The firn meltwater Retention Model Intercomparison Project (RetMIP): Evaluation of nine firn models at four weather station sites on the Greenland ice sheet
Bibliographic record
Abstract
Abstract. Perennial snow, or firn, covers 80 % of the Greenland ice sheet and has the capacity to retain part of the surface meltwater, buffering the ice sheet’s contribution to sea level. Multi-layer firn models are traditionally used to simulate the firn processes and estimate meltwater retention. We present the output from nine firn models, forced by weather-station-derived mass and energy fluxes at four sites representative of the dry snow, percolation, ice slab and firn aquifer areas. We compare the model outputs and evaluate them against in situ observations. Models that explicitly account for deep meltwater percolation overestimate percolation depth and consequently firn temperature at the percolation and ice slab sites although they accurately simulate the recharge of the firn aquifer. Models using Darcy's law and a bucket scheme compare favourably to observations at the percolation site but only the Darcy models accurately simulate firn temperature and thus meltwater percolation at the ice slab site. We find that Eulerian models, that transfer firn through fixed layers, smooth sharp gradients in firn temperature and density over time. From the model spread, we find that simulated densities (respectively temperature) have an uncertainty envelope of ±60 kg m−3 (resp. ±14 °C) in the dry snow area and up to ±280 kg m−3 (resp. ±15–18 °C) at warmer sites.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".