Lightning in a changing climate and its impacts on fire area burned
Bibliographic record
Abstract
Lightning is an important atmospheric process for igniting forest fires – often in remote locations where they are not easily suppressed – which results in potentially large emissions of many pollutants and short-lived climate forcers. Lightning also generates reactive nitrogen, resulting in the production of tropospheric ozone, the third most important greenhouse gas. Furthermore, the changing climate is expected to change the frequency and location of lightning. As such, lightning is an important component of climate models. The Canadian Atmospheric Model, CanAM, is one such climate model that did not contain an 'online' lightning parameterization. Fire ignition in CanAM was done via an unchanging climatological lightning input. In this study, we have added a new logistical regression lightning model (Etten-Bohm et al, 2021) into CanAM, creating the capacity for future lightning predictions with CanAM under different climate scenarios. The modelled lightning and fire area burned were evaluated against measurements in a historical period with good results. Then we simulate lightning and fire area burned in a future climate scenario in order to provide an estimate on how lightning and its impacts will change in the future. This study also presents the first time that CanAM’s land fire model was used online with its atmosphere to fully simulate fires in the global earth system.Reference:Etten-Bohm, M., J. Yang, C. Schumacher, and M. Jun : Evaluating the relationship between lightning and the large-scale environment and its use for lightning prediction in global climate models, JGR-atmospheres, 126, e2020JD033990, 2021.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".