Time-Domain Reflectometry Measurements and Modelling of Firn Meltwater Infiltration at DYE-2, Greenland
Bibliographic record
Abstract
Meltwater infiltration and refreezing in snow and firn are important processes for Greenland Ice Sheet mass balance, acting to reduce meltwater runoff and mass loss. To advance understanding of meltwater retention processes in firn, we deployed vertical arrays of time-domain reflectometry sensors and thermistors to continuously monitor meltwater infiltration, refreezing, and wetting-front propagation in the upper 4 m of snow and firn over the 2016 melt season at DYE-2, Greenland. The dataset provides a detailed record of the co-development of the firn wetting and thawing fronts through the melt season. These data are used to constrain a model of firn thermodynamics and hydrology that is then used in simulations of the long-term firn evolution at DYE-2, forced by ERA5 climate reanalyses over the period 1950-2020. Summer 2016 meltwater infiltration reached a depth of 1.8 m below the surface, which is close to the modelled long-term mean at this site. Modelled meltwater infiltration increased at DYE-2 from 1990-2020, driving increases in firn density, ice content, and temperature; 10-m firn temperatures increased by 1°C per decade over this period. Modelled meltwater infiltration reached 6 to 7 m depth during extreme melt seasons in Greenland such as 2012 and 2019, causing 3-4°C increases in 10-m firn temperatures which persist for several years. A similar event occurred in 1968 in the model reconstructions. These deep infiltration events strongly impact the firn at DYE-2, and may be more influential than the background warming trend in governing meltwater retention capacity in the Greenland percolation zone.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".