Modeling Wildfire Smoke Feedback Mechanisms Using a Coupled Fire‐Atmosphere Model With a Radiatively Active Aerosol Scheme

Abstract

During the summer of 2015, a number of large wildfires burned across Northern California in areas of localized topographic relief. Persistent valley smoke hindered fire‐fighting efforts, delayed helicopter operations, and exposed communities to extreme concentrations of particulate matter. It was hypothesized that smoke from the wildfires reduced the amount of incoming solar radiation reaching the ground, which resulted in near‐surface cooling, while smoke aerosols resulted in warming aloft. As a result of increased inversion‐like conditions, smoke from wildfires was trapped within mountain valleys adjacent to active wildfires. In this study, wildfire smoke‐induced inversion episodes across Northern California were examined using a modeling framework that couples an atmospheric, chemical, and fire spread model. Modeling results examined in this study indicate that wildfire smoke reduced incoming solar radiation during the afternoon, which lead to local surface cooling by up to 3 °C, which agrees with cooling observed at nearby surface stations. Direct heating from the fire itself did not significantly enhance atmospheric stability. However, midlevel warming (+0.5 °C) and pronounced surface cooling was observed in the smoke layer, indicating that smoke aerosols significantly enhanced atmospheric stability. A positive feedback associated with the presence of smoke was observed, where local smoke‐enhanced inversions inhibited the growth of the planetary boundary layer, and reduced surface winds, which resulted in smoke accumulation that further reduced near‐surface temperatures. This work suggests that the inclusion of fire‐smoke‐atmosphere feedback in a coupled modeling framework such as WRF‐SFIRE‐CHEM can forecast the dispersion of wildfire smoke and its radiative feedback, and potentially provide decision‐support for wildfire operations.

Evaluating Wildfire Smoke Transport Within a Coupled Fire‐Atmosphere Model Using a High‐Density Observation Network for an Episodic Smoke Event Along Utah's Wasatch Front

“…As wildfires continue to increase in size and frequency across the Western United States, it will become imperative to develop and utilize novel and cost‐efficient observation data sets to better understand the physical processes that govern smoke transport. Transient air quality events, especially those associated with wildfire smoke plumes in regions with significant topographic relief, can be difficult to characterize (Kelleher et al, 2018; Kochanski et al, 2019).…”

“…The purpose of this setup was to (1) test the performance of WRFSFC smoke simulations under the scenario where fire growth was well represented by the model and to (2) highlight key atmospheric processes that govern the transport of smoke in complex terrain. Here, fire arrival times were estimated by linearly interpolating fire arrival times between observed fire perimeters during the simulation period of interest, similar to the methodology described in Kochanski et al (2019). For the simulations carried out here, GeoMac infrared perimeters on 14 (18:00 LST) and 16 (21:00 LST) September were used to create the fire arrival time matrix ( Figure S1).…”

“…Journal of Geophysical Research: Atmospheres physical processes that govern smoke transport. Transient air quality events, especially those associated with wildfire smoke plumes in regions with significant topographic relief, can be difficult to characterize (Kelleher et al, 2018;Kochanski et al, 2019).…”

“…As a result, these models can explicitly resolve the wildfire plume rise instead of parameterizing it. In addition, finer grid spacing also allows coupled fire-atmosphere models to resolve finer-scale atmospheric flows often found in mountainous areas where wildfires are more common (Kochanski et al, 2019). While coupled fire-atmosphere models like WRF-SFIRE and WRF-Fire have already been used to make smoke forecasts in an operational setting (Mandel et al, 2014(Mandel et al, , 2019Muñoz-Esparza et al, 2018), more work is needed to evaluate the performance of these models (Kochanski et al, 2018).…”

“…Smoke modeling frameworks such as BlueSky and HRRR‐Smoke are currently used operationally for smoke forecasting across the Western United States (Ahmadov et al, 2017; Larkin et al, 2009). Forecasting smoke dispersion can be particularly challenging as there are a number of fire‐related processes that can be difficult to simulate such as the wildfire plume rise (Val Martin et al, 2012), smoke emissions (Herron‐Thorpe et al, 2014), and fire‐atmosphere feedbacks (Kochanski et al, 2019). Previous work has shown that properly characterizing the vertical smoke plume evolution is critical for forecasting smoke (Christian et al, 2019; Mallia et al, 2018, 2020; Stein et al, 2009; Walter et al, 2016).…”