| 18 May 2026
Horizontal sensible heat advection increases snow melt rates: Beyond point measurements
Abstract. In the atmospheric surface layer, it is generally assumed that vertical fluxes dominate near-surface heat transport and heat exchange between the atmosphere and the earth's surface. However, when the earth's surface is covered in patchy snow, near-surface horizontal advection of heat may be significant, and it's impact on snow melt rates is disputed. We estimated the contribution of horizontal sensible heat advection (QH) to snow melt using 8 days of measurements collected in May 2023 in the alpine East River basin in Colorado, Rocky Mountains, USA. We used an infrared video camera and polyester sheet to estimate QH, 3-meter resolution satellite imagery to track fractional snow covered area (fSCA), a scanning lidar to estimate snow melt rates, and micrometeorological and eddy covariance measurements to characterize the surface energy balance. Infrared camera measurements of QH over a single snow patch show that QH contributes to snow melt and is largest at the patch's upwind edge. Lidar-based snow melt measurements show that melt rates are highest at the upwind edge, further suggesting that QH causes snow melt. Over four days when fSCA was less than 62%, QH estimates ranged from 234–584 W m-2 and mid-day net radiation ranged from 331–508 W m-2. When we considered only vertical heat fluxes, radiative fluxes, and snow melt in a snow patch surface energy balance equation, we found a mid-day residual between 267–668 W m-2, and the residual increased as fSCA decreased. The approximate match between the energy balance residual and estimated QH suggests that advection is an important factor to consider when predicting snow melt. On days with low fSCA (< 62%), horizontal advection may contribute as much to snow melt as net radiation.
This study examines sensible heat advection over a snow patch over an 8-day period in an alpine valley in the Rocky Mountains of Colorado. The few studies that have examined this term have suggested that it may be significant, particularly during the snowmelt season when snow patches are present. An ambitious experimental setup was thus implemented to quantify the key terms of the energy balance over the snow patch. The main innovative feature is the deployment of a polyester sheet that is imaged by a thermal camera; this sheet has the property of capturing the air temperature distribution above the snow patch and visualizing the growth of the internal boundary layer (IBL). This setup is installed at the center of an array comprising several flux towers - which measure turbulent sensible and latent heat fluxes - radiometers, and satellite measurements to provide a detailed description of snow cover changes, as well as a lidar to measure changes in snow depth over the patch.
The introduction provides an excellent review of the literature, clearly outlining the current state of knowledge and the gaps that this article aims to fill. The theoretical frameowrk is clear and well-structured. This type of study is rare and important, as there is considerable uncertainty regarding the processes controlling snowmelt when snow cover becomes patchy. This study addresses two main research questions: 1) to quantify the magnitude of sensible heat advection; 2) to determine the contribution of sensible heat advection to the melting of the snow patch. Unfortunately, the experimental setup is not fully in line with these research objectives, forcing the authors to propose multiple assumptions, such as :
As such, the level of confidence in the results is, at best, moderate. Some of these assumptions need to be revisited and discussed in greater detail to improve the level of confidence in the results.
Main Comments
1) Wind Speed Profile
The assumption of a logarithmic wind profile under neutral atmospheric conditions does not hold up, in my opinion, especially when considering the estimated profiles above a snow patch shown in Figure 5 of Haugeneder et al. (2023). I encourage you to revisit your treatment of the wind profile and explore the possibility of using the “wind field estimation from infrared data” (WEIRD) approach presented in their article.
2) Height of the IBL
First, it would be helpful to present air temperature profiles at various locations along the snow patch, as shown in Fig. 5 of Haugeneder et al. (2023). A more robust approach than visual inspection should also be used to determine zb, which was set at 0.75 m.
3) Determination of QH
QH is determined based on 30 minutes of data. The evolution of QH is then linked to the relative position with respect to the leading edge of the snow patch, and a regression model is trained to estimate QH using the width of each snow patch in the following days. What were the weather conditions like during this 30-minute period? What was the main factor limiting the use of other data blocks to increase the robustness of this estimate? Is it possible to be a little more flexible regarding wind direction? The fits used do not seem to perform very well between 0 and 1 m. Overall, you need to improve the robustness of your approach to determining QH.
4) Disturbed Snow Patch
Well, it is not ideal, but the snow patch was manually shoveled around the screen for the purposes of the study (see lines 131–133). What are the effects of this disrupted snow on melting? For example, the surface is surely rougher than a natural snow patch, no? What is the impact on albedo? These effects need to be discussed.
5) Albedo
Is it possible to see the albedo data on which the assumption of a constant value of 0.6 was based? I find this value rather high for snow during the melting period. Why not show the data collected in situ just before the snow melted at the site?
More Specific Comments
Fig. 1: This figure would benefit from being revised to make it look more professional. The number of colors used should be minimized, font sizes should be standardized, all boxes should be properly aligned, the blue diagram of the towers should not be superimposed on the photo in (b), text elements within the boxes should be centered, etc. The focus should be on the towers relevant to the study. Furthermore, Figures (d) through (i) are a bit too small to be easily readable. Perhaps fewer days should be selected and the figures enlarged? Also, the fourth line of the legend should read “locations.”
Fig. 3. This figure has great potential, but would benefit from being simplified. Add downward-pointing lenses for the radiometer, even though that data is not used here. Add actual arrows at (a) and standardize the font size and line thickness. Minimize the number of frames to avoid cluttering the figure. Is the shape of the temperature profiles representative?
L2: its impact
L102: sensible
L123: µm should not be in italics
L145: add space between 4.5 and m
L179–180: What is the basis for the claim that heat fluxes between the ground and the base of the snow cover are negligible?
L345: within .01%, really?
REFERENCE
Haugeneder M, Lehning M, Reynolds D, Jonas T, Mott R. A Novel Method to Quantify Near-Surface Boundary-Layer Dynamics at Ultra-High Spatio-Temporal Resolution. Boundary Layer Meteorol. 2023;186(2):177-197.