Modelling interactive fires: climate-fire feedbacks on fire characteristics and multi-model projects
Bibliographic record
Abstract
There are multiple feedback mechanisms between wildfires and climate, such as temperature, emissions, cloud interactions, deposition, and land cover changes. Wildfires can also have large societal and ecological impacts and are considered as an extreme climate event. Despite this, most Earth System Models have, until recently, used prescribed fire emissions and fire plume injection heights for input into their atmospheric models that were unresponsive to climate changes. Fire plume heights, in particular, have a great influence on the radiative forcing and long-range transport of pollutants. This presentation will show recent results from global modelling of interactive fires (land-atmosphere) in the Canadian Earth System Model (CanESM), with a focus on key wildfire characteristics, such as aerosol emissions and fire plume height. These model improvements introduce the capacity to more accurately simulate future projections of wildfire characteristics under different climate scenarios. The upcoming applications of these improvements include experiments for the Hemispheric Transport of Air Pollution (HTAP) Fires project, AerChemMIP2, and Aerocom. HTAP Fires is a multi-model, multi-pollutant study with the goal of improving global fire modelling and using the multi-model ensembles to provide estimates of fire-related pollution for impact studies and policy makers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".