White blanket, blue waters: Tracing El Niño footprints in Canada
Bibliographic record
Abstract
• A comprehensive analysis of snow cover across Canada was done. • An assessment of El Niño and atmospheric, oceanic parameters was carried out. • Three recent El Niño and La Niña events were examined. • Long-term data were used to explore snow cover deviations in all provinces. • Impact of snow cover on water resources and the hydrological cycle was investigated. The El Niño Southern Oscillation (ENSO) significantly influences global climate patterns, with one of the strongest warm phases (El Niño) occurring in 2023, altering precipitation and temperature regimes. In this study, the spatiotemporal variability in snow cover across Canadian provinces from December 2023 to February 2024 relative to long-term averages is explored. The NOAA-OISST, NOAA-CSFV2, and MODIS MOD10A1 remote sensing datasets were selected to assess the impacts of El Niño on snow cover changes and the subsequent effects on water availability, agricultural productivity, the municipal water supply, natural ecosystems, and wildfire risk in Canada. An analysis of sea surface temperature anomalies in the equatorial Pacific revealed that El Niño intensity and progression are linked to regional snow cover deviations. Compared with the long-term average, Canada’s snow cover area experienced significant declines in December 2023, January 2024, and February 2024, with decreases of 135,938 km 2 (−7.43 %), 309,928 km 2 (−15.26 %), and 136,406 km 2 (−4.57 %), respectively. The findings indicate significant disparities among provinces, with Ontario, Quebec, and Manitoba experiencing marked decreases in snow cover, whereas in Saskatchewan and Alberta, initial increases were followed by subsequent variability. In British Columbia, a late-season increase in snow was observed, whereas minor changes were noted in the Maritime provinces and Northern territories. The findings of this study highlight the importance of snow cover as an important factor that has a considerable impact on the hydrological cycle and agricultural productivity, influences environmental health and economic resilience, and is crucial for both natural ecosystems and human livelihoods.
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".