Development of a Data-Driven Lightning Scheme for Implementation in Global Climate Models

Abstract

This study proposes a new lightning scheme applicable at the global scale, predicting lightning rates from 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 tree-based regression scheme, representing different lightning regimes with separate single hidden layer neural networks of low dimensionality. We apply multiple complexity constraints in the development stages, which makes our lightning scheme straightforward to implement within global climate models (GCMs). We demonstrate that, for years not used for training, our lightning scheme captures $71.8\%$ of the daily global spatio-temporal lightning variability, which corresponds to a $>43\%$ relative improvement compared to well-established lightning schemes. Similarly, the scheme correlates well with lightning observations for the monthly climatology $\left(r>0.92\right)$, inter-annual variability $\left(r>0.76\right)$, and latitudinal and longitudinal distributions $\left(r>0.87\right)$. Most notably, the lightning scheme 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 scheme 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 lightning scheme offers GCMs a straightforward and efficient tool to improve lightning simulation, which is critical for representing atmospheric chemistry and naturally ignited wildfires.