报告人：Dr. Xinguang CUI

　　报告人单位：Lawrence Berkeley National Laboratory

　　报告时间：2017年3月30日（星期四）15:00

　　报告地点：科研楼303会议室

　　报告简介：The climate change is not a threat in distant and it has become a focus of the human society today. In 2006, the state of California legislated an landmark bill of Assembly Bill 32 (AB32), targeting to reduce the greenhouse gases (GHGs) emissions in the state to 1990 levels by 2020. This leads to a plenty of efforts to measure, quantify, and mitigate emissions of a variety of key GHGs. Both the bottom-up inventory and top-down inversion are important methods to accurately estimate the GHGs emissions. Bottom-up inventories estimate the total GHGs emission by aggregating emission estimates from spatial and temporal distributions of known GHG sources. This approach has multiple strengths but it can be limited by incomplete knowledge of processes that contribute to GHGs emissions. Additionally, atmospheric inverse modeling is a complementary approach to qualify GHG emissions without requiring complete knowledge of individual sources and provide improving estimate of GHGs emissions. In this context, several projects have been conducted in LBNL towards qualifying the GHGs emissions in CA using atmospheric measurements and inverse modeling and most recent results will be introduced in this talk.