Abstract

Abstract. Mesoscale models are a class of atmospheric numerical model designed to simulate atmospheric phenomena with horizontal scales of about 2–200 km, although they are also applied to microscale phenomena with horizontal scales of less than about 2 km. Mesoscale models are capable of simulating wildland fire impacts on atmospheric flows if combustion byproducts (e.g., heat, smoke) are properly represented in the model. One of the primary challenges encountered in applying a mesoscale model to studies of fire-perturbed flows is the representation of the fire sensible heat source in the model. Two primary methods have been implemented previously: turbulent sensible heat flux, either in the form of an exponentially-decaying vertical heat flux profile or surface heat flux; and soil temperature perturbation. In this study, the ARPS-CANOPY model, a version of the Advanced Regional Prediction System (ARPS) model with a canopy submodel, is utilized to simulate the turbulent atmosphere during a low-intensity operational prescribed fire in the New Jersey Pine Barrens. The study takes place in two phases: model assessment and model sensitivity. In the model assessment phase, analysis is limited to a single control simulation in which the fire sensible heat source is represented as an exponentially decaying vertical profile of turbulent sensible heat flux. In the model sensitivity phase, a series of simulations are conducted to explore the sensitivity of model–observation agreement to (i) the method used to represent the fire sensible heat source in the model and (ii) parameters controlling the magnitude and vertical distribution of the sensible heat source. In both phases, momentum and scalar fields are compared between the model simulations and data obtained from six flux towers located within and adjacent to the burn unit. The multi-dimensional model assessment confirms that the model reproduces the background and fire-perturbed atmosphere as depicted by the tower observations, although the model underestimates the turbulent kinetic energy at the top of the canopy at several towers. The model sensitivity tests reveal that the best agreement with observations occurs when the fire sensible heat source is represented as a turbulent sensible heat flux profile, with surface heat flux magnitude corresponding to the peak 1 min mean observed heat flux averaged across the flux towers, and an e-folding extinction depth corresponding to the average canopy height in the burn unit. The study findings provide useful guidance for improving the representation of the sensible heat released from low-intensity prescribed fires in mesoscale models.

Highlights

  • Studies of wildland fire-perturbed atmospheric flows have relevance for our understanding of fire behavior, smoke transport and dispersion, and ecological effects such as tree mortality

  • Perturbation of atmospheric flows by lowintensity prescribed fires in forested landscapes has received less attention overall and remains poorly resolved in numerical modeling tools used by land managers despite the nearly 2 : 1 dominance of prescribed fire over wildfire in terms of area impacted, most of which is of low intensity (Melvin, 2018; Hiers et al, 2020; Melvin, 2020; Heilman et al, 2021)

  • We have examined different methods used to represent the sensible heat release from low-intensity prescribed fires in mesoscale models

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Summary

Introduction

Studies of wildland fire-perturbed atmospheric flows have relevance for our understanding of fire behavior, smoke transport and dispersion, and ecological effects such as tree mortality. Perturbation of atmospheric flows by lowintensity prescribed fires in forested landscapes has received less attention overall and remains poorly resolved in numerical modeling tools used by land managers despite the nearly 2 : 1 dominance of prescribed fire over wildfire in terms of area impacted, most of which is of low intensity (Melvin, 2018; Hiers et al, 2020; Melvin, 2020; Heilman et al, 2021). This importance is further underscored by the implementation of more than 1 million prescribed fires burning more than 24 million hectares in the US between 2000 and 2019 (National Interagency Fire Center, 2019). Improving the prediction of atmospheric flows during low-intensity fires may permit the refinement and operationalization of process-based modeling tools used during prescribed burning and wildland fire management operations (e.g., smoke dispersion models)

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