Intraseasonal and interannual variability of sea ice in the Gulf of St.Lawrence
Bibliographic record
Abstract
Intraseasonal and interannual variability of sea-ice cover (SIC) in the Gulf of St. Lawrence, including the time of first ice presence (TFIP), time of last ice presence (TLIP), and sea-ice duration (SID), were investigated, using weekly sea ice observations from 1963--1996. For the intraseasonal variations of sea ice, it was found that SIC in different sub-regions displays contrasting features. The largest intraseasonal variations of SIC occur in the Strait of Belle Isle region and in the southwestem Gulf, where the mean SIC is largest and SID is longest. For the interannual variability of sea ice, the largest variability of SIC occurs in the area off mid-Newfoundland, where the mean SIC is small. For the TFIP and TLIP, the largest interannual variability occurs in the area off western Newfoundland and along coasts in the northeast sector of the Gulf, respectively. In addition, sea ice appeared earlier in the coastal regions and disappeared later over the entire Gulf in severe ice years; while sea ice appeared later in the central and eastern Gulf and disappeared earlier over the entire Gulf in light ice years. Several of the forcing factors influencing sea ice variability in the Gulf of St. Lawrence were examined and mechanisms controlling this variability were discussed. It was found that surface air temperature (SAT), the eastward wind component (u-wind), sea surface temperature (SST), sea surface salinity (SSS), mixed layer depth (MLD), total river runoff, the ocean circulation pattern, and sea-ice advection from the Labrador Sea, all play important roles in explaining sea ice variability in the Gulf. However, the relative contributions of these factors to the observed sea ice variability differ in different subregions. Quantitative relationships between sea ice variability and various forcing factors were investigated using statistical analysis and a simplified Hibler's sea-ice model. Both approaches indicated that the December--April averaged SAT, u-wind, and November SST all contribute to the variability of December--June SIC in the Gulf, with SAT playing the most important role. The analysis also indicated that the dependence of SIC on various forcing factors varies with geographical location. For example, SAT influences sea ice variability mainly in the central Gulf, while the u-wind component effects SIC mainly in the eastern Gulf. In addition, statistical analyses also suggest that SSS values present in the previous November play an important role in determining SIC variability. The linear regression between SIC and three independent variables: December--April SAT, November SST and SSS, accounts for 81% of the total SIC variance. The statistical analysis and model study also indicated that December SAT, u-wind, November SST, and MLD control the time of first ice presence, with SAT and SST playing the dominant role. The linear regression between TFIP and three independent variables (u-wind, SST, and SSS) accounts for 76% of the total TFIP variance. For TLIP, both SAT and u-wind play an important role.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".