Microphysical Properties and Ice Crystal Habits of Low-Level Arctic Mixed-Phase Clouds from Four Airborne In Situ Campaigns over the Svalbard Seas

Dziduch, Aymeric; Baudoux, Antoine; Mioche, Guillaume; Canzi, Antoine; Doiteau, Benjamin; Moser, Manuel; Järvinen, Emma; Vignon, Étienne; Raut, Jean-Christophe; Dupuy, Régis; Gourbeyre, Christophe; Baray, Jean-Luc; Monier, Marie; Szczap, Frédéric; Penide, Guillaume; Delanoë, Julien; Sourdeval, Odran; Schwarzenboeck, Alfons; Mertes, Stephan; Schnaiter, Franz Martin; Voigt, Christiane; Mech, Mario; Crewell, Susanne; Herber, Andreas; Lüpkes, Christof; Ehrlich, André; Wendisch, Manfred; Jourdan, Olivier

doi:10.5194/egusphere-2026-3265

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https://doi.org/10.5194/egusphere-2026-3265

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19 Jun 2026

| 19 Jun 2026

Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

Microphysical Properties and Ice Crystal Habits of Low-Level Arctic Mixed-Phase Clouds from Four Airborne In Situ Campaigns over the Svalbard Seas

Aymeric Dziduch, Antoine Baudoux, Guillaume Mioche, Antoine Canzi, Benjamin Doiteau, Manuel Moser, Emma Järvinen, Étienne Vignon, Jean-Christophe Raut, Régis Dupuy, Christophe Gourbeyre, Jean-Luc Baray, Marie Monier, Frédéric Szczap, Guillaume Penide, Julien Delanoë, Odran Sourdeval, Alfons Schwarzenboeck, Stephan Mertes, Franz Martin Schnaiter, Christiane Voigt, Mario Mech, Susanne Crewell, Andreas Herber, Christof Lüpkes, André Ehrlich, Manfred Wendisch, and Olivier Jourdan

Abstract. This study presents a statistical analysis of the microphysical properties and ice crystal habits of low-level Arctic mixed-phase clouds (MPCs) using in situ observations from four aircraft campaigns conducted near Svalbard, Norway. The dataset comprises 45 hours of in-cloud measurements, with 731 600 ice crystal images classified using a convolutional neural network. Observations were categorized into four meteorological regimes using the Marine Cold Air Outbreak index: Marine Cold Air Outbreaks (MCAOs), Warm Air Advections (WAAs), and two transitional states. Vertical profiles of microphysical properties exhibit clear differences between regimes. MCAO conditions are associated with deeper mixed-phase layers, larger ice water contents, and higher concentrations of ice particles. In contrast, WAA cases are associated with shallower clouds dominated by liquid water with lower ice water content. The analysis indicates that temperature inversions influence cloud microphysics: weaker inversions support deeper clouds and enhanced ice development, while stronger inversions limit the extent of the liquid-containing layer. Analysis of ice crystal habits reveals distinct growth patterns based on the meteorological regime and surface type. Rimed particles appear more frequently during MCAOs, particularly over open ocean where increased turbulence likely promotes droplet–ice interactions. Over sea ice, pristine and aggregated crystals are more prevalent, implying that riming plays a lesser role while depositional growth and aggregation are more significant. This combined multi-campaign dataset offers a comprehensive statistical view on the interaction of synoptic regime, temperature inversion, and surface conditions in determining the microphysical structure and ice crystal habits of Arctic MPCs.

How to cite. Dziduch, A., Baudoux, A., Mioche, G., Canzi, A., Doiteau, B., Moser, M., Järvinen, E., Vignon, É., Raut, J.-C., Dupuy, R., Gourbeyre, C., Baray, J.-L., Monier, M., Szczap, F., Penide, G., Delanoë, J., Sourdeval, O., Schwarzenboeck, A., Mertes, S., Schnaiter, F. M., Voigt, C., Mech, M., Crewell, S., Herber, A., Lüpkes, C., Ehrlich, A., Wendisch, M., and Jourdan, O.: Microphysical Properties and Ice Crystal Habits of Low-Level Arctic Mixed-Phase Clouds from Four Airborne In Situ Campaigns over the Svalbard Seas, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2026-3265, 2026.

Received: 05 Jun 2026 – Discussion started: 19 Jun 2026

Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics.

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.

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RC1: 'Comment on egusphere-2026-3265', Anonymous Referee #1, 01 Oct 2026 reply

General comments
The manuscript presents an interesting and comprehensive analysis of low-level Arctic mixed-phase clouds based on airborne in situ observations from four campaigns around Svalbard. In particular, the combination of microphysical vertical profiles, thermodynamic and turbulence measurements, and automated classification of a very large number of ice-particle images is valuable. The manuscript presents several interesting results concerning differences between MCAO and WAA conditions and between open-ocean and ice-covered surfaces.
Overall, I find the study suitable for ACP, but several aspects of the methodology and interpretation need to be clarified before publication.
The data and results are very interesting, but the robustness of some analyses is not always clear. In particular, the treatment of uncertainty could be strengthened, especially for the CNN-based habit classification and the derived morphological fractions. In several places, conclusions also appear broader than what can clearly be supported by the data and displayed uncertainties. Examples include the attribution of differences between strong and weak WAA inversions, the interpretation of individual features in the ice-habit distributions in Fig. 14, and the interpretation of morphological mass fractions despite the large habit-dependent uncertainty of the mass–dimension relationship.
Specific comments
1. Introduction
The Introduction provides a useful overview of the relevant processes and previous work, though I found it somewhat long and think it could be shortened.
L. 46: The sentence describing “moist-air entrainment driven by cloud-top radiative cooling” is not fully clear to me. Please clarify the physical process meant here, in particular why the entrained air is described as moist
L. 59/60: The statement that interactions between the liquid and ice phases include riming, the WBF process, and aggregation seems misleading, since aggregation itself is primarily an interaction between ice particles rather than between the liquid and the ice phase.
L. 88 vs 110: Please use the introduced acronyms consistently. In particular, “warm air advections” and “marine cold air outbreaks” should use the corresponding acronyms once defined. Please also check the consistency between WAI and WAA.
2. Data, methods and meteorological settings
One general concern regarding the combined dataset is the comparability of the different campaigns. The campaigns differ not only in season and meteorological conditions but also partly in instrumentation. For instance, the 2D-S and CIP are used in different campaigns and have different resolutions and classification thresholds. Please discuss more explicitly how these differences affect comparability and whether campaign, season, meteorological regime, surface conditions, and instrumentation may partly confound each other.
2.1 Airborne campaigns
I found Section 2.1 difficult to follow because the manuscript repeatedly moves between campaigns, meteorological conditions, surface conditions, and instrumentation.
An overview table including campaign period, aircraft, number of flights, in-cloud measurement time, main instruments, and dominant meteorological/surface conditions would substantially improve readability and would allow some of the descriptive text to be shortened. I also suggest reconsidering the organization of Sections 2.1–2.3 to reduce repetition.
2.4 Processing microphysical data
The processing methodology is generally described in detail, but several aspects of the CNN classification remain unclear to me. I also could not find a report of the uncertainty for the CNN classification here (see comments to Section 4 for more details).
L. 265: It is unclear how the additional manually classified images were incorporated into the CNN training. Was the existing model fine-tuned, or was it retrained from scratch? Please clarify the training procedure. This also ties in with the uncertainty reporting for the CNN classification. Were some of these images retained as an independent test set?
265–270: The habit classification is restricted to particles larger than 300 µm for the 2D-S, 450 µm for the CIP, and 2000 µm for the PIP. Please discuss more explicitly what information is lost by excluding smaller particles from the habit analysis and whether the different thresholds affect comparisons between campaigns.
L. 280–285: The manuscript states that particles smaller than 50 µm cannot be reliably separated by phase and are therefore all considered liquid. I understand the practical reason for this assumption, and the comparison with D’Alessandro et al. (2019) provides support for it. Nevertheless, I would like a discussion how this assumption affects the derived LWC, IWC, and phase partitioning, and whether this effect is included in the uncertainty assessment. Why are these particles assigned to the liquid phase rather than excluded from the phase-partitioning analysis?
Figure 5 caption: Please check whether Bazantay et al. (2025) is introduced appropriately before it appears in the caption.
2.6 Profile selection and normalized altitudes
The normalized-altitude approach is useful, but the determination of cloud boundaries is still not entirely clear to me.
L. 355: “Figure 7 illustrates the method used to identify the top and the base of the cloud from the aircraft observations.” I do not clearly see from Fig. 7 how the actual boundary-selection method works.
L. 355–363: Please provide a clearer and more reproducible description of how the final \(Z_b\) and \(Z_t\) are selected for an individual profile, particularly when LWC, asymmetry parameter, radar, and lidar do not indicate the same boundary.
3. Microphysical profiles
The profiles show interesting differences between the regimes. However, in several places I found the interpretation stronger than what I can clearly identify from the figures and displayed uncertainty ranges.
L. 474: “Figures 10 (a–d) show the associated vertical profiles of cloud microphysical properties.” Please add that panels (a–d) refer to WAA conditions.
3.2 Influence of temperature inversions
The influence of inversion strength is interesting, but other cloud properties also vary between the inversion categories.
Table 3: In the WAA cases, stronger inversions are also associated with lower cloud-top temperatures. Could part of the difference in the ice phase therefore result from the lower cloud-top temperature rather than inversion strength itself? Please discuss this possible confounding effect.
3.3 Sea ice influence
The comparison between sea ice and open ocean is interesting, but the strength of the interpretation should reflect what can actually be distinguished in the profiles.
Figure 11: Here N_w is shown, whereas D_{eff, w} is not. Looking at the three quantities in parallel would help the interpretation of the liquid phase changes. It would be interesting to see how D_{eff, w} behaves near cloud top where the LWC decreases. Similarly, also in Fig. 10, including N_w would provide interesting additional context.
L. 529–530: The sentence “This weak vertical increase suggests a limited supply…” is duplicated.
4. Ice crystal habits
This is one of the most interesting parts of the manuscript, but it is also where I miss an uncertainty analysis most. In particular, I could not find uncertainty estimates for the CNN-derived morphological fractions, making it difficult to assess how robust some of the reported peaks and differences are.
Please clarify how particles are identified as “precipitating” particles. If the classification is based primarily on size (>2 mm), “precipitation-sized particles” may be more precise.
4.1 Overview for all campaign
Given that the four campaigns cover different seasons, meteorological conditions, and partly different instrumentation, please discuss more explicitly how representative and comparable the combined distribution is.
Some statements in the description of Fig. 14 are difficult for me to identify in the figure itself.
L. 580: “for” → “of”.
610: The wording that compact particles may grow under “environmental conditions that limit the development of pristine ice crystals” is unclear to me and could be explained more explicitly.
L. 611: The statement that compact particles occur somewhat more frequently in the upper cloud layers does not appear very clear visually. It is also unclear whether this refers to small particles, large particles, or both.
L. 616: The two distinct peaks in rimed aggregates around −7.5 °C and −25 °C are not obvious to me from the figure. If these features are quantitatively robust, please make this clearer or consider softening the wording.
L. 620: The identification of spherical particles as drizzle droplets (see comment above to L. 280–285).
4.2 Comparison of ice crystal habits between MCAO and WAA regimes
The manuscript changes here from morphological number fractions mass fractions. I assume that the mass fractions are calculated using the mass–dimension relationship in Eq. (1) but stating this would make the analysis easier to follow.
More importantly though, I could not find an uncertainty analysis for the mass fractions. Given the earlier discussion of very large and habit-dependent uncertainty in the mass–dimension relationship, a clarification of how these uncertainties affect the interpretation of Figs. 15 and 16 would be helpful.
4.3 Surface influence during cold air outbreak
L. 691-693: In the discussion of Fig. 16, the statement that the fraction of rimed particles is approximately 30% lower over ice-covered surfaces appears somewhat general, as this difference seems strongest only over part of the vertical profile. Please consider making the wording more precise.
5. Discussion
The Discussion provides a useful comparison with previous work. Given the uncertainties and possible confounding factors discussed above, I suggest distinguishing more consistently between relationships directly demonstrated by the dataset and interpretations that are consistent with the observations.
6. Summary and conclusions
Several conclusions are stated more strongly here than in the corresponding Results sections. Short summary statements at the end of the main Results subsections would help connect the presented evidence more clearly to the final conclusions.
L. 791: “Stronger temperature inversions promote favorable conditions for cloud glaciation.” I do not think this conclusion is sufficiently clear from the presented results. There is a potential confounding effect from the lower cloud-top temperatures that occur together with the stronger inversions. In addition, the statement is more strongly worded here than in the Results section.
L. 786–788: High ice crystal number concentrations alone do not demonstrate riming or secondary ice production. In particular, secondary ice production does not appear to be established anywhere in the Results, so I am not sure why it is introduced here.
L. 796–799: The reported 50–70% rimed mass fraction during MCAOs is not obvious to me from Section 4.2, where values of approximately 50–60% are reported, while the higher values appear to occur in the open-ocean data.
L. 800/807: Statements such as “the underlying surface strongly influences” and “jointly controlled” appear somewhat stronger than the results support.
L. 800–803: The statement that stronger surface turbulent fluxes favor both riming and aggregation is not entirely clear to me from Fig. 16. The enhancement of rimed particles over open ocean is clear, but large aggregates also appear abundant over ice-covered surfaces. Please clarify which result supports the conclusion regarding aggregation.

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Citation: https://doi.org/10.5194/egusphere-2026-3265-RC1

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Short summary

This study examines the microphysical properties and ice crystal habits of low-level Arctic mixed-phase clouds around Svalbard. Using airborne in situ observations from four campaigns, we show that cloud microphysical structure and ice crystal habits are influenced by weather conditions and surface type. These results provide quantitative observations to better understand low-level Arctic clouds, their ice formation processes, and their influence on the Arctic atmosphere and climate system.


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