Simulating boreal forest carbon dynamics after stand-replacing fire disturbance: insights from a global process-based vegetation model
Bibliographic record
Abstract
Abstract. Stand-replacing fires are the dominant fire type in North American boreal forest and leave a historical legacy of a mosaic landscape of different aged forest cohorts. To accurately quantify the role of fire in historical and current regional forest carbon balance using models, one needs to explicitly simulate the new forest cohort that is established after fire. The present study adapted the global process-based vegetation model ORCHIDEE to simulate boreal forest fire CO2 emissions and follow-up recovery after a stand-replacing fire, with representation of postfire new cohort establishment, forest stand structure and the following self-thinning process. Simulation results are evaluated against three clusters of postfire forest chronosequence observations in Canada and Alaska. Evaluation variables for simulated postfire carbon dynamics include: fire carbon emissions, CO2 fluxes (gross primary production, total ecosystem respiration and net ecosystem exchange), leaf area index (LAI), and biometric measurements (aboveground biomass carbon, forest floor carbon, woody debris carbon, stand individual density, stand basal area, and mean diameter at breast height). The model simulation results, when forced by local climate and the atmospheric CO2 history on each chronosequence site, generally match the observed CO2 fluxes and carbon stock data well, with model-measurement mean square root of deviation comparable with measurement accuracy (for CO2 flux ~100 g C m−2 yr−1, for biomass carbon ~1000 g C m−2 and for soil carbon ~2000 g C m−2). We find that current postfire forest carbon sink on evaluation sites observed by chronosequence methods is mainly driven by historical atmospheric CO2 increase when forests recover from fire disturbance. Historical climate generally exerts a negative effect, probably due to increasing water stress caused by significant temperature increase without sufficient increase in precipitation. Our simulation results demonstrate that a global vegetation model such as ORCHIDEE is able to capture the essential ecosystem processes in fire-disturbed boreal forests and produces satisfactory results in terms of both carbon fluxes and carbon stocks evolution after fire, making it suitable for regional simulations in boreal regions where fire regimes play a key role on ecosystem carbon balance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".