Ice, Fire, or Fizzle: The Climate Footprint of Earth's Supercontinental Cycles
Bibliographic record
Abstract
Abstract Supercontinent assembly and breakup can influence the rate and global extent to which insulated and relatively warm subcontinental mantle is mixed globally, potentially introducing lateral oceanic‐continental mantle temperature variations that regulate volcanic and weathering controls on Earth's long‐term carbon cycle for a few hundred million years. We propose that the relatively warm and unchanging climate of the Nuna supercontinental epoch (1.8–1.3 Ga) is characteristic of thorough mantle thermal mixing. By contrast, the extreme cooling‐warming climate variability of the Neoproterozoic Rodinia episode (1–0.63 Ga) and the more modest but similar climate change during the Mesozoic Pangea cycle (0.3–0.05 Ga) are characteristic features of the effects of subcontinental mantle thermal isolation with differing longevity. A tectonically modulated carbon cycle model coupled to a one‐dimensional energy balance climate model predicts the qualitative form of Mesozoic climate evolution expressed in tropical sea‐surface temperature and ice sheet proxy data. Applied to the Neoproterozoic, this supercontinental control can drive Earth into, as well as out of, a continuous or intermittently panglacial climate, consistent with aspects of proxy data for the Cryogenian‐Ediacaran period. The timing and magnitude of this cooling‐warming climate variability depends, however, on the detailed character of mantle thermal mixing, which is incompletely constrained. We show also that the predominant modes of chemical weathering and a tectonically paced abiotic methane production at mid‐ocean ridges can modulate the intensity of this climate change. For the Nuna epoch, the model predicts a relatively warm and ice‐free climate related to mantle dynamics potentially consistent with the intense anorogenic magmatism of this period.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".