Global sea-level contribution from Arctic land ice: 1971 to 2017
Bibliographic record
Abstract
Abstract The Arctic Monitoring and Assessment Program (AMAP) (AMAP, 2017) identifies the Arctic as the largest regional source of land ice to global sea-level rise in the 2003 to 2014 period. Yet, this contextualization ignores the longer perspective from in-situ records of glacier mass balance. Here, using 18 (> 55 °N latitude) glacier and ice cap mass balance series in the 1971 to 2017 period, we develop a semi-empirical estimate of annual sea-level contribution from seven Arctic regions by scaling the in-situ records to GRACE averages. We contend that our estimate represents the most accurate mass balance assessment so far available before the 1992 start of satellite altimetry. We estimate the 1971 to 2017 eustatic sea-level contribution from land ice north of ~55° N to be 23.0±12.3 mm sea-level equivalent (SLE). In all regions, the cumulative sea-level rise curves exhibit an acceleration, especially after 1988. Greenland is the source of 46% of the Arctic sea-level rise contribution (10.6±7.3 mm), followed by Alaska (5.7±2.2 mm), Arctic Canada (3.2±0.7 mm) and the Russian High Arctic (1.5±0.4 mm). Our annual results exhibit co-variability over a 43 year overlap (1971 to 2013) with the alternative dataset of Marzeion et al (2015) (M15). However, we find a 1.36x lower sea-level contribution, in agreement with satellite gravimetry. The IPCC Fifth Assessment report identified constraining the pre-satellite era sea-level budget as a topic of low scientific understanding that we address and specify sea-level contributions coinciding with IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC) “present day” (2005-2015) and “recent past” (1986-2005) reference periods. We assess an Arctic land ice loss of 8.3 mm SLE during the recent past and 12.4 mm SLE during the present day. The seven regional sea-level rise contribution time series of this study are available from AMAP.no.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".