., Engler, C., Hoose, C., and Tegen, I.: Parameterizing cloud condensation nuclei concentrations during HOPE, Atmos. Chem. Phys., 16, 12059–12079, https://doi.org/10.5194/acp-16-12059-2016, 2016.
Hawker, R. E., Miltenberger, A. K., Wilkinson, J. M., Hill, A. A., Shipway, B. J., Cui, Z., Cotton, R. J., Carslaw, K. S., Field, P. R., and Murray, B. J.: The temperature dependence of ice-nucleating particle concentrations affects the radiative properties of tropical convective cloud systems, Atmos. Chem. Phys., 21, 5439–5461, https://doi.org/10.5194/acp-21-5439-2021, 2021.
Heymsfield, A. J., Miloshevich, L. M., Schmitt, C., Bansemer, A., Twohy, C., Poellot, M. R., Fridlind, A., and Gerber, H.: Homogeneous ice nucleation in subtropical and tropical convection and its influence on cirrus anvil microphysics, J. Atmos. Sci., 62, 41–64, https://doi.org/10.1175/JAS-3360.1, 2005.
Hoose, C. and Möhler, O.: Heterogeneous ice nucleation on atmospheric aerosols: a review of results from laboratory experiments, Atmos. Chem. Phys., 12, 9817–9854, https://doi.org/10.5194/acp-12-9817-2012, 2012.
Hoose, C., Karrer, M., and Barthlott, C.: Cloud top phase distributions of simulated deep convective clouds, J. Geophys. Res.-Atmos., 123, 10464–10476, https://doi.org/10.1029/2018JD028381, 2018.
Kanji, Z. A., Ladino, L. A., Wex, H., Boose, Y., Burkert-Kohn, M., Cziczo, D. J., and Krämer, M.: Overview of ice nucleating particles, Meteorol. Monogr., 58, 1.1–1.33, https://doi.org/10.1175/AMSMONOGRAPHS-D-16-0006.1, 2017.
Kärcher, B., Hendricks, J., and Lohmann, U.: Physically based parameterization of cirrus cloud formation for use in global atmospheric models, J. Geophys. Res.-Atmos., 111, D01205, https://doi.org/10.1029/2005JD006219, 2006.
Kay, J. E., L'Ecuyer, T., Pendergrass, A., Chepfer, H., Guzman, R., and Yettella, V.: Scale-aware and definition-aware evaluation of modeled near-surface precipitation frequency using CloudSat observations, J. Geophys. Res.-Atmos., 123, 4294–4309, https://doi.org/10.1002/2017JD028213, 2018.
Keil, C., Baur, F., Bachmann, K., Rasp, S., Schneider, L., and Barthlott, C.: Relative contribution of soil moisture, boundary-layer and microphysical perturbations on convective predictability in different weather regimes, Q. J. Roy. Meteorol. Soc., 145, 3102–3115, https://doi.org/10.1002/qj.3607, 2019.
Korolev, A.: Limitations of the Wegener–Bergeron–Findeisen mechanism in the evolution of mixed-phase clouds, J. Atmos. Sci., 64, 3372–3375, https://doi.org/10.1175/JAS4035.1, 2007.
Korolev, A. and Isaac, G. A.: Relative humidity in liquid, mixed-phase, and ice clouds, J. Atmos. Sci., 63, 2865–2880, https://doi.org/10.1175/JAS3784.1, 2006.
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, Meteorol. Monogr., 58, 5.1–5.50, https://doi.org/10.1175/AMSMONOGRAPHS-D-17-0001.1, 2017.
Korolev, A. V., Isaac, G. A., Cober, S. G., Strapp, J. W., and Hallett, J.: Microphysical characterization of mixed-phase clouds, Q. J. Roy. Meteorol. Soc., 129, 39–65, https://doi.org/10.1256/qj.01.204, 2003.
Li, R. and Min, Q. L.: Impacts of mineral dust on the vertical structure of precipitation, J. Geophys. Res.-Atmos., 115, D09203, https://doi.org/10.1029/2009JD011925, 2010.
Li, X., Tao, W.-K., Masunaga, H., Masunaga, Gu, G., and Zeng, X.: Aerosol effects on cumulus congestus population over the tropical acific: A cloud-resolving modeling study, J. Meteorol. Soc. Jpn., 91, 817–833, https://doi.org/10.2151/jmsj.2013-607, 2013.
Lohmann, U. and Hoose, C.: Sensitivity studies of different aerosol indirect effects in mixed-phase clouds, Atmos. Chem. Phys., 9, 8917–8934, https://doi.org/10.5194/acp-9-8917-2009, 2009.
Matus, A. V. and L'Ecuyer, T. S.: The role of cloud phase in Earth's radiation budget, J. Geophys. Res.-Atmos., 122, 2559–2578, https://doi.org/10.1002/2016JD025951, 2017.
McCoy, D. T., Tan, I., Hartmann, D. L., Zelinka, M. D., and Storelvmo, T.: On the relationships among cloud cover, mixed-phase partitioning, and planetary albedo in GCMs, J. Adv. Model. Earth Syst., 8, 650–668, https://doi.org/10.1002/2015MS000589, 2016.
Mecikalski, J. R., Jewett, C. P., Apke, J. M., and Carey, L. D.: Analysis of Cumulus Cloud Updrafts as Observed with 1-Min Resolution Super Rapid Scan GOES Imagery, Mon. Weather Rev., 144, 811–830, https://doi.org/10.1175/MWR-D-14-00399.1, 2016.
Mellado, J. P.: Cloud-top entrainment in stratocumulus clouds, Annu. Rev. Fluid Mech., 49, 145–169, https://doi.org/10.1146/annurev-fluid-010816-060231, 2017.
Menzel, W. P., Smith, W. L., and Stewart, T. R.: Improved cloud motion wind vector and altitude assignment using VAS, J. Appl. Meteorol. Clim., 22, 377–384, https://doi.org/10.1175/1520-0450(1983)022<0377:ICMWVA>2.0.CO;2, 1983.
Miltenberger, A. K. and Field, P. R.: Sensitivity of mixed-phase moderately deep convective clouds to parameterizations of ice formation – an ensemble perspective, Atmos. Chem. Phys., 21, 3627–3642, https://doi.org/10.5194/acp-21-3627-2021, 2021.
Min, Q. L., Li, R., Lin, B., Joseph, E., Wang, S., Hu, Y., Morris, V., and Chang, F.: Evidence of mineral dust altering cloud microphysics and precipitation, Atmos. Chem. Phys., 9, 3223–3231, https://doi.org/10.5194/acp-9-3223-2009, 2009.
Nakajima, T. and King, M. D.: Determination of the optical thickness and effective particle radius of clouds from reflected solar radiation measurements. Part I: Theory, J. Atmos. Sci., 47, 1878–1893, https://doi.org/10.1175/1520-0469(1990)047<1878:DOTOTA>2.0.CO;2, 1990.
Noh, Y.-J., Seaman, C. J., Vonder Haar, T. H., and Liu, G.: In situ aircraft measurements of the vertical distribution of liquid and ice water content in midlatitude mixed-phase clouds, J. Appl. Meteorol. Clim., 52, 269–279, https://doi.org/10.1175/JAMC-D-11-0202.1, 2013.
Pavolonis, M. J., Heidinger, A. K., and Uttal, T.: Daytime global cloud typing from AVHRR and VIIRS: Algorithm description, validation, and comparisons, J. Appl. Meteorol., 44, 804–826, https://doi.org/10.1175/JAM2236.1, 2005.
Pinto, J. O.: Autumnal mixed-phase cloudy boundary layers in the Arctic, J. Atmos. Sci., 55, 2016–2038, https://doi.org/10.1175/1520-0469(1998)055<2016:AMPCBL>2.0.CO;2, 1998.
Pscheidt, I., Senf, F., Heinze, R., Deneke, H., Trömel, S., and Hohenegger, C.: How organized is deep convection over Germany?, Q. J. Roy. Meteorol. Soc., 145, 2366–2384, https://doi.org/10.1002/qj.3552, 2019.
Roebeling, R. A., Feijt, A. J., and Stammes, P.: Cloud property retrievals for climate monitoring: Implications of differences between Spinning Enhanced Visible and Infrared Imager (SEVIRI) on METEOSAT-8 and Advanced Very High Resolution Radiometer (AVHRR) on NOAA-17, J. Geophys. Res.-Atmos., 111, D20210, https://doi.org/10.1029/2005JD006990, 2006.
Rosenfeld, D. and Woodley, W. L.: Deep convective clouds with sustained supercooled liquid water down to −37.5 ∘C, Nature, 405, 440–442, https://doi.org/10.1038/35013030, 2000.
Rosenfeld, D., Yu, X., Liu, G., Xu, X., Zhu, Y., Yue, Z., Dai, J., Dong, Z., Dong, Y., and Peng, Y.: Glaciation temperatures of convective clouds ingesting desert dust, air pollution and smoke from forest fires, Geophys. Res. Lett., 38, L21804, https://doi.org/10.1029/2011GL049423, 2011.
Rybka, H., Burkhardt, U., Köhler, M., Arka, I., Bugliaro, L., Görsdorf, U., Horváth, Á., Meyer, C. I., Reichardt, J., Seifert, A., and Strandgren, J.: The behavior of high-CAPE (convective available potential energy) summer convection in large-domain large-eddy simulations with ICON, Atmos. Chem. Phys., 21, 4285–4318, https://doi.org/10.5194/acp-21-4285-2021, 2021.
Saunders, R., Hocking, J., Turner, E., Rayer, P., Rundle, D., Brunel, P., Vidot, J., Roquet, P., Matricardi, M., Geer, A., Bormann, N., and Lupu, C.: An update on the RTTOV fast radiative transfer model (currently at version 12), Geosci. Model Dev., 11, 2717–2737, https://doi.org/10.5194/gmd-11-2717-2018, 2018.
Schmetz, J., Holmlund, K., Hoffman, J., Strauss, B., Mason, B., Gaertner, V., Koch, A., and Van De Berg, L.: Operational cloud-motion winds from meteosat infrared images, J. Appl. Meteorol. Clim., 32, 1206–1225, https://doi.org/10.1175/1520-0450(1993)032<1206:OCMWFM>2.0.CO;2, 1993.
Schmetz, J., Pili, P., Tjemkes, S., Just, D., Kerkmann, J., Rota, S., and Ratier, A.: An introduction to meteosat second generation (MSG), B. Am. Meteorol. Soc., 83, 977–992, https://doi.org/10.1175/1520-0477(2002)083<0977:AITMSG>2.3.CO;2, 2002.
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.
Senf, F., Voigt, A., Clerbaux, N., Hünerbein, A., and Deneke, H.: Increasing resolution and resolving convection improve the simulation of cloud-radiative effects over the North Atlantic, J. Geophys. Res.-Atmos., 125, e2020JD032667, https://doi.org/10.1029/2020JD032667, 2020.
Sheffield, A. M., Saleeby, S. M., and van den Heever, S. C.: Aerosol-induced mechanisms for cumulus congestus growth, J. Geophys. Res.-Atmos., 120, 8941–8952, https://doi.org/10.1002/2015JD023743, 2015.
Sotiropoulou, G., Vignon, É., Young, G., Morrison, H., O'Shea, S. J., Lachlan-Cope, T., Berne, A., and Nenes, A.: Secondary ice production in summer clouds over the Antarctic coast: an underappreciated process in atmospheric models, Atmos. Chem. Phys., 21, 755–771, https://doi.org/10.5194/acp-21-755-2021, 2021.
Stengel, M., Kniffka, A., Meirink, J. F., Lockhoff, M., Tan, J., and Hollmann, R.: CLAAS: the CM SAF cloud property data set using SEVIRI, Atmos. Chem. Phys., 14, 4297–4311, https://doi.org/10.5194/acp-14-4297-2014, 2014.
Stephens, G. L.: Radiation profiles in extended water clouds. II: Parameterization schemes, J. Atmos. Sci., 35, 2123–2132, https://doi.org/10.1175/1520-0469(1978)035<2123:RPIEWC>2.0.CO;2, 1978.
Stith, J. L., Haggerty, J. A., Heymsfield, A., and Grainger, C. A.: Microphysical characteristics of tropical updrafts in clean conditions, J. Appl. Meteorol., 43, 779–794, https://doi.org/10.1175/2104.1, 2004.
Sullivan, S. C., Barthlott, C., Crosier, J., Zhukov, I., Nenes, A., and Hoose, C.: The effect of secondary ice production parameterization on the simulation of a cold frontal rainband, Atmos. Chem. Phys., 18, 16461–16480, https://doi.org/10.5194/acp-18-16461-2018, 2018.
Takeishi, A. and Storelvmo, T.: A study of enhanced heterogeneous ice nucleation in simulated deep convective clouds observed during DC3, J. Geophys. Res.-Atmos., 123, 13396–13420, https://doi.org/10.1029/2018JD028889, 2018.
Taylor, J. W., Choularton, T. W., Blyth, A. M., Liu, Z., Bower, K. N., Crosier, J., Gallagher, M. W., Williams, P. I., Dorsey, J. R., Flynn, M. J., Bennett, L. J., Huang, Y., French, J., Korolev, A., and Brown, P. R. A.: Observations of cloud microphysics and ice formation during COPE, Atmos. Chem. Phys., 16, 799–826, https://doi.org/10.5194/acp-16-799-2016, 2016.
Thomas, J., Barrett, A., and Hoose, C.: Temperature and cloud condensation nuclei (CCN) sensitivity of orographic precipitation enhanced by a mixed-phase seeder–feeder mechanism: a case study for the 2015 Cumbria flood, Atmos. Chem. Phys., 23, 1987–2002, https://doi.org/10.5194/acp-23-1987-2023, 2023.
Twohy, C. H.: Measurements of Saharan dust in convective clouds over the tropical eastern Atlantic ocean, J. Atmos. Sci., 72, 75–81, https://doi.org/10.1175/JAS-D-14-0133.1, 2015.
van den Heever, S. C., Carrió, G. G., Cotton, W. R., DeMott, P. J., and Prenni, A. J.: Impacts of nucleating aerosol on Florida storms. Part I: Mesoscale simulations, J. Atmos. Sci., 63, 1752–1775, https://doi.org/10.1175/JAS3713.1, 2006.
Vignon, É., Alexander, S. P., DeMott, P. J., Sotiropoulou, G., Gerber, F., Hill, T. C. J., Marchand, R., Nenes, A., and Berne, A.: Challenging and improving the simulation of mid-level mixed-phase clouds over the high-latitude southern ocean, J. Geophys. Res.-Atmos., 126, e2020JD033490, https://doi.org/10.1029/2020JD033490, 2021.
Wan, H., Giorgetta, M. A., Zängl, G., Restelli, M., Majewski, D., Bonaventura, L., Fröhlich, K., Reinert, D., Rípodas, P., Kornblueh, L., and Förstner, J.: The ICON-1.2 hydrostatic atmospheric dynamical core on triangular grids – Part 1: Formulation and performance of the baseline version, Geosci. Model Dev., 6, 735–763, https://doi.org/10.5194/gmd-6-735-2013, 2013.
Zängl, G., Reinert, D., Rípodas, P., and Baldauf, M.: The ICON (ICOsahedral Non-hydrostatic) modelling framework of DWD and MPI-M: Description of the non-hydrostatic dynamical core, Q. J. Roy. Meteorol. Soc., 141, 563–579, https://doi.org/10.1002/qj.2378, 2015.
Zhao, B., Wang, Y., Gu, Y., Liou, K.-N., Jiang, J. H., Fan, J., Liu, X., Huang, L., and Yung, Y. L.: Ice nucleation by aerosols from anthropogenic pollution, Nat. Geosci., 12, 602–607, https://doi.org/10.1038/s41561-019-0389-4, 2019.
Zhao, X., Liu, X., Burrows, S. M., and Shi, Y.: Effects of marine organic aerosols as sources of immersion-mode ice-nucleating particles on high-latitude mixed-phase clouds, Atmos. Chem. Phys., 21, 2305–2327, https://doi.org/10.5194/acp-21-2305-2021, 2021.
Short summary
Cloud phase has been found to significantly impact cloud thermodynamics and Earth’s radiation budget, and various factors influence it. This study investigates the sensitivity of the cloud-phase distribution to the ice-nucleating particle concentration and thermodynamics. Multiple simulation experiments were performed using the ICON model at the convection-permitting resolution of 1.2 km. Simulation results were compared to two different retrieval products based on SEVIRI measurements.
Cloud phase has been found to significantly impact cloud thermodynamics and Earth’s radiation...
Altmetrics
Final-revised paper
Preprint