Snow effect on North American ground temperatures, 1950–2002
Bibliographic record
Abstract
Changes in snow's influence on surface ground temperature (SGT) could create a bias in the borehole temperature record of climate change. Using a snow‐ground thermal model which predicts changes in the mean annual offset between SGT and surface air temperature (SAT), we calculate the response of SGT to changes in seasonal snow cover in North America from 1950 to 2002, the period for which comprehensive snow and air observations exist across the region. Daily snow and SAT observations come from the U.S. Historical Climatology Network, the Canadian Daily Climatic Dataset, and a set of National Weather Service cooperative stations in Alaska. For the period 1961–1990 the mean snow onset date in North America is 15 December, with mean snow cover duration of 81 days. There are no significant trends in either onset or duration from 1950 to 2002. Winter season air temperature, however, has warmed during this period, particularly from 1970 to 2002. The effect of the combination of a relatively stationary snow season with winter season SAT warming has been to diminish the mean annual SGT‐SAT offset by −0.05 K/decade over the past 30 years. This effect is most pronounced between 50° and 75°N in west central North America, coincident with the location of greatest winter season warming since 1970. Although comprehensive snow cover data do not exist prior to 1950, this analysis quantifies the changes in snow cover required to account for the difference between borehole temperature and multiproxy climate reconstructions.
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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 | 0.000 | 0.001 |
| 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".