Cloud-to-Ground Lightning Trends in Canada and Regions of the United States North of 40°N 1999–2023
Bibliographic record
Abstract
An assessment of the temporal and spatial trends in lightning activity in Canada and adjacent United States was undertaken using cloud-to-ground (CG) stroke data collected by the Canadian Lightning Detection Network from 1999 to 2023. The nonparametric Mann-Kendall test was used to identify monotonic trends in the data. The direction of trends was determined by the slope of a linear fit to the data. Common perceptions are that lightning will increase due to climate change, but at least in temperate latitudes, based on trends shown here the full picture is more nuanced. Total and negative-polarity CG lightning has shown a steady decline nationally in the observing period. The decline is most pronounced in Central Canada (Ontario and Quebec) and adjacent regions of the United States. Declines have also been detected over the eastern Prairies (SK and MB) and Atlantic Canada. An increasing trend, however, has been observed in the three northern territories, northern BC and northern AB. Sizeable areas of upward-trending positive CG lightning within wildfire environments have been detected. Observed trends in CG lightning may be explained by long-term changes in the continental-scale general circulation that are likely due to the onset of the positive phase of the Pacific Decadal Oscillation coupled with ENSO events occurring in a climate warming background.
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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 | 0.001 |
| 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".