Current climate policies will affect multi-century global glacier change
Bibliographic record
Abstract
Glaciers adapt slowly to changing climatic conditions, leading to a time lag between climate change and the resulting impacts, such as sea-level rise, water supply changes, and ecological impacts. While previous projections of all glaciers around the globe have mainly focused on the 21st century, longer timescales are essential to fully understand the glacier response to climate policies and associated warming. Using eight glacier evolution models, we simulate global glacier evolution over multi-centennial timescales, allowing glaciers to equilibrate with climate under various constant global temperature scenarios. We estimate that glaciers globally will lose about 40% of their mass, relative to 2020, corresponding to a global mean sea-level rise of more than 10 centimeters even if temperatures stabilized at present-day conditions. The effect of climate policies is very pronounced: under the +1.5°C target of the Paris Agreement, more than twice as much global glacier mass remains at equilibration compared to the mass projected under the warming level resulting from current policies (+2.7°C by 2100 above pre-industrial). Long-term global glacier mass loss is highly sensitive to global mean temperature, with each additional 0.1°C warming leading to a ca. 2% additional increase in global glacier mass loss. These long-term losses largely exceed those projected over the 21st century, implying that the most substantial impacts of today's climate policies on glacier mass will unfold after 2100. Notably, regions previously found to experience limited mass loss in the 21st century, such as Arctic Canada, Russian Arctic, and Subantarctic & Antarctic Islands, are projected to lose substantial mass on longer timescales. Our findings underscore the necessity of extending the focus of glacier studies beyond the 21st century to fully comprehend the long-term implications of today’s climate policies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".