Using cloud ice flux to parametrise large-scale lightning

Abstract

Lightning is an important natural source of nitrogen oxide especially in the middle and upper troposphere. Hence, it is essential to represent lightning in chemistry transport and coupled chemistry–climate models. Using ERA-Interim meteorological reanalysis data we compare the lightning flash density distributions produced using several existing lightning parametrisations, as well as a new parametrisation developed on the basis of upward cloud ice flux at 440 hPa. The use of ice flux forms a link to the non-inductive charging mechanism of thunderstorms. Spatial and temporal distributions of lightning flash density are compared to tropical and subtropical observations for 2007–2011 from the Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measuring Mission (TRMM) satellite. The well-used lightning flash parametrisation based on cloud-top height has large biases but the derived annual total flash density has a better spatial correlation with the LIS observations than other existing parametrisations. A comparison of flash density simulated by the different schemes shows that the cloud-top height parametrisation has many more instances of moderate flash densities and fewer low and high extremes compared to the other parametrisations. Other studies in the literature have shown that this feature of the cloud-top height parametrisation is in contrast to lightning observations over certain regions. Our new ice flux parametrisation shows a clear improvement over all the existing parametrisations with lower root mean square errors (RMSEs) and better spatial correlations with the observations for distributions of annual total, and seasonal and interannual variations. The greatest improvement with the new parametrisation is a more realistic representation of the zonal distribution with a better balance between tropical and subtropical lightning flash estimates. The new parametrisation is appropriate for testing in chemistry transport and chemistry–climate models that use a lightning parametrisation.