The Canadian Arctic Archipelago throughflow in a multiresolution global model: Model assessment and the driving mechanism of interannual variability
Bibliographic record
Abstract
The volume and freshwater transports through the Canadian Arctic Archipelago (CAA) are assessed using the unstructured‐mesh Finite Element Sea ice‐Ocean Model (FESOM) in a global setup with the CAA resolved at 5 km scale. The hindcast simulation realistically represents fluxes through the main gates of the Arctic Ocean and the Arctic sea ice conditions. During the period 1968–2007, the mean volume transports through Lancaster Sound and Nares Strait amount to 0.86 and 0.91 Sv, respectively. The monthly mean volume transport through western Lancaster Sound is highly correlated with the observational estimate (r = 0.81). The seasonal variability of the Lancaster Sound transport is well represented in the model. The simulated mean CAA freshwater export rate is 123 mSv, slightly higher than the observational estimate. The interannual variability of CAA volume transports is determined by sea surface height (SSH) gradients between the Arctic Ocean and northern Baffin Bay. The sea level upstream of Lancaster Sound is mainly determined by that along the Beaufort Sea coast, which can be explained by changes in the wind regimes (cyclonic versus anticyclonic) associated with release or accumulation of freshwater in the Beaufort Gyre. Sea level variations downstream of Lancaster Sound and Nares Strait are connected to SSH variations in the eastern Baffin Bay and in the Labrador Sea, which can be attributed to the variability of ocean‐atmosphere heat fluxes. Both processes upstream and downstream of the CAA are linked with the North Atlantic Oscillation type of atmospheric variability. The local mesh refinement of ∼5 km allows us to investigate the contribution of individual narrow straits to the Parry Channel volume transport. The volume transports through these straits show a very similar variability.
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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 | 0.004 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".