Articles | Volume 26, issue 3
https://doi.org/10.5194/acp-26-2293-2026
© 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-26-2293-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Feb 2026
Research article |
| 13 Feb 2026
| 13 Feb 2026
Remaining aerosol forcing uncertainty after observational constraint and the processes that cause it
Leighton A. Regayre
CORRESPONDING AUTHOR
Met Office Hadley Centre, Exeter, Fitzroy Road, Exeter, Devon, EX1 3PB, UK
Centre for Environmental Modelling and Computation, University of Leeds, Leeds, LS2 9JT, UK
School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UK
Léa M. C. Prévost
School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UK
Kunal Ghosh
School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UK
Jill S. Johnson
School of Mathematical and Physical Sciences, University of Sheffield, Sheffield, S3 7RH, UK
Jeremy E. Oakley
School of Mathematical and Physical Sciences, University of Sheffield, Sheffield, S3 7RH, UK
Jonathan Owen
School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UK
School of Mathematical and Physical Sciences, University of Sheffield, Sheffield, S3 7RH, UK
Iain Webb
School of Mathematical and Physical Sciences, University of Sheffield, Sheffield, S3 7RH, UK
Ken S. Carslaw
School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UK
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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.
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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.
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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.
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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.
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
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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
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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.
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
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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
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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.
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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.
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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
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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
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Kunal Ghosh and Leighton A. Regayre
EGUsphere, https://doi.org/10.5194/egusphere-2025-5533, https://doi.org/10.5194/egusphere-2025-5533, 2025
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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.
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Xinyi Huang, Paul R. Field, Benjamin J. Murray, Daniel P. Grosvenor, Floortje van den Heuvel, and Kenneth S. Carslaw
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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
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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.
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.
Alison Bain, Kunal Ghosh, Konstantin Tumashevich, Nønne L. Prisle, and Bryan R. Bzdek
Atmos. Chem. Phys., 25, 5633–5645, https://doi.org/10.5194/acp-25-5633-2025, https://doi.org/10.5194/acp-25-5633-2025, 2025
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Violet L. Patterson, Lauren J. Gregoire, Ruza F. Ivanovic, Niall Gandy, Stephen Cornford, Jonathan Owen, Sam Sherriff-Tadano, and Robin S. Smith
EGUsphere, https://doi.org/10.5194/egusphere-2024-3896, https://doi.org/10.5194/egusphere-2024-3896, 2025
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Simulations of the last two glacial periods are ran using a computer model in which the atmosphere and ice sheets interact. The model is able to produce ice sheet volumes, extents and dynamics in good agreement with data. Sensitivity analysis is undertaken and shows the Northern Hemisphere ice sheet size is particularly sensitive to the albedo of the ice in the model but the different ice sheets display different sensitivities to other processes.
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
Short summary
Short summary
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.
Kunal Ghosh, Rukhsar Parveen, and Yelia Shankaranarayana Mayya
Aerosol Research Discuss., https://doi.org/10.5194/ar-2024-23, https://doi.org/10.5194/ar-2024-23, 2024
Preprint withdrawn
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We investigated the space charge influence on the aerosol neutralization principle used in all aerosol measurement instruments. We found that the space charge cause ions to separate unevenly, leading to different rates of neutralization in different parts of the inlet, impacting the effectiveness of the neutralization process.
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
Short summary
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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
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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.
Cited articles
Albrecht, B. A.: Aerosols, Cloud Microphysics, and Fractional Cloudiness, Science, 245, 1227–1230, https://doi.org/10.1126/science.245.4923.1227, 1989.
Andersen, H., Cermak, J., Douglas, A., Myers, T. A., Nowack, P., Stier, P., Wall, C. J., and Wilson Kemsley, S.: Sensitivities of cloud radiative effects to large-scale meteorology and aerosols from global observations, Atmos. Chem. Phys., 23, 10775–10794, https://doi.org/10.5194/acp-23-10775-2023, 2023.
Andres, R. J. and Kasgnoc, A. D.: A time-averaged inventory of subaerial volcanic sulfur emissions, J. Geophys. Res., 103, 25251–25261, https://doi.org/10.1029/98JD02091, 1998.
Archibald, A. T., O'Connor, F. M., Abraham, N. L., Archer-Nicholls, S., Chipperfield, M. P., Dalvi, M., Folberth, G. A., Dennison, F., Dhomse, S. S., Griffiths, P. T., Hardacre, C., Hewitt, A. J., Hill, R. S., Johnson, C. E., Keeble, J., Köhler, M. O., Morgenstern, O., Mulcahy, J. P., Ordóñez, C., Pope, R. J., Rumbold, S. T., Russo, M. R., Savage, N. H., Sellar, A., Stringer, M., Turnock, S. T., Wild, O., and Zeng, G.: Description and evaluation of the UKCA stratosphere–troposphere chemistry scheme (StratTrop vn 1.0) implemented in UKESM1, Geosci. Model Dev., 13, 1223–1266, https://doi.org/10.5194/gmd-13-1223-2020, 2020.
Baker, A. R., Thomas, M., Bange, H. W., and Plasencia Sánchez, E.: Soluble trace metals in aerosols over the tropical south-east Pacific offshore of Peru, Biogeosciences, 13, 817–825, https://doi.org/10.5194/bg-13-817-2016, 2016.
Balkanski, Y., Schulz, M., Claquin, T., and Guibert, S.: Reevaluation of Mineral aerosol radiative forcings suggests a better agreement with satellite and AERONET data, Atmos. Chem. Phys., 7, 81–95, https://doi.org/10.5194/acp-7-81-2007, 2007.
Bellouin, N., Quaas, J., Morcrette, J.-J., and Boucher, O.: Estimates of aerosol radiative forcing from the MACC re-analysis, Atmos. Chem. Phys., 13, 2045–2062, https://doi.org/10.5194/acp-13-2045-2013, 2013.
Bellouin, N., Quaas, J., Gryspeerdt, E., Kinne, S., Stier, P., Watson-Parris, D., Boucher, O., Carslaw, K. S., Christensen, M., Daniau, A.-L., Dufresne, J.-L., Feingold, G., Fiedler, S., Forster, P., Gettelman, A., Haywood, J. M., Lohmann, U., Malavelle, F., Mauritsen, T., McCoy, D. T., Myhre, G., Mülmenstädt, J., Neubauer, D., Possner, A., Rugenstein, M., Sato, Y., Schulz, M., Schwartz, S. E., Sourdeval, O., Storelvmo, T., Toll, V., Winker, D., and Stevens, B.: Bounding Global Aerosol Radiative Forcing of Climate Change, Rev. Geophys., 58, https://doi.org/10.1029/2019RG000660, 2020.
Beven, K. and Freer, J.: Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology, J. Hydrol., 249, 11–29, https://doi.org/10.1016/S0022-1694(01)00421-8, 2001.
Bond, T. C., Doherty, S. J., Fahey, D. W., Forster, P. M., Berntsen, T., DeAngelo, B. J., Flanner, M. G., Ghan, S., Kärcher, B., Koch, D., Kinne, S., Kondo, Y., Quinn, P. K., Sarofim, M. C., Schultz, M. G., Schulz, M., Venkataraman, C., Zhang, H., Zhang, S., Bellouin, N., Guttikunda, S. K., Hopke, P. K., Jacobson, M. Z., Kaiser, J. W., Klimont, Z., Lohmann, U., Schwarz, J. P., Shindell, D., Storelvmo, T., Warren, S. G., and Zender, C. S.: Bounding the role of black carbon in the climate system: A scientific assessment, J. Geophys. Res. Atmos., 118, 5380–5552, https://doi.org/10.1002/jgrd.50171, 2013.
Brock, C. A., Froyd, K. D., Dollner, M., Williamson, C. J., Schill, G. P., Murphy, D. M., Wagner, N. L., Kupc, A., Jimenez, J. L., Campuzano-Jost, P., Nault, B. A., Schroder, J. C., Day, D. A., Price, D. J., Weinzierl, B. B., Schwarz, J. P., Katich, J. M., Wang, S., Zeng, L. H., Webb, R., Dibb, J. E., Scheuer, E., Diskin, G. S., DiGangi, J. P., Bui, T. P., Dean-Day, J. M., Thompson, C. R., Peischl, J., Ryerson, T. B., Bourgeois, I., Daube, B. C., Commane, R., and Wofsy, S. C.: Atmospheric Tomography Mission (ATom); ATom: Comprehensive Aerosol Properties, 2016–2018, Version 2, NASA Earthdata [data set], https://doi.org/10.3334/ORNLDAAC/2111, 2022.
Browse, J., Carslaw, K. S., Arnold, S. R., Pringle, K., and Boucher, O.: The scavenging processes controlling the seasonal cycle in Arctic sulphate and black carbon aerosol, Atmos. Chem. Phys., 12, 6775–6798, https://doi.org/10.5194/acp-12-6775-2012, 2012.
Budhavant, K., Manoj, M. R., Nair, H. R. C. R., Gaita, S. M., Holmstrand, H., Salam, A., Muslim, A., Satheesh, S. K., and Gustafsson, Ö.: Changing optical properties of black carbon and brown carbon aerosols during long-range transport from the Indo-Gangetic Plain to the equatorial Indian Ocean, Atmos. Chem. Phys., 24, 11911–11925, https://doi.org/10.5194/acp-24-11911-2024, 2024.
Cao, Y., Zhu, Y., Wang, M., Rosenfeld, D., Liang, Y., Liu, J., Liu, Z., and Bai, H.: Emission Reductions Significantly Reduce the Hemispheric Contrast in Cloud Droplet Number Concentration in Recent Two Decades, J. Geophy. Res. Atmos., 128, e2022JD037417, https://doi.org/10.1029/2022JD037417, 2023.
Carslaw, K. S., Lee, L. A., Reddington, C. L., Pringle, K. J., Rap, A., Forster, P. M., Mann, G. W., Spracklen, D. V., Woodhouse, M. T., Regayre, L. A., and Pierce, J. R.: Large contribution of natural aerosols to uncertainty in indirect forcing, Nature, 503, 67–71, https://doi.org/10.1038/nature12674, 2013.
Chemke, R. and Dagan, G.: The Effects of the Spatial Distribution of Direct Anthropogenic Aerosols Radiative Forcing on Atmospheric Circulation, J. Clim., 31, 7129–7145, https://doi.org/10.1175/JCLI-D-17-0694.1, 2018.
Chen, D., Rojas, M., Samset, B. H., Cobb, K., Diongue Niang, A., Edwards, P., Emori, S., Faria, S. H., Hawkins, E., Hope, P., Huybrechts, P., Meinshausen, M., Mustafa, S. K., Plattner, G.-K., and Treguier, A.-M.: Framing, Context, and Methods, in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom and New York, 147–286, https://doi.org/10.1017/9781009157896.003, 2021.
Craig, P. S., Goldstein, M., Seheult, A. H., and Smith, J. A.: Pressure Matching for Hydrocarbon Reservoirs: A Case Study in the Use of Bayes Linear Strategies for Large Computer Experiments, in: Case Studies in Bayesian Statistics, vol. 121, edited by: Gatsonis, C., Hodges, J. S., Kass, R. E., McCulloch, R., Rossi, P., and Singpurwalla, N. D., Springer New York, New York, 37–93, https://doi.org/10.1007/978-1-4612-2290-3_2, 1997.
Eidhammer, T., Gettelman, A., Thayer-Calder, K., Watson-Parris, D., Elsaesser, G., Morrison, H., van Lier-Walqui, M., Song, C., and McCoy, D.: An extensible perturbed parameter ensemble for the Community Atmosphere Model version 6, Geosci. Model Dev., 17, 7835–7853, https://doi.org/10.5194/gmd-17-7835-2024, 2024.
Elsaesser, G. S., Van Lier-Walqui, M., Yang, Q., Kelley, M., Ackerman, A. S., Fridlind, A. M., Cesana, G. V., Schmidt, G. A., Wu, J., Behrangi, A., Camargo, S. J., De, B., Inoue, K., Leitmann-Niimi, N. M., and Strong, J. D. O.: Using Machine Learning to Generate a GISS ModelE Calibrated Physics Ensemble (CPE), J. Adv. Model. Earth Syst., 17, e2024MS004713, https://doi.org/10.1029/2024MS004713, 2025.
Endo, T., Yagoh, H., Sato, K., Matsuda, K., Hayashi, K., Noguchi, I., and Sawada, K.: Regional characteristics of dry deposition of sulfur and nitrogen compounds at EANET sites in Japan from 2003 to 2008, Atmos. Env., 45, 1259–1267, https://doi.org/10.1016/j.atmosenv.2010.12.003, 2011.
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016.
Fierce, L., Bond, T. C., Bauer, S. E., Mena, F., and Riemer, N.: Black carbon absorption at the global scale is affected by particle-scale diversity in composition, Nat. Commun., 7, 12361, https://doi.org/10.1038/ncomms12361, 2016.
Fierce, L., Yao, Y., Easter, R., Ma, P.-L., Sun, J., Wan, H., and Zhang, K.: Quantifying structural errors in cloud condensation nuclei activity from reduced representation of aerosol size distributions, J. Aerosol Sci., 181, 106388, https://doi.org/10.1016/j.jaerosci.2024.106388, 2024.
Forster, P., Storelvmo, T., Armour, K., Collins, W., Dufresne, J., Frame, D., Lunt, D., Mauritsen, T., Palmer, M., Watanabe, M., Wild, M., and Zhang, H.: The Earth's energy budget, climate feedbacks, and climate sensitivity, in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Pean, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Aterfield, T., Yelekci, O., Yu, R., and Zhou, B., Cambridge University Press, https://doi.org/10.1017/9781009157896.009, 2021.
Gallo, F., Sanchez, K. J., Anderson, B. E., Bennett, R., Brown, M. D., Crosbie, E. C., Hostetler, C., Jordan, C., Yang Martin, M., Robinson, C. E., Russell, L. M., Shingler, T. J., Shook, M. A., Thornhill, K. L., Wiggins, E. B., Winstead, E. L., Wisthaler, A., Ziemba, L. D., and Moore, R. H.: Measurement report: Aerosol vertical profiles over the western North Atlantic Ocean during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES), Atmos. Chem. Phys., 23, 1465–1490, https://doi.org/10.5194/acp-23-1465-2023, 2023.
Gettelman, A., Eidhammer, T., Duffy, M. L., McCoy, D. T., Song, C., and Watson-Parris, D.: The Interaction Between Climate Forcing and Feedbacks, J. Geophys. Res. Atmos., 129, e2024JD040857, https://doi.org/10.1029/2024JD040857, 2024.
Ghan, S., Wang, M., Zhang, S., Ferrachat, S., Gettelman, A., Griesfeller, J., Kipling, Z., Lohmann, U., Morrison, H., Neubauer, D., Partridge, D. G., Stier, P., Takemura, T., Wang, H., and Zhang, K.: Challenges in constraining anthropogenic aerosol effects on cloud radiative forcing using present-day spatiotemporal variability, Proc. Natl. Acad. Sci. USA, 113, 5804–5811, https://doi.org/10.1073/pnas.1514036113, 2016.
Gosling, J. P.: SHELF: The Sheffield Elicitation Framework, in: Elicitation, vol. 261, edited by: Dias, L. C., Morton, A., and Quigley, J., Springer International Publishing, Cham, 61–93, https://doi.org/10.1007/978-3-319-65052-4_4, 2018.
Grosvenor, D. P. and Carslaw, K. S.: The decomposition of cloud–aerosol forcing in the UK Earth System Model (UKESM1), Atmos. Chem. Phys., 20, 15681–15724, https://doi.org/10.5194/acp-20-15681-2020, 2020.
Gryspeerdt, E., Quaas, J., and Bellouin, N.: Constraining the aerosol influence on cloud fraction: AEROSOLS AND CLOUD FRACTION, J. Geophys. Res. Atmos., 121, 3566–3583, https://doi.org/10.1002/2015JD023744, 2016.
Halmer, M. M., Schmincke, H.-U., and Graf, H.-F.: The annual volcanic gas input into the atmosphere, in particular into the stratosphere: a global data set for the past 100 years, J. Volcanol. Geotherm. Res., 115, 511–528, https://doi.org/10.1016/S0377-0273(01)00318-3, 2002.
Hamilton, D. S., Lee, L. A., Pringle, K. J., Reddington, C. L., Spracklen, D. V., and Carslaw, K. S.: Occurrence of pristine aerosol environments on a polluted planet, Proc. Natl. Acad. Sci. USA, 111, 18466–18471, https://doi.org/10.1073/pnas.1415440111, 2014.
Hoffmann, J., Bauer, P., Sandu, I., Wedi, N., Geenen, T., and Thiemert, D.: Destination Earth – A digital twin in support of climate services, Clim. Serv., 30, 100394, https://doi.org/10.1016/j.cliser.2023.100394, 2023.
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, Bull. Amer. Met. Soc., 98, 589–602, https://doi.org/10.1175/BAMS-D-15-00135.1, 2017.
Huebert, B. J., Bates, T., Russell, P. B., Shi, G., Kim, Y. J., Kawamura, K., Carmichael, G., and Nakajima, T.: An overview of ACE-Asia: Strategies for quantifying the relationships between Asian aerosols and their climatic impacts, J. Geophys. Res., 108, 2003JD003550, https://doi.org/10.1029/2003JD003550, 2003.
Intergovernmental Panel On Climate Change (IPCC): Climate Change 2021 – The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 1st edn., Cambridge University Press, https://doi.org/10.1017/9781009157896, 2023.
Jia, H. and Quaas, J.: Nonlinearity of the cloud response postpones climate penalty of mitigating air pollution in polluted regions, Nat. Clim. Chang., 13, 943–950, https://doi.org/10.1038/s41558-023-01775-5, 2023.
Johnson, J. S., Regayre, L. A., Yoshioka, M., Pringle, K. J., Turnock, S. T., Browse, J., Sexton, D. M. H., Rostron, J. W., Schutgens, N. A. J., Partridge, D. G., Liu, D., Allan, J. D., Coe, H., Ding, A., Cohen, D. D., Atanacio, A., Vakkari, V., Asmi, E., and Carslaw, K. S.: Robust observational constraint of uncertain aerosol processes and emissions in a climate model and the effect on aerosol radiative forcing, Atmos. Chem. Phys., 20, 9491–9524, https://doi.org/10.5194/acp-20-9491-2020, 2020.
Kahn, R. A., Andrews, E., Brock, C. A., Chin, M., Feingold, G., Gettelman, A., Levy, R. C., Murphy, D. M., Nenes, A., Pierce, J. R., Popp, T., Redemann, J., Sayer, A. M., Da Silva, A. M., Sogacheva, L., and Stier, P.: Reducing Aerosol Forcing Uncertainty by Combining Models With Satellite and Within-The-Atmosphere Observations: A Three-Way Street, Rev. Geophys., 61, e2022RG000796, https://doi.org/10.1029/2022RG000796, 2023.
King, M. D., Menzel, W. P., Kaufman, Y. J., Tanre, D., Bo-Cai Gao, Platnick, S., Ackerman, S. A., Remer, L. A., Pincus, R., and Hubanks, P. A.: Cloud and aerosol properties, precipitable water, and profiles of temperature and water vapor from MODIS, IEEE T. Geosci. Remote, 41, 442–458, https://doi.org/10.1109/TGRS.2002.808226, 2003.
Knutti, R., Masson, D., and Gettelman, A.: Climate model genealogy: Generation CMIP5 and how we got there: CLIMATE MODEL GENEALOGY, Geophys. Res. Lett., 40, 1194–1199, https://doi.org/10.1002/grl.50256, 2013.
Kokkola, H., Tonttila, J., Calderón, S. M., Romakkaniemi, S., Lipponen, A., Peräkorpi, A., Mielonen, T., Gryspeerdt, E., Virtanen, T. H., Kolmonen, P., and Arola, A.: Model analysis of biases in the satellite-diagnosed aerosol effect on the cloud liquid water path, Atmos. Chem. Phys., 25, 1533–1543, https://doi.org/10.5194/acp-25-1533-2025, 2025.
Kuma, P., Bender, F. A.-M., and Jönsson, A. R.: Climate Model Code Genealogy and Its Relation to Climate Feedbacks and Sensitivity, J. Adv. Model. Earth Syst., 15, e2022MS003588, https://doi.org/10.1029/2022MS003588, 2023.
Lack, D. A. and Cappa, C. D.: Impact of brown and clear carbon on light absorption enhancement, single scatter albedo and absorption wavelength dependence of black carbon, Atmos. Chem. Phys., 10, 4207–4220, https://doi.org/10.5194/acp-10-4207-2010, 2010.
Lee, L. A., Carslaw, K. S., Pringle, K. J., and Mann, G. W.: Mapping the uncertainty in global CCN using emulation, Atmos. Chem. Phys., 12, 9739–9751, https://doi.org/10.5194/acp-12-9739-2012, 2012.
Lee, L. A., Reddington, C. L., and Carslaw, K. S.: On the relationship between aerosol model uncertainty and radiative forcing uncertainty, Proc. Natl. Acad. Sci. USA, 113, 5820–5827, https://doi.org/10.1073/pnas.1507050113, 2016.
Liu, Z., Bollasina, M. A., and Wilcox, L. J.: Impact of Asian aerosols on the summer monsoon strongly modulated by regional precipitation biases, Atmos. Chem. Phys., 24, 7227–7252, https://doi.org/10.5194/acp-24-7227-2024, 2024.
Loeb, N. G., Doelling, D. R., Wang, H., Su, W., Nguyen, C., Corbett, J. G., Liang, L., Mitrescu, C., Rose, F. G., and Kato, S.: Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Top-of-Atmosphere (TOA) Edition-4.0 Data Product, J. Clim., 31, 895–918, https://doi.org/10.1175/JCLI-D-17-0208.1, 2018.
Mather, J. H. and Voyles, J. W.: The Arm Climate Research Facility: A Review of Structure and Capabilities, Bull. Amer. Meteor. Soc., 94, 377–392, https://doi.org/10.1175/BAMS-D-11-00218.1, 2013.
McCoy, I. L., McCoy, D. T., Wood, R., Regayre, L., Watson-Parris, D., Grosvenor, D. P., Mulcahy, J. P., Hu, Y., Bender, F. A.-M., Field, P. R., Carslaw, K. S., and Gordon, H.: The hemispheric contrast in cloud microphysical properties constrains aerosol forcing, Proc. Natl. Acad. Sci. USA, 117, 189980–19006, https://doi.org/10.1073/pnas.1922502117, 2020.
McNeall, D., Williams, J., Booth, B., Betts, R., Challenor, P., Wiltshire, A., and Sexton, D.: The impact of structural error on parameter constraint in a climate model, Earth Syst. Dynam., 7, 917–935, https://doi.org/10.5194/esd-7-917-2016, 2016.
Metzger, A., Verheggen, B., Dommen, J., Duplissy, J., Prevot, A. S. H., Weingartner, E., Riipinen, I., Kulmala, M., Spracklen, D. V., Carslaw, K. S., and Baltensperger, U.: Evidence for the role of organics in aerosol particle formation under atmospheric conditions, Proc. Natl. Acad. Sci. USA, 107, 6646–6651, https://doi.org/10.1073/pnas.0911330107, 2010.
Mikkelsen, A., McCoy, D. T., Eidhammer, T., Gettelman, A., Song, C., Gordon, H., and McCoy, I. L.: Constraining aerosol–cloud adjustments by uniting surface observations with a perturbed parameter ensemble, Atmos. Chem. Phys., 25, 4547–4570, https://doi.org/10.5194/acp-25-4547-2025, 2025.
Mulcahy, J. P., Johnson, C., Jones, C. G., Povey, A. C., Scott, C. E., Sellar, A., Turnock, S. T., Woodhouse, M. T., Abraham, N. L., Andrews, M. B., Bellouin, N., Browse, J., Carslaw, K. S., Dalvi, M., Folberth, G. A., Glover, M., Grosvenor, D. P., Hardacre, C., Hill, R., Johnson, B., Jones, A., Kipling, Z., Mann, G., Mollard, J., O'Connor, F. M., Palmiéri, J., Reddington, C., Rumbold, S. T., Richardson, M., Schutgens, N. A. J., Stier, P., Stringer, M., Tang, Y., Walton, J., Woodward, S., and Yool, A.: Description and evaluation of aerosol in UKESM1 and HadGEM3-GC3.1 CMIP6 historical simulations, Geosci. Model Dev., 13, 6383–6423, https://doi.org/10.5194/gmd-13-6383-2020, 2020.
Mülmenstädt, J. and Wilcox, L. J.: The Fall and Rise of the Global Climate Model, J. Adv. Model. Earth Syst., 13, e2021MS002781, https://doi.org/10.1029/2021MS002781, 2021.
Mülmenstädt, J., Ackerman, A. S., Fridlind, A. M., Huang, M., Ma, P.-L., Mahfouz, N., Bauer, S. E., Burrows, S. M., Christensen, M. W., Dipu, S., Gettelman, A., Leung, L. R., Tornow, F., Quaas, J., Varble, A. C., Wang, H., Zhang, K., and Zheng, Y.: Can general circulation models (GCMs) represent cloud liquid water path adjustments to aerosol–cloud interactions?, Atmos. Chem. Phys., 24, 13633–13652, https://doi.org/10.5194/acp-24-13633-2024, 2024.
Murphy, D. M., Froyd, K. D., Bian, H., Brock, C. A., Dibb, J. E., DiGangi, J. P., Diskin, G., Dollner, M., Kupc, A., Scheuer, E. M., Schill, G. P., Weinzierl, B., Williamson, C. J., and Yu, P.: The distribution of sea-salt aerosol in the global troposphere, Atmos. Chem. Phys., 19, 4093–4104, https://doi.org/10.5194/acp-19-4093-2019, 2019.
O'Hagan, A.: Bayesian analysis of computer code outputs: A tutorial, Reliab. Eng. Syst. Saf., 91, 1290–1300, https://doi.org/10.1016/j.ress.2005.11.025, 2006.
Peace, A. H., Carslaw, K. S., Lee, L. A., Regayre, L. A., Booth, B. B. B., Johnson, J. S., and Bernie, D.: Effect of aerosol radiative forcing uncertainty on projected exceedance year of a 1.5 °C global temperature rise, Environ. Res. Lett., 15, 0940a6, https://doi.org/10.1088/1748-9326/aba20c, 2020.
Peace, A. H., Booth, B. B. B., Regayre, L. A., Carslaw, K. S., Sexton, D. M. H., Bonfils, C. J. W., and Rostron, J. W.: Evaluating uncertainty in aerosol forcing of tropical precipitation shifts, Earth Syst. Dynam., 13, 1215–1232, https://doi.org/10.5194/esd-13-1215-2022, 2022.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., and Cournapeau, D.: Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011.
Petrenko, M., Kahn, R., Chin, M., Bauer, S. E., Bergman, T., Bian, H., Curci, G., Johnson, B., Kaiser, J. W., Kipling, Z., Kokkola, H., Liu, X., Mezuman, K., Mielonen, T., Myhre, G., Pan, X., Protonotariou, A., Remy, S., Skeie, R. B., Stier, P., Takemura, T., Tsigaridis, K., Wang, H., Watson-Parris, D., and Zhang, K.: Biomass burning emission analysis based on MODIS aerosol optical depth and AeroCom multi-model simulations: implications for model constraints and emission inventories, Atmos. Chem. Phys., 25, 1545–1567, https://doi.org/10.5194/acp-25-1545-2025, 2025.
Price, R., Orr, A., Field, P. F., Mace, G., and Protat, A.: Simulation of Cloud Processes Over Offshore Coastal Antarctica Using the High-Resolution Regional UK Met Office Unified Model With Interactive Aerosols, J. Geophys. Res. Atmos., 130, e2024JD042109, https://doi.org/10.1029/2024JD042109, 2025.
Proske, U., Ferrachat, S., Neubauer, D., Staab, M., and Lohmann, U.: Assessing the potential for simplification in global climate model cloud microphysics, Atmos. Chem. Phys., 22, 4737–4762, https://doi.org/10.5194/acp-22-4737-2022, 2022.
Qian, Y., Wan, H., Yang, B., Golaz, J., Harrop, B., Hou, Z., Larson, V. E., Leung, L. R., Lin, G., Lin, W., Ma, P., Ma, H., Rasch, P., Singh, B., Wang, H., Xie, S., and Zhang, K.: Parametric Sensitivity and Uncertainty Quantification in the Version 1 of E3SM Atmosphere Model Based on Short Perturbed Parameter Ensemble Simulations, J. Geophys. Res. Atmos., 123, https://doi.org/10.1029/2018JD028927, 2018.
Quaas, J., Arola, A., Cairns, B., Christensen, M., Deneke, H., Ekman, A. M. L., Feingold, G., Fridlind, A., Gryspeerdt, E., Hasekamp, O., Li, Z., Lipponen, A., Ma, P.-L., Mülmenstädt, J., Nenes, A., Penner, J. E., Rosenfeld, D., Schrödner, R., Sinclair, K., Sourdeval, O., Stier, P., Tesche, M., van Diedenhoven, B., and Wendisch, M.: Constraining the Twomey effect from satellite observations: issues and perspectives, Atmos. Chem. Phys., 20, 15079–15099, https://doi.org/10.5194/acp-20-15079-2020, 2020.
Regayre, L.: Figure creation code and dependencies for Reamining aerosol forcing uncertainty after observational constraint and the processes that cause it, Zenodo [code], https://doi.org/10.5281/zenodo.16686812, 2025.
Regayre, L. A., Pringle, K. J., Booth, B. B. B., Lee, L. A., Mann, G. W., Browse, J., Woodhouse, M. T., Rap, A., Reddington, C. L., and Carslaw, K. S.: Uncertainty in the magnitude of aerosol-cloud radiative forcing over recent decades, Geophys. Res. Lett., 41, 9040–9049, https://doi.org/10.1002/2014GL062029, 2014.
Regayre, L. A., Pringle, K. J., Lee, L. A., Rap, A., Browse, J., Mann, G. W., Reddington, C. L., Carslaw, K. S., Booth, B. B. B., and Woodhouse, M. T.: The Climatic Importance of Uncertainties in Regional Aerosol–Cloud Radiative Forcings over Recent Decades, J. Clim., 28, 6589–6607, https://doi.org/10.1175/JCLI-D-15-0127.1, 2015.
Regayre, L. A., Johnson, J. S., Yoshioka, M., Pringle, K. J., Sexton, D. M. H., Booth, B. B. B., Lee, L. A., Bellouin, N., and Carslaw, K. S.: Aerosol and physical atmosphere model parameters are both important sources of uncertainty in aerosol ERF, Atmos. Chem. Phys., 18, 9975–10006, https://doi.org/10.5194/acp-18-9975-2018, 2018.
Regayre, L. A., Schmale, J., Johnson, J. S., Tatzelt, C., Baccarini, A., Henning, S., Yoshioka, M., Stratmann, F., Gysel-Beer, M., Grosvenor, D. P., and Carslaw, K. S.: The value of remote marine aerosol measurements for constraining radiative forcing uncertainty, Atmos. Chem. Phys., 20, 10063–10072, https://doi.org/10.5194/acp-20-10063-2020, 2020.
Regayre, L. A., Carslaw, K. S., Deaconu, L., Symonds, C., Richardson, M., Langton, T., Watson-Parris, D., and Stier, P.: A-CURE: Monthly mean perturbed parameter ensemble data, https://catalogue.ceda.ac.uk/uuid/b735718d66c1403fbf6b93ba3bd3b1a9 (last access: 10 February 2026), 2021.
Regayre, L. A., Deaconu, L., Grosvenor, D. P., Sexton, D. M. H., Symonds, C., Langton, T., Watson-Paris, D., Mulcahy, J. P., Pringle, K. J., Richardson, M., Johnson, J. S., Rostron, J. W., Gordon, H., Lister, G., Stier, P., and Carslaw, K. S.: Identifying climate model structural inconsistencies allows for tight constraint of aerosol radiative forcing, Atmos. Chem. Phys., 23, 8749–8768, https://doi.org/10.5194/acp-23-8749-2023, 2023.
Reutter, P., Su, H., Trentmann, J., Simmel, M., Rose, D., Gunthe, S. S., Wernli, H., Andreae, M. O., and Pöschl, U.: Aerosol- and updraft-limited regimes of cloud droplet formation: influence of particle number, size and hygroscopicity on the activation of cloud condensation nuclei (CCN), Atmos. Chem. Phys., 9, 7067–7080, https://doi.org/10.5194/acp-9-7067-2009, 2009.
Rostron, J. W., Sexton, D. M. H., McSweeney, C. F., Yamazaki, K., Andrews, T., Furtado, K., Ringer, M. A., and Tsushima, Y.: The impact of performance filtering on climate feedbacks in a perturbed parameter ensemble, Clim. Dyn., 55, 521–551, https://doi.org/10.1007/s00382-020-05281-8, 2020.
Rostron, J. W., Sexton, D. M. H., Furtado, K., and Tsushima, Y.: A clearer view of systematic errors in model development: two practical approaches using perturbed parameter ensembles, Clim. Dyn., 63, 354, https://doi.org/10.1007/s00382-025-07717-5, 2025.
Saltelli, A., Aleksankina, K., Becker, W., Fennell, P., Ferretti, F., Holst, N., Li, S., and Wu, Q.: Why so many published sensitivity analyses are false: A systematic review of sensitivity analysis practices, Env. Model. Soft., 114, 29–39, https://doi.org/10.1016/j.envsoft.2019.01.012, 2019.
Salter, J. M., Williamson, D. B., Scinocca, J., and Kharin, V.: Uncertainty Quantification for Computer Models With Spatial Output Using Calibration-Optimal Bases, J. Am. Stat. Assoc., 114, 1800–1814, https://doi.org/10.1080/01621459.2018.1514306, 2019.
Sand, M., Samset, B. H., Myhre, G., Gliß, J., Bauer, S. E., Bian, H., Chin, M., Checa-Garcia, R., Ginoux, P., Kipling, Z., Kirkevåg, A., Kokkola, H., Le Sager, P., Lund, M. T., Matsui, H., van Noije, T., Olivié, D. J. L., Remy, S., Schulz, M., Stier, P., Stjern, C. W., Takemura, T., Tsigaridis, K., Tsyro, S. G., and Watson-Parris, D.: Aerosol absorption in global models from AeroCom phase III, Atmos. Chem. Phys., 21, 15929–15947, https://doi.org/10.5194/acp-21-15929-2021, 2021.
Sanderson, B. M., Knutti, R., and Caldwell, P.: A Representative Democracy to Reduce Interdependency in a Multimodel Ensemble, J. Clim., 28, 5171–5194, https://doi.org/10.1175/JCLI-D-14-00362.1, 2015.
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, J. Adv. Model. Earth Syst., 16, e2023MS004179, https://doi.org/10.1029/2023MS004179, 2024.
Schmale, J., Baccarini, A., Thurnherr, I., Henning, S., Efraim, A., Regayre, L., Bolas, C., Hartmann, M., Welti, A., Lehtipalo, K., Aemisegger, F., Tatzelt, C., Landwehr, S., Modini, R. L., Tummon, F., Johnson, J. S., Harris, N., Schnaiter, M., Toffoli, A., Derkani, M., Bukowiecki, N., Stratmann, F., Dommen, J., Baltensperger, U., Wernli, H., Rosenfeld, D., Gysel-Beer, M., and Carslaw, K. S.: Overview of the Antarctic Circumnavigation Expedition: Study of Preindustrial-like Aerosols and Their Climate Effects (ACE-SPACE), Bull. Am. Met. Soc., 100, 2260–2283, https://doi.org/10.1175/BAMS-D-18-0187.1, 2019.
Schutgens, N., Tsyro, S., Gryspeerdt, E., Goto, D., Weigum, N., Schulz, M., and Stier, P.: On the spatio-temporal representativeness of observations, Atmos. Chem. Phys., 17, 9761–9780, https://doi.org/10.5194/acp-17-9761-2017, 2017.
Sellar, A. A., Jones, C. G., Mulcahy, J. P., Tang, Y., Yool, A., Wiltshire, A., O'Connor, F. M., Stringer, M., Hill, R., Palmieri, J., Woodward, S., Mora, L., Kuhlbrodt, T., Rumbold, S. T., Kelley, D. I., Ellis, R., Johnson, C. E., Walton, J., Abraham, N. L., Andrews, M. B., Andrews, T., Archibald, A. T., Berthou, S., Burke, E., Blockley, E., Carslaw, K., Dalvi, M., Edwards, J., Folberth, G. A., Gedney, N., Griffiths, P. T., Harper, A. B., Hendry, M. A., Hewitt, A. J., Johnson, B., Jones, A., Jones, C. D., Keeble, J., Liddicoat, S., Morgenstern, O., Parker, R. J., Predoi, V., Robertson, E., Siahaan, A., Smith, R. S., Swaminathan, R., Woodhouse, M. T., Zeng, G., and Zerroukat, M.: UKESM1: Description and Evaluation of the U.K. Earth System Model, J. Adv. Model. Earth Syst., 11, 4513–4558, https://doi.org/10.1029/2019MS001739, 2019.
Servén, D., Brummitt, C., Abedi, H., and Hlink: dswah/pyGAM: v0.8.0, Zenodo [code], https://doi.org/10.5281/zenodo.1208723, 2018.
Sexton, D. M. H., Murphy, J. M., Collins, M., and Webb, M. J.: Multivariate probabilistic projections using imperfect climate models part I: outline of methodology, Clim. Dyn., 38, 2513–2542, https://doi.org/10.1007/s00382-011-1208-9, 2012.
Sexton, D. M. H., McSweeney, C. F., Rostron, J. W., Yamazaki, K., Booth, B. B. B., Murphy, J. M., Regayre, L., Johnson, J. S., and Karmalkar, A. V.: A perturbed parameter ensemble of HadGEM3-GC3.05 coupled model projections: part 1: selecting the parameter combinations, Clim. Dyn., 56, 3395–3436, https://doi.org/10.1007/s00382-021-05709-9, 2021.
Shindell, D. T.: Inhomogeneous forcing and transient climate sensitivity, Nat. Clim. Change, 4, 274–277, https://doi.org/10.1038/nclimate2136, 2014.
Sprintall, J., Coles, V. J., Reed, K. A., Butler, A. H., Foltz, G. R., Penny, S. G., and Seo, H.: Best Practice Strategies for Process Studies Designed to Improve Climate Modeling, Bull. Am. Met. Soc., 101, E1842–E1850, https://doi.org/10.1175/BAMS-D-19-0263.1, 2020.
Strong, M., Oakley, J. E., and Brennan, A.: Estimating Multiparameter Partial Expected Value of Perfect Information from a Probabilistic Sensitivity Analysis Sample: A Nonparametric Regression Approach, Med. Decis. Making, 34, 311–326, https://doi.org/10.1177/0272989X13505910, 2014.
Sun, C., Zhang, Y., Liang, B., Gao, M., Sun, X., Li, F., Ni, X., Sun, Q., Ou, H., Chen, D., Zhou, S., and Zhao, J.: Morphological and optical properties of carbonaceous aerosol particles from ship emissions and biomass burning during a summer cruise measurement in the South China Sea, Atmos. Chem. Phys., 24, 3043–3063, https://doi.org/10.5194/acp-24-3043-2024, 2024.
Walters, D., Baran, A. J., Boutle, I., Brooks, M., Earnshaw, P., Edwards, J., Furtado, K., Hill, P., Lock, A., Manners, J., Morcrette, C., Mulcahy, J., Sanchez, C., Smith, C., Stratton, R., Tennant, W., Tomassini, L., Van Weverberg, K., Vosper, S., Willett, M., Browse, J., Bushell, A., Carslaw, K., Dalvi, M., Essery, R., Gedney, N., Hardiman, S., Johnson, B., Johnson, C., Jones, A., Jones, C., Mann, G., Milton, S., Rumbold, H., Sellar, A., Ujiie, M., Whitall, M., Williams, K., and Zerroukat, M.: The Met Office Unified Model Global Atmosphere 7.0/7.1 and JULES Global Land 7.0 configurations, Geosci. Model Dev., 12, 1909–1963, https://doi.org/10.5194/gmd-12-1909-2019, 2019.
Wang, J., Wood, R., Jensen, M. P., Chiu, J. C., Liu, Y., Lamer, K., Desai, N., Giangrande, S. E., Knopf, D. A., Kollias, P., Laskin, A., Liu, X., Lu, C., Mechem, D., Mei, F., Starzec, M., Tomlinson, J., Wang, Y., Yum, S. S., Zheng, G., Aiken, A. C., Azevedo, E. B., Blanchard, Y., China, S., Dong, X., Gallo, F., Gao, S., Ghate, V. P., Glienke, S., Goldberger, L., Hardin, J. C., Kuang, C., Luke, E. P., Matthews, A. A., Miller, M. A., Moffet, R., Pekour, M., Schmid, B., Sedlacek, A. J., Shaw, R. A., Shilling, J. E., Sullivan, A., Suski, K., Veghte, D. P., Weber, R., Wyant, M., Yeom, J., Zawadowicz, M., and Zhang, Z.: Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA), Bull. Am. Met. Soc., 103, E619–E641, https://doi.org/10.1175/BAMS-D-19-0220.1, 2022.
Watson-Parris, D. and Smith, C. J.: Large uncertainty in future warming due to aerosol forcing, Nat. Clim. Change, 12, 1111–1113, https://doi.org/10.1038/s41558-022-01516-0, 2022.
Williams, A. I. L., Stier, P., Dagan, G., and Watson-Parris, D.: Strong control of effective radiative forcing by the spatial pattern of absorbing aerosol, Nat. Clim. Change, 12, 735–742, https://doi.org/10.1038/s41558-022-01415-4, 2022.
Williams, K. D., Copsey, D., Blockley, E. W., Bodas-Salcedo, A., Calvert, D., Comer, R., Davis, P., Graham, T., Hewitt, H. T., Hill, R., Hyder, P., Ineson, S., Johns, T. C., Keen, A. B., Lee, R. W., Megann, A., Milton, S. F., Rae, J. G. L., Roberts, M. J., Scaife, A. A., Schiemann, R., Storkey, D., Thorpe, L., Watterson, I. G., Walters, D. N., West, A., Wood, R. A., Woollings, T., and Xavier, P. K.: The Met Office Global Coupled Model 3.0 and 3.1 (GC3.0 and GC3.1) Configurations, J. Adv. Model. Earth Syst., 10, 357–380, https://doi.org/10.1002/2017MS001115, 2018.
Williamson, D., Goldstein, M., Allison, L., Blaker, A., Challenor, P., Jackson, L., and Yamazaki, K.: History matching for exploring and reducing climate model parameter space using observations and a large perturbed physics ensemble, Clim. Dyn., 41, 1703–1729, https://doi.org/10.1007/s00382-013-1896-4, 2013.
Wood, R., Mechoso, C. R., Bretherton, C. S., Weller, R. A., Huebert, B., Straneo, F., Albrecht, B. A., Coe, H., Allen, G., Vaughan, G., Daum, P., Fairall, C., Chand, D., Gallardo Klenner, L., Garreaud, R., Grados, C., Covert, D. S., Bates, T. S., Krejci, R., Russell, L. M., de Szoeke, S., Brewer, A., Yuter, S. E., Springston, S. R., Chaigneau, A., Toniazzo, T., Minnis, P., Palikonda, R., Abel, S. J., Brown, W. O. J., Williams, S., Fochesatto, J., Brioude, J., and Bower, K. N.: The VAMOS Ocean-Cloud-Atmosphere-Land Study Regional Experiment (VOCALS-REx): goals, platforms, and field operations, Atmos. Chem. Phys., 11, 627–654, https://doi.org/10.5194/acp-11-627-2011, 2011.
Yeom, J. M., Yum, S. S., Shaw, R. A., La, I., Wang, J., Lu, C., Liu, Y., Mei, F., Schmid, B., and Matthews, A.: Vertical Variations of Cloud Microphysical Relationships in Marine Stratocumulus Clouds Observed During the ACE-ENA Campaign, J. Geophys. Res. Atmos., 126, e2021JD034700, https://doi.org/10.1029/2021JD034700, 2021.
Yoshioka, M., Regayre, L. A., Pringle, K. J., Johnson, J. S., Mann, G. W., Partridge, D. G., Sexton, D. M. H., Lister, G. M. S., Schutgens, N., Stier, P., Kipling, Z., Bellouin, N., Browse, J., Booth, B. B. B., Johnson, C. E., Johnson, B., Mollard, J. D. P., Lee, L., and Carslaw, K. S.: Ensembles of Global Climate Model Variants Designed for the Quantification and Constraint of Uncertainty in Aerosols and Their Radiative Forcing, J. Adv. Model. Earth Syst., 11, 3728–3754, https://doi.org/10.1029/2019MS001628, 2019.
Zheng, G., Wang, Y., Aiken, A. C., Gallo, F., Jensen, M. P., Kollias, P., Kuang, C., Luke, E., Springston, S., Uin, J., Wood, R., and Wang, J.: Marine boundary layer aerosol in the eastern North Atlantic: seasonal variations and key controlling processes, Atmos. Chem. Phys., 18, 17615–17635, https://doi.org/10.5194/acp-18-17615-2018, 2018.
Zhong, Q., Schutgens, N., Van Der Werf, G. R., Takemura, T., Van Noije, T., Mielonen, T., Checa-Garcia, R., Lohmann, U., Kirkevåg, A., Olivié, D. J. L., Kokkola, H., Matsui, H., Kipling, Z., Ginoux, P., Le Sager, P., Rémy, S., Bian, H., Chin, M., Zhang, K., Bauer, S. E., and Tsigaridis, K.: Threefold reduction of modeled uncertainty in direct radiative effects over biomass burning regions by constraining absorbing aerosols, Sci. Adv., 9, eadi3568, https://doi.org/10.1126/sciadv.adi3568, 2023.
Zhuang, B. L., Liu, L., Shen, F. H., Wang, T. J., and Han, Y.: Semidirect radiative forcing of internal mixed black carbon cloud droplet and its regional climatic effect over China, J. Geophys. Res., 115, 2009JD013165, https://doi.org/10.1029/2009JD013165, 2010.
Short summary
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.
Tiny particles called aerosols affect how much sunlight the Earth reflects back into space – one...
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