Arctic Ocean and Hudson Bay Freshwater Exports: New Estimates from Seven Decades of Hydrographic Surveys on the Labrador Shelf
Bibliographic record
Abstract
Abstract While reasonable knowledge of multidecadal Arctic freshwater storage variability exists, we have little knowledge of Arctic freshwater exports on similar time scales. A hydrographic time series from the Labrador Shelf, spanning seven decades at annual resolution, is here used to quantify Arctic Ocean freshwater export variability west of Greenland. Output from a high-resolution coupled ice–ocean model is used to establish the representativeness of those hydrographic sections. Clear annual to decadal variability emerges, with high freshwater transports during the 1950s and 1970s–80s, and low transports in the 1960s and from the mid-1990s to 2016, with typical amplitudes of 30 mSv (1 Sv = 106m3s−1). The variability in both the transports and cumulative volumes correlates well both with Arctic and North Atlantic freshwater storage changes on the same time scale. We refer to the “inshore branch” of the Labrador Current as the Labrador Coastal Current, because it is a dynamically and geographically distinct feature. It originates as the Hudson Bay outflow, and preserves variability from river runoff into the Hudson Bay catchment. We find a need for parallel, long-term freshwater transport measurements from Fram and Davis Straits to better understand Arctic freshwater export control mechanisms and partitioning of variability between routes west and east of Greenland, and a need for better knowledge and understanding of year-round (solid and liquid) freshwater fluxes on the Labrador shelf. Our results have implications for wider, coherent atmospheric control on freshwater fluxes and content across the Arctic Ocean and northern North Atlantic Ocean.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".