Declining number of northern hemisphere land-surface frozen days under global warming and thinner snowpacks
Bibliographic record
Abstract
Abstract Freeze–thaw processes shape ecosystems, hydrology, and infrastructure across northern high latitudes. Here we use satellite-based observations from 1979–2021 across 47 northern hemisphere ecoregions to examine changes in the number of frozen land-surface days per year. We find widespread declines, with 70% of ecoregions showing significant reductions, primarily linked to rising air temperatures and thinning snowpacks. Causal analysis demonstrates that air temperature and snow depth exert consistent controls on the number of frozen days. A trend-informed assessment based on historical observations suggests a potential average loss of more than 30 frozen days per year by the end of the century, with the steepest decreases in Alaska, northern Canada, northern Europe, and eastern Russia. Scenario-based analysis indicates that each 1 °C increase in air temperature reduces frozen days by ~6-days, while each 1 cm decrease in snow depth leads to a ~ 3-day reduction. These shifts carry major ecological and socio-economic implications.
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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) | 0.002 | 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".