Spatiotemporal variation characteristics of global fires and their emissions
Bibliographic record
Abstract
Intense regional fires are a common occurrence in the context of climate warming and have progressively evolved into one of the major natural disasters in terrestrial ecosystems, posing a serious hazard to the atmosphere and climate change. We investigated the spatial distribution, intensity, emission changes, and meteorological differences between fires in different fire-active and fire-prone regions globally based on multi-source satellite remote sensing fire data, emission data, and meteorological data in order to better understand the change trend of fire activity at multiple spatial and temporal scales. The findings demonstrate that while the burned area (BA) has decreased slowly over the last 20 years, the burned fraction (BF), the fire count (FC), and the fire radiative power (FRP) all exhibit pronounced regional and seasonal variations. The physical characteristics of fires, including the BF, FC, and FRP, experience greater seasonal variation as latitude increases, with summer and autumn being the seasons with the most frequent fire occurrence worldwide. This study also shows that the emissions declined substantially between 2012 and 2020 in northern Canada, Alaska, and northeastern China, whereas it notably increased in the Siberia region during the same period, primarily due to a rise in summer emissions. The results based on classification show that the difference in CO 2 produced by fires among regions is relatively small. Excluding CO 2 , aerosol emissions (the total of organic carbon (OC), total carbon (TC), black carbon (BC)) ranged from 78.6 % to 84.2 %, while the least significant air pollutants (the total of PM 2.5 , SO 2 , and NO x ) ranged from 5.8 % to 11.7 %. The abundance of vegetation predominately affects the intensity change in fire development, while the weather conditions can also indirectly influence the incidence of fire by altering the growth condition of vegetation. Correspondingly, the increase in temperature in the Northern Hemisphere's middle- and high-latitude forest regions is likely the major cause for the increase in fires and emissions, while the change in fires in tropical regions was largely influenced by the decrease in precipitation and relative humidity. This study contributes to the understanding of regional variations in fire activity and emission variability and provides support for the control of fire activity across regions and seasons.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".