Spatial and Temporal Shifts in Historic and Future Temperature and Precipitation Patterns Related to Snow Accumulation and Melt Regimes in Alberta, Canada
Bibliographic record
Abstract
Shifts in winter temperature and precipitation patterns can profoundly affect snow accumulation and melt regimes. These shifts have varying impacts on local to large-scale hydro-ecological systems and freshwater distribution, especially in cold regions with high hydroclimatic heterogeneity. We evaluate winter climate changes in the six ecozones (Mountains, Foothills, Prairie, Parkland, Boreal, and Taiga) in Alberta, Canada, and identify regions of elevated susceptibility to change. Evaluation of historic trends and future changes in winter climate use high-resolution (~10 km) gridded data for 1950–2017 and projections for the 2050s (2041–2070) and 2080s (2071–2100) under medium (RCP 4.5) and high (RCP 8.5) emissions scenarios. Results indicate continued declines in winter duration and earlier onset of spring above-freezing temperatures from historic through future periods, with greater changes in Prairie and Mountain ecozones, and extremely short or nonexistent winter durations in future climatologies. Decreases in November–April precipitation and a shift from snow to rain dominate the historic period. Future scenarios suggest winter precipitation increases are expected to predominantly fall as rain. Additionally, shifts in precipitation distributions are likely to lead to historically-rare, high-precipitation extreme events becoming more common. This study increases our understanding of historic trends and projected future change effects on winter snowpack-related climate and can be used inform adaptive water resource management strategies.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".