The Initiation of Modern “Soft Snowball” and “Hard Snowball” Climates in CCSM3. Part I: The Influences of Solar Luminosity, CO2 Concentration, and the Sea Ice/Snow Albedo Parameterization
Bibliographic record
Abstract
Abstract The “Snowball Earth” hypothesis, proposed to explain the Neoproterozoic glacial episodes in the period 750–580 million years ago, suggested that the earth was globally covered by ice/snow during these events. This study addresses the problem of the forcings required for the earth to enter such a state of complete glaciation using the Community Climate System Model, version 3 (CCSM3). All of the simulations performed to address this issue employ the geography and topography of the present-day earth and are employed to explore the combination of factors consisting of total solar luminosity, CO2 concentration, and sea ice/snow albedo parameterization that would be required for such an event to occur. The analyses demonstrate that the critical conditions beyond which runaway ice–albedo feedback will lead to global freezing include 1) a 10%–10.5% reduction in solar radiation with preindustrial greenhouse gas concentrations; 2) a 6% reduction in solar radiation with 17.5 ppmv CO2; or 3) 6% less solar radiation and 286 ppmv CO2 if sea ice albedo is equal to or greater than 0.60 with a snow albedo of 0.78, or if sea ice albedo is 0.58 with a snow albedo equal to or greater than 0.80. These bifurcation points are very sensitive to the sea ice and snow albedo parameterizations. Moreover, “soft Snowball” solutions are found in which tropical open water oceans stably coexist with year-round snow-covered low-latitude continents, implying that tropical continental ice sheets would actually be present. The authors conclude that a “soft Snowball” is entirely plausible, in which the global sea ice fraction may reach as high as 76% and sea ice margins may extend to 10°S(N) latitudes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".