Variability of regional snow cover in spring over western Canada and its relationship to temperature and circulation anomalies
Bibliographic record
Abstract
Abstract This study documents the variability of spring snow cover over the western Canadian Prairies and Northern Boreal Forest and its relationship to sea surface temperature (SST) over the North Pacific and atmospheric circulation over the North Pacific–North America. The work is based on monthly snow cover extent (SCE) estimates derived from Advanced Very High Resolution Radiometer data during 1972–2008. Results show that the SCE along eastern parts of the Canadian Rocky Mountains has the largest variance during March and April. Two regional SCE indices are defined using area mean SCE anomalies over the region of 47–53°N, 104–111°W (SCE‐A) and 55–60°N, 111–120°W (SCE‐B) based on the leading empirical orthogonal function (EOF) patterns of SCE in March and April, respectively. These two SCE indices are not only significantly correlated with simultaneous and preceding winter 500 hPa heights over mid‐latitude northwestern North America and central North Pacific but also with SST in mid‐latitude eastern North Pacific in preceding autumn and winter. Furthermore, it is found that the SST anomalies in the mid‐latitudes along the western coast of North America in the preceding autumn–winter influence the 500 hPa height over northwestern North America in April and cause the variations of SCE‐B. Finally, it is shown that the SCE‐B can be used as a climatic index to characterize the connection of SCE with the regional skin temperature in late spring and early summer. Copyright © 2010 Royal Meteorological Society
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".