Spreading of warm ocean waters around Greenland as a possible cause for glacier acceleration
Bibliographic record
Abstract
Abstract We examine the pattern of spreading of warm subtropical-origin waters around Greenland for the years 1992–2009 using a high-resolution (4km horizontal grid) coupled ocean and sea-ice simulation. The simulation, provided by the Estimating the Circulation and Climate of the Ocean, Phase II (ECCO2) project, qualitatively reproduces the observed warming of subsurface waters in the subpolar gyre associated with changes of the North Atlantic atmospheric state that occurred in the mid-1990s. The modeled subsurface ocean temperature warmed by 1.5˚C in southeast and southwest Greenland during 1994–2005 and subsequently cooled by 0.5˚C; modeled subsurface ocean temperature increased by 2–2.5˚C in central and then northwest Greenland during 1997–2005 and stabilized thereafter, while it increased after 2005 by <0.5˚C in north Greenland. Comparisons with in situ measurements off the continental shelf in the Labrador and Irminger Seas indicate that the model initial conditions were 0.4˚C too warm in the south but the simulated warming is correctly reproduced; while measurements from eastern Baffin Bay reveal that the model initial conditions were 1.0˚C too cold in the northwest but the simulated ocean warming brought modeled temperature closer to observations, i.e. the simulated warming is 1.0˚C too large. At several key locations, the modeled oceanic changes off the shelf and below the seasonal mixed layer were rapidly transmitted to the shelf within troughs towards (model-unresolved) fjords. Unless blocked in the fjords by shallow sills, these warm subsurface waters had potential to propagate down the fjords and melt the glacier fronts. Based on model sensitivity simulations from an independent study (Xu and others, 2012), we show that the oceanic changes have very likely increased the subaqueous melt rates of the glacier fronts, and in turn impacted the rates of glacier flow.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".