The exceptionality of the 2022 fire season over Central Europe
Bibliographic record
Abstract
The frequency and intensity of temperature extremes have increased worldwide in the past couple of decades. The year 2018 was characterized by record-breaking temperatures in many parts of Europe during spring and summer, which lead to unusual and severe wildfires in central and northern Europe. For example, devastating fires destroyed large areas of intact forests, not only in countries with a long tradition of wildfires but also in countries, such as Sweden, Norway, Finland, and Latvia.  In 2022, Europe was characterized   by a prolonged spring drought, several summer heatwaves and fire activity without precedents for several European countries. In particular, a high number of fires occurred in Germany, Austria, Chechia, Hungary, Slovenia and Romania during the summer months, highlighting the increase of fire-prone conditions in the region linked with an increase of hot and dry conditions.This work analyses the exceptionality of the 2022 fire season over central Europe. Fire Radiate Power (FRP) from MODIS, burned area and number of fires from EFFIS were used to characterize fire occurrence in 2022. We used ERA5 meteorological parameters, such as the maximum and minimum air 2m temperature, minimum relative humidity, and wind speed to evaluate the severity of the heat extremes from April to August. SPEI for the time scales of 6 and 12 months were computed using ERA5 data to evaluate the drought conditions in spring and summer over the central European region. The Canadian Fire Weather Index (FWI) and sub-indices available from ERA5 data, were used to assess the exceptionality of meteorological fire danger over the region in the summer of 2022.  Moreover, possible FWI trends and sub-indices were also analysed for the period from 1979 to 2022. The impact of drought on vegetation productivity during the spring and summer of 2022 was also evaluated. Results highlight the new fire dynamics in Europe in recent years, with new emergent hot spots, in central and northern European countries. It is thus extremely important to assess of trends of fire danger and changes in fire activity over this region to better define the related activities of fire monitoring, as well as the definition of planning activities and suppression measures towards climate mitigation and adaptation.Acknowledgements: This study is partially supported by the European Union’s Horizon 2020 research project FirEUrisk (Grant Agreement no. 101003890) and by the Portuguese Fundação para a Ciência e a Tecnologia (FCT) I.P./MCTES through national funds (PIDDAC) – UIDB/50019/2020- IDL and DHEFEUS - 2022.09185.PTDC.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".