Global glacier climate disequilibrium from modelling steady state AAR
Bibliographic record
Abstract
Most glaciers and ice caps (GIPs) are out of balance with the current climate, exhibiting continuous retreat and thinning. These changes impact regional runoff and contribute to sea level rise, causing glacier-related hazards. In this study, we estimate the area and volume losses for current GIPs to reach equilibrium through modelling the steady state accumulation area ratios (AAR0) and time-averaged AAR of each GIP. The modelled global average AAR0 is 0.541 ± 0.082, which is lower than most previous studies due to the inclusion of numerous unobserved GIPs. Ice caps exhibit a higher AAR0 (0.612 ± 0.11) compared to glaciers (0.538 ± 0.08). For regional distribution, the largest AAR0 appears in northern Arctic Canada (0.608 ± 0.114) and low latitude areas (0.570 ± 0.067), while the smallest AAR0 occurs in Central Europe (0.519 ± 0.066) and north Asia (0.522 ± 0.071). Accounting for debris-cover reveals a decrease in AAR0 due to reduced sub-debris melting, while considering the frontal ablation of marine-terminating glaciers leads to an increase in AAR0. Assessing the imbalance between global GIPs and the current (2000-2019) climate, we project an additional loss of 23 ± 6% in area and 29 ± 8% in volume. This corresponds to a sea level rise equivalent of 128 ± 34 mm. The Antarctic and subantarctic are the primary contributor to global mean sea level rise, accounting for 60 ± 20 mm. The GIPs in central and eastern Himalaya, as well as the Mt. Hengduan exhibit significant instability, characterized by an average imbalance ratio (AAR/AAR0) of 0.72 ± 0.25.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".