Field‐calibrated model of melt, refreezing, and runoff for polar ice caps: Application to Devon Ice Cap
Bibliographic record
Abstract
Abstract Understanding the controls on the amount of surface meltwater that refreezes, rather than becoming runoff, over polar ice masses is necessary for modeling their surface mass balance and ultimately for predicting their future contributions to global sea level change. We present a modified version of a physically based model that includes an energy balance routine and explicit calculation of near‐surface meltwater refreezing capacity, to simulate the evolution of near‐surface density and temperature profiles across Devon Ice Cap in Arctic Canada. Uniquely, our model is initiated and calibrated using high spatial resolution measurements of snow and firn densities across almost the entire elevation range of the ice cap for the summer of 2004 and subsequently validated with the same type of measurements obtained during the very different meteorological conditions of summer 2006. The model captures the spatial variability across the transect in bulk snowpack properties although it slightly underestimates the flow of meltwater into the firn of previous years. The percentage of meltwater that becomes runoff is similar in both years; however, the spatial pattern of this melt‐runoff relationship is different in the 2 years. The model is found to be insensitive to variation in the depth of impermeable layers within the firn but is very sensitive to variation in air temperature, since the refreezing capacity of firn decreases with increasing temperature. We highlight that the sensitivity of the ice cap's surface mass balance to air temperature is itself dependent on air temperature.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".