A high-resolution pan-Arctic meltwater discharge dataset from 1950 to 2021
Bibliographic record
Abstract
Abstract. Arctic air temperatures have increased approximately 4 times faster than the global average since about 1980. Consequently, the Greenland ice sheet has lost approximately 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 affect physical, chemical and biological processes across a range of domains and spatiotemporal scales. To date, meltwater discharge data at Arctic coastlines are only available from two datasets that are limited by their spatial resolution and/or coverage. Here, we extend 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 – i.e. spatially integrated runoff that is assigned to the outflow points of drainage basins – were derived from Modèle Atmosphérique Régional (MAR) daily ice and land runoff products between 1950 and 2021, which we statistically downscaled from their original approximately 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 (Igneczi and Bamber, 2024; https://doi.org/10.1594/PANGAEA.967544).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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; both teacher heads agree on what is shown here.
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".