Articles | Volume 23, issue 20
https://doi.org/10.5194/acp-23-13523-2023
https://doi.org/10.5194/acp-23-13523-2023
Research article
 | 
27 Oct 2023
Research article |  | 27 Oct 2023

Evaluation of liquid cloud albedo susceptibility in E3SM using coupled eastern North Atlantic surface and satellite retrievals

Adam C. Varble, Po-Lun Ma, Matthew W. Christensen, Johannes Mülmenstädt, Shuaiqi Tang, and Jerome Fast

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Cited articles

Abdul-Razzak, H. and Ghan, S. J.: A parameterization of aerosol activation: 2. Multiple aerosol types, J. Geophys. Res., 105, 6837–6844, https://doi.org/10.1029/1999JD901161, 2000. 
Ackerman, A. S., Kirkpatrick, M. P., Stevens, D. E., and Toon, O. B.: The impact of humidity above stratiform clouds on indirect aerosol climate forcing, Nature, 432, 1014–1017, https://doi.org/10.1038/nature03174, 2004. 
Albrecht, B. A.: Aerosols, Cloud Microphysics, and Fractional Cloudiness, Science, 245, 1227–1230, https://doi.org/10.1126/science.245.4923.1227, 1989. 
ARM – Atmospheric Radiation Measurement user facility: Interpolated Sonde (INTERPOLATEDSONDE), 2016-01-01 to 2020-12-31, Eastern North Atlantic (ENA) Graciosa Island, Azores, Portugal (C1), compiled by: Jensen, M., Giangrande, S., Fairless, T., and Zhou, A., ARM Data Center [data set], https://doi.org/10.5439/1095316, 2013a.  
ARM – Atmospheric Radiation Measurement user facility: Minnis Cloud Products Using Visst Algorithm (VISSTGRIDM10MINNIS), 2016-01-01 to 2018-02-20, Eastern North Atlantic (ENA) External Data (satellites and others) (X1), ARM Data Center [data set], https://adc.arm.gov/discovery/#/results/datastream::enavisstgridm10minnisX1.c1 (last access: 12 August 2021), 2013b. 
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We evaluate how clouds change in response to changing atmospheric particle (aerosol) concentrations in a climate model and find that the model-predicted cloud brightness increases too much as aerosols increase because the cloud drop number increases too much. Excessive drizzle in the model mutes this difference. Many differences between observational and model estimates are explained by varying assumptions of how much liquid has been lost in clouds, which impacts the estimated cloud drop number.
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