The Climatological Context of Trends in the Onset of Northern Hemisphere Seasonal Snow Cover, 1972–2017
Bibliographic record
Abstract
Abstract Several studies based on the longest remotely sensed record of Northern Hemisphere seasonal snow cover have indicated that its extent has increased over large areas of Eurasia and North America during the transition from summer to winter. Given current understanding of widespread warming trends, this finding is somewhat surprising. It has been suggested that these increases are artifacts of technological improvements in the data set's production. Alternatively, if such trends do reflect actual changes in the timing and location of snow onset, it follows that associations should exist with altered spatiotemporal patterns of atmospheric activity likely to influence the probability of snowfall. This study places significant onset trends during September to December between 1972 and 2017 within the context of means and trends of a range of relevant metrics of middle‐to‐lower tropospheric activity between 1972 and 2014, summarized from monthly reanalysis data. The results suggest that clear explanatory links exist between earlier (and later) onset, and patterns of trends in 500 hPa geopotential heights and sea level pressure, airflows at 500 and 850 hPa, atmospheric humidity, and near‐surface temperature. These findings suggest that most incidences of progressively earlier Eurasian snow cover result from northward advection of moisture by stronger southerly winds, driven by altered zonal gradients in geopotential height north of the Himalayan ranges. Over North America, moisture has been supplied primarily from maritime sources along zonal airflows induced by meridional gradients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".