Development of a data-driven lightning model for implementation in Global Climate Models

This study proposes a new global-scale lightning model, predicting lightning rates from large-scale climatic variables. Using satellite lightning records spanning a period of 29 years, we apply machine learning methods to derive a functional relationship between lightning and climate reanalysis data. In particular, we design a model tree, representing different lightning regimes with separate single hidden layer neural networks of low dimensionality. We apply multiple complexity constraints in the model development stages, which makes the lightning model straightforward to implement as a lightning scheme for global climate models (GCMs). We demonstrate that, for years not used for model training, our lightning model captures 70.6% of the daily global spatio-temporal lightning variability, which corresponds to a >42% relative improvement compared to well-established lightning schemes. Similarly, the model correlates well with lightning observations for the monthly climatology (r>0.92), inter-annual variability (r>0.90), and latitudinal and longitudinal distributions (r>0.86). Most notably, the model brings a critical improvement in representing lightning magnitude and variability in the three tropical lightning chimney regions: central Africa, the Amazon, and the Maritime Continent. We implement the lightning model in the Community Earth System Model to verify its stability and performance as a GCM component, and we provide detailed implementation guidelines. As an intermediate approach between high-dimensional machine learning models and first-order lightning parameterizations, our model offers GCMs a straightforward and efficient tool to improve lightning simulation, which is critical for representing atmospheric chemistry and naturally-ignited wildfires.