Articles | Volume 26, issue 3
https://doi.org/10.5194/acp-26-1713-2026
© 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-26-1713-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Feb 2026
Research article |
| 03 Feb 2026
| 03 Feb 2026
Strong control of the stratocumulus-to-cumulus transition time by aerosol: analysis of the joint roles of several cloud-controlling factors using Gaussian process emulation
School of Earth and Environment, University of Leeds, Leeds, UK
Jill S. Johnson
School of Mathematical and Physical Sciences, University of Sheffield, Sheffield, UK
Leighton A. Regayre
School of Earth and Environment, University of Leeds, Leeds, UK
Met Office Hadley Centre, Exeter, UK
Centre for Environmental Modelling and Computation, University of Leeds, Leeds, UK
Lindsay A. Lee
Advanced Manufacturing Research Centre, University of Sheffield, Sheffield, UK
Ken S. Carslaw
School of Earth and Environment, University of Leeds, Leeds, UK
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Ross James Herbert, Larissa Lacher, Alexander Böhmländer, Mark D. Tarn, Antione Canzi, Aidan Pantoya, Evelyn Freney, Kristina Höhler, Pia Bogert, Celine Planche, Ping Tian, Michael Adams, Sarah Barr, David Brus, Nicole Büttner, Martin Daily, Konstantinos Doulgeris, Konstantinos Eleftheridadis, Grant Forster, Romy Fösig, Dimitrios Georgakopoulos, Maria Gini, A. Gannett Hallar, Radovan Krejci, Elke Ludewig, Mauro Mazzola, Ian B. McCubbin, Tuukka Petäjä, Joseph Robinson, Franziska Vogel, Paul Zieger, Stephen R. Arnold, Kenneth S. Carslaw, Naruki Hiranuma, Ottmar Möhler, and Benjamin J. Murray
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-41, https://doi.org/10.5194/essd-2026-41, 2026
Preprint under review for ESSD
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Ice formation in sub-zero clouds is influenced by airborne particles called ice-nucleating particles (INPs), whose concentrations vary substantially over short time and spatial scales. To assess the role of INPs in our climate, a comprehensive and consistent global dataset is essential. Our GloPINE model-ready dataset is a major step in this direction, comprising 36,000 measurements made using a single instrument design (PINE) over 70,000 hours of operation at 20 northern hemisphere sites.
Xinyi Huang, Paul R. Field, Ross J. Herbert, Benjamin J. Murray, Floortje van den Heuvel, Daniel P. Grosvenor, Rachel W. N. Sansom, and Kenneth S. Carslaw
EGUsphere, https://doi.org/10.5194/egusphere-2026-311, https://doi.org/10.5194/egusphere-2026-311, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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Aerosol-cloud interactions and ice formation processes are key to modelling mixed-phase clouds in cold-air outbreaks. However, no studies have investigated the joint effects of these processes. Here, we used perturbed parameter ensembles and model emulators to show that these processes have strongly interacting effects on the properties of cold air outbreak clouds. Single sensitivity tests may therefore provide incomplete information about cloud-controlling factors.
Yusuf A. Bhatti, Duncan Watson-Parris, Leighton A. Regayre, Hailing Jia, David Neubauer, Ulas Im, Carl Svenhag, Nick Schutgens, Athanasios Tsikerdekis, Athanasios Nenes, Muhammed Irfan, Bastiaan van Diedenhoven, Ardit Arifi, Guangliang Fu, and Otto P. Hasekamp
Atmos. Chem. Phys., 26, 269–293, https://doi.org/10.5194/acp-26-269-2026, https://doi.org/10.5194/acp-26-269-2026, 2026
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Aerosols (small airborne particles) impact Earth's climate, but their extent is unknown. By running climate model simulations and using machine learning to emulate millions of additional variants with different settings, we found that natural emissions like sea spray and sulfur are key sources of uncertainty in climate predictions. Our work shows that understanding these natural processes better can help improve climate models and make future climate projections more accurate.
Kunal Ghosh and Leighton A. Regayre
EGUsphere, https://doi.org/10.5194/egusphere-2025-5533, https://doi.org/10.5194/egusphere-2025-5533, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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Understanding which parts of climate models cause uncertainty requires many large computer experiments. We developed a new workflow that greatly improves the speed and efficiency of these studies. It can analyse millions of model variations up to 25 times faster without losing accuracy, allowing scientists to explore uncertainty in more detail and make climate predictions more reliable.
Barbara Ervens, Ken S. Carslaw, Thomas Koop, and Ulrich Pöschl
Atmos. Chem. Phys., 25, 13903–13952, https://doi.org/10.5194/acp-25-13903-2025, https://doi.org/10.5194/acp-25-13903-2025, 2025
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Over 25 years, the European Geosciences Union (EGU) has demonstrated the success, viability and benefits of interactive open-access (OA) publishing with public peer review in its journals, its publishing platform EGUsphere and virtual compilations. The article summarizes the evolution of the EGU/Copernicus publications and of OA publishing with interactive public peer review at large by placing the EGU/Copernicus publications in the context of current and future global open science.
Léa M. C. Prévost, Leighton A. Regayre, Jill S. Johnson, Doug McNeall, Sean Milton, and Kenneth S. Carslaw
EGUsphere, https://doi.org/10.5194/egusphere-2025-4795, https://doi.org/10.5194/egusphere-2025-4795, 2025
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Climate models rely on uncertain adjustable parameters. We tested millions of combinations of these inputs to see how well the model matches real-world data. We found that no single set of inputs can match several observations at the same time, which suggests that the issue lies in the model itself. We developed a method to detect these conflicts and trace them back trace them to their source. The aim is to help modellers target improvements that reduce uncertainty in climate projections.
Xinyi Huang, Paul R. Field, Benjamin J. Murray, Daniel P. Grosvenor, Floortje van den Heuvel, and Kenneth S. Carslaw
Atmos. Chem. Phys., 25, 11363–11406, https://doi.org/10.5194/acp-25-11363-2025, https://doi.org/10.5194/acp-25-11363-2025, 2025
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Cold-air outbreak (CAO) clouds play a vital role in climate prediction. This study explores the responses of CAO clouds to aerosols and ice production under different environmental conditions. We found that CAO cloud responses vary with cloud temperature and are strongly controlled by the liquid–ice partitioning in these clouds, suggesting the importance of good representations of cloud microphysics properties to predict the behaviours of CAO clouds in a warming climate.
Ken S. Carslaw, Leighton A. Regayre, Ulrike Proske, Andrew Gettelman, David M. H. Sexton, Yun Qian, Lauren Marshall, Oliver Wild, Marcus van Lier-Walqui, Annika Oertel, Saloua Peatier, Ben Yang, Jill S. Johnson, Sihan Li, Daniel T. McCoy, Benjamin M. Sanderson, Christina J. Williamson, Gregory S. Elsaesser, Kuniko Yamazaki, and Ben B. B. Booth
EGUsphere, https://doi.org/10.5194/egusphere-2025-4341, https://doi.org/10.5194/egusphere-2025-4341, 2025
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A major challenge in climate science is reducing projection uncertainty despite advances in models and observational constraints. Perturbed parameter ensembles (PPEs) offer a powerful tool to explore and reduce uncertainty by revealing model weaknesses and guiding development. PPEs are now widely applied across climate systems and scales. We argue they should be prioritized alongside complexity and resolution in model resource planning.
Leighton A. Regayre, Léa M. C. Prévost, Kunal Ghosh, Jill S. Johnson, Jeremy E. Oakley, Jonathan Owen, Iain Webb, and Ken S. Carslaw
EGUsphere, https://doi.org/10.5194/egusphere-2025-3755, https://doi.org/10.5194/egusphere-2025-3755, 2025
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Tiny particles called aerosols affect how much sunlight the Earth reflects back into space – one of the biggest climate uncertainties. We use a large set of climate model simulations and find that uncertainty drops in some regions, but persists in other areas, after comparing models to observations. By identifying the specific processes that cause the remaining uncertainty, we guide future efforts to reduce the aerosol forcing uncertainty so we can make more reliable climate predictions.
Xuemei Wang, Kenneth S. Carslaw, Daniel P. Grosvenor, and Hamish Gordon
Atmos. Chem. Phys., 25, 9685–9717, https://doi.org/10.5194/acp-25-9685-2025, https://doi.org/10.5194/acp-25-9685-2025, 2025
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Anthropogenic emissions can influence aerosol particle number concentrations and cloud formation. Our model simulations predict around a 10 % increase in the particle and cloud droplet number concentrations when doubling the emissions in the Manaus region in the Amazonian wet season. However, the corresponding changes in cloud water and rain mass are around 4 %. Such a weak response implies that this convective environment is not sensitive to the localized anthropogenic emission changes here.
Xu-Cheng He, Nathan Luke Abraham, Han Ding, Maria R. Russo, Daniel P. Grosvenor, Yao Ge, Xuemei Wang, Anthony C. Jones, Pedro Campuzano-Jost, Benjamin Nault, Agnieszka Kupc, Donald Blake, Jose L. Jimenez, Christina J. Williamson, Kenneth S. Carslaw, James Weber, Alexander T. Archibald, and Hamish Gordon
EGUsphere, https://doi.org/10.5194/egusphere-2025-3700, https://doi.org/10.5194/egusphere-2025-3700, 2025
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Aerosols affect clouds and climate. However, current climate models still struggle to simulate them accurately. We used aircraft data from a global mission to evaluate how well the UK Earth System Model represents aerosols and their precursors. Our results show that the model misses key formation processes in clean ocean regions, suggesting that future improvements should focus on better representing how aerosols form naturally in the atmosphere.
Xinyue Shao, Yaman Liu, Xinyi Dong, Minghuai Wang, Ruochong Xu, Joel A. Thornton, Duseong S. Jo, Man Yue, Wenxiang Shen, Manish Shrivastava, Stephen R. Arnold, and Ken S. Carslaw
EGUsphere, https://doi.org/10.5194/egusphere-2025-1526, https://doi.org/10.5194/egusphere-2025-1526, 2025
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Highly Oxygenated Organic Molecules (HOMs) are key precursors of secondary organic aerosols (SOA). Incorporating the HOMs chemical mechanism into a global climate model allows for a reasonable reproduction of observed HOM characteristics. HOM-SOA constitutes a significant fraction of global SOA, and its distribution and formation pathways exhibit strong sensitivity to uncertainties in autoxidation processes and peroxy radical branching ratios.
Xinyue Shao, Minghuai Wang, Xinyi Dong, Yaman Liu, Stephen R. Arnold, Leighton A. Regayre, Duseong S. Jo, Wenxiang Shen, Hao Wang, Man Yue, Jingyi Wang, Wenxin Zhang, and Ken S. Carslaw
EGUsphere, https://doi.org/10.5194/egusphere-2024-4135, https://doi.org/10.5194/egusphere-2024-4135, 2025
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This study uses a global chemistry-climate model to investigate how new particle formation (NPF) from highly oxygenated organic molecules (HOMs) contributes to cloud condensation nuclei (CCN) in both preindustrial (PI) and present-day (PD) environments, and its impact on aerosol indirect radiative forcing. The findings highlight the crucial role of biogenic emissions in climate change, providing new insights for carbon-neutral scenarios and enhancing understanding of aerosol-cloud interactions.
Ross J. Herbert, Alberto Sanchez-Marroquin, Daniel P. Grosvenor, Kirsty J. Pringle, Stephen R. Arnold, Benjamin J. Murray, and Kenneth S. Carslaw
Atmos. Chem. Phys., 25, 291–325, https://doi.org/10.5194/acp-25-291-2025, https://doi.org/10.5194/acp-25-291-2025, 2025
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Aerosol particles that help form ice in clouds vary in number and type around the world and with time. However, in many weather and climate models cloud ice is not linked to aerosols that are known to nucleate ice. Here we report the first steps towards representing ice-nucleating particles within the UK Earth System Model. We conclude that in addition to ice nucleation by sea spray and mineral components of soil dust, we also need to represent ice nucleation by the organic components of soils.
Erin N. Raif, Sarah L. Barr, Mark D. Tarn, James B. McQuaid, Martin I. Daily, Steven J. Abel, Paul A. Barrett, Keith N. Bower, Paul R. Field, Kenneth S. Carslaw, and Benjamin J. Murray
Atmos. Chem. Phys., 24, 14045–14072, https://doi.org/10.5194/acp-24-14045-2024, https://doi.org/10.5194/acp-24-14045-2024, 2024
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Ice-nucleating particles (INPs) allow ice to form in clouds at temperatures warmer than −35°C. We measured INP concentrations over the Norwegian and Barents seas in weather events where cold air is ejected from the Arctic. These concentrations were among the highest measured in the Arctic. It is likely that the INPs were transported to the Arctic from distant regions. These results show it is important to consider hemispheric-scale INP processes to understand INP concentrations in the Arctic.
Masaru Yoshioka, Daniel P. Grosvenor, Ben B. B. Booth, Colin P. Morice, and Ken S. Carslaw
Atmos. Chem. Phys., 24, 13681–13692, https://doi.org/10.5194/acp-24-13681-2024, https://doi.org/10.5194/acp-24-13681-2024, 2024
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A 2020 regulation has reduced sulfur emissions from shipping by about 80 %, leading to a decrease in atmospheric aerosols that have a cooling effect primarily by affecting cloud properties and amounts. Our climate model simulations predict a global temperature increase of 0.04 K over the next 3 decades as a result, which could contribute to surpassing the Paris Agreement's 1.5 °C target. Reduced aerosols may have also contributed to the recent temperature spikes.
Xinyue Shao, Minghuai Wang, Xinyi Dong, Yaman Liu, Wenxiang Shen, Stephen R. Arnold, Leighton A. Regayre, Meinrat O. Andreae, Mira L. Pöhlker, Duseong S. Jo, Man Yue, and Ken S. Carslaw
Atmos. Chem. Phys., 24, 11365–11389, https://doi.org/10.5194/acp-24-11365-2024, https://doi.org/10.5194/acp-24-11365-2024, 2024
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Highly oxygenated organic molecules (HOMs) play an important role in atmospheric new particle formation (NPF). By semi-explicitly coupling the chemical mechanism of HOMs and a comprehensive nucleation scheme in a global climate model, the updated model shows better agreement with measurements of nucleation rate, growth rate, and NPF event frequency. Our results reveal that HOM-driven NPF leads to a considerable increase in particle and cloud condensation nuclei burden globally.
Rolf Müller, Ulrich Pöschl, Thomas Koop, Thomas Peter, and Ken Carslaw
Atmos. Chem. Phys., 23, 15445–15453, https://doi.org/10.5194/acp-23-15445-2023, https://doi.org/10.5194/acp-23-15445-2023, 2023
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Paul J. Crutzen was a pioneer in atmospheric sciences and a kind-hearted, humorous person with empathy for the private lives of his colleagues and students. He made fundamental scientific contributions to a wide range of scientific topics in all parts of the atmosphere. Paul was among the founders of the journal Atmospheric Chemistry and Physics. His work will continue to be a guide for generations of scientists and environmental policymakers to come.
Hamza Ahsan, Hailong Wang, Jingbo Wu, Mingxuan Wu, Steven J. Smith, Susanne Bauer, Harrison Suchyta, Dirk Olivié, Gunnar Myhre, Hitoshi Matsui, Huisheng Bian, Jean-François Lamarque, Ken Carslaw, Larry Horowitz, Leighton Regayre, Mian Chin, Michael Schulz, Ragnhild Bieltvedt Skeie, Toshihiko Takemura, and Vaishali Naik
Atmos. Chem. Phys., 23, 14779–14799, https://doi.org/10.5194/acp-23-14779-2023, https://doi.org/10.5194/acp-23-14779-2023, 2023
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We examine the impact of the assumed effective height of SO2 injection, SO2 and BC emission seasonality, and the assumed fraction of SO2 emissions injected as SO4 on climate and chemistry model results. We find that the SO2 injection height has a large impact on surface SO2 concentrations and, in some models, radiative flux. These assumptions are a
hiddensource of inter-model variability and may be leading to bias in some climate model results.
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
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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.
Daniel P. Grosvenor and Kenneth S. Carslaw
Atmos. Chem. Phys., 23, 6743–6773, https://doi.org/10.5194/acp-23-6743-2023, https://doi.org/10.5194/acp-23-6743-2023, 2023
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We determine what causes long-term trends in short-wave (SW) radiative fluxes in two climate models. A positive trend occurs between 1850 and 1970 (increasing SW reflection) and a negative trend between 1970 and 2014; the pre-1970 positive trend is mainly driven by an increase in cloud droplet number concentrations due to increases in aerosol, and the 1970–2014 trend is driven by a decrease in cloud fraction, which we attribute to changes in clouds caused by greenhouse gas-induced warming.
Ernesto Reyes-Villegas, Douglas Lowe, Jill S. Johnson, Kenneth S. Carslaw, Eoghan Darbyshire, Michael Flynn, James D. Allan, Hugh Coe, Ying Chen, Oliver Wild, Scott Archer-Nicholls, Alex Archibald, Siddhartha Singh, Manish Shrivastava, Rahul A. Zaveri, Vikas Singh, Gufran Beig, Ranjeet Sokhi, and Gordon McFiggans
Atmos. Chem. Phys., 23, 5763–5782, https://doi.org/10.5194/acp-23-5763-2023, https://doi.org/10.5194/acp-23-5763-2023, 2023
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Organic aerosols (OAs), their sources and their processes remain poorly understood. The volatility basis set (VBS) approach, implemented in air quality models such as WRF-Chem, can be a useful tool to describe primary OA (POA) production and aging. However, the main disadvantage is its complexity. We used a Gaussian process simulator to reproduce model results and to estimate the sources of model uncertainty. We do this by comparing the outputs with OA observations made at Delhi, India, in 2018.
Xuemei Wang, Hamish Gordon, Daniel P. Grosvenor, Meinrat O. Andreae, and Ken S. Carslaw
Atmos. Chem. Phys., 23, 4431–4461, https://doi.org/10.5194/acp-23-4431-2023, https://doi.org/10.5194/acp-23-4431-2023, 2023
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New particle formation in the upper troposphere is important for the global boundary layer aerosol population, and they can be transported downward in Amazonia. We use a global and a regional model to quantify the number of aerosols that are formed at high altitude and transported downward in a 1000 km region. We find that the majority of the aerosols are from outside the region. This suggests that the 1000 km region is unlikely to be a
closed loopfor aerosol formation, transport and growth.
Ruth Price, Andrea Baccarini, Julia Schmale, Paul Zieger, Ian M. Brooks, Paul Field, and Ken S. Carslaw
Atmos. Chem. Phys., 23, 2927–2961, https://doi.org/10.5194/acp-23-2927-2023, https://doi.org/10.5194/acp-23-2927-2023, 2023
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Arctic clouds can control how much energy is absorbed by the surface or reflected back to space. Using a computer model of the atmosphere we investigated the formation of atmospheric particles that allow cloud droplets to form. We found that particles formed aloft are transported to the lowest part of the Arctic atmosphere and that this is a key source of particles. Our results have implications for the way Arctic clouds will behave in the future as climate change continues to impact the region.
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
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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.
Ville Leinonen, Harri Kokkola, Taina Yli-Juuti, Tero Mielonen, Thomas Kühn, Tuomo Nieminen, Simo Heikkinen, Tuuli Miinalainen, Tommi Bergman, Ken Carslaw, Stefano Decesari, Markus Fiebig, Tareq Hussein, Niku Kivekäs, Radovan Krejci, Markku Kulmala, Ari Leskinen, Andreas Massling, Nikos Mihalopoulos, Jane P. Mulcahy, Steffen M. Noe, Twan van Noije, Fiona M. O'Connor, Colin O'Dowd, Dirk Olivie, Jakob B. Pernov, Tuukka Petäjä, Øyvind Seland, Michael Schulz, Catherine E. Scott, Henrik Skov, Erik Swietlicki, Thomas Tuch, Alfred Wiedensohler, Annele Virtanen, and Santtu Mikkonen
Atmos. Chem. Phys., 22, 12873–12905, https://doi.org/10.5194/acp-22-12873-2022, https://doi.org/10.5194/acp-22-12873-2022, 2022
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We provide the first extensive comparison of detailed aerosol size distribution trends between in situ observations from Europe and five different earth system models. We investigated aerosol modes (nucleation, Aitken, and accumulation) separately and were able to show the differences between measured and modeled trends and especially their seasonal patterns. The differences in model results are likely due to complex effects of several processes instead of certain specific model features.
Amy H. Peace, Ben B. B. Booth, Leighton A. Regayre, Ken S. Carslaw, David M. H. Sexton, Céline J. W. Bonfils, and John W. Rostron
Earth Syst. Dynam., 13, 1215–1232, https://doi.org/10.5194/esd-13-1215-2022, https://doi.org/10.5194/esd-13-1215-2022, 2022
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Anthropogenic aerosol emissions have been linked to driving climate responses such as shifts in the location of tropical rainfall. However, the interaction of aerosols with climate remains one of the most uncertain aspects of climate modelling and limits our ability to predict future climate change. We use an ensemble of climate model simulations to investigate what impact the large uncertainty in how aerosols interact with climate has on predicting future tropical rainfall shifts.
Alexander D. Harrison, Daniel O'Sullivan, Michael P. Adams, Grace C. E. Porter, Edmund Blades, Cherise Brathwaite, Rebecca Chewitt-Lucas, Cassandra Gaston, Rachel Hawker, Ovid O. Krüger, Leslie Neve, Mira L. Pöhlker, Christopher Pöhlker, Ulrich Pöschl, Alberto Sanchez-Marroquin, Andrea Sealy, Peter Sealy, Mark D. Tarn, Shanice Whitehall, James B. McQuaid, Kenneth S. Carslaw, Joseph M. Prospero, and Benjamin J. Murray
Atmos. Chem. Phys., 22, 9663–9680, https://doi.org/10.5194/acp-22-9663-2022, https://doi.org/10.5194/acp-22-9663-2022, 2022
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The formation of ice in clouds fundamentally alters cloud properties; hence it is important we understand the special aerosol particles that can nucleate ice when immersed in supercooled cloud droplets. In this paper we show that African desert dust that has travelled across the Atlantic to the Caribbean nucleates ice much less well than we might have expected.
Rachel E. Hawker, Annette K. Miltenberger, Jill S. Johnson, Jonathan M. Wilkinson, Adrian A. Hill, Ben J. Shipway, Paul R. Field, Benjamin J. Murray, and Ken S. Carslaw
Atmos. Chem. Phys., 21, 17315–17343, https://doi.org/10.5194/acp-21-17315-2021, https://doi.org/10.5194/acp-21-17315-2021, 2021
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We find that ice-nucleating particles (INPs), aerosols that can initiate the freezing of cloud droplets, cause substantial changes to the properties of radiatively important convectively generated anvil cirrus. The number concentration of INPs had a large effect on ice crystal number concentration while the INP temperature dependence controlled ice crystal size and cloud fraction. The results indicate information on INP number and source is necessary for the representation of cloud glaciation.
Heather Guy, Ian M. Brooks, Ken S. Carslaw, Benjamin J. Murray, Von P. Walden, Matthew D. Shupe, Claire Pettersen, David D. Turner, Christopher J. Cox, William D. Neff, Ralf Bennartz, and Ryan R. Neely III
Atmos. Chem. Phys., 21, 15351–15374, https://doi.org/10.5194/acp-21-15351-2021, https://doi.org/10.5194/acp-21-15351-2021, 2021
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We present the first full year of surface aerosol number concentration measurements from the central Greenland Ice Sheet. Aerosol concentrations here have a distinct seasonal cycle from those at lower-altitude Arctic sites, which is driven by large-scale atmospheric circulation. Our results can be used to help understand the role aerosols might play in Greenland surface melt through the modification of cloud properties. This is crucial in a rapidly changing region where observations are sparse.
Mao Xiao, Christopher R. Hoyle, Lubna Dada, Dominik Stolzenburg, Andreas Kürten, Mingyi Wang, Houssni Lamkaddam, Olga Garmash, Bernhard Mentler, Ugo Molteni, Andrea Baccarini, Mario Simon, Xu-Cheng He, Katrianne Lehtipalo, Lauri R. Ahonen, Rima Baalbaki, Paulus S. Bauer, Lisa Beck, David Bell, Federico Bianchi, Sophia Brilke, Dexian Chen, Randall Chiu, António Dias, Jonathan Duplissy, Henning Finkenzeller, Hamish Gordon, Victoria Hofbauer, Changhyuk Kim, Theodore K. Koenig, Janne Lampilahti, Chuan Ping Lee, Zijun Li, Huajun Mai, Vladimir Makhmutov, Hanna E. Manninen, Ruby Marten, Serge Mathot, Roy L. Mauldin, Wei Nie, Antti Onnela, Eva Partoll, Tuukka Petäjä, Joschka Pfeifer, Veronika Pospisilova, Lauriane L. J. Quéléver, Matti Rissanen, Siegfried Schobesberger, Simone Schuchmann, Yuri Stozhkov, Christian Tauber, Yee Jun Tham, António Tomé, Miguel Vazquez-Pufleau, Andrea C. Wagner, Robert Wagner, Yonghong Wang, Lena Weitz, Daniela Wimmer, Yusheng Wu, Chao Yan, Penglin Ye, Qing Ye, Qiaozhi Zha, Xueqin Zhou, Antonio Amorim, Ken Carslaw, Joachim Curtius, Armin Hansel, Rainer Volkamer, Paul M. Winkler, Richard C. Flagan, Markku Kulmala, Douglas R. Worsnop, Jasper Kirkby, Neil M. Donahue, Urs Baltensperger, Imad El Haddad, and Josef Dommen
Atmos. Chem. Phys., 21, 14275–14291, https://doi.org/10.5194/acp-21-14275-2021, https://doi.org/10.5194/acp-21-14275-2021, 2021
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Experiments at CLOUD show that in polluted environments new particle formation (NPF) is largely driven by the formation of sulfuric acid–base clusters, stabilized by amines, high ammonia concentrations or lower temperatures. While oxidation products of aromatics can nucleate, they play a minor role in urban NPF. Our experiments span 4 orders of magnitude variation of observed NPF rates in ambient conditions. We provide a framework based on NPF and growth rates to interpret ambient observations.
Ramiro Checa-Garcia, Yves Balkanski, Samuel Albani, Tommi Bergman, Ken Carslaw, Anne Cozic, Chris Dearden, Beatrice Marticorena, Martine Michou, Twan van Noije, Pierre Nabat, Fiona M. O'Connor, Dirk Olivié, Joseph M. Prospero, Philippe Le Sager, Michael Schulz, and Catherine Scott
Atmos. Chem. Phys., 21, 10295–10335, https://doi.org/10.5194/acp-21-10295-2021, https://doi.org/10.5194/acp-21-10295-2021, 2021
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Thousands of tons of dust are emitted into the atmosphere every year, producing important impacts on the Earth system. However, current global climate models are not yet able to reproduce dust emissions, transport and depositions with the desirable accuracy. Our study analyses five different Earth system models to report aspects to be improved to reproduce better available observations, increase the consistency between models and therefore decrease the current uncertainties.
Rachel E. Hawker, Annette K. Miltenberger, Jonathan M. Wilkinson, Adrian A. Hill, Ben J. Shipway, Zhiqiang Cui, Richard J. Cotton, Ken S. Carslaw, Paul R. Field, and Benjamin J. Murray
Atmos. Chem. Phys., 21, 5439–5461, https://doi.org/10.5194/acp-21-5439-2021, https://doi.org/10.5194/acp-21-5439-2021, 2021
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The impact of aerosols on clouds is a large source of uncertainty for future climate projections. Our results show that the radiative properties of a complex convective cloud field in the Saharan outflow region are sensitive to the temperature dependence of ice-nucleating particle concentrations. This means that differences in the aerosol source or composition, for the same aerosol size distribution, can cause differences in the outgoing radiation from regions dominated by tropical convection.
Ananth Ranjithkumar, Hamish Gordon, Christina Williamson, Andrew Rollins, Kirsty Pringle, Agnieszka Kupc, Nathan Luke Abraham, Charles Brock, and Ken Carslaw
Atmos. Chem. Phys., 21, 4979–5014, https://doi.org/10.5194/acp-21-4979-2021, https://doi.org/10.5194/acp-21-4979-2021, 2021
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The effect aerosols have on climate can be better understood by studying their vertical and spatial distribution throughout the atmosphere. We use observation data from the ATom campaign and evaluate the vertical profile of aerosol number concentration, sulfur dioxide and condensation sink using the UKESM (UK Earth System Model). We identify uncertainties in key atmospheric processes that help improve their theoretical representation in global climate models.
Kamalika Sengupta, Kirsty Pringle, Jill S. Johnson, Carly Reddington, Jo Browse, Catherine E. Scott, and Ken Carslaw
Atmos. Chem. Phys., 21, 2693–2723, https://doi.org/10.5194/acp-21-2693-2021, https://doi.org/10.5194/acp-21-2693-2021, 2021
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Global models consistently underestimate atmospheric secondary organic aerosol (SOA), which has significant climatic implications. We use a perturbed parameter model ensemble and ground-based observations to reduce the uncertainty in modelling SOA formation from oxidation of volatile organic compounds. We identify plausible parameter spaces for the yields of extremely low-volatility, low-volatility, and semi-volatile organic compounds based on model–observation match for three key model outputs.
Cited articles
Abdul-Razzak, H. and Ghan, S. J.: A parameterization of aerosol activation: 2. Multiple aerosol types, Journal of Geophysical Research: Atmospheres, 105, 6837–6844, https://doi.org/10.1029/1999JD901161, 2000. a
Albrecht, B. A.: Aerosols, cloud microphysics, and fractional cloudiness, Science, 245, 1227–1230, https://doi.org/10.1126/science.245.4923.1227, 1989. a
Albrecht, B. A., Bretherton, C. S., Johnson, D., Scubert, W. H., and Frisch, A. S.: The Atlantic Stratocumulus Transition Experiment – ASTEX, Bulletin of the American Meteorological Society, 76, 889–904, https://doi.org/10.1175/1520-0477(1995)076<0889:TASTE>2.0.CO;2, 1995. a
Beckett, G., Beech-Brandt, J., Leach, K., Payne, Z., Simpson, A., Smith, L., Turner, A., and Whiting, A.: ARCHER2 Service Description, Zenodo, https://doi.org/10.5281/zenodo.14507040, 2024. a
Bellon, G. and Geoffroy, O.: Stratocumulus radiative effect, multiple equilibria of the well-mixed boundary layer and transition to shallow convection, Quarterly Journal of the Royal Meteorological Society, 142, 1685–1696, https://doi.org/10.1002/qj.2762, 2016. a
Blossey, P. N., Bretherton, C. S., and Mohrmann, J.: Simulating Observed Cloud Transitions in the Northeast Pacific during CSET, Monthly Weather Review, 149, 2633–2658, https://doi.org/10.1175/MWR-D-20-0328.1, 2021. a, b, c, d
Böing, S. J., Dritschel, D. G., Parker, D. J., and Blyth, A. M.: Comparison of the Moist Parcel-in-Cell (MPIC) model with large-eddy simulation for an idealized cloud, Quarterly Journal of the Royal Meteorological Society, 145, 1865–1881, https://doi.org/10.1002/qj.3532, 2019. a
Bony, S. and Dufresne, J.-L.: Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models, Geophysical Research Letters, 32, https://doi.org/10.1029/2005GL023851, 2005. a
Brendecke, J., Dong, X., Xi, B., and Wu, P.: Maritime Cloud and Drizzle Microphysical Properties Retrieved From Ship-Based Observations During MAGIC, Earth and Space Science, 8, e2020EA001588, https://doi.org/10.1029/2020EA001588, 2021. a
Bretherton, C. S.: Insights into low-latitude cloud feedbacks from high-resolution models, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, https://doi.org/10.1098/rsta.2014.0415, 2015. a
Bretherton, C. S. and Blossey, P. N.: Low cloud reduction in a greenhouse-warmed climate: Results from Lagrangian les of a subtropical marine cloudiness transition, Journal of Advances in Modeling Earth Systems, 6, 91–114, https://doi.org/10.1002/2013MS000250, 2014. a, b, c, d
Bretherton, C. S. and Pincus, R.: Cloudiness and marine boundary layer dynamics in the ASTEX Lagrangian experiments. Part I: synoptic setting and vertical structure, Journal of the Atmospheric Sciences, 52, 2707–2723, https://doi.org/10.1175/1520-0469(1995)052<2707:CAMBLD>2.0.CO;2, 1995. a
Bretherton, C. S. and Wyant, M. C.: Moisture transport, lower-tropospheric stability, and decoupling of cloud-topped boundary layers, Journal of the Atmospheric Sciences, 54, 148–167, https://doi.org/10.1175/1520-0469(1997)054<0148:MTLTSA>2.0.CO;2, 1997. a, b
Bretherton, C. S., Austin, P. H., and Siems, S. T.: Cloudiness and marine boundary layer dynamics in the ASTEX Lagrangian experiments. Part II: cloudiness, drizzle, surface fluxes, and entrainment, Journal of the Atmospheric Sciences, 52, 2724–2735, https://doi.org/10.1175/1520-0469(1995)052<2724:CAMBLD>2.0.CO;2, 1995. a
Bretherton, C. S., Krueger, S. K., Wyant, M. C., Bechtold, P., Van Meijgaard, E., Stevens, B., and Teixeira, J.: A GCSS boundary-layer cloud model intercomparison study of the first ASTEX Lagrangian experiment, Boundary-Layer Meteorology, 93, 341–380, https://doi.org/10.1023/A:1002005429969, 1999. a
Bretherton, C. S., Wood, R., George, R. C., Leon, D., Allen, G., and Zheng, X.: Southeast Pacific stratocumulus clouds, precipitation and boundary layer structure sampled along 20° S during VOCALS-REx, Atmospheric Chemistry and Physics, 10, 10639–10654, https://doi.org/10.5194/acp-10-10639-2010, 2010. a
Bretherton, C. S., McCoy, I. L., Mohrmann, J., Wood, R., Ghate, V., Gettelman, A., Bardeen, C. G., Albrecht, B. A., and Zuidema, P.: Cloud, aerosol, and boundary layer structure across the northeast Pacific stratocumulus-cumulus transition as observed during CSET, Monthly Weather Review, 147, 2083–2103, https://doi.org/10.1175/MWR-D-18-0281.1, 2019. a
Brown, A. R., Derbyshire, S. H., and Mason, P. J.: Large-eddy simulation of stable atmospheric boundary layers with a revised stochastic subgrid model, Quarterly Journal of the Royal Meteorological Society, 120, 1485–1512, https://doi.org/10.1002/qj.49712052004, 1994. a
Brown, N., Weiland, M., Hill, A., Shipway, B., Allen, T., Maynard, C., and Rezny, M.: A highly scalable met office NERC cloud model, arXiv [preprint], https://doi.org/10.48550/arXiv.2009.12849, 2020. a
Ceppi, P., Brient, F., Zelinka, M. D., and Hartmann, D. L.: Cloud feedback mechanisms and their representation in global climate models, Wiley Interdisciplinary Reviews: Climate Change, 8, e465, https://doi.org/10.1002/wcc.465, 2017. a
Christensen, M. W., Jones, W. K., and Stier, P.: Aerosols enhance cloud lifetime and brightness along the stratus-to-cumulus transition, Proceedings of the National Academy of Sciences of the United States of America, 117, 17591–17598, https://doi.org/10.1073/pnas.1921231117, 2020. a
Chun, J.-Y., Wood, R., Blossey, P. N., and Doherty, S. J.: Impact on the stratocumulus-to-cumulus transition of the interaction of cloud microphysics and macrophysics with large-scale circulation, Atmospheric Chemistry and Physics, 25, 5251–5271, https://doi.org/10.5194/acp-25-5251-2025, 2025. a
Chung, D., Matheou, G., and Teixeira, J.: Steady-State Large-Eddy Simulations to Study the Stratocumulus to Shallow Cumulus Cloud Transition, Journal of the Atmospheric Sciences, 69, 3264–3276, https://doi.org/10.1175/JAS-D-11-0256.1, 2012. a
Crameri, F.: Scientific colour maps, Zenodo [code], https://doi.org/10.5281/zenodo.8409685, 2023. a
Crameri, F., Shephard, G. E., and Heron, P. J.: The misuse of colour in science communication, Nature Communications, 11, 5444, https://doi.org/10.1038/s41467-020-19160-7, 2020. a
de Roode, S. R. and Duynkerke, P. G.: Dynamics of cumulus rising into stratocumulus as observed during the first “Lagrangian” experiment of ASTEX, Quarterly Journal of the Royal Meteorological Society, 122, 1597–1623, https://doi.org/10.1002/qj.49712253507, 1996. a
de Roode, S. R., Sandu, I., van der Dussen, J. J., Ackerman, A. S., Blossey, P., Jarecka, D., Lock, A., Siebesma, A. P., and Stevens, B.: Large-eddy simulations of EUCLIPSE-GASS lagrangian stratocumulus-to-cumulus transitions: Mean state, turbulence, and decoupling, Journal of the Atmospheric Sciences, 73, 2485–2508, https://doi.org/10.1175/JAS-D-15-0215.1, 2016. a, b, c, d, e, f
Dearden, C., Hill, A., Coe, H., and Choularton, T.: The role of droplet sedimentation in the evolution of low-level clouds over southern West Africa, Atmospheric Chemistry and Physics, 18, 14253–14269, https://doi.org/10.5194/acp-18-14253-2018, 2018. a
Denby, L., Symonds, C., Sansom, R. W. N., and Böing, S. J.: rwnsansom/monc: Stratocumulus to cumulus simulation transition adaptations, Zenodo [code], https://doi.org/10.5281/zenodo.17436433, 2025. a, b
Diamond, M. S., Saide, P. E., Zuidema, P., Ackerman, A. S., Doherty, S. J., Fridlind, A. M., Gordon, H., Howes, C., Kazil, J., Yamaguchi, T., Zhang, J., Feingold, G., and Wood, R.: Cloud adjustments from large-scale smoke–circulation interactions strongly modulate the southeastern Atlantic stratocumulus-to-cumulus transition, Atmospheric Chemistry and Physics, 22, 12113–12151, https://doi.org/10.5194/acp-22-12113-2022, 2022. a, b, c, d, e
Eastman, R., Terai, C. R., Grosvenor, D. P., and Wood, R.: Evaluating the Lagrangian Evolution of Subtropical Low Clouds in GCMs Using Observations: Mean Evolution, Time Scales, and Responses to Predictors, Journal of the Atmospheric Sciences, 78, 353–372, https://doi.org/10.1175/JAS-D-20-0178.1, 2021. a
Eastman, R., McCoy, I. L., and Wood, R.: Wind, Rain, and the Closed to Open Cell Transition in Subtropical Marine Stratocumulus, Journal of Geophysical Research: Atmospheres, 127, e2022JD036795, https://doi.org/10.1029/2022JD036795, 2022. a
Edwards, J. M. and Slingo, A.: Studies with a flexible new radiation code. I: Choosing a configuration for a large-scale model, Quarterly Journal of the Royal Meteorological Society, 122, 689–719, https://doi.org/10.1002/qj.49712253107, 1996. a
Erfani, E., Blossey, P., Wood, R., Mohrmann, J., Doherty, S. J., Wyant, M., and O, K.-T.: Simulating Aerosol Lifecycle Impacts on the Subtropical Stratocumulus-to-Cumulus Transition Using Large-Eddy Simulations, Journal of Geophysical Research: Atmospheres, 127, e2022JD037258, https://doi.org/10.1029/2022JD037258, 2022. a, b, c, d, e
Feingold, G., McComiskey, A., Yamaguchi, T., Johnson, J. S., Carslaw, K. S., and Schmidte, K. S.: New approaches to quantifying aerosol influence on the cloud radiative effect, Proceedings of the National Academy of Sciences of the United States of America, 113, 5812–5819, https://doi.org/10.1073/pnas.1514035112, 2016. a
Field, P. R., Hill, A., Shipway, B., Furtado, K., Wilkinson, J., Miltenberger, A., Gordon, H., Grosvenor, D. P., Stevens, R., and Van Weverberg, K.: Implementation of a Double Moment Cloud Microphysics Scheme in the UK Met Office Regional Numerical Weather Prediction Model, Quarterly Journal of the Royal Meteorological Society, https://doi.org/10.1002/qj.4414, 2023. a
Glassmeier, F., Hoffmann, F., Johnson, J. S., Yamaguchi, T., Carslaw, K. S., and Feingold, G.: An emulator approach to stratocumulus susceptibility, Atmospheric Chemistry and Physics, 19, 10191–10203, https://doi.org/10.5194/acp-19-10191-2019, 2019. a
Goren, T., Kazil, J., Hoffmann, F., Yamaguchi, T., and Feingold, G.: Anthropogenic Air Pollution Delays Marine Stratocumulus Breakup to Open Cells, Geophysical Research Letters, 46, 14135–14144, https://doi.org/10.1029/2019GL085412, 2019. a
Grosvenor, D. P., Field, P. R., Hill, A. A., and Shipway, B. J.: The relative importance of macrophysical and cloud albedo changes for aerosol-induced radiative effects in closed-cell stratocumulus: insight from the modelling of a case study, Atmospheric Chemistry and Physics, 17, 5155–5183, https://doi.org/10.5194/acp-17-5155-2017, 2017. a
Hill, A. A., Shipway, B. J., and Boutle, I. A.: How sensitive are aerosol-precipitation interactions to the warm rain representation?, Journal of Advances in Modeling Earth Systems, 7, 987–1004, https://doi.org/10.1002/2014MS000422, 2015. a
Hoffmann, F., Glassmeier, F., Yamaguchi, T., and Feingold, G.: Liquid Water Path Steady States in Stratocumulus: Insights from Process-Level Emulation and Mixed-Layer Theory, Journal of the Atmospheric Sciences, 77, 2203–2215, https://doi.org/10.1175/jas-d-19-0241.1, 2020. a
Johnson, J. S., Cui, Z., Lee, L. A., Gosling, J. P., Blyth, A. M., and Carslaw, K. S.: Evaluating uncertainty in convective cloud microphysics using statistical emulation, Journal of Advances in Modeling Earth Systems, 7, 162–187, https://doi.org/10.1002/2014MS000383, 2015. a, b
Jones, B. and Johnson, R. T.: Design and analysis for the Gaussian process model, Quality and Reliability Engineering International, 25, 515–524, https://doi.org/10.1002/qre.1044, 2009. a
Jones, C. R., Bretherton, C. S., and Leon, D.: Coupled vs. decoupled boundary layers in VOCALS-REx, Atmospheric Chemistry and Physics, 11, 7143–7153, https://doi.org/10.5194/acp-11-7143-2011, 2011. a
Kazil, J., Yamaguchi, T., and Feingold, G.: Mesoscale organization, entrainment, and the properties of a closed-cell stratocumulus cloud, Journal of Advances in Modeling Earth Systems, 9, 2214–2229, https://doi.org/10.1002/2017MS001072, 2017. a
Klein, S. A. and Hartmann, D. L.: The Seasonal Cycle of Low Stratiform Clouds, Journal of Climate, 6, 1587–1606, https://doi.org/10.1175/1520-0442(1993)006<1587:TSCOLS>2.0.CO;2, 1993. a
Klein, S. A., Hartmann, D. L., and Norris, J. R.: On the relationships among low-cloud structure, sea surface temperature, and atmospheric circulation in the summertime northeast Pacific, Journal of Climate, 8, 1140–1155, https://doi.org/10.1175/1520-0442(1995)008<1140:OTRALC>2.0.CO;2, 1995. a
Krueger, S. K., McLean, G. T., and Fu, Q.: Numerical simulation of the stratus-to-cumulus transition in the subtropical marine boundary layer. Part I: boundary-layer structure, Journal of the Atmospheric Sciences, 52, 2839–2850, https://doi.org/10.1175/1520-0469(1995)052<2839:NSOTST>2.0.CO;2, 1995. a, b
Lawrence, B. N., Bennett, V. L., Churchill, J., Juckes, M., Kershaw, P., Pascoe, S., Pepler, S., Pritchard, M., and Stephens, A.: Storing and manipulating environmental big data with JASMIN, in: 2013 IEEE International Conference on Big Data, 68–75, https://doi.org/10.1109/BigData.2013.6691556, 2013. a
Martin, G. M., Johnson, D. W., Rogers, D. P., Jonas, P. R., Minnis, P., and Hegg, D. A.: Observations of the Interaction between Cumulus Clouds and Warm Stratocumulus Clouds in the Marine Boundary Layer during ASTEX, Journal of Atmospheric Sciences, 52, 2902–2922, https://doi.org/10.1175/1520-0469(1995)052<2902:OOTIBC>2.0.CO;2, 1995. a
Mauger, G. S. and Norris, J. R.: Assessing the impact of meteorological history on subtropical cloud fraction, Journal of Climate, 23, 2926–2940, https://doi.org/10.1175/2010JCLI3272.1, 2010. a, b
McCoy, I. L., Bretherton, C. S., Wood, R., Twohy, C. H., Gettelman, A., Bardeen, C. G., and Toohey, D. W.: Influences of Recent Particle Formation on Southern Ocean Aerosol Variability and Low Cloud Properties, Journal of Geophysical Research: Atmospheres, 126, e2020JD033529, https://doi.org/10.1029/2020JD033529, 2021. a
McCoy, I. L., Wyant, M. C., Blossey, P. N., Bretherton, C. S., and Wood, R.: Aitken Mode Aerosols Buffer Decoupled Mid-Latitude Boundary Layer Clouds Against Precipitation Depletion, Journal of Geophysical Research: Atmospheres, 129, e2023JD039572, https://doi.org/10.1029/2023JD039572, 2024. a
McGibbon, J. and Bretherton, C. S.: Skill of ship-following large-eddy simulations in reproducing MAGIC observations across the northeast Pacific stratocumulus to cumulus transition region, Journal of Advances in Modeling Earth Systems, 9, 810–831, https://doi.org/10.1002/2017MS000924, 2017. a
Merikanto, J., Spracklen, D. V., Mann, G. W., Pickering, S. J., and Carslaw, K. S.: Impact of nucleation on global CCN, Atmospheric Chemistry and Physics, 9, 8601–8616, https://doi.org/10.5194/acp-9-8601-2009, 2009. a
Miller, M. A. and Albrecht, B. A.: Surface-based observations of mesoscale cumulus-stratocumulus interaction during ASTEX, Journal of the Atmospheric Sciences, 52, 2809–2826, https://doi.org/10.1175/1520-0469(1995)052<2809:SBOOMC>2.0.CO;2, 1995. a
Morris, M. D. and Mitchell, T. J.: Exploratory designs for computational experiments, Journal of Statistical Planning and Inference, 43, 381–402, https://doi.org/10.1016/0378-3758(94)00035-T, 1995. a
Nuijens, L. and Siebesma, A. P.: Boundary Layer Clouds and Convection over Subtropical Oceans in our Current and in a Warmer Climate, Current Climate Change Reports, 5, 80–94, https://doi.org/10.1007/S40641-019-00126-X, 2019. a
O, K.-T., Wood, R., and Bretherton, C. S.: Ultraclean Layers and Optically Thin Clouds in the Stratocumulus-to-Cumulus Transition. Part II: Depletion of Cloud Droplets and Cloud Condensation Nuclei through Collision–Coalescence, Journal of the Atmospheric Sciences, 75, 1653–1673, https://doi.org/10.1175/JAS-D-17-0218.1, 2018. a, b
O'Hagan, A.: Bayesian analysis of computer code outputs: A tutorial, Reliability Engineering and System Safety, 91, 1290–1300, https://doi.org/10.1016/j.ress.2005.11.025, 2006. a, b
Painemal, D., Minnis, P., and Nordeen, M.: Aerosol variability, synoptic-scale processes, and their link to the cloud microphysics over the northeast pacific during MAGIC, Journal of Geophysical Research, 120, 5122–5139, https://doi.org/10.1002/2015JD023175, 2015. a
Paluch, I. R. and Lenschow, D. H.: Stratiform Cloud Formation in the Marine Boundary Layer, Journal of Atmospheric Sciences, 48, 2141–2158, https://doi.org/10.1175/1520-0469(1991)048<2141:SCFITM>2.0.CO;2, 1991. a
Pincus, R., Baker, M. B., and Bretherton, C. S.: What controls stratocumulus radiative properties? Lagrangian observations of cloud evolution, Journal of the Atmospheric Sciences, 54, 2215–2236, https://doi.org/10.1175/1520-0469(1997)054<2215:WCSRPL>2.0.CO;2, 1997. a, b, c
Poku, C., Ross, A. N., Hill, A. A., Blyth, A. M., and Shipway, B.: Is a more physical representation of aerosol activation needed for simulations of fog?, Atmospheric Chemistry and Physics, 21, 7271–7292, https://doi.org/10.5194/acp-21-7271-2021, 2021. a
Rasmussen, C. E. and Williams, C. K. I.: Gaussian Processes for Machine Learning, The MIT Press, Massachusetts Institute of Technology, ISBN 026218253X, https://www.gaussianprocess.org/gpml/ (last access: 25 January 2026), 2006. a
Saltelli, A., Chan, K., and Scott, E. M.: Sensitivity Analysis, Wiley, Chichester, England, ISBN 10: 0471998923, 2000. a
Sandu, I., Brenguier, J.-L., Geoffroy, O., Thouron, O., and Masson, V.: Aerosol Impacts on the Diurnal Cycle of Marine Stratocumulus, Journal of the Atmospheric Sciences, 65, 2705–2718, https://doi.org/10.1175/2008JAS2451.1, 2008. a
Sandu, I., Stevens, B., and Pincus, R.: On the transitions in marine boundary layer cloudiness, Atmospheric Chemistry and Physics, 10, 2377–2391, https://doi.org/10.5194/acp-10-2377-2010, 2010. a, b, c
Sansom, R. W. N.: rwnsansom/sct_monc: Edits to analysis after peer review process, Zenodo [code], https://doi.org/10.5281/zenodo.18177076, 2026a. a, b
Sansom, R. W. N.: Stratocumulus-to-cumulus transition: a perturbed parameter ensemble of a large-eddy simulation model, Zenodo [data set], https://doi.org/10.5281/zenodo.18177089, 2026b. a
Sansom, R. W. N., Carslaw, K. S., Johnson, J. S., and Lee, L.: An Emulator of Stratocumulus Cloud Response to Two Cloud-Controlling Factors Accounting for Internal Variability, Journal of Advances in Modeling Earth Systems, 16, e2023MS004179, https://doi.org/10.1029/2023MS004179, 2024. a, b, c
Shipway, B. J. and Hill, A. A.: Diagnosis of systematic differences between multiple parametrizations of warm rain microphysics using a kinematic framework, Quarterly Journal of the Royal Meteorological Society, 138, 2196–2211, https://doi.org/10.1002/qj.1913, 2012. a
Siems, S. T., Lenschow, D. H., and Bretherton, C. S.: A Numerical Study of the Interaction between Stratocumulus and the Air Overlying It, Journal of Atmospheric Sciences, 50, 3663–3676, https://doi.org/10.1175/1520-0469(1993)050<3663:ANSOTI>2.0.CO;2, 1993. a
Sobol, I. M.: Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates, Mathematics and Computers in Simulation, 55, 271–280, https://doi.org/10.1016/S0378-4754(00)00270-6, 2001. a
Svensson, G., Tjernström, M., and Koračin, D.: The Sensitivity Of A Stratocumulus Transition: Model Simulations Of The Astex First Lagrangian, Boundary-Layer Meteorology, 95, 57–90, https://doi.org/10.1023/A:1002434314651, 2000. a, b
Teixeira, J., Cardoso, S., Bonazzola, M., Cole, J., Delgenio, A., Demott, C., Franklin, C., Hannay, C., Jakob, C., Jiao, Y., Karlsson, J., Kitagawa, H., Köhler, M., Kuwano-Yoshida, A., Ledrian, C., Li, J., Lock, A., Miller, M. J., Marquet, P., Martins, J., Mechoso, C. R., Meijgaard, E. v., Meinke, I., Miranda, P. M. A., Mironov, D., Neggers, R., Pan, H. L., Randall, D. A., Rasch, P. J., Rockel, B., Rossow, W. B., Ritter, B., Siebesma, A. P., Soares, P. M. M., Turk, F. J., Vaillancourt, P. A., Von Engeln, A., and Zhao, M.: Tropical and subtropical cloud transitions in weather and climate prediction models: The GCSS/WGNE pacific cross-section intercomparison (GPCI), Journal of Climate, 24, 5223–5256, https://doi.org/10.1175/2011JCLI3672.1, 2011. a
Tsai, J. Y. and Wu, C. M.: Critical transitions of stratocumulus dynamical systems due to perturbation in free-atmosphere moisture, Dynamics of Atmospheres and Oceans, 76, 1–13, https://doi.org/10.1016/j.dynatmoce.2016.08.002, 2016. a
van der Dussen, J. J., de Roode, S. R., Ackerman, A. S., Blossey, P. N., Bretherton, C. S., Kurowski, M. J., Lock, A. P., Neggers, R. A. J., Sandu, I., and Siebesma, A. P.: The GASS/EUCLIPSE model intercomparison of the stratocumulus transition as observed during ASTEX: LES results, Journal of Advances in Modeling Earth Systems, 5, 483–499, https://doi.org/10.1002/jame.20033, 2013. a
van der Dussen, J. J., de Roode, S. R., and Siebesma, A. P.: How large-scale subsidence affects stratocumulus transitions, Atmospheric Chemistry and Physics, 16, 691–701, https://doi.org/10.5194/acp-16-691-2016, 2016. a
Wang, Q. and Lenschow, D. H.: An observational study of the role of penetrating cumulus in a marine stratocumulus-topped boundary layer, Journal of the Atmospheric Sciences, 52, 2778–2787, https://doi.org/10.1175/1520-0469(1995)052<2778:aosotr>2.0.co;2, 1995. a
Wang, S.: Modeling Marine Boundary-Layer Clouds with a Two-Layer Model: A One-Dimensional Simulation, Journal of the Atmospheric Sciences, 50, 4001–4021, https://doi.org/10.1175/1520-0469(1993)050<4001:mmblcw>2.0.co;2, 1993. a
Wellmann, C., Barrett, A. I., Johnson, J. S., Kunz, M., Vogel, B., Carslaw, K. S., and Hoose, C.: Using Emulators to Understand the Sensitivity of Deep Convective Clouds and Hail to Environmental Conditions, Journal of Advances in Modeling Earth Systems, 10, 3103–3122, https://doi.org/10.1029/2018MS001465, 2018. a
Wellmann, C., Barrett, A. I., Johnson, J. S., Kunz, M., Vogel, B., Carslaw, K. S., and Hoose, C.: Comparing the impact of environmental conditions and microphysics on the forecast uncertainty of deep convective clouds and hail, Atmospheric Chemistry and Physics, 20, 2201–2219, https://doi.org/10.5194/acp-20-2201-2020, 2020. a
Wood, R. and Bretherton, C. S.: On the Relationship between Stratiform Low Cloud Cover and Lower-Tropospheric Stability, Journal of Climate, 19, 6425–6432, https://doi.org/10.1175/JCLI3988.1, 2006. a
Wood, R., O, K.-T., Bretherton, C. S., Mohrmann, J., Albrecht, B. A., Zuidema, P., Ghate, V., Schwartz, C., Eloranta, E., Glienke, S., Shaw, R. A., Fugal, J., and Minnis, P.: Ultraclean layers and optically thin clouds in the stratocumulus-to-cumulus transition. Part I: Observations, Journal of the Atmospheric Sciences, 75, 1631–1652, https://doi.org/10.1175/JAS-D-17-0213.1, 2018. a, b
Wyant, M. C., Bretherton, C. S., Rand, H. A., and Stevens, D. E.: Numerical simulations and a conceptual model of the stratocumulus to trade cumulus transition, Journal of the Atmospheric Sciences, 54, 168–192, https://doi.org/10.1175/1520-0469(1997)054<0168:NSAACM>2.0.CO;2, 1997. a
Wyant, M. C., Bretherton, C. S., Wood, R., Blossey, P. N., and McCoy, I. L.: High Free-Tropospheric Aitken-Mode Aerosol Concentrations Buffer Cloud Droplet Concentrations in Large-Eddy Simulations of Precipitating Stratocumulus, Journal of Advances in Modeling Earth Systems, 14, e2021MS002930, https://doi.org/10.1029/2021MS002930, 2022. a, b
Yamaguchi, T., Feingold, G., Kazil, J., and McComiskey, A.: Stratocumulus to cumulus transition in the presence of elevated smoke layers, Geophysical Research Letters, 42, 10478–10485, https://doi.org/10.1002/2015GL066544, 2015. a
Zheng, Y., Zhang, H., and Li, Z.: Role of Surface Latent Heat Flux in Shallow Cloud Transitions: A Mechanism-Denial LES Study, Journal of the Atmospheric Sciences, 78, 2709–2723, https://doi.org/10.1175/JAS-D-20-0381.1, 2021. a
Zhou, X., Kollias, P., and Lewis, E. R.: Clouds, precipitation, and marine boundary layer structure during the MAGIC field campaign, Journal of Climate, 28, 2420–2442, https://doi.org/10.1175/JCLI-D-14-00320.1, 2015. a
Zhou, X., Ackerman, A. S., Fridlind, A. M., Wood, R., and Kollias, P.: Impacts of solar-absorbing aerosol layers on the transition of stratocumulus to trade cumulus clouds, Atmospheric Chemistry and Physics, 17, 12725–12742, https://doi.org/10.5194/acp-17-12725-2017, 2017. a
Short summary
The cloud transition from stratocumulus to cumulus features a distinct decrease in cloud cover. We used a high-resolution model to simulate many instances of the transition with different environmental conditions. In low aerosol conditions, the transition occurred faster due to drizzle depleting the cloud of moisture and aerosol, whereas in high aerosol conditions, other factors were more important. Understanding different regimes is important for accurately simulating clouds in global models.
The cloud transition from stratocumulus to cumulus features a distinct decrease in cloud cover....
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