Bibliographic record
Abstract
Observational and modelling studies show that the warming of the Arctic is leading to shrub expansion. This shift in vegetation cover is expected to significantly alter the distribution of snow across the landscape and the interactions between the land surface and the atmosphere. Shrubs capture wind-blown snow, increasing snow depth and decreasing winter water loss through sublimation, and bend beneath the weight of snow, affecting albedo. Snow is highly insulative and affects the soil hydrological and thermal properties. Therefore, as the snow-vegetation-soil interactions is expected to be at the core of feedback loops leading to further shrub expansion, there is a need for models to be able to simulate these processes accurately. Initially using the community land surface model JULES (Joint UK Land Environment Simulator) this study investigates the effects of shrub-tundra on snow and runoff. Alternative formulations of soil processes are proposed, which are better adapted to the representation of subgrid heterogeneity in cold regions than the current model formulation, and evaluated over the Abisko and Torne-Kalix river basins. In addition, a high resolution shrub bending model, which calculates the exposed winter shrub fraction, is developed and parameterised for use alongside the snow cover parameterisation in JULES in order to provide a better representation of shrub-specific processes. This revised JULES more than doubles the efficiency coefficient and halfs the negative bias between modelled and observed runoff in the shrub-tundra Abisko basin. However, the current structure of the model is found to be inadequate for use in investigating the effect of shrub-tundra expansion because it calculates a single energy balance for the snow-free and the snow-covered areas. To address this issue, a distributed three-source (snow-shrub-ground) model (D3SM) is developed. D3SM is evaluated against snow and energy ux measurements from a shrub-tundra basin in the Yukon, Canada, and is found to reproduce snowmelt energetics well. The effects of shrub expansion on the energy balance of the basin during snowmelt are then investigated by increasing the vegetation fraction and canopy height of the current shrub distribution, which is found to be positively correlated with topography. D3SM shows that the most significant effects of shrub expansion in the basin are to reduce the spatial variability of snow depth and to increase the sensible heat flux from the surface to the atmosphere.
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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 | 0.001 |
| 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) | 0.001 | 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".