Sensitivity of net ecosystem exchange and heterotrophic respiration to parameterization uncertainty
Bibliographic record
Abstract
We examine the uncertainty in net ecosystem exchange due to the model treatment of heterotrophic respiration in a variety of hydroclimatic conditions using a land surface model. Multiple soil temperature‐respiration functions and soil moisture‐respiration functions are incorporated into the Carnegie‐Ames‐Stanford Approach with Carbon‐Nitrogen‐Phosphorus (CASA‐CNP) biogeochemical model coupled to the Community Atmosphere Biosphere Land Exchange land surface model. Every possible combination of the newly implemented functions is then used to simulate heterotrophic respiration and net ecosystem exchange at 10 different flux towers covering a large range of global vegetation types. Results show that a large uncertainty in the simulated net ecosystem exchange is attributable to differences in the soil respiration parameterization. No single combination of soil temperature and moisture‐respiration functions appears to show superior performance across all sites. Large variations in the simulated evolution of soil carbon storages emphasize the problem that to use an observationally based soil temperature or soil moisture response function requires a land surface model to capture the observed soil temperature and soil moisture mean and variability correctly. Land surface models are known to vary dramatically in their simulation of the soil moisture state and probably in their simulation of soil temperature. Resolving how to simulate heterotrophic respiration and net ecosystem exchange will therefore require an accurate simulation of temperature and moisture combined with a realistic soil heterotrophic respiration parameterization, and these cannot be developed and implemented in isolation.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".