The impact of climate change on fire danger over the contiguous United States
Bibliographic record
Abstract
Recent extreme heat events, especially those that have occurred in the Western United States (WUS), are fueling wildfires and funneling smoke at an unprecedented level, impacting air/water quality and leading to an increase in respiratory hospitalizations. Under greenhouse warming, extreme weather conditions that favor wildfire ignition are expected to occur more frequently over the contiguous United States (CONUS). Therefore, predicting wildfire danger under a changing climate is essential in managing future wildfires and protecting the welfare of people and the environment. In response to mitigating wildfire risks, the Canadian Fire Weather Index (FWI) was developed to provide a numeric rating representing the intensity of a spreading fire. In this work, we utilized fine-scale (0.25° x 0.25°) daily meteorological inputs from thirty-five general circulation models in NASA Earth Exchange Global Daily Downscaled Projections Coupled Model Intercomparison Project Phase 6 (NEX-GDDP-CMIP6) data to calculate the FWI. Using the daily maximum temperature, relative humidity, wind speed, and precipitation from NEX-GDDP-CMIP6, we calculated the FWI of historical and future simulations from the periods of 1950 to 2100 under different emission scenarios (Shared Socioeconomic Pathways 2-4.5 and 5-8.5). We have analyzed the FWI for the GISS-E2-1-G model, which indicates a 2-3% increase per decade in future fire danger under both emission-pathway-driven climate scenarios during the dry season in the Southwestern US. We have found that the FWI climatology in the Southwestern US during the Summer presents high to extreme fire danger (> 50) and higher FWI values in the future compared to historical observations. Moreover, we have explored the uncertainties across multiple models using NEX-GDDP-CMIP6 statistically downscaled data and found a significant spread of the FWI across the models for historical observations and future simulations. To correlate the link between the FWI and actual fire occurrence, we will calculate the FWI using reanalysis data (MERRA-2) and validate the FWI with actual fire occurrence data from Global Fire Emissions Database (GFED) with a special emphasis on the WUS. While supporting the US NCA and NASA’s Climate Adaptation Service Investigator (CASI), we will also try to contribute FWI to NASA’s FireSense, an initiative to bring an Earth systems approach to improving wildfire and wildland fire management.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".