the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 20 May 2026
Cloud-phase sensitivities of a simulated Arctic stratocumulus to aerosol and microphysical parameters
Abstract. Low-level, mixed-phase clouds are a key component of the Arctic energy budget and can impact the extent and thickness of sea ice. These clouds are influenced by aerosols and microphysical processes that can determine the phase partitioning and thereby cloud lifetime and radiative impacts. Atmospheric models often struggle to represent phase partitioning in Arctic mixed-phase clouds correctly. Aerosol number concentration (ANC), aerosol type (Atype), ice crystal number concentration (ICNC), and ice crystal morphology (ice crystal habit; IChab) have previously been shown to impact phase partitioning in Arctic clouds. In this study, we quantified the relative importance of these parameters for simulated liquid water path (LWP), ice water path (IWP), and downward longwave radiation at the surface (DWLW) of a slightly supercooled Arctic mixed-phase cloud by applying factorial analysis. Using MIMICA, the MISU-MIT Cloud Aerosol large-eddy simulation code, we found that ANC was the most important parameter for LWP and DWLW, while ICNC controlled IWP. IChab ranked third for all simulated variables, yet it crucially determined the final phase state of the cloud. The impact of Atype was negligible compared to the other three parameters. Recognizing the limits of relying on a single case study and model, our results suggest that future Arctic field campaigns should prioritize observations of ANC, ICNC, and, crucially, ice habit for slightly supercooled mixed-phase clouds. Models must also represent different ice habits to accurately simulate cloud phase partitioning and its subsequent impact on the Arctic energy budget.
Competing interests: Four coauthors are editors for Atmospheric Chemistry and Physics.
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Status: open (until 02 Jul 2026)
- RC1: 'Comment on egusphere-2026-2403', Anonymous Referee #1, 11 Jun 2026 reply
Data sets
MIMICA LES output to analyze cloud-phase sensitivities in an Arctic stratocumulus Hannah C. Frostenberg, Luisa Ickes https://doi.org/10.5281/zenodo.19761135
Model code and software
MIMICA v5 version for analyzing cloud-phase sensitivities of an Arctic stratocumulus Hannah C. Frostenberg, Luisa Ickes https://doi.org/10.5281/zenodo.19762208
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- 1
Review report
The authors simulate a case of Arctic Sc using the MIMICA model, perturbing several aerosol and cloud microphysical parameters to examine their impact to cloud macrophysical and radiative properties. The study addresses an important topic and is well-presented. I have a few concerns that the authors should address prior to publication.
Major concerns
Fixed aerosol (ll. 118-120) – What is the rationale behind fixing aerosol concentrations? It would be good if the authors could explain how strong of an assumption this is compared to a more realistic simulation using prognostic aerosol concentrations. Assuming the authors tested it, would prognostic aerosol create swift aerosol depletion? And would that suggest missing sources in the model, such as replenishment via free-tropospheric entrainment?
Evolution of thermodynamics – The authors present very little information on how temperature and humidity evolve during the simulation. I am a bit worried the simulations gloss over important temperature and moisture source terms that could substantially affect relative humidity profiles (wrt. water and ice) that are crucial for WBF process rates. Since there are 6-hourly radiosonde profiles, it would be good to show how simulations evolve compared to observations.
Spinup in cloudy conditions – the author mention inhomogeneities imposed to the temperature field at simulation start. Since there is an immediate cloud field, how do these inhomogeneities translate into cloud properties and subsequent vertical wind fields? Would the results change if the spatial scales of the inhomogeneities were different? Since these are rather idle conditions, it would be good if the authors could explain how long their spin-up takes?
Other measurable properties – the authors limits themselves to LWP, IWP, and radiative fluxes. I am wondering if other properties (that should be available from observations) are changing as well, such as cloud cover and surface precipitation rates. Even if there are no impacts to these quantities, it would good to briefly mention that. In case there are in-situ-probed hydrometeor size distributions, it would be great to compare these with simulations, thereby substantially strengthen the microphysical choices.
Unique response under atypeS in combination with icnc1 (Table 3) – IWP appears to generally increase going from ichabP to ichabC, with the exception of atypeS_icnc1 combinations. I wonder if the authors could expand their analysis in Section 3 to explain why that is.
Minor concerns
l. 78 Could also cite other recent studies here that fixed ice number concentrations: Wu et al., 2025, Tornow et al., 2021, Juliano et al., 2026.
l. 112 It would be good if the authors were more specific here, since they strongly advocate for replacing it. For example, what is the default configuration of MIMICA’s primary ice formation.
l. 126 A missing link between cloud ice and radiative transfer could substantially affect the cloud evolution (perhaps even explain the collapse of fully glaciated clouds). The authors should discuss this shortcoming in section 4.
Fig. 3 It would be good to list total aerosol number in the figure or in the caption.
Sec. 2.4.3. Please clarify if the filter collected interstitial aerosol particles only.
ll. 476ff Does ice habit affect the particle fall speed? And it would also be good to repeat here which habit was mostly seen via in-situ probes.
ll. 508ff Could a different INP parametrization help here, such as time-dependent activation (e.g., Knopf et al., 2023)?
References
Juliano, T. et al.: The Cold-Air Outbreaks in the Marine Boundary Layer Experiment model-observation intercomparison project (COMBLE-MIP), Part I: Model specification, observational constraints, and preliminary findings, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-6217, 2026.
Knopf, D. et al. (2023). A 1D model for nucleation of ice from aerosol particles: An application to a mixed-phase Arctic stratus cloud layer. Journal of Advances in Modeling Earth Systems, 15, e2023MS003663. https://doi.org/10.1029/2023MS003663
Tornow, F., et al.: Preconditioning of overcast-to-broken cloud transitions by riming in marine cold air outbreaks, Atmos. Chem. Phys., 21, 12049–12067, https://doi.org/10.5194/acp-21-12049-2021, 2021.
Wu, P et al. (2025). Effect of ice number concentration on the evolution of boundary layer clouds during Arctic marine cold-air outbreaks. Journal of Geophysical Research: Atmospheres, 130, e2024JD041282. https://doi.org/10.1029/2024JD041282