High-resolution fire weather projections for effective forest management and restoration across the Mediterranean region 
Bibliographic record
Abstract
The Mediterranean region has historically been prone to wildfire activity. However, many Mediterranean countries have been particularly impacted in recent years by an increase in fire intensity and fire season length, with hundreds of thousands of hectares burned both north and south of the basin. Larger and more frequent fires are anticipated across the Mediterranean region in the future, a key driver of which is the projected increase in so-called fire weather (the meteorological conditions conducive to fire ignition and spread) associated with a warming world. In view of the loss or degradation of forest areas due to wildfires, and in the context of the ongoing UN Decade on Ecosystem Restoration (2021-2030), Mediterranean countries are actively engaging in post-fire restoration actions. Developing new insights into the evolution of fire weather across Mediterranean ecosystems is crucial for effective forest management and restoration planning. For the Mediterranean, fire weather projections under climate change have typically been extrapolated from global-scale studies or otherwise focused predominantly on Southern European countries. By contrast, far less attention has been given to countries in North Africa and the Middle East. Here, we generate high-resolution fire weather projections for the entire Mediterranean region, using the latest generation of global climate models. We calculate the Canadian Fire Weather Index (FWI) following a multivariate bias correction and downscaling of the FWI’s underpinning meteorological variables (namely, maximum daily temperature, minimum daily relative humidity, mean daily wind speed and daily precipitation). Results show changes in the magnitude of FWI seasonal means, maxima and fire season length in different scenarios and areas of the Mediterranean region where fire danger is projected to increase in the forthcoming decades. We discuss potential implications for future land management and restoration activities, as current preventive and restorative strategies should consider these future scenarios to ensure their success. The high-resolution fire weather projections generated here will help to better target areas of intervention and types of measures to be implemented.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".