Cloudiness change with aerosol trend reversal over China: data-model synergy from regional to large scale

中国云量变化随气溶胶趋势逆转：从区域到大规模的数据模型协同

• 批准号: 448864730
• 负责人: Professor Dr. Johannes Quaas
• 金额: --
• 依托单位: School of Atmospheric Sciences (SAS)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/448864730?language=en
• 关键词: Cloudiness; change; aerosol; trend; reversal

The response of clouds to anthropogenic changes in atmospheric composition constitutes a large uncertainty when quantifying the effective forcing of global climate change. One key ele­ment is the response of clouds to aerosol emissions. In these aerosol-cloud interactions, it emerges currently as the key question how cloud horizontal extent, or cloud fraction, changes in adjustment to cloud droplet number concentration changes. Cloudtrend will substantially im­prove the understanding and quantification for this problem by building on three key ideas: (i) the very strong increasing then decreasing trends in anthropogenic aerosol emissions over China in the 21st Century provide a unique opportunity for a detection and attribution of aerosol-induced cloudiness changes in the high-quality data available from the surface and from satel­lites; (ii) new modelling and data analysis tools are available, including better satellite retrievals of cloud microphysics and new regime definitions, as well as the new CMIP6 multi-model data; and (iii) the large-scale data analysis and global climate modelling is proposed to systematically learn from the regional, high-quality data and modelling. The project is possible thanks to the synergy between the expertise in the teams at the Nanjing University of Information Science and Technology (regional focus) and the University of Leipzig (large-scale focus).

云对大气成分人为变化的响应在量化全球气候变化的有效强迫时构成很大的不确定性。其中一个关键要素是云对气溶胶排放的响应。在这些气溶胶-云相互作用中，云的水平范围（或云分数）如何随云滴数浓度的变化而变化是当前的关键问题。Cloudtrend将通过以下三个关键思想大大改善对这一问题的理解和量化：（i）21世纪中国上空人为气溶胶排放的强烈增加然后减少的趋势为在地面和卫星云图的高质量数据中检测和归因气溶胶引起的云量变化提供了一个独特的机会;（ii）有了新的模拟和数据分析工具，包括更好的云微物理卫星反演和新的状态定义，以及新的CMIP 6多模式数据;（iii）建议进行大规模数据分析和全球气候模拟，以系统地学习区域高质量数据和模拟。该项目之所以成为可能，要归功于南京信息科学技术大学（区域重点）和莱比锡大学（大规模重点）团队的专业知识之间的协同作用。