An investigation of carbon cycle dynamics since the Last Glacial Maximum: Complex interactions between the terrestrial biosphere, weathering, ocean alkalinity, and CO<sub>2</sub> radiative warming in an Earth system model of intermediate complexity
Bibliographic record
Abstract
Abstract. Proxy reconstructions and modeling studies of the glacial-interglacial changes in the global carbon cycle have led to a stimulating debate in the paleoclimate literature about the mechanisms leading to a 90–100 ppmv increase in atmospheric CO2. In this paper, we used the University of Victoria Earth System Climate Model v. 2.9 to simulate the carbon cycle response to ice sheet retreat and Milankovitch (insolation) forcing from the Last Glacial Maximum (LGM) to the present. In addition, we conducted sensitivity studies to address the contributions of CO2 radiative forcing, atmospheric carbon content, and weathering rates to climate and carbon cycle changes since 21 kyr BP. The simulations show that ice sheet and orbital changes by themselves do not lead to a notable increase in atmospheric CO2 over the course of deglaciation. However, with the application of CO2 radiative forcing and different weathering rates, the simulated atmospheric CO2 variations ranged over ~ 35 ppmv. Virtually all of the simulated net global vegetation carbon uptake since the LGM is attributable to CO2 fertilization rather than greater land availability or warmer temperatures. Furthermore, the ‘greening’ from CO2 fertilization significantly enhances total deglacial warming (by 0.14°C) and contributes to warmer intermediate and deep ocean temperatures during the interglacial period. We also found that CO2 radiative forcing was the dominant factor allowing for greater outgassing at the ocean surface and an earlier ventilation of deep-ocean DIC. The downwelling of high-alkalinity surface waters stimulated by a stronger, earlier overturning circulation led to greater deep sedimentation (alkalinity removal), which, in turn, permitted CO2 to continue to increase through much of the simulation 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".