Modified snow algorithms in the Canadian land surface scheme: Model runs and sensitivity analysis at three boreal forest stands
Bibliographic record
Abstract
Version 3.1 of the Canadian Land Surface Scheme (CLASS) contains a number of new algorithms of significance for snow simulations in the boreal forest. In particular, mixed precipitation is now modelled, the density of fresh snow varies with temperature and the maximum snowpack density varies with snow depth. A model for canopy interception and unloading of snow developed in the Canadian boreal forest has also been implemented. In this paper, nine‐month column runs of CLASS 3.1 are compared with CLASS 2.7, the current operational version. The model runs span the winter of 2002–03 at three boreal forest sites: a mature aspen stand, a mature jack pine stand and a mature black spruce stand, all located in central Saskatchewan. The focus is on the winter performance and the representation of snow. More accurate (lower) values of modelled snow density improve the modelled snowpack depth. The accuracy of the canopy interception algorithm could not be tested directly with respect to measured interception, but results suggest that the ability to unload intercepted snow is important for accurate estimates of sublimation loss, and that simulated snow water equivalent is sensitive to perceived canopy gap fraction, interception capacity, and unloading rate. Underestimation of the canopy gap fraction increases canopy interception and sublimation losses, and decreases snow water equivalent in the snowpack, and vice versa. Employing modified gap fraction values improved the modelled snow water equivalent at two of the sites. Modifications to the model are suggested to allow the total albedo to respond to changes in the modelled sub‐canopy albedo.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".