Ocean Model Derived Global Surface Circulation and Vertical Velocity
Bibliographic record
Abstract
Abstract In this article, the authors examine Sea surface temperature (SST), Sea surface circulation (SSC) and Vertical velocity (VV) fields from simulation of 25 layers coarse resolution Modular ocean model (MOM version 3.0) with prescribed wind forcing for the region 74.25°S to 65°N, 180°W-180°E. It is found that distribution of SST simulated by the model shows its consistency with the observed climatology. However, simulated SST in the areas of Arabian Sea, Bay of Bengal, Indonesian Throughflow (ITF) region and east of North America near equator exhibit slight warming with respect to observation, which may be due to model deficiency and forcing problems. Circulation features suggest that one of the strongest current viz. Antarctic circumpolar current (ACC) along with other major current systems viz. Gulf stream current, North and South Pacific current, Agulhas current, Labrador current, Canary current, etc are captured well by the model. In the Indian Ocean and other ocean basins, current patterns are well captured by the model simulation. Intense upwelling as well as downwelling areas is marked in the horizontal distribution of VV, which is as expected. VV show quasi-stagnant and convergent regions suggesting that floating materials may be accumulated during January/July in the real ocean and wind driven circulation may act as an important contribution for such transport of floating materials in these regions. An attempt has also been made to understand the fluctuations of the SST in NINO 3.4 region during the period of model simulation using SST anomalies.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".