| 12 Sep 2025
Predictability of mean summertime diurnal winds over ungauged mountain glaciers
Abstract. Glacier and valley winds are typical characteristics of the microclimate of glacierised valleys. The speed of such winds determines the turbulent heat flux, which contributes to ice melt. Sparse in-situ meteorological measurements and the inability of large-scale climate data products to capture such local winds introduce uncertainty into glacier- to global-scale mass-balance calculations. Here, we propose an empirical model having three parameters, namely, the mean wind speed, the sensitivity of the diurnal winds to temperature, and a response time, to predict the mean summertime diurnal wind speed on valley glaciers based only on reanalysis temperature. Utilising data from 28 weather stations on 18 valley glaciers across the globe, we show that the model reproduces the observed mean summertime diurnal wind speed reasonably well. Furthermore, we show that the three model parameters can be estimated at any glacier using a few topographic variables, allowing prediction of wind speed on ungauged glaciers. A leave-one-out analysis of the stations suggests a root-mean-squared error of 0.76 ms-1 on average, which is a ∼300 % improvement over a standard reanalysis product. The performance of the model is largely independent of the number of stations available for calibration, as long as it is 20 or more. More work is needed to explain the physical mechanisms underlying the predictability of the mean diurnal wind speed on ungauged glaciers based solely on reanalysis temperature and a few topographic variables. The presented model can improve wind speed estimates on ungauged glaciers, leading to better glacier mass-balance calculations at various spatial scales.
General Comments
In this paper, the authors present an empirical model designed to predict the mean summertime diurnal wind speed on valley glaciers. This model, which utilises reanalysis temperature data and a small set of topographic variables, provides a promising approach to overcoming the challenges associated with sparse in-situ meteorological measurements. The potential for applying this model to ungauged glaciers is particularly noteworthy, as it enables more accurate glacier mass-balance calculations in areas where observational data are lacking.
While the authors' methodology is an interesting contribution to the field, I believe some points need further clarification.
Specific comments
-It’s necessary to specify the reference systems used in the text (see also the following comment). 'Wind speed u' is mentioned, but the reference system is not explained. Since the flow is described over slopes, I’m unsure whether it refers to north-south, planar fit, or slope coordinates.
-Figure 1) It is not clear how this plot was created in terms of normalisation; I think it’s necessary to specify this either in the caption or the text. X and Y represent the longitudinal and transverse distances, but relative to which reference system? It would also be interesting to understand how many of these stations are positioned uphill, or if they follow the glacier downstream.
-In lines 94-96, the paper mentions that the ERA5L wind speed at 10m was not adjusted to 2m due to the complex nature of the boundary layer above glaciers. However, the model’s 10m data is later used to compare with the 2m wind speed observations. While I understand this is based on data availability and what ERA5L provides, this seems inconsistent. The paper acknowledges the challenges associated with sloped flows, which complicate comparisons between wind speeds at different heights. Yet, this issue is only briefly mentioned in lines 94-96, with no further exploration of how it might affect the comparison between the two heights. I suggest that this topic be addressed in more detail in the discussion and limitations sections.
-Technical corrections
20) You could mention 'anabatic wind' for symmetry.
24) Who do 'they' refer to? Valley wind and glacier winds, or up-valley winds and down-valley winds? The statement is clearly true for both cases.
141) This is not a bilinear regression, but rather a multivariate linear regression (?).
177) Include where these results are presented.
186) Similar to the previous point.
It is advisable to ensure that the order of panels mentioned in the text matches the order in the figure. For example, in Figure 1, panel b is mentioned before panel a.
In general, all figures should be self-explanatory. This means the caption must include explanations of the symbols used. For example, in Figure 2, all abbreviations should be introduced in the caption, and the same applies to the other figures.