Distinct Ocean Responses to Greenland's Liquid Runoff and Iceberg Melt
Bibliographic record
Abstract
Abstract While Greenland discharge has been increasing in the last decades, its impact on the Meridional Overturning Circulation (MOC) is not clearly established. Because of that, the accuracy of this discharge representation in ocean models has not been a priority in large‐scale circulation studies. Many models prescribe Greenland discharge solely as liquid runoff from the coast—even though around half of this mass loss is attributed to solid discharge. In this study, we use sensitivity experiments carried out with the Nucleus for European Modeling of the Ocean general circulation model to show the most relevant impacts that different Greenland solid discharge parameterizations (transforming it to liquid runoff or explicitly representing it through an iceberg model) have on the western subpolar Atlantic. We find that icebergs act as freshwater reservoirs that affect how much , when , and where freshwater is delivered to the ocean. They carry large amounts of freshwater away from boundary currents, releasing it in the interior of the subpolar gyre. Moreover, the amount and variability of freshwater delivered to the ocean depend not only on the characteristics of Greenland discharge itself but also on the environmental conditions icebergs are subjected to. We also find a large difference in subsurface temperatures in the Gulf of Saint Lawrence, which suggests that different Greenland discharge parameterizations might have far reaching implications beyond the MOC. Although differences in ocean fields between the simulations are usually small and within their interannual variability, they might be relevant as Greenland calving rates increase with global warming.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".