Soil Thermal and Ecological Impacts of Rain on Snow Events in the Circumpolar Arctic
Bibliographic record
Abstract
Abstract Rain on snow (ROS) events are rare in most parts of the circumpolar Arctic, but have been shown to have great impact on soil surface temperatures and serve as triggers for avalanches in the midlatitudes, and they have been implicated in catastrophic die-offs of ungulates. The study of ROS is inherently challenging due to the difficulty of both measuring rain and snow in the Arctic and representing ROS events in numerical weather predictions and climate models. In this paper these challenges are addressed, and the occurrence of these events is characterized across the Arctic. Incidents of ROS in Canadian meteorological station data and in the 40-yr ECMWF Re-Analysis (ERA-40) are compared to evaluate the suitability of these datasets for characterizing ROS. The ERA-40 adequately represents the large-scale synoptic fields of ROS, but too often has a tendency toward drizzle. Using the ERA-40, a climatology of ROS events is created for thresholds that impact ungulate populations and permafrost. It is found that ROS events with the potential to harm ungulate mammals are widespread, but the large events required to impact permafrost are limited to the coastal margins of Beringia and the island of Svalbard. The synoptic conditions that led to ROS events on Banks Island in October of 2003, which killed an estimated 20 000 musk oxen, and on Svalbard, which led to significant permafrost warming in December of 1995, are examined. Compositing analyses are used to show the prevailing synoptic conditions that lead to ROS in four disparate parts of the Arctic. Analysis of ROS in the daily output of a fully coupled GCM under a future climate change scenario finds an increase in the frequency and areal extent of these events for many parts of the Arctic over the next 50 yr and that expanded regions of permafrost become vulnerable to ROS.
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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 | 0.000 |
| 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 | 0.001 | 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".