Looking for convective invigoration in the Southern Great Plains with causal inference

UK Conference on Environmental Data Science

Time Series Causality for Aerosol Cloud Invigoration Aerosol has been argued (Williams et al. 2002, Rosenfeld et al. 2008) to 'invigorate' convective clouds, such as in very energetic thunderstorms. Aerosol would, in polluted regions, decrease cloud droplet radii and therefore delay precipitation. Droplets are transported to higher altitudes than they would be in clean conditions. Here, they freeze and release latent energy. The ice particles fall and melt again in lower regions. This increases the heat transport in the cloud, which in turn means more rainfall for the same amount of convective available potential energy (CAPE). This is the proposed mechanism for convective invigoration. Li et al. (2011) claim to have found evidence of such invigoration in the datasets of the Southern Great Plains Site of the ARM. They show that in mixed phase clouds, cloud-top height and thickness increase with aerosol concentration. They also show that when there is more aerosol, rainfall increases in the case of high-liquid water content clouds. According to the authors, these observations are evidence for convective invigoration. Conversely, Varble (2018) argues that while there is a correlation between aerosol loading and cloud top height, there is no causal link between the two. He shows that meteorological variables, especially the level of neutral buoyancy (LNB) and CAPE, are correlated to both aerosol and cloud top height, and that the addition of aerosol as a predictor does not add to a regression model for cloud top height. They propose that rain could be at the origin of the observed correlations, being impacted by meteorological variables and washing out aerosol. To untangle the causal links, here we use the methodology of time series causality (Runge et al., 2019), in particular the PCMCI algorithm, to elucidate the links between aerosol, precipitation, cloud height, and meteorology. Time series of a wide array of data (Active Remotely-Sensed Cloud Location product, MERGESONDE, condensation nuclei, radiosonde, and Arkansas-Red Basin River Forecast Center hourly rainfall data, among others) are used and fed into the PCMCI algorithm in order to construct a directed acyclic graph representing the causal links inferred from this time series data. By applying causal inference techniques to real world data, we present new perspectives for both the aerosol and the causal inference communities.