Reconstruction of Glacier Mass Balance with Surface Energy Balance Modeling across Southwestern Canada
Bibliographic record
Abstract
Current state-of-the-art glacier models for regional and global scales mostly rely on empirical models, such as temperature-index models, which require glacier-specific calibration with in-situ mass balance measurements. In the absence of these measurements, the models suffer from large uncertainties in their projections of glacier mass changes, especially at local scales. One way to address this issue is to transition from the empirical models toward more physics-based models, such as surface energy balance (SEB) models of glacier melt. In this study, we evaluate the performance of a glacier evolution model based on a SEB model with minimal calibration for nearly 15,000 glaciers in Southwestern Canada for the period of 1979–2021. The SEB model is forced with ERA5 reanalysis data with minimal bias corrections or statistical downscaling. The empirical models for accumulation and albedo are, however, calibrated to maximize the match between simulated and observed glaciological mass balance availabe for about 20 glaciers in this region. The simulated regional mass balance and area change are then evaluated against the geodetic mass balance as observed for all glaciers in the region over the last two decades. This study contributes to a better understanding of the applicability of SEB models with minimal calibration in regional glaciation modeling in order to narrow uncertainties in glacier melt projections.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".