Sources of Eddy Energy Simulated by a Model of the Northeast Pacific Ocean
Bibliographic record
Abstract
Abstract This paper examines the energy sources for eddy variability in the Gulf of Alaska using a numerical model and a novel form of data assimilation referred to as spectral nudging. Spectral nudging is distinguished from conventional nudging by its ability to operate only on specified frequency and wavenumber bands; in the present case, only the subannual variability is nudged, and only on spatial scales of 100 km or more. By using this approach, the broad-brush aspects of the model’s mean state are constrained to remain near the mean climatological conditions, while the simulated eddy field is determined by the model dynamics. Simulations of the North Pacific Ocean with a 0.25° horizontal resolution and spectral nudging have been previously shown to produce eddy fields that are significantly more energetic and more realistic than those produced by prognostic (i.e., not nudged) simulations. The analysis of the spectrally nudged model results undertaken here reveals the tendency of the circulation to be both baroclinically and barotropically unstable in different regions and to differing degrees. Along the north coast of the Gulf of Alaska, the simulation suggests that barotropic instability is more important overall as an energy source for eddies than is baroclinic instability. Along the east coast of the Gulf of Alaska, the simulation suggests that both baroclinic and barotropic instabilities are important. Although the overall energy transfer is from the mean state to the eddy field, there are regions of the model, particularly along the north coast of the Gulf of Alaska, where the transfer of energy is from the eddy field to the mean flow.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".