Reducing Drift and Bias of a Global Ocean Model by Frequency-Dependent Nudging
Bibliographic record
Abstract
Frequency-dependent nudging is applied to a coarse resolution (nominal 1°) global ocean model to suppress its drift and bias, and the impact of the nudging on the skill of the model is assessed. The nudging is applied to temperature and salinity in frequency bands centred on 0 and 1 cycles per year. As expected, the nudging significantly reduces the biases in the long-term mean and annual cycle of temperature, salinity, and sea level. By comparing the simulated (i) sea surface temperature with operational analyses based on observations, (ii) vertical profiles of temperature and salinity with observations made by Argo floats, and (iii) sea level with altimeter observations, it is shown that the skill of the model in simulating variability about the annual cycle is also improved. The potential benefit of applying frequency-dependent nudging to the ocean component of a coupled atmosphere–ocean model is discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".