The role of GCM resolution in simulating glacial inception
Bibliographic record
Abstract
We test the influence of model resolution on glacial inception using a coupled atmosphere–slab ocean version of NCAR’s CCSM3 GCM. Simulations employ a modern orbital configuration and greenhouse gas concentrations representing both recent (year 1990) and hypothetically lower values based on Ruddiman’s Early Anthropogenic Hypothesis (240 ppm CO 2 , 450 ppb CH 4 ). We ran the model at two different horizontal resolutions: relatively coarse (spectral T42, approximately 2.8°) and comparatively fine (T85, approximately 1.4°). Under contemporary greenhouse forcing, permanent boreal snow cover extent in the two model configurations is similar, but lowering greenhouse gas concentrations generates much more extensive glacial inception in the T85 experiment (150% increase) than at T42 resolution (80% increase). Furthermore, the spatial patterns of glacial inception differ considerably. Only the T85 version produces permanent snow cover over the Rocky Mountains and Baffin Island, consistent with geologic evidence for early glaciation in the northern Rockies and Laurentide ice sheet nucleation in northeastern Canada. Although enhanced sensitivity at higher resolution is largely attributable to the expected colder and wetter conditions over elevated topography, much of the response is also driven dynamically. Atmospheric pressure changes at the surface and aloft differ considerably between model resolutions. The T85 circulation anomalies favor a stronger onshore moisture flux and therefore more snowfall over the northern Rockies and Baffin Island. Although these experiments are driven by greenhouse forcing rather than orbital anomalies, our findings may apply to general mechanisms of glacial inception.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".