Evaluating Empirical Lightning Parameterizations in Global Atmospheric Models. Issue 4 (12th February 2021) - Stolz, Douglas C.; Bilsback, Kelsey R.; Pierce, Jeffrey R.; Rutledge, Steven A.
Abstract: Lightning fundamentally influences atmospheric chemistry as it is the largest natural source of NOx in the upper troposphere. To include lightning, global models rely on experimental parameterizations. We use global atmospheric model diagnostics (2.0 × 2.5° horizontal resolution, 38°S–38°N, for the years 2012–2013) to evaluate three lightning parameterizations that are based on: (1) cloud top height, (2) vertical ice‐mass flux, and (3) meteorological variables including aerosols. We apply conversions between convective/cloud area and model grid box area in these calculations to compare each parameterization's spatial, temporal, and spectral characteristics with an observed lightning climatology from the Tropical Rainfall Measuring Mission Lightning Imaging Sensor. Domain‐wide median values of parameterized lightning differ from observations by a factor of 0.6–3.1. The three parameterizations depict spatial (Pearson correlation ( r = 0.80–0.84) and temporal patterns ( r = 0.79–0.95) that match observations well. The parameterized median land‐ocean lightning contrast ranges from a factor of 2.82 (using environmental factors with aerosols) to 596.0 (using cloud top height), compared to 22.9 in observations. One‐to‐one comparisons suggest that lightning parameterizations can explain approximately 55%–64% of the observed monthly lightning variance, depending on the parameterization. The highest correlation and minimum error bias statistics (e.g., Logarithmic Mean Bias, LMB) are found for the lightning parameterization that employs environmental factors with aerosols ( r = 0.80, LMB = +0.002), whereas comparable correlation and generally higher error bias are found using vertical ice fluxes ( r = 0.80, LMB = +0.27) and cloud top height ( r = 0.74, LMB = −0.08). We show how the biases in lightning estimates are sensitive to uncertainties in the convective area and suggest ways to minimize bias overall. Plain Language Summary: This study investigates how lightning can be more accurately represented in global atmospheric models, using three different approaches: (1) based on environmental characteristics (potential energy, warm cloud depth, humidity, wind shear and atmospheric aerosols); (2) based on the vertical transport of ice particles; or (3) based on the peak thunderstorm height. A novel aspect of this research is that implementations of monthly lightning estimates incorporate the approximate areal extent of thunderstorms (convective area)/number of thunderstorms within model grid boxes. We document the performance of each lightning approximation method and its respective predictions for the frequency of occurrence for various lightning intensities over land and ocean using a single atmospheric model for simplicity. The results suggest that these lightning estimation approaches account for about 55%–64% of the monthly variations in a test sample of satellite lightning observations (without artificial scaling). Recent approaches to model lightning occurrence (e.g., approaches [1] and [2] above) show modest improvements in accuracy and reduced error bias compared to the conventional cloud top height approach (i.e., approach [3] above). We discuss ways to further improve monthly lightning estimates in global atmospheric models as well as the relationship between lightning and atmospheric chemistry under future climate change scenarios. Key Points: Lightning parameterizations are evaluated against monthly Tropical Rain Measuring Mission lightning climatology over the tropics and subtropics Lightning parameterization approaches that account for thunderstorm area explain 55%–64% of observed monthly lightning variance Using normalized convective available potential energy, warm‐cloud depth, cloud condensation nuclei concentration, SHEAR and relative humidity or ice mass fluxes improves on conventional cloud top height approaches for lightning parameterization