A strategy to represent impacts of subgrid-scale topography on snow evolution in the Canadian Land Surface Scheme
Bibliographic record
Abstract
ABSTRACT This sensitivity study applies the offline Canadian Land Surface Scheme (CLASS) version 3.6 to simulate snowpack evolution in idealized topography using observations at Likely, British Columbia, Canada over 1 July 2008 to 30 June 2009. A strategy for a subgrid-scale snow (SSS) parameterization is developed to incorporate two key features: ten elevation bands at 100 m intervals to capture air temperature lapse rates, and five slope angles on four aspects to resolve solar radiation impacts on the evolution of snow depth and SWE. Simulations reveal strong elevational dependencies of snow depth and SWE when adjusting temperatures using a moist adiabatic lapse rate with elevation, with 26% peak SWE differences between that at the average elevation versus the mean of the remainder of the elevation bands. Differences in peak SWE on north- and south-facing slopes increase from 3.0 mm at 10° slope to 17.9 mm at 50° slope. When applied to elevation, slope and aspect combinations derived from a high-resolution digital elevation model, elevation dominates the control of peak SWE values. Inclusion of the range of SSS effects into a regional climate model will improve snowpack and hydrological simulations of western North America's snow-dominated, mountainous watersheds.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".