EAGER: A Stochastic Approach for Radiative Forcing Studies Involving Black Carbon Aging/Life Cycle and Mountain Snow

点击翻译按钮获取中文摘要

The project will use a stochastic approach based on probabilistic process to unlock the mystery of multiple internal mixing of uncoated black carbon (BC or soot) aggregates and coated BC core-shell spherical structures in the atmosphere, their associated role in cloud and precipitation processes, and the interaction of BC wet and dry depositions with surface snow layers. The fundamental principles of aerosol and cloud physics (deterministic process) cannot be applied to quantifying multiple internal mixing of uncoated/coated BC in snowflakes since many possibilities exist due to random collision and coalescence events. The subject has never been addressed in the literature. However, it is critically important in the understanding of snow albedo reduction over mountains in connection with the BC deposition from the atmosphere.Intellectual Merit:The research and subsequent results will for the first time provide models and datasets necessary to address a number of unsolved issues engendering new physical insights about the effects of atmospheric BC (uncoated aggregates and coated core-shell sphere) on cloud formation and precipitation processes and the consequence of wet and dry depositions of BC particles in forms of snowflakes and graupels onto 3-D and intense snow topography.Broader Impacts:The research efforts will add a new dimension to parameterization of BC-snow processes in regional and global climate models. The research will have a significant impact on a broad range of climate and atmospheric sciences, providing interactive BC-snow parameterization datasets based on the stochastic principle for reducing uncertainties in the projection and prediction of surface temperature and water resources over mountainous areas by means of computer models.