Risk of forest fire in Sweden under historical and future climate projections from 1971 to 2100t fire in Sweden under historical and future climate projections from 1971 to 2
Bibliographic record
Abstract
With the large forest fire in Sala 2014 and the forest fires during summer 2018 in mind, evaluating the tendency of high-risk fire season (HRS) under a changing climate shows its importance for risk management.This study focuses on exploring the behaviours of several user-defined fire-risk indicators concerning start, end, length, HRS and frequency of HRS, and impact of preconditions, e.g., snow cover and overwintering conditions. Here, we carry out the study by driving a Canadian forest fire model, the Fire Weather Index (FWI, Van Wagner,1987), using meteorological forcing from an ensemble of regional climate projections compiled from the Coupled Model Intercomparison Project Phase 5 (CMIP5, Taylor et al., 2012). The used CMIP5 data covers historical and representative concentration pathway projections (RCPs) from 1971 to 2100. The bias in the climate model projections is adjusted using the MultI-scale bias AdjuStment (MIdAS, Berg et al., 2022) with Copernicus regional reanalysis for Europe (CERRA, Schimankes et al., 2021) as a reference. The impact of climate change on the fire risk for three future periods (i.e., 2011–2040, 2041–2070 and 2071–2100) is explored under three RCPs (RCP2.6, 4.5 and 8.5). The ensemble agreement is used to evaluate the robustness of the fire risk indicators. The results show that all robust changes are toward increasing risk. More specifically, the length of HRS increases in southern and eastern Sweden. The start of HRS shifts to earlier in the eastern coastal and northern regions of Sweden in RCP4.5 and 8.5. In all RCPs the end of HRS is delayed by a couple of weeks in the southern regions in the period after 2041. The HRS is likely to become more frequent in the regions along the east coast and in southern Sweden.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".