Multidekadische und NAO bezogene Variabilität in einem numerischen Modell des Nordatlantiks
Bibliographic record
Abstract
The natural variability of the North Atlantic and Arctic Ocean is still not completely understood. Especially on interannual to multidecadal time scales, where the amount of bservations is limited, many open questions remain. The objective of this study is therefore to investigate the reaction of a numerical model of the North Atlantic and Arctic Ocean to changes in the atmospheric surface forcing and to improve the understanding of the internal variability of the model and furthermore the real ocean-sea ice system. The ocean model is based on the MOM-2 code, which is coupled to a dynamic-thermodynamic sea ice model with a viscous-plastic rheology. A major mode of atmospheric variability over the North Atlantic Ocean is the North Atlantic Oscillation (NAO). Two 200 year integrations of the coupled ocean-sea ice model with surface boundary conditions which are related to periods of positive and negative NAO are compared to an integration with climatological atmospheric forcing over thesame number of years. The experiment with climatological forcing reveals a self-sustaining oscillation, with a period of 40 years. The interplay between the Deep Western Boundary Current (DWBC) and Gulf Stream is important to sustain the oscillation. A switch from a long time NAO state to an enduring NAO- situation is also investigated in this study. The sea ice extent increases quickly after this switch, whereas the changes in sea ice volume adapt slower to the changed forcing. The ocean adjusts with a fast barotropic circulation anomaly, accompanied by an enhancement of meridional overturning and northward heat transport at 48sup:o:/sup:N. The slow response is a substantial decrease of the northward heat transport, which is caused by a reduction of the strength of subpolar and subtropical gyres. One year of NAO- forcing shifts the area of convection in the Labrador Sea to a different position, which turns out to be a new state of equilibrium for the ocean.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".