Global Sensitivity Analysis of the Historical Carbon Sink across Biomes
Bibliographic record
Abstract
The terrestrial biosphere currently acts as a carbon sink, mitigating atmospheric CO2 increases caused by human activities. However, the sink's strength remains highly uncertain, with recent terrestrial biosphere model estimates ranging from 1.0 to 3.2 PgC yr−1 during 2014–2023. Some of this broad inter-model difference is due to input parameter uncertainties. To better understand the effects of parameter uncertainties, we conducted a global sensitivity analysis (GSA) using the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC). We assessed how input parameter uncertainties influence simulated historical carbon cycle variables over long timescales. Our two-step GSA was applied to seven grid cells, each located in a different biome, and for two statistical measures, the 30-year mean and 30-year trends. In the first step, we applied the Morris method, a qualitative, coarse-sample screening approach, to 124 input parameters and reduced the set to fewer than 20 influential parameters per biome. In the second step, we used the Sobol' method, a fine-sample quantitative technique, to estimate the absolute effects of these parameters. The analysis identified that the maximum carboxylation rate was consistently the most influential across five of the seven biomes for both statistical measures of net biome productivity. Despite six weeks of computation on 120 parallel cores, the confidence intervals for sensitivity indices remained broad, preventing definitive parameter rankings. Influential parameters were associated to ecosystem processes such as photosynthesis, phenology, rooting, respiration, mortality, and carbon allocation. Notably, the sensitivity indices for trends were less robust than those for means. Overall, our results indicate that only 13–15 parameters account for most of the uncertainty in output variables for different variables, statistical measures, and biome locations. Our study shows that GSA is a crucial step before model calibration, helping to prioritise parameters that most influence carbon cycle simulations.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".