Extreme wind-driven autumn wildfires are hazardous to life and property, due to their rapid rate of spread. Recent catastrophic autumn wildfires in the western United States co-occurred with record- or near-record autumn fire weather indices that are a byproduct of extreme fuel dryness and strong offshore dry winds. Here, we use a formal, probabilistic, extreme event attribution analysis to investigate anthropogenic influence on recent extreme autumn fire weather events. We show that while present-day anthropogenic climate change has slightly decreased the prevalence of strong offshore downslope winds, it has increased the likelihood of extreme fire weather indices by 40%, primarily through increased autumn fuel aridity and warmer temperatures during dry wind events. These findings illustrate that anthropogenic climate change is exacerbating autumn fire weather extremes that contribute to high-impact catastrophic fires in populated regions of the western US.

Mechanistic representations of biogeochemical processes in ecosystem models are rapidly advancing, requiring advancements in model evaluation approaches. Here we quantify multiple aspects of model functional performance to evaluate improved process representations in ecosystem models. We compare semi-empirical stomatal models with hydraulic constraints against more mechanistic representations of stomatal and hydraulic functioning at a semi-arid pine site using a suite of metrics and analytical tools. We find that models generally perform similarly under unstressed conditions, but performance diverges under atmospheric and soil drought. The more empirical models better capture synergistic information flows between soil water potential and vapor pressure deficit to transpiration, while the more mechanistic models are overly deterministic. Additionally, both multilayer canopy and big-leaf models were unable to capture the magnitude of canopy temperature divergence from air temperature. Lastly, modeled stable carbon isotope fractionation differed under canopy water stress which illustrates the value of carbon isotopes in helping to characterize ecosystem function and elucidate differences attributable to model structure. This study demonstrates the value of merging underutilized observational data streams with emerging analytical tools to characterize ecosystem function and discriminate among model process representations.