报告题目：Driving mechanisms and feedbacks of the land greening
报 告 人：毛嘉富 博士
报告题目：Investigating the effects of CO2 and human intervention on the water cycle
报 告 人：施小英 博士
时 间： 2018年6月13日（周三）10:00~12:00
地 点：气象楼423报告厅
主 持 人：陈海山 教授
欢迎广大师生踊跃参加！
气象灾害教育部重点实验室
气象灾害预报预警与评估协同创新中心
大气科学学院
2018.6.8
专家简介：
毛嘉富，博士，橡树岭国家实验室研究员。2001年获得南京气象学院气象学学士学位；2007年获得中国科学院大气物理所大气科学博士学位。2005年、2007年英国谢菲尔德大学访问学者，2016年法国国家气象研究中心访问学者。2006-2008年在中国科学院大气物理所任助理研究员，2008-2009年在澳大利亚新南威尔士大学的气候变化研究中心和CSIRO海洋与大气研究中心任联合研究科学家，2009-2011年在橡树岭国家实验室从事博士后研究，2015-2016年任美国田纳西州大学工业与系统工程系助理教授，2016年至今任美国田纳西州大学工业与系统工程系副教授。主要从事陆地表面对环境变化的响应与反馈，包括水文、碳循环和植被动力学等。获得2016年ORNL Super Performance Award，2014年、2015年Significant Event Award (SEA) 。迄今在Nature Climate Change, Nature Communications, Scientific Reports, Biogeosciences, Global Change Biology and Journal of Climate等国际著名期刊上发表论文20余篇。
邮箱：maoj@ornl.gov
施小英，博士，橡树岭国家实验室研究员。1998年获得南京气象学院气象学学士学位；2004年、2007年分别获得中国科学院大气物理所气象学硕士、大气科学博士学位。2007-2008年在中国气象科学研究院灾害性天气国家重点实验室担任助理研究员，2008-2009年在澳大利亚新南威尔士大学的气候变化研究中心和CSIRO海洋与大气研究中心担任联合研究科学家，2009-2011年在橡树岭国家实验室从事博士后研究。主要研究领域为生物地球化学、地球系统模式、气候响应等。获得2016年ORNL Super Performance Award，2014年Significant Event Award (SEA) ，2014年计算机与地球科学最佳论文奖，2007年中国气候委员会优秀论文奖，2000年广西气象局优秀预报员奖。迄今在 Environmental Research Letters, Geophysical Research Letters, Journal of Hydraulic Engineering, Journal of Applied meteorological Science等国际著名期刊上发表第一作者论文10余篇。
邮箱：shix@ornl.gov
报告摘要：
Driving mechanisms and feedbacks of the land greening
The land greening, characterized by the change of vegetation indices, has been documented to significantly increase over the past 3 decades, especially in the northern extratropical land surface. Drivers of this enhanced vegetation growth have been extensively investigated using multiple estimates from observed and modeled datasets and various statistical methods. Spatialtemporal changes of the main climate drivers (e.g., temperature, precipitation and radiation) have been widely accessed to modulate the variation in vegetation growth. The combined anthropogenic effects, particularly the rising atmospheric concentrations of well-mixed GHGs, was recently clearly identified as the dominant factor controlling this observed land greening. On the other hand, the enhancement of vegetation activity can potentially accelerate the photosynthetic removal of atmospheric CO2 and decrease the surface air temperature causing a negative forcing on climate system. Also, through the complicated biophysical feedbacks (e.g., the increased evapotranspiration and absorption of solar radiation), the land greening can intensify or diminish negative climate forcing induced via its biogeochemical feedbacks. Future vegetation growth and feedbacks, however, remain to be determined because of nonlinear human-ecosystem-climate interactions under global warming.
Investigating the effects of CO2 and human intervention on the water cycle
In the traditional CMIP approach, human system information required as forcing for climate prediction is generated in advance by economic integrated assessment models (IAMs) that include both energy and agricultural sectors and passed to earth system models in an asynchronous fashion. An integrated Earth System Model (iESM), which combines the Global Change Assessment Model, Global Land-use Model, and community Earth System Model, was recently developed. This synchronous (coupled) approach provides a tremendous opportunity for modeling experiments to examine interactions between the human and natural systems. A series of experiments was performed using the iESM under the RCP8.5 scenario to explore the radiative and physiological effects of atmospheric CO2 concentration and Earth-human interactions in the context of future hydrological projections. We found that CO2 concentration has significant effects on the hydrological cycle by modulating the evapotranspiration of the plant and soil system. Including Earth and human system interactions also has significant impacts on future water cycle through interactive land use and land cover change, greenhouse gas emissions, and water management. On the basis of these results, we suggest utilizing more integrated modeling systems to better project future environmental changes.