Articles | Volume 6, issue 4
https://doi.org/10.5194/wcd-6-1605-2025
© Author(s) 2025. This work is distributed under the Creative Commons Attribution 4.0 License.
An extensive investigation of the ability of the ICOLMDZ model to simulate a katabatic wind event in Antarctica
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- Final revised paper (published on 28 Nov 2025)
- Preprint (discussion started on 02 Jun 2025)
Interactive discussion
Status: closed
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
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- RC1: 'Comment on egusphere-2025-2046', Anonymous Referee #1, 14 Jul 2025
- RC2: 'Comment on egusphere-2025-2046', Anonymous Referee #2, 29 Jul 2025
- AC1: 'Comment on egusphere-2025-2046', Valentin Wiener, 14 Oct 2025
Peer review completion
AR – Author's response | RR – Referee report | ED – Editor decision | EF – Editorial file upload
AR by Valentin Wiener on behalf of the Authors (14 Oct 2025)
Author's response
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ED: Referee Nomination & Report Request started (21 Oct 2025) by Tim Woollings
RR by Anonymous Referee #2 (24 Oct 2025)
ED: Publish as is (12 Nov 2025) by Tim Woollings
AR by Valentin Wiener on behalf of the Authors (12 Nov 2025)
Manuscript
Review of An extensive investigation of the ability of the ICOLMDZ model to simulate a katabatic wind event in Antarctica By V Wiener
The manuscript entitled An extensive investigation of the ability of the ICOLMDZ model to simulate a katabatic wind event in Antarctica by V Wiener and colleagues aims to describe and evaluate surface wind in continental Antarctica, simulated by the ICOLMDZ, the newest generation of the LMDZ climate model with a new dynamical core that allows for localized high spatial resolution. They chose one 24hr strong katabatic wind event, and evaluated the model against observations from D18, on the coast, and D47 on the escarpment, situated in the same Adelie Land region of Antarctica. They used a large parametric ensemble of simulations to evaluate what would be the best tuning strategy to ensure good model performance. They find that the most critical parameter is roughness length, and evaluate the optimal horizontal resolution for accurately modeling surface winds. These results are well supported, and have validity beyond this particular model (ICOLMDZ), thus this paper will be of great value to the atmospheric modeling community. Generally speaking, I find the paper very well written, the scientific results well argued, and the illustrations very clear.
Major comments:
My major criticism of the approach in this paper is that the vertical structure of the wind is not adequately discussed, even though the authors use tower data to validate their model. Specifically, the “roughness length” is a parameter that tunes the vertical profile of the wind, but the hypothesis that a logarithmic vertical profile of the wind is valid is never discussed or demonstrated. The vertical wind shear is very important for vertical momentum and heat transport, and it should be within the scope of the paper to discuss this aspect also. (It is mentioned briefly starting line 296, but never evaluated). The wind profiles of the ensemble are shown on Figure 6, but not evaluated against observations. I understand you may not have observations for this particular event, but other climatological validation must be available, or a more detailed critical examination of the literature and its known limitations is needed.
Also, in section 4.1, you show the limits of the existing roughness length parametrisations, and how using z0 to tune the wind results in over-tuning other deficiencies in representing near surface wind. Since the roughness length is the critical parameter you have identified, it need to be explained and assessed a bit better.
Here are a few line-by-line comments:
Line 345: space before “;” to be removed
Figure 9: To ease the comparison between the various resolutions, it would be better tp put a,b and c in the same graph, or at least to have the range of the other simulations put on each graph. You can use transparency to put all the simulations together, or you could simplify your display by showing a swath between the min and max of your parametric ensemble, that could be overlayed between the 3 resolutions.
Figure 12: put a, b, and c in the same plot, it will save you a lot of space.