Preprints
https://doi.org/10.5194/egusphere-2026-5114
https://doi.org/10.5194/egusphere-2026-5114
03 Sep 2026
 | 03 Sep 2026
Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

Vertical differences in liquid water content between warm and cold liquid-phase clouds over the North China Plain and their controlling factors: insights from in situ aircraft observations and ensemble learning models

Yulin Wang, Yichen Lu, Honglei Wang, Chunsong Lu, Sihan Liu, Yang Yang, Zirui Liu, Yan Yin, Deyu Liu, Yue Chen, and Tianliang Zhao

Abstract. Liquid water content (LWC) governs cloud radiative forcing and precipitation efficiency, and its contrast between warm and supercooled liquid-phase clouds shapes cloud lifetime and the water cycle. Aircraft campaigns over the North China Plain have characterized droplet spectra, vertical structure, and aerosol responses, but LWC has mostly been diagnosed with single-factor or linear analyses, which cannot distinguish the individual contributions of different factors. It thus remains unclear which factors govern LWC in each regime, and whether any acts differently between them. Using in-situ observations from 13 K350 aircraft flights during 2019–2021, we compared the two cloud types and quantified predictor contributions and interactions in an LWC prediction model with SHAP. An equal-weighted AdaBoost–LightGBM ensemble performed best. Adding further members provided no improvement, indicating that model complementarity outweighs ensemble size. Despite similar cloud-base distributions, cold clouds developed more deeply with stronger winds but lower LWC. The principal vertical contrast occurred at 1600–2300 m, where warm-cloud LWC temporarily exceeded cold-cloud LWC. Wind-related dynamical factors dominated LWC predictions in both cloud types (~41 % combined). From warm to cold clouds, the temperature contribution decreased from 15.5 % to 6.9 %, while aerosol-related contributions increased. The SHAP interaction between temperature and vertical wind changed from positive to negative, indicating that the two regimes share a dynamic framework in which the temperature-wind coupling, not temperature itself, differs. By making these contributions directly comparable, the study identifies this interaction reversal as the key regime difference and provides an observational constraint for LWC parameterizations.

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 paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
Share
Yulin Wang, Yichen Lu, Honglei Wang, Chunsong Lu, Sihan Liu, Yang Yang, Zirui Liu, Yan Yin, Deyu Liu, Yue Chen, and Tianliang Zhao

Status: open (until 15 Oct 2026)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
Yulin Wang, Yichen Lu, Honglei Wang, Chunsong Lu, Sihan Liu, Yang Yang, Zirui Liu, Yan Yin, Deyu Liu, Yue Chen, and Tianliang Zhao
Yulin Wang, Yichen Lu, Honglei Wang, Chunsong Lu, Sihan Liu, Yang Yang, Zirui Liu, Yan Yin, Deyu Liu, Yue Chen, and Tianliang Zhao
Metrics will be available soon.
Latest update: 03 Sep 2026
Download
Short summary
Cloud water shapes rain and climate, and droplets can stay liquid even below freezing, but differences between warm and supercooled clouds remain unclear. We analysed aircraft observations and used machine learning to identify the controls. The cold clouds were deeper but held less water. Wind was the main control in both, while temperature mattered mainly through its interaction with wind rather than on its own. This should help improve weather and climate models.
Share