Coupled Sea Ice–Ocean-State Estimation in the Labrador Sea and Baffin Bay
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Sea ice variability in the Labrador Sea is of climatic interest because of its relationship to deep convection, mode-water formation, and the North Atlantic atmospheric circulation. Historically, quantifying the relationship between sea ice and ocean variability has been difficult because of in situ observation paucity and technical challenges associated with synthesizing observations with numerical models. Here the relationship between ice and ocean variability is explored by analyzing new estimates of the ocean–ice state in the northwest North Atlantic. The estimates are syntheses of in situ and satellite hydrographic and ice data with a regional ⅓° coupled ocean–sea ice model. The synthesis of sea ice data is achieved with an improved adjoint of a thermodynamic ice model. Model and data are made consistent, in a least squares sense, by iteratively adjusting control variables, including ocean initial and lateral boundary conditions and the atmospheric state, to minimize an uncertainty-weighted model–data misfit cost function. The utility of the state estimate is demonstrated in an analysis of energy and buoyancy budgets in the marginal ice zone (MIZ). In mid-March the system achieves a state of quasi-equilibrium during which net ice growth and melt approaches zero; newly formed ice diverges from coastal areas and converges via wind and ocean forcing in the MIZ. The convergence of ice mass in the MIZ is ablated primarily by turbulent ocean–ice enthalpy fluxes. The primary source of the enthalpy required for sustained MIZ ice ablation is the sensible heat reservoir of the subtropical-origin subsurface waters.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it