Forest-fire aerosol–weather feedbacks over western North America using a high-resolution, online coupled air-quality model
Bibliographic record
Abstract
Abstract. The influence of both anthropogenic and forest-fire emissions, and their subsequent chemical and physical processing, on the accuracy of weather and air-quality forecasts, was studied using a high-resolution, online coupled air-quality model. Simulations were carried out for the period 4 July through 5 August 2019, at 2.5 km horizontal grid cell size, over a 2250×3425 km2 domain covering western Canada and USA, prior to the use of the forecast system as part of the FIREX-AQ ensemble forecast. Several large forest fires took place in the Canadian portion of the domain during the study period. A feature of the implementation was the incorporation of a new online version of the Canadian Forest Fire Emissions Prediction System (CFFEPSv4.0). This inclusion of thermodynamic forest-fire plume-rise calculations directly into the online air-quality model allowed us to simulate the interactions between forest-fire plume development and weather. Incorporating feedbacks resulted in weather forecast performance that exceeded or matched the no-feedback forecast, at greater than 90 % confidence, at most times and heights in the atmosphere. The feedback forecast outperformed the feedback forecast at 35 out of 48 statistical evaluation scores, for PM2.5, NO2, and O3. Relative to the climatological cloud condensation nuclei (CCN) and aerosol optical properties used in the no-feedback simulations, the online coupled model's aerosol indirect and direct effects were shown to result in feedback loops characterized by decreased surface temperatures in regions affected by forest-fire plumes, decreases in stability within the smoke plume, increases in stability further aloft, and increased lower troposphere cloud droplet and raindrop number densities. The aerosol direct and indirect effect reduced oceanic cloud droplet number densities and increased oceanic raindrop number densities, relative to the no-feedback climatological simulation. The aerosol direct and indirect effects were responsible for changes to the near-surface PM2.5 and NO2 concentrations at greater than the 90 % confidence level near the forest fires, with O3 changes remaining below the 90 % confidence level. The simulations show that incorporating aerosol direct and indirect effect feedbacks can significantly improve the accuracy of weather and air-quality forecasts and that forest-fire plume-rise calculations within an online coupled model change the predicted fire plume dispersion and emissions, the latter through changing the meteorology driving fire intensity and fuel consumption.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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 teacher head, 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".