Active and passive ocean regimes in a low-order climate model
Bibliographic record
Abstract
A low-order climate model is studied which combines the Lorenz-84 model for the atmosphereon a fast time scale and a box model for the ocean on a slow time scale. In this climate model, the ocean is forced strongly by the atmosphere. The feedback to the atmosphere is weak. Thebehaviour of the model is studied as a function of the feedback parameters. We find regions inparameter space with dominant atmospheric dynamics, i.e., a passive ocean, as well as regionswith an active ocean, where the oceanic feedback is essential for the qualitative dynamics. Theocean is passive if the coupled system is fully chaotic. This is illustrated by comparing theKaplan–Yorke dimension and the correlation dimension of the chaotic attractor to the valuesfound in the uncoupled Lorenz-84 model. The active ocean behaviour occurs at parametervalues between fully chaotic and stable periodic motion. Here, intermittency is observed. Bymeans of bifurcation analysis of periodic orbits, the intermittent behaviour, and the ro†le playedby the ocean model, is clarified. A comparison of power spectra in the active ocean regime andthe passive ocean regime clearly shows an increase of energy in the low frequency modes of theatmospheric variables. The results are discussed in terms of itinerancy and quasi-stationarystates observed in realistic atmosphere and climate models.
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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) | 0.000 | 0.000 |
| 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) | 0.002 | 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".