Impact of Shrubs on Winter Surface Albedo and Snow Specific Surface Area at a Low Arctic Site: In Situ Measurements and Simulations
Bibliographic record
Abstract
Abstract Erect shrubs in the Arctic reduce surface albedo when branches protrude above the snow and modify snow properties, in particular specific surface area (SSA). Important consequences are changes in the land surface–atmosphere energy exchange and the increase of snow melting in autumn, possibly inducing reduced soil thermal insulation and in turn permafrost cooling. Near Umiujaq (56.5°N, 76.5°W) in the Canadian low Arctic where dwarf birches ( Betula glandulosa ) are expanding, spectral albedo (400–1080 nm) under diffuse light and vertical profiles of SSA were measured in November and December 2015 at four sites: three with protruding branches and one with only snow. At the beginning of the snow season (8 November), shrub-induced albedo reductions were found to be wavelength dependent and as high as 55% at 500 nm and 18% at 1000 nm, which, integrated over the measurement range (400–1080 nm), corresponds to 70 W m −2 of additional absorbed energy. The impact of shrubs is not just snow darkening. They also affect snow SSA in multiple ways, by accumulating snow with high SSA during cold windy precipitation and favoring SSA decrease by inducing melting during warm spells. However, the impact on the radiation budget of direct darkening from shrubs likely dominates over the indirect change in SSA. Spectral albedo was simulated with a linear mixing equation (LME), which fitted well with observed spectra. The average root-mean-square error was 0.009. We conclude that LMEs are a suitable tool to parameterize mixed surface albedo in snow and climate models.
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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) | 0.003 | 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".