A Global Sensitivity Analysis of Parameter Uncertainty in the CLASSIC Model
Bibliographic record
Abstract
Land surface models (LSMs) have become indispensable for understanding the role of the terrestrial biosphere in the global climate system. However, the ability of LSMs to reproduce observed carbon, water, and energy fluxes varies considerably among models. Some of these deficiencies can be attributed to parameter uncertainties. Global sensitivity analysis (GSA) quantifies model output uncertainties caused by the uncertainty in model inputs. Our study conducts, for the very first time, a GSA for the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC) model. Focusing on a site in the humid tropics, we evaluate the model's sensitivity for a wide range of ecosystem variables (17 in total). Considering a total of 90 parameters, we identify the top five most influential parameters using the qualitative Morris method per output variable. These influential parameters are then analysed using the quantitative Sobol' method. The analysis shows that the maximum carboxylation rate parameter has the greatest influence on almost all output variables considered. The impact of the maximum carboxylation rate is partially regulated by the canopy extinction coefficient's uncertainty. The results of this research will guide future efforts to optimize the model's performance more efficiently, focussing on a small subset of the 90 parameters.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".