The study of interactions among clouds, aerosols and dynamics in the earth system using casual inference approaches

The aerosols from multiple sources, including dust, marine, biomass and anthropogenic activities play critical role in the Earth system through impacting the energy balance and cloud dynamics. It is an active research area to understand the interactions among clouds, aerosols and dynamics in the systems through well-thought observational and modeling strategies in order to disentangle all coupled processes. The study of cloud and aerosol properties requires investigations of their morphology and variability in relation to other atmospheric variables, which can be measured through multiple instruments (e.g. MODIS, MISR, MLS, AIRS, etc.). Recently, the rapidly increasing amount of observational and simulated data opens up doors to many exciting researches in use of data-driven machine learning methods. Among them, the observational causal inference methods, is not yet widely applied in the geoscience community for the difficulties in operating real experiments in the large-scale and complex dynamical earth systems. Therefore, the traditional correlation techniques are still the common methods in revealing the relationships between various variables in the context of the agreement that "correlation does not imply causation". This study intends to use the casual inference method to explore the relationship between aerosol and cloud interactions and the impacts of aerosol-cloud variabilities on climate and climate change.