Parameter Optimization for Global Soil Carbon Simulations: Not a Simple Problem
Bibliographic record
Abstract
Abstract Accurate simulation of soil organic carbon (SOC) dynamics by terrestrial biosphere models is hampered by poorly constrained parameters and parameter equifinality, amongst other issues. To address this, we use Bayesian optimization to constrain the 16 SOC‐related parameters in the Canadian Land Surface Scheme Including biogeochemical Cycles (CLASSIC). We employed a global sensitivity analysis (Sobol’) to develop four parameter sets based upon different sensitivity criteria. We then optimized each set against observed SOC (World Soil Information Service; WoSIS) and soil respiration (Soil Respiration Database; SRDB). Using two different loss functions; one focused on reproducing the observational mean value, and the other explicitly accounting for an estimated observational uncertainty. The best optimized parameter sets for each loss function had an average relative difference of 61%. Thus, the choice of loss function impacts what parameter values are deemed optimal and should be considered carefully. The final set of selected optimal parameters saw a 12% improvement against WoSIS and SRDB, had global SOC totals in line with literature estimates, and better simulated high‐latitude SOC stocks evaluated against the Northern Circumpolar Soil Carbon Database (RMSD: 16.39 vs. 17.61; bias: −5.57 vs. −10.78 kg C ) compared to the default CLASSIC parameters. However, some parameters were not well constrained, in particular those of needle‐leaf deciduous trees that dominate the Siberian boreal forests, a region relatively poorly observed in WoSIS and SRDB. Future work should apply further constraints on the optimization framework and address observational gaps.
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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 | 0.000 |
| 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".