The annual cycle of snowfall at Summit, Greenland
Bibliographic record
Abstract
Abstract While snow accumulation over central Greenland has been extensively studied, interannual variability of snowfall in the region is not well understood due to a dearth of observations. The Integrated Characterization of Energy, Clouds, Atmospheric state and Precipitation at Summit (ICECAPS) project at Summit, Greenland, offers a unique, ground‐based opportunity to study precipitation in central Greenland where the surface mass balance is positive. Combining data from a Precipitation Occurrence Sensor System (POSS), Millimeter‐wavelength Cloud Radar (MMCR), and snow stake field, the annual cycle of precipitation at Summit is examined. Average daily snowfall is higher by a factor of 3 from June to October compared to November to May, while surface height change is only higher by 15% during the same timeframes. This reduced variability in surface height is explained by the seasonally varying nature of latent heat flux, compaction, and wind contributions. The ICECAPS remote sensors and stake field measurements do not agree as far as total annual water equivalent. This discrepancy is likely due to a low bias in the POSS and MMCR snowfall retrievals for Summit. To further examine the seasonal cycle, snowfall measurements by the POSS were linked to local meteorological parameters, including wind direction, liquid water path (LWP), 2 m temperature, and precipitable water vapor. An observed wind direction and moisture dependence are consistent with snowfall being linked to pulses of moist air that originate over nearby, ice‐free ocean, a resource that becomes more readily available in summertime as the winter sea ice retreats. LWP is shown to have little relationship to snowfall, indicating that ice‐phase precipitation processes are quite important for snowfall at Summit.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".