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Record W4408431531 · doi:10.5194/egusphere-egu25-12890

Assessing the Impact of Climate Change on Forest Fire Weather Index Using Downscaled Climate Model Data

2025· preprint· en· W4408431531 on OpenAlexaboutno aff
Anton Laakso, Meeri Palokangas, Taejun Park, Antti Lipponen, Laura Utriainen, Tero Mielonen

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changeEnvironmental scienceIndex (typography)ClimatologyMeteorologyClimate change scenarioGeographyComputer scienceEcology

Abstract

fetched live from OpenAlex

In recent years, fire activity at high latitudes has reached unprecedented levels, driven in part by global warming, which increases fire danger. Climate projections of fire risk rely on indices like the Canadian Forest Fire Weather Index (FWI), which are often derived from coarse-resolution climate models. Thus, there is the need for finer-scale fire weather projections to enable more effective planning and resource allocation as wildfire threats grow. High-resolution climate projections can be achieved through various methods, including dynamical and statistical downscaling, each potentially yielding different estimates of FWI and its future changes. We calculated the FWI based on HCLIM - Nordic Convection Permitting Climate Projections (NorCP) over Fennoscandia. The simulations include 12 x 12 km resolution models using HCLIM-ALADIN and convection-permitting simulation at 3 x 3 km resolution with HCLIM-AROME, covering both historical and future periods under the RCP8.5 scenario. Results were compared against FWI estimates from other climate datasets, such as CORDEX and statistically downscaled NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP).As expected, all the simulations indicate that the annual and summer mean FWI indices will increase significantly in warmer future climates, along with an increase in days with moderate and high fire weather risk. However, the magnitude of the risk depends heavily on the climate dataset used. For instance, HCLIM-AROME simulations generally show higher FWI values in the historical period even when compared to the future projections of HCLIM-ALADIN, due to generally lower summer precipitation in the former model. Additionally, there are notable regional disparities between the HCLIM simulations, with the highest FWI values observed in coastal areas of southern Finland and Sweden. According to the HCLIM-AROME simulations under the RCP8.5 scenario, these regions experience a moderate fire risk (FWI > 11) on roughly one out of three summer days, whereas HCLIM-ALADIN simulations indicate an average of 7–20 days per summer with such risk. There are also differences in the magnitude and regional distribution of FWIs calculated from HCLIM, NEX-GDDP, and CORDEX simulations. However, all future FWI predictions consistently indicate that, without effective mitigation of global warming, conditions for forest fires will worsen 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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.224

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.080
GPT teacher head0.360
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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