Laboratory of Tree-Ring Research and School of Natural Resources and the Environment
Bibliographic record
Abstract
Understanding the role of climate variation in governing fire regimes remains one of the central needs in contemporary fire science and management. Ideally, this understanding should encompass both historical and current fire-climatology, and inform both basic science and ecosystem management. In this project, Fire and Climate Synthesis (FACS) we undertook a detailed synthesis of both paleofire and modern fire based on compilations of existing data sets. We also analyzed three major thematic pathways by which climate has impacted fire policy, including direct and indirect climate effects on fire policy. Paleofire. We assembled the largest and most comprehensive data set of cross-dated, georeferenced fire-scar paleofire records ever compiled for western North America. Data were provided by over 60 researchers in the field, in the form of data files, published reports, and individual study records. We also accessed the most recent holdings of the International Multiproxy Paleofire Database (IMPD) for inclusion in our compilation. These efforts resulted in the compilation of 1,248 fire history studies from 64 contributors meeting our quality criteria. Study locations extend from southern Canada to north-central Mexico, and cover the 3,248-yr period 1248 BCE to 2011 CE, with sample size > 600 sites covering the period 1700-1990. Seven major forest types are represented, including piñon-juniper, pine-oak woodland, ponderosa pine woodland, dry and mesic mixed conifer, fir, and subalpine forests. Mean annual precipitation ranges from < 30 cm to > 200 cm, while mean annual temperature ranges from 5.0 to 25.3 °C. We identified numerous west-wide fire years in the paleofire record indicating the strong top-down influence of synoptic climate conditions and regulation by major climate oscillatory modes. Our work included the development of new software tools to facilitate future analyses of paleofire data sets (see Decision Support Tools, below). Modern fire. For our synthesis of modern fire-climatology, we focused on analyzing trends and drivers in area burned in western forests, particularly the influence of snowpack duration and climate variables to annual area burned. We compiled annual area burned (AAB) for the western US based on data provide by Dr. AH Westerling, University of California – Merced, for the period 1972-2006. 12,596 fires met data quality standards and were included in analysis. Similar data were obtained from the Canadian Large Fire Database. Spatiotemporal climate layers (monthly mean, minimum, and maximum temperature and precipitation) were obtained from the National Climate Data Center for the same time period. We obtained snowpack data estimating the presence or absence of snowpack from satellite reflectance data aggregated to 25 km2 pixels. Snowpack data were converted to a continuous variable, LDPS (Last Date of Permanent Snowpack). We evaluated all time series for trend over the period of analysis at the scale of each 1° grid cell. For AAB we analyzed the period 1972-1999, and climate variables and snowpack for the period 1972-2006. To separate the influence of multiple drivers, we employed path analysis to identify the relative contributions of seasonalized temperature, precipitation, and snowpack duration to AAB. AAB increased significantly over most of the study area during the period 1972-1999. Seasonalized temperatures increased, and winter precipitation and snow cover duration decreased, over most of the study area significantly over the period of analysis (1972-2006). Winter temperature and precipitation had the strongest effect on snowpack duration. In turn, snowpack duration affected AAB, but results were spatially heterogeneous. Overall, the strongest effects on AAB were the direct effects of winter and temperature, followed by direct effects of spring precipitation. Most indirect effects, i.e. mediated by climate effects on snowpack, were a relatively small component of variability. Effects of snowpack on AAB were spatially variable in strength and sign. These results suggest that snowpack duration may be an indicator of the factors that control AAB, rather than a mechanism of control. We also compiled the first complete set of “pyroclimographs” for the western US, visualizations that integrate monthly mean temperature, precipitation, and area burned from both lightning- and human-caused fires.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.126 | 0.043 |
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".