Winter rain on snow and its association with air temperature in northern Eurasia
Bibliographic record
Abstract
Abstract This study examines the characteristics of winter (Dec–Feb) rain‐on‐snow events and their relationship to surface air temperatures to reveal potential changes in rain‐on‐snow days under a warming climate over northern Eurasia. We found that rain‐on‐snow events mostly occur over European Russia during winter. Rain‐on‐snow days increase as air temperature increases and are primarily attributable to the increase in rainfall days. Air temperature is the primary cause for these changes, while the North Atlantic Oscillation has some influence on the rain on snow and rainfall over the northern part of European Russia. The magnitude of rain‐on‐snow increase ranges from 0·5 day to 2·5 days per degree Celsius increase in air temperature. Higher rates of increase in rain‐on‐snow days occur in the northern and eastern parts of European Russia where the air temperature is lower, in contrast to rainfall days which have higher rates at locations with higher air temperatures. This suggests that a decrease in snowfall days might be limiting the rate of increase in rain‐on‐snow events over warmer regions where the temperature is about − 8 °C or higher. This study also implies that rain‐on‐snow days will become more common over regions in which it is currently a rare event as air temperatures increase. Copyright © 2008 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".