Modelled sensitivity of the snow regime to topography, shrub fraction and shrub height
Bibliographic record
Abstract
Abstract. Recent studies show that shrubs are colonizing higher latitudes and altitudes in the Arctic. Shrubs affect the wind transport, accumulation and melt of snow, but there have been few sensitivity studies of how shrub expansion might affect snowmelt rates and timing. Here, a blowing snow transport and sublimation model is used to simulate premelt snow distributions and a 3-source energy balance model, which calculates vertical and horizontal energy fluxes between the atmosphere, snow, snow-free ground and vegetation, is used to simulate melt. Vegetation is parametrized as shrub cover and the parametrization includes shrub bending and burial in winter and emergence in spring. The models are used to investigate the sensitivity of the snow regime in an upland tundra valley to varying shrub cover and topography. Results show that topography dominates the spatial variability of snow accumulation, which in turn dominates the pre and early melt energy budget. With topography removed from the simulations, modelled snow cover is uniform when there is no vegetation but increasing vegetation introduces spatial variability in snow accumulation which is then decreased as further increases in shrub cover suppress wind-induced redistribution of snow. The domain-averaged simulations of premelt snow accumulation also increases with increasing shrub cover because suppression of blowing snow by shrubs decreases sublimation. In simulations with topography, the increase in snow accumulation and its spatial variability with increasing vegetation is less marked because snow is also held in topography-driven drifts. With topography, the existence of wind-scoured snow-free patches at the onset of snowmelt causes exposed ground to contribute to the energy balance such that sensible, advective and radiative heat fluxes are higher than in the flat domain during this period. However, as snowmelt evolves, differences in the energy budget between runs with and without topography dramatically diminish. These results suggest that, to avoid overestimating the effect of shrub expansion on the energy budget of the Arctic, future large scale investigations should consider wind redistribution of snow, shrub bending and emergence, and sub-grid topography as they affect the variability of snowcover.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".