Articles | Volume 22, issue 5
Atmos. Chem. Phys., 22, 3303–3319, 2022
https://doi.org/10.5194/acp-22-3303-2022
Atmos. Chem. Phys., 22, 3303–3319, 2022
https://doi.org/10.5194/acp-22-3303-2022

Research article 14 Mar 2022

Research article | 14 Mar 2022

Quantifying albedo susceptibility biases in shallow clouds

Graham Feingold et al.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2021-859', Anonymous Referee #1, 28 Nov 2021
  • RC2: 'Comment on acp-2021-859', Anonymous Referee #2, 21 Dec 2021
  • RC3: 'Comment on acp-2021-859', Anonymous Referee #3, 23 Dec 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Graham Feingold on behalf of the Authors (18 Jan 2022)  Author's response    Author's tracked changes
ED: Referee Nomination & Report Request started (22 Jan 2022) by Timothy Garrett
RR by Anonymous Referee #3 (04 Feb 2022)
RR by Anonymous Referee #1 (04 Feb 2022)
RR by Anonymous Referee #2 (11 Feb 2022)
ED: Publish as is (12 Feb 2022) by Timothy Garrett
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Short summary
The evaluation of radiative forcing associated with aerosol–cloud interactions remains a significant source of uncertainty in future climate projections. Using high-resolution numerical model output, we mimic typical satellite retrieval methodologies to show that data aggregation can introduce significant error (hundreds of percent) in the cloud albedo susceptibility metric. Spatial aggregation errors tend to be countered by temporal aggregation errors.
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