Influence of Surface Topography on the Critical Carbon Dioxide Level Required for the Formation of a Modern Snowball Earth
Bibliographic record
Abstract
The influence of continental topography on the initiation of a global glaciation (i.e., snowball Earth) is studied with both a fully coupled atmosphere–ocean general circulation model (AOGCM), CCSM3, and an atmospheric general circulation model (AGCM), CAM3 coupled to a slab ocean model. It is found that when the climate is very cold, snow cover over the central region of the Eurasian continent decreases when the atmospheric CO2 concentration ( pCO2) is reduced. In the coupled model, this constitutes a negative feedback due to the reduction of land surface albedo that counteracts the positive feedback due to sea ice expansion toward the equator. When the solar insolation is 94% of the present-day value, Earth enters a snowball state when pCO2 is ~35 ppmv. On the other hand, if the continents are assumed to be flat topographically (with the global mean elevation as in the more realistic present-day case), Earth enters a snowball state more easily at pCO2 = ~60 ppmv. Therefore, the presence of topography may increase the stability of Earth against descent into a snowball state. On the contrary, a snowball Earth is found to form much more easily when complex topography is present than when it is not in CAM3. This happens despite the fact that the mid- to high-latitude climate is much warmer (by ~10°C) when topography is present than when it is not. Analyses show that neglecting sea ice dynamics in this model prevents the warming anomaly in the mid- to high latitudes from being efficiently transmitted into the tropics.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".