A high-resolution pan-Arctic meltwater discharge dataset from 1950 to 2021
Bibliographic record
Abstract
Abstract. Arctic air temperatures have increased about four times faster than the global average since about 1980. Consequently the Greenland Ice Sheet has lost about twice as much ice as the Antarctic Ice Sheet between 2003 and 2019, and mass loss from glaciers and ice caps is also dominated by those that lie in the Arctic. Thus, Arctic land ice loss is currently a major contributor to global sea level rise. This increasing freshwater flux into the Arctic and North Atlantic oceans, will also impact physical, chemical and biological processes across a range of domains and spatiotemporal scales. Although, meltwater discharge data at Arctic coastlines are available from two existing datasets, these are limited by their spatial resolution and/or coverage. Here, we improve upon previous work and provide a high-resolution coastal meltwater discharge data product that covers all Arctic regions, where land ice is present, i.e. the Canadian Arctic Archipelago, Greenland, Iceland, Svalbard, Russian Arctic Islands. Coastal meltwater discharge data were derived from Modèle Atmosphérique Régional daily ice and land runoff products between 1950 and 2021, which we statistically downscaled from their original ~6 km resolution to 250 m. The complete data processing algorithm, including downscaling, is fully documented and relies on open-source software. The coastal discharge database is disseminated in easily accessible and storage efficient netCDF files.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.003 | 0.003 |
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".