the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Monitoring dry snow metamorphism using 4D tomography across 20 experimental conditions
Abstract. Refined observations of the temporal evolution of snow microstructure are crucial for improving the understanding and modeling of snow metamorphism. X-ray tomography has opened new possibilities for observing the microstructure of dry snow by enabling 3D imaging of the ice and air arrangement with micrometric resolution. The development of cells that control the thermal boundary conditions of a snow sample during scanning has made in-situ monitoring of microstructural changes during metamorphism possible. However, such data sets remain scarce and are often limited in terms of the snow evolution conditions explored. In this work, we use highly resolved X-ray tomography to characterize the temporal evolution of dry snow microstructure under a wide range of thermal boundary conditions. We designed a snow-metamorphism cell to continuously control the temperature at the boundaries of a centimeter-sized snow sample directly inside the tomograph. Using this setup, we conducted a total of 20 snow metamorphism experiments, covering mean snow temperatures from -3 to -17 °C, snow temperature gradients from 0 to 100 K m⁻¹, and five initial snow samples with varying snow types, densities, and specific surface areas. Each experiment lasted 7 days, during which tomographic measurements were performed every 4 hours at a spatial resolution of 8.5 µm. We provide a unique set of 4D data in .zarr format, consisting of time series of binary 3D images of snow undergoing the aforementioned experiments. These images are particularly well-suited for investigating local processes, such as the interface growth velocity, as well as for computing various physical properties of snow. In addition, videos showing the temporal evolution of the snow microstructure for the 20 experiments are provided.
- Preprint
(7684 KB) - Metadata XML
- BibTeX
- EndNote
Status: final response (author comments only)
-
RC1: 'Comment on essd-2026-36', Anonymous Referee #1, 08 Mar 2026
- AC1: 'Reply on RC1', Oscar Dick, 25 Mar 2026
-
RC2: 'Comment on essd-2026-36', Anonymous Referee #2, 13 Mar 2026
Review:
Monitoring dry snow metamorphism using 4D tomography across 20 experimental conditions
General comment:
The paper is very well organized, well written, does not leave open questions and is perfectly suited for the scope of the journal. Therefore, I suggest publication after considering minor revisions.
Detailed comments:
L14-16: Maybe be more specific for what these measurements are useful than just “interface growth velocity” and “various physical properties”. Missing here something like: Basis for developing physical models for different types of dry snow metamorphism, including SSA evolution, densification, shape (dendricity & sphericity), structural anisotropy, polydisperity evolution.
L122: Schneebeli
L135: Why aluminium side walls? Plastic not better? Plastic has lower thermal conductivity, lower x-ray absorption.
L140: Isn't a high thermal conductivity along the side walls of the cylinder unfavourable?
L205: DF … DH, as you mention these her for the first time, you maybe better write the full names and put abbreviations in brackets.
L230: Regarding ice layer at the bottom: I think this needs a bit more discussions about how this may or may not affect TG metamorphism. Basically, what you simulate is a certain snow stratigraphy with an ice layer below your snow layer. -> I see, in Fig. 3 it looks very homogeneous the microstructure at the interface ice-snow, but I guess in Fig. 3 it is the initial sample before metamorphism. You should mention it in the figure caption what snow type it is, and if before or after the experiment. Could not find this information. In Fig. 3 I would rather prefer seeing both, a sample before (left, as is) and after (right, instead of the enlargement which does not provide any more information). This will show if the ice-snow transition affects metamorphism locally or not. Anyway, the cropped parts Fig. 4-5-6 always looks good (i.e. homogeneous over height) I think.
L243: Above you give absolute temperatures in Kelvin, e.g. 270 K. Should be consistent throughout the paper. I would prefer everything in °C as more typical for Water / Ice / Snow.
L245-246: Why not all combinations? Due to time constraints or because you were not expecting to see any more differences for the other combinations?
L312: GB instead of Go
Fig. 4 – FC: Crystals do not look really faceted to me. Also, with densities in Table 1 seems to be something wrong when compared to the structures in Fig. 4.
L352: I would give the images in all Figures sub labels (a, b, c, …) and then reference individual figures more often: E.g. here: Compare Fig. 4e with Fig 5e instead of referring to the video. Generally, this would help readability if you directly and more often refer to the right sub-image in the text.
L363-365: This is a nice result, however, the perception that "doubling the time is similar to doubling the gradient" should be referenced if somewhere published, maybe Pinzer et al? Or is it just a widely accepted assumption? I don't know.
L365-370: Maybe you want to provide some physical explanation here, i.e. pore scale vapor pressure gradients depend on the absolute temperature due to exponentially increasing saturation vapor pressure at higher temperatures? Furthermore, not just the pore space is smaller, also the depth hoar crystals are smaller, no? And maybe estimating a total re-crystallization turnover rate would be interesting, i.e. how long it takes until a crystal is totally sublimated and recrystallized into a new crystal. From the videos, it seems that at 100K/m-1 and -3°C, the crystals recrystallized like 3-4 times during the entire experiment, whereas just once at -17°C.
Fig. 6: Could be interesting, if you could cut in this figure (and Fig. 5) each image in half, and make the left half the initial microstructure and the right half the final, then you have a nice side by side comparison. Otherwise it is difficult to judge the changes from the beginning to the end. And you still can keep the arrangement as it is in Fig. 6 to compare the effects of absolute temperature and temperature gradient.Citation: https://doi.org/10.5194/essd-2026-36-RC2 - AC2: 'Reply on RC2', Oscar Dick, 25 Mar 2026
Data sets
Time series and videos of 3D snow images: 20 scenarios of dry snow metamorphism monitored by X-ray tomography Oscar Dick et al. https://sdrive.cnrs.fr/s/HHJt56dj63sNTYg
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 221 | 147 | 21 | 389 | 41 | 40 |
- HTML: 221
- PDF: 147
- XML: 21
- Total: 389
- BibTeX: 41
- EndNote: 40
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
The manuscript outlines the results obtained from a detailed snow metamorphism study utilizing time and spatial resolution.
ESSD review criteria:
Is the article itself appropriate to support the publication of a data set? - Yes. The experimental procedure is clearly described and scientifically sound. A suggestion would be to plot time traces of the snow microstructure evolution (density, SSA, mean curvature, etc.) instead of or in addition to the 3D visualizations. This could serve as a 'preview' for further analysis and already yield some interesting results.
Is the data set significant – unique, useful, and complete? - Yes. The data set fits well within a history of time and spatially resolved microCT investigations of snow metamorphism and the resolution and detail will be useful for further investigations into these phenomena.
Is the data set itself of high quality? - Yes. The data set quality is adequate for the described effects of snow metamorphism. A few issues are discussed in the detailed comments below.