Spring warming in Yukon mountains is not amplified by the snow albedo feedback
Bibliographic record
Abstract
Decreasing spring snow cover may amplify Arctic warming through the snow albedo feedback. To examine the impact of snowmelt on increasing temperature we used a 5,000 m elevation gradient in Yukon, Canada, extending from valley-bottom conifer forests, through middle elevation tundra, to high elevation icefields, to compare validated downscaled reanalysis air temperature patterns across elevational bands characterized by different patterns of spring snowmelt. From 2000 to 2014 we observed surface warming of 0.01 °C/a·1,000 m in May (0.14 °C/a at 1,000 m to 0.19 °C/a at 5,000 m), and uniform cooling of 0.09 °C/a in June at all elevations. May temperature trends across elevationally dependent land cover types were highly correlated with each other despite large variations in albedo and snow cover trends. Furthermore, a clear dependency of infrared skin temperature on snow cover mediated albedo decline was observed in tundra, but this was insufficient to influence average diurnal air temperature. We observed negative June temperature trends which we attribute to increasing daytime cloud cover because albedo and snow cover trends were unchanging. We conclude that 8-day and monthly averaged Spring air temperature trends are responding to a synoptic external forcing that is much stronger than the snow albedo feedback in sub-Arctic mountains.
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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".