A new lightning scheme in the Canadian Atmospheric Model (CanAM5.1): implementation, evaluation, and projections of lightning and fire in future climates
Bibliographic record
Abstract
Abstract. Lightning is an important atmospheric process for generating reactive nitrogen, resulting in the 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 in CanESM5.1's atmospheric model, CanAM5.1 (Canadian Atmospheric Model), 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 had a small negative bias and excellent land–ocean ratio compared to satellite measurements. The modified version of CanESM5.1 was used to simulate two future climate scenarios (SSP2-4.5 and SSP5-8.5; Shared Socioeconomic Pathway) to assess how lightning and burned area change in the future. Under the higher-emissions scenario (SSP5-8.5), CanESM5.1 predicts almost no change to the global mean lightning flash rate by the end of the century (2081–2100 vs. 2015–2035 average). However, there are substantial regional changes to lightning – particularly over land – such as a mean increase of 6 % in the northern mid-latitudes and decrease of −8 % in the tropics. By the century's end, the change in global total burned area with prescribed climatological lightning was about 2 times greater than that with interactive lightning (42 % vs. 26 % increase, respectively). Conversely, in the northern mid-latitudes the use of interactive lightning resulted in 3 times more burned area compared to that with unchanging lightning (48 % vs. 16 % 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".