Preprints
https://doi.org/10.5194/acp-2022-688
https://doi.org/10.5194/acp-2022-688
 
04 Oct 2022
04 Oct 2022
Status: this preprint is currently under review for the journal ACP.

Possible evidence of increased global cloudiness due to aerosol-cloud interactions

Alyson Rose Douglas1 and Tristan L'Ecuyer2,3 Alyson Rose Douglas and Tristan L'Ecuyer
  • 1Atmospheric, Oceanic and Planetary Physics Department, Department of Physics, University of Oxford, Sherrington Rd, Oxford OX1 3PU, United Kingdom
  • 2Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin-Madison, 1225 W Dayton St, Madison, WI, USA
  • 3Department of Atmospheric and Oceanic Sciences, University of Wisconsin-Madison, 1225 W Dayton St, Madison, WI, USA

Abstract. Aerosol-cloud interactions remain a large source of uncertainty in global climate models due to uncertainty in how pre-industrial clouds, aerosols, and the environment behaved. We employ three machine learning models, a random forest, a stochastic gradient boosting, and an extreme gradient boosting regressor to derive a pre-industrial proxy for warm cloudiness predicted using only their environmental controls. We train our models on boundary layer stability, relative humidity of the free atmosphere, upper level vertical motion, and sea surface temperature to predict a simulated, pristine cloud fraction as a one-for-one proxy for a pre-industrial warm cloud fraction. Using a multivariate linear regression as a proxy for sensitivity studies, we show that the non-linear signatures derived using the simple machine learning models are pivotal in deriving an accurate estimate. We find that aerosols may have increased global cloudiness by 1.27 % since pre-industrial times, leading to −0.42 (0.39–0.46 at 95 % confidence intervals) of cooling. Our methodology reduces the covariability between aerosol, the environment, and cloud adjustments by aiming only to estimate an initial, unperturbed state of the cloud based on the environment alone.

Alyson Rose Douglas and Tristan L'Ecuyer

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2022-688', Anonymous Referee #1, 02 Nov 2022
  • RC2: 'Comment on acp-2022-688', Anonymous Referee #2, 03 Nov 2022
  • RC3: 'Comment on acp-2022-688', Anonymous Referee #3, 11 Nov 2022

Alyson Rose Douglas and Tristan L'Ecuyer

Alyson Rose Douglas and Tristan L'Ecuyer

Viewed

Total article views: 262 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
189 64 9 262 4 5
  • HTML: 189
  • PDF: 64
  • XML: 9
  • Total: 262
  • BibTeX: 4
  • EndNote: 5
Views and downloads (calculated since 04 Oct 2022)
Cumulative views and downloads (calculated since 04 Oct 2022)

Viewed (geographical distribution)

Total article views: 256 (including HTML, PDF, and XML) Thereof 256 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 30 Nov 2022
Download
Short summary
Aerosol, or small particles released by human activities, enter the atmosphere and eventually interact with clouds in what we term aerosol-cloud interactions. As more aerosol enter a cloud, they act as cloud droplet nuclei, increasing the number of cloud droplets in a cloud and delaying rain formation, leading to a larger cloud. We use machine learning and found that these interactions lead to 1.27 % more cloudiness on Earth and offset ~1/4 of the warming due to CO2.
Altmetrics