Preprints
https://doi.org/10.5194/egusphere-2023-862
https://doi.org/10.5194/egusphere-2023-862
22 May 2023
 | 22 May 2023

Examination of varying mixed-phase stratocumulus clouds in terms of their properties, ice processes and aerosol-cloud interactions between polar and midlatitude cases: An attempt to propose a microphysical factor to explain the variation

Seoung Soo Lee, Chang-Hoon Jung, Young Jun Yoon, Junshik Um, Youtong Zheng, Jianping Guo, Manguttathil G. Manoj, and Sang-Keun Song

Abstract. This study examines the ratio of ice crystal number concentration (ICNC) to cloud droplet number concentration (CDNC), which is ICNC/CDNC, as a microphysical factor that induces differences in cloud development, its interactions with aerosols and impacts of ice processes on them among cases of mixed-phase clouds. This examination is performed using a large-eddy simulation (LES) framework and one of efforts toward a more general understanding of mechanisms controlling those development and impacts in mixed-phase clouds. For the examination, this study compares a case of polar mixed-phase clouds to that of midlatitude mixed-phase clouds with weak precipitation. It is found that ICNC/CDNC plays a critical role in making differences in cloud development with respect to the relative proportion of liquid and ice mass between the cases by affecting in-cloud latent-heat processes. Note that this proportion has an important implication for cloud radiative properties and thus climate. It is also found that ICNC/CDNC plays a critical role in making differences in clouds and their interactions with aerosols and impacts of ice processes on them between the cases by affecting in-cloud latent-heat processes. Findings of this study suggest that ICNC/CDNC can be a simplified general factor that contributes to a more general understanding of mixed-phase clouds and roles of ice processes and aerosols in them and thus, to the development of more general parameterizations of those clouds and roles.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Seoung Soo Lee, Chang-Hoon Jung, Young Jun Yoon, Junshik Um, Youtong Zheng, Jianping Guo, Manguttathil G. Manoj, and Sang-Keun Song

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-862', Anonymous Referee #1, 10 Jul 2023
    • AC1: 'Reply on RC1', Seoung Soo Lee, 08 Oct 2023
  • RC2: 'Comment on egusphere-2023-862', Anonymous Referee #2, 06 Sep 2023
    • AC2: 'Reply on RC2', Seoung Soo Lee, 08 Oct 2023
Seoung Soo Lee, Chang-Hoon Jung, Young Jun Yoon, Junshik Um, Youtong Zheng, Jianping Guo, Manguttathil G. Manoj, and Sang-Keun Song
Seoung Soo Lee, Chang-Hoon Jung, Young Jun Yoon, Junshik Um, Youtong Zheng, Jianping Guo, Manguttathil G. Manoj, and Sang-Keun Song

Viewed

Total article views: 574 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
392 142 40 574 48 36
  • HTML: 392
  • PDF: 142
  • XML: 40
  • Total: 574
  • BibTeX: 48
  • EndNote: 36
Views and downloads (calculated since 22 May 2023)
Cumulative views and downloads (calculated since 22 May 2023)

Viewed (geographical distribution)

Total article views: 556 (including HTML, PDF, and XML) Thereof 556 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 12 Jul 2024
Download
Short summary
This study is motivated by the fact that there are no general factors that represent the overall properties of mixed-phase clouds. The absence of these factors contributes to the high uncertainty in the prediction of climate change. Hence, this study finds a general factor that explains differences in the properties of different mixed-phase clouds, using a modeling tool. This factor is useful to develop a general way of using climate models to better predict climate change.