Consistency of global carbon budget between concentration- and emission-driven historical experiments simulated by CMIP6 Earth system models and suggestions for improved simulation of CO <sub>2</sub> concentration
Bibliographic record
Abstract
Abstract. Anthropogenically emitted CO2 from fossil fuel use and land use change is partly absorbed by terrestrial ecosystems and the ocean, while the remainder retained in the atmosphere adds to the ongoing increase in atmospheric CO2 concentration. Earth system models (ESMs) can simulate such dynamics of the global carbon cycle and consider its interaction with the physical climate system. The ESMs that participated in the Coupled Model Intercomparison Project phase 6 (CMIP6) performed historical simulations to reproduce past climate–carbon cycle dynamics. This study investigated the cause of CO2 concentration biases in ESMs and identified how they might be reduced. First, we compared simulated historical carbon budgets in two types of experiments: one with prescribed CO2 emissions (the emission-driven experiment, “E-HIST”) and the other with a prescribed CO2 concentration (the concentration-driven experiment, “C-HIST”). Because the design of CMIP7 is being considered, it is important to explore any differences or implications associated with such variations. The findings of this confirmed that the multi-model means of the carbon budgets simulated by one type of experiment generally showed good agreement with those simulated by the other. However, the multi-model average of cumulative compatible fossil fuel emission diagnosed from the C-HIST experiment was lower by 35 PgC than that used as the prescribed input data to drive the E-HIST experiment; the multi-model average of the simulated CO2 concentration for 2014 in E-HIST was higher by 7 ppmv than that used to drive C-HIST. Regarding individual models, some showed a distinctly different magnitude of ocean carbon uptake from C-HIST because the E-HIST setting allows ocean carbon fluxes to be dependent on land carbon fluxes via CO2 concentration. Second, we investigated the potential linkages of two types of carbon cycle indices: simulated CO2 concentration in E-HIST and compatible fossil fuel emission in C-HIST. It was confirmed quantitatively that the two indices are reasonable indicators of overall model performance in the context of carbon cycle feedbacks, although most models cannot accurately reproduce the cumulative compatible fossil fuel emission and thus cannot reproduce the CO2 concentration precisely. Third, analysis of the atmospheric CO2 concentration in five historical eras enabled the identification of periods that caused the concentration bias in individual models. Fourth, it is suggested that this non-CO2 effect is likely to be the reason why the magnitude of the natural land carbon sink in historical simulations is difficult to explain based on analysis of idealized experiments. Finally, accurate reproduction of land use change emission is critical for better reproduction of the global carbon budget and CO2 concentration. The magnitude of simulated land use change emission not only affects the level of net land carbon uptake but also determines the magnitude of the ocean carbon sink in the emission-driven experiment. This study confirmed that E-HIST enables an evaluation of the full span of the uncertainty range covering the entire carbon–climate system and allows for an explicit simulation of the interlinking process of the carbon cycle between land and ocean. By isolating the forced responses and feedback processes of the carbon cycle processes, the usefulness of C-HIST in elucidating climate–carbon cycle systems and in identifying the cause of CO2 biases was confirmed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".