Global Sensitivity Analysis of the Future Land Carbon Sink
Bibliographic record
Abstract
The terrestrial biosphere absorbs more CO2 than it emits, slowing the accumulation of atmospheric CO2. However, Earth System Models differ in their Net Biome Productivity (NBP) projections, with inter-model ranges of 2 to 7 PgC yr−1 by the late 21st century under a fossil-fuel-intensive scenario. We notice that the uncertainty in NBP simulated by the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC) model is vastly impacted by parameter uncertainty. To address this uncertainty, we conduct a global sensitivity analysis (GSA) for seven grid cells across different biomes. Results of the preliminary screening test show that only 11–15 of 124 input parameters drive the output uncertainty at each location. Among them, the maximum carboxylation rate (vmax) consistently influences multiple output variables. Through the secondary quantitative test, we notice that vmax's impact on NBP declines over time, but other photosynthetic and rooting parameters become more influential. In some locations, higher vmax values reduce NBP, as increased ecosystem respiration and wildfire emissions outweigh gross primary productivity. Despite using approximately eight weeks of computational effort using 120 cores, the sampling uncertainty among the 11–15 parameters is broad. Ranking the parameters based on robustness is difficult. Expanding the analysis to an additional metric, we find that the same parameters that drive the uncertainty of the projected future NBP also drive the uncertainty of the change from the late historical to late future NBP. These results indicate that future optimization efforts related to NBP must consider multiple parameters rather than focussing solely on vmax. By identifying influential parameters and processes, this study enhances our understanding of parametric uncertainty in carbon sink projections and defines a low-dimensional space of influential parameters, aiding future model refinement.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".