Articles | Volume 19, issue 17
https://doi.org/10.5194/gmd-19-8191-2026
© 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/gmd-19-8191-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
04 Sep 2026
Model evaluation paper |
| 04 Sep 2026
| 04 Sep 2026
Sensitivity of cloud structure and precipitation to cloud microphysics schemes in ICON and implications for global km-scale simulations
Department of Physics, University of Oxford, Oxford, UK
Philipp Weiss
European Centre for Medium-Range Weather Forecasts, Bonn, Germany
Philip Stier
Department of Physics, University of Oxford, Oxford, UK
Related authors
Carynelisa Haspel, Cuiqi Zhang, Martin J. Wolf, Daniel J. Cziczo, and Maor Sela
Atmos. Chem. Phys., 23, 10091–10115, https://doi.org/10.5194/acp-23-10091-2023, https://doi.org/10.5194/acp-23-10091-2023, 2023
Short summary
Short summary
Small particles, commonly termed aerosols, can be found throughout the atmosphere and come from both natural and anthropogenic sources. One important type of aerosol is black carbon (BC). In this study, we conducted laboratory measurements of light scattering by particles meant to mimic atmospheric BC and compared them to calculations of scattering. We find that it is likely that calculations underpredict the scattering by BC particles of certain polarizations of light in certain directions.
Hailing Jia, David Neubauer, Yusuf Bhatti, Duncan Watson-Parris, Philip Stier, Johannes Quaas, Daniel Partridge, Ardit Arifi, Anne Kubin, Athanasios Nenes, Muhammed Irfan, Ulas Im, Carl Svenhag, Nick Schutgens, Bastiaan van Diedenhoven, Sylvaine Ferrachat, Ulrike Lohmann, Ina Tegen, Alice Henkes, Guangliang Fu, and Otto Hasekamp
EGUsphere, https://doi.org/10.5194/egusphere-2026-3275, https://doi.org/10.5194/egusphere-2026-3275, 2026
Short summary
Short summary
Tiny aerosol particles affect Earth’s climate by interacting with clouds and sunlight, but their impact remains uncertain. Combining climate model simulations and emulators, we explored millions of model variants across different aerosol and cloud settings to identify the causes of the uncertainty. We found that aerosol emissions and cloud convection are key contributors. Our work suggests that better integration of existing observations could help reduce these uncertainties in the future.
Bernd Heinold, Philipp Weiss, Sadhitro De, Anne Kubin, Jason Müller, Fabian Senf, Philip Stier, and Ina Tegen
Geosci. Model Dev., 19, 6167–6187, https://doi.org/10.5194/gmd-19-6167-2026, https://doi.org/10.5194/gmd-19-6167-2026, 2026
Short summary
Short summary
Understanding climate effects of airborne particles requires computer models that can represent small-scale dynamics. We introduce a new regional configuration of a kilometre-scale aerosol–climate model and apply it to air pollution in Central Europe, sea spray in the Atlantic Arctic, and smoke and dust over Australia. The simulations reproduce key particle patterns and variability, but also reveal limitations due to simplified sources and chemistry. The results guide future model improvements.
Tim Reichelt, Juniper Tyree, Milan Klöwer, Peter Dueben, Bryan N. Lawrence, Allison H. Baker, Sara Faghih-Naini, Torsten Hoefler, and Philip Stier
Geosci. Model Dev., 19, 5933–5960, https://doi.org/10.5194/gmd-19-5933-2026, https://doi.org/10.5194/gmd-19-5933-2026, 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.
Mathilde Ritman, William Jones, and Philip Stier
Atmos. Chem. Phys., 26, 7105–7126, https://doi.org/10.5194/acp-26-7105-2026, https://doi.org/10.5194/acp-26-7105-2026, 2026
Short summary
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.
Tim Butler, Tabish Ansari, Claudio A. Belis, Elisa Bergas-Masso, Willem van Caspel, Hilde Fagerli, Johannes Flemming, Marta Garcia Vivanco, Paul Griffiths, Douglas S. Hamilton, Coralina Hernandez Trujillo, Lena Höglund-Isaksson, Vincent Huijnen, Matthew Kasoar, Johannes W. Kaiser, Gerbrand Koren, Zbigniew Klimont, Florian Lindl, Aura Lupascu, Mariano Mertens, Martijn Schaap, Steven T. Turnock, Oliver Wild, Philipp Weiss, Jacek Kaminski, Rosa Wu, and Terry Keating
EGUsphere, https://doi.org/10.5194/egusphere-2026-1367, https://doi.org/10.5194/egusphere-2026-1367, 2026
Short summary
Short summary
Air pollution travels across continents, meaning emissions in one region can affect air quality far away. To better understand this, scientists from many groups are planning to run coordinated computer simulations of the atmosphere. By comparing results across models and emission scenarios, the planned study will show how pollution moves between regions and which sources matter most, helping governments design more effective air quality policies.
William K. Jones and Philip Stier
EGUsphere, https://doi.org/10.5194/egusphere-2025-6391, https://doi.org/10.5194/egusphere-2025-6391, 2025
Preprint archived
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.
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.
Carynelisa Haspel, Cuiqi Zhang, Martin J. Wolf, Daniel J. Cziczo, and Maor Sela
Atmos. Chem. Phys., 23, 10091–10115, https://doi.org/10.5194/acp-23-10091-2023, https://doi.org/10.5194/acp-23-10091-2023, 2023
Short summary
Short summary
Small particles, commonly termed aerosols, can be found throughout the atmosphere and come from both natural and anthropogenic sources. One important type of aerosol is black carbon (BC). In this study, we conducted laboratory measurements of light scattering by particles meant to mimic atmospheric BC and compared them to calculations of scattering. We find that it is likely that calculations underpredict the scattering by BC particles of certain polarizations of light in certain directions.
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.
Cited articles
Adams-Selin, R. D., van den Heever, S. C., and Johnson, R. H.: Impact of graupel parameterization schemes on idealized bow echo simulations, Mon. Weather Rev., 141, 1241–1262, 2013. a
Anber, U., Gentine, P., Wang, S., and Sobel, A. H.: Fog and rain in the Amazon, P. Natl. Acad. Sci. USA, 112, 11473–11477, https://doi.org/10.1073/pnas.1505077112, 2015. a
Andreae, M. O., Acevedo, O. C., Araùjo, A., Artaxo, P., Barbosa, C. G. G., Barbosa, H. M. J., Brito, J., Carbone, S., Chi, X., Cintra, B. B. L., da Silva, N. F., Dias, N. L., Dias-Júnior, C. Q., Ditas, F., Ditz, R., Godoi, A. F. L., Godoi, R. H. M., Heimann, M., Hoffmann, T., Kesselmeier, J., Könemann, T., Krüger, M. L., Lavric, J. V., Manzi, A. O., Lopes, A. P., Martins, D. L., Mikhailov, E. F., Moran-Zuloaga, D., Nelson, B. W., Nölscher, A. C., Santos Nogueira, D., Piedade, M. T. F., Pöhlker, C., Pöschl, U., Quesada, C. A., Rizzo, L. V., Ro, C.-U., Ruckteschler, N., Sá, L. D. A., de Oliveira Sá, M., Sales, C. B., dos Santos, R. M. N., Saturno, J., Schöngart, J., Sörgel, M., de Souza, C. M., de Souza, R. A. F., Su, H., Targhetta, N., Tóta, J., Trebs, I., Trumbore, S., van Eijck, A., Walter, D., Wang, Z., Weber, B., Williams, J., Winderlich, J., Wittmann, F., Wolff, S., and Yáñez-Serrano, A. M.: The Amazon Tall Tower Observatory (ATTO): overview of pilot measurements on ecosystem ecology, meteorology, trace gases, and aerosols, Atmos. Chem. Phys., 15, 10723–10776, https://doi.org/10.5194/acp-15-10723-2015, 2015. a
Angelis, C. F., McGregor, G. R., and Kidd, C.: Diurnal cycle of rainfall over the Brazilian Amazon, Clim. Res., 26, 139–149, http://www.jstor.org/stable/24868716 (last access: 2 September 2026), 2004. a
Artaxo, P., Martins, J. V., Yamasoe, M. A., Procópio, A. S., Pauliquevis, T. M., Andreae, M. O., Guyon, P., Gatti, L. V., and Leal, A. M. C.: Physical and chemical properties of aerosols in the wet and dry seasons in Rondônia, Amazonia, J. Geophys. Res.-Atmos., 107, LBA 49-1–LBA 49-14, https://doi.org/10.1029/2001JD000666, 2002. 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
Bogerd, L., Overeem, A., Leijnse, H., and Uijlenhoet, R.: A Comprehensive Five-Year Evaluation of IMERG Late Run Precipitation Estimates over the Netherlands, J. Hydrometeorol., 22, 1855–1868, https://doi.org/10.1175/JHM-D-21-0002.1, 2021. a
Bulovic, N., McIntyre, N., and Johnson, F.: Evaluation of IMERG V05B 30-Min Rainfall Estimates over the High-Elevation Tropical Andes Mountains, J. Hydrometeorol., 21, 2875–2892, https://doi.org/10.1175/JHM-D-20-0114.1, 2020. a, b, c
Chen, H., Yong, B., Qi, W., Wu, H., Ren, L., and Hong, Y.: Investigating the Evaluation Uncertainty for Satellite Precipitation Estimates Based on Two Different Ground Precipitation Observation Products, J. Hydrometeorol., 21, 2595–2606, https://doi.org/10.1175/JHM-D-20-0103.1, 2020. a
Christensen, M. W., Ma, P.-L., Wu, P., Varble, A. C., Mülmenstädt, J., and Fast, J. D.: Evaluation of aerosol–cloud interactions in E3SM using a Lagrangian framework, Atmos. Chem. Phys., 23, 2789–2812, https://doi.org/10.5194/acp-23-2789-2023, 2023. a
Clark, P., Roberts, N., Lean, H., Ballard, S. P., and Charlton-Perez, C.: Convection-permitting models: a step-change in rainfall forecasting, Meteorol. Appl., 23, 165–181, https://doi.org/10.1002/met.1538, 2016. a
Costa-Surós, M., Sourdeval, O., Acquistapace, C., Baars, H., Carbajal Henken, C., Genz, C., Hesemann, J., Jimenez, C., König, M., Kretzschmar, J., Madenach, N., Meyer, C. I., Schrödner, R., Seifert, P., Senf, F., Brueck, M., Cioni, G., Engels, J. F., Fieg, K., Gorges, K., Heinze, R., Siligam, P. K., Burkhardt, U., Crewell, S., Hoose, C., Seifert, A., Tegen, I., and Quaas, J.: Detection and attribution of aerosol–cloud interactions in large-domain large-eddy simulations with the ICOsahedral Non-hydrostatic model, Atmos. Chem. Phys., 20, 5657–5678, https://doi.org/10.5194/acp-20-5657-2020, 2020. a
Dagan, G., Koren, I., Altaratz, O., and Lehahn, Y.: Shallow convective cloud field lifetime as a key factor for evaluating aerosol effects, iScience, 10, 192–202, 2018. a
Dagan, G., Stier, P., and Watson-Parris, D.: Analysis of the atmospheric water budget for elucidating the spatial scale of precipitation changes under climate change, Geophys. Res. Lett., 46, 11252–11261, https://doi.org/10.1029/2019GL084173, 2019. a
Dagan, G., Stier, P., Dingley, B., and Williams, A. I. L.: Examining the Regional Co-Variability of the Atmospheric Water and Energy Imbalances in Different Model Configurations – Linking Clouds and Circulation, J. Adv. Model. Earth Sy., 14, e2021MS002951, https://doi.org/10.1029/2021MS002951, 2022a. a
Dagan, G., Stier, P., Spill, G., Harbert, R., Heikenfeld, M., and Van den Heever, S.: Boundary conditions representation can determine simulated aerosol effects on convective cloud fields, Commun. Earth Environ., 3, 71, https://doi.org/10.1038/s43247-022-00399-5, 2022b. a
da Silva, L. d. D. d. J., Mahmoud, M., González-Rodríguez, L., Mohammed, S., Rodríguez-López, L., and Arias, M. I. A.: Assessment of the IMERG Early-Run Precipitation Estimates over South American Country of Chile, Remote Sensing, 15, https://doi.org/10.3390/rs15030573, 2023. a
Derin, Y., Kirstetter, P.-E., and Gourley, J. J.: Evaluation of IMERG Satellite Precipitation over the Land–Coast–Ocean Continuum. Part I: Detection, J. Hydrometeorol., 22, 2843–2859, https://doi.org/10.1175/JHM-D-21-0058.1, 2021. a
Dipankar, A., Stevens, B., Heinze, R., Moseley, C., Zängl, G., Giorgetta, M., and Brdar, S.: Large eddy simulation using the general circulation model ICON, J. Adv. Model. Earth Sy., 7, 963–986, https://doi.org/10.1002/2015MS000431, 2015. a
DWD: ICON tutorial – Working with the ICON Model, DWD pub/nwv/icon tutorial2020, https://doi.org/10.5676/DWD_pub/nwv/icon_tutorial2020, 2020. a
Emanuel, K. A.: Atmospheric Convection, Oxford University Press, New York, Oxford, ISBN 9780195066302, 1994. a
Ferrari, F., Cassola, F., Tuju, P. E., Stocchino, A., Brotto, P., and Mazzino, A.: Impact of Model Resolution and Initial/Boundary Conditions in Forecasting Flood-Causing Precipitations, Atmosphere, 11, https://doi.org/10.3390/atmos11060592, 2020. a
Field, P., Hogan, R., Brown, P., Illingworth, A., Choulartona, T., and Cotton, R.: Parametrization of ice-particle size distributions for mid-latitude stratiform cloud, Q. J. Roy. Meteor. Soc., 131, 1997–2017, 2005. a
Gadelha, A. N., Coelho, V. H. R., Xavier, A. C., Barbosa, L. R., Melo, D. C., Xuan, Y., Huffman, G. J., Petersen, W. A., and das Almeida, C. N.: Grid box-level evaluation of IMERG over Brazil at various space and time scales, Atmos. Res., 218, 231–244, https://doi.org/10.1016/j.atmosres.2018.12.001, 2019. a, b
Gettelman, A. and Morrison, H.: Advanced Two-Moment Bulk Microphysics for Global Models. Part I: Off-Line Tests and Comparison with Other Schemes, J. Climate, 28, 1268–1287, https://doi.org/10.1175/JCLI-D-14-00102.1, 2015. a
Gilewski, P. and Nawalany, M.: Inter-Comparison of Rain-Gauge, Radar, and Satellite (IMERG GPM) Precipitation Estimates Performance for Rainfall-Runoff Modeling in a Mountainous Catchment in Poland, Water, 10, https://doi.org/10.3390/w10111665, 2018. a
Grabowski, W. W., Yano, J.-I., and Moncrieff, M. W.: Cloud Resolving Modeling of Tropical Circulations Driven by Large-Scale SST Gradients, J. Atmos. Sci., 57, 2022–2040, https://doi.org/10.1175/1520-0469(2000)057<2022:CRMOTC>2.0.CO;2, 2000. a, b
Guichard, F. and Couvreux, F.: A short review of numerical cloud-resolving models, Tellus A, 69, 1373578, https://doi.org/10.1080/16000870.2017.1373578, 2017. a, b, c
Guo, H., Golaz, J.-C., Donner, L. J., Wyman, B., Zhao, M., and Ginoux, P.: CLUBB as a unified cloud parameterization: Opportunities and challenges, Geophys. Res. Lett., 42, 4540–4547, 2015. a
Hagemann, S. and Dümenil, L.: A parametrization of the lateral waterflow for the global scale, Clim. Dynam., 14, 17–31, https://doi.org/10.1007/s003820050205, 1997. a
Hagemann, S. and Stacke, T.: Impact of the soil hydrology scheme on simulated soil moisture memory, Clim. Dynam., 44, 1731–1750, https://doi.org/10.1007/s00382-014-2221-6, 2015. a
Hartke, S. H. and Wright, D. B.: Where Can IMERG Provide a Better Precipitation Estimate than Interpolated Gauge Data?, Remote Sensing, 14, https://doi.org/10.3390/rs14215563, 2022. a
Herbert, R., Murray, B. J., Dobbie, S. J., and Koop, T.: Sensitivity of liquid clouds to homogenous freezing parameterizations, Geophys. Res. Lett., 42, 1599–1605, 2015. 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., 126, https://doi.org/10.1029/2021JD034615, 2021. a
Herbert, R. J., Williams, A. I. L., Weiss, P., Watson-Parris, D., Dingley, E., Klocke, D., and Stier, P.: Regional variability of aerosol impacts on clouds and radiation in global kilometer-scale simulations, Atmos. Chem. Phys., 25, 7789–7814, https://doi.org/10.5194/acp-25-7789-2025, 2025. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: Complete ERA5 from 1940: Fifth generation of ECMWF atmospheric reanalyses of the global climate, Copernicus Climate Change Service (C3S) Data Store (CDS) [data set], https://doi.org/10.24381/cds.143582cf, 2017. a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023. a
Heymsfield, A. J. and Kajikawa, M.: An improved approach to calculating terminal velocities of plate-like crystals and graupel, J. Atmos. Sci., 44, 1088–1099, 1987. a
Heymsfield, A. J., Miloshevich, L. M., Schmitt, C., Bansemer, A., Twohy, C., Peollot, 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. 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, b
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, b, c, d
Hourdin, F., Mauritsen, T., Gettelman, A., Golaz, J.-C., Balaji, V., Duan, Q., Folini, D., Ji, D., Klocke, D., Qian, Y., Rauser, F., Rio, C., Tomassini, L., Watanabe, M., and Williamson, D.: The Art and Science of Climate Model Tuning, B. Am. Meteorol. Soc., 98, 589–602, https://doi.org/10.1175/BAMS-D-15-00135.1, 2017. a, b
Huffman, G. J., Bolvin, D. T., Nelkin, E. J., and Tan, J.: Integrated Multi-satellitE Retrievals for GPM (IMERG) Technical Documentation, NASA Goddard Space Flight Center, NASA Technical Document https://gpm.nasa.gov/sites/default/files/document_files/IMERG_doc_190313.pdf (last access: 7 May 2025), 2019. a, b
Ibebuchi, C.: On the representation of atmospheric circulation modes in regional climate models over Western Europe, Int. J. Climatol., 43, 668–682, https://doi.org/10.1002/joc.7807, 2022. 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, b, c, d
Jiang, L. and Bauer-Gottwein, P.: How do GPM IMERG precipitation estimates perform as hydrological model forcing? Evaluation for 300 catchments across Mainland China, J. Hydrol., 572, 486–500, https://doi.org/10.1016/j.jhydrol.2019.03.042, 2019. a
Karydis, V. A., Capps, S. L., Russell, A. G., and Nenes, A.: Adjoint sensitivity of global cloud droplet number to aerosol and dynamical parameters, Atmos. Chem. Phys., 12, 9041–9055, https://doi.org/10.5194/acp-12-9041-2012, 2012. a
Khain, A. P., Beheng, K. D., Heymsfield, A., Korolev, A., Krichak, S. O., Levin, Z., Pinsky, M., Phillips, V., Prabhakaran, T., Teller, A., van den Heever, S. C., and Yano, J.-I.: Representation of microphysical processes in cloud-resolving models: Spectral (bin) microphysics versus bulk parameterization, Rev. Geophys., 53, 247–322, 2015. a, b, c, d
Kipling, Z., Stier, P., Labbouz, L., and Wagner, T.: Dynamic subgrid heterogeneity of convective cloud in a global model: description and evaluation of the Convective Cloud Field Model (CCFM) in ECHAM6–HAM2, Atmos. Chem. Phys., 17, 327–342, https://doi.org/10.5194/acp-17-327-2017, 2017. a
Klemp, J. B., Dudhia, J., and Hassiotis, A. D.: An Upper Gravity-Wave Absorbing Layer for NWP Applications, Mon. Weather Rev., 136, 3987–4004, https://doi.org/10.1175/2008MWR2596.1, 2008. a
Köcher, G., Zinner, T., and Knote, C.: Influence of cloud microphysics schemes on weather model predictions of heavy precipitation, Atmos. Chem. Phys., 23, 6255–6269, https://doi.org/10.5194/acp-23-6255-2023, 2023. a, b
Kodama, C., Noda, A. T., and Satoh, M.: An assessment of the cloud signals simulated by NICAM using ISCCP, CALIPSO, and CloudSat satellite simulators, J. Geophys. Res.-Atmos., 117, https://doi.org/10.1029/2011JD017317, 2012. a
Labbouz, L., Kipling, Z., Stier, P., and Protat, A.: How Well Can We Represent the Spectrum of Convective Clouds in a Climate Model? Comparisons between Internal Parameterization Variables and Radar Observations, J. Atmos. Sci., 75, 1509–1524, https://doi.org/10.1175/JAS-D-17-0191.1, 2018. a
Lebo, Z. J., Morrison, H., and Seinfeld, J. H.: Are simulated aerosol-induced effects on deep convective clouds strongly dependent on saturation adjustment?, Atmos. Chem. Phys., 12, 9941–9964, https://doi.org/10.5194/acp-12-9941-2012, 2012. a, b
Leduc, M. and Laprise, R.: Regional climate model sensitivity to domain size, Clim. Dynam., 32, 833–854, https://doi.org/10.1007/s00382-008-0400-z, 2009. a
Lee, H.-T., Laszlo, I., and Gruber, A.: ABI Earth Radiation Budget Upward Longwave Radiation: TOA (Outgoing Longwave Radiation), https://www.goes-r.gov/products/ATBDs/option2/RadBud_OLR_v2.0_no_color.pdf (last access: 6 October 2025), 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, J., Ding, C., Li, F., and Chen, Y.: Effects of single- and double-moment microphysics schemes on the intensity of super typhoon Sarika (2016), Atmos. Res., 238, 104894, https://doi.org/10.1016/j.atmosres.2020.104894, 2020. a
Li, J., Zhang, K., Hassan, T., Zhang, S., Ma, P.-L., Singh, B., Yan, Q., and Huang, H.: Assessing the sensitivity of aerosol mass budget and effective radiative forcing to horizontal grid spacing in E3SMv1 using a regional refinement approach, Geosci. Model Dev., 17, 1327–1347, https://doi.org/10.5194/gmd-17-1327-2024, 2024. a
Li, Z., Wright, D. B., Hartke, S. H., Kirschbaum, D. B., Khan, S., Maggioni, V., and Kirstetter, P.-E.: Toward a Globally-Applicable Uncertainty Quantification Framework for Satellite Multisensor Precipitation Products Based on GPM DPR, IEEE T. Geosci. Remote, 61, 1–15, https://doi.org/10.1109/TGRS.2023.3235270, 2023. a
Marinescu, P. J., van den Heever, S. C., Heikenfeld, M., Barrett, A. I., Barthlott, C., Hoose, C., Fan, J., Fridlind, A. M., Matsui, T., Miltenberger, A. K., Stier, P., Vie, B., White, B. A., and Zhang, Y.: Impacts of Varying Concentrations of Cloud Condensation Nuclei on Deep Convective Cloud Updrafts – A Multimodel Assessment, J. Atmos. Sci., 78, 1147–1172, https://doi.org/10.1175/JAS-D-20-0200.1, 2021. a
Martins, G., von Randow, C., Sampaio, G., and Dolman, A. J.: Precipitation in the Amazon and its relationship with moisture transport and tropical Pacific and Atlantic SST from the CMIP5 simulation, Hydrol. Earth Syst. Sci. Discuss., 12, 671–704, https://doi.org/10.5194/hessd-12-671-2015, 2015. a
Matte, D., Laprise, R., Thériault, J., and Lucas-Picher, P.: Spatial spin-up of fine scales in a regional climate model simulation driven by low-resolution boundary conditions, Clim. Dynam., 49, 563–574, https://doi.org/10.1007/s00382-016-3358-2, 2017. a
Morrison, H. and Milbrandt, J.: Comparison of Two-Moment Bulk Microphysics Schemes in Idealized Supercell Thunderstorm Simulations, Mon. Weather Rev., 139, 1103–1130, https://doi.org/10.1175/2010MWR3433.1, 2011. 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
Moseley, C., Pscheidt, I., Cioni, G., and Heinze, R.: Impact of resolution on large-eddy simulation of midlatitude summertime convection, Atmos. Chem. Phys., 20, 2891–2910, https://doi.org/10.5194/acp-20-2891-2020, 2020. a
Muñoz de la Torre, E., González Trinidad, J., González Ramírez, E., Bautista Capetillo, C. F., Júnez Ferreira, H. E., Badillo Almaraz, H., and Rivas Recendez, M. I.: Estimation of Rainfall via IMERG-FR and Its Relationship with the Records of a Rain Gauge Network with Spatio-Temporal Variation, Case of Study: Mexican Semi-Arid Region, Remote Sensing, 16, https://doi.org/10.3390/rs16020273, 2024. a, b
NASA Earth Observatory: Blue Marble Next Generation, https://visibleearth.nasa.gov/collection/1484/blue-marble (last access: 6 October 2025), 2024. a
Naumann, A. K., Esch, M., and Stevens, B.: How the representation of microphysical processes affects tropical condensate in the global storm-resolving model ICON, Atmos. Chem. Phys., 25, 6429–6444, https://doi.org/10.5194/acp-25-6429-2025, 2025. a
Ohring, G., Gruber, A., and Ellingson, R.: Satellite Determinations of the Relationship between Total Longwave Radiation Flux and Infrared Window Radiance, J. Appl. Meteorol. Clim., 23, 416–425, https://doi.org/10.1175/1520-0450(1984)023<0416:SDOTRB>2.0.CO;2, 1984. a
Omanovic, N., Goger, B., and Lohmann, U.: The impact of the mesh size and microphysics scheme on the representation of mid-level clouds in the ICON model in hilly and complex terrain, Atmos. Chem. Phys., 24, 14145–14175, https://doi.org/10.5194/acp-24-14145-2024, 2024. a
Palmer, T. and Stevens, B.: The scientific challenge of understanding and estimating climate change, P. Natl. Acad. Sci. USA, 116, 24390–24395, https://doi.org/10.1073/pnas.1906691116, 2019. a
Pascale, S., Lucarini, V., and Feng, X.: Analysis of rainfall seasonality from observations and climate models, Clim. Dynam., 44, 3281–3301, https://doi.org/10.1007/s00382-014-2278-2, 2015. 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
Pöhlker, M. L., Pöhlker, C., Ditas, F., Klimach, T., Hrabe de Angelis, I., Araújo, A., Brito, J., Carbone, S., Cheng, Y., Chi, X., Ditz, R., Gunthe, S. S., Kesselmeier, J., Könemann, T., Lavrič, J. V., Martin, S. T., Mikhailov, E., Moran-Zuloaga, D., Rose, D., Saturno, J., Su, H., Thalman, R., Walter, D., Wang, J., Wolff, S., Barbosa, H. M. J., Artaxo, P., Andreae, M. O., and Pöschl, U.: Long-term observations of cloud condensation nuclei in the Amazon rain forest – Part 1: Aerosol size distribution, hygroscopicity, and new model parametrizations for CCN prediction, Atmos. Chem. Phys., 16, 15709–15740, https://doi.org/10.5194/acp-16-15709-2016, 2016. a
Pope, N. H. and Igel, A. L.: Identifying Important Microphysical Properties and Processes for Marine Fog Forecasts, Mon. Weather Rev., 151, 2427–2441, https://doi.org/10.1175/MWR-D-22-0294.1, 2023. a
Radović, J., Belda, M., Resler, J., Eben, K., Bureš, M., Geletič, J., Krč, P., Řezníček, H., and Fuka, V.: Challenges of constructing and selecting the “perfect” boundary conditions for the large-eddy simulation model PALM, Geosci. Model Dev., 17, 2901–2927, https://doi.org/10.5194/gmd-17-2901-2024, 2024. a
Randall, D. A., man Xu, K., Somerville, R. J. C., and Iacobellis, S.: Single-Column Models and Cloud Ensemble Models as Links between Observations and Climate Models, J. Climate, 9, 1683–1697, https://doi.org/10.1175/1520-0442(1996)009<1683:SCMACE>2.0.CO;2, 1996. a
Rao, C. N., Stowe, L., McClain, E., Sapper, J., and McCormick, M.: Development and application of aerosol remote sensing with AVHRR data from the NOAA satellites, edited by: Hobbs, P. V., Aerosols and Climate, A. Deepak Publishing, Hampton, VA, USA, 69–80, 1988. a
Rehbein, A. and Ambrizzi, T.: Mesoscale convective systems over the Amazon basin in a changing climate under global warming, Clim. Dynam., 61, 1815–1827, https://doi.org/10.1007/s00382-022-06657-8, 2023. a
Richtmyer, R. D. and Morton, K. W.: Difference methods for initial-value problems, Interscience Publishers, John Wiley and Sons, New York, 2nd Edn., ISBN 0894647636, 1967. a
Rocheta, E., Evans, J. P., and Sharma, A.: Can Bias Correction of Regional Climate Model Lateral Boundary Conditions Improve Low-Frequency Rainfall Variability?, J. Climate, 30, 9785–9806, https://doi.org/10.1175/JCLI-D-16-0654.1, 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
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. a
Satoh, M., Matsugishi, S., Roh, W., Yasutaka, I., Naomi, K., Tatsuya, S., Tempei, H., and Hajime, O.: Evaluation of cloud and precipitation processes in regional and global models with ULTIMATE (ULTra-sIte for Measuring Atmosphere of Tokyo metropolitan Environment): a case study using the dual-polarization Doppler weather radars, Prog. Earth Planet. Sci., 9, 51, https://doi.org/10.1186/s40645-022-00511-5, 2022. a
Schmidt, H., Rast, S., Bao, J., Cassim, A., Fang, S.-W., Jimenez-de la Cuesta, D., Keil, P., Kluft, L., Kroll, C., Lang, T., Niemeier, U., Schneidereit, A., Williams, A. I. L., and Stevens, B.: Effects of vertical grid spacing on the climate simulated in the ICON-Sapphire global storm-resolving model, Geosci. Model Dev., 17, 1563–1584, https://doi.org/10.5194/gmd-17-1563-2024, 2024. a
Schmit, T. J. and Gunshor, M. M.: Chapter 4 – ABI Imagery from the GOES-R Series, in: The GOES-R Series, edited by: Goodman, S. J., Schmit, T. J., Daniels, J., and Redmon, R. J., Elsevier, 23–34, ISBN 978-0-12-814327-8, https://doi.org/10.1016/B978-0-12-814327-8.00004-4, 2020. a, b
Schär, C., Fuhrer, O., Arteaga, A., Ban, N., Charpilloz, C., Girolamo, S. D., Hentgen, L., Hoefler, T., Lapillonne, X., Leutwyler, D., Osterried, K., Panosetti, D., Rüdisühli, S., Schlemmer, L., Schulthess, T. C., Sprenger, M., Ubbiali, S., and Wernli, H.: Kilometer-Scale Climate Models: Prospects and Challenges, B. Ame. Meteorol. Soc., 101, E567– E587, https://doi.org/10.1175/BAMS-D-18-0167.1, 2020. a
Seifert, A. and Beheng, K. D.: A double-moment parameterization for simulating autoconversion, accretion and self-collection, Atmos. Res., 59–60, 265–281, 2001. a
Seifert, A. and Beheng, K. D.: A two-moment cloud microphysics parameterization for mixed-phase clouds. Part 1: Model description, Meteorol. Atmos. Phys., 92, 45–66, https://doi.org/10.1007/s00703-005-0112-4, 2006. a, b
Seiki, T., Roh, W., and Satoh, M.: Cloud Microphysics in Global Cloud Resolving Models, Atmos.-Ocean, 60, 477–505, https://doi.org/10.1080/07055900.2022.2075310, 2022. a
Sela, M.: Data for “Sensitivity of cloud structure and precipitation to cloud microphysics schemes in ICON and implications for global km-scale simulations”, Zenodo [data set], https://doi.org/10.5281/zenodo.17592760, 2025. a
Sela, M., Weiss, P., and Stier, P.: Code for “Sensitivity of cloud structure and precipitation to cloud microphysics schemes in ICON and implications for global km-scale simulations”, Zenodo [code], https://doi.org/10.5281/zenodo.17581721, 2025. a
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
Song, Q., Yang, J., Luo, H., Li, C., and Fu, S.: Idealized 2D Cloud-resolving Simulations for Tidally Locked Habitable Planets, Astrophys. J., 934, 149, https://doi.org/10.3847/1538-4357/ac7879, 2022. a, b
Song, X. and Zhang, G. J.: Microphysics parameterization for convective clouds in a global climate model: Description and single-column model tests, J. Geophys. Res.-Atmos., 116, https://doi.org/10.1029/2010JD014833, 2011. a
Stephens, G. L.: Cloud Feedbacks in the Climate System: A Critical Review, J. Climate, 18, 237–273, https://doi.org/10.1175/JCLI-3243.1, 2005. a
Stevens, B., Satoh, M., Auger, L., Biercamp, J., Bretherton, C. S., Chen, X., Düben, P., Judt, F., Khairoutdinov, M., Klocke, D., Kodama, C., Kornblueh, L., Lin, S.-J., Neumann, P., Putman, W. M., Röber, N., Shibuya, R., Vanniere, B., Vidale, P. L., Wedi, N., and Zhou, L.: DYAMOND: the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains, Prog. Earth Pl. Sci., 6, 61, https://doi.org/10.1186/s40645-019-0304-z, 2019. a
Sullivan, S. C., Lee, D., Oreopoulos, L., and Nenes, A.: Role of updraft velocity in temporal variability of global cloud hydrometeor number, P. Natl. Acad. Sci. USA, 113, 5791–5796, https://doi.org/10.1073/pnas.1514039113, 2016. a
Talchabhadel, R., Shah, S., and Aryal, B.: Evaluation of the Spatiotemporal Distribution of Precipitation Using 28 Precipitation Indices and 4 IMERG Datasets over Nepal, Remote Sensing, 14, https://doi.org/10.3390/rs14235954, 2022. a
Tan, J., Petersen, W. A., Kirstetter, P.-E., and Tian, Y.: Performance of IMERG as a Function of Spatiotemporal Scale, J. Hydrometeorol., 18, 307–319, https://doi.org/10.1175/JHM-D-16-0174.1, 2017. a, b
Tang, G., Clark, M. P., Papalexiou, S. M., Ma, Z., and Hong, Y.: Have satellite precipitation products improved over last two decades? A comprehensive comparison of GPM IMERG with nine satellite and reanalysis datasets, Remote Sens. Environ., 240, 111697, https://doi.org/10.1016/j.rse.2020.111697, 2020. a
Tao, W., Shi, J., Chen, S., Lang, S., Lin, P.-L., Hong, S.-Y., Peters-Lidard, C., and Hou, A.: The impact of microphysical schemes on hurricane intensity and track, Asia-Pacific J. Atmos. Sci., 47, 1–16, https://doi.org/10.1007/s13143-011-1001-z, 2011. a
Tao, W.-K., Chen, J.-P., Li, Z., Wang, C., and Zhang, C.: Impact of aerosols on convective clouds and precipitation, Rev. Geophys., 50, https://doi.org/10.1029/2011RG000369, 2012. a
Tao, W.-K., Wu, D., Lang, S., Chern, J.-D., Peters-Lidard, C., Fridlind, A., and Matsui, T.: High-resolution NU-WRF simulations of a deep convective-precipitation system during MC3E: Further improvements and comparisons between Goddard microphysics schemes and observations, J. Geophys. Res., 121, 1278–1305, 2016. a
Tardif, R.: Precipitation and Fog, Springer International Publishing, Cham, 395–423, ISBN 978-3-319-45229-6, https://doi.org/10.1007/978-3-319-45229-6_8, 2017. a
Tomita, H., Miura, H., Iga, S., Nasuno, T., and Satoh, M.: A global cloud-resolving simulation: Preliminary results from an aqua planet experiment, Geophys. Res. Lett., 32, https://doi.org/10.1029/2005GL022459, 2005. a, b
Tompkins, A. M.: Organization of Tropical Convection in Low Vertical Wind Shears: The Role of Cold Pools, J. Atmos. Sci., 58, 1650–1672, https://doi.org/10.1175/1520-0469(2001)058<1650:OOTCIL>2.0.CO;2, 2001. a
Trenberth, K. E., Fasullo, J. T., and Kiehl, J.: Earth's Global Energy Budget, B. Am. Meteorol. Soc., 90, 311–324, https://doi.org/10.1175/2008BAMS2634.1, 2009. a
Uchida, J., Mori, M., Hara, M., Satoh, M., Goto, D., Kataoka, T., Suzuki, K., and Nakajima, T.: Impact of Lateral Boundary Errors on the Simulation of Clouds with a Nonhydrostatic Regional Climate Model, Mon. Weather Rev., 145, 5059–5082, https://doi.org/10.1175/MWR-D-17-0158.1, 2017. a
Van Weverberg, K., Goudenhoofdt, E., Blahak, U., Brisson, E., Demuzere, M., Marbaix, P., and van Ypersele, J.-P.: Comparison of one-moment and two-moment bulk microphysics for high-resolution climate simulations of intense precipitation, Atmos. Res., 147–148, 145–161, https://doi.org/10.1016/j.atmosres.2014.05.012, 2014. a
Varble, A.: Erroneous Attribution of Deep Convective Invigoration to Aerosol Concentration, J. Atmos. Sci., 75, 1351–1368, https://doi.org/10.1175/JAS-D-17-0217.1, 2018. a
Weigum, N., Schutgens, N., and Stier, P.: Effect of aerosol subgrid variability on aerosol optical depth and cloud condensation nuclei: implications for global aerosol modelling, Atmos. Chem. Phys., 16, 13619–13639, https://doi.org/10.5194/acp-16-13619-2016, 2016. a
White, B., Gryspeerdt, E., Stier, P., Morrison, H., Thompson, G., and Kipling, Z.: Uncertainty from the choice of microphysics scheme in convection-permitting models significantly exceeds aerosol effects, Atmos. Chem. Phys., 17, 12145–12175, https://doi.org/10.5194/acp-17-12145-2017, 2017. a, b, c, d
Wisner, C., Orville, H. D., and Myers, C.: A Numerical Model of a Hail-Bearing Cloud, J. Atmos. Sci., 29, 1160–1181, https://doi.org/10.1175/1520-0469(1972)029<1160:ANMOAH>2.0.CO;2, 1972. a
WMO: Manual on Codes – International Codes, Volume I.1, Annex II to the WMO Technical Regulations: Part A – Alphanumeric Codes, Tech. Rep. WMO-No. 306, World Meteorological Organization, Geneva, Switzerland, https://library.wmo.int/idurl/4/35713 (last access: 6 October 2025), 2019. a
Wu, L. and Petty, G. W.: Intercomparison of Bulk Microphysics Schemes in Model Simulations of Polar Lows, Mon. Weather Rev., 138, 2211–2228, https://doi.org/10.1175/2010MWR3122.1, 2010. a
Wyngaard, J. C.: Toward Numerical Modeling in the “Terra Incognita”, J. Atmos. Sci., 61, 1816–1826, https://doi.org/10.1175/1520-0469(2004)061<1816:TNMITT>2.0.CO;2, 2004. a
Zhang, K., Fu, R., Shaikh, M. J., Ghan, S., Wang, M., Leung, L. R., Dickinson, R. E., and Marengo, J.: Influence of Superparameterization and a Higher-Order Turbulence Closure on Rainfall Bias Over Amazonia in Community Atmosphere Model Version 5, J. Geophys. Res.-Atmos., 122, 9879–9902, https://doi.org/10.1002/2017JD026576, 2017. a
Zhang, M., Wang, H., Zhang, X., Peng, Y., and Che, H.: Applying the WRF Double-Moment Six-Class Microphysics Scheme in the GRAPES_Meso Model: A Case Study, J. Meteorol. Res., 32, 246–264, https://doi.org/10.1007/s13351-018-7066-1, 2018. a
Zhou, C., Gao, W., Hu, J., Du, L., and Du, L.: Capability of IMERG V6 Early, Late, and Final Precipitation Products for Monitoring Extreme Precipitation Events, Remote Sensing, 13, https://doi.org/10.3390/rs13040689, 2021. a
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 representation, and that the behaviour of features such as rain, fog, and ice becomes even more variable when moving to the global scale.
Clouds play a key role in Earth’s climate, but their representation in models remains uncertain....
