Very high fire danger in UK in 2022 at least 6 times more likely due to human-caused climate change
Bibliographic record
Abstract
Abstract The UK experienced an unprecedented heatwave in 2022, with temperatures reaching 40 °C for the first time in recorded history. This extreme heat was accompanied by widespread fires across London and elsewhere in England, which destroyed houses and prompted evacuations. While attribution studies have identified a strong human fingerprint contributing to the heatwave, no studies have attributed the associated fires to anthropogenic influence. In this study, we assess the contribution of human-induced climate change to fire weather conditions over the summer of 2022 using simulations from the HadGEM3-A model with and without anthropogenic emissions and apply the Canadian Fire Weather Index. Our analysis reveals at least a 6-fold increase in the probability of very high fire weather in the UK due to human influence, most of which is driven by high fire conditions across England. These findings highlight the significant role of human-induced climate change in emerging UK wildfires. As we experience more hotter and drier summers as temperatures continue to rise the frequency and severity of fires are likely to increase, posing significant risks to both natural ecosystems and human populations. This study underscores the need for further research to quantify the changing fire risk due to our changing climate and the urgent requirement for mitigation and adaptation efforts to address the growing wildfire threat in the UK.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".