基于WRF-STILT模型对高塔CO_2浓度的模拟研究  

Tall tower CO_2 concentration simulation using the WRF-STILT model

作　　者：胡诚[1,2] 张弥[1,2] 肖薇[1,2] 王咏薇[1,2] 王伟[1,2] Tim Griffis 刘寿东[1,2] 李旭辉[1] HU Cheng;ZHANG Mi;XIAO Wei;WANG Yong-wei;WANG Wei;LIU Shou-dong;LI Xu-hui(Yale-NUIST Center on Atmospheric Environment, International Joint Laboratory on Climate and Environment Change (ILCEC), Nanjing University of Information Science & Technology, Nanjing 210044, China;Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CICFEMD) Nanjing University of Information Science & Technology, Nanjing 210044, China;University of MinnesotaTwin Cities, Saint Paul 55108, U.S.A)

机构地区：[1]南京信息工程大学气候与环境变化国际合作联合实验室大气环境中心,江苏南京210044 [2]南京信息工程大学气象灾害预报预警与评估协同创新中心/气象灾害教育部重点实验室,江苏南京210044 [3]明尼苏达大学,美国圣保罗市55108

出　　处：《中国环境科学》2017年第7期2424-2437,共14页China Environmental Science

基　　金：国家自然科学基金资助项目(41575147;41475141;41505005);江苏省高校优势学科建设工程项目(PAPD);教育部长江学者和创新团队发展计划项目(PCSIRT);2016年度江苏省高校研究生科技创新项目(KY2216-0348);国家公派联合培养博士研究生项目(201508320287)

摘　　要：利用下垫面均一的美国最大农业种植区已有高塔CO_2浓度观测资料,结合EDGAR的13种不同类型人为化石源CO_2通量和Carbon Tracker的植被NEE数据,评估了WRF-STILT拉格朗日大气传输模型的模拟能力.结果表明,WRF-STILT模型能够很好地模拟出高塔100m处观测到的CO_2浓度强季节和日变化特征,全年模拟的大气CO_2浓度的均方根误差为10.6×10^(-6),相关系数为0.44(n=7784,P<0.001);生长季(6~9月)观测和模拟的浓度增加值线性拟合斜率为1.08(R=0.52,P<0.001),说明一致性高;截距为7.26×10^(-6)则反映了使用人为化石燃烧的CO_2通量的高估或者植被NEE的低估.2008年全年高塔观测到的CO_2浓度增加值为4.83×10^(-6),小于模拟得到化石燃烧贡献的增加量6.61×10^(-6)与植被NEE的贡献值3.23×10^(-6)之和.其中原油生产和提炼以及能源工业分别贡献了化石燃料燃烧总量的2.55×10^(-6)(38.6%)和1.43×10^(-6)(21.6%).而对生长季观测到的强CO_2浓度日变化特征模拟结果显示,其模拟的平均日振幅为24.30×10^(-6);生物质燃烧产生的CO_2浓度贡献值为0.06×10^(-6),相对于植被NEE和化石源的贡献,可以被忽略.该方法可为将来应用高塔衡量气体浓度观测来反演中国区域尺度的温室气体通量提供参考.By using high spatial and temporal resolution EDGAR fossil emissions(13categories)and Carbon Tracker NEE flux,WRF-STILT model was evaluated with one year(2008)CO2concentration observations at a homogeneous agricultural underlying surface,which located in U.S.corn belt.The results showed that this model could capture the strong seasonal and daily variation,with RMSE be10.6×10-6,R=0.44(n=7784,P<0.001).The linear regression slope of growing season concentration enhancement was1.08(R=0.52,P<0.001),indicating high consistency,while the intercept(7.26×10-6)reflects the overestimation of fossil emission or underestimation of NEE.During this year round,observed enhancement was4.83×10-6,smaller than sum of the fossil enhancement contribution(6.61×10-6)and NEE contribution(3.23×10-6).The oil production and refineries and energy industry contributed2.55×10-6(38.6%)and1.43×10-6(21.6%)of all fossil enhancements,separately.Biomass burning only contributes0.06×10-6to the total enhancement which was ignorable compared with fossil and NEE.At the end,it can be concluded that this method can be used to retrieve regional scale greenhouse gas flux in China.

关 键 词：WRF-STILT模型 高塔CO2 浓度模拟 涡度相关 区域尺度 美国玉米带 

分 类 号：X511[环境科学与工程—环境工程]