Improving Terrestrial Carbon Flux Simulations With Machine Learning and Global Earth Observations
Bibliographic record
Abstract
Abstract. The land carbon cycle can act as both a negative and positive climate feedback. Currently, it serves as a negative feedback, absorbing about one-third of anthropogenic CO2 emissions. However, multi-model studies project a weakening of this sink, with the potential for a future shift to a carbon source. Significant inter-model differences persist, limiting confidence in these projections. Some of these discrepancies may arise from parameter uncertainty. Advances in artificial intelligence, computing, and Earth observations now offer new opportunities to better constrain key model parameters. While previous studies have shown that parameter optimization can substantially improve model performance, they have not explored its impact on the future carbon balance. To address this gap, I use a machine learning algorithm to optimize 28 model parameters based on 13 global Earth observation datasets. The resulting parameter set is then applied in carbon cycle simulations under historical conditions and a high-emissions future scenario. Results show that optimization significantly improves model performance, particularly for gross primary productivity (GPP), leaf area index, and sensible heat flux. Globally, optimized net biome productivity is lower than in the default simulation (33 % lower from 1960 to 2022 and 43 % lower from 2015 to 2100) due to reduced GPP and increased autotrophic respiration. Regionally, optimization tends to weaken both carbon sinks and sources, reducing the contrast between them. In conclusion, parameter tuning can substantially alter historical and future carbon fluxes, with effects comparable to adding new processes. To reduce inter-model spread, modeling groups should integrate advanced parameter optimization frameworks into their model development cycle.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".