Articles | Volume 20, issue 22
https://doi.org/10.5194/acp-20-13771-2020
https://doi.org/10.5194/acp-20-13771-2020
Research article
 | 
16 Nov 2020
Research article |  | 16 Nov 2020

Snow-induced buffering in aerosol–cloud interactions

Takuro Michibata, Kentaroh Suzuki, and Toshihiko Takemura

Related authors

A new method for diagnosing effective radiative forcing from aerosol-cloud interactions in climate models
Brandon M. Duran, Casey J. Wall, Nicholas J. Lutsko, Takuro Michibata, Po-Lun Ma, Yi Qin, Margaret L. Duffy, Brian Medeiros, and Matvey Debolskiy
EGUsphere, https://doi.org/10.5194/egusphere-2024-3063,https://doi.org/10.5194/egusphere-2024-3063, 2024
Short summary
Droplet collection efficiencies inferred from satellite retrievals constrain effective radiative forcing of aerosol–cloud interactions
Charlotte M. Beall, Po-Lun Ma, Matthew W. Christensen, Johannes Mülmenstädt, Adam Varble, Kentaroh Suzuki, and Takuro Michibata
Atmos. Chem. Phys., 24, 5287–5302, https://doi.org/10.5194/acp-24-5287-2024,https://doi.org/10.5194/acp-24-5287-2024, 2024
Short summary
Incorporation of inline warm rain diagnostics into the COSP2 satellite simulator for process-oriented model evaluation
Takuro Michibata, Kentaroh Suzuki, Tomoo Ogura, and Xianwen Jing
Geosci. Model Dev., 12, 4297–4307, https://doi.org/10.5194/gmd-12-4297-2019,https://doi.org/10.5194/gmd-12-4297-2019, 2019
Short summary
The source of discrepancies in aerosol–cloud–precipitation interactions between GCM and A-Train retrievals
Takuro Michibata, Kentaroh Suzuki, Yousuke Sato, and Toshihiko Takemura
Atmos. Chem. Phys., 16, 15413–15424, https://doi.org/10.5194/acp-16-15413-2016,https://doi.org/10.5194/acp-16-15413-2016, 2016
Short summary
The effects of aerosols on water cloud microphysics and macrophysics based on satellite-retrieved data over East Asia and the North Pacific
T. Michibata, K. Kawamoto, and T. Takemura
Atmos. Chem. Phys., 14, 11935–11948, https://doi.org/10.5194/acp-14-11935-2014,https://doi.org/10.5194/acp-14-11935-2014, 2014
Short summary

Related subject area

Subject: Clouds and Precipitation | Research Activity: Atmospheric Modelling and Data Analysis | Altitude Range: Troposphere | Science Focus: Physics (physical properties and processes)
The impact of the mesh size and microphysics scheme on the representation of mid-level clouds in the ICON model in hilly and complex terrain
Nadja Omanovic, Brigitta Goger, and Ulrike Lohmann
Atmos. Chem. Phys., 24, 14145–14175, https://doi.org/10.5194/acp-24-14145-2024,https://doi.org/10.5194/acp-24-14145-2024, 2024
Short summary
The role of ascent timescales for warm conveyor belt (WCB) moisture transport into the upper troposphere and lower stratosphere (UTLS)
Cornelis Schwenk and Annette Miltenberger
Atmos. Chem. Phys., 24, 14073–14099, https://doi.org/10.5194/acp-24-14073-2024,https://doi.org/10.5194/acp-24-14073-2024, 2024
Short summary
Estimating the concentration of silver iodide needed to detect unambiguous signatures of glaciogenic cloud seeding
Jing Yang, Jiaojiao Li, Meilian Chen, Xiaoqin Jing, Yan Yin, Bart Geerts, Zhien Wang, Yubao Liu, Baojun Chen, Shaofeng Hua, Hao Hu, Xiaobo Dong, Ping Tian, Qian Chen, and Yang Gao
Atmos. Chem. Phys., 24, 13833–13848, https://doi.org/10.5194/acp-24-13833-2024,https://doi.org/10.5194/acp-24-13833-2024, 2024
Short summary
Ice-nucleating particle concentration impacts cloud properties over Dronning Maud Land, East Antarctica, in COSMO-CLM2
Florian Sauerland, Niels Souverijns, Anna Possner, Heike Wex, Preben Van Overmeiren, Alexander Mangold, Kwinten Van Weverberg, and Nicole van Lipzig
Atmos. Chem. Phys., 24, 13751–13768, https://doi.org/10.5194/acp-24-13751-2024,https://doi.org/10.5194/acp-24-13751-2024, 2024
Short summary
Numerical simulation of aerosol concentration effects on cloud droplet size spectrum evolutions of warm stratiform clouds in Jiangxi, China
Yi Li, Xiaoli Liu, and Hengjia Cai
Atmos. Chem. Phys., 24, 13525–13540, https://doi.org/10.5194/acp-24-13525-2024,https://doi.org/10.5194/acp-24-13525-2024, 2024
Short summary

Cited articles

Abdul-Razzak, H. and Ghan, J.: A parameterization of aerosol activation 2. Multiple aerosol types, J. Geophys. Res., 105, 6837–6844, 2000. a
Albrecht, B. A.: Aerosols, cloud microphysics, and fractional cloudiness, Science, 245, 1227–1230, 1989. a, b
Beheng, K. D.: A parameterization of warm cloud microphysical conversion processes, Atmos. Res., 33, 193–206, 1994. a
Bellouin, N., Quaas, J., Morcrette, J.-J., and Boucher, O.: Estimates of aerosol radiative forcing from the MACC re-analysis, Atmos. Chem. Phys., 13, 2045–2062, https://doi.org/10.5194/acp-13-2045-2013, 2013. a
Bellouin, N., Quaas, J., Gryspeerdt, E., Kinne, S., Stier, P., Watson‐Parris, D., Boucher, O., Carslaw, K., Christensen, M., Daniau, A., Dufresne, J., Feingold, G., Fiedler, S., Forster, P., Gettelman, A., Haywood, J., Lohmann, U., Malavelle, F., Mauritsen, T., McCoy, D., Myhre, G., Mülmenstädt, J., Neubauer, D., Possner, A., Rugenstein, M., Sato, Y., Schulz, M., Schwartz, S., 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, b
Download
Short summary
This work reveals that prognostic precipitation significantly reduces the magnitude of aerosol–cloud interactions (ERFaci), mainly due to the collection process associated with snowflakes and underlying cloud droplets. This precipitation-driven buffering effect, which is missing in traditional GCMs, can explain the model–observation discrepancy in ERFaci. These results underscore the necessity for a prognostic precipitation framework in GCMs for more reliable climate simulations.
Altmetrics
Final-revised paper
Preprint