Sea Ice Interannual Variability and Sensitivity to Fall Oceanic Conditions and Winter Air Temperature in the Gulf of St. Lawrence, Canada
Bibliographic record
Abstract
Abstract The Gulf of St. Lawrence has been nearly free of sea ice in the winter six times in its recorded history, four of which have occurred since 2010. This study examines the inter‐annual variability of sea ice cover characteristics (1969–2024) and winter mixed layer heat content (1996–2024), their sensitivity to fall oceanic conditions (since fall of 1995) and to winter air temperatures. The study finds no relationship between fall oceanic conditions with either the first occurrence of sea ice, maximum seasonal estimated volume or winter mixed layer heat content. However, it shows that the first occurrence of sea ice in the northwestern Gulf is related to the timing of sea surface temperature crossing the 1°C threshold with a lag time of 30–37 days, and with air temperature dropping below −2°C with a lag of 37–44 days; longer lags have weak correlations. The seasonal maximum conditions in area or estimated volume can be estimated by the preceding measurements of the same metrics with a lead time of only 29 days for volume and 36 days for area. The average air temperature over the Gulf between December and February or March is highly correlated to seasonal maximum sea ice area and estimated volume, as well as ice season duration. The six nearly ice‐free winters correspond to the warmest December to February (or December to March) average air temperatures over the Gulf. A warming of >1.9°C–2.4°C (DJFM) or >2.2°C–2.9°C (DJF) above the 1991–2020 climatology leads to nearly ice‐free conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".