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

EAGER：涉及黑碳老化/生命周期和山雪的辐射强迫研究的随机方法

• 批准号: 1523296
• 负责人: Kuo-Nan Liou
• 金额: $ 24万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-02-15 至 2017-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1523296&HistoricalAwards=false
• 关键词: EAGER; Stochastic; Approach; Radiative; Forcing

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.

该项目将使用基于概率过程的随机方法来解开大气中未涂覆的黑碳（BC或烟灰）聚集体和涂覆的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。 气溶胶和云物理学的基本原理（确定性过程）不能应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聚结事件，存在许多可能性。 这个问题在文献中从未被提及。然而，这对于理解与大气中BC沉积有关的山区积雪减少至关重要。知识价值：这项研究和随后的结果将首次提供解决许多未解决问题所需的模型和数据集，从而产生关于大气BC影响的新物理见解（无涂层的聚集体和有涂层的核壳球）对云的形成和降水过程的影响，以及BC颗粒以雪花和谷粒的形式在三维和强烈的雪地形上的干湿沉积的后果。研究工作将为区域和全球气候模式中BC雪过程的参数化增加一个新的维度。 该研究将对广泛的气候和大气科学产生重大影响，提供基于随机原理的交互式BC-雪参数化数据集，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报的不确定性。