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

该项目将使用基于概率过程的随机方法来揭开大气中未涂层黑碳(BC或烟灰)聚集体和涂层BC核壳球形结构的多重内部混合之谜，它们在云和降水过程中的相关作用，以及BC湿沉积和干沉积与表面雪层的相互作用。气溶胶和云物理的基本原理(确定性过程)不能用于量化雪花中未涂层/涂层BC的多重内部混合，因为由于随机碰撞和合并事件存在许多可能性。这一主题在文学作品中从未被提及。然而，这在理解与大气中BC沉积有关的山区积雪反照率减少方面至关重要。智力价值：这项研究和后续结果将首次提供必要的模式和数据集，以解决一些尚未解决的问题，这些问题产生了关于大气BC(未涂层的集合体和涂层的核壳球体)对云形成和降水过程的影响，以及BC粒子以雪花和碎屑的形式湿干沉积到3D和强雪地形上的后果的新的物理见解。这项研究将对广泛的气候和大气科学产生重大影响，根据随机原理提供交互式BC-Snow参数化数据集，以减少通过计算机模型对山区地表温度和水资源进行预测和预测的不确定性。

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.