The Role of Thermodynamics on Northern Labrador Sea Ice Trends and Variability
Bibliographic record
Abstract
Long-term changes and year-to-year variability in sea ice conditions on the northern Labrador (Nunatsiavut) coast and shelf have important influences on regional climate, marine ecosystems, and coastal communities. The drivers of sea ice variability in this region are poorly understood despite being critical for planning for future changes. Here, we evaluate the spatial and temporal trends and variability of sea ice area, concentration, thickness, and volume over the Labrador Shelf between 1979 and 2021 based on Canadian Ice Service sea ice charts. We characterise the seasonal cycle into two phases: a growth phase (December to January) and a peak phase (February to April). We then use Empirical Orthogonal Function analysis on mean ice thickness to identify the dominant modes of variability, and use correlations and simple physical models to investigate the relationships between these modes and thermodynamic forcing variables. Around 68% of the total variability can be explained by the first two modes (Mode 1: 52.6%; Mode 2: 15.2%). The first mode represents sea ice volume changes across the entire shelf, mainly driven by remote air temperature variations, with a smaller but non-negligible influence from local anomalies. The second mode represents a cross-shelf dipole structure that may be linked to the dynamic effects of winds and ocean currents.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".