Impacts of Climate Change in the Gulf of St. Lawrence
Bibliographic record
Abstract
To explore modifications in water temperature and salinity under warmer climate change conditions, we performed simulations from 1970 to 2069 with the CANadian Océan PArallélisé (CANOPA) model for the Gulf of St. Lawrence and the Scotian Shelf. The surface fields to drive CANOPA were provided by the Canadian Regional Climate Model (CRCM), driven by the outputs from the third-generation Canadian Global Climate Model (CGCM3) following the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A1B climate change scenario. The sea-ice concentration and volume simulated by CANOPA are shown to have patterns consistent with those seen in observations; CANOPA is also shown to simulate sea surface temperature (SST) well. Although CANOPA can simulate the observed vertical structure of water temperature and salinity, it tends to underestimate the cold intermediate layer and overestimate water salinity in the central Gulf of St. Lawrence (GSL). In terms of the possible future climate, CANOPA simulations suggest that the GSL will be largely ice free in January, with ice volume in March steadily decreasing from about 80 km3 in the 1980s to near zero by the late 2060s. On average, the GSL water will become warmer and fresher over this time period. In January, maximum SST increases occur near eastern Cabot Strait, with amplitudes of about 1.5°–2.5°C, corresponding to reduced sea ice in that area, and there is no notable change along the western and northern coasts of the GSL. In July, maximum SST increases occur over the western GSL corresponding to the largest increases in surface air temperature in the region. The maximum decreases in surface salinity also occur near western coastal areas and the Scotian Shelf, whereas reductions in the eastern GSL are relatively weak. Finally, compared with the present climate, the cold intermediate layer is significantly weaker in 2040–2069 than in 1980–2009.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".