Assessment of current methods of positive degree-day calculation using in situ observations from glaciated regions
Bibliographic record
Abstract
Abstract The continued use of the positive degree-day (PDD) method to predict ice-sheet melt is generally favoured over surface energy-balance methods partly due to the computational efficiency of the algorithm and the requirement of only one input variable (temperature). In this paper, we revisit some of the assumptions governing the application of the PDD method. Using hourly temperature data from the GC-Net network we test the assumption that monthly PDD total (PDD M ) can be represented by a Gaussian distribution with fixed standard deviation of monthly temperature ( σ M ). The results presented here show that the common assumption of fixed σ M does not hold, and that σ M may be represented more accurately as a quadratic function of average monthly temperature. For Greenland, the mean absolute error in predicting PDD M using our methodology is 3.9°C d, representing a significant improvement on current methods (7.8°C d, when σ M = 4.5°C). Over a range of glaciated settings, our method reproduces PDD M , on average, to within 1.5–8.5°C d, compared to 4.4–15.7°C d when σ M = 4.5°C. The improvement arises because we capture the systematic reduction in temperature variance that is observed over melting snow and ice, when surface temperatures cannot warm above 0°C.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".