A high-resolution, operational pan-Arctic meltwater discharge database from 1950 to 202
Bibliographic record
Abstract
The Arctic has warmed about four times faster than the global average during the last four decades. One of the consequences of this intensive warming is increasing Arctic land ice loss. In particular, mass loss from the Greenland Ice Sheet has been estimated to have increased sixfold between 1980 and 2020. Glaciers and ice caps outside of Greenland, though receiving less attention, have also been reported to be losing mass at an increasing rate. This is caused by a combination of negative surface mass balance – due to decreasing snowfall and/or increasing melting and runoff – and increasing ice discharge. However, negative surface mass balance due to increasing melting and runoff has become the dominant cause of mass loss in Greenland and the Canadian Arctic during the last 10-15 years. This indicates the increasing role of meltwater discharge into fjords and coastal seas, influencing a wide-range of physical, chemical and biological processes and also the large-scale oceanic circulation. Despite recent advancements, no meltwater discharge data products are available that cover the entire Arctic at a high spatial (< 1 km) and temporal (sub-monthly) resolution. To fill this data gap, we use daily ~6km runoff data from a regional climate model, Modéle Atmosphérique Régional (MAR), for the period of 1950-2021 – covering Greenland, Arctic Canada, Iceland, Svalbard, and Arctic Russia. We employ a statistical downscaling algorithm that utilises a high resolution (250 m) DEM, land mask (Copernicus GLO-90), and ice mask (GIMP, RGI). A hydrological routing scheme is also applied to the downscaled runoff to provide meltwater runoff data at coastal outflow points. Meltwater components coming from non glacierized land, bare glacier ice, and glacierized area above the snowline are separated to aid further analyses. The software pipeline is designed to be fully operational so that it can be used to update the time series as soon as the input data are available, so providing a continuous time series for the entire Arctic within the framework of a project aimed to develop a holistic, integrated observing system for the Arctic (www.arctipassion.eu).
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".