Hypsometric control on glacier mass balance sensitivity in Alaska and northwest Canada
Bibliographic record
Abstract
Abstract Glacier hypsometry provides a first‐order approach for assessing a glacier's response to climate forcings. We couple the Randolph Glacier Inventory to a suite of in situ observations and climate model output to examine potential change for the ∼27,000 glaciers in Alaska and northwest Canada through the end of the 21st century. By 2100, based on Representative Concentration Pathways (RCPs) 4.5–8.5 forcings, summer temperatures are predicted to increase between +2.1 and +4.6°C, while solid precipitation (snow) is predicted to decrease by −6 to −11%, despite a +9 to +21% increase in total precipitation. Snow is predicted to undergo a pronounced decrease in the fall, shifting the start of the accumulation season back by ∼1 month. In response to these forcings, the regional equilibrium line altitude (ELA) may increase by +105 to +225 m by 2100. The mass balance sensitivity to this increase is highly variable, with the most substantive impact for glaciers with either limited elevation ranges (often small (<1 km2) glaciers, which account for 80% of glaciers in the region) or those with top‐heavy geometries, like icefields. For more than 20% of glaciers, future ELAs, given RCP 6.0 forcings, will exceed the maximum elevation of the glacier, resulting in their eventual demise, while for others, accumulation area ratios will decrease by >60%. Our results highlight the first‐order control of hypsometry on individual glacier response to climate change, and the variability that hypsometry introduces to a regional response to a coherent climate perturbation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".