Articles | Volume 18, issue 15
https://doi.org/10.5194/gmd-18-4899-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-4899-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
11 Aug 2025
Development and technical paper |
| 11 Aug 2025
| 11 Aug 2025
Assessing modifications to the Abdul-Razzak and Ghan aerosol activation parameterization (version ARG2000) to improve simulated aerosol–cloud radiative effects in the UK Met Office Unified Model (UM version 13.0)
Pratapaditya Ghosh
Department of Civil and Environmental Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
Center for Atmospheric Particle Studies, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
Katherine J. Evans
Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
Daniel P. Grosvenor
Met Office Hadley Centre, Exeter, UK
School of Earth and Environment, University of Leeds, Leeds, UK
Hyun-Gyu Kang
Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
Salil Mahajan
Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
Min Xu
Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
Wei Zhang
Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
Center for Atmospheric Particle Studies, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
Department of Chemical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
Related authors
Pratapaditya Ghosh, Xue Zheng, Hassan Beydoun, Peter Bogenschutz, and Yunyan Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2026-3700, https://doi.org/10.5194/egusphere-2026-3700, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Marine low clouds play a crucial role in cooling Earth by reflecting sunlight, but storm-resolving models struggle to simulate them accurately. Using ship-based observations and machine learning, we identify which parameter combinations best reduce errors across different cloud types. We find that fixes effective for thick stratocumulus clouds do not work equally well for broken cumulus clouds, suggesting that improving these models requires different strategies for different cloud regimes.
Han Ding, Pratapaditya Ghosh, Xu-Cheng He, R. Lee Mauldin III, David O'Neal, John Ortega, James Norman Smith, and Hamish Gordon
EGUsphere, https://doi.org/10.5194/egusphere-2026-932, https://doi.org/10.5194/egusphere-2026-932, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
About half of the particles in the atmosphere that affect the properties of clouds are formed when gas molecules cluster together. This clustering process is usually very simplified in atmospheric models. Here we test a more sophisticated clustering mechanism in a regional setup of a climate model, using aircraft measurements in Colorado. Including ammonia in the mechanism can substantially improve predictions of atmospheric particle concentrations, but other species probably contribute as well.
Pratapaditya Ghosh, Ian Boutle, Paul Field, Adrian Hill, Anthony Jones, Marie Mazoyer, Katherine J. Evans, Salil Mahajan, Hyun-Gyu Kang, Min Xu, Wei Zhang, Noah Asch, and Hamish Gordon
Atmos. Chem. Phys., 25, 11129–11156, https://doi.org/10.5194/acp-25-11129-2025, https://doi.org/10.5194/acp-25-11129-2025, 2025
Short summary
Short summary
We study aerosol–fog interactions near Paris using a weather and climate model with high spatial resolution. We show that our model can simulate the fog life cycle effectively. We find that the fog droplet number concentrations, the amount of liquid water in the fog, and the vertical structure of the fog are highly sensitive to the parameterization that simulates droplet formation and growth. The changes we propose could improve fog forecasts significantly without increasing computational costs.
Pratapaditya Ghosh, Ian Boutle, Paul Field, Adrian Hill, Marie Mazoyer, Katherine J. Evans, Salil Mahajan, Hyun-Gyu Kang, Min Xu, Wei Zhang, and Hamish Gordon
Atmos. Chem. Phys., 25, 11157–11182, https://doi.org/10.5194/acp-25-11157-2025, https://doi.org/10.5194/acp-25-11157-2025, 2025
Short summary
Short summary
We study the life cycle of fog events in Europe using a weather and climate model. By incorporating droplet formation and growth driven by radiative cooling, our model better simulates the total liquid water in foggy atmospheric columns. We show that both adiabatic and radiative cooling play significant, often equally important, roles in driving droplet formation and growth. We discuss strategies to address droplet number overpredictions by improving model physics and addressing model artifacts.
Forrest M. Hoffman, Birgit Hassler, Ranjini Swaminathan, Jared Lewis, Bouwe Andela, Nathan Collier, Dóra Hegedűs, Jiwoo Lee, Charlotte Pascoe, Mika Pflüger, Martina Stockhause, Paul Ullrich, Min Xu, Lisa Bock, Felicity Chun, Bettina K. Gier, Douglas I. Kelley, Axel Lauer, Julien Lenhardt, Manuel Schlund, Mohanan G. Sreeush, Katja Weigel, Ed Blockley, Rebecca Beadling, Romain Beucher, Demiso D. Dugassa, Valerio Lembo, Jianhua Lu, Swen Brands, Jerry Tjiputra, Elizaveta Malinina, Brian Medeiros, Enrico Scoccimarro, Jeremy Walton, Phil Kershaw, André Lanfer Marquez, Malcolm J. Roberts, Eleanor O'Rourke, Beth Dingley, Briony Turner, Helene Hewitt, and John P. Dunne
Geosci. Model Dev., 19, 7415–7455, https://doi.org/10.5194/gmd-19-7415-2026, https://doi.org/10.5194/gmd-19-7415-2026, 2026
Short summary
Short summary
The Rapid Evaluation Framework (REF) is a community-driven platform for benchmarking and performance assessment of Earth system models. Built upon four disparate community evaluation tools, the REF is designed to provide model-data comparisons for the Assessment Fast Track for the Seventh Phase of the Coupled Model Intercomparison Project. The REF will be run at the Earth System Grid Federation to enable model devleopers and scientists to quickly identify model biases and performance issues.
Pratapaditya Ghosh, Xue Zheng, Hassan Beydoun, Peter Bogenschutz, and Yunyan Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2026-3700, https://doi.org/10.5194/egusphere-2026-3700, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Marine low clouds play a crucial role in cooling Earth by reflecting sunlight, but storm-resolving models struggle to simulate them accurately. Using ship-based observations and machine learning, we identify which parameter combinations best reduce errors across different cloud types. We find that fixes effective for thick stratocumulus clouds do not work equally well for broken cumulus clouds, suggesting that improving these models requires different strategies for different cloud regimes.
Bin Wang, M. Luke McCormack, Daniel M. Ricciuto, Xiaojuan Yang, Min Xu, and Forrest M. Hoffman
EGUsphere, https://doi.org/10.5194/egusphere-2026-2759, https://doi.org/10.5194/egusphere-2026-2759, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Plants send about a third of their captured carbon below ground, to roots and soil fungi that take up water and nutrients. Most climate models still treat the entire root system as one uniform mass. We built a new land-model version that separates roots into three types: transport roots, absorptive roots, and fungal partners. Tested at twenty sites worldwide, it cut errors in simulated ecosystem carbon exchange by more than half, sharpening projections of how land will respond to climate change.
Neil M. Donahue, Lubna Dada, Dominik Stolzenburg, Eva Sommer, Mario Simon, Meredith Schervish, Jenna DeVivo, Alexandra Stinchfield, Natalie Burton, Nirvan Bhattacharyya, Brandon Lopez, Mingyi Wang, Wiebke Scholz, João Almeida, Bin Zhao, Martin Heinritzi, Hamish Gordon, Armin Hansel, Joachim Curtius, Katrianne Lehtipalo, Imad El Haddad, Jasper Kirkby, Richard Flagan, Markku Kulmala, and Douglas Worsnop
Atmos. Chem. Phys., 26, 8311–8339, https://doi.org/10.5194/acp-26-8311-2026, https://doi.org/10.5194/acp-26-8311-2026, 2026
Short summary
Short summary
A new theory has the potential to accurately describe changes to atmospheric particle formation from the pre-industrial to the present and onwards along socioeconomic pathways addressing air pollution and climate. The model places organic nucleation in the context of the Volatility Basis Set and reveals a competition between chemistry, which accelerates as temperature rises, and vapor pressure, which drops as temperature decreases. The model reproduces observations from the CERN (European Organization for Nuclear Research) CLOUD (Cosmics Leaving Outdoor Droplets) chamber.
Han Ding, Pratapaditya Ghosh, Xu-Cheng He, R. Lee Mauldin III, David O'Neal, John Ortega, James Norman Smith, and Hamish Gordon
EGUsphere, https://doi.org/10.5194/egusphere-2026-932, https://doi.org/10.5194/egusphere-2026-932, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
About half of the particles in the atmosphere that affect the properties of clouds are formed when gas molecules cluster together. This clustering process is usually very simplified in atmospheric models. Here we test a more sophisticated clustering mechanism in a regional setup of a climate model, using aircraft measurements in Colorado. Including ammonia in the mechanism can substantially improve predictions of atmospheric particle concentrations, but other species probably contribute as well.
Neil M. Donahue, Victoria Hofbauer, Henning Finkenzeller, Dominik Stolzenburg, Paulus S. Bauer, Randall Chiu, Lubna Dada, Jonathan Duplissy, Xu-Cheng He, Martin Heinritzi, Christopher R. Hoyle, Andreas Kürten, Aleksandr Kvashnin, Katrianne Lehtipalo, Naser Mahfouz, Vladimir Makhmutov, Roy L. Mauldin III, Ugo Molteni, Lauriane L. J. Quéléver, Matti Rissanen, Siegfried Schobesberger, Mario Simon, Andrea C. Wagner, Mingyi Wang, Chao Yan, Penglin Ye, Ilona Riipinen, Hamish Gordon, Joachim Curtius, Armin Hansel, Imad El Haddad, Markku Kulmala, Douglas R. Worsnop, Rainer Volkamer, Paul M. Winkler, Jasper Kirkby, and Richard Flagan
Atmos. Meas. Tech., 19, 3363–3396, https://doi.org/10.5194/amt-19-3363-2026, https://doi.org/10.5194/amt-19-3363-2026, 2026
Short summary
Short summary
We describe accurate measurement of particle formation and growth in the CERN CLOUD (European Council for Nuclear Research Cosmics Leaving OUtdoor Droplets) chamber, using a suite of gas- and particle-phase instruments. The interconnected measurements establish high accuracy in key particle properties and critically important gas-phase sulfuric acid. This is a template for accurate calibration of similar experiments and thus accurate determination of aerosol nucleation and growth rates, which are an important source of uncertainty in climate science.
Moetasim Ashfaq, Erika Coppola, Chris Lennard, Claas Teichmann, Deeksha Rastogi, Elias Massoud, Erasmo Buonomo, Eun-Soon Im, George Zittis, Jason P. Evans, Jesus Fernandez, Katherine J. Evans, Maria Leidinice da Silva, Marianna Adinolfi, Melissa Bukovsky, Rosmeri Porfirio da Rocha, Shabeh ul Hasson, Silvina A. Solman, Stefan Sobolowski, Sushant Das, Swen Brands, Tereza Cavazos, Thanh Ngo-Duc, and Xuejie Gao
EGUsphere, https://doi.org/10.5194/egusphere-2026-2649, https://doi.org/10.5194/egusphere-2026-2649, 2026
Short summary
Short summary
Producing reliable regional climate projections requires carefully selecting which global climate models drive them. We developed a transparent and reproducible framework to identify a balanced, representative subset of models for the Coordinated Regional Climate Downscaling Experiment, a major international effort to generate high-resolution climate projections worldwide. While designed for this initiative, the framework is broadly applicable to other models sub-selection efforts.
Mark R. Petersen, Xylar S. Asay-Davis, Alice M. Barthel, Carolyn Branecky Begeman, Siddhartha Bishnu, Steven R. Brus, Philip W. Jones, Hyun-Gyu Kang, Youngsung Kim, Azamat Mametjanov, Brian J. O'Neill, James R. Overfelt, Kieran K. Ringel, Katherine M. Smith, Sarat Sreepathi, Luke P. Van Roekel, and Maciej Waruszewski
Geosci. Model Dev., 19, 3569–3594, https://doi.org/10.5194/gmd-19-3569-2026, https://doi.org/10.5194/gmd-19-3569-2026, 2026
Short summary
Short summary
Ocean models are used to predict currents, temperature, and salinity of the earth's oceans, much like weather forecasting. As supercomputer hardware changes with evolving technology, models must be updated, and sometimes rewritten. Here we document Omega, a new ocean model that was designed to run on the world's fastest supercomputers. Testing shows that Omega accurately solves the model equations, and runs efficiently on many different computer architectures, including exascale computers.
Masaru Yoshioka, Daniel P. Grosvenor, Amy H. Peace, Jim M. Haywood, Ying Chen, and Paul R. Field
Atmos. Chem. Phys., 26, 4341–4358, https://doi.org/10.5194/acp-26-4341-2026, https://doi.org/10.5194/acp-26-4341-2026, 2026
Short summary
Short summary
We used advanced computer simulations to study how aerosol particles from a volcanic eruption in Iceland affected clouds. The eruption plume increased small droplets, but changes in cloud water and horizontal extent were not clear. Satellite comparisons between plume and non-plume regions can miss volcanic effects due to spatial variability in weather and aerosol, but simulations can isolate the impact by comparing cases with and without the eruption.
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, James Weber, Alexander T. Archibald, and Hamish Gordon
Atmos. Chem. Phys., 26, 3805–3851, https://doi.org/10.5194/acp-26-3805-2026, https://doi.org/10.5194/acp-26-3805-2026, 2026
Short summary
Short summary
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.
Shweta Bhati, Theethai-Jacob Anurose, Aravindakshan Jayakumar, Hamish Gordon, Anthony Jones, Saji Mohandas, and Vijapurapu Srinivasa Prasad
EGUsphere, https://doi.org/10.5194/egusphere-2025-6088, https://doi.org/10.5194/egusphere-2025-6088, 2026
Short summary
Short summary
This study shows that aerosols that absorb or scatter sunlight play a crucial role in the growth and sustenance of fog, with the Indo-Gangetic Plains (IGP) as a case study. Five simulations a numerical weather prediction model exhibited that absorbing aerosols have a greater influence on shaping fog over the IGP compared to scattering aerosols. These insights can help with fog forecasts and pollution-control policies over IGP, which are known for high pollution and dense fog episodes in winters.
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
Short summary
Short summary
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.
Michael E. Kelleher and Salil Mahajan
Earth Syst. Dynam., 17, 23–39, https://doi.org/10.5194/esd-17-23-2026, https://doi.org/10.5194/esd-17-23-2026, 2026
Short summary
Short summary
Building numerical models of the Earth is a complex task that scientists and engineers around the world work on. It is important that results are repeatable accurately to help advance science. This study uses a statistical method to reduce errors when comparing two sets of simulations to see if they agree with each other. This approach helps identify if changes made to the model's code result in unexpected or unintended effects.
Eric Giuffrida, Kate Johnson, Tyler Tatro, Paquita Zuidema, and Hamish Gordon
Atmos. Chem. Phys., 25, 14879–14907, https://doi.org/10.5194/acp-25-14879-2025, https://doi.org/10.5194/acp-25-14879-2025, 2025
Short summary
Short summary
Smoke aerosols emitted from summer African fires periodically travel across the ocean and interact with one of Earth’s largest permanent cloud decks. Researchers quantify the heating and cooling effects of this interaction using climate models. However, the use of different historical weather matching methods has produced a large variation in results. Here, we test method variations commonly used today and conclude with new guidelines for achieving the most accurate results.
August Mikkelsen, Virendra P. Ghate, Daniel T. McCoy, and Hamish Gordon
EGUsphere, https://doi.org/10.5194/egusphere-2025-4434, https://doi.org/10.5194/egusphere-2025-4434, 2025
Short summary
Short summary
In this study, nearly 10 years of data from a radar wind profiler data stationed in the Azores is processed. The sensitivity and dynamic range of the radar are evaluated over time, and a methodology is developed and implemented to remove degraded data. With the remaining data, measurements of turbulence are retrieved and a climatology of wind data during marine conditions is created, showing increased turbulence in the marine boundary layer during autumn and winter months.
K. S. Apsara, Aravindakshan Jayakumar, Theethai Jacob Anurose, Saji Mohandas, Paul R. Field, Thara Prabhakaran, Mahen Konwar, and Vijayapurapu Srinivasa Prasad
Atmos. Chem. Phys., 25, 11423–11439, https://doi.org/10.5194/acp-25-11423-2025, https://doi.org/10.5194/acp-25-11423-2025, 2025
Short summary
Short summary
Science has made significant strides in weather prediction, especially for intense tropical rainfall that can lead to floods and landslides. Our study aims to improve monsoon rainfall forecasts by analyzing raindrop sizes. Using a new approach to model raindrop growth, we achieved a more accurate depiction of large rainfall events. These improvements can be generalized to enhance early warning systems, offering reliable predictions that help reduce risks from severe tropical weather events.
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
Short summary
Short summary
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.
Pratapaditya Ghosh, Ian Boutle, Paul Field, Adrian Hill, Anthony Jones, Marie Mazoyer, Katherine J. Evans, Salil Mahajan, Hyun-Gyu Kang, Min Xu, Wei Zhang, Noah Asch, and Hamish Gordon
Atmos. Chem. Phys., 25, 11129–11156, https://doi.org/10.5194/acp-25-11129-2025, https://doi.org/10.5194/acp-25-11129-2025, 2025
Short summary
Short summary
We study aerosol–fog interactions near Paris using a weather and climate model with high spatial resolution. We show that our model can simulate the fog life cycle effectively. We find that the fog droplet number concentrations, the amount of liquid water in the fog, and the vertical structure of the fog are highly sensitive to the parameterization that simulates droplet formation and growth. The changes we propose could improve fog forecasts significantly without increasing computational costs.
Pratapaditya Ghosh, Ian Boutle, Paul Field, Adrian Hill, Marie Mazoyer, Katherine J. Evans, Salil Mahajan, Hyun-Gyu Kang, Min Xu, Wei Zhang, and Hamish Gordon
Atmos. Chem. Phys., 25, 11157–11182, https://doi.org/10.5194/acp-25-11157-2025, https://doi.org/10.5194/acp-25-11157-2025, 2025
Short summary
Short summary
We study the life cycle of fog events in Europe using a weather and climate model. By incorporating droplet formation and growth driven by radiative cooling, our model better simulates the total liquid water in foggy atmospheric columns. We show that both adiabatic and radiative cooling play significant, often equally important, roles in driving droplet formation and growth. We discuss strategies to address droplet number overpredictions by improving model physics and addressing model artifacts.
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
Short summary
Short summary
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.
Emma Sands, Ruth M. Doherty, Fiona M. O'Connor, Richard J. Pope, James Weber, and Daniel P. Grosvenor
Atmos. Chem. Phys., 25, 7269–7297, https://doi.org/10.5194/acp-25-7269-2025, https://doi.org/10.5194/acp-25-7269-2025, 2025
Short summary
Short summary
We perform a detailed satellite–model comparison for isoprene, formaldehyde and aerosol optical depth in an Earth system model. We quantify the impacts of several processes that affect how biosphere–atmosphere interactions influence atmospheric chemistry and aerosols. Our findings highlight that the aerosol direct effect is sensitive to the processes studied. These results can inform future investigations of how the biosphere can affect atmospheric composition and climate.
August Mikkelsen, Daniel T. McCoy, Trude Eidhammer, Andrew Gettelman, Ci Song, Hamish Gordon, and Isabel L. McCoy
Atmos. Chem. Phys., 25, 4547–4570, https://doi.org/10.5194/acp-25-4547-2025, https://doi.org/10.5194/acp-25-4547-2025, 2025
Short summary
Short summary
Whether increased aerosol increases or decreases liquid cloud mass has been a longstanding question. Observed correlations suggest that aerosols thin liquid cloud, but we are able to show that observations were consistent with an increase in liquid cloud in response to aerosols by leveraging a model where causality could be traced.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Leighton A. Regayre, Lucia Deaconu, Daniel P. Grosvenor, David M. H. Sexton, Christopher Symonds, Tom Langton, Duncan Watson-Paris, Jane P. Mulcahy, Kirsty J. Pringle, Mark Richardson, Jill S. Johnson, John W. Rostron, Hamish Gordon, Grenville Lister, Philip Stier, and Ken S. Carslaw
Atmos. Chem. Phys., 23, 8749–8768, https://doi.org/10.5194/acp-23-8749-2023, https://doi.org/10.5194/acp-23-8749-2023, 2023
Short summary
Short summary
Aerosol forcing of Earth’s energy balance has persisted as a major cause of uncertainty in climate simulations over generations of climate model development. We show that structural deficiencies in a climate model are exposed by comprehensively exploring parametric uncertainty and that these deficiencies limit how much the model uncertainty can be reduced through observational constraint. This provides a future pathway towards building models with greater physical realism and lower uncertainty.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Chengzhu Zhang, Jean-Christophe Golaz, Ryan Forsyth, Tom Vo, Shaocheng Xie, Zeshawn Shaheen, Gerald L. Potter, Xylar S. Asay-Davis, Charles S. Zender, Wuyin Lin, Chih-Chieh Chen, Chris R. Terai, Salil Mahajan, Tian Zhou, Karthik Balaguru, Qi Tang, Cheng Tao, Yuying Zhang, Todd Emmenegger, Susannah Burrows, and Paul A. Ullrich
Geosci. Model Dev., 15, 9031–9056, https://doi.org/10.5194/gmd-15-9031-2022, https://doi.org/10.5194/gmd-15-9031-2022, 2022
Short summary
Short summary
Earth system model (ESM) developers run automated analysis tools on data from candidate models to inform model development. This paper introduces a new Python package, E3SM Diags, that has been developed to support ESM development and use routinely in the development of DOE's Energy Exascale Earth System Model. This tool covers a set of essential diagnostics to evaluate the mean physical climate from simulations, as well as several process-oriented and phenomenon-based evaluation diagnostics.
Leighton A. Regayre, Lucia Deaconu, Daniel P. Grosvenor, David Sexton, Christopher C. Symonds, Tom Langton, Duncan Watson-Paris, Jane P. Mulcahy, Kirsty J. Pringle, Mark Richardson, Jill S. Johnson, John Rostron, Hamish Gordon, Grenville Lister, Philip Stier, and Ken S. Carslaw
EGUsphere, https://doi.org/10.5194/egusphere-2022-1330, https://doi.org/10.5194/egusphere-2022-1330, 2022
Preprint archived
Short summary
Short summary
We show that potential structural deficiencies in a climate model can be exposed by comprehensively exploring its parametric uncertainty, and that these deficiencies limit how much the model uncertainty can be reduced through observational constraint. Combined consideration of parametric and structural uncertainties provides a future pathway towards building models that have greater physical realism and lower uncertainty.
Johannes Quaas, Hailing Jia, Chris Smith, Anna Lea Albright, Wenche Aas, Nicolas Bellouin, Olivier Boucher, Marie Doutriaux-Boucher, Piers M. Forster, Daniel Grosvenor, Stuart Jenkins, Zbigniew Klimont, Norman G. Loeb, Xiaoyan Ma, Vaishali Naik, Fabien Paulot, Philip Stier, Martin Wild, Gunnar Myhre, and Michael Schulz
Atmos. Chem. Phys., 22, 12221–12239, https://doi.org/10.5194/acp-22-12221-2022, https://doi.org/10.5194/acp-22-12221-2022, 2022
Short summary
Short summary
Pollution particles cool climate and offset part of the global warming. However, they are washed out by rain and thus their effect responds quickly to changes in emissions. We show multiple datasets to demonstrate that aerosol emissions and their concentrations declined in many regions influenced by human emissions, as did the effects on clouds. Consequently, the cooling impact on the Earth energy budget became smaller. This change in trend implies a relative warming.
Michael S. Diamond, Pablo E. Saide, Paquita Zuidema, Andrew S. Ackerman, Sarah J. Doherty, Ann M. Fridlind, Hamish Gordon, Calvin Howes, Jan Kazil, Takanobu Yamaguchi, Jianhao Zhang, Graham Feingold, and Robert Wood
Atmos. Chem. Phys., 22, 12113–12151, https://doi.org/10.5194/acp-22-12113-2022, https://doi.org/10.5194/acp-22-12113-2022, 2022
Short summary
Short summary
Smoke from southern Africa blankets the southeast Atlantic from June-October, overlying a major transition region between overcast and scattered clouds. The smoke affects Earth's radiation budget by absorbing sunlight and changing cloud properties. We investigate these effects in regional climate and large eddy simulation models based on international field campaigns. We find that large-scale circulation changes more strongly affect cloud transitions than smoke microphysical effects in our case.
Haochi Che, Philip Stier, Duncan Watson-Parris, Hamish Gordon, and Lucia Deaconu
Atmos. Chem. Phys., 22, 10789–10807, https://doi.org/10.5194/acp-22-10789-2022, https://doi.org/10.5194/acp-22-10789-2022, 2022
Short summary
Short summary
Extensive stratocumulus clouds over the south-eastern Atlantic (SEA) can lead to a cooling effect on the climate. A key pathway by which aerosols affect cloud properties is by acting as cloud condensation nuclei (CCN). Here, we investigated the source attribution of CCN in the SEA as well as the cloud responses. Our results show that aerosol nucleation contributes most to CCN in the marine boundary layer. In terms of emissions, anthropogenic sources contribute most to the CCN and cloud droplets.
Matthew W. Christensen, Andrew Gettelman, Jan Cermak, Guy Dagan, Michael Diamond, Alyson Douglas, Graham Feingold, Franziska Glassmeier, Tom Goren, Daniel P. Grosvenor, Edward Gryspeerdt, Ralph Kahn, Zhanqing Li, Po-Lun Ma, Florent Malavelle, Isabel L. McCoy, Daniel T. McCoy, Greg McFarquhar, Johannes Mülmenstädt, Sandip Pal, Anna Possner, Adam Povey, Johannes Quaas, Daniel Rosenfeld, Anja Schmidt, Roland Schrödner, Armin Sorooshian, Philip Stier, Velle Toll, Duncan Watson-Parris, Robert Wood, Mingxi Yang, and Tianle Yuan
Atmos. Chem. Phys., 22, 641–674, https://doi.org/10.5194/acp-22-641-2022, https://doi.org/10.5194/acp-22-641-2022, 2022
Short summary
Short summary
Trace gases and aerosols (tiny airborne particles) are released from a variety of point sources around the globe. Examples include volcanoes, industrial chimneys, forest fires, and ship stacks. These sources provide opportunistic experiments with which to quantify the role of aerosols in modifying cloud properties. We review the current state of understanding on the influence of aerosol on climate built from the wide range of natural and anthropogenic laboratories investigated in recent decades.
Sarah J. Doherty, Pablo E. Saide, Paquita Zuidema, Yohei Shinozuka, Gonzalo A. Ferrada, Hamish Gordon, Marc Mallet, Kerry Meyer, David Painemal, Steven G. Howell, Steffen Freitag, Amie Dobracki, James R. Podolske, Sharon P. Burton, Richard A. Ferrare, Calvin Howes, Pierre Nabat, Gregory R. Carmichael, Arlindo da Silva, Kristina Pistone, Ian Chang, Lan Gao, Robert Wood, and Jens Redemann
Atmos. Chem. Phys., 22, 1–46, https://doi.org/10.5194/acp-22-1-2022, https://doi.org/10.5194/acp-22-1-2022, 2022
Short summary
Short summary
Between July and October, biomass burning smoke is advected over the southeastern Atlantic Ocean, leading to climate forcing. Model calculations of forcing by this plume vary significantly in both magnitude and sign. This paper compares aerosol and cloud properties observed during three NASA ORACLES field campaigns to the same in four models. It quantifies modeled biases in properties key to aerosol direct radiative forcing and evaluates how these biases propagate to biases in forcing.
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
Short summary
Short summary
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.
Cited articles
Abdul-Razzak, H., Ghan, S. J., and Rivera-Carpio, C.: A parameterization of aerosol activation: 1. Single aerosol type, J. Geophys. Res.-Atmos., 103, 6123–6131, https://doi.org/10.1029/97JD03735, 1998. a
Barahona, D., West, R. E. L., Stier, P., Romakkaniemi, S., Kokkola, H., and Nenes, A.: Comprehensively accounting for the effect of giant CCN in cloud activation parameterizations, Atmos. Chem. Phys., 10, 2467–2473, https://doi.org/10.5194/acp-10-2467-2010, 2010. a
Baudin, M.: pyDOE, Github, https://github.com/tisimst/pyDOE (last access: 22 July 2025), 2015. a
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, e2019RG000660, https://doi.org/10.1029/2019RG000660, 2020. a
Binkowski, F. S. and Roselle, S. J.: Models-3 Community Multiscale Air Quality (CMAQ) model aerosol component 1. Model description, J. Geophys. Res.-Atmos., 108, 4183, https://doi.org/10.1029/2001JD001409, 2003. a
Chand, D., Wood, R., Anderson, T., Satheesh, S., and Charlson, R.: Satellite-derived direct radiative effect of aerosols dependent on cloud cover, Nat. Geosci., 2, 181–184, https://doi.org/10.1038/ngeo437, 2009. a
Christensen, M. W., Ma, P.-L., Wu, P., Varble, A. C., Mülmenstädt, J., and Fast, J. D.: Evaluation of aerosol–cloud interactions in E3SM using a Lagrangian framework, Atmos. Chem. Phys., 23, 2789–2812, https://doi.org/10.5194/acp-23-2789-2023, 2023. a, b
Clark, D. B., Mercado, L. M., Sitch, S., Jones, C. D., Gedney, N., Best, M. J., Pryor, M., Rooney, G. G., Essery, R. L. H., Blyth, E., Boucher, O., Harding, R. J., Huntingford, C., and Cox, P. M.: The Joint UK Land Environment Simulator (JULES), model description – Part 2: Carbon fluxes and vegetation dynamics, Geosci. Model Dev., 4, 701–722, https://doi.org/10.5194/gmd4-701-2011, 2011 (code available at: http://jules-lsm.github.io/access_req/JULES_access.html, last access: 23 July 2025). a
Cohard, J.-M., Pinty, J.-P., and Bedos, C.: Extending Twomey's Analytical Estimate of Nucleated Cloud Droplet Concentrations from CCN Spectra, J. Atmos. Sci., 55, 3348–3357, https://doi.org/10.1175/1520-0469(1998)055<3348:ETSAEO>2.0.CO;2, 1998. a
Easter, R. C., Ghan, S. J., Zhang, Y., Saylor, R. D., Chapman, E. G., Laulainen, N. S., Abdul-Razzak, H., Leung, L. R., Bian, X., and Zaveri, R. A.: MIRAGE: Model description and evaluation of aerosols and trace gases, J. Geophys. Res.-Atmos., 109, D20210, https://doi.org/10.1029/2004JD004571, 2004. a
Feng, L., Smith, S. J., Braun, C., Crippa, M., Gidden, M. J., Hoesly, R., Klimont, Z., van Marle, M., van den Berg, M., and van der Werf, G. R.: The generation of gridded emissions data for CMIP6, Geosci. Model Dev., 13, 461–482, https://doi.org/10.5194/gmd-13-461-2020, 2020. a
Forster, P., Storelvmo, T., Armour, K., Collins, W., Dufresne, J.-L., Frame, D., Lunt, D. J., Mauritsen, T., Palmer, M. D., Watanabe, M., Wild, M., and Zhang, X.: 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., Péan, 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., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 923–1054, https://doi.org/10.1017/9781009157896.001, 2021. a, b
Ghan, S. J.: Technical Note: Estimating aerosol effects on cloud radiative forcing, Atmos. Chem. Phys., 13, 9971–9974, https://doi.org/10.5194/acp-13-9971-2013, 2013. a
Ghan, S. J., Abdul-Razzak, H., Nenes, A., Ming, Y., Liu, X., Ovchinnikov, M., Shipway, B., Meskhidze, N., Xu, J., and Shi, X.: Droplet nucleation: Physically-based parameterizations and comparative evaluation, J. Adv. Model. Earth Sy., 3, M10001, https://doi.org/10.1029/2011MS000074, 2011. a, b, c, d, e, f
Ghosh, P., Evans, K. J., Grosvenor, D. P., Kang, H.-G., Mahajan, S., Xu, M., Zhang, W., and Gordon, H.: Improving the Abdul-Razzak & Ghan (2000) aerosol activation parameterization impacts simulated cloud radiative effects (shown in the Unified Model, version 13.0), Zenodo [code], https://doi.org/10.5281/zenodo.15062206, 2025. a
Grosvenor, D. and Wood, R.: Daily MODIS (MODerate Imaging Spectroradiometer) derived cloud droplet number concentration global dataset for 2003–2015, Centre for Environmental Data Analysis [data set], https://catalogue.ceda.ac.uk/uuid/cf97ccc802d348ec8a3b6f2995dfbbff (last access: 22 July 2025), 2018. a
Grosvenor, D. P., Sourdeval, O., Zuidema, P., Ackerman, A., Alexandrov, M. D., Bennartz, R., Boers, R., Cairns, B., Chiu, J. C., Christensen, M., Deneke, H., Diamond, M., Feingold, G., Fridlind, A., Hünerbein, A., Knist, C., Kollias, P., Marshak, A., McCoy, D., Merk, D., Painemal, D., Rausch, J., Rosenfeld, D., Russchenberg, H., Seifert, P., Sinclair, K., Stier, P., van Diedenhoven, B., Wendisch, M., Werner, F., Wood, R., Zhang, Z., and Quaas, J.: Remote Sensing of Droplet Number Concentration in Warm Clouds: A Review of the Current State of Knowledge and Perspectives, Rev. Geophys., 56, 409–453, https://doi.org/10.1029/2017RG000593, 2018. a, b, c, d
Gustafson, W. I. and Yu, S.: Generalized approach for using unbiased symmetric metrics with negative values: normalized mean bias factor and normalized mean absolute error factor, Atmos. Sci. Lett., 13, 262–267, https://doi.org/10.1002/asl.393, 2012. a, b
Helton, J. and Davis, F.: Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems, Reliab. Eng. Syst. Safe., 81, 23–69, https://doi.org/10.1016/S0951-8320(03)00058-9, 2003. a
Kaiser, J. C., Hendricks, J., Righi, M., Riemer, N., Zaveri, R. A., Metzger, S., and Aquila, V.: The MESSy aerosol submodel MADE3 (v2.0b): description and a box model test, Geosci. Model Dev., 7, 1137–1157, https://doi.org/10.5194/gmd-7-1137-2014, 2014. a
Kohler, H.: The nucleus in and the growth of hygroscopic droplets, T. Faraday Soc., 32, 1152–1161, https://doi.org/10.1039/tf9363201152, 1936. a
Kreidenweis, S. M., Walcek, C. J., Feingold, G., Gong, W., Jacobson, M. Z., Kim, C.-H., Liu, X., Penner, J. E., Nenes, A., and Seinfeld, J. H.: Modification of aerosol mass and size distribution due to aqueous-phase SO2 oxidation in clouds: Comparisons of several models, J. Geophys. Res.-Atmos., 108, 4213, https://doi.org/10.1029/2002JD002697, 2003. a
Laaksonen, A., Vesala, T., Kulmala, M., Winkler, P. M., and Wagner, P. E.: Commentary on cloud modelling and the mass accommodation coefficient of water, Atmos. Chem. Phys., 5, 461–464, https://doi.org/10.5194/acp-5-461-2005, 2005. a
Liu, X., Easter, R. C., Ghan, S. J., Zaveri, R., Rasch, P., Shi, X., Lamarque, J.-F., Gettelman, A., Morrison, H., Vitt, F., Conley, A., Park, S., Neale, R., Hannay, C., Ekman, A. M. L., Hess, P., Mahowald, N., Collins, W., Iacono, M. J., Bretherton, C. S., Flanner, M. G., and Mitchell, D.: Toward a minimal representation of aerosols in climate models: description and evaluation in the Community Atmosphere Model CAM5, Geosci. Model Dev., 5, 709–739, https://doi.org/10.5194/gmd-5-709-2012, 2012. a, b
Liu, X., Ma, P.-L., Wang, H., Tilmes, S., Singh, B., Easter, R. C., Ghan, S. J., and Rasch, P. J.: Description and evaluation of a new four-mode version of the Modal Aerosol Module (MAM4) within version 5.3 of the Community Atmosphere Model, Geosci. Model Dev., 9, 505–522, https://doi.org/10.5194/gmd-9-505-2016, 2016. a, b
Mann, G. W., Carslaw, K. S., Spracklen, D. V., Ridley, D. A., Manktelow, P. T., Chipperfield, M. P., Pickering, S. J., and Johnson, C. E.: Description and evaluation of GLOMAP-mode: a modal global aerosol microphysics model for the UKCA composition-climate model, Geosci. Model Dev., 3, 519–551, https://doi.org/10.5194/gmd-3-519-2010, 2010. a, b, c
Mann, G. W., Carslaw, K. S., Ridley, D. A., Spracklen, D. V., Pringle, K. J., Merikanto, J., Korhonen, H., Schwarz, J. P., Lee, L. A., Manktelow, P. T., Woodhouse, M. T., Schmidt, A., Breider, T. J., Emmerson, K. M., Reddington, C. L., Chipperfield, M. P., and Pickering, S. J.: Intercomparison of modal and sectional aerosol microphysics representations within the same 3-D global chemical transport model, Atmos. Chem. Phys., 12, 4449–4476, https://doi.org/10.5194/acp-12-4449-2012, 2012. a, b, c, d
Mckay, M. D., Beckman, R. J., and Conover, W. J.: A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code, Technometrics, 42, 55–61, https://doi.org/10.1080/00401706.2000.10485979, 2000. a
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, P. Natl. Acad. Sci. USA, 107, 6646–6651, https://doi.org/10.1073/pnas.0911330107, 2010. a
Ming, Y., Ramaswamy, V., Donner, L. J., and Phillips, V. T. J.: A New Parameterization of Cloud Droplet Activation Applicable to General Circulation Models, J. Atmos. Sci., 63, 1348–1356, https://doi.org/10.1175/JAS3686.1, 2006. a, b
Morales Betancourt, R. and Nenes, A.: Droplet activation parameterization: the population-splitting concept revisited, Geosci. Model Dev., 7, 2345–2357, https://doi.org/10.5194/gmd-7-2345-2014, 2014. a
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. a, b
Mulcahy, J. P., Jones, C. G., Rumbold, S. T., Kuhlbrodt, T., Dittus, A. J., Blockley, E. W., Yool, A., Walton, J., Hardacre, C., Andrews, T., Bodas-Salcedo, A., Stringer, M., de Mora, L., Harris, P., Hill, R., Kelley, D., Robertson, E., and Tang, Y.: UKESM1.1: development and evaluation of an updated configuration of the UK Earth System Model, Geosci. Model Dev., 16, 1569–1600, https://doi.org/10.5194/gmd-16-1569-2023, 2023. a, b, c, d, e
Nenes, A., Ghan, S., Abdul-Razzak, H., Chuang, P. Y., and Seinfeld, J. H.: Kinetic limitations on cloud droplet formation and impact on cloud albedo, Tellus B, 53, 133–149, https://doi.org/10.1034/j.1600-0889.2001.d01-12.x, 2001. a, b
Oliver, H. J., Shin, M., Sanders, O., Fitzpatrick, B., Clark, A., Kinoshita, B. P., Dutta, R., Pillinger, T., Bartholomew, S. L., Hall, M., Valters, D., Sutherland, D., Trzeciak, T., Challurip, Gaist, S., Matthews, D., Wales, S., ColemanTom, Menezes, G., Haiducek, J., Williams, J., Lhuggett, Osprey, A., jonty at BoM, Hatcher, R., Veselov, D., Reinecke, A., Andrew, Pulo, K., and Dix, M.: cylc/cylc-flow: cylc-7.8.8, Zenodo [code], https://doi.org/10.5281/zenodo.4638360, 2021. a
Phinney, L. A., Lohmann, U., and Leaitch, W. R.: Limitations of using an equilibrium approximation in an aerosol activation parameterization, J. Geophys. Res.-Atmos., 108, 4371, https://doi.org/10.1029/2002JD002391, 2003. a, b
Pringle, K. J., Tost, H., Message, S., Steil, B., Giannadaki, D., Nenes, A., Fountoukis, C., Stier, P., Vignati, E., and Lelieveld, J.: Description and evaluation of GMXe: a new aerosol submodel for global simulations (v1), Geosci. Model Dev., 3, 391–412, https://doi.org/10.5194/gmd-3-391-2010, 2010. a
Pruppacher, H. and Klett, J.: Microphysics of Clouds and Precipitation, vol. 18, in: Atmospheric and Oceanographic Sciences Library, Springer Dordrecht, https://doi.org/10.1007/978-0-306-48100-0, 2010. a, b
Raatikainen, T., Nenes, A., Seinfeld, J. H., Morales, R., Moore, R. H., Lathem, T. L., Lance, S., Padró, L. T., Lin, J. J., Cerully, K. M., Bougiatioti, A., Cozic, J., Ruehl, C. R., Chuang, P. Y., Anderson, B. E., Flagan, R. C., Jonsson, H., Mihalopoulos, N., and Smith, J. N.: Worldwide data sets constrain the water vapor uptake coefficient in cloud formation, P. Natl. Acad. Sci. USA, 110, 3760–3764, https://doi.org/10.1073/pnas.1219591110, 2013. a
Ranjithkumar, A., Gordon, H., Williamson, C., Rollins, A., Pringle, K., Kupc, A., Abraham, N. L., Brock, C., and Carslaw, K.: Constraints on global aerosol number concentration, SO2 and condensation sink in UKESM1 using ATom measurements, Atmos. Chem. Phys., 21, 4979–5014, https://doi.org/10.5194/acp-21-4979-2021, 2021. a
Rothenberg, D. and Wang, C.: Metamodeling of Droplet Activation for Global Climate Models, J. Atmos. Sci., 73, 1255–1272, https://doi.org/10.1175/JAS-D-15-0223.1, 2016. a, b
Rothenberg, D. and Wang, C.: An aerosol activation metamodel of v1.2.0 of the pyrcel cloud parcel model: development and offline assessment for use in an aerosol–climate model, Geosci. Model Dev., 10, 1817–1833, https://doi.org/10.5194/gmd-10-1817-2017, 2017. a, b, c
Rothenberg, D., Avramov, A., and Wang, C.: On the representation of aerosol activation and its influence on model-derived estimates of the aerosol indirect effect, Atmos. Chem. Phys., 18, 7961–7983, https://doi.org/10.5194/acp-18-7961-2018, 2018. a
Salzmann, M., Ferrachat, S., Tully, C., Münch, S., Watson-Parris, D., Neubauer, D., Siegenthaler-Le Drian, C., Rast, S., Heinold, B., Crueger, T., Brokopf, R., Mülmenstädt, J., Quaas, J., Wan, H., Zhang, K., Lohmann, U., Stier, P., and Tegen, I.: The Global Atmosphere-aerosol Model ICON-A-HAM2.3–Initial Model Evaluation and Effects of Radiation Balance Tuning on Aerosol Optical Thickness, J. Adv. Model. Earth Sy., 14, e2021MS002699, https://doi.org/10.1029/2021MS002699, 2022. a
Saponaro, G., Sporre, M. K., Neubauer, D., Kokkola, H., Kolmonen, P., Sogacheva, L., Arola, A., de Leeuw, G., Karset, I. H. H., Laaksonen, A., and Lohmann, U.: Evaluation of aerosol and cloud properties in three climate models using MODIS observations and its corresponding COSP simulator, as well as their application in aerosol–cloud interactions, Atmos. Chem. Phys., 20, 1607–1626, https://doi.org/10.5194/acp-20-1607-2020, 2020. a
Shin, M., Fitzpatrick, B., Clark, A., Sanders, O., Bartholomew, S. L., Whitehouse, S., Pillinger, T., Wardle, S., Matthews, D., Oxley, S., Trzeciak, T., Valters, D., Kinoshita, B. P., Mancell, J., harry shepherd, Oliver, H. J., Wales, S., Hall, M., Seddon, J., Osprey, A., Dix, M., Sharp, R., and Cresswell, P.: metomi/rose: 2019.01.3, Zenodo [code], https://doi.org/10.5281/zenodo.3800775, 2020. a
Shipway, B. and Abel, S.: Analytical estimation of cloud droplet nucleation based on an underlying aerosol population, Atmos. Res., 96, 344–355, https://doi.org/10.1016/j.atmosres.2009.10.005, 2010. a
Silva, S. J., Ma, P.-L., Hardin, J. C., and Rothenberg, D.: Physically regularized machine learning emulators of aerosol activation, Geosci. Model Dev., 14, 3067–3077, https://doi.org/10.5194/gmd-14-3067-2021, 2021. a, b
Smith, C. J., Kramer, R. J., Myhre, G., Alterskjær, K., Collins, W., Sima, A., Boucher, O., Dufresne, J.-L., Nabat, P., Michou, M., Yukimoto, S., Cole, J., Paynter, D., Shiogama, H., O'Connor, F. M., Robertson, E., Wiltshire, A., Andrews, T., Hannay, C., Miller, R., Nazarenko, L., Kirkevåg, A., Olivié, D., Fiedler, S., Lewinschal, A., Mackallah, C., Dix, M., Pincus, R., and Forster, P. M.: Effective radiative forcing and adjustments in CMIP6 models, Atmos. Chem. Phys., 20, 9591–9618, https://doi.org/10.5194/acp-20-9591-2020, 2020. a
Stier, P., Feichter, J., Kinne, S., Kloster, S., Vignati, E., Wilson, J., Ganzeveld, L., Tegen, I., Werner, M., Balkanski, Y., Schulz, M., Boucher, O., Minikin, A., and Petzold, A.: The aerosol-climate model ECHAM5-HAM, Atmos. Chem. Phys., 5, 1125–1156, https://doi.org/10.5194/acp-5-1125-2005, 2005. a, b
Tegen, I., Neubauer, D., Ferrachat, S., Siegenthaler-Le Drian, C., Bey, I., Schutgens, N., Stier, P., Watson-Parris, D., Stanelle, T., Schmidt, H., Rast, S., Kokkola, H., Schultz, M., Schroeder, S., Daskalakis, N., Barthel, S., Heinold, B., and Lohmann, U.: The global aerosol–climate model ECHAM6.3–HAM2.3 – Part 1: Aerosol evaluation, Geosci. Model Dev., 12, 1643–1677, https://doi.org/10.5194/gmd-12-1643-2019, 2019. a
Telford, P. J., Braesicke, P., Morgenstern, O., and Pyle, J. A.: Technical Note: Description and assessment of a nudged version of the new dynamics Unified Model, Atmos. Chem. Phys., 8, 1701–1712, https://doi.org/10.5194/acp-8-1701-2008, 2008. a
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. a, b
West, R. E. L., Stier, P., Jones, A., Johnson, C. E., Mann, G. W., Bellouin, N., Partridge, D. G., and Kipling, Z.: The importance of vertical velocity variability for estimates of the indirect aerosol effects, Atmos. Chem. Phys., 14, 6369–6393, https://doi.org/10.5194/acp-14-6369-2014, 2014. a
Wilson, D. R. and Ballard, S. P.: A microphysically based precipitation scheme for the UK meteorological office unified model, Q. J. Roy. Meteor. Soc., 125, 1607–1636, https://doi.org/10.1002/qj.49712555707, 1999. a
Zhang, K., Wan, H., Liu, X., Ghan, S. J., Kooperman, G. J., Ma, P.-L., Rasch, P. J., Neubauer, D., and Lohmann, U.: Technical Note: On the use of nudging for aerosol–climate model intercomparison studies, Atmos. Chem. Phys., 14, 8631–8645, https://doi.org/10.5194/acp-14-8631-2014, 2014. a
Short summary
The most popular algorithm for calculating cloud droplet number concentrations in climate models is sensitive to parameters that control simulated aerosol particle number concentrations at different sizes. We recommend small modifications to functions in the algorithm to improve its performance. Implementing the changes in the UK Met Office climate model reduced average bias in simulated global droplet number concentrations, leading to more reflected solar radiation and a net cooling effect.
The most popular algorithm for calculating cloud droplet number concentrations in climate models...
