Modeling surface response of the Greenland Ice Sheet to interglacial climate
Bibliographic record
Abstract
Abstract. This study presents a~new approach to parameterizing surface mass balance (SMB) of the Greenland Ice Sheet (GIS) under interglacial climate validated against recent satellite observations and the results of a high-resolution model on a regional scale. Based on detailed analysis of the modeled SMB, we conclude that existing parameterizations fail to capture either spatial pattern or amplitude of the observed surface responses of the GIS. This is due to multiple simplifying assumptions adopted by the majority of modeling studies within the framework of a positive degree-day method. Modeled surface melting is found to be highly sensitive to a choice of daily temperature standard deviation (SD), which is generally assumed to have uniform distribution across Greenland. The range of commonly used SD values does not however receive support from climate datasets available. In this region, SD distribution is highly inhomogeneous and characterized by low values during summer months in areas where most surface melting occurs. Our approach is to make use of spatially variable SD and here we show that this leads to significant improvements in the modeled SMB over the instrumental record. Our findings necessitate evaluating potential consequences of the simplified SMB treatment for assessment of the history and future of glaciation on Earth.
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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 | 0.000 |
| 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 | 0.001 | 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 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".