Attributing uncertainties in simulated biospheric carbon fluxes to different error sources
Bibliographic record
Abstract
[1] Estimating the current sources and sinks of carbon and projecting future levels of CO2 and climate require biospheric carbon models that cover the landscape. Such models inevitably suffer from deficiencies and uncertainties. This paper addresses how to quantify errors in modeled carbon fluxes and then trace them to specific input variables. To date, few studies have examined uncertainties in biospheric models in a quantitative fashion that are relevant to landscape-scale simulations. In this paper, we introduce a general framework to quantify errors in biospheric carbon models that “unmix” the contributions to the total uncertainty in simulated carbon fluxes and attribute the error to different variables. To illustrate this framework we apply and use a simple biospheric model, the Vegetation Photosynthesis and Respiration Model (VPRM), in boreal forests of central Canada, using eddy covariance flux measurement data from two main sites of the Canadian Carbon Program (CCP). We explicitly distinguish between systematic errors (“biases”) and random errors and focus on the impact of errors present in biospheric parameters as well as driver data sets (satellite indices, temperature, solar radiation, and land cover). Biases in downward shortwave radiation accumulated to the most significant amount out of the driver data sets and accounted for a significant percentage of the annually summed carbon uptake. However, the largest cumulative errors were shown to stem from biospheric parameters controlling the light-use efficiency and respiration-temperature relationships. This work represents a step toward a carbon model-data fusion system because in such systems the outcome is determined as much by uncertainties as by the measurements themselves.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 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".