Global Lightning Derived from Satellite-Based Lightning Climatology and CMIP5 Climate Model Output
While lightning is an important phenomenon in the Earth system, global climate models do not directly simulate the process of lightning initiation and discharge. Atmospheric chemistry models, which are critical components of global climate models, require lightning to simulate the natural formation of nitrogen oxides and rely on climatologies of lightning from satellite-based data sets of lightning or empirically-derived lightning parameterizations. Global fire models rely almost entirely on monthly climatologies of lightning from satellite-based data sets. Diverse communities of researchers, such as those in atmospheric chemistry, global fire modeling, and even paleoclimatology would benefit from methods to estimate the spatiotemporal patterns of lightning in the past and future.
Global climate models simulate many parameters related to convection, including total precipitation rate, convective precipitation rate, and convective mass flux. Theoretical, field, and higher resolution modeling studies of thunderstorm dynamics have found that the mass flux of ice in the presence of supercooled water is related to lightning flash rate. Thus, there is a physical basis for exploring relationships between simulated convective parameters and observed lightning.
This presentation examines how satellite observations of lightning and convective parameters simulated using current climate models from the Fifth Coupled Model Intercomparison Project (CMIP5) can be used to derive an empirical, non-linear parameterization of monthly lightning. The satellite-derived lightning is a data product that combines Optical Transient Detector (OTD) and Lightning Imaging Sensor (LIS) observations, the latter of which is on the NASA Tropical Rainfall Measurement Mission (TRMM) satellite. CMIP5 model output is readily available from online data archives, and that same model output served as the basis for the discussions in the recently released Fifth Assessment Report by the Intergovernmental Panel on Climate Change (IPCC AR5). This research is unique in that CMIP5 convective parameters have never been used to derive lightning.
The results show that the parameterization based on convective mass flux from CMIP5 climate models best captures the spatiotemporal distribution of observed lightning from OTD/LIS. Temporally, simulated lightning seasonality is captured with 95% confidence over 69% of land, and only 30% of ocean. Spatially, the correlation of simulated mean annual lightning and observed lightning is 0.74. Overall, OTD/LIS data suggests lightning occurs at an annual rate of 47 flashes per second, while lightning from the parameterization occurs at 44 flashes per second. The parameterization works considerably better over land. Also, as would be expected from a parameterization developed from average lightning, the parameterization tends to underpredict lightning over regions with the highest flash rates and overpredicting lightning for regions with the lowest flash rates.
A feature of the parameterization between monthly lightning and monthly convective parameters that emerges from this study is linearity for the low values of convective precipitation and convective mass flux. Whereas a non-linear function is needed to parameterize the full range of simulated convective precipitation and convective mass flux, a linear function captures 70-90% of the OTD/LIS variability for a range of convective precipitation and convective mass flux values that account for ~90% of the total range. The linearity found in this study suggests that any changes in convective precipitation and convective mass flux in the future would result in lightning flash rates that change proportionally. This linearity could be a valuable way to assess future lightning distributions suggested by climate model projections of convective parameters such as convective mass flux.
This presentation focuses on the methods, findings, and ways that satellite data and CMIP5 model output could further be used to understand past, present, and future spatiotemporal distributions of global lightning.