Attribution of the role of climate change in the forest fires in Sweden2018
Bibliographic record
Abstract
Abstract. In this study we analyse the role of climate change in the forest fires that raged through large parts of Sweden in the summer of 2018 from a meteorological perspective. This is done by studying the Canadian Fire Weather Index (FWI) based on sub-daily data, both in reanalysis datasets (ERA-Interim, ERA5, JMA55 and MERRA2) and three large ensemble climate models (EC-Earth, W@H and CESM) simulations. The FWI based on reanalysis correlates well with observed area burned in summer (r = 0.6 to 0.8). We find that the maximum forest fire risk in July 2018 had return times of ∼ 24 years for Southern and Northern Sweden. Further, we find a negative trend of the FWI for Southern Sweden over the 1979 to 2017 time period, yielding a decreasing risk of such an event solely based on reanalysis data. However, given the short observational record, large uncertainty between the reanalysis products and large natural variability of the FWI we cannot draw robust conclusions from reanalysis data. The 3 large-ensembles with climate models on the other hand point to a roughly 1.1 times increased risk for such events in the current climate relative to pre-industrial climate. For a future climate (2C warming) we find a roughly 2 times increased risk for such events relative to pre-industrial climate. The increased fire weather risk is mainly attributed to the increase in temperature. The other main factor, precipitation during summer months, is projected to increase for Northern Sweden, and decrease for Southern Sweden. We however do not find a clear change of prolonged dry periods in summer months that could explain the increased fire weather risk. In summary, we find a small but positive role of global warming up to now in the 2018 forest fires in Sweden, but a more robust increase in the risk for such events in the future.
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 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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".