Climate Change Impacts on Forest Carbon: Drought, Fire, Biotic Disturbances and Long-Term Sink Resilience
Bibliographic record
Abstract
Abstract Forests currently absorb a substantial fraction of anthropogenic CO₂ emissions, buffering the pace of climate change. However, rising temperatures, altered precipitation regimes and increasing disturbance intensity are undermining the stability of forest carbon stocks. This review synthesizes evidence on four key pathways by which climate change affects forest carbon: (1) drought impacts on tree growth and mortality, (2) changing fire regimes, (3) pest and disease outbreaks, and (4) long-term resilience of forest carbon sinks. Hotter droughts reduce growth, increase mortality and create “legacy effects” that depress carbon uptake for years after drought events. Increasing fire frequency and severity, particularly in boreal and Mediterranean regions, directly consume biomass, erode soil carbon, and can shift vegetation to lower-carbon states. Pest and disease outbreaks—exemplified by the mountain pine beetle in western Canada—are amplified by warming and drought, transforming vast forest areas from carbon sinks into net sources. At larger scales, disturbance amplification and drought-induced “sink saturation” are weakening forest contributions to national and global climate targets, as shown in Europe, North America and the Amazon. Nonetheless, management that promotes structural and species diversity, reduces fuel loads and limits high-risk monocultures can enhance the resilience of forest carbon sinks under a warming climate. Keywords: forest carbon, drought, wildfire, insect outbreaks, tree mortality, carbon sink resilience
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".