the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Exploring the potential of forest snow modelling at the tree and snowpack layer scale
Abstract. Boreal and subalpine forests host seasonal snow for multiple months per year, however snow regimes in these environments are rapidly changing due to rising temperatures and forest disturbances. Accurate prediction of forest snow dynamics, relevant for ecohydrology, biogeochemistry, cryosphere, and climate sciences, requires process-based models. While snow schemes that track the microstructure of individual snow layers have been proposed for avalanche research, tree-scale process resolving canopy representations so far only exist in a few snow-hydrological models. A framework that enables layer and microstructure resolving forest snow simulations at the meter scale is lacking to date. To fill this research gap, this study introduces the forest snow modelling framework FSMCRO, which combines two detailed, state-of-the art model components: the canopy representation from the Flexible Snow Model (FSM2), and the snowpack representation of the Crocus ensemble model system (ESCROC). We apply FSMCRO to discontinuous forests at boreal and subalpine sites to showcase how tree-scale forest snow processes affect layer-scale snowpack properties. Simulations at contrasting locations reveal marked differences in stratigraphy throughout the winter. These arise due to different prevailing processes at under-canopy versus gap locations, and due to variability in snow metamorphism dictated by a spatially variable snowpack energy balance. Ensemble simulations allow us to assess the robustness and uncertainties of simulated stratigraphy. Spatially explicit simulations unravel the dependencies of snowpack properties on canopy structure at a previously unfeasible level of detail. Our findings thus demonstrate how hyper-resolution forest snow simulations can complement observational approaches to improve our understanding of forest snow dynamics, highlighting the potential of such models as research tool in interdisciplinary studies.
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RC1: 'Comment on egusphere-2023-2781', Anonymous Referee #1, 03 Apr 2024
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Summary and recommendation
Mazotti et al. develop and present a new physics-based, multi-layer, hyper-resolution snow model (FSMCRO) that can represent high spatial and vertical resolution snow properties including grain type, density, temperature, and other snow parameters. This was achieved through a one way coupling between the FSM2 canopy model and the ensemble Crocus model, with the added benefit that ensemble simulations provides a mean for assessing uncertainty. The paper focuses on introducing and demonstrating the model at two well studied snow sites (Finland and Switzerland), with only qualitative validation (“plausibility”). The model shows reasonable representation of snow depth patterns in Switzerland (focus in the main paper) but less so in Finland (supp. material). Overall, the model shows realistic spatial variations in key snow properties (grain size, SSA) and their evolution in time along a transect spanning a forest gap with variable radiation and interception dynamics. Through the use of the ensembles and spatial simulations, the study also finds that snowpack variability (due to canopy effects on snow processes) is more important than model uncertainty.
Overall, I find this paper potentially offers a significant advance in our ability to resolve very localized snow properties which will be of interest and use to research in snow-forest interactions, wildlife ecology, and possibly avalanche studies. I think the scientific and presentation are generally of high quality, though I offer some comments and suggestion for further improvement. My main concern is about the minimal validation effort and the apparent deficiencies in snow depth simulation at one of the sites (See #1 below), and therefore request the authors consider these before publication. I emphasize this paper should be published following attention to these comments.
MAIN COMMENTS
- While the paper does not present a detailed validation but rather a demonstration of the new model, it seems there is still an opportunity to provide additional analysis to understand the “plausibility” of the model and needs for future improvements. For instance, the paper references weekly snow pit data at the Finland site, but does not make use of them due to issues with geolocation. I would argue that the geolocation issue with the pits does not preclude such a comparison, as multiple location from the domain could be selected, along with the ensemble members in order to understand the range of possible snow profiles simulated by FSMCRO. I think that a comparison between the FSMCRO ensemble and the snow pit data (grain type, density, etc.) could still be informative, even if done on a qualitative basis given the recognized challenges in comparing multi-layer snow models to snow pits. This might help to identify the plausibility of the model as well as possible deficiencies and areas for future development in the model. At the same time, this may require attention to the prominent errors in FSMCRO snow depth that are apparent at the Finland site (Figure S2, where even normalized snow depths are quite different from observations). As noted by the authors: “an adequate reproduction of observed snow depth patterns is a prerequisite for a meaningful subsequent analysis of snowpack vertical properties” (L. 285-286). Comparing to the Finland snowpit data might be helpful for diagnosing possible reasons for the deficient snow depth representation (e.g., bulk snow density?).
- Several figures in the paper are not readable for someone with a red-green vision deficiency. As such, those readers may not be able to distinguish (for instance) the different snow grain types (e.g., melt forms vs. precipitation particles). I recognize this is not the fault of the authors as they are following the conventions from the Fierz et al. (2009) international snow classification report. However, I would suggest the authors consider whether something can be done to help these readers (e.g., adding a small hatch pattern to the green colors).
- I recommend adding snow hardness and snow liquid water content (LWC) as new figures in the supplement (similar to Figures S3-S4), as the capability for mapping these variables spatially may be of high interest to other researchers. The paper references wildlife ecology, and for that the snow hardness is a relevant parameter. Likewise, snowmelt studies and microwave remote sensing (e.g., GPR) may benefit from a model that can resolve spatial variations in LWC.
Line Comments
- L. 27: This should be “tools”.
- L. 30-35: The opening sentence is rather long and cumbersome. I recommend breaking it into two or more sentences.
- L. 215: Add “an” after “as”.
- L. 256: The sentence begins with awkward wording. Please rephrase.
- L. 287: This is somewhat subjective and I think the sentence would be stronger if you cited the quantitative metrics here.
- L. 289: The phrase “not exactly recorded locations” is awkward wording. Please rephrase.
- L. 311: Should be “Sturm”.
- L. 452: Add “a” before “main”.
- L. 475-482: Can you please clarify whether blowing snow is simulated in the model or not? I think wind redistribution should be noted here as an important process for spatial variability of snow.
- L. 494: The sentence has awkward wording (“did not allow to evaluate”). Please rephrase.
- L. 506: Should be “prey” instead of “pray”.
- L. 518: Add “a” before “benchmark”.
- L. 520-521: The sentence begins with awkward wording. Please rephrase.
- L. 531-534: Could the new snow density and snow compaction parameterizations also be impacting the snow depth overestimation?
- L. 531: This focuses on one of the evaluations of the modeled snow depth, however, I think it is best to also acknowledge the prominent deficiencies in modeled snow depth at the Finland site in April (Figure S2). See my first major comment above.
FIGURES
- Figure 2, Figure S1, and Figure S2: Please add a scale bar.
- Figure 2: Please clarify in the caption what blue represents in the hemispherical photos. I believe it is in the sky portion outside the solar track but it would be helpful to state this in the caption.
- Figure 4: I wonder if it would be useful to show a plot of mean direct beam transmissivity at each location on the transect? This could go just below the Fveg and could have similar dimensions/scale. This is not a required revision but merely a suggestion if it helps to show the shaded area in the open gap on the left side of the figure.
- Figures 4, 5, 6, S3, S4, … : It could be helpful to add “S” on the left and “N” on the right at the top to indicate the south-to-north orientation of the transect.
- Figure 7: I suggest adding a map on mean canopy transmissivity, which I suspect might aid in interpretation of the spatial patterns here.
Citation: https://doi.org/10.5194/egusphere-2023-2781-RC1
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