19 Apr 2022
19 Apr 2022
Status: a revised version of this preprint is currently under review for the journal ACP.

Southern Ocean cloud and shortwave radiation biases in a nudged climate model simulation: does the model ever get it right?

Sonya L. Fiddes1,2, Alain Protat3,1, Marc D. Mallet1, Simon P. Alexander4,1, and Matthew T. Woodhouse2,1 Sonya L. Fiddes et al.
  • 1Australian Antarctic Program Partnership, Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Australia
  • 2Climate Science Centre, Oceans and Atmosphere, Commonwealth Scientific and Industrial Research Organisation, Aspendale, Australia
  • 3Bureau of Meteorology, Melbourne, Australia
  • 4Australian Antarctic Division, Hobart, Australia

Abstract. The Southern Ocean radiative bias continues to impact climate and weather models, including the Australian Community Climate and Earth System Simulator (ACCESS). The radiative bias, characterised by too much shortwave radiation reaching the surface, is attributed to the incorrect simulation of cloud frequency and phase. In this work, we use k-means cloud clustering, combined with nudged simulations of the latest generation ACCESS atmosphere model, to evaluate cloud and radiation biases when cloud types are correctly and incorrectly simulated.

We find that even if the ACCESS model correctly simulates the cloud type, biases of equivalent, or in some cases greater, magnitude then when they are incorrectly simulated remain in the cloud and radiation fields examined. Furthermore, we find that even when radiative biases appear small on average, cloud property biases, such as liquid or ice water paths or cloud fractions remain large. Our results suggest that simply getting the right cloud type (or the cloud macrophysics) is not enough to reduce the Southern Ocean radiative bias. Furthermore, in instances where the radiative bias is small, it may be so for the wrong reasons. Considerable effort is still required to improve cloud microphysics, with a particular focus on cloud phase.

Sonya L. Fiddes et al.

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-2022-259', Anonymous Referee #1, 01 May 2022
    • AC1: 'Reply on RC1', Sonya Fiddes, 01 Jul 2022
  • RC2: 'Comment on acp-2022-259', Alex Schuddeboom, 10 May 2022
    • AC2: 'Reply on RC2', Sonya Fiddes, 01 Jul 2022

Sonya L. Fiddes et al.

Sonya L. Fiddes et al.


Total article views: 694 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
517 166 11 694 9 8
  • HTML: 517
  • PDF: 166
  • XML: 11
  • Total: 694
  • BibTeX: 9
  • EndNote: 8
Views and downloads (calculated since 19 Apr 2022)
Cumulative views and downloads (calculated since 19 Apr 2022)

Viewed (geographical distribution)

Total article views: 731 (including HTML, PDF, and XML) Thereof 731 with geography defined and 0 with unknown origin.
Country # Views %
  • 1


Latest update: 23 Sep 2022
Short summary
Climate models have difficulty simulating Southern Ocean clouds, impacting how much sunlight reaches the surface. We use machine learning to group different cloud types observed from satellites and simulated in a climate model. We find the model does a poor job of simulating the correct cloud type and even when it does, the cloud properties and amount of reflected sunlight is incorrect. We have a lot of work to do to model clouds correctly over the Southern Ocean.