Seasonal Characteristics and Trends in Precipitation Partitioning in the Arctic
Bibliographic record
Abstract
Abstract. Driven by growing impacts of changing precipitation amounts and phase on the Arctic’s natural and built environment, we examine seasonal patterns and trends in Arctic precipitation and partitioning between its liquid and solid forms. Use is made of data from the ERA5 reanalysis, Automated Surface Observing System stations over land, and a climatology based on present weather reports over the Arctic Ocean. In the Atlantic sector of the Arctic, most precipitation falls as rain in all seasons in the extreme south, but snowfall is high over its northern parts. Annual precipitation over the dry central Arctic Ocean and terrestrial polar deserts almost always falls as snow. Even during the summer, typically 50 % of precipitation over the central Arctic Ocean falls as snow. Over land, nearly all summer precipitation falls as rain, except in the Canadian Arctic Archipelago where summer snowfall is still common. Annual precipitation has increased since 1979, primarily in the Barents Sea sector, accompanied by generally downward trends in snowfall and, hence, upward trends in liquid precipitation. Across much of the Arctic, the rainfall to total precipitation ratio has increased only in summer, while in the Atlantic sector, the rainfall to total precipitation ratio has increased in all seasons.
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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 | 0.001 |
| 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".