The Importance of Snow Sublimation on a Himalayan Glacier
Bibliographic record
Abstract
Snow sublimation is a loss of water from the snowpack to the atmosphere. So far, snow sublimation has remained unquantified in the Himalaya, prohibiting a full understanding of the water balance and glacier mass balance. Hence, we measured surface latent heat fluxes with an eddy covariance system on Yala Glacier (5350 m a.s.l) in the Nepalese Himalaya to quantify the role snow sublimation plays in the water and glacier mass budget. Observations reveal that cumulative sublimation is 32 mm for a 32-day period from October to November 2016, which is high compared to observations in other regions in the world. Multiple turbulent flux parameterizations were subsequently tested against this observed sublimation. The bulk-aerodynamic method offered the best performance, and we subsequently used this method to estimate cumulative sublimation and evaporation at the location of the eddy covariance system for the 2016-2017 winter season, which is 125 and 9 mm respectively. This is equivalent to 21% of the annual snowfall. In addition, the spatial variation of total daily sublimation over Yala Glacier was simulated with the bulk-aerodynamic method for a humid and non-humid day. Required spatial fields of meteorological variables were obtained from high-resolution WRF simulations of the region in combination with field observations. The cumulative daily sublimation at the location of the eddy covariance system equals the simulated sublimation averaged over the entire glacier. Therefore, this location appears to be representative for Yala Glacier sublimation. The spatial distribution of sublimation is primarily controlled by wind speed. Close to the ridge of Yala Glacier cumulative daily sublimation is a factor 1.7 higher than at the location of the eddy covariance system, whereas it is a factor 0.8 lower at the snout of the glacier. This illustrates that the fraction of snowfall returned to the atmosphere may be much higher than 21% at wind-exposed locations. This is a considerable loss of water and illustrates the importance and need to account for sublimation in future hydrological and mass balance studies in the Himalaya.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".