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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The south-eastern Atlantic is semi-permanently covered by some of the largest stratocumulus clouds and is influenced by one-third of the biomass burning emissions from African fires. A UKEMS1 model simulation shows that the absorption effect of biomass burning aerosols is the most significant on clouds and radiation. The dominate cooling and rapid adjustments induced by the radiative effects of biomass burning aerosols result in an overall cooling in the south-eastern Atlantic.
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
Geosci. Model Dev., 13, 6383–6423, https://doi.org/10.5194/gmd-13-6383-2020, https://doi.org/10.5194/gmd-13-6383-2020, 2020
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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
Atmos. Chem. Phys., 20, 15681–15724, https://doi.org/10.5194/acp-20-15681-2020, https://doi.org/10.5194/acp-20-15681-2020, 2020
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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.
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...
