Interacting effects of droplet number and ice formation processes on mixed-phase cold-air outbreak clouds
- 1University of Leeds (Leeds, United Kingdom)
Abstract
Shallow, mixed-phase clouds associated with cold-air outbreak (CAO) events are natural laboratories for studying mixed-phase cloud processes which are important for estimating cloud-phase feedback in the warming climate. Recent studies show that CAO clouds are sensitive to aerosol-cloud interactions and ice formation processes, but many modelling studies perturbed model parameters individually, limiting the investigation of joint effects from multiple processes on cloud properties. Here we investigated how six cloud microphysics parameters jointly affect CAO cloud properties by building model emulators trained on output from perturbed parameter ensembles of a high-resolution regional model. The parameters are cloud droplet number concentration ( N d ), ice-nucleating particle concentration ( N INP ), efficiencies of three secondary ice production processes, as well as the mixed-phase overlap factor (mpof). For the CAO case studied, N d and N INP most strongly control the cloud radiative properties in the stratocumulus region; whereas in the cumulus region, effects from varying N d and mpof are the strongest. Our results show that these parameters have non-linear joint effects such that the magnitude and even sign of cloud responses to a parameter are highly dependent on the values of other parameters. For example, the sensitivity of cloud albedo to increases in N INP varies between near zero to strongly negative across the sampled parameter space. Therefore, perturbing parameters individually is an inadequate method for determining the cloud responses to model parameters. This work demonstrates the power of model emulation and the importance of a full exploration of parameter space in order to understand the factors controlling cloud properties.
Versions
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BibTeX
@article{huang2026interacting,
title = {{Interacting effects of droplet number and ice formation processes on mixed-phase cold-air outbreak clouds}},
author = {Xinyi Huang and Paul R. Field and Ross J. Herbert and Benjamin J. Murray and Floortje van den Heuvel and Daniel P. Grosvenor and Rachel W. N. Sansom and Kenneth S. Carslaw},
journal = {Atmospheric Chemistry and Physics},
year = {2026},
month = oct,
doi = {10.5194/acp-26-14577-2026},
url = {https://acp.copernicus.org/articles/26/14577/2026/},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Interacting effects of droplet number and ice formation processes on mixed-phase cold-air outbreak clouds
AU - Xinyi Huang
AU - Paul R. Field
AU - Ross J. Herbert
AU - Benjamin J. Murray
AU - Floortje van den Heuvel
AU - Daniel P. Grosvenor
AU - Rachel W. N. Sansom
AU - Kenneth S. Carslaw
PY - 2026
DA - 2026/10/15
JO - Atmospheric Chemistry and Physics
DO - 10.5194/acp-26-14577-2026
UR - https://acp.copernicus.org/articles/26/14577/2026/
AB - Shallow, mixed-phase clouds associated with cold-air outbreak (CAO) events are natural laboratories for studying mixed-phase cloud processes which are important for estimating cloud-phase feedback in the warming climate. Recent studies show that CAO clouds are sensitive to aerosol-cloud interactions and ice formation processes, but many modelling studies perturbed model parameters individually, limiting the investigation of joint effects from multiple processes on cloud properties. Here we investigated how six cloud microphysics parameters jointly affect CAO cloud properties by building model emulators trained on output from perturbed parameter ensembles of a high-resolution regional model. The parameters are cloud droplet number concentration ( N d ), ice-nucleating particle concentration ( N INP ), efficiencies of three secondary ice production processes, as well as the mixed-phase overlap factor (mpof). For the CAO case studied, N d and N INP most strongly control the cloud radiative properties in the stratocumulus region; whereas in the cumulus region, effects from varying N d and mpof are the strongest. Our results show that these parameters have non-linear joint effects such that the magnitude and even sign of cloud responses to a parameter are highly dependent on the values of other parameters. For example, the sensitivity of cloud albedo to increases in N INP varies between near zero to strongly negative across the sampled parameter space. Therefore, perturbing parameters individually is an inadequate method for determining the cloud responses to model parameters. This work demonstrates the power of model emulation and the importance of a full exploration of parameter space in order to understand the factors controlling cloud properties.
SN - 1680-7324
ER -