No apparent state-dependency of equilibrium climate sensitivity between the Pleistocene glacial and interglacial climate states
Bibliographic record
Abstract
Quantifying climate sensitivity is essential for future climate projections, yet it varies with major Earth system changes. We present a glacial CO₂ reconstruction using paleosols from the Chinese Loess Plateau, covering 2580 to 800 thousand years ago. A stepwise decline in glacial CO₂ levels from ~300 ppm to <200 ppm is observed. Our paleosol-based CO₂ estimates support the key role of atmospheric CO₂ in driving major climate transitions during the Pleistocene, such as the long-term global cooling and the amplification of the glacial cycles. Based on compiled glacial and interglacial CO2 records, Earth system sensitivity, defined as the global temperature change for a doubling of CO2 once the whole Earth system has reached equilibrium, is estimated to be ~6.2–7.4 K (3.2–12.0 K, 95% confidence). Equilibrium climate sensitivity, after accounting for the different efficacy between ice-sheet and CO2 forcing and other slow feedbacks, is estimated to be 3.3 K (2.1–6.3 K, 95% confidence) and 3.7 K (1.7–6.3 K, 95% confidence), respectively. The lack of a significant difference between these values suggests no apparent state-dependency of climate sensitivity between glacial and interglacial climate states. A new paleosol-based CO₂ record from the Chinese Loess Plateau reveals a stepwise decline in glacial CO₂ over the past 2.6 million years and suggests that climate sensitivity remained stable across glacial–interglacial cycles.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".