On the importance of sublimation to an alpine snow mass balance in the Canadian Rocky Mountains
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Bibliographic record
Abstract
Abstract. A modelling study was undertaken to evaluate the contribution of sublimation to an alpine snow mass balance in the Canadian Rocky Mountains. Snow redistribution and sublimation by wind, snowpack sublimation and snowmelt were simulated for two winters over an alpine ridge transect located in the Canada Rocky Mountains. The resulting snowcover regimes were compared to those from manual snow surveys. Simulations were performed using physically based blowing snow (PBSM) and snowpack ablation (SNOBAL) models. A hydrological response unit (HRU)-based spatial discretization was used rather than a more computationally expensive fully-distributed one. The HRUs were set up to follow an aerodynamic sequence, whereby eroded snow was transported from windswept, upwind HRUs to drift accumulating, downwind HRUs. That snow redistribution by wind can be adequately simulated in computationally efficient HRUs over this ridge has important implications for representing snow transport in large-scale hydrology models and land surface schemes. Alpine snow sublimation losses, in particular blowing snow sublimation losses, were significant. Snow mass losses to sublimation as a percentage of cumulative snowfall were estimated to be 20–32% with the blowing snow sublimation loss amounting to 17–19% of cumulative snowfall. This estimate is considered to be a conservative estimate of the blowing snow sublimation loss in the Canadian Rocky Mountains because the study transect is located in the low alpine zone where the topography is more moderate than the high alpine zone and windflow separation was not observed. An examination of the suitability of PBSM's sublimation estimates in this environment and of the importance of estimating blowing snow sublimation on the simulated snow accumulation regime was conducted by omitting sublimation calculations. Snow accumulation in HRUs was overestimated by 30% when neglecting blowing snow sublimation calculations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it