Estimating the contribution of Arctic glaciers to sea-level change in the next 100 years
Bibliographic record
Abstract
Abstract In this paper, we report on an approach to estimate the contribution of Arctic glaciers to sea-level change. In our calculation we assume that a static approach is feasible. We only calculate changes in the surface balance from modelled sensitivities. These sensitivities, summarized in the seasonal sensitivity characteristic, can be used to calculate the change in the surface mass budget for given anomalies of monthly temperature and precipitation. We have based our calculations on a subdivision of all Arctic ice into 13 regions: four sectors of the Greenland ice sheet; the Canadian Arctic >74˚N; the Canadian Arctic <74˚N; Alaska, USA; Iceland; Svalbard; Zemlya Frantsa Iosifa, Russia; Novaya Zemlya, Russia; Severnaya Zemlya, Russia; and Norway/Sweden >60˚N. As forcing for the calculations, we have used the output from five climate models, for the period 2000–2100. These models were forced by the same greenhouse-gas scenario (IPCC-B2). The calculated contributions to sea-level rise in the year 2100 vary from almost zero to about 6 cm. The differences among the models stem first of all from differences in the precipitation. The largest contribution to sea-level change comes from the Greenland ice sheet. The glaciers in Alaska also make a large contribution, not because of the area they cover, but because they are more sensitive than other glaciers in the Arctic. The climate models do not agree on regional patterns. The runoff from Svalbard glaciers, for instance, increases for two models and decreases for the three other models. We conclude that the uncertainty due to a simple representation of the glaciological processes is probably smaller than the uncertainty induced by the differences in the climate-change scenarios produced by the models.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| 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".