Recent Arctic change simulated with a coupled ice‐ocean model
Bibliographic record
Abstract
A high‐resolution coupled ice‐ocean model, forced with 1983–1997 European Center for Medium‐Range Weather Forecasts data, is used to explore recent Arctic change. In response to changes in atmospheric circulation, stronger cyclonic circulation is present in Arctic sea ice and upper ocean in the late 1980s and early 1990s as compared to the early 1980s, manifested as the weakening of the Beaufort Gyre and the shifting of the Transpolar Drift Stream. Corroborating previous studies, ice divergence in the central Arctic Ocean is highly correlated with surface atmospheric vorticity in summer, suggesting that summer atmospheric circulation is more important than winter for inducing interannual variability of the central Arctic ice divergence and growth rate. The weakening of the summer atmospheric cyclonic circulation from the earlier period to the later period over the Canadian Basin leads to decreased ice divergence there, which then has significant impact on the ice growth rate by reducing ice formation in fall and winter. For the 15 year period, variability in the spatial distribution of ice concentration and thickness is largely determined by the ice dynamics, which is dominated by the atmospheric circulation, except over the Greenland and Labrador Seas, where the ice thermodynamics plays a more important role. The model simulation supports the recent observations of increased presence of Atlantic Water in the Arctic Ocean. The spatial pattern of warming and salinization of the Arctic Atlantic layer follows the pathways of the strengthened boundary currents along the continental slopes and over the ridges, thereby slowly spreading more Atlantic Water downstream from the eastern Arctic into the western Arctic. The integrations with and without surface temperature restoring indicate that the restoring leads to a warmer ocean surface temperature. However, the restoring has little impact on its interannual variability for the 15 year period.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".