the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 20 May 2025
Observed and modeled Arctic airmass transformations during warm air intrusions and cold air outbreaks
Abstract. Profiles of thermodynamic and cloud properties and their transformations during Arctic warm air intrusions (WAIs) and cold air outbreaks (CAOs) were observed during an aircraft campaign, and simulated using the ICON weather prediction model. The data were collected along flight patterns aimed at sampling the same air parcels multiple times, enabling Eulerian and quasi-Lagrangian measurement-model comparisons and model process studies. Within the Eulerian framework, the temperature profiles agreed well with the ICON output although a small model bias of -0.9 K was detected over sea ice during CAOs. Also, the air parcels did not adjust to the changing surface skin temperature quickly enough. The specific humidity profiles were reproduced by ICON with mean deviations of 6.0 % and 19.5 % for WAIs and CAOs, respectively. Radar reflectivities based on ICON output captured the vertical cloud distributions during the airmass transformations. The simulated process rates of temperature and humidity along the trajectories showed that adiabatic processes dominated the heating and cooling of the air parcels over diabatic effects during WAIs and CAOs. Of the diabatic processes, latent heating and turbulence had a stronger impact on the temperature process rates than terrestrial radiative effects, especially over the warm ocean surface during CAOs. Finally, a quasi-Lagrangian observation-model comparison was performed. For WAIs, the observed change rates of temperature and humidity were not well captured in the simulations. For the CAOs, the calculated heating and moistening of the airmasses were represented by ICON with remaining problems close to the surface.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-2062', Anonymous Referee #1, 06 Jul 2025
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RC2: 'Comment on egusphere-2025-2062', Anonymous Referee #2, 14 Jul 2025
Review of “Observed and modeled Arctic airmass transformations during warm air intrusions and cold air outbreaks” by Wendisch et al., for potential publication in ACP.
Summary:
This manuscript closely examines airmass transformations in both warm air outbreak (WAI) and cold air outbreak (CAO) events during the HALO-AC3 campaign. The introduction is lengthy, but very well structured and both appropriately and clearly motivates the specific science questions addressed in this manuscript. A quasi-Lagrangian sampling strategy is employed for sampling WAI and CAO events, with over 2.2 million trajectories calculated for each HALO flight. The caveats of this quasi-Lagrangian approach are clearly defined and discussed in the text (for example: that a true Lagrangian experiment is not possible for aircraft measurements). The figures throughout the manuscript are highly descriptive and very easy (in my view) for the casual reader to understand quickly. ICON does not reproduce the near-surface temperature inversion and has a cold bias below 1 km. ICON specific humidity near the surface is slightly drier compared to the dropsonde data. For the WAI case, ICON performed better over the sea ice than over the open ocean, with MBL evolution well captured. For the CAO case, the lack of a properly simulated inversion in ICON may explain why the difference between the dropsonde & ICON temperatures jumps from a -4K cold bias to a +2K warm bias. I agree with the conclusion that the simulations produce both events accurately, despite noted biases in temperature/specific humidity as well as the differences likely caused by simplified microphysics. I also agree with the conclusion that the change rates for the WAI case are representative of the WAI dataset (from Table 2). The CAO case is representative too – with noted overestimation of the rate of change of RH increase due in part to an overestimation of the heating rate & underestimation of moistening rate near the surface. WAI change rates, overall, were not accurately captured in the simulations. One of the key findings in this manuscript was the dominance of latent heating and turbulence compared to radiative effects during CAO events. For both CAOs and WAIs, adiabatic processes dominated along trajectories for both process rates of temperature & humidity.
One aspect of this manuscript that I appreciate is the relation to previous & related studies on the subject matter (e.g., L388-393 comparing present temperature & humidity change rates to those results found in Kahnert et al. 2021). The authors throughout the manuscript demonstrate up-to-date knowledge on the topic of WAIs and CAOs, and convey the latest set of articles in a way that (in my opinion) is very friendly to the non-Arctic weather or climate researcher who may read this paper. This manuscript is exceptionally well written, organized, and clear – not an easy task given the length and density of this manuscript. The manuscript represents a very important and timely contribution as well given the broader community’s interest in WAIs and CAOs on high-latitude climate. I have nothing substantive to add or opinionate on with regards to suggested revisions, aside from a couple very minor comments listed below. In my view, the manuscript is ready for publication.
General Comments:
Somewhere in the last two paragraphs of your Introduction, I recommend explicitly listing Objectives 1-3 numerically. This will make it easier for the reader to hop back-and-forth in the paper, so they are fully clear which data/methods address their respective objective(s).
Specific Comments:
L108: (objective 2) I could consider listing the objectives numerically (1., 2., and 3.) somewhere in the introduction, especially since this 2nd objective is discussed before the 1st objective.
L120-125: This paragraph highlights dropsondes, but the dropsonde variables are not listed/discussed until L192. Consider briefly describing in a sentence or two what data the dropsondes provide. Since dropsondes are central to the analysis, I would also recommend adding some basic information about what sensors are contained in each dropsondes, manufacturer information, etc. and provide an additional appropriate reference or two.
L135: The average reader may not know what a “moist tongue” is. I personally think it’s quite descriptive & implies what it means, but it may be worth adding a parenthetical clarification (referring to the region of 200 kg/(m s) IVT?).
L142: “with only sea ice being present at latitudes higher than 80N”... this is a bit misleading, as Figure 1 on the next page shows the mean sea ice concentrations at latitudes south of 80N, albeit in regions where fewer of the flight measurements took place.
L245: Forgot “diagram” after “so-called contoured frequency by altitude”.
L315: This is a great note to add to the manuscript, but I think it may be worth repeating once in Figure 6 (i.e., that the subset is chosen specifically to improve clarity/reduce overcrowding in the plot).
L371: “wider” atmospheric layers?
Citation: https://doi.org/10.5194/egusphere-2025-2062-RC2 -
RC3: 'Comment on egusphere-2025-2062', Anonymous Referee #3, 30 Jul 2025
Review of “Observed and modeled Arctic airmass transformations during warm air intrusions and cold air outbreaks” submitted to EGUsphere
Paper summary:
This paper evaluates the performance of a NWP model, ICON in limited-area form, through Eulerian and quasi-Lagrangian comparisons against measurements during airmass transformation periods (both WAIs and CAOs) in the Arctic, specifically over the Norwegian Sea and the Arctic sea ice. The focus is on profiles of thermodynamic and cloud properties. The measurements were made aboard an aircraft, which sampled the same air parcels multiple times. That allows observationally-based estimation of quasi-Lagrangian change (i.e., the rate of airmass modification through turbulent and radiative fluxes), that can be compared against matching trajectories from the ICON simulation.
Overall evaluation:
This is a thorough evaluation of a model’s performance in capturing Arctic airmass modification in both directions. The analysis is rigorous, the illustrations are in-depth, and the results on the roles of adiabatic and diabatic processes at the surface and in the atmosphere robust. The story across the paper is excellent, starting with a broad-brush up to date Introduction of previous work, followed by enough depth to address the findings and limitations of the figures shown, and at the same time enough vision from above not to be mired in details but rather focus on the main take-aways.
General comments:
- The paper could build a stronger rationale for the focus on periods of rapid airmass transformation and change. A critic could argue that such periods should be avoided because much of the model-observation differences can be due to initial condition uncertainty (because of the rapid change and the poor time resolution of the model’s driver dataset, i.e. the operational global ICON initialized at 00Z)? A lot of campaigns and studies have focused on more balanced Arctic conditions, when model deficiencies usually are more persistent and model biases are more attributable to cloud, radiative, or surface exchange processes. But the HALO-AC3 flight campaign and this study expressly targeted periods of rapid change, precisely to understand how well models can capture such change. Still, initial condition uncertainty remains and the paper hardly addresses this “noise” source.
- It is not clear how this paper builds on and is different from other recent papers led by the same lead author and mentioned in the Introduction, (Wendisch et al. 2023a, b, Wendisch et al. 2024)
- We like key results listed clearly, like a set of bullets. The long paragraph starting on L451 (“The observational and modeling results …”) lends itself well to a break-up in a specific list.
Specific comments, typos, and technical issues:
Line 148: I suggest interpolating the hourly model data to the time of the drop sonde, like done later in the paper.
Line 230: The dropsonde drift is mentioned but not accounted for. “As the dropsonde is traveling in space, while the model column is constant, this introduces some uncertainty, especially in highly variable situations, such as the MIZ.” (Lines 213-215). This drift can easily be accounted for. Rather than using the dropsonde’s final location (where the sonde hit the Earth surface), why not compare geographically more precise profiles, using the actual GPS location of the sonde at each level? Anyway, the dropsonde should not drift more than a couple of model grid points, so this uncertainty should be small.
Line 313: Add ‘change’: “Lastly, the magnitude of humidity change rates are compared ... “
Line 319: Fig. 6 suggests that most cloud ice is below 3 km altitude, mostly dry air above 3.5 km altitude for the WAI. The next sentence mentions correctly that most solid precipitation is below 3 km.
Line 330: Typo: The time-series of the 1-hourly ...
Line 380: How can there be sub-hour variability in some of the variables when it is a linear interpolation between hourly data points?
Citation: https://doi.org/10.5194/egusphere-2025-2062-RC3 -
AC1: 'Comment on egusphere-2025-2062', Manfred Wendisch, 15 Aug 2025
Dear Editor, dear Reviewers,
Please find our replies to your reviews of our manuscript attached.
We would like to sincerely thank the reviewers for their careful examination of our manuscript. We have carefully taken the reviewers' comments and suggestions into account.
We truly appreciate your suggestions and the time you have spent reading this extensive manuscript.
With kind regards,
Manfred Wendisch, Benjamin Kirbus, Davide Ori, Matthew D. Shupe, Susanne Crewell, Harald Sodemann, and Vera Schemann
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- 1
The manuscript presents a detailed study on Arctic airmass transformations during on-ice and off-ice airflows. The study is based on extensive data set collected during the HALO-AC3 flight campaign mostly over the Barents Sea and Fram Strait. The research aircraft observations, including those made using tethersondes, are strongly supplemented by simulations using the high-resolution numerical weather prediction model ICON and by sophisticated trajectory calculations, including identification of matching trajectories of observations and simulations. According to my knowledge, the material gathered is more extensive than in any previous studies addressing Arctic airmass transformations. The analyses appear carefully made and yielded interesting result on the roles of adiabatic and diabatic processes, the latter including condensation/evaporation, radiative transport, and turbulent surface heat flux. In addition to improved process understanding, the study yielded new information on the performance of the ICON model. I suggest acceptance of the manuscript subject to minor revisions.
Detailed comments:
Introduction: It would be good to clearly summarize the relationship between this manuscript and the papers by Wendisch et al. (2023a, 2023b, 2024) cited in various parts of the manuscript. There seems to be a bit of overlap between them.
Lines 17-18: Be more specific about the 50% decline of the Arctic sea ice cover: extent, thickness or volume? Annual mean of a certain season?
Line 63: Do you mean “In addition to these model difficulties …”?
Line 106-107: It is somewhat misleading to call energy and mass fluxes as surface properties. The fluxes may change in time even if such surface properties as ice concentration and thickness remain constant.
Lines 147-148: Is this a good argument to not consider the drift of drop sondes? A bit less than 30 km may matter quite a lot in the ice-edge zone, in particular during cold-air outbreaks.
Lines 226-227: One would expect that successful modelling of crossing of the ice edge could be a challenge. Hence, it is somewhat surprising that during a warm-air intrusion the ICON model performs somewhat better over sea ice than over the open ocean. On lines 283-284 you refer to systematically better ICON results over the open ocean than sea ice. Any ideas on this?
Line 238: Specify the altitude, as the number (5 K) is probably very sensitive to it.
Lines 267-270: Could the bias be due to challenges in modelling the Lagrangian evolution of the airmass? By “too low reflectivities”, do you mean errors in the PAMTRA algorithm?
Lines 288-291: Near-surface warm bias over Arctic sea ice is indeed common, and I fully agree on the two reasons mentioned in the text. In addition, the warm bias is often related to too large roughness lengths and exchange coefficients applied in parameterization of turbulent surface fluxes under stable stratification (e.g., Cuxart et al., 2006). Also, overabundance of clouds causes excessive longwave heating of the snow/ice surface (Tjernström et al.,2008), which is reflected as a warm bias in near-surface air temperature.
Line 313: humidity change rates
Line 315: What do you exactly mean by “such that the plots are well covered”?
Line 321: Do you mean “moves far enough over the sea ice”?
Lines 341-342: Can you identify the reasons for descending flow upstream and ascending flow downstream in the cases of CAO and WAI?
Figures 6-9: These are excellent figures with so many interesting and interpretable findings! In this respect, I am not sure if Figure 10 is the highlight of this paper (as stated on line 401).
Section 4.2.2: Referring to diabatic effects due to turbulence sounds vague. I suggest writing about convergence of surface sensible heat flux in the case of diabatic heating (divergence in the case of cooling).
Line 383: Fig. 9c
Line 385: Fig.9b
Lines 415-416: But in Figure 9d, near-surface relative humidity decreases downwind over sea ice. Any comment on this?
Line 425: This is interesting, as in many models the turbulent exchange coefficients for the turbulent surface fluxes of heat and moisture are identical. Is this the case also in ICON? Naturally the underestimation and overestimation may be related to later advection instead of vertical surface fluxes.
Line 454: the error was smaller rather than better.
Lines 451-474: To make it easier for a reader, I suggest dividing this long paragraph into two or three paragraphs, perhaps starting on lines 460 and 466.
Lines 483-488: Considering true Lagrangian observations, the potential of controlled meteorological balloons deserves to be mentioned. There are papers by, e.g., Lars Hole and Paul Voss.
References
Cuxart J., Holtslag, A. A. M., Beare, R., Beljaars, A., Cheng, A., Conangla, L., Ek, M., Freedman, F., Hamdi, R., Kerstein, A., Kitagawa, H., Lenderik, G., Lewellen. D., Mailhot, J., Mauritsen, T., Perov, V., Schayes, G., Steeneveld, G.-J., Svensson, G., Taylor, P., Wunsch, S., Weng, W., and Xu, K.-M. (2006). Single-column intercomparison for a stably stratified atmospheric boundary layer, Bound. Layer Meteorol., 118, 273–303.
Tjernström, M., Sedlar, J., and Shupe, M. D. (2008). How well do regional climate models reproduce radiation and clouds in the Arctic? An evaluation of ARCMIP simulations. J. Appl. Meteorol. Climatol., 47, 2405–2422.