Influence of Arctic Freshwater Sources on the Circulation in the Arctic Mediterranean and the North Atlantic in a Prognostic Ocean/Sea-Ice Model
Bibliographic record
Abstract
The thesis documents the design and development of a three-dimensional prognostic ocean/sea-ice model of the Arctic Mediterranean and the North Atlantic. The model has been set up on the basis of the z-coordinate ocean model MOM 2 coupled to a dynamic/thermodynamic sea-ice model with viscous-plastic rheology. Adding arctic freshwater sources step by step leads to a progressive improvement of the coupled model, and allows to analyse the sensitivity of the ocean/sea-ice system with respect to freshwater forcing. The results reveal that freshwater plays a major role in Arctic Ocean dynamics. In particular, the path of the Transpolar Drift and the strength of the East Greenland Current in the western Fram Strait are strongly influenced by the input of freshwater. Thus, freshwater favours the exchange of water masses between the Nordic Seas and the Arctic Ocean. Moreover, freshwater input controls vertical oceanic heat fluxes into the ice by forming a stable density stratification. The model requires a total freshwater input of approx. 6800 km^3/yr to the Arctic Ocean in order to maintain a realistic hydrography. More than 40% of this freshwater leaves the Arctic Ocean as sea-ice through Fram Strait. The sum of liquid freshwater exports through Fram Strait and the Canadian Arctic Archipelago is of similar magnitude (c. 1500 km^3/yr through each passage). Taking the volume input of surface freshwater fluxes into account by applying an open surface, the model presented here is superior to other models of the Arctic Mediterranean, which are driven by virtual salinity fluxes. Experiments with different salinity/freshwater flux boundary conditions reveal the shortcomings of salinity-flux formulations. It is concluded that other prognostic models of the Arctic Ocean can be improved substantially by implementing an open surface.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".