Snow cover validation and sensitivity to CO<sub>2</sub>in the UVic ESCM
Bibliographic record
Abstract
Abstract The University of Victoria's (UVic) Earth System Climate model is used to conduct equilibrium atmospheric CO2 sensitivity experiments over the range 200–1600 ppm in order to explore changes in northern hemisphere snow cover and feedbacks on terrestrial surface air temperature (SAT). Simulations of warmer climates predict a retreat of snow cover over northern continents, in a northeasterly direction. The decline in northern hemisphere global snow mass is estimated to reach 33% at 600 ppm and 54% at 1200 ppm. In the most northerly regions, annual mean snow depth increases for simulations with CO2 levels higher than present day. The shift in the latitude of maximum snowfall is estimated to be inversely proportional to the CO2 concentration. The northern hemisphere net shortwave radiation changes are found to be greater over land than over the ocean, suggesting a stronger albedo feedback from changes in terrestrial snow cover than from changes in sea ice. Results also reveal high sensitivity of the snow mass balance under low CO2 conditions. The amplification feedback (defined as the zonal SAT anomaly caused by doubling CO2 divided by the equatorial anomaly) is greatest for scenarios with less than 300 ppm, reaching 1.9 at the pole for 250 ppm. The stronger feedback is attributed to the significant albedo changes over land areas. The simulation with 200 ppm triggers continuous accumulation of snow ('glaciation') in regions which, according to paleo‐reconstructions, were covered by ice during the last glacial cycle (the Canadian Arctic, Scandinavia, and the Taymir Peninsula).
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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".