Lightning Over Central Canada: Skill Assessment for Various Land‐Atmosphere Model Configurations and Lightning Indices Over a Boreal Study Area. Issue 1 (29th December 2022) - Mortelmans, Jonas; Bechtold, Michel; Brisson, Erwan; Lynn, Barry; Kumar, Sujay; De Lannoy, Gabriëlle
Abstract: Current lightning predictions are uncertain because they rely on empirical diagnostic relationships and often use coarse‐scale climate scenario simulations in which deep convection is parameterized. Previous studies demonstrated that simulations with convection‐permitting resolutions improve lightning predictions compared to coarser‐grid simulations using convection parameterizations for different geographical locations but not over the boreal zone. In this study, lightning simulations with the NASA Unified‐Weather Research and Forecasting model are evaluated over a domain including the Great Slave Lake in Canada, for six lightning seasons. The simulations are performed at convection‐parameterized (9 km) and convection‐permitting (3 km) resolution using the Goddard 4ICE and the Thompson microphysics schemes. Four lightning indices are evaluated against observations from the Canadian Lightning Detection Network, in terms of spatiotemporal frequency distribution, spatial pattern, daily climatology, and an event‐based overall skill assessment. The Thompson scheme is, regardless of the spatial resolution, superior to the Goddard 4ICE scheme in predicting daily climatology but worse in predicting the spatial patterns of lightning occurrence. Results indicate that lightning estimation benefits from modeling at convection‐permitting resolution, in particular for the ice‐based lightning indices. In contrast, the product of convective available potential energy and precipitation rate proved to be the most robust index that was largely invariant to varying spatial resolution. Finally, this study reveals issues of the models to reproduce the observed spatial pattern of lightning well, which might be related to an insufficient representation of land surface heterogeneity, including peatlands, in the study area. Plain Language Summary: Lightning is a phenomenon known by everyone. To predict when and where lightning will occur, numerical models are used. These models combine complex mathematical equations to make a model of the real world. By starting from a real‐life example, models could predict the future. In our everyday life, models are present everywhere. One of the most obvious examples is the weather forecast. This study uses such a model to try to predict lightning occurrence while starting from known past conditions. But since the small‐scale processes that lead to lightning are so difficult to model, different people came up with different methods to predict lightning. In this study, four of these methods are compared for the boreal zone where lightning could increase with global warming and mobilize carbon through wildfire ignition. Our results show that there is no single best method to predict lightning, but a finer model resolution does improve the accuracy of the models. Finally, this study shows that the model still cannot predict accurately where lightning will occur. This might be due to problems of the models to simulate the water and energy cycle over specific boreal land elements such as peatlands. Key Points: The NASA Unified‐Weather Research and Forecasting modeling framework is run at two resolutions to predict lightning over the boreal zone Lightning estimation benefits from modeling at convection‐permitting resolution Ice‐based lightning indices are more sensitive to model resolution than nonice‐based lightning indices