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

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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.
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