Comment on gmd-2021-254

My first concern is the lack of consideration for a sublimation flux. Sublimation of blowing snow and of snow packs has been identified as a major contributor to mass loss in many environments (Mott 2018 ) . The authors do not describe the humidity of this location. Indeed, perhaps it is sufficiently humid that such an approximation is warranted. However, this is never described nor justified in anyway. I see that the MOSAiC companion paper Wagner (2021; TCD) notes low sublimation fluxes for this period, perhaps 6%. This seems derived from modelling studies and not observations. The simulation period is short and perhaps sublimation is negligible. However, that has not been demonstrated and to wave it away, especially when the model is noted to have deviations from observations, seems problematic. I would like to see a sublimation sink added and evidence that, in a distributed context that this process is indeed negligible.

models. These areas may have substantial impacts on blowing snow simulations and deserve attention. I would like to see, at a minimum, a description and placement in the literature of these features and if the authors think they are real. Wagenbrenner (2019) identifies that they are somewhat dependent upon the upwinding scheme used. Do the authors think that is the case here?
This brings me to my last concern -a lack of uncertainty analysis. I would like to see the authors quantify the impact of any meteorological forcing, e.g., their input precipitation and the values in Table 2. Certain values can have massive impacts, e.g., friction velocity threshold, and it would be useful to understand how sensitive the model is to these parameters. It is not 100% clear to me how the simulations were done. It seems to be developing a steady-state (?) simulation of 1000 s at which point the wind speed is reduced? How sensitive are the results to this (spin up?) 1000 s period?
The large temporal periods over which the simulation is run from mean values of wind is concerning or at least requires further discussion. The temporal scales that impact blowing snow are quite small, < 1 s (Aksamit & Pomeroy, 2016, 2018, although at 15 m to 1 h r scales, mean shear-stress models tend to be successful. However it is not clear how successful a many-hour mean wind structure is for representing these features. To me it seems a mis-match to run a sub-metre spatial model, but drive it with many-hour mean windflow that we know doesn't represent any of the wind structure known to drive blowing snow events. In summary I would recommend this for major revisions. It has the potential to be a unique contribution to the blowing snow literature, however I do not believe to be there in its current form.  There are more recent descriptions of the blowing snow transport outside of wind tunnels such as: Aksamit, N. O. & Pomeroy, J. W. Near-surface snow particle dynamics from particle tracking velocimetry and turbulence measurements during alpine blowing snow storms. The Cryosphere 10, 3043-3062 (2016). that should be referenced.

L195 > Bagnold, 1941)
Bagnold 1941 is sand, not snow. Odd to include it in this list. Would suggest replace it with the early efforts to adopt Bagnold to blowing snow that I list above. L200 > a threshold value defined as (Bagnold, 1941) It is not clear to me why a sand-grain threshold is used. Where is the constant A from? Is this a snow value or a sand value? L 207 > empirical parameter set to 1.5 (Doorschot and Lehning, 2002), I had assumed equation (11)  L 290 > the preferential deposition Preferential deposition arises due to terrain impacts on local meteorological conditions, causing increased deposition on the leeward slopes and decreased deposition on the windward, and is typically a critical process in mountain terrain (e.g., Gerber et al., 2019;Mott et al., 2014;Vionnet et al., 2017). A few places in the manuscript it seems like blowing snow process and the deposition of suspended snow to be called 'preferential deposition'. I would like to see this tightened up so the reader is not potentially confused.
L 300 > (280m range gate) 280 m, but also what is a range gate? L 302 > threshold formulation (Bagnold, 1941), Ah, so this is where A is defined. Please add this to the description of eqn (10).
Page 14 L320 > Additional limitations arise from the forcing of the model.
It is not totally clear to me not why simulate the whole time series? I realize 'compute' is offered up, but how prohibitively long would it be? It's probably outside the scope of this project (but perhaps not) however it would be interesting to know how much worse these assumptions made the model output v. running the model for the entirety of the observation period

Legend needs units (even if its in the caption)
L327 > extrapolated areal What does "extrapolated" mean in this context?
Page 15 L336 > in blue in the surface friction velocity plots (Fig. 5).
These low friction velocity streaks require more description and a quantification if the authors believe they are 'real'.
Page 16 L369 > Quantitatively, our model appears only partially successful I would like to see RMSE + CV for the domain.
L 387 > may be multiple reasons for the Density assumption is not addressed here.
Page 17 L391 > used four averaged values for wind speed and direction in OpenFOAM to represent a one-week period of measurements. I am not I surprised this didn't when we know blowing snow is at higher temporal scale! L392 > many specific wind conditions Would be good to muse on if the neglected conditions are similar to those shown by Aksamit 2016, for instance. I would also explicitly note "specific wind conditions" to include, e.g., gusts, etc.
L 420 > Could some of this be due to ignoring sublimation? Figure 6 (left) suggests an over estimation of deposition in areas such as due north of the middle of B, on the flatter (?) section. The elevation (?) isolines between these two figures are different though, making a qualitative comparison difficult.
Page 18 L425 > tendency to overestimate precipitation By how much, exactly?
Page 20 L496 > snow distribution patterns were accurately captured You note the following in the. results section "we observe that the quantitative performance of our o model is not optimal" and Fig 6 shows