the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 28 Mar 2025
Impact of topography and meteorological forcing on snow simulation in the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC)
Abstract. Our study evaluates the impacts of an alternate snow cover fraction (SCF) parameterization on snow simulation in the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC). Three reanalysis-based meteorological datasets are used to drive the model to account for uncertainties in the forcing data. While the default parameterization assumes a simple linear relationship between SCF and snow depth with no dependence on topography, the alternate parameterization accounts for the topographic effects of sub-grid terrain on SCF. We show that the alternate parameterization improves SCF simulated in CLASSIC during winter and spring in mountainous areas for all three choices of meteorological datasets. Annual mean bias, unbiased root mean squared area, and correlation improve by 75 %, 32 %, and 7 % when evaluated with MODIS SCF observations over the Northern Hemisphere. We also demonstrate that the improvements to simulated SCF lead to further improvements in variables related to surface radiation, energy fluxes, and the water cycle. Finally, we link relative biases in the meteorological forcing data to differences in simulated snow water equivalent and SCF. Assessment of simulations with different combinations of SCF parameterizations and meteorological datasets reveals the large impact of meteorological forcing on snow simulation in CLASSIC. Two out of the three meteorological datasets were bias-adjusted using observation-based datasets. However, simulations forced by the dataset without bias correction outperform relative to simulations forced by datasets with bias correction, suggesting that there are large uncertainties in the observation-based datasets and/or methods used for bias correction. This study underscores the importance of accounting for topographic effects of sub-grid terrain and accurate meteorological forcing on snow simulation in land surface models.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
(4193 KB)
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
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- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2025-1264', Anonymous Referee #1, 10 Apr 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1264/egusphere-2025-1264-RC1-supplement.pdfCitation: https://doi.org/
10.5194/egusphere-2025-1264-RC1 -
AC1: 'Reply on RC1', Libo Wang, 20 Jun 2025
Publisher’s note: this comment is a copy of AC4 and its content was therefore removed on 26 June 2025.
Citation: https://doi.org/10.5194/egusphere-2025-1264-AC1 -
AC4: 'Reply on RC1', Libo Wang, 25 Jun 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1264/egusphere-2025-1264-AC4-supplement.pdf
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AC1: 'Reply on RC1', Libo Wang, 20 Jun 2025
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RC2: 'Comment on egusphere-2025-1264', Anonymous Referee #2, 23 Apr 2025
Review of Wang et al., "Impact of topography and meteorological forcing on snow simulation in the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC)"
This study examines the impact of replacing the default CLASSIC snow cover fraction (SCF) parameterization with an alternative one. The study has two objectives: 1) comparing the two SCF parameterizations and 2) examining the role of meteorological forcing on the simulation of snow. The role of meteorological forcing is highlighted by using three input datasets to force the model. The default parameterization predicts SCF from snow depth using a linear relationship for snow depth below 0.1m; above 0.1m, SCF is 1. The alternative considers the accumulation and ablation seasons separately, and also incorporates topographic information to adjust its behavior spatially based on the topographic variability within a region. The authors find that comparisons to MODIS SCF are more favorable when using the alternate SCF parameterization. In addition, other metrics related to water and energy fluxes show improvement.
General comments:
The authors state that tests changing the lone parameter (0.1) in the CTL parameterization show minimal impacts to their simulations. In contrast, in addition to a structural change, SL12 offers opportunities to improve the SCF simulation by modifying and calibrating equations 1 and 3. While the authors mention a couple of changes to the k_acc and N_melt parameters that led to positive results, they choose not to explore the parameter sensitivity in more detail "because none of the three meteorological forcing datasets used in this study are exempt from biases, there is a limit to how well optimal parameter values can be chosen for use in CLASSIC". The results shown in figure 5 of the manuscript seem to contradict this statement, as the biases seem consistent across forcings. The authors support this by stating "On the global scale, the spatial patterns of SCF bias are similar for all three meteorological forcing choices."
I believe that this study would be improved if the authors were to pursue this path. The authors could perform a few shorter, initialized runs (e.g. from 1980 onwards) to do a sensitivity study of the N_melt parameter. They could then use these results to see if a better function for N_melt as a function of sigma_topo becomes apparent. For example, figure 5 indicates that lower SCF values in winter and spring, irrespective of forcing input, are preferred for flat regions. This implies that the N_melt equation increase too rapidly for small values of sigma_topo. This is perhaps not surprising, given that 1/x blows up as x goes to zero. A bounded function, e.g. a decaying exponential, might improve the results for flat regions, while maintaining the good results for mountainous regions. Similarly, adding a simple dependence on sigma_topo to k_acc might improve the fall bias shown in figure 5 b) without degrading other regions.
Specific comments:
Lines 99,100: add references for CLM5, CESM2
Line 130: how do the four sub-areas relate to SCF? Do the snow/snow-free areas change dynamically?
Line 144: "all vertical layers": I thought there was only 1 layer (line 132)?
Line 160: is there reason to think k_acc should vary spatially? If so, how might one parameterize it? (discussed in sec 5.2). Also, SL12 mentions that eq 1 assumes snowfall is randomly distributed in the region; is this a valid assumption?
Line 173: how are the parameters 200 and 10 chosen? How sensitive are the results to these parameters, and could they instead be calibrated?
Line 181: does SL12 implemented in CLM5 use time of year to determine which equation to use?
Why is equation 4 used? Isn't W_max based on the evolution of W in the model, i.e. is it the peak SWE of each snow season?Line 262: does the resolution of the DEM affect the calculation of the standard deviation of the sub-grid terrain?
Line 271: 'high mountainous asia' or 'high mountain asia'?
Line 297: IMS data could be converted to 1 degree fractional values, then treated similarly to MODIS; is that how IMS data is processed?
Line 315: SND is related to SWE via snow density; how is snow density calculated in CLASSIC?
Line 363: are there 21 datasets, or 7 datasets?
Figure 2: add units to (d) colorbar
Figure 3: please label figures with NH or HMA. Also adding a dashed line to indicate zero for each y-axis would be helpful.
Figure 4: why only show NH, but not HMA like other sections? Does HMASR not provide SWE?
Figure 4: perhaps replace 'mount' and 'flat' with 'Mountainous Regions (sigma > 200m)' and 'Flat Regions (sigma < 200m)'
Figure 4: how do errors compare to magnitude of SWE, e.g. figure 2 d), which shows maximum values of 115?
Line 403: CTL and SL12 do not cause any albedo feedbacks to cause changes in SWE (via surface energy balance)?
Figure 5: improvements mainly in 2nd half of snow season for mountainous regions; is this due to the ablation part of the SL12 parameterization?
Figure 5: SL12 shows similar results in the NA and EA flat regions (the flat region biases begin early in the season (around Dec/Jan)), can that be improved by calibrating with the parameters in eqn 3?
Figure 6: I would use a colormap that was not white in the middle for panels a) and b). Perhaps simply linear white-to-blue?
Figure 7: SWE evaluation for HMA in section 4 would help understand differences in forcing data. Is the cruja/era5 overestimate due to a SWE high bias, or does it come from the SCF parameterizations?
Line 441: SL12 is shown to perform slightly worse than CTL in fall (SON) in mountainous regions but not flat regions in NA. What might cause this? Is it more due to the accumulation equation or the ablation equation? For the flat regions, the spring bias is similar for both SL12 and CTL. What does that say about SL12, i.e. would a more rapid SCF decrease improve the results? Does that imply that equation 3 is not optimal for flat regions, and the 1/sigma_topo behavior might be too large for small sigma_topo?
Line 664: does the 'wet day' dependence indicate that one of the accumulation / ablation equations in SL12 has a bigger impact on the SCF evolution?
Citation: https://doi.org/10.5194/egusphere-2025-1264-RC2 -
AC2: 'Reply on RC2', Libo Wang, 21 Jun 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1264/egusphere-2025-1264-AC2-supplement.pdf
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AC2: 'Reply on RC2', Libo Wang, 21 Jun 2025
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RC3: 'Comment on egusphere-2025-1264', Anonymous Referee #3, 07 May 2025
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AC3: 'Reply on RC3', Libo Wang, 21 Jun 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1264/egusphere-2025-1264-AC3-supplement.pdf
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AC3: 'Reply on RC3', Libo Wang, 21 Jun 2025
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2025-1264', Anonymous Referee #1, 10 Apr 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1264/egusphere-2025-1264-RC1-supplement.pdf
-
AC1: 'Reply on RC1', Libo Wang, 20 Jun 2025
Publisher’s note: this comment is a copy of AC4 and its content was therefore removed on 26 June 2025.
Citation: https://doi.org/10.5194/egusphere-2025-1264-AC1 -
AC4: 'Reply on RC1', Libo Wang, 25 Jun 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1264/egusphere-2025-1264-AC4-supplement.pdf
-
AC1: 'Reply on RC1', Libo Wang, 20 Jun 2025
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RC2: 'Comment on egusphere-2025-1264', Anonymous Referee #2, 23 Apr 2025
Review of Wang et al., "Impact of topography and meteorological forcing on snow simulation in the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC)"
This study examines the impact of replacing the default CLASSIC snow cover fraction (SCF) parameterization with an alternative one. The study has two objectives: 1) comparing the two SCF parameterizations and 2) examining the role of meteorological forcing on the simulation of snow. The role of meteorological forcing is highlighted by using three input datasets to force the model. The default parameterization predicts SCF from snow depth using a linear relationship for snow depth below 0.1m; above 0.1m, SCF is 1. The alternative considers the accumulation and ablation seasons separately, and also incorporates topographic information to adjust its behavior spatially based on the topographic variability within a region. The authors find that comparisons to MODIS SCF are more favorable when using the alternate SCF parameterization. In addition, other metrics related to water and energy fluxes show improvement.
General comments:
The authors state that tests changing the lone parameter (0.1) in the CTL parameterization show minimal impacts to their simulations. In contrast, in addition to a structural change, SL12 offers opportunities to improve the SCF simulation by modifying and calibrating equations 1 and 3. While the authors mention a couple of changes to the k_acc and N_melt parameters that led to positive results, they choose not to explore the parameter sensitivity in more detail "because none of the three meteorological forcing datasets used in this study are exempt from biases, there is a limit to how well optimal parameter values can be chosen for use in CLASSIC". The results shown in figure 5 of the manuscript seem to contradict this statement, as the biases seem consistent across forcings. The authors support this by stating "On the global scale, the spatial patterns of SCF bias are similar for all three meteorological forcing choices."
I believe that this study would be improved if the authors were to pursue this path. The authors could perform a few shorter, initialized runs (e.g. from 1980 onwards) to do a sensitivity study of the N_melt parameter. They could then use these results to see if a better function for N_melt as a function of sigma_topo becomes apparent. For example, figure 5 indicates that lower SCF values in winter and spring, irrespective of forcing input, are preferred for flat regions. This implies that the N_melt equation increase too rapidly for small values of sigma_topo. This is perhaps not surprising, given that 1/x blows up as x goes to zero. A bounded function, e.g. a decaying exponential, might improve the results for flat regions, while maintaining the good results for mountainous regions. Similarly, adding a simple dependence on sigma_topo to k_acc might improve the fall bias shown in figure 5 b) without degrading other regions.
Specific comments:
Lines 99,100: add references for CLM5, CESM2
Line 130: how do the four sub-areas relate to SCF? Do the snow/snow-free areas change dynamically?
Line 144: "all vertical layers": I thought there was only 1 layer (line 132)?
Line 160: is there reason to think k_acc should vary spatially? If so, how might one parameterize it? (discussed in sec 5.2). Also, SL12 mentions that eq 1 assumes snowfall is randomly distributed in the region; is this a valid assumption?
Line 173: how are the parameters 200 and 10 chosen? How sensitive are the results to these parameters, and could they instead be calibrated?
Line 181: does SL12 implemented in CLM5 use time of year to determine which equation to use?
Why is equation 4 used? Isn't W_max based on the evolution of W in the model, i.e. is it the peak SWE of each snow season?Line 262: does the resolution of the DEM affect the calculation of the standard deviation of the sub-grid terrain?
Line 271: 'high mountainous asia' or 'high mountain asia'?
Line 297: IMS data could be converted to 1 degree fractional values, then treated similarly to MODIS; is that how IMS data is processed?
Line 315: SND is related to SWE via snow density; how is snow density calculated in CLASSIC?
Line 363: are there 21 datasets, or 7 datasets?
Figure 2: add units to (d) colorbar
Figure 3: please label figures with NH or HMA. Also adding a dashed line to indicate zero for each y-axis would be helpful.
Figure 4: why only show NH, but not HMA like other sections? Does HMASR not provide SWE?
Figure 4: perhaps replace 'mount' and 'flat' with 'Mountainous Regions (sigma > 200m)' and 'Flat Regions (sigma < 200m)'
Figure 4: how do errors compare to magnitude of SWE, e.g. figure 2 d), which shows maximum values of 115?
Line 403: CTL and SL12 do not cause any albedo feedbacks to cause changes in SWE (via surface energy balance)?
Figure 5: improvements mainly in 2nd half of snow season for mountainous regions; is this due to the ablation part of the SL12 parameterization?
Figure 5: SL12 shows similar results in the NA and EA flat regions (the flat region biases begin early in the season (around Dec/Jan)), can that be improved by calibrating with the parameters in eqn 3?
Figure 6: I would use a colormap that was not white in the middle for panels a) and b). Perhaps simply linear white-to-blue?
Figure 7: SWE evaluation for HMA in section 4 would help understand differences in forcing data. Is the cruja/era5 overestimate due to a SWE high bias, or does it come from the SCF parameterizations?
Line 441: SL12 is shown to perform slightly worse than CTL in fall (SON) in mountainous regions but not flat regions in NA. What might cause this? Is it more due to the accumulation equation or the ablation equation? For the flat regions, the spring bias is similar for both SL12 and CTL. What does that say about SL12, i.e. would a more rapid SCF decrease improve the results? Does that imply that equation 3 is not optimal for flat regions, and the 1/sigma_topo behavior might be too large for small sigma_topo?
Line 664: does the 'wet day' dependence indicate that one of the accumulation / ablation equations in SL12 has a bigger impact on the SCF evolution?
Citation: https://doi.org/10.5194/egusphere-2025-1264-RC2 -
AC2: 'Reply on RC2', Libo Wang, 21 Jun 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1264/egusphere-2025-1264-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Libo Wang, 21 Jun 2025
-
RC3: 'Comment on egusphere-2025-1264', Anonymous Referee #3, 07 May 2025
-
AC3: 'Reply on RC3', Libo Wang, 21 Jun 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1264/egusphere-2025-1264-AC3-supplement.pdf
-
AC3: 'Reply on RC3', Libo Wang, 21 Jun 2025
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Gesa Meyer
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Mickaël Lalande
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(4193 KB) - Metadata XML