Preprints
https://doi.org/10.5194/acp-2021-859
https://doi.org/10.5194/acp-2021-859

  08 Nov 2021

08 Nov 2021

Review status: this preprint is currently under review for the journal ACP.

Quantifying Albedo Susceptibility Biases in Shallow Clouds

Graham Feingold1, Tom Goren2, and Takanobu Yamaguchi1,3 Graham Feingold et al.
  • 1National Oceanic and Atmospheric Administration (NOAA), Chemical Sciences Laboratory, Boulder, Colorado, USA
  • 2University of Leipzig, Leipzig, Germany
  • 3Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado, Boulder, Colorado, USA

Abstract. The evaluation of radiative forcing associated with aerosol-cloud interactions remains a significant source of uncertainty in future climate projections. The problem is confounded by the fact that aerosol particles influence clouds locally, and that averaging to larger spatial and/or temporal scales carries biases that depend on the heterogeneity and spatial correlation of the interacting fields and the non-linearity of the responses. Mimicking commonly applied satellite data analyses for calculation of albedo susceptibility So, we quantify So aggregation biases using an ensemble of 127 large eddy simulations of marine stratocumulus. We explore the cloud field properties that control this spatial aggregation bias, and quantify the bias for a large range of shallow stratocumulus cloud conditions manifesting a variety of morphologies and range of cloud fractions. We show that So spatial aggregation biases can be on the order of 100s of percent, depending on methodology. Key uncertainties emanate from the typically applied adiabatic drop concentration Nd retrieval, the correlation between aerosol and cloud fields, and the extent to which averaging reduces the variance in cloud albedo Ac and Nd. Biases are more often positive than negative. So biases are highly correlated to biases in the adjustment. Temporal aggregation biases are shown to offset spatial averaging biases. Both spatial and temporal biases have significant implications for observationally based assessments of aerosol indirect effects and our inferences of underlying aerosol-cloud-radiation effects.

Graham Feingold et al.

Status: open (until 20 Dec 2021)

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 reply

Graham Feingold et al.

Graham Feingold et al.

Viewed

Total article views: 333 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
225 103 5 333 2 3
  • HTML: 225
  • PDF: 103
  • XML: 5
  • Total: 333
  • BibTeX: 2
  • EndNote: 3
Views and downloads (calculated since 08 Nov 2021)
Cumulative views and downloads (calculated since 08 Nov 2021)

Viewed (geographical distribution)

Total article views: 242 (including HTML, PDF, and XML) Thereof 242 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 01 Dec 2021
Download
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 (100s of %) in the cloud albedo susceptibility metric. Spatial aggregation errors tend to be countered by temporal aggregation errors.
Altmetrics