the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 25 Apr 2025
Saharan dust impacts on the surface mass balance of Argentière Glacier (French Alps)
Abstract. Saharan dust deposits frequently color alpine glaciers orange. Together with other light-absorbing particles, mineral dust reduces snow albedo, increases snow melt rate, and lowers the surface mass balance of glaciers. Since the surface mass balance drives the evolution of alpine glaciers, assessing the impact of impurities helps to understand their current and future evolution. The location of impurities within the snowpack and their effect on snow albedo can be estimated through physical modelling. In this study, we quantified the impact of dust, taking into account mineral dust and black carbon in snow, on the Argentière Glacier over the period 2019–2022. Our results show that during the three years preceding 2022, the contribution of mineral dust to the annual decrease in surface mass balance was between 0.19–0.28 m w.e., while it reached the double in 2022 with 0.47 m w.e. [0.41, 0.50] (median, [Q10–Q90]), and up to 1.00 m w.e. [0.78, 1.12] at specific locations. The impact of dust in snow was unevenly distributed over the glacier, especially in 2022. The highest simulated impacts occurred where firn layers from previous years were exposed after the total melt of the snowpack of the previous winter. The gravitational redistribution of the snow from avalanches was not taken into account, which can reduce the impact of dust at specific locations. Increasing the modelled scavenging efficiency of black carbon can double the impact of dust alone at the glacier scale. In general, the contribution of mineral dust to the melt represents between 6 and 12 % of Argentière Glacier summer melt depending on the year. Hence, we recommend to account for impurities to simulate the distributed surface mass balance of glaciers.
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
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RC1: 'Comment on egusphere-2025-1741', Anonymous Referee #1, 16 May 2025
This is an interesting and rigorous study investigating the impacts of Saharan dust on a French Alpine glacier. The study nicely combines remote sensing data of albedo, in-situ measurements of glacier surface mass balance, snowpack radiative transfer modeling, and glacier surface mass balance modeling. Uncertainty is explored via numerous perturbed simulations. Overall, I find the conclusions to be compelling and robustly supported through analysis and discussion. The authors find a large impact on glacier SMB from the dust deposition in 2022, supporting previous studies, and adding context to earlier studies (e.g., Gabbi et al, 2015) that explored impacts of dust and black carbon on other glaciers in the Alps. Moreover, the manuscript is well-written and the figures are excellent. I recommend publication after relatively minor issues are addressed.
More important issues:Section 2.3: Please include more detail about how the dust and black carbon emissions and deposition fluxes are simulated in ALADIN, as the deposition fluxes critically impact the results of this study. In particular, are the dust emissions simulated prognostically, or are they prescribed? Which inventory of BC emissions is used? Do the BC emissions vary between years? Does the regional domain of the model include the Sahara desert?
Figure 8 and associated discussion: The sensitivity of dust impacts to the BC melt scavenging factor is certainly an interesting result. The authors may disagree, but I suspect that uncertainty in melt scavenging efficiency of dust itself is similarly large, and if so, the simulated SMB impacts would also be sensitive to uncertainty in this parameter. One way of exploring and presenting this would be to add additional markers to Figure 8 that depict the sensitivity to dust scavenging. Regardless, I think it would be helpful to include a bit more acknowledgment and discussion of this.
Minor issues:line 30-31: "LAPs have advanced the snow melt-out date on average by 18 days in the French Alps in the past 40 years..." - As written, there is potential for ambiguity in whether this a trend towards earlier melt-out date over the last 40 years, or a mean impact over the last 40 years. Please clarify.
Section 2.2.3: Please list the wavelengths of the spectral bands used for the NDSI and RGND calculations.
Section 2.2.5: Here, BC is varied within SNICAR to match the Sentinel-2 albedos. Dust also could have been varied to match the satellite albedos. Are the results at all sensitive to the choice of LAP used for this purpose?
line 138: The use of "(-)" is unfamiliar to me, but is perhaps used to indicate that albedo is a dimensionless quantity. I don't think this is needed, but if there is precedence for using such notation, it is fine.
line 171: "right-hand side moraine" seems to be perspective-dependent, but perhaps there is precedence for using this terminology. Regardless, please clarify.
line 203: Please clarify this sentence. It may be as simple as changing "then" to "so".
Equation 1: Is there an upper bound of temperature to which this scaling is applied?
Section 2.3.6: Although this is clarified in the appendix, please briefly communicate here whether the perturbations are random within the confines of individual variances, as in a Monte Carlo approach, or whether variables are perturbed in combination by their full variance. (Or in general, please provide a little more detail on the perturbation approach here).
Figure 5: The month abbreviations on the x-axis appear to be in French.
line 457: "be" -> "been"
line 489: "We could not directly compare the MAC of dust..." - Despite this, please include the MAC values of dust that were assumed in your study.
Citation: https://doi.org/10.5194/egusphere-2025-1741-RC1 -
RC2: 'Comment on egusphere-2025-1741', Anonymous Referee #2, 21 Jun 2025
General comments
This study examines the impact of mineral dust and BC on the surface mass balance of a glacier in the French Alps. The study uses a variety of assimilated and reanalysis data to inform a multilayer snow model, CROCUS, which was adapted to account for LAP layer deposition and exposure within the snowpack structure. Results show that dust contributed significantly to water equivalent loss from the glacier over all years investigated, but especially in 2022 when compounding effects of dust deposition during the previous season resulted in exception melt loss. I think this study is very well structured and organized. I appreciate the wide array of data used and the thorough discussion of methods and limitations. The Figure and results are mostly well designed and can be clearly understood. My primary concern lies within the discussion associated with BC scavenging and increased dust impact, detailed in the comments below. I would also request some clarity regarding the choice of both the LAP representation within the snow representation, as well as the choice of CROCUS itself. Overall, I think the approach in this study is great for assessing specific, well-observed glaciers. However, I question some broader claims of scalability and transferability to other regions.
Specific comments
Line 530, Section 4.2, Figure 8, and elsewhere: There is mention of higher dust impacts when BC is low or highly scavenged within the snowpack. I agree that this increases the relative impact of dust on snowmelt (compared to BC), but as I understand it, this shouldn’t directly affect the impact that dust has in generating melt water. In other words, 1g/m2 of exposed dust would generate X m w.e of melt, and 1 g/m2 of dust with 0.1 g/m2 black carbon would generate X m w.e. of melt (from dust) and Y m w.e. of melt (from BC). The point being that X m w.e. is the same in both scenarios. If this is not the case, then it is currently unclear what the mechanism is that would drive the same amount of dust to have a greater impact on melt in the absence of BC (because of higher scavenging). I would argue that having more BC retained in the snowpack with dust would enhance the impact of dust indirectly, by resulting in more rapid exposure of buried deeper dust layers from X+Y vs. just X melt. Please explain and/or clarify.
Section 2.4.1: As I understand it, the LAP implementation and resulting modeled albedo depends on optical properties derived from dust during previous years at the same glacier. Is there ample evidence that the optical properties do not vary that much? Explain. The LAP representation could be assessed indirectly by comparison to remotely sensed snow albedo. Why was a snow albedo comparison from Sentinel or another sensor omitted? Remotely sensed snow albedo could also inform modeling directly and may be more scalable. Explain why this approach was not used in this paper. If Sentinel has a pixel saturation issue that prevents good snow albedo detection, mention this.
Section 4.3: I appreciate the thorough discussion of uncertainties regarding LAPs, but think there also needs to be more discussion of broader model uncertainties. What are the limits of CROCUS? How would using different NWPs and physically based snow models vary the results? What if there is no information about the optical properties of snow? Is CROCUS best suited for very large glaciers? This also relates to my comment on line 623. If word count is an issue, I would recommend condensing some lines from the detailed discussion of SMB and LAPs.
Figure 6: Basic cartography issues. Please add a scale bar and a north arrow to the maps. Some indication of elevations (maybe very faint topo lines) would help the reader understand the distribution of these changes over terrain. I would recommend including the same basemap as Figure 1 (d) in these panels. This is the take-home figure of this paper, and it would be helpful to orient readers, especially those just “skimming” and those not familiar with the glacier.
Line 623: Is this truly “easily” scalable if this modeling work relies heavily on bias correction and adjustments from automated in situ measurements (as discussed in section 2.3.3)? Are these relationships scalable to other glaciers at other latitudes? Most glaciers do not have automated measurements, let alone a glacier observatory, and modeled forcings have much higher uncertainties in more remote regions (Himalayas, Andes, Rockies). Would this approach to such glaciers truly be reasonable? Please clarify how such an approach would be transferable with ease.
Line 447: Do you mean the sensible heat flux? Latent heat fluxes are generally negative during melt.
Technical corrections
Line 1: color -> turn (Just a recommendation for slightly smoother wording. Also consider “…deposits frequently darken alpine glaciers”)
Line 15: to account -> accounting
Line 588: depositions -> deposition, radiations -> radiation
Figure 5c: Change month abbreviations to English (Jan., Jul.) or spell out the month. Also, decrease the size of x-axis labels (or rotate slightly) on red/purple plots on the right. Currently, it looks like “19202122” instead of “19 20 21 22”. Please specify that this is year.
Line 620: their -> its
Citation: https://doi.org/10.5194/egusphere-2025-1741-RC2
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