Shrubs covered by snow in the high Arctic cool down permafrost in winter by thermal bridging through frozen branches
Bibliographic record
Abstract
Warming-induced shrub expansion on Arctic tundra is generally thought to warm up permafrost, as shrubs trap blowing snow and increase the thermal insulation effect of snow, limiting permafrost winter cooling. We have monitored the thermal regime of permafrost on Bylot Island, 73°N in the Canadian high Arctic at nearby herb tundra and shrub tundra sites. Once adjusted for differences in air temperature, we find that shrubs actually cool permafrost by 0.6°C over November-March 2019, despite a snowpack twice as insulating in shrubs. By simulating the rate of propagation of thermal perturbations and using finite element calculations, we show that heat conduction through frozen shrub branches have a winter cooling effect of 1.5°C which compensates the warming effect induced by the more insulating snow in shrubs. In spring shrub branches under snow absorb solar radiation and accelerate permafrost warming. Over the whole snow season, simulations indicate that heat and radiation transfer through shrub branches result in a 0.3°C cooling effect. This is contrary to many previous studies, which concluded to a warming effect, sometimes based on environmental manipulations that may perturb the natural environment. The impact of shrubs on the permafrost thermal regime may need to be re-evaluated.
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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 | 0.000 | 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) | 0.001 | 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".