Interaction of ocean temperature advection, surface heat fluxes and sea ice in the marginal ice zone during the North Atlantic Oscillation in the 1990s: A modeling study
Bibliographic record
Abstract
A moderately fine‐resolution (0.4°, 40 vertical levels), global, coupled ice‐ocean model was configured and run for 24 years (1979–2002), forced with high‐frequency National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) atmospheric fluxes. The model consists of the Los Alamos National Laboratory Parallel Ocean Program (POP) and sea ice model (CICE). The fidelity of the simulated mean climatological state and variability of key variables such as ice concentration, total ice area, ice thickness and drift were compared to observational data sets from satellite and ice drift buoy measurements. Basin‐scale changes in the lower atmosphere/surface ocean/sea‐ice in the simulated Arctic and Nordic Seas before and after the North Atlantic Oscillation (NAO) phase switch in 1995 were examined using winter composite analyses over the period 1990–1999. Ice cover changes between the two NAO phases were consistent with observations in that reduced concentrations were found in the Nordic and Barents Seas and increased values occurred in the Labrador Sea. Next we regionally evaluated the relative importance of winter anomalies of upper‐ocean mixed layer net heat fluxes and of ocean temperature advection on marginal ice zone variability in the Irminger, Greenland, and Barents Seas for this ten‐year period. We found that the net heat flux winter anomaly was at least four times more important than the winter anomaly of ocean temperature advection in the Greenland and Barents Seas, while it was twice as important in the Irminger Sea. The Ekman ocean temperature advection component generally dominated the geostrophic component in all three regions.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".