Long-term contributions of Baffin and Bylot Island Glaciers to sea level rise: an integrated approach using airborne and satellite laser altimetry, stereoscopic imagery and satellite gravimetry
Bibliographic record
Abstract
Abstract. Canadian Arctic glaciers have recently contributed large volumes of meltwater to the world's oceans. To place recently observed glacier wastage into a historical perspective and to determine the region's longer-term (~50 years) contribution to sea level, we estimate mass and volume changes for the glaciers of Baffin and Bylot Islands using Digital Elevation Models generated from airborne and satellite stereoscopic imagery and elevation postings from repeat airborne and satellite laser altimetry. In addition, we update existing glacier mass change records from GRACE satellite gravimetry to cover the period from 2003 to 2011. Using an integrated approach we find that the rate of mass loss from the region's glaciers increased from 11.1 ± 1.8 Gt a−1 (–270 ± 40 kg m−2 a−1) in 1963–2006 to 23.8 ± 3.1 Gt a−1 (–580 ± 80 kg m2 a−1) in 2003–2011. The doubling of the rate of mass loss is attributed to higher temperatures in summer with little change in annual precipitation. Through both direct and indirect effects, changes in summer temperatures accounted for 68–98 % of the variance in the rate of mass loss to which the Barnes Ice Cap was found to be 1.6 times more sensitive than either the Penny Ice Cap or the regions glaciers as a whole. Between 2003 and 2011 the glaciers of Baffin and Bylot Islands contributed 0.07 ± 0.01 mm a−1 to sea level rise, a rate equivalent to the contribution coming from Patagonian glaciers. Over the 48-year period between 1963 and 2011 the glaciers of Baffin and Bylot Islands contributed 1.7 mm to the world's oceans.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 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".