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 forests. They leave a historical legacy of a mosaic landscape of different aged forest cohorts. This forest age dynamics must be included in vegetation models to accurately quantify the role of fire in the historical and current regional forest carbon balance. The present study adapted the global process-based vegetation model ORCHIDEE to simulate the CO2 emissions from boreal forest fire and the subsequent recovery after a stand-replacing fire; the model represents postfire new cohort establishment, forest stand structure and the self-thinning process. Simulation results are evaluated against observations of three clusters of postfire forest chronosequences in Canada and Alaska. The variables evaluated include: fire carbon emissions, CO2 fluxes (gross primary production, total ecosystem respiration and net ecosystem exchange), leaf area index, and biometric measurements (aboveground biomass carbon, forest floor carbon, woody debris carbon, stand individual density, stand basal area, and mean diameter at breast height). When forced by local climate and the atmospheric CO2 history at each chronosequence site, the model simulations generally match the observed CO2 fluxes and carbon stock data well, with model-measurement mean square root of deviation comparable with the 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 the current postfire forest carbon sink at the evaluation sites, as observed by chronosequence methods, is mainly due to a combination of historical CO2 increase and forest succession. Climate change and variability during this period offsets some of these expected carbon gains. The negative impacts of climate were a likely consequence of increasing water stress caused by significant temperature increases that were not matched by concurrent increases 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-stock evolution after fire. This makes the model suitable for regional simulations in boreal regions where fire regimes play a key role in the 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".