Seasonal surface layer dynamics and sensitivity to runoff in a high Arctic fjord (Young Sound/Tyrolerfjord, 74°N)
Bibliographic record
Abstract
Abstract Runoff from the Greenland Ice Sheet, local glaciers, and snowmelt along the northeastern Greenland coastline has a significant impact on coastal water masses flowing south toward Denmark Strait. Very few direct measurements of runoff currently exist in this large area, and the water masses near the coast are also difficult to measure due to the presence of icebergs and sea ice. Measurements from the Zackenberg Research station, located in Young Sound/Tyrolerfjord in northeast Greenland (74°N), provide some of the few observations of hydrographic, hydrologic, and atmospheric parameters from this remote area. Here we analyze measurements from the fjord and also measurements in the ambient water masses, which are found in the outer fjord and between the fjord and the East Greenland Current and validate and apply a numerical model of the fjord. A model sensitivity study allows us to constrain runoff estimates for the area. We also show that a total runoff between 0.9 and 1.4 km3 in 2006 is in accordance with observed surface salinities and calculated freshwater content in the fjord. This indicates that earlier reported runoff to the area is significantly underestimated and that melt from glaciers and the Greenland Ice Sheet in this region may be up to 50% larger than the current estimate. Model simulations indicate the presence of a cold low‐saline coastal water mass formed by runoff from fjords north of the Young Sound/Tyrolerfjord system. Simulations of passive and age tracers show that residence time of river water during the summer period is about 1 month in the inner part of the fjord.
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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 | 0.000 |
| 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 | 0.000 | 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".