Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 20, issue 22
Articles | Volume 20, issue 22
https://doi.org/10.5194/acp-20-13771-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-20-13771-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 20, issue 22
Research article
 | 
16 Nov 2020
Research article |  | 16 Nov 2020

Snow-induced buffering in aerosol–cloud interactions

Takuro Michibata, Kentaroh Suzuki, and Toshihiko Takemura

Viewed

Total article views: 2,485 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,845 584 56 2,485 249 41 55
  • HTML: 1,845
  • PDF: 584
  • XML: 56
  • Total: 2,485
  • Supplement: 249
  • BibTeX: 41
  • EndNote: 55
Views and downloads (calculated since 31 Mar 2020)
Cumulative views and downloads (calculated since 31 Mar 2020)

Viewed (geographical distribution)

Total article views: 2,485 (including HTML, PDF, and XML) Thereof 2,530 with geography defined and -45 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 26 Dec 2024
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.
Read more
Altmetrics
Final-revised paper
Preprint