the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 28 May 2026
Assessment of Snow Depth Retrievability from Passive Microwave Observations over Arctic Sea Ice: A Global Sensitivity Analysis
Abstract. The complexity of passive microwave (PM) retrieval of snow depth over Arctic sea ice stems from non-linear interactions between snow microstructure, wetness, and basal ice properties. These mechanisms remain insufficiently quantified, resulting in large uncertainties in PM-based snow products. We employ the snow microwave radiative transfer (SMRT) model together with a global sensitivity analysis, i.e. the Extended Fourier Amplitude Sensitivity Test, to decompose SMRT-simulated TB variance into contributions from individual parameters and their interactions. Averaging kernel analysis is then used to quantify snow depth retrievability across standard PM channels from 6 to 89 GHz under single- and multi-layer snowpack scenarios. 1) For single-layer dry snow, snow depth, density and grain radius are strongly coupled to each other, dominating the PM signals. When liquid water is present in the snow, the PM signals are primarily controlled by snow density and liquid water content. 2) In multi-layer dry snow, channels below 23 GHz are strongly influenced by the basal snow ice, while those above or equal to 23 GHz are dominated by depth hoar. At 6 GHz, retrievability is limited to dry snow with grain radius ≥ 0.5 mm and density ≤ 250 kg m-³, expanding toward finer grains and higher densities with increasing frequency. Regarding gradient ratio (GR), GR(18/6) provides limited retrievability for grain radius < 0.5 mm, whereas GR(36/18) remains effective for grain radius ＞0.2 mm. Notably, incorporating 89 GHz in GR improves the retrievability for new snow. Furthermore, sea ice type exerts a significant constraint on GR retrievability of snow depth and becomes increasingly pronounced under fine-grained snow conditions.
Competing interests: At least one of the (co-)authors is a member of the editorial board of The Cryosphere.
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.- Preprint
(3015 KB) - Metadata XML
-
Supplement
(1054 KB) - BibTeX
- EndNote
Status: open (until 12 Jul 2026)
- RC1: 'Comment on egusphere-2026-1680', Anonymous Referee #1, 30 Jun 2026 reply
-
RC2: 'Comment on egusphere-2026-1680', Anonymous Referee #2, 01 Jul 2026
reply
I have reviewed the manuscript titled „Assessment of Snow Depth Retrievability from Passive Microwave Observations over Arctic Sea Ice: A Global Sensitivity Analysis “ by Yan et al..
Their study evaluates the retrievability of snow depth over Arctic sea ice for the AMSR2 frequency channels. I appreciate the effort of taking a more fundamental view on the question: instead of trying out different snow depth retrievals the authors use radiative transfer modeling to test sensitivities depending on background conditions.
The introduction is well written. The methods are clearly described but could benefit from a bit of additional information (see below) and more caution regarding the use of snow ice. The Data section describes the MOSAiC data but remains a bit too vague as to how the MOSAiC data is actually used later in the model setup. The Results section is well structured, describing first the results of the different snow setups and then the retrievable ranges, see some comments for improvement below. The conclusions provide nice guidance for further developments, highlighting also the importance of snow grain sizes. The figures are well done and I congratulate the authors for visualizing the information/coupling effects of such high dimensions, without oversimplifying it.
Overall, the study is a valuable contribution for the scientific community and well suited for publication in this journal. However, before publication I have one bigger concern that needs to be addressed (minor comments are found below): how do the authors ensure representative sampling? From the method section I understand that the parameter are uniformly sampled, thus unrealistic input cases, that is unrealistic combinations of layer parameters or values that are rare (like very deep snow), are possible and could influence the sensitivity, is that right? Or is there a mechanism to ensure that the (observed) gradients of, e.g., temperature and density, are preserved or that the output is weighted according to the likelihood of occurrence? The use of the MOSAiC data here is not clear to me here.
Another general remark related to that: I think the confidence in the modeling results/snow configuration could be increased by a direct comparison, possibly in the supplemental material, of the modeled brightness temperature distributions to pan-Arctic satellite-measured brightness temperature distributions (possibly corrected for atmosphere), to see how modeled and measured TB variabilities compare (with no perfect agreement expected).
Below, I provide more specific comments:
- Section 2.1: I recommend that the authors follow the guidelines from the official SMRT documentation: https://smrt.readthedocs.io/en/v1.5.1/publish.html providing a table with additional information about the model setup (in the supplemental material) including the surface roughness assumptions, the version number of the model and the exact way the salty snow (snow ice in Table 2) is modeled (what permittivity formulation is used)
- L. 116 : is i going from 1 to N or from 1 to n (as suggested by line 113)?
- Section 2.3: While the general description of the the averaging kernel as matrix is of course correct, you could add that is is 1D in your case, am I correct?
- Section 3.1., Line 190: how are these measurements used exactly? To constrain the parameter ranges? Or to sustain realistic parameter combinations?
- Line 201/Table 4: you write about BVF for temperatures warmer than -2°C, but you only model 265 K ice. How did you come up with BVF of 5% in Table 4?
- Section 3.2.: I suggest to add AMSR2 footprint sizes
- Section 3.3.: snow ice formation: I suggest a bit more caution with using this term in the context of Arctic sea ice. To my knowledge flooding is common in the Antarctic (the Maksym and Jeffries paper quoted in line 217 and line 239 is also about Antarctica, which should be clarified), but the occurrence in the Arctic is still not clear. Also, the Merkouriadi study quoted later in line 360 talks about potential and not observations at large scale, please formulate accordingly. It is not clear to me whether the use of snow ice is backed by the MOSAiC observations. That is not to say that there is no salty snow (commonly observed e.g. on fast ice) but to be careful with the flooding hypothesis. Finally, the density values listed in Table 2 go up very high, and scattering formulations tend to break down at intermediate/high densities, see Picard, G., Löwe, H., and Mätzler, C.: Brief communication: A continuous formulation of microwave scattering from fresh snow to bubbly ice from first principles, The Cryosphere, 16, 3861–3866, https://doi.org/10.5194/tc-16-3861-2022, 2022. Please have a look whether any issues are observed with regard to that.
-Section 3.4/ Table 4: what is the source of e.g. the radius values and the BVF values? Is it based on literature or MOSAiC data?
- Figure 1: You could consider adding a panel giving the number of observations per snow depth/horizontal bars.
- Section 4, Line 307: why is the temperature not shown for wet snow? I would think it is the biggest contributor with wet snow being more similar to a black body?
- Line 383: “finite depth” please be more specific, I assume snow is always of finite depth
- Section 4.2: I could not follow on which simulation setup this section is based. Are these extra simulations? Is the single-layer snow used? But then, how can we get snow depths up to 1 m when the range was limited to 60 cm in the previous section? Is wet snow excluded? Please clarify.
- Figure 6: I am surprised to see no sharper gradient between 0 cm snow and the rest, is 0 snow actually modeled or is there always a thin snow layer present?
Technical:
- Line 35: “most weather and all-sky conditions”: redundant
Citation: https://doi.org/10.5194/egusphere-2026-1680-RC2
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 38 | 24 | 2 | 64 | 6 | 6 | 4 |
- HTML: 38
- PDF: 24
- XML: 2
- Total: 64
- Supplement: 6
- BibTeX: 6
- EndNote: 4
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
Review of the manuscript “Assessment of Snow Depth Retrievability from Passive Microwave Observations over Arctic Sea Ice: A Global Sensitivity Analysis” by Yan et al.
This manuscript investigates the non-linear interactions between different key snow parameters on brightness temperature using radiative transfer model simulations with parametrization scenarios based on MOSAiC (besides others) observations.
The authors show that most of the variability with the TBs above 18GHz are mostly driven by non-linear interaction between the tested snow parameters.
Comments
Overall, I found this study well written, straight to the point and very interesting. The EFAST decomposition is a simple yet robust method to explore whether an individual parameter contribute more or less than its coupling with other parameter in a qualitative AND quantitative way. The paper does a really good job describing the coupling and non-coupling effect of the different snow parameters on the TBs and the different GRs. Given the fact that this paper relies on MOSAiC snow data to estimate the range of variability of the snow and ice parameters, I think it would be valuable to have results of this parametrization that fits the MOSAiC TBs to confirm that the proposed configuration still falls within the observations range.
I think this study is well suited for publication in The Cryosphere as it fits very well its scope, but some minor adjustments are needed prior publication.
Specific comments
Although the author mention in the text that the “global” term refers to the full parameter space, having it in the title can be quite confusing as it mostly focuses on Arctic snow. In general, the paper does a very good job describing the theoretical coupling between the ASMR2-3 frequencies, but given the title there is this feeling it would be more connected to observations. The Section 3.2 “Microwave radiometer configuration” is in data yet just describes the AMSR2 frequencies. If no observational TBs are used, this should be moved to the Method section.
Still in the section 3.2 or later in the text, I’m surprised nothing is said about the major footprint’s size difference between the 6GHz and the higher frequency channels (36 and 89 GHz. For snow depth retrieval from satellite measurement, this cannot be ignored as the 6GHz footprint is tens of km wider than the 36GHz, so the GR36/6 may also show something artificial due to the heterogeneity between the two footprints.
L255-260 and Tables 2-3: how is snow ice and superimposed ice modeled in SMRT? Are you using fresh ice layer? Snow with such high density (>500) can be tricky to configure in SMRT as some permittivity models can’t characterize snow with density that high. I would maybe suggest to better describe the permittivity model used for each layer (snow, ice, fresh, dry, wet etc..)
How saline the was the snow? A 5% of brine volume fraction isn’t something we can easily represent, as BVF is not something we directly measure on the field. It could be better to explicitly say the salinity value set in SMRT to get this BVF of 5%, as Figure 2 aslo shows the salinity of the snow in PSU.
Figure 3, 4 and 5: the author’s choice of underlying the TSI value is quite confusing and sometime makes the results not readable. It would be better to just write the the numbers (such as for Figure 3-b 18H where the author forgot to put the underline)
Figure 4 is mostly empty, I wonder if there would be a better way to present these sparse results.
For the figures showing the GR as function of snow depth and snow density (Figures 6, 7, 8 and 9), I would suggest removing the X-Y projection as it takes a lot of space and decrease the readability of the results and in a lot of case, they are showing no variability at all. They could be added as Supplementary material, according to the author’s decision.
L446: “The 89 GHz TBs are also susceptible to atmospheric effects…” This is also true for the 36GHz and in a lower extent the 18GHz.