Exploring the effect of vegetation photosynthesis phenology on wildfire dynamics
Bibliographic record
Abstract
2023 has witnessed a record-breaking extreme wildfire season in Canada from coast to coast, following closely to the unprecedented wildfire outbreaks in 2019/20 Australia and 2021 Siberia, causing far-reaching threats on terrestrial carbon stock, air quality, and human society. The heightened wildfire activity in specific regions prompts us to rethink the underlying factors driving the global wildfire dynamics. Climate change has been recognized as an important factor in amplifying wildfire risk, mainly through increasing temperature and reducing relative humidity. However, the role of vegetation productivity and phenology on wildfire dynamics remains elusive, even though which can exacerbate or mitigate the climate-induced fire risk. Importantly, changes in vegetation phenology can cause biophysical feedback to the climate system and land surface by modulating the exchanges of water and energy between land and the atmosphere. Considering the climate feedback of vegetation phenology, we hypothesize that peak photosynthesis timing (PPT) can contribute to wildfire activity. To explore it, we provide comprehensive analyses using multiple satellite-based photosynthesis observations from solar-induced chlorophyll fluorescence (SIF), and wildfire activity from national fire perimeters and MODIS global burned area records from 2001 to 2018, as well as diverse methodologies and models. In response to changes in various biological and climatic factors, we find PPT has advanced 1.10 ± 0.57 days per decade at a global scale. This earlier PPT acts to expand the extent of wildfires, with an increase in the global average burned fraction by 0.021% (~2.20 Mha) for every additional day of PPT advancement. Satellite observations and the Earth system modeling consistently reveal that this expansion is attributed to the intensified drought conditions during the potential fire season, induced by the earlier PPT that can modulate the global patterns of temperature, precipitation, and surface soil moisture. Furthermore, current fire-vegetation models participating in the FireMIP project underestimate the sensitivity of burned area to PPT, despite reproducing their negative correlation. Our findings highlight the importance of climate-vegetation-fire feedback loops in future prediction of wildfire dynamics and the strategy of climate change adaptation and mitigation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".