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The Haines index (HI) is a fire-weather index that is widely used as an indicator of the potential for dry, low-static-stability air in the lower atmosphere to contribute to erratic fire behavior or large fire growth. This study examines the interannual variability of HI over North America and its relationship to indicators of large-scale circulation anomalies. The results show that the first three HI empirical orthogonal function modes are related respectively to El Niño-Southern Oscillation (ENSO), the Arctic Oscillation (AO), and the interdecadal sea surface temperature variation over the tropical Pacific Ocean. During the negative ENSO phase, an anomalous ridge (trough) is evident over the western (eastern) United States, with warm/dry weather and more days with high HI values in the western and southeastern United States. During the negative phase of the AO, an anomalous trough is found over the western United States, with wet/cool weather and fewer days with high HI, while an anomalous ridge occurs over the southern United States-northern Mexico, with an increase in the number of days with high HI. After the early 1990s, the subtropical high over the eastern Pacific Ocean and the Bermuda high were strengthened by a wave train that was excited over the tropical western Pacific Ocean and resulted in warm/dry conditions over the southwestern United States and western Mexico and wet weather in the southeastern United States. The above conditions are reversed during the positive phase of ENSO and AO and before the early 1990s. [ABSTRACT FROM AUTHOR]

The major source of uncertainty in wildfire behavior prediction is the transient behavior of wildfire due to changes in flow in the fire's environment. The changes in flow are dominated by two factors. The first is the interaction or 'coupling' between the fire and the fire-induced flow. The second is the interaction or 'coupling' between the fire and the ambient flow driven by turbulence due to wind gustiness and eddies in the atmospheric boundary layer (ABL). In the present study, coupled wildfire-atmosphere large-eddy simulations of grassland fires are used to examine the differences in the rate of spread and area burnt by grass fires in two types of ABL, a buoyancy-dominated ABL and a roll-dominated ABL. The simulations show how a buoyancy-dominated ABL affects fire spread, how a roll-dominated ABL affects fire spread, and how fire lines interact with these two different ABL flow types. The simulations also show how important are fire-atmosphere couplings or fire-induced circulations to fire line spread compared with the direct impact of the turbulence in the two different ABLs. The results have implications for operational wildfire behavior prediction. Ultimately, it will be important to use techniques that include an estimate of uncertainty in wildfire behavior forecasts. [ABSTRACT FROM AUTHOR]

The ventilation index (VI) is an index that describes the potential for smoke or other pollutants to disperse from a source. In this study, a Lagrangian particle dispersion model was utilized to examine smoke dispersion and the diagnostic value of VI during a September 2018 prescribed fire in southwestern Colorado. Smoke dispersion in the vicinity of the fire was simulated using the FLEXPART-WRF particle dispersion model, driven by meteorological outputs from Advanced Regional Prediction System (ARPS) simulations of the background (non-fire) conditions. Two research questions are posed: (1) Is a horizontal grid spacing of 4 km comparable to the finest grid spacing currently used in operational weather models and sufficient to capture the spatiotemporal variability in wind and planetary boundary layer (PBL) structure during the fire? (2) What is the relationship between VI and smoke dispersion during the prescribed fire event, as measured by particle residence time within a given horizontal or vertical distance from each particle's release point? The ARPS no-fire simulations are shown to generally reproduce the observed variability in weather variables, with greatest fidelity to observations found with horizontal grid spacing of approximately 1 km or less. It is noted that there are considerable differences in particle residence time (i.e., dispersion) at different elevations, with VI exhibiting greater diagnostic value in the southern half of the domain, farthest from the higher terrain across the north. VI diagnostic value is also found to vary temporally, with diagnostic value greatest during the mid-morning to mid-afternoon period, and lowest during thunderstorm outflow passage in the late afternoon. Results from this study are expected to help guide the application of VI in complex terrain, and possibly inform development of new dispersion potential metrics. [ABSTRACT FROM AUTHOR]

The impact of a forest canopy on smoke concentration is assessed by applying a numerical weather prediction model coupled with a Lagrangian particle dispersion model to two low-intensity wildland (prescribed) fires in the New Jersey Pine Barrens. A comparison with observations indicates that the coupled numerical model can reproduce some of the observed variations in surface smoke concentrations and plume heights. Model sensitivity analyses highlight the effect of the forest canopy on simulated meteorological conditions, smoke concentrations, and plume heights. The forest canopy decreases near-surface wind speed, increases buoyancy, and increases turbulent mixing. Sensitivities to the time of day, plant area density profiles, and fire heat fluxes are documented. Analyses of temporal variations in smoke concentrations indicate that the effect of the transition from a daytime to a nocturnal planetary boundary layer is weaker when sensible heat fluxes from the fires are stronger. The results illustrate the challenges in simulating meteorological conditions and smoke concentrations at scales where interactions between the fire, fuels, and atmosphere are critically important. The study demonstrates the potential for predictive tools to be developed and implemented that could help fire and air-quality managers assess local air-quality impacts during low-intensity wildland fires in forested environments. [ABSTRACT FROM AUTHOR]

• The study offers insights into atmospheric turbulence during intense grass fires. • These findings are significant as they provide the first-ever in-situ turbulence data collected during Red Flag Warnings. • The data establishes an upper limit for turbulence in prescribed grass fires, offering valuable reference points. • These results have the potential to enhance turbulence schemes in fire behavior models, contributing to better fire predictions. • This study underscores the complexities of studying fire type effects in a real-world context. A wildland fire experiment was conducted on a 40-ha tall grass prairie in Texas to study fire-atmosphere interactions. The experiment coincided with a Red Flag Warning, signifying specific weather conditions associated with an elevated risk of wildfire ignition and rapid spread. This provided a rare opportunity to observe wildland fire behavior and its impact on atmospheric flows under conditions deliberately avoided in similar experiments. This study analyzed the high frequency meteorological data collected during the experiment using three-dimensional sonic anemometers mounted on a 43-m tower (at 5.8, 10 and 20 m) and on three 10-m towers (at 5.3 m) within the burn block. The tower placements were carefully chosen to capture the impact of both head and flank fires on the mean and turbulent flows. The hypothesis is that fire-induced perturbations in the lower atmosphere, quantified through parameters like turbulent kinetic energy (TKE) and vertical fluxes, would be substantially stronger than those observed in similar wildland fires under less fire-prone atmospheric conditions. While this hypothesis was validated for heat flux and TKE, it did not hold for momentum fluxes or friction velocity. Additionally, the observations failed to confirm the hypothesis that head fires would produce more intense perturbations than flank fires. Given the homogeneity of fuel, terrain and meteorological elements across the burn unit, this underscores the challenge of disentangling the effects of fire type from other natural factors in a real-world setting. Nevertheless, the finding holds promise for improving turbulence parameterizations in fire-behavior and smoke dispersion models. [ABSTRACT FROM AUTHOR]

• Outbreaks of spruce budworm currently affect fir/spruce forests in eastern Canada. • We redeveloped an agent-based, weather-driven model of above-canopy moth dispersal. • New formulations of moth flight response to the environment improve on prior work. • Moth morphology and response differ by sex, producing different dispersal outcomes. • Sex-based roles and dispersal patterns may inform analyses of ongoing outbreaks. Long-term studies of insect populations in the North American boreal forest have shown the vital importance of long-distance dispersal to the maintenance and expansion of insect outbreaks. In this work, we extend several concepts established previously in an empirically-based dispersal flight model with recent work on the physiology and behavior of the adult eastern spruce budworm (SBW) moth, Choristoneura fumiferana (Clemens). An outbreak of defoliating SBW in Quebec, ongoing since the mid-2000s, already covers millions of hectares of forests in eastern Canada and threatens to spread into neighboring areas through annual summertime episodes of long-distance dispersal. Such flight events in favorable conditions frequently include billions of SBW moths dispersing in the warm atmospheric boundary layer, typically starting around sunset and often lasting through several hours of wind-driven transport over hundreds of kilometers. Successful SBW dispersal to possibly distant host forest areas depends acutely on the weather. Here we describe the components and results of SBW–pyATM, an open-source individual-based modeling framework developed in Python for the simulation of these weather-driven SBW dispersal events. Using seasonal SBW phenology results from BioSIM at known outbreak locations and high-resolution Weather Research and Forecasting (WRF) model output, we focus on modeling dispersal flights over two successive nights in July 2013 in southern Quebec. Our flight model closely reproduces the SBW spatial patterns and motions observed by weather surveillance radar over the St. Lawrence estuary. With SBW–pyATM we can estimate landing locations for both male and female SBW and the resulting spatial patterns of egg distribution, allowing us eventually to forecast future larval defoliation activity in new locations where immigration could help overcome local limitations on SBW populations. This information could then support forest management decisions where SBW outbreaks threaten valuable resources. [ABSTRACT FROM AUTHOR]