Articles | Volume 26, issue 10
https://doi.org/10.5194/acp-26-7105-2026
© Author(s) 2026. 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-26-7105-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 May 2026
Research article |
| 22 May 2026
| 22 May 2026
Convective controls on anvil cloud evolution in the ICON km-scale global climate model
Mathilde Ritman
CORRESPONDING AUTHOR
Department of Physics, University of Oxford, Oxford, United Kingdom
William Jones
Department of Physics, University of Oxford, Oxford, United Kingdom
European Space Agency, Harwell, United Kingdom
Philip Stier
Department of Physics, University of Oxford, Oxford, United Kingdom
Related authors
No articles found.
Bernd Heinold, Philipp Weiss, Sadhitro De, Anne Kubin, Jason Müller, Fabian Senf, Philip Stier, and Ina Tegen
EGUsphere, https://doi.org/10.5194/egusphere-2026-328, https://doi.org/10.5194/egusphere-2026-328, 2026
Short summary
Short summary
A limited-area aerosol-climate model based on ICON coupled to HAM-lite is introduced for regional studies of natural and anthropogenic aerosols and interactions with clouds and radiation. Case studies over Central Europe, the Atlantic Arctic, and Australia exemplarily show the model’s capability to capture key aerosol patterns and variability, while remaining affected by simplified emissions and chemistry. The results guide future HAM-lite development.
Tim Reichelt, Juniper Tyree, Milan Klöwer, Peter Dueben, Bryan N. Lawrence, Allison H. Baker, Sara Faghih-Naini, Torsten Hoefler, and Philip Stier
EGUsphere, https://doi.org/10.5194/egusphere-2026-60, https://doi.org/10.5194/egusphere-2026-60, 2026
Short summary
Short summary
The growing size of datasets used in climate science makes it difficult to store, analyze, and distribute dataset. Lossy compression algorithms can significantly reduce the disk space required to store datasets, but it can be difficult to understand and compare the behavior of different compression algorithms. ClimateBenchPress provides a benchmark to standardize comparisons between lossy compression algorithms and guide development of novel algorithms specifically targeted towards climate data.
William K. Jones and Philip Stier
EGUsphere, https://doi.org/10.5194/egusphere-2025-6391, https://doi.org/10.5194/egusphere-2025-6391, 2025
Short summary
Short summary
Deep convective storm clouds produce large areas of high-altitude anvil clouds which are vital to balancing the Earth's energy budget due to both the reflection of sunlight and absorption of infrared radiation. Recent research has highlighted important changes in the anvil cloud thickness. In this study, we investigate how changes of convective intensity and convective organisation have different effects on anvil structure across the entire anvil cloud lifecycle using a cloud tracking approach.
Maor Sela, Philipp Weiss, and Philip Stier
EGUsphere, https://doi.org/10.5194/egusphere-2025-5803, https://doi.org/10.5194/egusphere-2025-5803, 2025
Short summary
Short summary
Clouds play a key role in Earth’s climate, but their representation in models remains uncertain. We use high-resolution simulations to examine how two statistical representations of cloud processes influence cloud and rain formation, and how these effects manifest in global models. We find that simulated clouds are highly sensitive to the chosen method, and that features such as rain, fog, and ice become even more variable at the global scale.
Hans Segura, Xabier Pedruzo-Bagazgoitia, Philipp Weiss, Sebastian K. Müller, Thomas Rackow, Junhong Lee, Edgar Dolores-Tesillos, Imme Benedict, Matthias Aengenheyster, Razvan Aguridan, Gabriele Arduini, Alexander J. Baker, Jiawei Bao, Swantje Bastin, Eulàlia Baulenas, Tobias Becker, Sebastian Beyer, Hendryk Bockelmann, Nils Brüggemann, Lukas Brunner, Suvarchal K. Cheedela, Sushant Das, Jasper Denissen, Ian Dragaud, Piotr Dziekan, Madeleine Ekblom, Jan Frederik Engels, Monika Esch, Richard Forbes, Claudia Frauen, Lilli Freischem, Diego García-Maroto, Philipp Geier, Paul Gierz, Álvaro González-Cervera, Katherine Grayson, Matthew Griffith, Oliver Gutjahr, Helmuth Haak, Ioan Hadade, Kerstin Haslehner, Shabeh ul Hasson, Jan Hegewald, Lukas Kluft, Aleksei Koldunov, Nikolay Koldunov, Tobias Kölling, Shunya Koseki, Sergey Kosukhin, Josh Kousal, Peter Kuma, Arjun U. Kumar, Rumeng Li, Nicolas Maury, Maximilian Meindl, Sebastian Milinski, Kristian Mogensen, Bimochan Niraula, Jakub Nowak, Divya Sri Praturi, Ulrike Proske, Dian Putrasahan, René Redler, David Santuy, Domokos Sármány, Reiner Schnur, Patrick Scholz, Dmitry Sidorenko, Dorian Spät, Birgit Sützl, Daisuke Takasuka, Adrian Tompkins, Alejandro Uribe, Mirco Valentini, Menno Veerman, Aiko Voigt, Sarah Warnau, Fabian Wachsmann, Marta Wacławczyk, Nils Wedi, Karl-Hermann Wieners, Jonathan Wille, Marius Winkler, Yuting Wu, Florian Ziemen, Janos Zimmermann, Frida A.-M. Bender, Dragana Bojovic, Sandrine Bony, Simona Bordoni, Patrice Brehmer, Marcus Dengler, Emanuel Dutra, Saliou Faye, Erich Fischer, Chiel van Heerwaarden, Cathy Hohenegger, Heikki Järvinen, Markus Jochum, Thomas Jung, Johann H. Jungclaus, Noel S. Keenlyside, Daniel Klocke, Heike Konow, Martina Klose, Szymon Malinowski, Olivia Martius, Thorsten Mauritsen, Juan Pedro Mellado, Theresa Mieslinger, Elsa Mohino, Hanna Pawłowska, Karsten Peters-von Gehlen, Abdoulaye Sarré, Pajam Sobhani, Philip Stier, Lauri Tuppi, Pier Luigi Vidale, Irina Sandu, and Bjorn Stevens
Geosci. Model Dev., 18, 7735–7761, https://doi.org/10.5194/gmd-18-7735-2025, https://doi.org/10.5194/gmd-18-7735-2025, 2025
Short summary
Short summary
The Next Generation of Earth Modeling Systems project (nextGEMS) developed two Earth system models that use horizontal grid spacing of 10 km and finer, giving more fidelity to the representation of local phenomena, globally. In its fourth cycle, nextGEMS simulated the Earth System climate over the 2020–2049 period under the SSP3-7.0 scenario. Here, we provide an overview of nextGEMS, insights into the model development, and the realism of multi-decadal, kilometer-scale simulations.
Ross J. Herbert, Andrew I. L. Williams, Philipp Weiss, Duncan Watson-Parris, Elisabeth Dingley, Daniel Klocke, and Philip Stier
Atmos. Chem. Phys., 25, 7789–7814, https://doi.org/10.5194/acp-25-7789-2025, https://doi.org/10.5194/acp-25-7789-2025, 2025
Short summary
Short summary
Clouds exist at scales that climate models struggle to represent, limiting our knowledge of how climate change may impact clouds. Here we use a new kilometer-scale global model representing an important step towards the necessary scale. We focus on how aerosol particles modify clouds, radiation, and precipitation. We find the magnitude and manner of responses tend to vary from region to region, highlighting the potential of global kilometer-scale simulations and a need to represent aerosols in climate models.
Philipp Weiss, Ross Herbert, and Philip Stier
Geosci. Model Dev., 18, 3877–3894, https://doi.org/10.5194/gmd-18-3877-2025, https://doi.org/10.5194/gmd-18-3877-2025, 2025
Short summary
Short summary
Aerosols strongly influence Earth's climate as they interact with radiation and clouds. New Earth system models run at resolutions of a few kilometers. To simulate the Earth system with interactive aerosols, we developed a new aerosol module. It represents aerosols as an ensemble of lognormal modes with given sizes and compositions. We present a year-long simulation with four modes at a resolution of 5 km. It captures key processes like the formation of dust storms in the Sahara.
Mariya Petrenko, Ralph Kahn, Mian Chin, Susanne E. Bauer, Tommi Bergman, Huisheng Bian, Gabriele Curci, Ben Johnson, Johannes W. Kaiser, Zak Kipling, Harri Kokkola, Xiaohong Liu, Keren Mezuman, Tero Mielonen, Gunnar Myhre, Xiaohua Pan, Anna Protonotariou, Samuel Remy, Ragnhild Bieltvedt Skeie, Philip Stier, Toshihiko Takemura, Kostas Tsigaridis, Hailong Wang, Duncan Watson-Parris, and Kai Zhang
Atmos. Chem. Phys., 25, 1545–1567, https://doi.org/10.5194/acp-25-1545-2025, https://doi.org/10.5194/acp-25-1545-2025, 2025
Short summary
Short summary
We compared smoke plume simulations from 11 global models to each other and to satellite smoke amount observations aimed at constraining smoke source strength. In regions where plumes are thick and background aerosol is low, models and satellites compare well. However, the input emission inventory tends to underestimate in many places, and particle property and loss rate assumptions vary enormously among models, causing uncertainties that require systematic in situ measurements to resolve.
Anna Tippett, Edward Gryspeerdt, Peter Manshausen, Philip Stier, and Tristan W. P. Smith
Atmos. Chem. Phys., 24, 13269–13283, https://doi.org/10.5194/acp-24-13269-2024, https://doi.org/10.5194/acp-24-13269-2024, 2024
Short summary
Short summary
Ship emissions can form artificially brightened clouds, known as ship tracks, and provide us with an opportunity to investigate how aerosols interact with clouds. Previous studies that used ship tracks suggest that clouds can experience large increases in the amount of water (LWP) from aerosols. Here, we show that there is a bias in previous research and that, when we account for this bias, the LWP response to aerosols is much weaker than previously reported.
Sarah Wilson Kemsley, Paulo Ceppi, Hendrik Andersen, Jan Cermak, Philip Stier, and Peer Nowack
Atmos. Chem. Phys., 24, 8295–8316, https://doi.org/10.5194/acp-24-8295-2024, https://doi.org/10.5194/acp-24-8295-2024, 2024
Short summary
Short summary
Aiming to inform parameter selection for future observational constraint analyses, we incorporate five candidate meteorological drivers specifically targeting high clouds into a cloud controlling factor framework within a range of spatial domain sizes. We find a discrepancy between optimal domain size for predicting locally and globally aggregated cloud radiative anomalies and identify upper-tropospheric static stability as an important high-cloud controlling factor.
G. Alexander Sokolowsky, Sean W. Freeman, William K. Jones, Julia Kukulies, Fabian Senf, Peter J. Marinescu, Max Heikenfeld, Kelcy N. Brunner, Eric C. Bruning, Scott M. Collis, Robert C. Jackson, Gabrielle R. Leung, Nils Pfeifer, Bhupendra A. Raut, Stephen M. Saleeby, Philip Stier, and Susan C. van den Heever
Geosci. Model Dev., 17, 5309–5330, https://doi.org/10.5194/gmd-17-5309-2024, https://doi.org/10.5194/gmd-17-5309-2024, 2024
Short summary
Short summary
Building on previous analysis tools developed for atmospheric science, the original release of the Tracking and Object-Based Analysis (tobac) Python package, v1.2, was open-source, modular, and insensitive to the type of gridded input data. Here, we present the latest version of tobac, v1.5, which substantially improves scientific capabilities and computational efficiency from the previous version. These enhancements permit new uses for tobac in atmospheric science and potentially other fields.
Johannes Mülmenstädt, Edward Gryspeerdt, Sudhakar Dipu, Johannes Quaas, Andrew S. Ackerman, Ann M. Fridlind, Florian Tornow, Susanne E. Bauer, Andrew Gettelman, Yi Ming, Youtong Zheng, Po-Lun Ma, Hailong Wang, Kai Zhang, Matthew W. Christensen, Adam C. Varble, L. Ruby Leung, Xiaohong Liu, David Neubauer, Daniel G. Partridge, Philip Stier, and Toshihiko Takemura
Atmos. Chem. Phys., 24, 7331–7345, https://doi.org/10.5194/acp-24-7331-2024, https://doi.org/10.5194/acp-24-7331-2024, 2024
Short summary
Short summary
Human activities release copious amounts of small particles called aerosols into the atmosphere. These particles change how much sunlight clouds reflect to space, an important human perturbation of the climate, whose magnitude is highly uncertain. We found that the latest climate models show a negative correlation but a positive causal relationship between aerosols and cloud water. This means we need to be very careful when we interpret observational studies that can only see correlation.
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.
Bjorn Stevens, Stefan Adami, Tariq Ali, Hartwig Anzt, Zafer Aslan, Sabine Attinger, Jaana Bäck, Johanna Baehr, Peter Bauer, Natacha Bernier, Bob Bishop, Hendryk Bockelmann, Sandrine Bony, Guy Brasseur, David N. Bresch, Sean Breyer, Gilbert Brunet, Pier Luigi Buttigieg, Junji Cao, Christelle Castet, Yafang Cheng, Ayantika Dey Choudhury, Deborah Coen, Susanne Crewell, Atish Dabholkar, Qing Dai, Francisco Doblas-Reyes, Dale Durran, Ayoub El Gaidi, Charlie Ewen, Eleftheria Exarchou, Veronika Eyring, Florencia Falkinhoff, David Farrell, Piers M. Forster, Ariane Frassoni, Claudia Frauen, Oliver Fuhrer, Shahzad Gani, Edwin Gerber, Debra Goldfarb, Jens Grieger, Nicolas Gruber, Wilco Hazeleger, Rolf Herken, Chris Hewitt, Torsten Hoefler, Huang-Hsiung Hsu, Daniela Jacob, Alexandra Jahn, Christian Jakob, Thomas Jung, Christopher Kadow, In-Sik Kang, Sarah Kang, Karthik Kashinath, Katharina Kleinen-von Königslöw, Daniel Klocke, Uta Kloenne, Milan Klöwer, Chihiro Kodama, Stefan Kollet, Tobias Kölling, Jenni Kontkanen, Steve Kopp, Michal Koran, Markku Kulmala, Hanna Lappalainen, Fakhria Latifi, Bryan Lawrence, June Yi Lee, Quentin Lejeun, Christian Lessig, Chao Li, Thomas Lippert, Jürg Luterbacher, Pekka Manninen, Jochem Marotzke, Satoshi Matsouoka, Charlotte Merchant, Peter Messmer, Gero Michel, Kristel Michielsen, Tomoki Miyakawa, Jens Müller, Ramsha Munir, Sandeep Narayanasetti, Ousmane Ndiaye, Carlos Nobre, Achim Oberg, Riko Oki, Tuba Özkan-Haller, Tim Palmer, Stan Posey, Andreas Prein, Odessa Primus, Mike Pritchard, Julie Pullen, Dian Putrasahan, Johannes Quaas, Krishnan Raghavan, Venkatachalam Ramaswamy, Markus Rapp, Florian Rauser, Markus Reichstein, Aromar Revi, Sonakshi Saluja, Masaki Satoh, Vera Schemann, Sebastian Schemm, Christina Schnadt Poberaj, Thomas Schulthess, Cath Senior, Jagadish Shukla, Manmeet Singh, Julia Slingo, Adam Sobel, Silvina Solman, Jenna Spitzer, Philip Stier, Thomas Stocker, Sarah Strock, Hang Su, Petteri Taalas, John Taylor, Susann Tegtmeier, Georg Teutsch, Adrian Tompkins, Uwe Ulbrich, Pier-Luigi Vidale, Chien-Ming Wu, Hao Xu, Najibullah Zaki, Laure Zanna, Tianjun Zhou, and Florian Ziemen
Earth Syst. Sci. Data, 16, 2113–2122, https://doi.org/10.5194/essd-16-2113-2024, https://doi.org/10.5194/essd-16-2113-2024, 2024
Short summary
Short summary
To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines is proposed as an international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.
Karoline Block, Mahnoosh Haghighatnasab, Daniel G. Partridge, Philip Stier, and Johannes Quaas
Earth Syst. Sci. Data, 16, 443–470, https://doi.org/10.5194/essd-16-443-2024, https://doi.org/10.5194/essd-16-443-2024, 2024
Short summary
Short summary
Aerosols being able to act as condensation nuclei for cloud droplets (CCNs) are a key element in cloud formation but very difficult to determine. In this study we present a new global vertically resolved CCN dataset for various humidity conditions and aerosols. It is obtained using an atmospheric model (CAMS reanalysis) that is fed by satellite observations of light extinction (AOD). We investigate and evaluate the abundance of CCNs in the atmosphere and their temporal and spatial occurrence.
Peter Manshausen, Duncan Watson-Parris, Matthew W. Christensen, Jukka-Pekka Jalkanen, and Philip Stier
Atmos. Chem. Phys., 23, 12545–12555, https://doi.org/10.5194/acp-23-12545-2023, https://doi.org/10.5194/acp-23-12545-2023, 2023
Short summary
Short summary
Aerosol from burning fuel changes cloud properties, e.g., the number of droplets and the content of water. Here, we study how clouds respond to different amounts of shipping aerosol. Droplet numbers increase linearly with increasing aerosol over a broad range until they stop increasing, while the amount of liquid water always increases, independently of emission amount. These changes in cloud properties can make them reflect more or less sunlight, which is important for the earth's climate.
Hendrik Andersen, Jan Cermak, Alyson Douglas, Timothy A. Myers, Peer Nowack, Philip Stier, Casey J. Wall, and Sarah Wilson Kemsley
Atmos. Chem. Phys., 23, 10775–10794, https://doi.org/10.5194/acp-23-10775-2023, https://doi.org/10.5194/acp-23-10775-2023, 2023
Short summary
Short summary
This study uses an observation-based cloud-controlling factor framework to study near-global sensitivities of cloud radiative effects to a large number of meteorological and aerosol controls. We present near-global sensitivity patterns to selected thermodynamic, dynamic, and aerosol factors and discuss the physical mechanisms underlying the derived sensitivities. Our study hopes to guide future analyses aimed at constraining cloud feedbacks and aerosol–cloud interactions.
Leighton A. Regayre, Lucia Deaconu, Daniel P. Grosvenor, David M. H. Sexton, Christopher Symonds, Tom Langton, Duncan Watson-Paris, Jane P. Mulcahy, Kirsty J. Pringle, Mark Richardson, Jill S. Johnson, John W. Rostron, Hamish Gordon, Grenville Lister, Philip Stier, and Ken S. Carslaw
Atmos. Chem. Phys., 23, 8749–8768, https://doi.org/10.5194/acp-23-8749-2023, https://doi.org/10.5194/acp-23-8749-2023, 2023
Short summary
Short summary
Aerosol forcing of Earth’s energy balance has persisted as a major cause of uncertainty in climate simulations over generations of climate model development. We show that structural deficiencies in a climate model are exposed by comprehensively exploring parametric uncertainty and that these deficiencies limit how much the model uncertainty can be reduced through observational constraint. This provides a future pathway towards building models with greater physical realism and lower uncertainty.
Ross Herbert and Philip Stier
Atmos. Chem. Phys., 23, 4595–4616, https://doi.org/10.5194/acp-23-4595-2023, https://doi.org/10.5194/acp-23-4595-2023, 2023
Short summary
Short summary
We provide robust evidence from multiple sources showing that smoke from fires in the Amazon rainforest significantly modifies the diurnal cycle of convection and cools the climate. Low to moderate amounts of smoke increase deep convective clouds and rain, whilst beyond a threshold amount, the smoke starts to suppress the convection and rain. We are currently at this threshold, suggesting increases in fires from agricultural practices or droughts will reduce cloudiness and rain over the region.
William K. Jones, Matthew W. Christensen, and Philip Stier
Atmos. Meas. Tech., 16, 1043–1059, https://doi.org/10.5194/amt-16-1043-2023, https://doi.org/10.5194/amt-16-1043-2023, 2023
Short summary
Short summary
Geostationary weather satellites have been used to detect storm clouds since their earliest applications. However, this task remains difficult as imaging satellites cannot observe the strong vertical winds that are characteristic of storm clouds. Here we introduce a new method that allows us to detect the early development of storms and continue to track them throughout their lifetime, allowing us to study how their early behaviour affects subsequent weather.
Leighton A. Regayre, Lucia Deaconu, Daniel P. Grosvenor, David Sexton, Christopher C. Symonds, Tom Langton, Duncan Watson-Paris, Jane P. Mulcahy, Kirsty J. Pringle, Mark Richardson, Jill S. Johnson, John Rostron, Hamish Gordon, Grenville Lister, Philip Stier, and Ken S. Carslaw
EGUsphere, https://doi.org/10.5194/egusphere-2022-1330, https://doi.org/10.5194/egusphere-2022-1330, 2022
Preprint archived
Short summary
Short summary
We show that potential structural deficiencies in a climate model can be exposed by comprehensively exploring its parametric uncertainty, and that these deficiencies limit how much the model uncertainty can be reduced through observational constraint. Combined consideration of parametric and structural uncertainties provides a future pathway towards building models that have greater physical realism and lower uncertainty.
Johannes Quaas, Hailing Jia, Chris Smith, Anna Lea Albright, Wenche Aas, Nicolas Bellouin, Olivier Boucher, Marie Doutriaux-Boucher, Piers M. Forster, Daniel Grosvenor, Stuart Jenkins, Zbigniew Klimont, Norman G. Loeb, Xiaoyan Ma, Vaishali Naik, Fabien Paulot, Philip Stier, Martin Wild, Gunnar Myhre, and Michael Schulz
Atmos. Chem. Phys., 22, 12221–12239, https://doi.org/10.5194/acp-22-12221-2022, https://doi.org/10.5194/acp-22-12221-2022, 2022
Short summary
Short summary
Pollution particles cool climate and offset part of the global warming. However, they are washed out by rain and thus their effect responds quickly to changes in emissions. We show multiple datasets to demonstrate that aerosol emissions and their concentrations declined in many regions influenced by human emissions, as did the effects on clouds. Consequently, the cooling impact on the Earth energy budget became smaller. This change in trend implies a relative warming.
Haochi Che, Philip Stier, Duncan Watson-Parris, Hamish Gordon, and Lucia Deaconu
Atmos. Chem. Phys., 22, 10789–10807, https://doi.org/10.5194/acp-22-10789-2022, https://doi.org/10.5194/acp-22-10789-2022, 2022
Short summary
Short summary
Extensive stratocumulus clouds over the south-eastern Atlantic (SEA) can lead to a cooling effect on the climate. A key pathway by which aerosols affect cloud properties is by acting as cloud condensation nuclei (CCN). Here, we investigated the source attribution of CCN in the SEA as well as the cloud responses. Our results show that aerosol nucleation contributes most to CCN in the marine boundary layer. In terms of emissions, anthropogenic sources contribute most to the CCN and cloud droplets.
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.
Duncan Watson-Parris, Andrew Williams, Lucia Deaconu, and Philip Stier
Geosci. Model Dev., 14, 7659–7672, https://doi.org/10.5194/gmd-14-7659-2021, https://doi.org/10.5194/gmd-14-7659-2021, 2021
Short summary
Short summary
The Earth System Emulator (ESEm) provides a fast and flexible framework for emulating a wide variety of Earth science datasets and tools for constraining (or tuning) models of any complexity. Three distinct use cases are presented that demonstrate the utility of ESEm and provide some insight into the use of machine learning for emulation in these different settings. The open-source Python package is freely available so that it might become a valuable tool for the community.
Maria Sand, Bjørn H. Samset, Gunnar Myhre, Jonas Gliß, Susanne E. Bauer, Huisheng Bian, Mian Chin, Ramiro Checa-Garcia, Paul Ginoux, Zak Kipling, Alf Kirkevåg, Harri Kokkola, Philippe Le Sager, Marianne T. Lund, Hitoshi Matsui, Twan van Noije, Dirk J. L. Olivié, Samuel Remy, Michael Schulz, Philip Stier, Camilla W. Stjern, Toshihiko Takemura, Kostas Tsigaridis, Svetlana G. Tsyro, and Duncan Watson-Parris
Atmos. Chem. Phys., 21, 15929–15947, https://doi.org/10.5194/acp-21-15929-2021, https://doi.org/10.5194/acp-21-15929-2021, 2021
Short summary
Short summary
Absorption of shortwave radiation by aerosols can modify precipitation and clouds but is poorly constrained in models. A total of 15 different aerosol models from AeroCom phase III have reported total aerosol absorption, and for the first time, 11 of these models have reported in a consistent experiment the contributions to absorption from black carbon, dust, and organic aerosol. Here, we document the model diversity in aerosol absorption.
Shipeng Zhang, Philip Stier, and Duncan Watson-Parris
Atmos. Chem. Phys., 21, 10179–10197, https://doi.org/10.5194/acp-21-10179-2021, https://doi.org/10.5194/acp-21-10179-2021, 2021
Short summary
Short summary
The relationship between aerosol-induced changes in atmospheric energetics and precipitation responses across different scales is studied in terms of fast (radiatively or microphysically mediated) and slow (temperature-mediated) responses. We introduced a method to decompose rainfall changes into contributions from clouds, aerosols, and clear–clean sky from an energetic perspective. It provides a way to better interpret and quantify the precipitation changes caused by aerosol perturbations.
Cited articles
Allan, D. B., Caswell, T., Keim, N. C., van der Wel, C. M., and Verweij, R. W.: Soft-Matter/Trackpy: V0.6.4, Zenodo [code], https://doi.org/10.5281/ZENODO.1213240, 2024. a
Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M., and Reinhardt, T.: Operational Convective-Scale Numerical Weather Prediction with the COSMO Model: Description and Sensitivities, Mon. Weather Rev., 139, 3887–3905, https://doi.org/10.1175/MWR-D-10-05013.1, 2011. a
Becker, T., Bechtold, P., and Sandu, I.: Characteristics of convective precipitation over tropical Africa in storm-resolving global simulations, Q. J. Roy. Meteor. Soc., 147, 4388–4407, https://doi.org/10.1002/qj.4185, 2021. a
Bolot, M., Roca, R., Fiolleau, T., and Muller, C.: No decrease of tropical convection in individual deep convective systems with global warming, npj Clim. Atmos. Sci., 9, 14, https://doi.org/10.1038/s41612-025-01285-5, 2025. a
Bony, S., Stevens, B., Coppin, D., Becker, T., Reed, K. A., Voigt, A., and Medeiros, B.: Thermodynamic Control of Anvil Cloud Amount, P. Natl. Acad. Sci., 113, 8927–8932, https://doi.org/10.1073/pnas.1601472113, 2016. a, b
Byers, H. R. and Braham, R. R.: The Thunderstorm: Report of the Thunderstorm Project, US Government Printing Office, 1949. a
Crook, J., Klein, C., Folwell, S., Taylor, C. M., Parker, D. J., Stratton, R., and Stein, T.: Assessment of the Representation of West African Storm Lifecycles in Convection-Permitting Simulations, Earth and Space Science, 6, 818–835, https://doi.org/10.1029/2018EA000491, 2019. a, b, c
Deutloff, J., Buehler, S. A., Brath, M., and Naumann, A. K.: Insights on Tropical High-Cloud Radiative Effect From a New Conceptual Model, J. Adv. Model. Earth. Sy., 17, e2024MS004615, https://doi.org/10.1029/2024MS004615, 2025. a, b
Emanuel, K. A.: Atmospheric Convection, Oxford University Press, ISBN 978-0-19-506630-2, 1994. a
Feng, Z., Dong, X., Xi, B., McFarlane, S. A., Kennedy, A., Lin, B., and Minnis, P.: Life cycle of midlatitude deep convective systems in a Lagrangian framework, J. Geophys. Res.-Atmos., 117, https://doi.org/10.1029/2012JD018362, 2012. a
Feng, Z., Leung, L. R., Liu, N., Wang, J., Houze Jr, R. A., Li, J., Hardin, J. C., Chen, D., and Guo, J.: A Global High-Resolution Mesoscale Convective System Database Using Satellite-Derived Cloud Tops, Surface Precipitation, and Tracking, J. Geophys. Res.-Atmos., 126, e2020JD034202, https://doi.org/10.1029/2020JD034202, 2021. a, b
Feng, Z., Hardin, J., Barnes, H. C., Li, J., Leung, L. R., Varble, A., and Zhang, Z.: PyFLEXTRKR: a flexible feature tracking Python software for convective cloud analysis, Geosci. Model Dev., 16, 2753–2776, https://doi.org/10.5194/gmd-16-2753-2023, 2023a. a, b
Feng, Z., Leung, L. R., Hardin, J., Terai, C. R., Song, F., and Caldwell, P.: Mesoscale Convective Systems in DYAMOND Global Convection-Permitting Simulations, Geophys. Res. Lett., 50, e2022GL102603, https://doi.org/10.1029/2022GL102603, 2023b. a, b, c
Feng, Z., Prein, A. F., Kukulies, J., Fiolleau, T., Jones, W. K., Maybee, B., Moon, Z. L., Núnéz Ocasio, K. M., Dong, W., Molina, M. J., Albright, M. G., Rajagopal, M., Robledo, V., Song, J., Song, F., Leung, L. R., Varble, A. C., Klein, C., Roca, R., Feng, R., and Mejia, J. F.: Mesoscale Convective Systems Tracking Method Intercomparison (MCSMIP): Application to DYAMOND Global km-Scale Simulations, J. Geophys. Res.-Atmos., 130, e2024JD042204, https://doi.org/10.1029/2024JD042204, 2025. a, b, c, d, e
Fiolleau, T. and Roca, R.: An Algorithm for the Detection and Tracking of Tropical Mesoscale Convective Systems Using Infrared Images From Geostationary Satellite, IEEE T. Geosci. Remote, 51, 4302–4315, https://doi.org/10.1109/TGRS.2012.2227762, 2013. a
Fiolleau, T. and Roca, R.: A database of deep convective systems derived from the intercalibrated meteorological geostationary satellite fleet and the TOOCAN algorithm (2012–2020), Earth Syst. Sci. Data, 16, 4021–4050, https://doi.org/10.5194/essd-16-4021-2024, 2024. a
Freeman, S. W., Posselt, D. J., Reid, J. S., and van den Heever, S. C.: Dynamic and Thermodynamic Environmental Modulation of Tropical Congestus and Cumulonimbus in Maritime Tropical Regions, https://doi.org/10.1175/JAS-D-24-0055.1, J. Atmos. Sci., 2024. a, b
Futyan, J. M. and Genio, A. D. D.: Deep Convective System Evolution over Africa and the Tropical Atlantic, J. Climate, 20, 5041–5060, https://doi.org/10.1175/JCLI4297.1, 2007. a, b, c
Gasparini, B., Blossey, P. N., Hartmann, D. L., Lin, G., and Fan, J.: What Drives the Life Cycle of Tropical Anvil Clouds?, J. Adv. Model. Earth. Sy., 11, 2586–2605, https://doi.org/10.1029/2019MS001736, 2019. a
Gasparini, B., Rasch, P. J., Hartmann, D. L., Wall, C. J., and Dütsch, M.: A Lagrangian Perspective on Tropical Anvil Cloud Lifecycle in Present and Future Climate, J. Geophys. Res.-Atmos., 126, e2020JD033487, https://doi.org/10.1029/2020JD033487, 2021. a, b
Gasparini, B., Sokol, A. B., Wall, C. J., Hartmann, D. L., and Blossey, P. N.: Diurnal Differences in Tropical Maritime Anvil Cloud Evolution, J. Climate, 35, 1655–1677, https://doi.org/10.1175/JCLI-D-21-0211.1, 2022. a
Gasparini, B., Sullivan, S. C., Sokol, A. B., Kärcher, B., Jensen, E., and Hartmann, D. L.: Opinion: Tropical cirrus – from micro-scale processes to climate-scale impacts, Atmos. Chem. Phys., 23, 15413–15444, https://doi.org/10.5194/acp-23-15413-2023, 2023 . a, b, c
Ge, J., Hu, X., Mu, Q., Liu, B., Zhu, Z., Du, J., Su, J., Li, Q., and Zhang, C.: Contrasting characteristics of continental and oceanic deep convective systems at different life stages from CloudSat observations, Atmos. Res., 298, 107157, https://doi.org/10.1016/j.atmosres.2023.107157, 2024. a, b
Genio, A. D. D. and Kovari, W.: Climatic Properties of Tropical Precipitating Convection under Varying Environmental Conditions, J. Climate, 15, 2597–2615, https://doi.org/10.1175/1520-0442(2002)015<2597:CPOTPC>2.0.CO;2, 2002. a
Giangrande, S. E., Toto, T., Jensen, M. P., Bartholomew, M. J., Feng, Z., Protat, A., Williams, C. R., Schumacher, C., and Machado, L.: Convective Cloud Vertical Velocity and Mass-Flux Characteristics from Radar Wind Profiler Observations during GoAmazon2014/5, J. Geophys. Res.-Atmos., 121, 12891–12913, https://doi.org/10.1002/2016JD025303, 2016. a
Gupta, S., Wang, D., Giangrande, S. E., Biscaro, T. S., and Jensen, M. P.: Lifecycle of updrafts and mass flux in isolated deep convection over the Amazon rainforest: insights from cell tracking, Atmos. Chem. Phys., 24, 4487–4510, https://doi.org/10.5194/acp-24-4487-2024, 2024. a, b
Heikenfeld, M., Marinescu, P. J., Christensen, M., Watson-Parris, D., Senf, F., van den Heever, S. C., and Stier, P.: tobac 1.2: towards a flexible framework for tracking and analysis of clouds in diverse datasets, Geosci. Model Dev., 12, 4551–4570, https://doi.org/10.5194/gmd-12-4551-2019, 2019a. a, b, c
Heikenfeld, M., White, B., Labbouz, L., and Stier, P.: Aerosol effects on deep convection: the propagation of aerosol perturbations through convective cloud microphysics, Atmos. Chem. Phys., 19, 2601–2627, https://doi.org/10.5194/acp-19-2601-2019, 2019b. a
Herbert, R. and Stier, P.: Satellite observations of smoke–cloud–radiation interactions over the Amazon rainforest, Atmos. Chem. Phys., 23, 4595–4616, https://doi.org/10.5194/acp-23-4595-2023, 2023. a
Herbert, R., Stier, P., and Dagan, G.: Isolating Large-Scale Smoke Impacts on Cloud and Precipitation Processes Over the Amazon With Convection Permitting Resolution, J. Geophys. Res.-Atmos., 126, e2021JD034615, https://doi.org/10.1029/2021JD034615, 2021. a
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. a
Hohenegger, C., Kornblueh, L., Klocke, D., Becker, T., Cioni, G., Engels, J. F., Schulzweida, U., and Stevens, B.: Climate Statistics in Global Simulations of the Atmosphere, from 80 to 2.5 Km Grid Spacing, J. Meteorol. Soc. Jpn., Ser. II, 98, 73–91, https://doi.org/10.2151/jmsj.2020-005, 2020. a
Hohenegger, C., Korn, P., Linardakis, L., Redler, R., Schnur, R., Adamidis, P., Bao, J., Bastin, S., Behravesh, M., Bergemann, M., Biercamp, J., Bockelmann, H., Brokopf, R., Brüggemann, N., Casaroli, L., Chegini, F., Datseris, G., Esch, M., George, G., Giorgetta, M., Gutjahr, O., Haak, H., Hanke, M., Ilyina, T., Jahns, T., Jungclaus, J., Kern, M., Klocke, D., Kluft, L., Kölling, T., Kornblueh, L., Kosukhin, S., Kroll, C., Lee, J., Mauritsen, T., Mehlmann, C., Mieslinger, T., Naumann, A. K., Paccini, L., Peinado, A., Praturi, D. S., Putrasahan, D., Rast, S., Riddick, T., Roeber, N., Schmidt, H., Schulzweida, U., Schütte, F., Segura, H., Shevchenko, R., Singh, V., Specht, M., Stephan, C. C., von Storch, J.-S., Vogel, R., Wengel, C., Winkler, M., Ziemen, F., Marotzke, J., and Stevens, B.: ICON-Sapphire: simulating the components of the Earth system and their interactions at kilometer and subkilometer scales, Geosci. Model Dev., 16, 779–811, https://doi.org/10.5194/gmd-16-779-2023, 2023. a
Igel, A. L., Igel, M. R., and van den Heever, S. C.: Make It a Double? Sobering Results from Simulations Using Single-Moment Microphysics Schemes, J. Atmos. Sci., 72, 910–925, https://doi.org/10.1175/JAS-D-14-0107.1, 2015. a
Igel, M. R. and van den Heever, S. C.: The relative influence of environmental characteristics on tropical deep convective morphology as observed by CloudSat, J. Geophys. Res.-Atmos., 120, 4304–4322, https://doi.org/10.1002/2014JD022690, 2015. a
Jeevanjee, N. and Zhou, L.: On the Resolution-Dependence of Anvil Cloud Fraction and Precipitation Efficiency in Radiative-Convective Equilibrium, J. Adv. Model. Earth. Sy., 14, e2021MS002759, https://doi.org/10.1029/2021MS002759, 2022. a, b
Jensen, M. P. and Genio, A. D. D.: Factors Limiting Convective Cloud-Top Height at the ARM Nauru Island Climate Research Facility, J. Climate, 19, 2105–2117, https://doi.org/10.1175/JCLI3722.1, 2006. a
Jo, E., Feng, Z., Varble, A. C., Marquis, J. N., and Gustafson, W. I.: The Effect of Updraft Entrainment on Convective Cell Deepening in Realistic Large-Eddy Simulations, J. Atmos. Sci., 82, 1361–1379, https://doi.org/10.1175/JAS-D-24-0218.1, 2025. a
Jones, W. K., Stengel, M., and Stier, P.: A Lagrangian perspective on the lifecycle and cloud radiative effect of deep convective clouds over Africa, Atmos. Chem. Phys., 24, 5165–5180, https://doi.org/10.5194/acp-24-5165-2024, 2024. a, b, c, d
Klingebiel, M., Konow, H., and Stevens, B.: Measuring Shallow Convective Mass Flux Profiles in the Trade Wind Region, J. Atmos. Sci., 78, 3205–3214, https://doi.org/10.1175/JAS-D-20-0347.1, 2021. a
Koren, I., Remer, L. A., Altaratz, O., Martins, J. V., and Davidi, A.: Aerosol-induced changes of convective cloud anvils produce strong climate warming, Atmos. Chem. Phys., 10, 5001–5010, https://doi.org/10.5194/acp-10-5001-2010, 2010. a
Leuenberger, D., Koller, M., Fuhrer, O., and Schär, C.: A Generalization of the SLEVE Vertical Coordinate, Mon. Weather Rev., 138, 3683–3689, https://doi.org/10.1175/2010MWR3307.1, 2010. a
Li, W. and Schumacher, C.: Thick Anvils as Viewed by the TRMM Precipitation Radar, J. Climate, 24, 1718–1735 https://doi.org/10.1175/2010JCLI3793.1, 2011. a, b
Lilly, D. K.: On the Numerical Simulation of Buoyant Convection, Tellus, 14, 148–172, https://doi.org/10.1111/j.2153-3490.1962.tb00128.x, 1962. a
Lochbihler, K., Lenderink, G., and Siebesma, A. P.: The spatial extent of rainfall events and its relation to precipitation scaling, Geophys. Res. Lett., 44, 8629–8636, https://doi.org/10.1002/2017GL074857, 2017. a
Machado, L. A. T. and Rossow, W. B.: Structural Characteristics and Radiative Properties of Tropical Cloud Clusters, Mon. Weather Rev., 121, 3234–3260, https://doi.org/10.1175/1520-0493(1993)121<3234:SCARPO>2.0.CO;2, 1993. a, b, c
Machado, L. A. T., Rossow, W. B., Guedes, R. L., and Walker, A. W.: Life Cycle Variations of Mesoscale Convective Systems over the Americas, 126, 1630–1654, Mon. Weather Rev., https://doi.org/10.1175/1520-0493(1998)126<1630:LCVOMC>2.0.CO;2, 1998. a
McKim, B., Bony, S., and Dufresne, J.-L.: Weak Anvil Cloud Area Feedback Suggested by Physical and Observational Constraints, Nat. Geosci., 17, 392–397, https://doi.org/10.1038/s41561-024-01414-4, 2024. 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 Sy., 12, e2019MS001689, https://doi.org/10.1029/2019MS001689, 2020. a
Müller, S. K., Caillaud, C., Chan, S., de Vries, H., Bastin, S., Berthou, S., Brisson, E., Demory, M.-E., Feldmann, H., Goergen, K., Kartsios, S., Lind, P., Keuler, K., Pichelli, E., Raffa, M., Tölle, M. H., and Warrach-Sagi, K.: Evaluation of Alpine-Mediterranean precipitation events in convection-permitting regional climate models using a set of tracking algorithms, Clim. Dynam., 61, 939–957, https://doi.org/10.1007/s00382-022-06555-z, 2023. a
Nugent, J. M., Turbeville, S. M., Bretherton, C. S., Blossey, P. N., and Ackerman, T. P.: Tropical Cirrus in Global Storm-Resolving Models: 1. Role of Deep Convection, Earth and Space Science, 9, e2021EA001965, https://doi.org/10.1029/2021EA001965, 2022. a
Ocasio, K. M. N., Evans, J. L., and Young, G. S.: Tracking Mesoscale Convective Systems that are Potential Candidates for Tropical Cyclogenesis, Mon. Weather Rev., 148, 655–669, https://doi.org/10.1175/MWR-D-19-0070.1, 2020. a
Pincus, R., Mlawer, E. J., and Delamere, J. S.: Balancing Accuracy, Efficiency, and Flexibility in Radiation Calculations for Dynamical Models, J. Adv. Model. Earth. Sy., 11, 3074–3089, https://doi.org/10.1029/2019MS001621, 2019. a
Prein, A. F., Langhans, W., Fosser, G., Ferrone, A., Ban, N., Goergen, K., Keller, M., Tölle, M., Gutjahr, O., Feser, F., Brisson, E., Kollet, S., Schmidli, J., van Lipzig, N. P. M., and Leung, R.: A review on regional convection-permitting climate modeling: Demonstrations, prospects, and challenges, Rev. Geophys., 53, 323–361, https://doi.org/10.1002/2014RG000475, 2015. a
Prein, A. F., Feng, Z., Fiolleau, T., Moon, Z. L., Nünéz Ocasio, K. M., Kukulies, J., Roca, R., Varble, A. C., Rehbein, A., Liu, C., Ikeda, K., Mu, Y., and Rasmussen, R. M.: Km-Scale Simulations of Mesoscale Convective Systems Over South America – Feature Tracker Intercomparison, J. Geophys. Res.-Atmos., 129, e2023JD040254, https://doi.org/10.1029/2023JD040254, 2024. a
Raghuraman, S. P., Medeiros, B., and Gettelman, A.: Observational Quantification of Tropical High Cloud Changes and Feedbacks, J. Geophys. Res.-Atmos., 129, e2023JD039364, https://doi.org/10.1029/2023JD039364, 2024. a, b
Respati, M. R., Dommenget, D., Segura, H., and Stassen, C.: Diagnosing drivers of tropical precipitation biases in coupled climate model simulations, Clim. Dynam., 62, 8691–8709, https://doi.org/10.1007/s00382-024-07355-3, 2024. a
Ritman, M.: ICON tracked deep convective cloud statistics and data accompanying the manuscript “Convective controls on anvil cloud evolution in the ICON km-scale global climate model”, Zenodo [code], https://doi.org/10.5281/zenodo.18413874, 2026a. a
Ritman, M.: Scripts to produces results presented in “Convective controls on anvil cloud evolution in the ICON km-scale global climate model”, Zenodo [code], https://doi.org/10.5281/zenodo.18414234, 2026b. a
Roca, R., Fiolleau, T., and Bouniol, D.: A Simple Model of the Life Cycle of Mesoscale Convective Systems Cloud Shield in the Tropics, J. Climate, 30, 4283–4298, https://doi.org/10.1175/JCLI-D-16-0556.1, 2017. a
Roh, W., Satoh, M., and Hohenegger, C.: Intercomparison of Cloud Properties in DYAMOND Simulations over the Atlantic Ocean, J. Meteorol. Soc. Jpn., Ser. II, 99, 1439–1451, https://doi.org/10.2151/jmsj.2021-070, 2021. a
Saleeby, S. M., van den Heever, S. C., Marinescu, P. J., Oue, M., Barrett, A. I., Barthlott, C., Cherian, R., Fan, J., Fridlind, A. M., Heikenfeld, M., Hoose, C., Matsui, T., Miltenberger, A. K., Quaas, J., Shpund, J., Stier, P., Vie, B., White, B. A., and Zhang, Y.: Model Intercomparison of the Impacts of Varying Cloud Droplet-Nucleating Aerosols on the Life Cycle and Microphysics of Isolated Deep Convection, J. Atmos. Sci., 82, 2197–2217, https://doi.org/10.1175/JAS-D-24-0181.1, 2025. a, b
Savazzi, A. C. M., Jakob, C., and Siebesma, A. P.: Convective Mass-Flux From Long Term Radar Reflectivities Over Darwin, Australia, J. Geophys. Res.-Atmos., 126, e2021JD034910, https://doi.org/10.1029/2021JD034910, 2021. a
Schär, C., Leuenberger, D., Fuhrer, O., Lüthi, D., and Girard, C.: A New Terrain-Following Vertical Coordinate Formulation for Atmospheric Prediction Models, Mon. Weather Rev., 130, 2459–2480, https://doi.org/10.1175/1520-0493(2002)130<2459:ANTFVC>2.0.CO;2, 2002. a
Segura, H., Pedruzo-Bagazgoitia, X., Weiss, P., Müller, S. K., Rackow, T., Lee, J., Dolores-Tesillos, E., Benedict, I., Aengenheyster, M., Aguridan, R., Arduini, G., Baker, A. J., Bao, J., Bastin, S., Baulenas, E., Becker, T., Beyer, S., Bockelmann, H., Brüggemann, N., Brunner, L., Cheedela, S. K., Das, S., Denissen, J., Dragaud, I., Dziekan, P., Ekblom, M., Engels, J. F., Esch, M., Forbes, R., Frauen, C., Freischem, L., García-Maroto, D., Geier, P., Gierz, P., González-Cervera, Á., Grayson, K., Griffith, M., Gutjahr, O., Haak, H., Hadade, I., Haslehner, K., ul Hasson, S., Hegewald, J., Kluft, L., Koldunov, A., Koldunov, N., Kölling, T., Koseki, S., Kosukhin, S., Kousal, J., Kuma, P., Kumar, A. U., Li, R., Maury, N., Meindl, M., Milinski, S., Mogensen, K., Niraula, B., Nowak, J., Praturi, D. S., Proske, U., Putrasahan, D., Redler, R., Santuy, D., Sármány, D., Schnur, R., Scholz, P., Sidorenko, D., Spät, D., Sützl, B., Takasuka, D., Tompkins, A., Uribe, A., Valentini, M., Veerman, M., Voigt, A., Warnau, S., Wachsmann, F., Wacławczyk, M., Wedi, N., Wieners, K.-H., Wille, J., Winkler, M., Wu, Y., Ziemen, F., Zimmermann, J., Bender, F. A.-M., Bojovic, D., Bony, S., Bordoni, S., Brehmer, P., Dengler, M., Dutra, E., Faye, S., Fischer, E., van Heerwaarden, C., Hohenegger, C., Järvinen, H., Jochum, M., Jung, T., Jungclaus, J. H., Keenlyside, N. S., Klocke, D., Konow, H., Klose, M., Malinowski, S., Martius, O., Mauritsen, T., Mellado, J. P., Mieslinger, T., Mohino, E., Pawłowska, H., Peters-von Gehlen, K., Sarré, A., Sobhani, P., Stier, P., Tuppi, L., Vidale, P. L., Sandu, I., and Stevens, B.: nextGEMS: entering the era of kilometer-scale Earth system modeling, Geosci. Model Dev., 18, 7735–7761, https://doi.org/10.5194/gmd-18-7735-2025, 2025. a
Sela, M., Weiss, P., and Stier, P.: Sensitivity of cloud structure and precipitation to cloud microphysics schemes in ICON and implications for global km-scale simulations, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-5803, 2025. a
Senf, F., Klocke, D., and Brueck, M.: Size-Resolved Evaluation of Simulated Deep Tropical Convection, Mon. Weather Rev., 146, 2161–2182, https://doi.org/10.1175/MWR-D-17-0378.1, 2018. a, b
Sherwood, S. C., Webb, M. J., Annan, J. D., Armour, K. C., Forster, P. M., Hargreaves, J. C., Hegerl, G., Klein, S. A., Marvel, K. D., Rohling, E. J., Watanabe, M., Andrews, T., Braconnot, P., Bretherton, C. S., Foster, G. L., Hausfather, Z., von der Heydt, A. S., Knutti, R., Mauritsen, T., Norris, J. R., Proistosescu, C., Rugenstein, M., Schmidt, G. A., Tokarska, K. B., and Zelinka, M. D.: An Assessment of Earth's Climate Sensitivity Using Multiple Lines of Evidence, Rev. Geophys., 58, e2019RG000678, https://doi.org/10.1029/2019RG000678, 2020. a, b
Smagorinsky, J.: General Circulation Experiments with the Primitive Equations: I. The Basic Experiment, Mon. Weather Rev., 91, 99–164, https://doi.org/10.1175/1520-0493(1963)091<0099:GCEWTP>2.3.CO;2, 1963. a
Sokol, A. B. and Hartmann, D. L.: Tropical Anvil Clouds: Radiative Driving Toward a Preferred State, J. Geophys. Res.-Atmos., 125, e2020JD033107, https://doi.org/10.1029/2020JD033107, 2020. a
Sokol, A. B., Wall, C. J., and Hartmann, D. L.: Greater Climate Sensitivity Implied by Anvil Cloud Thinning, Nat. Geosci., 17, 398–403, https://doi.org/10.1038/s41561-024-01420-6, 2024. a, b
Sokolowsky, G. A., Freeman, S. W., Jones, W. K., Kukulies, J., Senf, F., Marinescu, P. J., Heikenfeld, M., Brunner, K. N., Bruning, E. C., Collis, S. M., Jackson, R. C., Leung, G. R., Pfeifer, N., Raut, B. A., Saleeby, S. M., Stier, P., and van den Heever, S. C.: tobac v1.5: introducing fast 3D tracking, splits and mergers, and other enhancements for identifying and analysing meteorological phenomena, Geosci. Model Dev., 17, 5309–5330, https://doi.org/10.5194/gmd-17-5309-2024, 2024. a, b, c
Stein, T. H. M., Hogan, R. J., Hanley, K. E., Nicol, J. C., Lean, H. W., Plant, R. S., Clark, P. A., and Halliwell, C. E.: The Three-Dimensional Morphology of Simulated and Observed Convective Storms over Southern England, Mon. Weather Rev., 142, 3264–3283, https://doi.org/10.1175/MWR-D-13-00372.1, 2014. a
Takahashi, H., Luo, Z. J., Stephens, G., and Mulholland, J. P.: Revisiting the Land-Ocean Contrasts in Deep Convective Cloud Intensity Using Global Satellite Observations, Geophys. Res. Lett., 50, e2022GL102089, https://doi.org/10.1029/2022GL102089, 2023. a
Tompkins, A. M. and Craig, G. C.: Sensitivity of Tropical Convection to Sea Surface Temperature in the Absence of Large-Scale Flow, J. Climate, 12, 462–476, https://doi.org/10.1175/1520-0442(1999)012<0462:SOTCTS>2.0.CO;2, 1999. a
Turbeville, S. M., Nugent, J. M., Ackerman, T. P., Bretherton, C. S., and Blossey, P. N.: Tropical Cirrus in Global Storm-Resolving Models: 2. Cirrus Life Cycle and Top-of-Atmosphere Radiative Fluxes, Earth and Space Science, 9, e2021EA001978, https://doi.org/10.1029/2021EA001978, 2022. a
Varble, A. C., Feng, Z., Marquis, J. N., Zhang, Z., Geiss, A., Hardin, J. C., and Jo, E.: Updraft Width Modulates Ambient Atmospheric Controls on Convective Cloud Depth, J. Geophys. Res.-Atmos., 129, e2024JD041769, https://doi.org/10.1029/2024JD041769, 2024. a, b
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, Ä., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., and van Mulbregt, P.: SciPy 1.0: fundamental algorithms for scientific computing in Python, Nature Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020. a
Wall, C. J., Hartmann, D. L., Thieman, M. M., Smith, W. L., and Minnis, P.: The Life Cycle of Anvil Clouds and the Top-of-Atmosphere Radiation Balance over the Tropical West Pacific, J. Climate, 31, 10059–10080, https://doi.org/10.1175/JCLI-D-18-0154.1, 2018. a
Wang, D., Giangrande, S. E., Schiro, K. A., Jensen, M. P., and Houze Jr., R. A.: The Characteristics of Tropical and Midlatitude Mesoscale Convective Systems as Revealed by Radar Wind Profilers, J. Geophys. Res.-Atmos., 124, 4601–4619, https://doi.org/10.1029/2018JD030087, 2019. a
Wang, D., Giangrande, S. E., Feng, Z., Hardin, J. C., and Prein, A. F.: Updraft and Downdraft Core Size and Intensity as Revealed by Radar Wind Profilers: MCS Observations and Idealized Model Comparisons, J. Geophys. Res.-Atmos., 125, e2019JD031774, https://doi.org/10.1029/2019JD031774, 2020. a
Weiss, P., Herbert, R., and Stier, P.: ICON-HAM-lite 1.0: simulating the Earth system with interactive aerosols at kilometer scales, Geosci. Model Dev., 18, 3877–3894, https://doi.org/10.5194/gmd-18-3877-2025, 2025. a
Wieners, K.-H., Rackow, T., Aguridan, R., Becker, T., Beyer, S., Cheedela, S. K., Dreier, N.-A., Engels, J. F., Esch, M., Frauen, C., Klocke, D., Kölling, T., Pedruzo-Bagazgoitia, X., Putrasahan, D., Sidorenko, D., Schnur, R., Stevens, B., and Zimmermann, J.: nextGEMS: output of the production simulations for ICON and IFS, https://www.wdc-climate.de/ui/entry?acronym=nextGEMS_prod (last access: 15 January 2026), 2024. a
Wilcox, E. M., Yuan, T., and Song, H.: Deep convective cloud system size and structure across the global tropics and subtropics, Atmos. Meas. Tech., 16, 5387–5401, https://doi.org/10.5194/amt-16-5387-2023, 2023. a
Wild, M., Hakuba, M. Z., Folini, D., Dörig-Ott, P., Schär, C., Kato, S., and Long, C. N.: The Cloud-Free Global Energy Balance and Inferred Cloud Radiative Effects: An Assessment Based on Direct Observations and Climate Models, Clim. Dynam., 52, 4787–4812, https://doi.org/10.1007/s00382-018-4413-y, 2019. a
Yuan, J. and Houze, R. A.: Global Variability of Mesoscale Convective System Anvil Structure from A-Train Satellite Data, J. Climate, 23, 5864–5888, https://doi.org/10.1175/2010JCLI3671.1, 2010. a, b
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. Meteor. Soc., 141, 563–579, https://doi.org/10.1002/qj.2378, 2015. a
Zipser, E. J.: Some Views On “Hot Towers” after 50 Years of Tropical Field Programs and Two Years of TRMM Data, Meteor. Mon., 29, 49–58, https://doi.org/10.1175/0065-9401(2003)029<0049:CSVOHT>2.0.CO;2, 2003. a
Zipser, E. J. and LeMone, M. A.: Cumulonimbus Vertical Velocity Events in GATE. Part II: Synthesis and Model Core Structure, J. Atmos. Sci., 37, 2458–2469, https://doi.org/10.1175/1520-0469(1980)037<2458:CVVEIG>2.0.CO;2, 1980. a
Short summary
The link between storm updrafts and the high clouds they produce has been difficult to study at large scale but may be important for understanding climate sensitivity. This study uses a new high-resolution global climate model and cloud tracking approach to study this link. More intense updrafts saw larger clouds, but this relationship was much stronger when the updrafts themselves were wider. That means changes in the usual size of updrafts could link to changes in high-cloud area.
The link between storm updrafts and the high clouds they produce has been difficult to study at...
Altmetrics
Final-revised paper
Preprint