Winter Warm Spells and snowpack ablation in western North America
Bibliographic record
Abstract
Winter warm spells (WWS) are extreme temperature anomalies that might impact the snowpack. WWS amplify snowmelt and sublimation in mountain regions with uncertain consequences to timing and volume of water resources. Most studies focus on the spring season when snowmelt rates and streamflow response are high. However, winter snowmelt events are important in places where the snowpack and air temperatures are closer to the freezing point during winter, and thus it will become important in other regions in a warmer climate.This study aims to understand the effect of WWS on snowpack ablation patterns in the mountainous western North America and how they might change under a warmer climate. For this, we use two convection permitting regional climate model simulations to represent historical (2001-2013) and future atmospheric and the surface conditions. The future simulation is performed with a Pseudo Global Warming approach for a high emission scenario (RCP8.5). We verify WWS using gridded maximum daily temperature observation, and winter ablation using snow pillows. Then we characterize WWS and relate them to snowpack ablation.Although days with ablation during WWS represent a small fraction (8.3%, 0.6 days on average), 55% of total ablation occurs during WWS over regions with significant snowpack (mean peak snow water equivalent over 150 mm). Consistently, a larger ablation rate (53%) is found during WWS than non-WWS events. Total ablation during WWS increases about 157% in a warmer climate; however, the extreme ablation (99th percentile) rates show slight decrease (5%). Classifying the domain based on its humidity and temperature, we found that ablation rates during WWS in humid regions are larger in a warmer climate than those of the dry regions, which is explained by the differences in the energy balance and the snowpack cold content. WWS predominantly drive snowmelt (93.8%) rather than sublimation (6.2%), which has relevance to water resources such as flood risk, soil moisture, and streamflow response. Furthermore, the median snowmelt rate during WWS found to increase in response to warming by 179% compared to the median sublimation rate (125%). This study provides a comprehensive description of the impact of extreme temperature events and a warmer climate over our changing snowpack. We acknowledge financial support by Centro CRHIAM Project ANID/FONDAP/15130015, and the Anillo project ACT-210080.
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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 | 0.000 | 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".