Chaotic fluctuations in Greenland outlet glaciers limit predictability of a future ice sheet collapse
Bibliographic record
Abstract
Abstract. The future evolution of the Greenland ice sheet (GrIS) depends on the intensity and the speed of climate change. By applying different rates of temperature change in a state-of-the-art comprehensive ice-sheet model coupled to a regional energy-moisture balance atmospheric model, oscillations in the total ice-sheet volume are found under warming magnitudes between 1.0 and 1.3 K above present-day temperatures. These are located in the northwestern drainage basin of the GrIS and are due to two ice streams which alternate between fast and slow basal velocities, manifesting in a build-up/surge variability. These ice streams interact due to their spatial proximity, resulting in irregular periodicity. The ice streams appear in a region where tipping of the entire GrIS begins, leading the oscillations to affect the tipping behaviour. These oscillations directly impact the time it takes before the ice sheet collapses at a given external forcing magnitude by hundreds of thousands of years for an ensemble of rates of forcing and initial conditions. These long tipping times are proposed to be due to chaotic transients. Our results suggest that ice-stream oscillations are a potential source of internal chaotic variability in ice sheets that affect tipping behaviour, thereby complicating prospects of anticipating such a tipping.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".