Optimization of snow cover fraction parameterization in the Community Land Model: implementation and preliminary validation over Tibetan Plateau

Yang, Kai; Wang, Chenghai; Cui, Yang; Ai, Lingyun; Zhang, Feimin; Zhaoye, Pinghan

doi:10.5194/egusphere-2025-6490

Preprints

https://doi.org/10.5194/egusphere-2025-6490

Preprints

27 Feb 2026

| 27 Feb 2026

Status: this preprint is open for discussion and under review for Geoscientific Model Development (GMD).

Optimization of snow cover fraction parameterization in the Community Land Model: implementation and preliminary validation over Tibetan Plateau

Kai Yang, Chenghai Wang, Yang Cui, Lingyun Ai, Feimin Zhang, and Pinghan Zhaoye

Abstract. Snow cover over Tibetan Plateau (TP) is not only a key land forcing for the regional and global climate but also an important water resource for surround regions. However, state-of-the-art climate models still exhibit substantial biases in simulating winter snow cover over the TP, which constitutes one of the major sources of uncertainty in climate prediction. Using satellite-based snow cover datasets, this study reveals that the Community Land Model version 5 (CLM5) systematically overestimates the winter snow cover fraction (SCF) over the TP. This bias mainly arises because the original SCF parameterization scheme neglects the spatially varying probability distribution of snowfall accumulation and underestimates snow depletion over barren land during the melting period. By accounting for the effects of non-growing-season low vegetation (i.e., withered grass stems) and topographic relief, we parameterize the snow accumulation probability factor (k_accum) instead of prescribing it as a constant. In addition, a revised factor is introduced to modify the snow depletion curve shape parameter (N_melt), thereby optimizing the SCF parameterization scheme. Preliminary validation indicates that the optimized scheme substantially reduces positive winter SCF biases over barren land and grassland by 34 %~88 %, and improves surface albedo simulations, thereby alleviating cold surface temperature biases by approximately 1~2 °C in snow-affected regions.

Received: 26 Dec 2025 – Discussion started: 27 Feb 2026

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.

Download & links

Kai Yang, Chenghai Wang, Yang Cui, Lingyun Ai, Feimin Zhang, and Pinghan Zhaoye

Status: open (until 24 Apr 2026)

Post a comment Subscribe to comment alert

RC1:
'Comment on egusphere-2025-6490', Anonymous Referee #1, 25 Mar 2026 reply
This study evaluates the performance of Community Land Model version 5 (CLM5) in simulating snow cover during winter over the Tibetan Plateau (TP) and presents a revised snow cover fraction (SCF) parameterization scheme implemented in the model. This is a very interesting and innovative investigation. The results show that the revised scheme appears to reduce SCF biases and alleviates the cold bias over the TP. Generally, the topic is interesting, as it addresses uncertainties in land surface model simulations of snow processes in alpine cold regions. The findings may therefore provide useful insights for further development and improvement of land surface models. I recommend a minor revision. My comments and concerning that need be clarified are as follows.
This study focuses on TP snow cover during winter rather than other seasons, the reason behind it shouldbeexplained.

The revised scheme parameterizing k_accumand revising N_meltare based on the consideration of withered grass stems (WGS) and terrain characteristics. While, the proposed formulation appears largely empirical, the physical mechanisms linking these factors (WGS and terrain) to the spatial distribution of snow cover seem not be sufficiently revealed. The authors should provide a clearer theoretical justification, explain the choice of functional form.

Results show a large reduction in SCF bias (up to ~88%) after optimizing the parameterization. But,it is unclear whether these improvements are spatially uniform across the TP. To identify the advantages and limitations of the revised scheme, it’s suggested to add analysis showing regions where the revised scheme performs particularly well and other regions where biases remain significant.

The authors are encouraged to provide a detailed analysis of the energy budget, analyze shortwave and longwave radiation fluxes, sensible and latent heat fluxes, potential changes in snow–albedo feedback, which would help clarify the physical consequences of the revised parameterization scheme.

This study does not indicate whether the revised scheme was tested in multi-year or long-term simulations. It is necessary to evaluate that the revised scheme remains stable over longer periods. Specifically, whether interannual variability of snow cover over TP is captured.

Reply
Citation: https://doi.org/10.5194/egusphere-2025-6490-RC1
RC2: 'Comment on egusphere-2025-6490', xu zhou, 29 Mar 2026 reply

Dear authors, Please see attached file my detailed comments. Please let me know if there are unclear points. Best wishes, Xu

Reply

Citation: https://doi.org/10.5194/egusphere-2025-6490-RC2
RC3:
'Comment on egusphere-2025-6490', Anonymous Referee #3, 07 Apr 2026 reply
Comments on “Optimization of snow cover fraction parameterization in the Community Land Model: implementation and preliminary validation over Tibetan Plateau” (egusphere-2025-6490)

General comment:
This study shows a systematic overestimation of wintertime snow cover fraction (SCF) over Tibetan Plateau (TP) by the Community Land Model version 5 (CLM5), and proposes an optimized SCF parameterization scheme through incorporating the effects of non-growing-season vegetation (withered grass stems) and topographic characteristics, and modifying the accumulation parameter (k_accum) and the melt parameter (N_melt). The optimized scheme is evaluated through preliminary simulations over the TP. According to the results presented, the optimized scheme substantially reduces SCF overestimation and improves the simulation of surface albedo and surface temperature. Overall, the manuscript is generally well organized and clearly written. The proposed optimization has the potential to contribute to improvements of snow cover and related surface energy budgets simulations in CLM5. However, several aspects of the methodology and interpretation still require clarification before the manuscript can be considered for publication. I recommend a moderate revision.

Major comments:
Physical basis of the optimized parameterization scheme seems unclear. This study proposes modifying k_accum by introducing the influence of non-growing-season vegetation (withered grass stems) and topographic relief. While the idea is plausible, the physical reasoning behind the selected functional form is not sufficiently explained. The authors are encouraged to clarify why these specific factors are expected to control subgrid snow accumulation probability and how the mathematical form of the parameterization was derived.

Universality of the optimized scheme and its applicability beyond the TP should be discussed. Currently, the validation is conducted only over the TP. Given that CLM5 is widely used in globe and it’s implied for other models, it is important to discuss the potential applicability of the optimized parameterization in other regions. Whether the parameters are region-specific or globally applicable, possible limitations when applied to different vegetation types also need be discussed.

More evaluations of the optimized scheme should be added. The evaluation mainly focuses on SCF, surface albedo, and surface temperature. While these variables are relevant indicators of snow processes, the evaluation could be strengthened by including additional snow-related variables, such as snow depth or snow water equivalent (SWE).

Minor comments:
Line 98: “0.05° × 0.05° dataset” should be “0.05° × 0.05° and 500 m spatial resolution dataset”.

It’s suggested to use snow water equivalent instead of snow depth in section 2.4, if the data is available.

Figure 1: It's suggested to analyze Fig. 1 in section 3.1 for better understanding the original SCF parameterization scheme, which might be the foundation for the proposed optimization

Lines 142-143: it's better to add references to support this perspective.

In section 3.4: more detailed information of experimental design should be given, such as integration step, output frequency, component set.

Please carefully proofread the manuscript for minor grammatical issues, and consider simplifying several long or complex sentences to improve readability.

Reply
Citation: https://doi.org/10.5194/egusphere-2025-6490-RC3

Kai Yang, Chenghai Wang, Yang Cui, Lingyun Ai, Feimin Zhang, and Pinghan Zhaoye

Viewed

Total article views: 272 (including HTML, PDF, and XML)

HTML	PDF	XML	Total	BibTeX	EndNote
178	80	14	272	11	22

HTML: 178
PDF: 80
XML: 14
Total: 272
BibTeX: 11
EndNote: 22

Views and downloads (calculated since 27 Feb 2026)

Month	HTML	PDF	XML	Total
Feb 2026	35	18	3	56
Mar 2026	124	55	10	189
Apr 2026	19	7	1	27

Cumulative views and downloads (calculated since 27 Feb 2026)

Month	HTML	PDF	XML	Total
Feb 2026	35	18	3	56
Mar 2026	124	55	10	189
Apr 2026	19	7	1	27

Viewed (geographical distribution)

Total article views: 259 (including HTML, PDF, and XML) Thereof 259 with geography defined and 0 with unknown origin.

Country	#	Views	%

Latest update: 15 Apr 2026

Short summary

In this study, focusing on a well-developed LSM—CLM5, we optimized the SCF parameterization scheme through considering effects of withered grass steam and topographic relief on the probability distribution and the depletion of snow, reducing the positive biases of SCF by 34 %~88 % and the surface cold biases by 1~2 ℃ in snow-affected regions over Tibetan Plateau.


Total:	0
HTML:	0
PDF:	0
XML:	0