Characterizing lightning-ignited wildfire occurrences at sub-grid scales in orography-aware NOAA/GFDL land model LM4.2

Lightning ignitions are the dominant causes of wildfires in many regions, responsible for 80% of burned areas at high latitudes and about 70% of fires in the Amazon rainforest. With global wildfire activities and extreme fire events (e.g., intensity, duration, and size) increasing under the changing climate conditions, understanding the interactions between lighting, landscape characteristics, and wildfires is crucial for predicting and mitigating the impacts of climate change. Cloud-to-ground lightning activities are driven by a combination of large- and local-scale factors, e.g., local atmospheric circulations and convection and topography. Furthermore, the number of lightning strikes is predicted to increase by 10 – 30 % per degree warming. Decadal satellite observations have revealed Earth’s lightning hotspots at very high resolution, however, there is a paucity of fine-scale lightning strikes and lightning-ignited wildfires (LIW) in the Earth system and climate models. Currently, many climate and ESM  models do not include fires at all or simulate them with meteorological inputs and grid-average lightning at the scale of atmospheric models (25 to 100 km), introducing large uncertainties of LIW due to the lack of information at the scales relevant to fire dynamics.  Lack of information about lightning trends and variability hinders the prediction and projection of fires and their contribution to carbon and other atmospheric tracers and global warming. For example, in the US National Oceanic and Atmospheric Administration (NOAA) Geophysical Fluid Dynamics Laboratory (GFDL) ESM4.1 model, the fire model uses a climatology of lightning strikes from preindustrial to 2100.

In this presentation, we will demonstrate the implications of capturing subgrid lightning distributions in the GFDL land model LM4.2 for the global simulations of wildfire dynamics over the available records (1998-2013) and provide insights into future projections. LM4.2 captures sub-grid heterogeneity of land cover and use, soil geomorphology, and topography, facilitating the understanding of LIW distribution across global to regional and sub-grid scales. In this study, we leverage 0.1° × 0.1° lightning observations from the Lightning Imaging Sensor (LIS) and Optical Transient Detector (OTD) in the GFDL LM4-HB to characterize fine-scale lightning strike distribution and associated LIW.