Articles | Volume 15, issue 7
https://doi.org/10.5194/gmd-15-2949-2022
© Author(s) 2022. 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-15-2949-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
07 Apr 2022
Model evaluation paper |
| 07 Apr 2022
| 07 Apr 2022
Evaluation of a quasi-steady-state approximation of the cloud droplet growth equation (QDGE) scheme for aerosol activation in global models using multiple aircraft data over both continental and marine environments
Hengqi Wang
Department of Earth System Science, Ministry of Education Key
Laboratory for Earth System Modeling, Institute for Global Change Studies,
Tsinghua University, Beijing 100084, China
Yiran Peng
CORRESPONDING AUTHOR
Department of Earth System Science, Ministry of Education Key
Laboratory for Earth System Modeling, Institute for Global Change Studies,
Tsinghua University, Beijing 100084, China
Knut von Salzen
CORRESPONDING AUTHOR
Canadian Centre for Climate Modelling and Analysis, Environment and
Climate Change Canada, Victoria, British Columbia, Canada
Yan Yang
Beijing Weather Modification Office, Beijing, 100101, China
Wei Zhou
Beijing Weather Modification Office, Beijing, 100101, China
Delong Zhao
Beijing Weather Modification Office, Beijing, 100101, China
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Min Yuan, Di Wang, Weijia Wang, Lei Yin, Xiaobo Dong, Delong Zhao, and Fan Ping
EGUsphere, https://doi.org/10.5194/egusphere-2025-3657, https://doi.org/10.5194/egusphere-2025-3657, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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This study applies the Thompson-Eidhammer aerosol-aware microphysics scheme to an in-flight icing event over the Sichuan Basin. Simulations with clean and polluted aerosol conditions show that pollution enhances supercooled liquid water mass, droplet number, and cloud lifetime. Copernicus Atmosphere Monitoring Service Reanalysis best matches in situ data, highlighting the value of real-time aerosol input for improving SLW and aerosol-cloud interaction simulations.
Yibo Huangfu, Ziyang Liu, Bin Yuan, Sihang Wang, Xianjun He, Wei Zhou, Fei Wang, Ping Tian, Wei Xiao, Yuanmou Du, Jiujiang Sheng, and Min Shao
EGUsphere, https://doi.org/10.5194/egusphere-2025-2988, https://doi.org/10.5194/egusphere-2025-2988, 2025
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Severe air pollution over the North China Plain has posed significant threats to human health. Emerging evidence highlights the vital role of vertical pollutant transport in influencing surface air quality. In this study, we summarized the vertical profiles of key pollutants based on aircraft surveys up to 4,000 m. The influence of regional transport on the vertical distribution patterns was analyzed, offering essential data for evaluating the impact of aloft pollutants on surface air quality.
Kang Hu, Hong Liao, Dantong Liu, Jianbing Jin, Lei Chen, Siyuan Li, Yangzhou Wu, Changhao Wu, Shitong Zhao, Xiaotong Jiang, Ping Tian, Kai Bi, Ye Wang, and Delong Zhao
Geosci. Model Dev., 18, 3623–3634, https://doi.org/10.5194/gmd-18-3623-2025, https://doi.org/10.5194/gmd-18-3623-2025, 2025
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This study combines machine learning with concentration-weighted trajectory analysis to quantify regional transport PM2.5. From 2013–2020, local emissions dominated Beijing's pollution events. The Air Pollution Prevention and Control Action Plan reduced regional transport pollution, but the eastern region showed the smallest decrease. Beijing should prioritize local emission reduction while considering the east region's contributions in future strategies.
Ruth A. R. Digby, Knut von Salzen, Adam H. Monahan, Nathan P. Gillett, and Jiangnan Li
Atmos. Chem. Phys., 25, 3109–3130, https://doi.org/10.5194/acp-25-3109-2025, https://doi.org/10.5194/acp-25-3109-2025, 2025
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The refractive index of black carbon (BCRI), which determines how much energy black carbon absorbs and scatters, is difficult to measure, and different climate models use different values. We show that varying the BCRI across commonly used values can increase absorbing aerosol optical depth by 42 % and the warming effect from interactions between black carbon and radiation by 47 %, an appreciable fraction of the overall spread between models reported in recent literature assessments.
Yuanmou Du, Dantong Liu, Delong Zhao, Mengyu Huang, Ping Tian, Dian Wen, Wei Xiao, Wei Zhou, Hui He, Baiwan Pan, Dongfei Zuo, Xiange Liu, Yingying Jing, Rong Zhang, Jiujiang Sheng, Fei Wang, Yu Huang, Yunbo Chen, and Deping Ding
Atmos. Chem. Phys., 24, 13429–13444, https://doi.org/10.5194/acp-24-13429-2024, https://doi.org/10.5194/acp-24-13429-2024, 2024
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By conducting in situ measurements, we investigated ice production processes in stratiform clouds with embedded convection over the North China Plain. The results show that the ice number concentration is strongly related to the distance to the cloud top, and the level with a larger distance to the cloud top has more graupel falling from upper levels, which promotes collision and coalescence between graupel and droplets and enhances secondary ice production.
Cynthia Whaley, Montana Etten-Bohm, Courtney Schumacher, Ayodeji Akingunola, Vivek Arora, Jason Cole, Michael Lazare, David Plummer, Knut von Salzen, and Barbara Winter
Geosci. Model Dev., 17, 7141–7155, https://doi.org/10.5194/gmd-17-7141-2024, https://doi.org/10.5194/gmd-17-7141-2024, 2024
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This paper describes how lightning was added as a process in the Canadian Earth System Model in order to interactively respond to climate changes. As lightning is an important cause of global wildfires, this new model development allows for more realistic projections of how wildfires may change in the future, responding to a changing climate.
Zizhan Hu, Yiran Peng, Mengke Zhu, and Jonathon S. Wright
EGUsphere, https://doi.org/10.5194/egusphere-2024-828, https://doi.org/10.5194/egusphere-2024-828, 2024
Preprint archived
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Clouds and precipitation are among the most difficult features of the climate system to simulate. Water isotopes provide valuable information about how clouds and precipitation develop and evolve, but most models that simulate water isotopes cannot resolve individual clouds. Here we introduce a new isotope-enabled model, iPyCLES, that simulates liquid and ice clouds on scales of 10 to 100 meters. This model can help to translate isotopic observations into constraints for larger-scale models.
Ruth A. R. Digby, Nathan P. Gillett, Adam H. Monahan, Knut von Salzen, Antonis Gkikas, Qianqian Song, and Zhibo Zhang
Atmos. Chem. Phys., 24, 2077–2097, https://doi.org/10.5194/acp-24-2077-2024, https://doi.org/10.5194/acp-24-2077-2024, 2024
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The COVID-19 lockdowns reduced aerosol emissions. We ask whether these reductions affected regional aerosol optical depth (AOD) and compare the observed changes to predictions from Earth system models. Only India has an observed AOD reduction outside of typical variability. Models overestimate the response in some regions, but when key biases have been addressed, the agreement is improved. Our results suggest that current models can realistically predict the effects of future emission changes.
Michael Sigmond, James Anstey, Vivek Arora, Ruth Digby, Nathan Gillett, Viatcheslav Kharin, William Merryfield, Catherine Reader, John Scinocca, Neil Swart, John Virgin, Carsten Abraham, Jason Cole, Nicolas Lambert, Woo-Sung Lee, Yongxiao Liang, Elizaveta Malinina, Landon Rieger, Knut von Salzen, Christian Seiler, Clint Seinen, Andrew Shao, Reinel Sospedra-Alfonso, Libo Wang, and Duo Yang
Geosci. Model Dev., 16, 6553–6591, https://doi.org/10.5194/gmd-16-6553-2023, https://doi.org/10.5194/gmd-16-6553-2023, 2023
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We present a new activity which aims to organize the analysis of biases in the Canadian Earth System model (CanESM) in a systematic manner. Results of this “Analysis for Development” (A4D) activity includes a new CanESM version, CanESM5.1, which features substantial improvements regarding the simulation of dust and stratospheric temperatures, a second CanESM5.1 variant with reduced climate sensitivity, and insights into potential avenues to reduce various other model biases.
Jason Neil Steven Cole, Knut von Salzen, Jiangnan Li, John Scinocca, David Plummer, Vivek Arora, Norman McFarlane, Michael Lazare, Murray MacKay, and Diana Verseghy
Geosci. Model Dev., 16, 5427–5448, https://doi.org/10.5194/gmd-16-5427-2023, https://doi.org/10.5194/gmd-16-5427-2023, 2023
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The Canadian Atmospheric Model version 5 (CanAM5) is used to simulate on a global scale the climate of Earth's atmosphere, land, and lakes. We document changes to the physics in CanAM5 since the last major version of the model (CanAM4) and evaluate the climate simulated relative to observations and CanAM4. The climate simulated by CanAM5 is similar to CanAM4, but there are improvements, including better simulation of temperature and precipitation over the Amazon and better simulation of cloud.
Cynthia H. Whaley, Kathy S. Law, Jens Liengaard Hjorth, Henrik Skov, Stephen R. Arnold, Joakim Langner, Jakob Boyd Pernov, Garance Bergeron, Ilann Bourgeois, Jesper H. Christensen, Rong-You Chien, Makoto Deushi, Xinyi Dong, Peter Effertz, Gregory Faluvegi, Mark Flanner, Joshua S. Fu, Michael Gauss, Greg Huey, Ulas Im, Rigel Kivi, Louis Marelle, Tatsuo Onishi, Naga Oshima, Irina Petropavlovskikh, Jeff Peischl, David A. Plummer, Luca Pozzoli, Jean-Christophe Raut, Tom Ryerson, Ragnhild Skeie, Sverre Solberg, Manu A. Thomas, Chelsea Thompson, Kostas Tsigaridis, Svetlana Tsyro, Steven T. Turnock, Knut von Salzen, and David W. Tarasick
Atmos. Chem. Phys., 23, 637–661, https://doi.org/10.5194/acp-23-637-2023, https://doi.org/10.5194/acp-23-637-2023, 2023
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This study summarizes recent research on ozone in the Arctic, a sensitive and rapidly warming region. We find that the seasonal cycles of near-surface atmospheric ozone are variable depending on whether they are near the coast, inland, or at high altitude. Several global model simulations were evaluated, and we found that because models lack some of the ozone chemistry that is important for the coastal Arctic locations, they do not accurately simulate ozone there.
Siyuan Li, Dantong Liu, Shaofei Kong, Yangzhou Wu, Kang Hu, Huang Zheng, Yi Cheng, Shurui Zheng, Xiaotong Jiang, Shuo Ding, Dawei Hu, Quan Liu, Ping Tian, Delong Zhao, and Jiujiang Sheng
Atmos. Chem. Phys., 22, 6937–6951, https://doi.org/10.5194/acp-22-6937-2022, https://doi.org/10.5194/acp-22-6937-2022, 2022
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The understanding of secondary organic aerosols is hindered by the aerosol–gas evolution by different oxidation mechanisms. By concurrently measuring detailed mass spectra of aerosol and gas phases in a megacity online, we identified the primary and secondary source sectors and investigated the transformation between gas and aerosol phases influenced by photooxidation and moisture. The results will help us to understand the respective evolution of major sources in a typical urban environment.
Cynthia H. Whaley, Rashed Mahmood, Knut von Salzen, Barbara Winter, Sabine Eckhardt, Stephen Arnold, Stephen Beagley, Silvia Becagli, Rong-You Chien, Jesper Christensen, Sujay Manish Damani, Xinyi Dong, Konstantinos Eleftheriadis, Nikolaos Evangeliou, Gregory Faluvegi, Mark Flanner, Joshua S. Fu, Michael Gauss, Fabio Giardi, Wanmin Gong, Jens Liengaard Hjorth, Lin Huang, Ulas Im, Yugo Kanaya, Srinath Krishnan, Zbigniew Klimont, Thomas Kühn, Joakim Langner, Kathy S. Law, Louis Marelle, Andreas Massling, Dirk Olivié, Tatsuo Onishi, Naga Oshima, Yiran Peng, David A. Plummer, Olga Popovicheva, Luca Pozzoli, Jean-Christophe Raut, Maria Sand, Laura N. Saunders, Julia Schmale, Sangeeta Sharma, Ragnhild Bieltvedt Skeie, Henrik Skov, Fumikazu Taketani, Manu A. Thomas, Rita Traversi, Kostas Tsigaridis, Svetlana Tsyro, Steven Turnock, Vito Vitale, Kaley A. Walker, Minqi Wang, Duncan Watson-Parris, and Tahya Weiss-Gibbons
Atmos. Chem. Phys., 22, 5775–5828, https://doi.org/10.5194/acp-22-5775-2022, https://doi.org/10.5194/acp-22-5775-2022, 2022
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Air pollutants, like ozone and soot, play a role in both global warming and air quality. Atmospheric models are often used to provide information to policy makers about current and future conditions under different emissions scenarios. In order to have confidence in those simulations, in this study we compare simulated air pollution from 18 state-of-the-art atmospheric models to measured air pollution in order to assess how well the models perform.
Ming Li, Husi Letu, Yiran Peng, Hiroshi Ishimoto, Yanluan Lin, Takashi Y. Nakajima, Anthony J. Baran, Zengyuan Guo, Yonghui Lei, and Jiancheng Shi
Atmos. Chem. Phys., 22, 4809–4825, https://doi.org/10.5194/acp-22-4809-2022, https://doi.org/10.5194/acp-22-4809-2022, 2022
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To build on the previous investigations of the Voronoi model in the remote sensing retrievals of ice cloud products, this paper developed an ice cloud parameterization scheme based on the single-scattering properties of the Voronoi model and evaluate it through simulations with the Community Integrated Earth System Model (CIESM). Compared with four representative ice cloud schemes, results show that the Voronoi model has good capabilities of ice cloud modeling in the climate model.
Chenjie Yu, Dantong Liu, Kang Hu, Ping Tian, Yangzhou Wu, Delong Zhao, Huihui Wu, Dawei Hu, Wenbo Guo, Qiang Li, Mengyu Huang, Deping Ding, and James D. Allan
Atmos. Chem. Phys., 22, 4375–4391, https://doi.org/10.5194/acp-22-4375-2022, https://doi.org/10.5194/acp-22-4375-2022, 2022
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In this study, we applied a new technique to investigate the aerosol properties on both a mass and number basis and CCN abilities in Beijing suburban regions. The size-resolved aerosol chemical compositions and CCN activation measurement enable a detailed analysis of BC-containing particle hygroscopicity and its size-dependent contribution to the CCN activation. The results presented in this study will affect future models and human health studies.
Julia Schmale, Sangeeta Sharma, Stefano Decesari, Jakob Pernov, Andreas Massling, Hans-Christen Hansson, Knut von Salzen, Henrik Skov, Elisabeth Andrews, Patricia K. Quinn, Lucia M. Upchurch, Konstantinos Eleftheriadis, Rita Traversi, Stefania Gilardoni, Mauro Mazzola, James Laing, and Philip Hopke
Atmos. Chem. Phys., 22, 3067–3096, https://doi.org/10.5194/acp-22-3067-2022, https://doi.org/10.5194/acp-22-3067-2022, 2022
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Long-term data sets of Arctic aerosol properties from 10 stations across the Arctic provide evidence that anthropogenic influence on the Arctic atmospheric chemical composition has declined in winter, a season which is typically dominated by mid-latitude emissions. The number of significant trends in summer is smaller than in winter, and overall the pattern is ambiguous with some significant positive and negative trends. This reflects the mixed influence of natural and anthropogenic emissions.
Donglin Chen, Hong Liao, Yang Yang, Lei Chen, Delong Zhao, and Deping Ding
Atmos. Chem. Phys., 22, 1825–1844, https://doi.org/10.5194/acp-22-1825-2022, https://doi.org/10.5194/acp-22-1825-2022, 2022
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The black carbon (BC) vertical profile plays a critical role in BC–meteorology interaction, which also influences PM2.5 concentrations. More BC mass was assigned into high altitudes (above 1000 m) in the model, which resulted in a stronger cooling effect near the surface, a larger temperature inversion below 421 m, more reductions in PBLH, and a larger increase in near-surface PM2.5 in the daytime caused by the direct radiative effect of BC.
Quan Liu, Dantong Liu, Yangzhou Wu, Kai Bi, Wenkang Gao, Ping Tian, Delong Zhao, Siyuan Li, Chenjie Yu, Guiqian Tang, Yunfei Wu, Kang Hu, Shuo Ding, Qian Gao, Fei Wang, Shaofei Kong, Hui He, Mengyu Huang, and Deping Ding
Atmos. Chem. Phys., 21, 14749–14760, https://doi.org/10.5194/acp-21-14749-2021, https://doi.org/10.5194/acp-21-14749-2021, 2021
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Through simultaneous online measurements of detailed aerosol compositions at both surface and surface-influenced mountain sites, the evolution of aerosol composition during daytime vertical transport was investigated. The results show that, from surface to the top of the planetary boundary layer, the oxidation state of organic aerosol had been significantly enhanced due to evaporation and further oxidation of these evaporated gases.
Dongfei Zuo, Deping Ding, Yichen Chen, Ling Yang, Delong Zhao, Mengyu Huang, Ping Tian, Wei Xiao, Wei Zhou, Yuanmou Du, and Dantong Liu
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2021-221, https://doi.org/10.5194/amt-2021-221, 2021
Publication in AMT not foreseen
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According to the echo attenuation analysis of mixed precipitation, the melting layer is found to be the key factor affecting the attenuation correction. This study hereby proposes an adaptive echo attenuation correction method based on the melting layer, and uses the ground-based S-band radar to extract the echo on the aircraft trajectory to verify the correction results. The results show that the echo attenuation correction value above the melting layer is related to the flight position.
Shuo Ding, Dantong Liu, Kang Hu, Delong Zhao, Ping Tian, Fei Wang, Ruijie Li, Yichen Chen, Hui He, Mengyu Huang, and Deping Ding
Atmos. Chem. Phys., 21, 681–694, https://doi.org/10.5194/acp-21-681-2021, https://doi.org/10.5194/acp-21-681-2021, 2021
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In this study, we for the first time characterized the detailed black carbon (BC) microphysics at a mountain site located at the top of the planetary boundary layer (PBL) influenced by surface emission over the North China Plain. We investigated the optical and hygroscopic properties of BC at this level as influenced by microphysical properties. Such information will constrain the impacts of BC in influencing the PBL dynamics and low-level cloud formation over anthropogenically polluted regions.
Cited articles
Abdul-Razzak, H. and Ghan, S. J.: A parameterization of aerosol activation
2. Multiple aerosol types, J. Geophys. Res.-Atmos., 105, 6837–6844,
https://doi.org/10.1029/1999JD901161, 2000.
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.
Albanese, D., Riccadonna, S., Donati, C., and Franceschi, P.: A practical
tool for maximal information coefficient analysis, Gigascience, 7, 1–8,
https://doi.org/10.1093/gigascience/giy032, 2018.
Albrecht, B.: 2011 CIRPAS Twin Otter Navigation and State Parameters. Version 1.0, UCAR/NCAR – Earth Observing Laboratory [data set], https://doi.org/10.26023/36PD-HZKW-RJ03, 2008.
ARM (Atmospheric Radiation Measurement) user facility: Ambient Winds – AIMMS, updated hourly, 2014-02-15 to 2014-10-15, ARM Mobile Facility (MAO) Manacapuru, Amazonas, Brazil, compiled by: Hobbe, J., ARM Data Center [data set], https://adc.arm.gov/discovery/#/results/site_code::mao (last access: 26 October 2020), 2014a.
ARM (Atmospheric Radiation Measurement) user facility: Fast Cloud Droplet Probe (FCDP), updated hourly, 2014-02-15 to 2014-10-15, ARM Mobile Facility (MAO) Manacapuru, Amazonas, Brazil, compiled by: Mei, F., ARM Data Center [data set], https://adc.arm.gov/discovery/#/results/site_code::mao (last access: 26 October 2020), 2014b.
ARM (Atmospheric Radiation Measurement) user facility: Passive Cavity Aerosol Spectrometer, updated hourly, 2014-02-22 to 2014-3-23, ARM Mobile Facility (MAO) Manacapuru, Amazonas, Brazil, compiled by: Tomlinson, J., ARM Data Center [data set], https://adc.arm.gov/discovery/#/results/site_code::mao (last access: 26 October 2020), 2014c.
ARM (Atmospheric Radiation Measurement) user facility: Time of Flight Aerosol Mass Spectrometer, updated hourly, 2014-02-22 to 2014-03-23, ARM Mobile Facility (MAO) Manacapuru, Amazonas, Brazil, compiled by: Shilling, J., ARM Data Center [data set], https://adc.arm.gov/discovery/#/results/site_code::mao (last access: 26 October 2020), 2014d.
Barahona, D. and Nenes, A.: Parameterization of cloud droplet formation in
large-scale models: Including effects of entrainment, J. Geophys. Res.-Atmos., 112, D16206, https://doi.org/10.1029/2007JD008473, 2007.
Boucher, O. and Lohmann, U.: The sulfate-CCN-cloud albedo effect, Tellus B,
47, 281–300, https://doi.org/10.1034/j.1600-0889.47.issue3.1.x, 1995.
Brenguier, J. L.: Parameterization of the condensation process: a
theoretical approach, J. Atmos. Sci., 48, 264–282,
https://doi.org/10.1175/1520-0469(1991)048<0264:POTCPA>2.0.CO;2,
1991.
Chen, J., Liu, Y., Zhang, M., and Peng, Y.: New understanding and
quantification of the regime dependence of aerosol-cloud interaction for
studying aerosol indirect effects, Geophys. Res. Lett., 43, 1780–1787,
https://doi.org/10.1002/2016GL067683, 2016.
Cohard, J. M., Pinty, J. P., and Suhre, K.: On the parameterization of
activation spectra from cloud condensation nuclei microphysical properties,
J. Geophys. Res.-Atmos., 105, 11753–11766, https://doi.org/10.1029/1999JD901195,
2000.
Emanuel, K. A.: Atmospheric convection, Oxford University Press, ISBN 0195066308, 1994.
Ferek, R. J., Hegg, D. A., Hobbs, P. V., Durkee, P., and Nielsen, K.: Measurements of ship-induced tracks in clouds off the Washington coast, J. Geophys. Res.-Atmos., 103, 23199–23206, https://doi.org/10.1029/98JD02121, 1998.
Flossmann, A. I., Hall, W. D., and Pruppacher, H. R.: A Theoretical Study of the Wet Removal of Atmospheric Pollutants. Part I: The Redistribution of Aerosol Particles Captured through Nucleation and Impaction Scavenging by Growing Cloud Drops, J. Atmos. Sci., 42, 583–606, https://doi.org/10.1175/1520-0469(1985)042<0583:ATSOTW>2.0.CO;2, 1985.
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, H.: The Earth's Energy Budget, Climate Feedbacks, and Climate Sensitivity, in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., 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, in press, 2022.
Forster, P. M., Richardson, T., Maycock, A. C., Smith, C. J., Samset, B. H.,
Myhre, G., Andrews, T., Pincus, R., and Schulz, M.: Recommendations for
diagnosing effective radiative forcing from climate models for CMIP6, J.
Geophys. Res., 121, 12460–12475, https://doi.org/10.1002/2016JD025320, 2016.
Fountoukis, C. and Nenes, A.: Continued development of a cloud droplet
formation parameterization for global climate models, J. Geophys. Res. D
Atmos., 110, 1–10, https://doi.org/10.1029/2004JD005591, 2005.
Fountoukis, C., Nenes, A., Meskhidze, N., Bahreini, R., Conant, W. C.,
Jonsson, H., Murphy, S., Sorooshian, A., Varutbangkul, V., Brechtel, F.,
Flagan, R. C., and Seinfeld, J. H.: Aerosol-cloud drop concentration closure
for clouds sampled during the International Consortium for Atmospheric
Research on Transport and Transformation 2004 campaign, J. Geophys. Res.-Atmos., 112, D10, https://doi.org/10.1029/2006JD007272, 2007.
Gerber, H. E., Frick, G. M., Jensen, J. B., and Hudson, J. G.: Entrainment,
mixing, and microphysics in trade-wind cumulus, J. Meteorol. Soc. Japan,
86A, 87–106, https://doi.org/10.2151/jmsj.86a.87, 2008.
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.
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 Sys., 3, M10001, https://doi.org/10.1029/2011MS000074, 2011.
Guibert, S., Snider, J. R., and Brenguier, J. L.: Aerosol activation in
marine stratocumulus clouds: 1. Measurement validation for a closure study,
J. Geophys. Res.-Atmos., 108, 8628, https://doi.org/10.1029/2002jd002678, 2003.
Hallberg, A., Wobrock, W., Flossmann, A. I., Bower, K. N., Noone, K. J.,
Wiedensohler, A., Hansson, H. C., Wendisch, M., Berner, A., Kruisz, C., Laj,
P., Facchini, M. C., Fuzzi, S., and Arends, B. G.: Microphysics of clouds:
Model vs measurements, Atmos. Environ., 31, 2453–2462,
https://doi.org/10.1016/S1352-2310(97)00041-1, 1997.
Herrington, A. R. and Reed, K. A.: On resolution sensitivity in the
Community Atmosphere Model, Q. J. R. Meteorol. Soc., 146, 3789–3807,
https://doi.org/10.1002/qj.3873, 2020.
Jarecka, D., Pawlowska, H., Grabowski, W. W., and Wyszogrodzki, A. A.: Modeling microphysical effects of entrainment in clouds observed during EUCAARI-IMPACT field campaign, Atmos. Chem. Phys., 13, 8489–8503, https://doi.org/10.5194/acp-13-8489-2013, 2013.
Jones, A. and Slingo, A.: Predicting cloud-droplet effective radius and
indirect sulphate aerosol forcing using a general circulation model, Q. J.
R. Meteorol. Soc., 122, 1573–1595, https://doi.org/10.1002/qj.49712253506, 1996.
Jones, A., Roberts, D. L., and Slingo, A.: A climate model study of indirect
radiative forcing by anthropogenic sulphate aerosols, Nature, 370,
450–453, https://doi.org/10.1038/370450a0, 1994.
Kang, I. S., Yang, Y. M., and Tao, W. K.: GCMs with implicit and explicit
representation of cloud microphysics for simulation of extreme precipitation
frequency, Clim. Dyn., 45, 325–335, https://doi.org/10.1007/s00382-014-2376-1,
2015.
Khain, A. P., Beheng, K. D., Heymsfield, A., Korolev, A., Krichak, S. O.,
Levin, Z., Pinsky, M., Phillips, V., Prabhakaran, T., Teller, A., Van Den
Heever, S. C., and Yano, J. I.: Representation of microphysical processes in
cloud-resolving models: Spectral (bin) microphysics versus bulk
parameterization, Rev. Geophys., 53, 247–322, https://doi.org/10.1002/2014RG000468,
2015.
Khvorostyanov, V. I. and Curry, J. A.: Parameterization of cloud drop
activation based on analytical asymptotic solutions to the supersaturation
equation, J. Atmos. Sci., 66, 1905–1925, https://doi.org/10.1175/2009JAS2811.1,
2009.
Kiehl, J. T., Schneider, T. L., Rasch, P. J., Barth, M. C., and Wong, J.:
Radiative forcing due to sulfate aerosols from simulations with the National
Center for Atmospheric Research Community Climate Model, Version 3, J.
Geophys. Res.-Atmos., 105, 1441–1457, https://doi.org/10.1029/1999JD900495, 2000.
Kivekäs, N., Kerminen, V. M., Anttila, T., Korhonen, H., Lihavainen, H.,
Komppula, M., and Kulmala, M.: Parameterization of cloud droplet activation
using a simplified treatment of the aerosol number size distribution, J.
Geophys. Res.-Atmos., 113, D15207, https://doi.org/10.1029/2007JD009485, 2008.
Kleinman, L. I., Daum, P. H., Lee, Y.-N., Lewis, E. R., Sedlacek III, A. J., Senum, G. I., Springston, S. R., Wang, J., Hubbe, J., Jayne, J., Min, Q., Yum, S. S., and Allen, G.: Aerosol concentration and size distribution measured below, in, and above cloud from the DOE G-1 during VOCALS-REx, Atmos. Chem. Phys., 12, 207–223, https://doi.org/10.5194/acp-12-207-2012, 2012.
Kreidenweis, S. M., Petters, M. D., and DeMott, P. J.: Single-parameter estimates of aerosol water content, Environ. Res. Lett., 3, 035002, https://doi.org/10.1088/1748-9326/3/3/035002, 2008.
Li, S., Zhang, F., Jin, X., Sun, Y., Wu, H., Xie, C., Chen, L., Liu, J., Wu,
T., Jiang, S., Cribb, M., and Li, Z.: Characterizing the ratio of nitrate to
sulfate in ambient fine particles of urban Beijing during 2018–2019, Atmos.
Environ., 237, 117662, https://doi.org/10.1016/j.atmosenv.2020.117662, 2020.
Li, S. M., Strawbridge, K. B., Leaitch, W. R., and Macdonald, A. M.: Aerosol
backscattering determined from chemical and physical properties and lidar
observations over the east coast of Canada, Geophys. Res. Lett., 25,
1653–1656, https://doi.org/10.1029/98GL00910, 1998.
Liu, Q., Liu, D., Gao, Q., Tian, P., Wang, F., Zhao, D., Bi, K., Wu, Y., Ding, S., Hu, K., Zhang, J., Ding, D., and Zhao, C.: Vertical characteristics of aerosol hygroscopicity and impacts on optical properties over the North China Plain during winter, Atmos. Chem. Phys., 20, 3931–3944, https://doi.org/10.5194/acp-20-3931-2020, 2020.
Lohmann, U.: Impact of sulfate aerosols on albedo and lifetime of clouds: A
sensitivity study with the ECHAM4 GCM, J. Geophys. Res.-Atmos., 102,
13685–13700, https://doi.org/10.1029/97JD00631, 1997.
Martin, S. T., Artaxo, P., Machado, L. A. T., Manzi, A. O., Souza, R. A. F., Schumacher, C., Wang, J., Andreae, M. O., Barbosa, H. M. J., Fan, J., Fisch, G., Goldstein, A. H., Guenther, A., Jimenez, J. L., Pöschl, U., Silva Dias, M. A., Smith, J. N., and Wendisch, M.: Introduction: Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5), Atmos. Chem. Phys., 16, 4785–4797, https://doi.org/10.5194/acp-16-4785-2016, 2016.
Martin, S. T., Artaxo, P., Machado, L., Manzi, A. O., Souza, R. A. F.,
Schumacher, C., Wang, J., Biscaro, T., Brito, J., Calheiros, A., Jardine,
K., Medeiros, A., Portela, B., De Sá, S. S., Adachi, K., Aiken, A. C.,
Alblbrecht, R., Alexander, L., Andreae, M. O., Barbosa, H. M. J., Buseck,
P., Chand, D., Comstmstmstock, J. M., Day, D. A., Dubey, M., Fan, J.,
Fastst, J., Fisch, G., Fortner, E., Giangrande, S., Gilllles, M.,
Goldststein, A. H., Guenther, A., Hubbbbe, J., Jensen, M., Jimenez, J. L.,
Keutstsch, F. N., Kim, S., Kuang, C., Laskskin, A., McKinney, K., Mei, F.,
Millller, M., Nascimento, R., Pauliquevis, T., Pekour, M., Peres, J.,
Petäjä, T., Pöhlklker, C., Pöschl, U., Rizzo, L., Schmid,
B., Shilllling, J. E., Silva Dias, M. A., Smith, J. N., Tomlmlinson, J. M.,
Tóta, J., and Wendisch, M.: The green ocean amazon experiment
(GOAMAZON2014/5) observes pollution affecting gases, aerosols, clouds, and
rainfall over the rain forest, B. Am. Meteorol. Soc., 98, 981–997,
https://doi.org/10.1175/BAMS-D-15-00221.1, 2017.
Menon, S., Del Genio, A. D., Koch, D., and Tselioudis, G.: GCM simulations of
the aerosol indirect effect: Sensitivity to cloud parameterization and
aerosol Burden, J. Atmos. Sci., 59, 692–713,
https://doi.org/10.1175/1520-0469(2002)059<0692:gsotai>2.0.co;2,
2002.
Meskhidze, N., Nenes, A., Conant, W. C., and Seinfeld, J. H.: Evaluation of a
new cloud droplet activation parameterization with in situ data from
CRYSTAL-FACE and CSTRIPE, J. Geophys. Res. D Atmos., 110, 1–10,
https://doi.org/10.1029/2004JD005703, 2005.
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.
Morrison, H. and Pinto, J. O.: Mesoscale modeling of springtime arctic
mixed-phase stratiform clouds using a new two-moment bulk microphysics
scheme, J. Atmos. Sci., 62, 3683–3704, https://doi.org/10.1175/JAS3564.1, 2005.
Murray, F. W.: On the Computation of Saturation Vapor Pressure, J. Appl.
Meteorol., 6, 203–204, https://doi.org/10.1175/1520-0450(1967)006<0203:otcosv>2.0.co;2, 1967.
Nakao, S., Tang, P., Tang, X., Clark, C. H., Qi, L., Seo, E., Asa-Awuku, A.,
and Cocker, D.: Density and elemental ratios of secondary organic aerosol:
Application of a density prediction method, Atmos. Environ., 68, 273–277,
https://doi.org/10.1016/j.atmosenv.2012.11.006, 2013.
Nenes, A. and Seinfeld, J. H.: Parameterization of cloud droplet formation
in global climate models, J. Geophys. Res.-Atmos., 108, 4415,
https://doi.org/10.1029/2002jd002911, 2003.
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/10.3402/tellusb.v53i2.16569, 2001.
Pandis, S. N., Seinfeld, J. H., and Pilinis, C.: Chemical composition
differences in fog and cloud droplets of different sizes, Atmos. Environ.
Part A, Gen. Top., 24, 1957–1969, https://doi.org/10.1016/0960-1686(90)90529-V,
1990.
Peng, Y., Lohmann, U., Leaitch, R., Banic, C., and Couture, M.: The cloud
albedo-cloud droplet effective radius relationship for clean and polluted
clouds from RACE and FIRE.ACE, J. Geophys. Res.-Atmos., 107, AAC
1-1–AAC 1-6, https://doi.org/10.1029/2000JD000281, 2002.
Peng, Y., Lohmann, U., and Leaitch, W. R.: Importance of vertical velocity
variations in the cloud droplet nucleation process of marine stratus clouds,
J. Geophys. Res.-Atmos., 110, 1–13, https://doi.org/10.1029/2004JD004922, 2005.
Petters, M. D. and Kreidenweis, S. M.: A single parameter representation of hygroscopic growth and cloud condensation nucleus activity, Atmos. Chem. Phys., 7, 1961–1971, https://doi.org/10.5194/acp-7-1961-2007, 2007.
Pruppacher, H. R., Klett, J. D., and Wang, P. K.: Microphysics of Clouds and
Precipitation, Aerosol Sci. Technol., 28, 381–382,
https://doi.org/10.1080/02786829808965531, 1998.
Ramaswamy, V., Collins, W., Haywood, J., Lean, J., Mahowald, N., Myhre, G., Naik, V., Shine, K. P., Soden, B., Stenchikov, G., and Storelvmo, T.: Radiative Forcing of Climate: The Historical Evolution of the Radiative Forcing Concept, the Forcing Agents and their Quantification, and Applications, Meteorol. Monogr., 59, 14.1–14.101, https://doi.org/10.1175/amsmonographs-d-19-0001.1, 2019.
Reshef, D. N., Reshef, Y. A., Finucane, H. K., Grossman, S. R., McVean, G.,
Turnbaugh, P. J., Lander, E. S., Mitzenmacher, M., and Sabeti, P. C.:
Detecting novel associations in large data sets, Science,
334, 1518–1524, https://doi.org/10.1126/science.1205438, 2011.
Seinfeld, J. H. and Pandis, S. N.: Atmospheric Chemistry and Physics: From
Air Pollution to Climate Change, 3rd edn., John Wiley and Sons Inc., New York, USA, ISBN 978-1-118-94740-1, 2016.
Shilling, J. E., Pekour, M. S., Fortner, E. C., Artaxo, P., de Sá, S., Hubbe, J. M., Longo, K. M., Machado, L. A. T., Martin, S. T., Springston, S. R., Tomlinson, J., and Wang, J.: Aircraft observations of the chemical composition and aging of aerosol in the Manaus urban plume during GoAmazon 2014/5, Atmos. Chem. Phys., 18, 10773–10797, https://doi.org/10.5194/acp-18-10773-2018, 2018.
Shipway, B. J. and Abel, S. J.: 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.
Slawinska, J., Grabowski, W. W., Pawlowska, H., and Morrison, H.: Droplet
activation and mixing in large-eddy simulation of a shallow cumulus field,
J. Atmos. Sci., 69, 444–462, https://doi.org/10.1175/JAS-D-11-054.1, 2012.
Snider, J. R. and Brenguier, J. L.: Cloud condensation nuclei and cloud
droplet measurements during ACE-2, Tellus B,
52, 828–842, https://doi.org/10.1034/j.1600-0889.2000.00044.x, 2000.
Snider, J. R., Guibert, S., Brenguier, J. L., and Putaud, J. P.: Aerosol
activation in marine stratocumulus clouds: 2. Köhler and parcel theory
closure studies, J. Geophys. Res.-Atmos., 108, 8629, https://doi.org/10.1029/2002jd002692,
2003.
Twomey, S.: Pollution and the planetary albedo, Atmos. Environ., 8, 1251–1256, https://doi.org/10.1016/0004-6981(74)90004-3, 1974.
Twomey, S.: The Influence of Pollution on the Shortwave Albedo of Clouds, J. Atmos. Sci., 34, 1149–1152, https://doi.org/10.1175/1520-0469(1977)034<1149:TIOPOT>2.0.CO;2, 1977.
UCAR/NCAR – Earth Observing Laboratory: 1970. DOE Gulf Stream 1 (G-1) CAS Data, Version 1.0, UCAR/NCAR – Earth Observing Laboratory [data set], https://data.eol.ucar.edu/dataset/89.262 (last access: 30 January 2018), 2008a.
UCAR/NCAR – Earth Observing Laboratory: 1970. DOE Gulf Stream 1 (G-1) LWC (Gerber Probe) Data, Version 1.0. UCAR/NCAR – Earth Observing Laboratory [data set], https://data.eol.ucar.edu/dataset/89.288 (last access: 30 January 2018), 2008b.
UCAR/NCAR – Earth Observing Laboratory: 1970. DOE Gulf Stream 1 (G-1) Navigation and State Parameters, Version 1.0. UCAR/NCAR – Earth Observing Laboratory [data set], https://data.eol.ucar.edu/dataset/89.217 (last access: 30 January 2018), 2008c.
UCAR/NCAR – Earth Observing Laboratory: 1970. DOE Gulf Stream 1 (G-1) PCASP Data, Version 1.0, UCAR/NCAR – Earth Observing Laboratory [data set], https://data.eol.ucar.edu/dataset/89.289 (last access: 30 January 2018), 2008d.
Wang, H., Peng, Y., Salzen, K. von, Yang, Y., Zhou, W., and Zhao, D.:
Evaluation of a numerically efficient aerosol activation scheme by using
cloud data from multiple aircraft campaigns in continental and marine
regions, Zenodo [data set and code], https://doi.org/10.5281/ZENODO.4841035, 2021.
Wang, M., Peng, Y., Liu, Y., Liu, Y., Xie, X., and Guo, Z.: Understanding
Cloud Droplet Spectral Dispersion Effect Using Empirical and Semi-Analytical
Parameterizations in NCAR CAM5.3, Earth Sp. Sci., 7, e2020EA001276,
https://doi.org/10.1029/2020EA001276, 2020.
Wexler, A. S. and Clegg, S. L.: Atmospheric aerosol models for systems
including the ions H+, NH4+, Na+, so42−, NO3−, Cl−, Br−, and H2O, J.
Geophys. Res.-Atmos., 107, 4207, https://doi.org/10.1029/2001JD000451, 2002.
Whitby, K. T.: The physical characteristics of sulfur aerosols, Atmos.
Environ., 12, 135–159, https://doi.org/10.1016/0004-6981(78)90196-8, 1978.
Wood, R., Mechoso, C. R., Bretherton, C. S., Weller, R. A., Huebert, B., Straneo, F., Albrecht, B. A., Coe, H., Allen, G., Vaughan, G., Daum, P., Fairall, C., Chand, D., Gallardo Klenner, L., Garreaud, R., Grados, C., Covert, D. S., Bates, T. S., Krejci, R., Russell, L. M., de Szoeke, S., Brewer, A., Yuter, S. E., Springston, S. R., Chaigneau, A., Toniazzo, T., Minnis, P., Palikonda, R., Abel, S. J., Brown, W. O. J., Williams, S., Fochesatto, J., Brioude, J., and Bower, K. N.: The VAMOS Ocean-Cloud-Atmosphere-Land Study Regional Experiment (VOCALS-REx): goals, platforms, and field operations, Atmos. Chem. Phys., 11, 627–654, https://doi.org/10.5194/acp-11-627-2011, 2011.
Zamora, M. L., Peng, J., Hu, M., Guo, S., Marrero-Ortiz, W., Shang, D., Zheng, J., Du, Z., Wu, Z., and Zhang, R.: Wintertime aerosol properties in Beijing, Atmos. Chem. Phys., 19, 14329–14338, https://doi.org/10.5194/acp-19-14329-2019, 2019.
Zhang, Y., Zhang, X., Wang, K., He, J., Leung, L. R., Fan, J., and Nenes, A.:
Incorporating an advanced aerosol activation parameterization into WRF-CAM5:
Model evaluation and parameterization intercomparison, J. Geophys. Res.,
120, 6952–6979, https://doi.org/10.1002/2014JD023051, 2015.
Zhou, W., Gao, M., He, Y., Wang, Q., Xie, C., Xu, W., Zhao, J., Du, W., Qiu, Y., Lei, L., Fu, P., Wang, Z., Worsnop, D. R., Zhang, Q., and Sun, Y.: Response of aerosol chemistry to clean air action in Beijing, China: Insights from two-year ACSM measurements and model simulations, Environ. Pollut., 255, 113345, https://doi.org/10.1016/j.envpol.2019.113345, 2019.
Short summary
The aerosol activation scheme is an important part of the general circulation model, but evaluations using observed data are mostly regional. This research introduced a numerically efficient aerosol activation scheme and evaluated it by using stratus and stratocumulus cloud data sampled during multiple aircraft campaigns in Canada, Chile, Brazil, and China. The decent performance indicates that the scheme is suitable for simulations of cloud droplet number concentrations over wide conditions.
The aerosol activation scheme is an important part of the general circulation model, but...
