A novel framework for studying oceanic freshwater transports, and its application in discerning the modelled fate of freshwater around the coast of Greenland
Bibliographic record
Abstract
In the sub-polar North Atlantic, the accumulation of fresh meltwaters from Greenland and the Arctic can impact the strength of the climatically important Atlantic Meridional Overturning Circulation. In this study I investigate and map out the processes that contribute to the accumulation of freshwater in four different regions around Greenland, quantifying horizontal transports of freshwater and the expansion and depletion of freshwater reservoirs by surface sources and interior mixing. Rather than using traditional freshwater budgets, whose flaws are well documented, I propose the novel use of the freshwater transformation framework and apply it to outputs from an eddy resolving coupled climate model (10 km atmosphere and 5 km ocean). Analysing volume transports in salinity space we observe the salinification of the boundary currents surrounding Greenland as they flow from Fram Strait towards the Labrador Sea. Using the freshwater transformation framework we are able to link the salinification to mixing, sea-ice formation or the accumulation of freshwaters stored in the waters surrounding Greenland. The balance changes depending upon the region and season under question. The mixing of freshwaters is found to be stronger during wintertime than in summertime. Furthermore, mixing plays a more dominant role in the freshwater transformation budget off Southern Greenland, where sea-ice cover is low, than off Northern Greenland, where sea-ice cover is high.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".