the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 02 Sep 2026
Precipitation phase-partitioning schemes introduce substantial uncertainty into simulations of the permafrost thermal regime over the Tibetan Plateau
Abstract. Precipitation phase-partitioning schemes determine how precipitation forcing is divided into snowfall and rainfall at each time step in land surface modelling. However, their influence on simulated permafrost thermal regimes remains poorly quantified. In this study, we use the one-dimensional transient permafrost model CryoGridLite to assess the uncertainty in permafrost simulations induced by different schemes over the Tibetan Plateau during 1951–2024. By keeping the meteorological forcing dataset, model configurations, and input parameters identical, we implemented eight commonly used schemes, including binary threshold schemes, temperature-ramp schemes, piecewise schemes, and wet-bulb/humidity-dependent schemes, allowing the effect of precipitation phase assignment to be isolated. Our simulations indicate that precipitation phase-partitioning schemes produce substantial differences in snowfall and rainfall forcing, which propagate into simulated ground thermal regimes. Across the ensemble-defined permafrost area, 83.4 % of the area shows an inter-scheme range in mean annual ground temperature at 10m depth exceeding 0.5 °C, and 48.9 % exceeds 1.0 °C. Uncertainty in simulated thaw depth is more spatially localized than that in mean annual ground temperature, but 47.2 % and 18.3 % of the ensemble-defined permafrost area still show inter-scheme thaw depth ranges exceeding 0.25 and 0.5 m, respectively. These thermal differences further affect threshold-based permafrost diagnosis. Differences among schemes lead to measurable changes in diagnosed permafrost area, with the largest diagnostic uncertainty occurring in marginal permafrost regions where simulated ground temperatures are close to 0 °C. Although precipitation phase assignment is commonly treated as a preprocessing step in meteorological forcing preparation, our controlled experiments show that the choice of scheme can generate pronounced differences in simulated ground thermal states and permafrost area diagnostics. Phase partitioning should therefore be considered a previously underexamined forcing-related source of uncertainty in high-elevation permafrost thermal simulations.
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Status: open (until 29 Nov 2026)
- RC1: 'Comment on egusphere-2026-4004', Anonymous Referee #1, 27 Sep 2026 reply
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RC2: 'Comment on egusphere-2026-4004', Anonymous Referee #2, 26 Oct 2026
reply
Review of the manuscript: "Precipitation phase-partitioning schemes introduce substantial uncertainty into simulations of the permafrost thermal regime over the Tibetan Plateau" (egusphere-2026-4004)
General comments
In this manuscript, Chen et al. investigate the sensitivity of simulated permafrost thermal regimes across the Tibetan Plateau (TP) to eight different precipitation phase-partitioning (PPP) schemes using the one-dimensional transient permafrost model CryoGridLite over the historical period 1951–2024. The experimental setup is rigorously controlled: meteorological forcing (CMFD v2.0), model physics, and subsurface parameters are held identical, isolating the direct effects of rainfall–snowfall partitioning. The authors show that PPP schemes lead to substantial spreads in snowfall amounts (up to ~400 mm locally), which translate into widespread differences in mean annual ground temperature at 10 m depth (MAGT_10m > 1.0 °C over 48.9% of the permafrost area) and localized differences in maximum thaw depth (TD). Interestingly, high-snowfall schemes (e.g., Wet-Bulb and Ding) lead to colder ground temperatures due to warm-season snow persistence and albedo-induced shortwave radiation deficit, rather than winter thermal insulation.
Overall, this is a well-written, timely, and conceptually clear study. Precipitation phase partitioning has long been relegated to an overlooked preprocessing step in cold-region land-surface and permafrost modeling. This paper delivers a compelling demonstration that PPP is, in fact, a first-order source of structural uncertainty.
However, several critical aspects require further clarification, deeper discussion, or refinement before the manuscript can be recommended for final publication:
- Lack of ground-truth validation: While the authors position this study purely as a sensitivity experiment, the practical community will inevitably wonder which scheme is physically most realistic over the TP. A benchmark comparison against available borehole temperatures or snow observation stations is strongly recommended.
- Sensitivity to the snow cover fraction (SCF) and albedo decay parameterizations: The central cooling mechanism hinges entirely on summer snow albedo feedback. The authors need to discuss whether this cooling might be artificially amplified by the sub-grid snow cover and albedo schemes implemented in CryoGridLite.
- Forcing dataset bias interaction: The interaction between near-surface forcing temperature/moisture biases in CMFD v2.0 and the precipitation phase transition functions needs more thorough critical assessment. Below are detailed major and specific comments.
Major comments
- Absence of observational benchmarking (Validation vs. Sensitivity):
- In lines 199–204, the authors state: "...this study focuses on quantifying the effects of PPP schemes on simulated permafrost thermal regime, rather than assessing the performance of CryoGridLite."
- While isolating uncertainty in an ensemble framework is valuable, presenting spreads ranging from -1.0 °C to +1.0 °C in MAGT_10m without any validation leaves the modeling community without actionable guidance. Can the authors include a comparison with a few benchmark boreholes (e.g., from the comprehensive Zhao et al., 2021 dataset along the Qinghai-Tibet Highway/Railway or in the West Kunlun) to demonstrate which scheme (or cluster of schemes, e.g., temperature-based vs. wet-bulb-based) yields the lowest model-data misfit?
- Dominance of the warm-season snow-albedo cooling mechanism and model sensitivity:
- In Section 4.1 (Lines 342–359, Figure 7), the authors argue that high-snow schemes cause ground cooling because snow persists into the warm season (April–October), increasing surface albedo (+0.08) and upward shortwave radiation (+20.9 W/m²).
- This is physically sound, but how realistic is warm-season snow persistence across the TP in CryoGridLite? The Tibetan Plateau snowpack is notoriously thin, patchy, and ephemeral during the summer monsoon season.
- Does CryoGridLite implement a fractional snow-cover (SCF) parameterization that accounts for sub-grid bare ground exposure, or does a thin layer of summer snow instantly cover 100% of the grid cell, drastically exaggerating cell-averaged albedo? Please clarify how fractional snow cover is computed and whether this may lead to an overestimation of the summer cooling effect.
- Representation and parameterization of snow albedo (Table C2):
- In Table C2, maximum fresh snow albedo is 0.85 and old snow albedo is 0.50. Does the model account for the reduction in snow albedo caused by dust/light-absorbing impurities, which are heavily deposited across the TP during spring and early summer? An albedo of 0.50 for melting summer snow might be high if dust is prevalent, which would damp the upward shortwave radiation anomaly. This should be addressed in the limitations section.
- Clarification of Equation (2) and Notation in Binary Schemes:
- In Equation (2) (Line 160), there appears to be a typographic formatting error:
"min = {0, T_air <= T_th; 1, T_air > T_th }"
Presumably, "min" should be "f_rain" (rainfall fraction), matching Equation (1) and Equation (3). Moreover, "T_thi" in the condition needs to be corrected to "T_th".
- Mathematical completeness for the Ding et al. (2014) Scheme:
- In Equations (9) and (10) (Lines 182–185), the conditions state "Delta T/Delta S > ln 2" and "Delta T / Delta S <= ln 2". - However, Delta S = 2.374 - 1.634 * RH (Eq. 7). When RH approaches 1, Delta S is positive (~0.74). But in Eq. (9), inside the natural logarithm, we have: exp(Delta T / Delta S) - 2 * exp(-Delta T / Delta S). The argument of the logarithm must be strictly positive. Does this expression always remain positive for all physically possible RH [0, 1] and elevations Z on the TP? A brief note confirming that the mathematical formulation never yields negative arguments or domain singularities under TP climate conditions is warranted.
- CMFD v2.0 Forcing Biases in the Near-Freezing Regime:
- In Appendix D (Figure D1), the authors show that near-freezing precipitation (-2 °C to +2 °C) constitutes a significant fraction (~100 mm/yr) of total precipitation.
- However, reanalysis and merged gridded products often exhibit cold biases at high elevations or positive precipitation biases in mountainous terrain. If CMFD overestimates precipitation occurring near 0 °C, the inter-scheme divergence reported here could be systematically elevated compared to real-world conditions. Please expand the discussion in Section 4.4 on how forcing-specific temperature/humidity biases specifically interact with the steep slope of the PPP transfer functions.
Specific/minor comments
- Line 36: "Fig. 1" is cited in the Introduction, which is fine, but please ensure that data sources and the rationale for the 1.46 M km² permafrost area (Obu et al., 2019) vs. the 1.09 M km² (Cao et al., 2023) mentioned on Line 37 are explicitly reconciled in the text to avoid confusing the reader.
- Line 101–103: "updated the soil thermal property parameterizations of Dai et al. (2019a, b) and the Balland and Arp scheme... allowing the effect of gravel... to be represented explicitly." -> Excellent addition for Tibetan Plateau modeling, where gravel content is very high.
- Line 120: "radiation and precipitation were processed as 3-hourly mean fluxes to preserve the corresponding energy and water amounts." -> Please specify whether the phase partitioning was evaluated at the hourly interpolated step or at the original 3-hourly step. If hourly, air temperature interpolation can artificially alter the crossing of the rain-snow threshold compared to 3-hourly blocks.
- Line 160, Eq. (2): Correct "min=" to "f_rain=" and fix "T_thi" to "T_th".
- Line 184: Typo in the subscript: "T_mik" in Eq. (10) should be "T_min".
- Line 279: Typo: "MAG Gamma_10m" -> replace with "MAGT_10m".
- Line 345: "Because incoming shortwave radiation was identical across the experiments, stronger upward shortwave radiation indicates reduced net shortwave radiation at the surface." -> This is a very clear deduction. Consider referencing Figure 7 directly here.
- Line 434: "...clay- and silt-rich soils, where matric and adsorptive forces allow part of the pore water to remain unfrozen below 0 °C." -> Good point. Could the lack of an SFCC also affect the sensitivity of thaw depth (TD) to high/low summer ground temperatures? Please add a sentence connecting this to TD sensitivity.
- Figure 3d and Figure 4d: The y-axis uses a broken/split log-linear scale around zero (10^0, 10^-1, 0, -10^-1, -10^0). Please explain in the caption how values between -0.1 and +0.1 are scaled (symmetric log scale / symlog) to prevent misinterpretation of the distribution near zero.
- Figure 7 Caption: "between high-snow schemes (Wet-Bulb and Ding) and low-snow schemes (Simple and VIC)..." -> Typo: "Simple" should be "Simple0".
Citation: https://doi.org/10.5194/egusphere-2026-4004-RC2
Model code and software
Model code for "Precipitation phase-partitioning schemes introduce substantial uncertainty into simulations of the permafrost thermal regime over the Tibetan Plateau" Rui Chen https://doi.org/10.5281/zenodo.20637282
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This manuscript presents an investigation into the uncertainty introduced by precipitation phase-partitioning (PPP) schemes into numerical simulations of the permafrost thermal regime across the Tibetan Plateau (TP). Using the one-dimensional numerical permafrost model CryoGridLite driven by the China Meteorological Forcing Dataset (CMFD v2.0) over the period 1951–2024, the authors evaluate eight widely used PPP formulations (binary threshold, linear ramp, piecewise, and wet-bulb/humidity-dependent schemes). The paper is well structured, timely, and addresses an overlooked aspect of permafrost modeling. While PPP uncertainty has been explored extensively in catchment hydrology and snowpack modeling, its downstream propagation into ground thermal regimes and threshold-based permafrost diagnostics has rarely been isolated. The authors highlight a notable regional physical mechanism: over the TP, schemes that partition more precipitation into snowfall lead to colder ground thermal regimes, primarily due to warm-season snow persistence and the associated albedo cooling effect, which contrasts with the classic winter snow-insulation warming observed across much of the Arctic. Overall, I consider the manuscript suitable for publication after revision, provided that the authors address the comments below.
1. The Methods state that TD is reported only for grid cells where MAGT10m is below 0 °C, whereas Section 3.3 states that TD is evaluated using the same fixed ensemble-defined permafrost mask used for the MAGT10m analysis. Please clarify how TD is treated when a grid cell belongs to the ensemble-defined permafrost mask but is diagnosed as non-permafrost in one or more individual PPP experiments. Are these values treated as missing values, excluded from the range calculation, or handled in another way? This choice could directly affect the estimated TD uncertainty, particularly in marginal permafrost regions where the PPP schemes most frequently differ in permafrost diagnosis. The authors should carefully check the calculation procedure and ensure that the descriptions in the Methods, Results, and Fig. 4 caption are fully consistent.
2. Figure 7 shows that the high-snowfall schemes produce greater warm-season snow depth, higher surface albedo, greater upward shortwave radiation, and lower ground surface temperature than the low-snowfall schemes. These results support an important role of warm-season snow persistence and associated shortwave radiative effects in linking PPP differences to simulated ground temperatures. However, the manuscript further states that the differences are “primarily attributable” to this pathway and describes it as the “dominant scheme-induced signal”. Because the complete surface energy balance is not shown, the relative contributions from longwave radiation, sensible and latent heat fluxes, ground heat flux, rainfall infiltration, soil-moisture changes, and other related processes cannot be quantitatively separated. I therefore suggest that the authors either (i) provide additional energy budget diagnostics to support the relative importance of this pathway, or (ii) moderate the corresponding statements and describe the results as suggesting an important role of warm-season snow persistence and shortwave radiative effects.
3.Figure 1: The map legend refers to the “Third Pole region boundary”, whereas the manuscript consistently describes the study domain as the Tibetan Plateau, and Table 1 refers to a “Tibetan Plateau Boundary Dataset”. Please use consistent terminology.
4.Lines 178-180: Please provide brief explanations of ΔT, ΔS, and T0 in Eqs. (6)-(8). In addition, please clarify how relative humidity (RH) was calculated, since CMFD2.0 is described as providing specific humidity rather than relative humidity, and specifies the source of the surface elevation used in the Ding scheme.
5.Line 413: “Appendix D1” appears to be an incorrect cross-reference. Please change this to “Fig. D1” or “Appendix D”, as appropriate.
6.Equation (A1): Please check whether the contribution of air to volumetric heat capacity has been intentionally neglected. The model description includes an air volume fraction, and the volumetric heat capacity of air (ca), is listed in Table C3, whereas the term θaca is absent from Eq. (A1).
7.Around Eq. (A10): The text states that both km,sat and ko,sat “are given by”, whereas Eq. (A10) only defines km,sat. Since the ko,sat has been provided in Table C3, please revise the text accordingly.
6.Table C3: Please verify the listed ice density, ρi=1000 kg m−3. If using the same density for liquid water and ice is an intentional simplification in CryoGridLite, please state this explicitly or provide the corresponding model reference.
8.Figure D1: CMFD2.0 shows a pronounced change in precipitation during the early part of the record, particularly for total precipitation and precipitation occurring under near-freezing conditions. Please verify that this feature is indeed present in the original CMFD2.0 dataset.
There are some minor grammatical issues in the manuscript, like in Line 480, “a new soil thermal conductivity and heat capacity scheme ... that account for the effect of gravel” should be changed to “... that accounts for the effect of gravel”. Please check the grammatical again throughout the manuscript.