Climate Change both Increases and Decreases Winter Snowmelt across North America
Bibliographic record
Abstract
Snowmelt is a vital source of freshwater for a large proportion of North America’s population. Sudden snowmelt can also lead to various extreme events and environmental hazards, such as floods in the cold season and droughts in the upcoming warmer months. However, this natural water resource is at risk due to climate change and variability. Temperature and precipitation are significant climatic controllers that regulate snowmelt dynamics. Warmer temperatures can affect snowmelt extremes, persistence, and distribution, while changing precipitation alters the available snow budget and, consequently, the snowmelt amount. Yet, the precise role of the compound changes in temperature and precipitation under changing climate on future snowmelt dynamics is unknown. To address this knowledge gap, we use observation-driven data and future projections to quantify the response of winter snowmelt to changes in temperature and precipitation across North America (United States and Canada). Our analysis of far-future (2091-2100) changes reveals a significant increase (> 60%) in winter (November-March) snowmelt in northern latitudes, while it declined (by up to< 38%) in southern latitudes. Higher temperatures proved to be the primary driver of the increased snowmelt, whereas decreased snowfall modulated the declines in snowmelt, with variability seen across the study domain. Our findings suggest that the probability of an increase in winter snowmelt is high under the warmer and wetter climatic conditions prevailing in northern regions. In contrast, winter snowmelt across southern latitudes is likely to decline. These findings have significant implications for freshwater availability in the future in the affected areas.
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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 | 0.001 | 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".