Simulating past and future mass balance of Place Glacier using a physically-based, distributed glacier mass balance model
Bibliographic record
Abstract
The objective of this study is to develop a physically-based distributed glacier mass balance (GMB) model for Place Glacier, British Columbia, Canada, and apply the model to develop the historic and the future mass balance. The model is forced with climate data from Regional Atmospheric Modeling System (RAMS) mesoscale atmospheric model output from 1979-2008 for developing historic mass balance on Place Glacier. The model is also run in the future (2009-2040) to develop a projection of mass balance. The model simulated the historic glacier-wide summer and winter balance on Place Glacier satisfactorily. For all years, root mean squared error (RMSE) in simulated summer and winter balance are 0.43 m water equivalent (w.e.) and 0.27 m w.e., respectively. Over the period of 29 years, the model simulated a cumulative net mass balance of -33.72 m w.e. The model outperformed both empirical temperature index (TI) and enhanced TI models in simulating summer balance on Place Glacier when forced with the same RAMS variables. A linear regression model based on Singular Value Decomposition (SVD) technique is used for downscaling future climate projections from a suite of Global Climate Models (GCMs). The cross-validation of downscaled daily air temperature showed a strong correlation with the validation dataset (r~ =0.85, p <0.05). However, the RMSE in downscaled daily air temperature is large (=2.4~C). With spatially average correlation of 0.38 and RMSE of 7.5 mm day\u207b~ , the model for daily precipitation performed less satisfactorily in downscaling large-scale precipitation. For all variables, the error statistics improved with the monthly model. Future GCM projections form CanESM2, MIROC-ESM, MPI-ESM-LR, and HadGEM2-ES, are considered for downscaling. CanESM2 predicted a large negative glacier-wide net mass balance of -2.50 m w.e. for Place Glacier in the future. For the remaining GCMs, the average of net mass balance is \u20130.96 m w.e. The average of the cumulative mass loss predicted from GCMs other than CanESM2 is -31 m w.e. From 2009-2040, CanESM2, MIROC, MPI and HadGEM2 predicted an area loss of 52%, 28% and 22%, respectively. Overall, all downscaled GCMs, except CanESM2, performed better in predicting future mass balance for Place Glacier.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".