Bibliographic record
Abstract
Abstract. Light-absorbing particles (LAPs) deposited at the snow surface significantly reduce its albedo and strongly affect the snow melt dynamics. The explicit simulation of these effects with advanced snow radiative transfer models is generally associated with a large computational cost. Consequently, many albedo schemes used in snowpack models still rely on empirical parameterizations that do not account for the spatial variability of LAP deposition. In this study, a new strategy of intermediate complexity that includes the effects of spatially variable LAP deposition on snow albedo is tested with the snowpack model Crocus. It relies on an optimization of the parameter that controls the evolution of snow albedo in the visible range. Optimized values for multi-year snow albedo simulations with Crocus were generated at ten reference experimental sites spanning a large variety of climates across the world. A regression was then established between these optimal values and climatological deposition of LAP on snow at the location of the experimental sites extracted from a global climatology developed in this study. This regression was finally combined with the global climatology to obtain an LAP-informed and spatially variable parameter for the Crocus albedo parameterization. The revised parameter improved snow albedo simulations on average by 10 % with the largest improvements found in the Arctic (more than 25 %). This methodology can be applied to other land surface models using the global climatology of LAP deposition on snow developed for this study.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.017 | 0.008 |
| Insufficient payload (model declined to judge) | 0.381 | 0.292 |
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".