Effects of Spectral Nudging on Oceanic States in a Coarse-Resolution Model
Bibliographic record
Abstract
A 1-degree global model is used to investigate the skill of spectral nudging at coarse resolution by performing two numerical experiments, one with spectral nudging and the other without. In the spectral nudging experiment, the model temperature and salinity are nudged to an observed climatological monthly-mean field. The study compares the model mean state, as well as the interannual and decadal variability of oceanic quantities with observations, (e.g., sea surface height (SSH) and sea surface temperature (SST)). Spectral nudging is found to be effective in constraining model drift from the observed mean state of temperature and salinity in the global ocean, which has been reported in previous studies. The present study further shows that spectral nudging significantly improves the model skill of topostrophy (a measure of currents flowing along the topography) in water depth below 2000 m with no clear improvement elsewhere. Despite its known ability to damp oceanic variability at various time scales, spectral nudging can still represent the interannual and decadal variability of SSH and SST well, to a degree comparable to the other experiment.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".