A 4.5 km resolution Arctic Ocean simulation with the globalmulti-resolution model FESOM1.4
Bibliographic record
Abstract
Abstract. In the framework of developing a global modeling system which can facilitate modeling studies on Arctic Ocean and high-mid latitude linkage, we evaluate the Arctic Ocean simulated by the multi-resolution ocean sea-ice model FESOM. To explore the value of using high horizontal resolution for Arctic Ocean modeling, we use two global meshes differing in the horizontal resolution only in the Arctic Ocean (24 km vs. 4.5 km). The high resolution significantly improves the model's representation of the Arctic Ocean. The most pronounced improvement is in the Arctic intermediate layer, in terms of both Atlantic Water (AW) mean state and variability. The deepening and thickening bias of the AW layer, a common issue found in coarse resolution simulations, is significantly alleviated by using higher resolution. The topographic steering of the AW is stronger and the seasonal and interannual temperature variability along the ocean bottom topography is enhanced in the high resolution simulation. The high resolution also improves the ocean surface circulation, mainly through a better representation of the narrow straits in the Canadian Arctic Archipelago (CAA). The representation of CAA throughflow not only influences the release of water masses through the other gateways, but also the circulation pathways inside the Arctic Ocean. However, the mean state and variability of Arctic freshwater content and the variability of freshwater transport through the Arctic gateways appear not to be very sensitive to the increase in resolution employed here. We also discuss model issues that are not directly linked to resolution, highlighting the need for further model development including improving parameterizations.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".