Partitioning the Uncertainty of Ensemble Projections of Global Glacier Mass Change
Bibliographic record
Abstract
Abstract Glacier mass loss is recognized as a major contributor to current sea level rise. However, large uncertainties remain in projections of glacier mass loss on global and regional scales. We present an ensemble of 288 glacier mass and area change projections for the 21st century based on 11 glacier models using up to 10 general circulation models and four Representative Concentration Pathways (RCPs) as boundary conditions. We partition the total uncertainty into the individual contributions caused by glacier models, general circulation models, RCPs, and natural variability. We find that emission scenario uncertainty is growing throughout the 21st century and is the largest source of uncertainty by 2100. The relative importance of glacier model uncertainty decreases over time, but it is the greatest source of uncertainty until the middle of this century. The projection uncertainty associated with natural variability is small on the global scale but can be large on regional scales. The projected global mass loss by 2100 relative to 2015 (79 ± 56 mm sea level equivalent for RCP2.6, 159 ± 86 mm sea level equivalent for RCP8.5) is lower than, but well within, the uncertainty range of previous projections.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".