Wildfires in earth system: Driver, transport and feedback
Bibliographic record
Abstract
Wildfires release large amounts of greenhouse gases, carbonaceous aerosols, and other pollutants, therefore having complex impacts on the earth climate, local weather, and air quality. To study the transport of the wildfire emissions, a plume height dataset has been developed. The resulting dataset from 2002 to 2010 captured well the observed MISR plume height distribution. By adding the plume height dataset in the climate model, the plume-rise enhanced AOD downstream of the wildfire spots by 20 to 50%. Moreover, an online plume rise module for CAM5 has been developed, allowing for the feedbacks of climate/weather on fire plume rise. As an application of this developed plume height dataset, the impact of West Canada wildfires (WCWs) on Northeast United States (NEUS) have been investigated. The observed OC/EC ratios over the NEUS show significant correlations with WCWs burned area since 2001. Detailed analysis and modeling simulations show that the strength of wildfire explains 48% variance of OC/EC disturbance while the transport effect explains another 35% variance. Africa wildfires response to half of global wildfire emissions. To investigate the driver of this wildfire variability, this study examined relationship between fire, climate, and ecosystem in arid, intermediate and mesic regions. The results show that the LAI caused fuel limitation dominates the wildfire variability in Africa. As an important feedback from wildfires, the fire-forest interaction is recognized as an important disturbance to produce the savanna-forest landscape in Africa. This study presents new observational evidence, showing significant negative lag correlations between the burned area and the forest amount in both hemisphere Africa. Ensemble runs of a modified ecosystem model have been performed with broad range of parameter values, suggesting that 90% of the fire needs to be reduced compared to 2005 level to reach the RCP4.5 forest target in 2100.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".