Phenology and species determine growing‐season albedo increase at the altitudinal limit of shrub growth in the sub‐Arctic
Bibliographic record
Abstract
Arctic warming is resulting in reduced snow cover and increased shrub growth, both of which have been associated with altered land surface-atmospheric feedback processes involving sensible heat flux, ground heat flux and biogeochemical cycling. Using field measurements, we show that two common Arctic shrub species (Betula glandulosa and Salix pulchra), which are largely responsible for shrub encroachment in tundra, differed markedly in albedo and that albedo of both species increased as growing season progressed when measured at their altitudinal limit. A moveable apparatus was used to repeatedly measure albedo at six precise spots during the summer of 2012, and resampled in 2013. Contrary to the generally accepted view of shrub-covered areas having low albedo in tundra, full-canopy prostrate B. glandulosa had almost the highest albedo of all surfaces measured during the peak of the growing season. The higher midsummer albedo is also evident in localized MODIS albedo aggregated from 2000 to 2013, which displays a similar increase in growing-season albedo. Using our field measurements, we show the ensemble summer increase in tundra albedo counteracts the generalized effect of earlier spring snow melt on surface energy balance by approximately 40%. This summer increase in albedo, when viewed in absolute values, is as large as the difference between the forest and tundra transition. These results indicate that near future (<50 years) changes in growing-season albedo related to Arctic vegetation change are unlikely to be particularly large and might constitute a negative feedback to climate warming in certain circumstances. Future efforts to calculate energy budgets and a sensible heating feedback in the Arctic will require more detailed information about the relative abundance of different ground cover types, particularly shrub species and their respective growth forms and phenology.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".