Understanding the aerosol impact on the electrification of PyroCb thunderstorm

Abstract

Understanding and predicting pyrocumulonimbus (pyroCb) thunderclouds is challenging due to their unpredictable nature and potential to exacerbate wildfires. This study employs a three-dimensional (3D) numerical model with a non-inductive electrification mechanism to explore how fire-produced aerosol concentrations influence the charge structure and lightning activity in pyroCb. Aerosol particle concentrations ranging from 100 to 20,000 cm⁻³ were considered to generate realistic thundercloud charge structures, with the cumulus cloud setup based on environmental conditions derived from an inverted-V sounding profile. The SP98 non-inductive electrification scheme models charge transfer in pyroCb, and the Poisson equation is solved to identify regions where lightning discharges are likely to initiate, though actual discharges are not simulated. For aerosol concentrations between 500 and 5000 cm⁻³, negative non-inductive charging dominates, while extreme aerosol cases ($\ge$5000 cm⁻³) enhance positive charging at altitudes of 3–7 km. These findings suggest that non-inductive charge separation between ice crystals and graupel leads to realistic charge structures under pyroconvective conditions simulated in this study, reflecting moderate-to-high aerosol environments characteristic of wildfire-driven pyroCb events. As aerosol concentrations increase from 500 to 5000 cm⁻³, the charge structure evolves from a dipole to a tripole (at 1000 cm⁻³) and eventually to an inverted dipole, enabling intra-cloud (IC) and negative cloud-to-ground (-CG) lightning. At higher concentrations (10,000–20,000 cm⁻³), a multi-layer charge configuration emerges, favoring intense positive cloud-to-ground (+CG) strokes. Sensitivity analysis reveals that low-to-moderate aerosol levels with strong wind shear promote IC lightning, while high aerosol levels favor +CG flashes. These findings highlight the complex interactions driving pyroCb dynamics and the importance of integrated modelling to predict wildfire-induced thunderstorms.