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
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Preprint archived
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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
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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
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Haochi Che, Philip Stier, Duncan Watson-Parris, Hamish Gordon, and Lucia Deaconu
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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
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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
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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
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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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Jim M. Haywood, Steven J. Abel, Paul A. Barrett, Nicolas Bellouin, Alan Blyth, Keith N. Bower, Melissa Brooks, Ken Carslaw, Haochi Che, Hugh Coe, Michael I. Cotterell, Ian Crawford, Zhiqiang Cui, Nicholas Davies, Beth Dingley, Paul Field, Paola Formenti, Hamish Gordon, Martin de Graaf, Ross Herbert, Ben Johnson, Anthony C. Jones, Justin M. Langridge, Florent Malavelle, Daniel G. Partridge, Fanny Peers, Jens Redemann, Philip Stier, Kate Szpek, Jonathan W. Taylor, Duncan Watson-Parris, Robert Wood, Huihui Wu, and Paquita Zuidema
Atmos. Chem. Phys., 21, 1049–1084, https://doi.org/10.5194/acp-21-1049-2021, https://doi.org/10.5194/acp-21-1049-2021, 2021
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Every year, the seasonal cycle of biomass burning from agricultural practices in Africa creates a huge plume of smoke that travels many thousands of kilometres over the Atlantic Ocean. This study provides an overview of a measurement campaign called the cloud–aerosol–radiation interaction and forcing for year 2017 (CLARIFY-2017) and documents the rationale, deployment strategy, observations, and key results from the campaign which utilized the heavily equipped FAAM atmospheric research aircraft.
Haochi Che, Philip Stier, Hamish Gordon, Duncan Watson-Parris, and Lucia Deaconu
Atmos. Chem. Phys., 21, 17–33, https://doi.org/10.5194/acp-21-17-2021, https://doi.org/10.5194/acp-21-17-2021, 2021
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Jane P. Mulcahy, Colin Johnson, Colin G. Jones, Adam C. Povey, Catherine E. Scott, Alistair Sellar, Steven T. Turnock, Matthew T. Woodhouse, Nathan Luke Abraham, Martin B. Andrews, Nicolas Bellouin, Jo Browse, Ken S. Carslaw, Mohit Dalvi, Gerd A. Folberth, Matthew Glover, Daniel P. Grosvenor, Catherine Hardacre, Richard Hill, Ben Johnson, Andy Jones, Zak Kipling, Graham Mann, James Mollard, Fiona M. O'Connor, Julien Palmiéri, Carly Reddington, Steven T. Rumbold, Mark Richardson, Nick A. J. Schutgens, Philip Stier, Marc Stringer, Yongming Tang, Jeremy Walton, Stephanie Woodward, and Andrew Yool
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Aerosols are an important component of the Earth system. Here, we comprehensively document and evaluate the aerosol schemes as implemented in the physical and Earth system models, HadGEM3-GC3.1 and UKESM1. This study provides a useful characterisation of the aerosol climatology in both models, facilitating the understanding of the numerous aerosol–climate interaction studies that will be conducted for CMIP6 and beyond.
Daniel P. Grosvenor and Kenneth S. Carslaw
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Particles arising from human activity interact with clouds and affect how much of the Sun's energy is reflected away. Lack of understanding about how to represent this in models leads to large uncertainties in climate predictions. We quantify cloud responses to particles in the latest UK Met Office climate model over the North Atlantic Ocean, showing that, in contrast to suggestions elsewhere, increases in cloud coverage and thickness are important over large areas.
Martin Heinritzi, Lubna Dada, Mario Simon, Dominik Stolzenburg, Andrea C. Wagner, Lukas Fischer, Lauri R. Ahonen, Stavros Amanatidis, Rima Baalbaki, Andrea Baccarini, Paulus S. Bauer, Bernhard Baumgartner, Federico Bianchi, Sophia Brilke, Dexian Chen, Randall Chiu, Antonio Dias, Josef Dommen, Jonathan Duplissy, Henning Finkenzeller, Carla Frege, Claudia Fuchs, Olga Garmash, Hamish Gordon, Manuel Granzin, Imad El Haddad, Xucheng He, Johanna Helm, Victoria Hofbauer, Christopher R. Hoyle, Juha Kangasluoma, Timo Keber, Changhyuk Kim, Andreas Kürten, Houssni Lamkaddam, Tiia M. Laurila, Janne Lampilahti, Chuan Ping Lee, Katrianne Lehtipalo, Markus Leiminger, Huajun Mai, Vladimir Makhmutov, Hanna Elina Manninen, Ruby Marten, Serge Mathot, Roy Lee Mauldin, Bernhard Mentler, Ugo Molteni, Tatjana Müller, Wei Nie, Tuomo Nieminen, Antti Onnela, Eva Partoll, Monica Passananti, Tuukka Petäjä, Joschka Pfeifer, Veronika Pospisilova, Lauriane L. J. Quéléver, Matti P. Rissanen, Clémence Rose, Siegfried Schobesberger, Wiebke Scholz, Kay Scholze, Mikko Sipilä, Gerhard Steiner, Yuri Stozhkov, Christian Tauber, Yee Jun Tham, Miguel Vazquez-Pufleau, Annele Virtanen, Alexander L. Vogel, Rainer Volkamer, Robert Wagner, Mingyi Wang, Lena Weitz, Daniela Wimmer, Mao Xiao, Chao Yan, Penglin Ye, Qiaozhi Zha, Xueqin Zhou, Antonio Amorim, Urs Baltensperger, Armin Hansel, Markku Kulmala, António Tomé, Paul M. Winkler, Douglas R. Worsnop, Neil M. Donahue, Jasper Kirkby, and Joachim Curtius
Atmos. Chem. Phys., 20, 11809–11821, https://doi.org/10.5194/acp-20-11809-2020, https://doi.org/10.5194/acp-20-11809-2020, 2020
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With experiments performed at CLOUD, we show how isoprene interferes in monoterpene oxidation via RO2 termination at atmospherically relevant concentrations. This interference shifts the distribution of highly oxygenated organic molecules (HOMs) away from C20 class dimers towards C15 class dimers, which subsequently reduces both biogenic nucleation and early growth rates. Our results may help to understand the absence of new-particle formation in isoprene-rich environments.
Yohei Shinozuka, Pablo E. Saide, Gonzalo A. Ferrada, Sharon P. Burton, Richard Ferrare, Sarah J. Doherty, Hamish Gordon, Karla Longo, Marc Mallet, Yan Feng, Qiaoqiao Wang, Yafang Cheng, Amie Dobracki, Steffen Freitag, Steven G. Howell, Samuel LeBlanc, Connor Flynn, Michal Segal-Rosenhaimer, Kristina Pistone, James R. Podolske, Eric J. Stith, Joseph Ryan Bennett, Gregory R. Carmichael, Arlindo da Silva, Ravi Govindaraju, Ruby Leung, Yang Zhang, Leonhard Pfister, Ju-Mee Ryoo, Jens Redemann, Robert Wood, and Paquita Zuidema
Atmos. Chem. Phys., 20, 11491–11526, https://doi.org/10.5194/acp-20-11491-2020, https://doi.org/10.5194/acp-20-11491-2020, 2020
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In the southeast Atlantic, well-defined smoke plumes from Africa advect over marine boundary layer cloud decks; both are most extensive around September, when most of the smoke resides in the free troposphere. A framework is put forth for evaluating the performance of a range of global and regional atmospheric composition models against observations made during the NASA ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) airborne mission in September 2016.
Hamish Gordon, Paul R. Field, Steven J. Abel, Paul Barrett, Keith Bower, Ian Crawford, Zhiqiang Cui, Daniel P. Grosvenor, Adrian A. Hill, Jonathan Taylor, Jonathan Wilkinson, Huihui Wu, and Ken S. Carslaw
Atmos. Chem. Phys., 20, 10997–11024, https://doi.org/10.5194/acp-20-10997-2020, https://doi.org/10.5194/acp-20-10997-2020, 2020
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The Met Office's Unified Model is widely used both for weather forecasting and climate prediction. We present the first version of the model in which both aerosol and cloud particle mass and number concentrations are allowed to evolve separately and independently, which is important for studying how aerosols affect weather and climate. We test the model against aircraft observations near Ascension Island in the Atlantic, focusing on how aerosols can "activate" to become cloud droplets.
James Weber, Scott Archer-Nicholls, Paul Griffiths, Torsten Berndt, Michael Jenkin, Hamish Gordon, Christoph Knote, and Alexander T. Archibald
Atmos. Chem. Phys., 20, 10889–10910, https://doi.org/10.5194/acp-20-10889-2020, https://doi.org/10.5194/acp-20-10889-2020, 2020
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Highly oxygenated organic molecules (HOMs) are important for aerosol growth and new particle formation, particularly in air masses with less sulphuric acid. This new chemical mechanism reproduces measured [HOM] and [HOM precursors] and is concise enough for use in global climate models. The mechanism also reproduces the observed suppression of HOMs by isoprene, suggesting enhanced emissions may not necessarily lead to more aerosols. Greater HOM importance in the pre-industrial era is also shown.
Leighton A. Regayre, Julia Schmale, Jill S. Johnson, Christian Tatzelt, Andrea Baccarini, Silvia Henning, Masaru Yoshioka, Frank Stratmann, Martin Gysel-Beer, Daniel P. Grosvenor, and Ken S. Carslaw
Atmos. Chem. Phys., 20, 10063–10072, https://doi.org/10.5194/acp-20-10063-2020, https://doi.org/10.5194/acp-20-10063-2020, 2020
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The amount of energy reflected back into space because of man-made particles is highly uncertain. Processes related to naturally occurring particles cause most of the uncertainty, but these processes are poorly constrained by present-day measurements. We show that measurements over the Southern Ocean, far from pollution sources, efficiently reduce climate model uncertainties. Our results pave the way to designing experiments and measurement campaigns that reduce this uncertainty even further.
Mario Simon, Lubna Dada, Martin Heinritzi, Wiebke Scholz, Dominik Stolzenburg, Lukas Fischer, Andrea C. Wagner, Andreas Kürten, Birte Rörup, Xu-Cheng He, João Almeida, Rima Baalbaki, Andrea Baccarini, Paulus S. Bauer, Lisa Beck, Anton Bergen, Federico Bianchi, Steffen Bräkling, Sophia Brilke, Lucia Caudillo, Dexian Chen, Biwu Chu, António Dias, Danielle C. Draper, Jonathan Duplissy, Imad El-Haddad, Henning Finkenzeller, Carla Frege, Loic Gonzalez-Carracedo, Hamish Gordon, Manuel Granzin, Jani Hakala, Victoria Hofbauer, Christopher R. Hoyle, Changhyuk Kim, Weimeng Kong, Houssni Lamkaddam, Chuan P. Lee, Katrianne Lehtipalo, Markus Leiminger, Huajun Mai, Hanna E. Manninen, Guillaume Marie, Ruby Marten, Bernhard Mentler, Ugo Molteni, Leonid Nichman, Wei Nie, Andrea Ojdanic, Antti Onnela, Eva Partoll, Tuukka Petäjä, Joschka Pfeifer, Maxim Philippov, Lauriane L. J. Quéléver, Ananth Ranjithkumar, Matti P. Rissanen, Simon Schallhart, Siegfried Schobesberger, Simone Schuchmann, Jiali Shen, Mikko Sipilä, Gerhard Steiner, Yuri Stozhkov, Christian Tauber, Yee J. Tham, António R. Tomé, Miguel Vazquez-Pufleau, Alexander L. Vogel, Robert Wagner, Mingyi Wang, Dongyu S. Wang, Yonghong Wang, Stefan K. Weber, Yusheng Wu, Mao Xiao, Chao Yan, Penglin Ye, Qing Ye, Marcel Zauner-Wieczorek, Xueqin Zhou, Urs Baltensperger, Josef Dommen, Richard C. Flagan, Armin Hansel, Markku Kulmala, Rainer Volkamer, Paul M. Winkler, Douglas R. Worsnop, Neil M. Donahue, Jasper Kirkby, and Joachim Curtius
Atmos. Chem. Phys., 20, 9183–9207, https://doi.org/10.5194/acp-20-9183-2020, https://doi.org/10.5194/acp-20-9183-2020, 2020
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Highly oxygenated organic compounds (HOMs) have been identified as key vapors involved in atmospheric new-particle formation (NPF). The molecular distribution, HOM yield, and NPF from α-pinene oxidation experiments were measured at the CLOUD chamber over a wide tropospheric-temperature range. This study shows on a molecular scale that despite the sharp reduction in HOM yield at lower temperatures, the reduced volatility counteracts this effect and leads to an overall increase in the NPF rate.
Dominik Stolzenburg, Mario Simon, Ananth Ranjithkumar, Andreas Kürten, Katrianne Lehtipalo, Hamish Gordon, Sebastian Ehrhart, Henning Finkenzeller, Lukas Pichelstorfer, Tuomo Nieminen, Xu-Cheng He, Sophia Brilke, Mao Xiao, António Amorim, Rima Baalbaki, Andrea Baccarini, Lisa Beck, Steffen Bräkling, Lucía Caudillo Murillo, Dexian Chen, Biwu Chu, Lubna Dada, António Dias, Josef Dommen, Jonathan Duplissy, Imad El Haddad, Lukas Fischer, Loic Gonzalez Carracedo, Martin Heinritzi, Changhyuk Kim, Theodore K. Koenig, Weimeng Kong, Houssni Lamkaddam, Chuan Ping Lee, Markus Leiminger, Zijun Li, Vladimir Makhmutov, Hanna E. Manninen, Guillaume Marie, Ruby Marten, Tatjana Müller, Wei Nie, Eva Partoll, Tuukka Petäjä, Joschka Pfeifer, Maxim Philippov, Matti P. Rissanen, Birte Rörup, Siegfried Schobesberger, Simone Schuchmann, Jiali Shen, Mikko Sipilä, Gerhard Steiner, Yuri Stozhkov, Christian Tauber, Yee Jun Tham, António Tomé, Miguel Vazquez-Pufleau, Andrea C. Wagner, Mingyi Wang, Yonghong Wang, Stefan K. Weber, Daniela Wimmer, Peter J. Wlasits, Yusheng Wu, Qing Ye, Marcel Zauner-Wieczorek, Urs Baltensperger, Kenneth S. Carslaw, Joachim Curtius, Neil M. Donahue, Richard C. Flagan, Armin Hansel, Markku Kulmala, Jos Lelieveld, Rainer Volkamer, Jasper Kirkby, and Paul M. Winkler
Atmos. Chem. Phys., 20, 7359–7372, https://doi.org/10.5194/acp-20-7359-2020, https://doi.org/10.5194/acp-20-7359-2020, 2020
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Sulfuric acid is a major atmospheric vapour for aerosol formation. If new particles grow fast enough, they can act as cloud droplet seeds or affect air quality. In a controlled laboratory set-up, we demonstrate that van der Waals forces enhance growth from sulfuric acid. We disentangle the effects of ammonia, ions and particle hydration, presenting a complete picture of sulfuric acid growth from molecular clusters onwards. In a climate model, we show its influence on the global aerosol budget.
Daniel T. McCoy, Paul Field, Hamish Gordon, Gregory S. Elsaesser, and Daniel P. Grosvenor
Atmos. Chem. Phys., 20, 4085–4103, https://doi.org/10.5194/acp-20-4085-2020, https://doi.org/10.5194/acp-20-4085-2020, 2020
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Incomplete understanding of how aerosol affects clouds degrades our ability to predict future climate. In particular, it is unclear how aerosol affects the lifetime of clouds. Does it increase or decrease it? This confusion is partially because causality flows from aerosol to clouds and clouds to aerosol, and it is hard to tell what is happening in observations. Here, we use simulations to tell us about how clouds affect aerosol and use this to interpret observations, showing increased lifetime.
Matthew J. Rowlinson, Alexandru Rap, Stephen R. Arnold, Richard J. Pope, Martyn P. Chipperfield, Joe McNorton, Piers Forster, Hamish Gordon, Kirsty J. Pringle, Wuhu Feng, Brian J. Kerridge, Barry L. Latter, and Richard Siddans
Atmos. Chem. Phys., 19, 8669–8686, https://doi.org/10.5194/acp-19-8669-2019, https://doi.org/10.5194/acp-19-8669-2019, 2019
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Wildfires and meteorology have a substantial effect on atmospheric concentrations of greenhouse gases such as methane and ozone. During the 1997 El Niño event, unusually large fire emissions indirectly increased global methane through carbon monoxide emission, which decreased the oxidation capacity of the atmosphere. There were also large regional changes to tropospheric ozone concentrations, but contrasting effects of fire and meteorology resulted in a small change to global radiative forcing.
Katherine J. Evans, Joseph H. Kennedy, Dan Lu, Mary M. Forrester, Stephen Price, Jeremy Fyke, Andrew R. Bennett, Matthew J. Hoffman, Irina Tezaur, Charles S. Zender, and Miren Vizcaíno
Geosci. Model Dev., 12, 1067–1086, https://doi.org/10.5194/gmd-12-1067-2019, https://doi.org/10.5194/gmd-12-1067-2019, 2019
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A robust validation of ice sheet models is presented using LIVVkit, version 2.1. It targets ice sheet and coupled Earth system models, and handles datasets and operations that require high-performance computing and storage. We apply LIVVkit to a Greenland ice sheet simulation to show the degree to which it captures the surface mass balance. LIVVkit identifies a positive bias due to insufficient melting compared to observations that is focused largely around Greenland's southwest region.
William H. Lipscomb, Stephen F. Price, Matthew J. Hoffman, Gunter R. Leguy, Andrew R. Bennett, Sarah L. Bradley, Katherine J. Evans, Jeremy G. Fyke, Joseph H. Kennedy, Mauro Perego, Douglas M. Ranken, William J. Sacks, Andrew G. Salinger, Lauren J. Vargo, and Patrick H. Worley
Geosci. Model Dev., 12, 387–424, https://doi.org/10.5194/gmd-12-387-2019, https://doi.org/10.5194/gmd-12-387-2019, 2019
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This paper describes the Community Ice Sheet Model (CISM) version 2.1. CISM solves equations for ice flow, heat conduction, surface melting, and other processes such as basal sliding and iceberg calving. It can be used for ice-sheet-only simulations or as the ice sheet component of the Community Earth System Model. Model solutions have been verified for standard test problems. CISM can efficiently simulate the whole Greenland ice sheet, with results that are broadly consistent with observations.
Hamish Gordon, Paul R. Field, Steven J. Abel, Mohit Dalvi, Daniel P. Grosvenor, Adrian A. Hill, Ben T. Johnson, Annette K. Miltenberger, Masaru Yoshioka, and Ken S. Carslaw
Atmos. Chem. Phys., 18, 15261–15289, https://doi.org/10.5194/acp-18-15261-2018, https://doi.org/10.5194/acp-18-15261-2018, 2018
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Smoke from African fires is frequently transported across the Atlantic Ocean, where it interacts with clouds. We simulate the interaction of the smoke with the clouds, and the consequences of this for the solar radiation the clouds reflect. The simulations use a new regional configuration of the UK Met Office climate model. Our simulations indicate that the properties of the clouds, in particular their height and reflectivity, and the fractional cloud cover, are strongly affected by the smoke.
Daniel P. Grosvenor, Odran Sourdeval, and Robert Wood
Atmos. Meas. Tech., 11, 4273–4289, https://doi.org/10.5194/amt-11-4273-2018, https://doi.org/10.5194/amt-11-4273-2018, 2018
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We provide a parameterized correction to the retrieval of cloud effective radius from satellite instruments to account for the assumption that the retrieved value is representative of that at cloud top, whereas in reality it is representative of that lower down. The error leads to errors (which we quantify) in the retrieved cloud droplet concentrations of up to 38 % for stratocumulus regions and also to liquid water path errors, both of which can be corrected using our parameterizations.
Daniel T. McCoy, Paul R. Field, Anja Schmidt, Daniel P. Grosvenor, Frida A.-M. Bender, Ben J. Shipway, Adrian A. Hill, Jonathan M. Wilkinson, and Gregory S. Elsaesser
Atmos. Chem. Phys., 18, 5821–5846, https://doi.org/10.5194/acp-18-5821-2018, https://doi.org/10.5194/acp-18-5821-2018, 2018
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Here we use a combination of global convection-permitting models, satellite observations and the Holuhraun volcanic eruption to demonstrate that aerosol enhances the cloud liquid content and brightness of midlatitude cyclones. This is important because the strength of anthropogenic radiative forcing is uncertain, leading to uncertainty in the climate sensitivity consistent with observed temperature record.
Daniel T. McCoy, Frida A.-M. Bender, Daniel P. Grosvenor, Johannes K. Mohrmann, Dennis L. Hartmann, Robert Wood, and Paul R. Field
Atmos. Chem. Phys., 18, 2035–2047, https://doi.org/10.5194/acp-18-2035-2018, https://doi.org/10.5194/acp-18-2035-2018, 2018
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The interaction between clouds and aerosols represents the largest source of uncertainty in the anthropogenic radiative forcing. Cloud droplet number concentration (CDNC) is the state variable that moderates the interaction between aerosol and clouds. Here we show that CDNC decreases off the coasts of East Asia and North America due to controls on emissions. We support this analysis through an examination of volcanism in Hawaii and Vanuatu.
Robert Wagner, Chao Yan, Katrianne Lehtipalo, Jonathan Duplissy, Tuomo Nieminen, Juha Kangasluoma, Lauri R. Ahonen, Lubna Dada, Jenni Kontkanen, Hanna E. Manninen, Antonio Dias, Antonio Amorim, Paulus S. Bauer, Anton Bergen, Anne-Kathrin Bernhammer, Federico Bianchi, Sophia Brilke, Stephany Buenrostro Mazon, Xuemeng Chen, Danielle C. Draper, Lukas Fischer, Carla Frege, Claudia Fuchs, Olga Garmash, Hamish Gordon, Jani Hakala, Liine Heikkinen, Martin Heinritzi, Victoria Hofbauer, Christopher R. Hoyle, Jasper Kirkby, Andreas Kürten, Alexander N. Kvashnin, Tiia Laurila, Michael J. Lawler, Huajun Mai, Vladimir Makhmutov, Roy L. Mauldin III, Ugo Molteni, Leonid Nichman, Wei Nie, Andrea Ojdanic, Antti Onnela, Felix Piel, Lauriane L. J. Quéléver, Matti P. Rissanen, Nina Sarnela, Simon Schallhart, Kamalika Sengupta, Mario Simon, Dominik Stolzenburg, Yuri Stozhkov, Jasmin Tröstl, Yrjö Viisanen, Alexander L. Vogel, Andrea C. Wagner, Mao Xiao, Penglin Ye, Urs Baltensperger, Joachim Curtius, Neil M. Donahue, Richard C. Flagan, Martin Gallagher, Armin Hansel, James N. Smith, António Tomé, Paul M. Winkler, Douglas Worsnop, Mikael Ehn, Mikko Sipilä, Veli-Matti Kerminen, Tuukka Petäjä, and Markku Kulmala
Atmos. Chem. Phys., 17, 15181–15197, https://doi.org/10.5194/acp-17-15181-2017, https://doi.org/10.5194/acp-17-15181-2017, 2017
Daniel P. Grosvenor, Paul R. Field, Adrian A. Hill, and Benjamin J. Shipway
Atmos. Chem. Phys., 17, 5155–5183, https://doi.org/10.5194/acp-17-5155-2017, https://doi.org/10.5194/acp-17-5155-2017, 2017
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We used a weather model to simulate low-level layer clouds that lie off the coast of Chile and tested how they would be affected by airborne particulate matter (aerosols) according to the model. We found that as aerosols were increased, the clouds reflected more and more of the sun’s incoming energy due to the combined effects of the cloud droplets becoming smaller, the thickening of clouds, and increased areal coverage. However, the latter two effects were only important at low aerosol levels.
Stephen F. Price, Matthew J. Hoffman, Jennifer A. Bonin, Ian M. Howat, Thomas Neumann, Jack Saba, Irina Tezaur, Jeffrey Guerber, Don P. Chambers, Katherine J. Evans, Joseph H. Kennedy, Jan Lenaerts, William H. Lipscomb, Mauro Perego, Andrew G. Salinger, Raymond S. Tuminaro, Michiel R. van den Broeke, and Sophie M. J. Nowicki
Geosci. Model Dev., 10, 255–270, https://doi.org/10.5194/gmd-10-255-2017, https://doi.org/10.5194/gmd-10-255-2017, 2017
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We introduce the Cryospheric Model Comparison Tool (CmCt) and propose qualitative and quantitative metrics for evaluating ice sheet model simulations against observations. Greenland simulations using the Community Ice Sheet Model are compared to gravimetry and altimetry observations from 2003 to 2013. We show that the CmCt can be used to score simulations of increasing complexity relative to observations of dynamic change in Greenland over the past decade.
Karoliina Ignatius, Thomas B. Kristensen, Emma Järvinen, Leonid Nichman, Claudia Fuchs, Hamish Gordon, Paul Herenz, Christopher R. Hoyle, Jonathan Duplissy, Sarvesh Garimella, Antonio Dias, Carla Frege, Niko Höppel, Jasmin Tröstl, Robert Wagner, Chao Yan, Antonio Amorim, Urs Baltensperger, Joachim Curtius, Neil M. Donahue, Martin W. Gallagher, Jasper Kirkby, Markku Kulmala, Ottmar Möhler, Harald Saathoff, Martin Schnaiter, Antonio Tomé, Annele Virtanen, Douglas Worsnop, and Frank Stratmann
Atmos. Chem. Phys., 16, 6495–6509, https://doi.org/10.5194/acp-16-6495-2016, https://doi.org/10.5194/acp-16-6495-2016, 2016
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Viscous solid or semi-solid secondary organic aerosol (SOA) may influence cloud properties through ice nucleation in the atmosphere. Here, we observed heterogeneous ice nucleation of viscous α-pinene SOA at temperatures between −39 °C and −37.2 °C with ice saturation ratios significantly below the homogeneous freezing limit. Global modelling suggests that viscous biogenic SOA are present in regions where cirrus formation takes place and could contribute to the global ice nuclei budget.
Emma Järvinen, Karoliina Ignatius, Leonid Nichman, Thomas B. Kristensen, Claudia Fuchs, Christopher R. Hoyle, Niko Höppel, Joel C. Corbin, Jill Craven, Jonathan Duplissy, Sebastian Ehrhart, Imad El Haddad, Carla Frege, Hamish Gordon, Tuija Jokinen, Peter Kallinger, Jasper Kirkby, Alexei Kiselev, Karl-Heinz Naumann, Tuukka Petäjä, Tamara Pinterich, Andre S. H. Prevot, Harald Saathoff, Thea Schiebel, Kamalika Sengupta, Mario Simon, Jay G. Slowik, Jasmin Tröstl, Annele Virtanen, Paul Vochezer, Steffen Vogt, Andrea C. Wagner, Robert Wagner, Christina Williamson, Paul M. Winkler, Chao Yan, Urs Baltensperger, Neil M. Donahue, Rick C. Flagan, Martin Gallagher, Armin Hansel, Markku Kulmala, Frank Stratmann, Douglas R. Worsnop, Ottmar Möhler, Thomas Leisner, and Martin Schnaiter
Atmos. Chem. Phys., 16, 4423–4438, https://doi.org/10.5194/acp-16-4423-2016, https://doi.org/10.5194/acp-16-4423-2016, 2016
Leonid Nichman, Claudia Fuchs, Emma Järvinen, Karoliina Ignatius, Niko Florian Höppel, Antonio Dias, Martin Heinritzi, Mario Simon, Jasmin Tröstl, Andrea Christine Wagner, Robert Wagner, Christina Williamson, Chao Yan, Paul James Connolly, James Robert Dorsey, Jonathan Duplissy, Sebastian Ehrhart, Carla Frege, Hamish Gordon, Christopher Robert Hoyle, Thomas Bjerring Kristensen, Gerhard Steiner, Neil McPherson Donahue, Richard Flagan, Martin William Gallagher, Jasper Kirkby, Ottmar Möhler, Harald Saathoff, Martin Schnaiter, Frank Stratmann, and António Tomé
Atmos. Chem. Phys., 16, 3651–3664, https://doi.org/10.5194/acp-16-3651-2016, https://doi.org/10.5194/acp-16-3651-2016, 2016
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Processes in the atmosphere are often governed by the physical and chemical properties of small cloud particles. Ice, water, and mixed clouds, as well as viscous aerosols, were formed under controlled conditions at the CLOUD-CERN facility. The experimental results show a link between cloud particle properties and their unique optical fingerprints. The classification map presented here allows easier discrimination between various particles such as viscous organic aerosol, salt, ice, and liquid.
C. R. Hoyle, C. Fuchs, E. Järvinen, H. Saathoff, A. Dias, I. El Haddad, M. Gysel, S. C. Coburn, J. Tröstl, A.-K. Bernhammer, F. Bianchi, M. Breitenlechner, J. C. Corbin, J. Craven, N. M. Donahue, J. Duplissy, S. Ehrhart, C. Frege, H. Gordon, N. Höppel, M. Heinritzi, T. B. Kristensen, U. Molteni, L. Nichman, T. Pinterich, A. S. H. Prévôt, M. Simon, J. G. Slowik, G. Steiner, A. Tomé, A. L. Vogel, R. Volkamer, A. C. Wagner, R. Wagner, A. S. Wexler, C. Williamson, P. M. Winkler, C. Yan, A. Amorim, J. Dommen, J. Curtius, M. W. Gallagher, R. C. Flagan, A. Hansel, J. Kirkby, M. Kulmala, O. Möhler, F. Stratmann, D. R. Worsnop, and U. Baltensperger
Atmos. Chem. Phys., 16, 1693–1712, https://doi.org/10.5194/acp-16-1693-2016, https://doi.org/10.5194/acp-16-1693-2016, 2016
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A significant portion of sulphate, an important constituent of atmospheric aerosols, is formed via the aqueous phase oxidation of sulphur dioxide by ozone. The rate of this reaction has previously only been measured over a relatively small temperature range. Here, we use the state of the art CLOUD chamber at CERN to perform the first measurements of this reaction rate in super-cooled droplets, confirming that the existing extrapolation of the reaction rate to sub-zero temperatures is accurate.
D. P. Grosvenor, J. C. King, T. W. Choularton, and T. Lachlan-Cope
Atmos. Chem. Phys., 14, 9481–9509, https://doi.org/10.5194/acp-14-9481-2014, https://doi.org/10.5194/acp-14-9481-2014, 2014
D. P. Grosvenor and R. Wood
Atmos. Chem. Phys., 14, 7291–7321, https://doi.org/10.5194/acp-14-7291-2014, https://doi.org/10.5194/acp-14-7291-2014, 2014
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OpenBench: a land models evaluation system
From weather data to river runoff: using spatiotemporal convolutional networks for discharge forecasting
A Fortran–Python interface for integrating machine learning parameterization into earth system models
ROCKE-3D 2.0: An updated general circulation model for simulating the climates of rocky planets
A rapid-application emissions-to-impacts tool for scenario assessment: Probabilistic Regional Impacts from Model patterns and Emissions (PRIME)
The sensitivity of EC-Earth3 decadal predictions to the choice of volcanic forcing dataset: Insights for the next major eruption
A first calibration of the JULES-crop version 7.4 for rice using the novel O3-FACE experiment in China
The DOE E3SM version 2.1: overview and assessment of the impacts of parameterized ocean submesoscales
WRF-ELM v1.0: a regional climate model to study land–atmosphere interactions over heterogeneous land use regions
Modeling commercial-scale CO2 storage in the gas hydrate stability zone with PFLOTRAN v6.0
DiuSST: a conceptual model of diurnal warm layers for idealized atmospheric simulations with interactive sea surface temperature
High-Resolution Model Intercomparison Project phase 2 (HighResMIP2) towards CMIP7
T&C-CROP: representing mechanistic crop growth with a terrestrial biosphere model (T&C, v1.5) – model formulation and validation
An updated non-intrusive, multi-scale, and flexible coupling interface in WRF 4.6.0
Monitoring and benchmarking Earth system model simulations with ESMValTool v2.12.0
The Earth Science Box Modeling Toolkit (ESBMTK 0.14.0.11): a Python library for research and teaching
CropSuite v1.0 – a comprehensive open-source crop suitability model considering climate variability for climate impact assessment
ICON ComIn – the ICON Community Interface (ComIn version 0.1.0, with ICON version 2024.01-01)
Using feature importance as an exploratory data analysis tool on Earth system models
A new metrics framework for quantifying and intercomparing atmospheric rivers in observations, reanalyses, and climate models
nextGEMS: entering the era of kilometer-scale Earth system modeling
Andrea Storto, Sergey Frolov, Laura Slivinski, and Chunxue Yang
Geosci. Model Dev., 18, 4789–4804, https://doi.org/10.5194/gmd-18-4789-2025, https://doi.org/10.5194/gmd-18-4789-2025, 2025
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Inaccuracies in air–sea heat fluxes severely degrade the accuracy of ocean numerical simulations. Here, we use artificial neural networks to correct air–sea heat fluxes as a function of oceanic and atmospheric state predictors. The correction successfully improves surface and subsurface ocean temperatures beyond the training period and in prediction experiments.
Chang Liao, L. Ruby Leung, Yilin Fang, Teklu Tesfa, and Robinson Negron-Juarez
Geosci. Model Dev., 18, 4601–4624, https://doi.org/10.5194/gmd-18-4601-2025, https://doi.org/10.5194/gmd-18-4601-2025, 2025
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Understanding horizontal groundwater flow is important for understanding how water moves through the ground. Current climate models often simplify this process because they do not have information about the land surface that is detailed enough. Our study developed a new model that divides the land surface into hillslopes to better represent how groundwater flows. This model can help improve predictions of water availability and how it affects ecosystems.
Sebastian Müller, Martin Lange, Thomas Fischer, Sara König, Matthias Kelbling, Jeisson Javier Leal Rojas, and Stephan Thober
Geosci. Model Dev., 18, 4483–4498, https://doi.org/10.5194/gmd-18-4483-2025, https://doi.org/10.5194/gmd-18-4483-2025, 2025
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This study presents FINAM (
FINAM is not a model), a new coupling framework written in Python to dynamically connect independently developed models. Python, as the ultimate glue language, enables the use of codes from nearly any programming language like Fortran, C++, Rust, and others. FINAM is designed to simplify the integration of various models with minimal effort, as demonstrated through various examples ranging from simple to complex systems.
Nathan P. Gillett, Isla R. Simpson, Gabi Hegerl, Reto Knutti, Dann Mitchell, Aurélien Ribes, Hideo Shiogama, Dáithí Stone, Claudia Tebaldi, Piotr Wolski, Wenxia Zhang, and Vivek K. Arora
Geosci. Model Dev., 18, 4399–4416, https://doi.org/10.5194/gmd-18-4399-2025, https://doi.org/10.5194/gmd-18-4399-2025, 2025
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Climate model simulations of the response to human and natural influences together, natural climate influences alone and greenhouse gases alone are key to quantifying human influence on the climate. The last set of such coordinated simulations underpinned key findings in the last Intergovernmental Panel on Climate Change (IPCC) report. Here we propose a new set of such simulations to be used in the next generation of attribution studies and to underpin the next IPCC report.
Pengfei Xue, Chenfu Huang, Yafang Zhong, Michael Notaro, Miraj B. Kayastha, Xing Zhou, Chuyan Zhao, Christa Peters-Lidard, Carlos Cruz, and Eric Kemp
Geosci. Model Dev., 18, 4293–4316, https://doi.org/10.5194/gmd-18-4293-2025, https://doi.org/10.5194/gmd-18-4293-2025, 2025
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This study introduces a new 3D lake–ice–atmosphere coupled model that significantly improves winter climate simulations for the Great Lakes compared to traditional 1D lake model coupling. The key contribution is the identification of critical hydrodynamic processes – ice transport, heat advection, and shear-driven turbulence production – that influence lake thermal structure and ice cover and explain the superior performance of 3D lake models to their 1D counterparts.
Anisbel Leon-Marcos, Moritz Zeising, Manuela van Pinxteren, Sebastian Zeppenfeld, Astrid Bracher, Elena Barbaro, Anja Engel, Matteo Feltracco, Ina Tegen, and Bernd Heinold
Geosci. Model Dev., 18, 4183–4213, https://doi.org/10.5194/gmd-18-4183-2025, https://doi.org/10.5194/gmd-18-4183-2025, 2025
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This study represents the primary marine organic aerosol (PMOA) emissions, focusing on their sea–atmosphere transfer. Using the FESOM2.1–REcoM3 model, concentrations of key organic biomolecules were estimated and integrated into the ECHAM6.3–HAM2.3 aerosol–climate model. Results highlight the influence of marine biological activity and surface winds on PMOA emissions, with reasonably good agreement with observations improving aerosol representation in the southern oceans.
Ziming Ke, Qi Tang, Jean-Christophe Golaz, Xiaohong Liu, and Hailong Wang
Geosci. Model Dev., 18, 4137–4153, https://doi.org/10.5194/gmd-18-4137-2025, https://doi.org/10.5194/gmd-18-4137-2025, 2025
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This study assesses volcanic aerosol representation in E3SM (Energy Exascale Earth System Model), showing that an emission-based approach moderately improves temperature variability and cloud responses compared to a prescribed forcing approach, yet significant bias persists.
Manuel Schlund, Bouwe Andela, Jörg Benke, Ruth Comer, Birgit Hassler, Emma Hogan, Peter Kalverla, Axel Lauer, Bill Little, Saskia Loosveldt Tomas, Francesco Nattino, Patrick Peglar, Valeriu Predoi, Stef Smeets, Stephen Worsley, Martin Yeo, and Klaus Zimmermann
Geosci. Model Dev., 18, 4009–4021, https://doi.org/10.5194/gmd-18-4009-2025, https://doi.org/10.5194/gmd-18-4009-2025, 2025
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The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool for the evaluation of Earth system models. Here, we describe recent significant improvements of ESMValTool’s computational efficiency including parallel, out-of-core, and distributed computing. Evaluations with the enhanced version of ESMValTool are faster, use less computational resources, and can handle input data larger than the available memory.
Philipp Weiss, Ross Herbert, and Philip Stier
Geosci. Model Dev., 18, 3877–3894, https://doi.org/10.5194/gmd-18-3877-2025, https://doi.org/10.5194/gmd-18-3877-2025, 2025
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Aerosols strongly influence Earth's climate as they interact with radiation and clouds. New Earth system models run at resolutions of a few kilometers. To simulate the Earth system with interactive aerosols, we developed a new aerosol module. It represents aerosols as an ensemble of lognormal modes with given sizes and compositions. We present a year-long simulation with four modes at a resolution of 5 km. It captures key processes like the formation of dust storms in the Sahara.
Yan Bo, Hao Liang, Tao Li, and Feng Zhou
Geosci. Model Dev., 18, 3799–3817, https://doi.org/10.5194/gmd-18-3799-2025, https://doi.org/10.5194/gmd-18-3799-2025, 2025
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This study proposed an advancing framework for modeling regional rice production, water use, and greenhouse gas emissions. The framework integrated a process-based soil-crop model with vital physiological effects, a novel model upscaling method, and the NSGA-II multi-objective optimization algorithm at a parallel computing platform. The framework provides a valuable tool for multi-objective optimization of rice irrigation schemes at a large scale.
Carolina A. Bieri, Francina Dominguez, Gonzalo Miguez-Macho, and Ying Fan
Geosci. Model Dev., 18, 3755–3779, https://doi.org/10.5194/gmd-18-3755-2025, https://doi.org/10.5194/gmd-18-3755-2025, 2025
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Access to deep moisture below the Earth's surface is important for vegetation in areas of the Amazon where there is little precipitation for part of the year. Most existing numerical models of the Earth system do not adequately capture where and when deep root water uptake occurs. We address this by adding deep soil layers and a root water uptake feature to an existing model. Out modifications lead to increased dry-month transpiration and improved simulation of the annual transpiration cycle.
Sergi Palomas, Mario C. Acosta, Gladys Utrera, and Etienne Tourigny
Geosci. Model Dev., 18, 3661–3679, https://doi.org/10.5194/gmd-18-3661-2025, https://doi.org/10.5194/gmd-18-3661-2025, 2025
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We present an automatic tool that optimizes resource distribution in coupled climate models, enhancing speed and reducing computational costs without requiring expert knowledge. Users can set energy/time criteria or limit resource usage. Tested on various European Community Earth System Model (EC-Earth) configurations and high-performance computing (HPC) platforms, it achieved up to 34 % faster simulations with fewer resources.
Maria Lucia Ferreira Barbosa, Douglas I. Kelley, Chantelle A. Burton, Igor J. M. Ferreira, Renata Moura da Veiga, Anna Bradley, Paulo Guilherme Molin, and Liana O. Anderson
Geosci. Model Dev., 18, 3533–3557, https://doi.org/10.5194/gmd-18-3533-2025, https://doi.org/10.5194/gmd-18-3533-2025, 2025
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As fire seasons in Brazil become increasingly severe, confidently understanding the factors driving fires is more critical than ever. To address this challenge, we developed FLAME (Fire Landscape Analysis using Maximum Entropy), a new model designed to predict fires and to analyse the spatial influence of both environmental and human factors while accounting for uncertainties. By adapting the model to different regions, we can enhance fire management strategies, making FLAME a powerful tool for protecting landscapes in Brazil and beyond.
Victor Couplet, Marina Martínez Montero, and Michel Crucifix
Geosci. Model Dev., 18, 3081–3129, https://doi.org/10.5194/gmd-18-3081-2025, https://doi.org/10.5194/gmd-18-3081-2025, 2025
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We present SURFER v3.0, a simple climate model designed to estimate the impact of CO2 and CH4 emissions on global temperatures, sea levels, and ocean pH. We added new carbon cycle processes and calibrated the model to observations and results from more complex models, enabling use over timescales ranging from decades to millions of years. SURFER v3.0 is fast, transparent, and easy to use, making it an ideal tool for policy assessments and suitable for educational purposes.
Yong-He Liu and Zong-Liang Yang
Geosci. Model Dev., 18, 3157–3174, https://doi.org/10.5194/gmd-18-3157-2025, https://doi.org/10.5194/gmd-18-3157-2025, 2025
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NMH-CS 3.0 is a C#-based ecohydrological model reconstructed from the WRF-Hydro/Noah-MP model by translating the Fortran code of WRF-Hydro 3.0 and integrating a parallel river routing module. It enables efficient execution on multi-core personal computers. Simulations in the Yellow River basin demonstrate its consistency with WRF-Hydro outputs, providing a reliable alternative to the original Noah-MP model.
Conor T. Doherty, Weile Wang, Hirofumi Hashimoto, and Ian G. Brosnan
Geosci. Model Dev., 18, 3003–3016, https://doi.org/10.5194/gmd-18-3003-2025, https://doi.org/10.5194/gmd-18-3003-2025, 2025
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We present, analyze, and validate a methodology for quantifying uncertainty in gridded meteorological data products produced by spatial interpolation. In a validation case study using daily maximum near-surface air temperature (Tmax), the method works well and produces predictive distributions with closely matching theoretical versus actual coverage levels. Application of the method reveals that the magnitude of uncertainty in interpolated Tmax varies significantly in both space and time.
Martin Juckes, Karl E. Taylor, Fabrizio Antonio, David Brayshaw, Carlo Buontempo, Jian Cao, Paul J. Durack, Michio Kawamiya, Hyungjun Kim, Tomas Lovato, Chloe Mackallah, Matthew Mizielinski, Alessandra Nuzzo, Martina Stockhause, Daniele Visioni, Jeremy Walton, Briony Turner, Eleanor O'Rourke, and Beth Dingley
Geosci. Model Dev., 18, 2639–2663, https://doi.org/10.5194/gmd-18-2639-2025, https://doi.org/10.5194/gmd-18-2639-2025, 2025
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The Baseline Climate Variables for Earth System Modelling (ESM-BCVs) are defined as a list of 135 variables which have high utility for the evaluation and exploitation of climate simulations. The list reflects the most frequently used variables from Earth system models based on an assessment of data publication and download records from the largest archive of global climate projects.
Yucheng Lin, Robert E. Kopp, Alexander Reedy, Matteo Turilli, Shantenu Jha, and Erica L. Ashe
Geosci. Model Dev., 18, 2609–2637, https://doi.org/10.5194/gmd-18-2609-2025, https://doi.org/10.5194/gmd-18-2609-2025, 2025
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PaleoSTeHM v1.0 is a state-of-the-art framework designed to reconstruct past environmental conditions using geological data. Built on modern machine learning techniques, it efficiently handles the sparse and noisy nature of paleo-records, allowing scientists to make accurate and scalable inferences about past environmental change. By using flexible statistical models, PaleoSTeHM separates different sources of uncertainty, improving the precision of historical climate reconstructions.
Ingo Richter, Ping Chang, Ping-Gin Chiu, Gokhan Danabasoglu, Takeshi Doi, Dietmar Dommenget, Guillaume Gastineau, Zoe E. Gillett, Aixue Hu, Takahito Kataoka, Noel S. Keenlyside, Fred Kucharski, Yuko M. Okumura, Wonsun Park, Malte F. Stuecker, Andréa S. Taschetto, Chunzai Wang, Stephen G. Yeager, and Sang-Wook Yeh
Geosci. Model Dev., 18, 2587–2608, https://doi.org/10.5194/gmd-18-2587-2025, https://doi.org/10.5194/gmd-18-2587-2025, 2025
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Tropical ocean basins influence each other through multiple pathways and mechanisms, referred to here as tropical basin interaction (TBI). Many researchers have examined TBI using comprehensive climate models but have obtained conflicting results. This may be partly due to differences in experiment protocols and partly due to systematic model errors. The Tropical Basin Interaction Model Intercomparison Project (TBIMIP) aims to address this problem by designing a set of TBI experiments that will be performed by multiple models.
Daniel F. J. Gunning, Kerim H. Nisancioglu, Emilie Capron, and Roderik S. W. van de Wal
Geosci. Model Dev., 18, 2479–2508, https://doi.org/10.5194/gmd-18-2479-2025, https://doi.org/10.5194/gmd-18-2479-2025, 2025
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This work documents the first results from ZEMBA: an energy balance model of the climate system. The model is a computationally efficient tool designed to study the response of climate to changes in the Earth's orbit. We demonstrate that ZEMBA reproduces many features of the Earth's climate for both the pre-industrial period and the Earth's most recent cold extreme – the Last Glacial Maximum. We intend to develop ZEMBA further and investigate the glacial cycles of the last 2.5 million years.
Pengfei Shi, L. Ruby Leung, and Bin Wang
Geosci. Model Dev., 18, 2443–2460, https://doi.org/10.5194/gmd-18-2443-2025, https://doi.org/10.5194/gmd-18-2443-2025, 2025
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Improving climate predictions has significant socio-economic impacts. In this study, we develop and apply a new weakly coupled ocean data assimilation (WCODA) system to a coupled climate model. The WCODA system improves simulations of ocean temperature and salinity across many global regions. This system is meant to advance our understanding of the ocean's role in climate predictability.
Liwen Wang, Qian Li, Qi Lv, Xuan Peng, and Wei You
Geosci. Model Dev., 18, 2427–2442, https://doi.org/10.5194/gmd-18-2427-2025, https://doi.org/10.5194/gmd-18-2427-2025, 2025
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Our research presents a novel deep learning approach called "TemDeep" for downscaling atmospheric variables at arbitrary time resolutions based on temporal coherence. Results show that our method can accurately recover evolution details superior to other methods, reaching 53.7 % in the restoration rate. Our findings are important for advancing weather forecasting models and enabling more precise and reliable predictions to support disaster preparedness, agriculture, and sustainable development.
Teo Price-Broncucia, Allison Baker, Dorit Hammerling, Michael Duda, and Rebecca Morrison
Geosci. Model Dev., 18, 2349–2372, https://doi.org/10.5194/gmd-18-2349-2025, https://doi.org/10.5194/gmd-18-2349-2025, 2025
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The ensemble consistency test (ECT) and its ultrafast variant (UF-ECT) have become powerful tools in the development community for the identification of unwanted changes in the Community Earth System Model (CESM). We develop a generalized setup framework to enable easy adoption of the ECT approach for other model developers and communities. This framework specifies test parameters to accurately characterize model variability and balance test sensitivity and computational cost.
Sergei Petrov, Andreas Will, and Beate Geyer
EGUsphere, https://doi.org/10.5194/egusphere-2025-710, https://doi.org/10.5194/egusphere-2025-710, 2025
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This study introduces a new method that helps improve the accuracy of climate models by automatically selecting the best parameters to match real-world observations. Instead of manually adjusting many parameters, the method uses a mathematical tool to find the most appropriate settings for the model. It can be especially helpful for researchers who introduce new physical parameters into climate models to assess their impact on model results and optimize them along with the old ones.
Michael Meier, Christof Bigler, and Isabelle Chuine
EGUsphere, https://doi.org/10.5194/egusphere-2025-460, https://doi.org/10.5194/egusphere-2025-460, 2025
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Formulated according to the leaf development process, the DP3 model of leaf coloring considerably contrasts previous models and allows to set up new hypotheses, e.g. on aging versus stress caused color changes. The DP3 model was as accurate as previous models and a comparison to the constant simulation of the mean date of leaf coloring indicated that noisy leaf coloring data forced the models to resort to this mean, which hinders model evaluation.
Esteban Fernández Villanueva and Gary Shaffer
Geosci. Model Dev., 18, 2161–2192, https://doi.org/10.5194/gmd-18-2161-2025, https://doi.org/10.5194/gmd-18-2161-2025, 2025
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We describe, calibrate and test the Danish Center for Earth System Science (DCESS) II model, a new, broad, adaptable and fast Earth system model. DCESS II is designed for global simulations over timescales of years to millions of years using limited computer resources like a personal computer. With its flexibility and comprehensive treatment of the global carbon cycle, DCESS II is a useful, computationally friendly tool for simulations of past climates as well as for future Earth system projections.
Gang Tang, Zebedee Nicholls, Alexander Norton, Sönke Zaehle, and Malte Meinshausen
Geosci. Model Dev., 18, 2193–2230, https://doi.org/10.5194/gmd-18-2193-2025, https://doi.org/10.5194/gmd-18-2193-2025, 2025
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We studied carbon–nitrogen coupling in Earth system models by developing a global carbon–nitrogen cycle model (CNit v1.0) within the widely used emulator MAGICC. CNit effectively reproduced the global carbon–nitrogen cycle dynamics observed in complex models. Our results show persistent nitrogen limitations on plant growth (net primary production) from 1850 to 2100, suggesting that nitrogen deficiency may constrain future land carbon sequestration.
Ngoc Thi Nhu Do, Kengo Sudo, Akihiko Ito, Louisa K. Emmons, Vaishali Naik, Kostas Tsigaridis, Øyvind Seland, Gerd A. Folberth, and Douglas I. Kelley
Geosci. Model Dev., 18, 2079–2109, https://doi.org/10.5194/gmd-18-2079-2025, https://doi.org/10.5194/gmd-18-2079-2025, 2025
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Understanding historical isoprene emission changes is important for predicting future climate, but trends and their controlling factors remain uncertain. This study shows that long-term isoprene trends vary among Earth system models mainly due to partially incorporating CO2 effects and land cover changes rather than to climate. Future models that refine these factors’ effects on isoprene emissions, along with long-term observations, are essential for better understanding plant–climate interactions.
Gang Tang, Zebedee Nicholls, Chris Jones, Thomas Gasser, Alexander Norton, Tilo Ziehn, Alejandro Romero-Prieto, and Malte Meinshausen
Geosci. Model Dev., 18, 2111–2136, https://doi.org/10.5194/gmd-18-2111-2025, https://doi.org/10.5194/gmd-18-2111-2025, 2025
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We analyzed carbon and nitrogen mass conservation in data from various Earth system models. Our findings reveal significant discrepancies between flux and pool size data, where cumulative imbalances can reach hundreds of gigatons of carbon or nitrogen. These imbalances appear primarily due to missing or inconsistently reported fluxes – especially for land-use and fire emissions. To enhance data quality, we recommend that future climate data protocols address this issue at the reporting stage.
Zhongwang Wei, Qingchen Xu, Fan Bai, Xionghui Xu, Zixin Wei, Wenzong Dong, Hongbin Liang, Nan Wei, Xingjie Lu, Lu Li, Shupeng Zhang, Hua Yuan, Laibo Liu, and Yongjiu Dai
EGUsphere, https://doi.org/10.5194/egusphere-2025-1380, https://doi.org/10.5194/egusphere-2025-1380, 2025
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Land surface models are used for simulating earth's surface interacts with the atmosphere. As models grow more complex and detailed, researchers need better tools to evaluate their performance. OpenBench, a new software system that makes evaluation process more comprehensive and efficient. It stands out by incorporating various factors and working with data at any scale which enabling scientists to incorporate new types of models and measurements as our understanding of Earth’s systems evolves.
Florian Börgel, Sven Karsten, Karoline Rummel, and Ulf Gräwe
Geosci. Model Dev., 18, 2005–2019, https://doi.org/10.5194/gmd-18-2005-2025, https://doi.org/10.5194/gmd-18-2005-2025, 2025
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Forecasting river runoff, which is crucial for managing water resources and understanding climate impacts, can be challenging. This study introduces a new method using convolutional long short-term memory (ConvLSTM) networks, a machine learning model that processes spatial and temporal data. Focusing on the Baltic Sea region, our model uses weather data as input to predict daily river runoff for 97 rivers.
Tao Zhang, Cyril Morcrette, Meng Zhang, Wuyin Lin, Shaocheng Xie, Ye Liu, Kwinten Van Weverberg, and Joana Rodrigues
Geosci. Model Dev., 18, 1917–1928, https://doi.org/10.5194/gmd-18-1917-2025, https://doi.org/10.5194/gmd-18-1917-2025, 2025
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Earth system models (ESMs) struggle with the uncertainties associated with parameterizing subgrid physics. Machine learning (ML) algorithms offer a solution by learning the important relationships and features from high-resolution models. To incorporate ML parameterizations into ESMs, we develop a Fortran–Python interface that allows for calling Python functions within Fortran-based ESMs. Through two case studies, this interface demonstrates its feasibility, modularity, and effectiveness.
Kostas Tsigaridis, Andrew S. Ackerman, Igor Aleinov, Mark A. Chandler, Thomas L. Clune, Christopher M. Colose, Anthony D. Del Genio, Maxwell Kelley, Nancy Y. Kiang, Anthony Leboissetier, Jan P. Perlwitz, Reto A. Ruedy, Gary L. Russell, Linda E. Sohl, Michael J. Way, and Eric T. Wolf
EGUsphere, https://doi.org/10.5194/egusphere-2025-925, https://doi.org/10.5194/egusphere-2025-925, 2025
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We present the second generation of ROCKE-3D, a generalized 3-dimensional model for use in Solar System and exoplanetary simulations of rocky planet climates. We quantify how the different component choices affect model results, and discuss strengths and limitations of using each component, together with how one can select which component to use. ROCKE-3D is publicly available and tutorial sessions are available for the community, greatly facilitating its use by any interested group.
Camilla Mathison, Eleanor J. Burke, Gregory Munday, Chris D. Jones, Chris J. Smith, Norman J. Steinert, Andy J. Wiltshire, Chris Huntingford, Eszter Kovacs, Laila K. Gohar, Rebecca M. Varney, and Douglas McNeall
Geosci. Model Dev., 18, 1785–1808, https://doi.org/10.5194/gmd-18-1785-2025, https://doi.org/10.5194/gmd-18-1785-2025, 2025
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We present PRIME (Probabilistic Regional Impacts from Model patterns and Emissions), which is designed to take new emissions scenarios and rapidly provide regional impact information. PRIME allows large ensembles to be run on multi-centennial timescales, including the analysis of many important variables for impact assessments. Our evaluation shows that PRIME reproduces the climate response for known scenarios, providing confidence in using PRIME for novel scenarios.
Roberto Bilbao, Thomas J. Aubry, Matthew Toohey, and Pablo Ortega
EGUsphere, https://doi.org/10.5194/egusphere-2025-609, https://doi.org/10.5194/egusphere-2025-609, 2025
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Large volcanic eruptions are unpredictable and can have significant climatic impacts. If one occurs, operational decadal forecasts will become invalid and must be rerun including the volcanic forcing. By analyzing the climate response in EC-Earth3 retrospective predictions, we show that idealised forcings produced with two simple models could be used in operational decadal forecasts to account for the radiative impacts of the next major volcanic eruption.
Beiyao Xu, Steven Dobbie, Huiyi Yang, Lianxin Yang, Yu Jiang, Andrew Challinor, Karina Williams, Yunxia Wang, and Tijian Wang
EGUsphere, https://doi.org/10.5194/egusphere-2024-4077, https://doi.org/10.5194/egusphere-2024-4077, 2025
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Ozone (O3) pollution harms rice production and threatens food security. To understand these impacts, we calibrated a crop model using unique data from experiments where rice was grown in open fields under controlled O3 exposure (free air). This is the first time such data has been used to improve a model’s ability to predict how rice responds to O3 pollution. Our work provides a more accurate tool to study O3’s effects and guide strategies to protect agriculture.
Katherine M. Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golaz, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautam Bisht, Yan Feng, Walter Hannah, Bryce E. Harrop, Nicole Jeffery, Wuyin Lin, Po-Lun Ma, Mathew E. Maltrud, Mark R. Petersen, Balwinder Singh, Qi Tang, Teklu Tesfa, Jonathan D. Wolfe, Shaocheng Xie, Xue Zheng, Karthik Balaguru, Oluwayemi Garuba, Peter Gleckler, Aixue Hu, Jiwoo Lee, Ben Moore-Maley, and Ana C. Ordoñez
Geosci. Model Dev., 18, 1613–1633, https://doi.org/10.5194/gmd-18-1613-2025, https://doi.org/10.5194/gmd-18-1613-2025, 2025
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Version 2.1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) adds the Fox-Kemper et al. (2011) mixed-layer eddy parameterization, which restratifies the ocean surface layer through an overturning streamfunction. Results include surface layer bias reduction in temperature, salinity, and sea ice extent in the North Atlantic; a small strengthening of the Atlantic meridional overturning circulation; and improvements to many atmospheric climatological variables.
Huilin Huang, Yun Qian, Gautam Bisht, Jiali Wang, Tirthankar Chakraborty, Dalei Hao, Jianfeng Li, Travis Thurber, Balwinder Singh, Zhao Yang, Ye Liu, Pengfei Xue, William J. Sacks, Ethan Coon, and Robert Hetland
Geosci. Model Dev., 18, 1427–1443, https://doi.org/10.5194/gmd-18-1427-2025, https://doi.org/10.5194/gmd-18-1427-2025, 2025
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We integrate the E3SM Land Model (ELM) with the WRF model through the Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) Earth System Modeling Framework (ESMF). This framework includes a top-level driver, LILAC, for variable communication between WRF and ELM and ESMF caps for ELM initialization, execution, and finalization. The LILAC–ESMF framework maintains the integrity of the ELM's source code structure and facilitates the transfer of future ELM model developments to WRF-ELM.
Michael Nole, Jonah Bartrand, Fawz Naim, and Glenn Hammond
Geosci. Model Dev., 18, 1413–1425, https://doi.org/10.5194/gmd-18-1413-2025, https://doi.org/10.5194/gmd-18-1413-2025, 2025
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Safe carbon dioxide (CO2) storage is likely to be critical for mitigating some of the most severe effects of climate change. We present a simulation framework for modeling CO2 storage beneath the seafloor, where CO2 can form a solid. This can aid in permanent CO2 storage for long periods of time. Our models show what a commercial-scale CO2 injection would look like in a marine environment. We discuss what would need to be considered when designing a subsea CO2 injection.
Reyk Börner, Jan O. Haerter, and Romain Fiévet
Geosci. Model Dev., 18, 1333–1356, https://doi.org/10.5194/gmd-18-1333-2025, https://doi.org/10.5194/gmd-18-1333-2025, 2025
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The daily cycle of sea surface temperature (SST) impacts clouds above the ocean and could influence the clustering of thunderstorms linked to extreme rainfall and hurricanes. However, daily SST variability is often poorly represented in modeling studies of how clouds cluster. We present a simple, wind-responsive model of upper-ocean temperature for use in atmospheric simulations. Evaluating the model against observations, we show that it performs significantly better than common slab models.
Malcolm J. Roberts, Kevin A. Reed, Qing Bao, Joseph J. Barsugli, Suzana J. Camargo, Louis-Philippe Caron, Ping Chang, Cheng-Ta Chen, Hannah M. Christensen, Gokhan Danabasoglu, Ivy Frenger, Neven S. Fučkar, Shabeh ul Hasson, Helene T. Hewitt, Huanping Huang, Daehyun Kim, Chihiro Kodama, Michael Lai, Lai-Yung Ruby Leung, Ryo Mizuta, Paulo Nobre, Pablo Ortega, Dominique Paquin, Christopher D. Roberts, Enrico Scoccimarro, Jon Seddon, Anne Marie Treguier, Chia-Ying Tu, Paul A. Ullrich, Pier Luigi Vidale, Michael F. Wehner, Colin M. Zarzycki, Bosong Zhang, Wei Zhang, and Ming Zhao
Geosci. Model Dev., 18, 1307–1332, https://doi.org/10.5194/gmd-18-1307-2025, https://doi.org/10.5194/gmd-18-1307-2025, 2025
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HighResMIP2 is a model intercomparison project focusing on high-resolution global climate models, that is, those with grid spacings of 25 km or less in the atmosphere and ocean, using simulations of decades to a century in length. We are proposing an update of our simulation protocol to make the models more applicable to key questions for climate variability and hazard in present-day and future projections and to build links with other communities to provide more robust climate information.
Jordi Buckley Paules, Simone Fatichi, Bonnie Warring, and Athanasios Paschalis
Geosci. Model Dev., 18, 1287–1305, https://doi.org/10.5194/gmd-18-1287-2025, https://doi.org/10.5194/gmd-18-1287-2025, 2025
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We present and validate enhancements to the process-based T&C model aimed at improving its representation of crop growth and management practices. The updated model, T&C-CROP, enables applications such as analysing the hydrological and carbon storage impacts of land use transitions (e.g. conversions between crops, forests, and pastures) and optimizing irrigation and fertilization strategies in response to climate change.
Sébastien Masson, Swen Jullien, Eric Maisonnave, David Gill, Guillaume Samson, Mathieu Le Corre, and Lionel Renault
Geosci. Model Dev., 18, 1241–1263, https://doi.org/10.5194/gmd-18-1241-2025, https://doi.org/10.5194/gmd-18-1241-2025, 2025
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This article details a new feature we implemented in the popular regional atmospheric model WRF. This feature allows for data exchange between WRF and any other model (e.g. an ocean model) using the coupling library Ocean–Atmosphere–Sea–Ice–Soil Model Coupling Toolkit (OASIS3-MCT). This coupling interface is designed to be non-intrusive, flexible and modular. It also offers the possibility of taking into account the nested zooms used in WRF or in the models with which it is coupled.
Axel Lauer, Lisa Bock, Birgit Hassler, Patrick Jöckel, Lukas Ruhe, and Manuel Schlund
Geosci. Model Dev., 18, 1169–1188, https://doi.org/10.5194/gmd-18-1169-2025, https://doi.org/10.5194/gmd-18-1169-2025, 2025
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Earth system models are important tools to improve our understanding of current climate and to project climate change. Thus, it is crucial to understand possible shortcomings in the models. New features of the ESMValTool software package allow one to compare and visualize a model's performance with respect to reproducing observations in the context of other climate models in an easy and user-friendly way. We aim to help model developers assess and monitor climate simulations more efficiently.
Ulrich G. Wortmann, Tina Tsan, Mahrukh Niazi, Irene A. Ma, Ruben Navasardyan, Magnus-Roland Marun, Bernardo S. Chede, Jingwen Zhong, and Morgan Wolfe
Geosci. Model Dev., 18, 1155–1167, https://doi.org/10.5194/gmd-18-1155-2025, https://doi.org/10.5194/gmd-18-1155-2025, 2025
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The Earth Science Box Modeling Toolkit (ESBMTK) is a user-friendly Python library that simplifies the creation of models to study earth system processes, such as the carbon cycle and ocean chemistry. It enhances learning by emphasizing concepts over programming and is accessible to students and researchers alike. By automating complex calculations and promoting code clarity, ESBMTK accelerates model development while improving reproducibility and the usability of scientific research.
Florian Zabel, Matthias Knüttel, and Benjamin Poschlod
Geosci. Model Dev., 18, 1067–1087, https://doi.org/10.5194/gmd-18-1067-2025, https://doi.org/10.5194/gmd-18-1067-2025, 2025
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CropSuite is a new open-source crop suitability model. It provides a GUI and a wide range of options, including a spatial downscaling of climate data. We apply CropSuite to 48 staple and opportunity crops at a 1 km spatial resolution in Africa. We find that climate variability significantly impacts suitable areas but also affects optimal sowing dates and multiple cropping potential. The results provide valuable information for climate impact assessments, adaptation, and land-use planning.
Kerstin Hartung, Bastian Kern, Nils-Arne Dreier, Jörn Geisbüsch, Mahnoosh Haghighatnasab, Patrick Jöckel, Astrid Kerkweg, Wilton Jaciel Loch, Florian Prill, and Daniel Rieger
Geosci. Model Dev., 18, 1001–1015, https://doi.org/10.5194/gmd-18-1001-2025, https://doi.org/10.5194/gmd-18-1001-2025, 2025
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The ICOsahedral Non-hydrostatic (ICON) model system Community Interface (ComIn) library supports connecting third-party modules to the ICON model. Third-party modules can range from simple diagnostic Python scripts to full chemistry models. ComIn offers a low barrier for code extensions to ICON, provides multi-language support (Fortran, C/C++, and Python), and reduces the migration effort in response to new ICON releases. This paper presents the ComIn design principles and a range of use cases.
Daniel Ries, Katherine Goode, Kellie McClernon, and Benjamin Hillman
Geosci. Model Dev., 18, 1041–1065, https://doi.org/10.5194/gmd-18-1041-2025, https://doi.org/10.5194/gmd-18-1041-2025, 2025
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Machine learning has advanced research in the climate science domain, but its models are difficult to understand. In order to understand the impacts and consequences of climate interventions such as stratospheric aerosol injection, complex models are often necessary. We use a case study to illustrate how we can understand the inner workings of a complex model. We present this technique as an exploratory tool that can be used to quickly discover and assess relationships in complex climate data.
Bo Dong, Paul Ullrich, Jiwoo Lee, Peter Gleckler, Kristin Chang, and Travis A. O'Brien
Geosci. Model Dev., 18, 961–976, https://doi.org/10.5194/gmd-18-961-2025, https://doi.org/10.5194/gmd-18-961-2025, 2025
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A metrics package designed for easy analysis of atmospheric river (AR) characteristics and statistics is presented. The tool is efficient for diagnosing systematic AR bias in climate models and useful for evaluating new AR characteristics in model simulations. In climate models, landfalling AR precipitation shows dry biases globally, and AR tracks are farther poleward (equatorward) in the North and South Atlantic (South Pacific and Indian Ocean).
Hans Segura, Xabier Pedruzo-Bagazgoitia, Philipp Weiss, Sebastian K. Müller, Thomas Rackow, Junhong Lee, Edgar Dolores-Tesillos, Imme Benedict, Matthias Aengenheyster, Razvan Aguridan, Gabriele Arduini, Alexander J. Baker, Jiawei Bao, Swantje Bastin, Eulàlia Baulenas, Tobias Becker, Sebastian Beyer, Hendryk Bockelmann, Nils Brüggemann, Lukas Brunner, Suvarchal K. Cheedela, Sushant Das, Jasper Denissen, Ian Dragaud, Piotr Dziekan, Madeleine Ekblom, Jan Frederik Engels, Monika Esch, Richard Forbes, Claudia Frauen, Lilli Freischem, Diego García-Maroto, Philipp Geier, Paul Gierz, Álvaro González-Cervera, Katherine Grayson, Matthew Griffith, Oliver Gutjahr, Helmuth Haak, Ioan Hadade, Kerstin Haslehner, Shabeh ul Hasson, Jan Hegewald, Lukas Kluft, Aleksei Koldunov, Nikolay Koldunov, Tobias Kölling, Shunya Koseki, Sergey Kosukhin, Josh Kousal, Peter Kuma, Arjun U. Kumar, Rumeng Li, Nicolas Maury, Maximilian Meindl, Sebastian Milinski, Kristian Mogensen, Bimochan Niraula, Jakub Nowak, Divya Sri Praturi, Ulrike Proske, Dian Putrasahan, René Redler, David Santuy, Domokos Sármány, Reiner Schnur, Patrick Scholz, Dmitry Sidorenko, Dorian Spät, Birgit Sützl, Daisuke Takasuka, Adrian Tompkins, Alejandro Uribe, Mirco Valentini, Menno Veerman, Aiko Voigt, Sarah Warnau, Fabian Wachsmann, Marta Wacławczyk, Nils Wedi, Karl-Hermann Wieners, Jonathan Wille, Marius Winkler, Yuting Wu, Florian Ziemen, Janos Zimmermann, Frida A.-M. Bender, Dragana Bojovic, Sandrine Bony, Simona Bordoni, Patrice Brehmer, Marcus Dengler, Emanuel Dutra, Saliou Faye, Erich Fischer, Chiel van Heerwaarden, Cathy Hohenegger, Heikki Järvinen, Markus Jochum, Thomas Jung, Johann H. Jungclaus, Noel S. Keenlyside, Daniel Klocke, Heike Konow, Martina Klose, Szymon Malinowski, Olivia Martius, Thorsten Mauritsen, Juan Pedro Mellado, Theresa Mieslinger, Elsa Mohino, Hanna Pawłowska, Karsten Peters-von Gehlen, Abdoulaye Sarré, Pajam Sobhani, Philip Stier, Lauri Tuppi, Pier Luigi Vidale, Irina Sandu, and Bjorn Stevens
EGUsphere, https://doi.org/10.5194/egusphere-2025-509, https://doi.org/10.5194/egusphere-2025-509, 2025
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The nextGEMS project developed two Earth system models that resolve processes of the order of 10 km, giving more fidelity to the representation of local phenomena, globally. In its fourth cycle, nextGEMS performed simulations with coupled ocean, land, and atmosphere over the 2020–2049 period under the SSP3-7.0 scenario. Here, we provide an overview of nextGEMS, insights into the model development, and the realism of multi-decadal, kilometer-scale simulations.
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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
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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
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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
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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
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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...
