Recent North Greenland temperature warming and accumulation
Bibliographic record
Abstract
Abstract. In a warming climate concise knowledge of the mass balance of the Greenland ice sheet is of utter importance. Speculations that current warming will increase the snow accumulation and mitigate mass balance losses are unconstrained as accumulation data across large regions of the northern ice sheet are scarce. We reconstructed the accumulation from six north Greenland shallow firn cores (~10 m) and eight snow cores (~2 m) to constrain recent accumulation patterns in northern Greenland and calculated recent warming in the same area using borehole temperature measurements. We find an increase in temperatures in the north Greenland interior of 0.9 to 2.5 °C (method and site dependent) per decade over the past two decades in line with an Arctic amplified anthropogenic warming. We compare annual reconstructed accumulation from the firn cores (1966–2015) to radar estimates and to annual re-analysis data (1980–2016) of precipitation subtracted evaporation from the regional climate model HIRHAM5, operated by the Danish Meteorological Institute. The spatial variability resembles that observed in earlier estimates with a clear increase west of the topographic divide and a low accumulation area across the north-eastern ice sheet. Our accumulation results are comparable to earlier firn core estimates, despite being larger in the east. We only find a positive significant trend in the accumulation for the period 2000–2010 to the northwest. In the vicinity of the EGRIP deep ice core drilling site, we find variable accumulation patterns for two 15 km apart firn cores likely owing to local topographic effects as a result of the North East Greenland Ice Stream dynamics.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".