Abstract

Lightning is an important atmospheric process for igniting forest fires – often in remote locations where they are not easily suppressed – which results in potentially large emissions of many pollutants and short-lived climate forcers. Lightning also generates reactive nitrogen, resulting in the production of tropospheric ozone, the third most important greenhouse gas. Furthermore, the changing climate is expected to change the frequency and location of lightning. As such, lightning is an important component of climate models. The Canadian Atmospheric Model, CanAM, is one such climate model that did not contain an 'online' lightning parameterization. Fire ignition in CanAM was done via an unchanging climatological lightning input. In this study, we have added a new logistical regression lightning model (Etten-Bohm et al, 2021) into CanAM, creating the capacity for future lightning predictions with CanAM under different climate scenarios. The modelled lightning and fire area burned were evaluated against measurements in a historical period with good results. Then we simulate lightning and fire area burned in a future climate scenario in order to provide an estimate on how lightning and its impacts will change in the future. This study also presents the first time that CanAM’s land fire model was used online with its atmosphere to fully simulate fires in the global earth system.Reference:Etten-Bohm, M., J. Yang, C. Schumacher, and M. Jun : Evaluating the relationship between lightning and the large-scale environment and its use for lightning prediction in global climate models, JGR-atmospheres, 126, e2020JD033990, 2021.

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