q 2002 American Meteorological Society Sea Ice Response to Wind Forcing from AMIP Models
Bibliographic record
Abstract
The Arctic surface circulation simulated by atmospheric general circulation models is assessed in the context of driving sea ice motion. A sea ice model is forced by geostrophic winds from eight models participating in the first Atmospheric Model Intercomparison Project (AMIP1), and the results are compared to simulations with the sea ice model forced by observed winds. The mean sea level pressure in the AMIP models is generally too high over the Arctic Ocean, except in the Beaufort and Chukchi Seas, where it is too low. This pattern creates anomalous winds that tend to transport too much ice away from the coast of Greenland and the Canadian Archipalego, and into the East Siberian Sea, producing a pattern of ice thickness in the Arctic that is rotated by roughly 1808 relative to what is expected based on observations. AMIP winds also drive too little ice transport through Fram Strait and too much transport east of Svalbard by way of the Barents Sea. These errors in ice thickness and transport influence ice growth and melt rates and hence the freshwater flux into the ocean. Sensitivity experiments that test the model response to the wind composition show the ice thickness patterns depend primarily on the climatological mean annual cycle of the geostrophic winds. Daily wind variability is necessary to create sufficient ice deformation and open water, but the sea ice behavior is rather insensitive to the details of the daily variations. 1.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.018 | 0.006 |
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".