Atmospheric flow indices, regional climate, and Glacier mass balance in the Canadian Rocky mountains
Bibliographic record
Abstract
Abstract Glacier mass balance is governed by cumulative temperature and precipitation patterns in a region, making it a sensitive indicator of climate variability and trends. Many studies have drawn the link between local meteorological conditions and glacier mass balance, but these statistical relationships are difficult to extrapolate to other sites or to apply in sensitivity studies of future climate change. In this paper, we explore the ability to predict regional climate anomalies and glacier mass balance in the Canadian Rockies on the basis of 500‐mb circulation indices derived from the NCEP‐NCAR reanalysis dataset. Daily precipitation amounts and variance‐weighted seasonal temperature and precipitation anomalies at a suite of six long‐term meteorological stations in the Canadian Rockies (1953–2002) demonstrate a coherent dependence on the daily and mean seasonal atmospheric flow indices. The Peyto Glacier, Alberta, Canada offers the best available mass balance time‐series in the Canadian Rockies (1966–2004). Regression models for Peyto Glacier winter, summer, and annual mass balance variability were constructed from (1) Jasper climate anomalies, (2) regional climate anomalies, and (3) atmospheric flow indices. Model performance was examined in terms of the multiple coefficient of determination and of the variables retained in the stepwise regression analysis. Flow indices were the stronger predictors of mass balance. This offers important advantages for mass balance forecasts, because large‐scale circulation patterns are better captured than surface weather in mountain regions, in both reanalysed climatology and model‐generated climate change scenarios. Copyright © 2006 Royal Meteorological Society.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".