Bibliographic record
Abstract
Abstract. Lightning is an important atmospheric process for generating reactive nitrogen, resulting in production of tropospheric ozone, as well as igniting wildland fires, which result in potentially large emissions of many pollutants and short-lived climate forcers. Lightning is also expected to change in frequency and location with the changing climate. As such, lightning is an important component of Earth system models. Until now, the Canadian Earth System Model (CanESM) did not contain an interactive lightning parameterization. The fire parameterization in CanESM5.1 was designed to use prescribed monthly climatological lightning. In this study, we have added a logistical regression lightning model that predicts lightning occurrence interactively based on three environmental variables and their interactions into CanESM5.1’s atmospheric model, CanAM5.1, creating the capacity to interactively model lightning, allowing for future projections under different climate scenarios. The modelled lightning and resulting burned area were evaluated against satellite measurements over the historical period and model biases were found to be acceptable. Modelled lightning was within a factor of two of the measurements and had exceptionally accurate land/ocean ratios. The modified version of CanESM5.1 was used to simulate two future climate scenarios (SSP2-4.5 and SSP5-8.5) to assess how lightning and burned area change in the future. Under the higher emission scenario (SSP5-8.5), CanESM5.1 predicts an increase in northern mid-latitude lightning flashrate of 5 %, but a decrease in tropical lightning of -10 %, resulting in almost no change to the global mean lightning amount by the end-of-the century (2081–2100 vs 2015–2035 average). By century’s end, the change in global total burned area with prescribed climatological lightning was about two times greater than that with interactive lightning (43 % vs 19 % increase, respectively). Conversely, in the northern mid-latitudes the use of interactive lightning resulted in three times more area burned as that with unchanging lightning (36 % vs 13 % increase, respectively). These results show that the future changes to burned area are greatly dependent on a model’s lightning scheme, both spatially and overall.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.015 | 0.014 |
| Insufficient payload (model declined to judge) | 0.338 | 0.246 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".