资助课题: 国家级气象科学数据资源建设(2005DKA31700-02), 中国气象局气象新技术推广项目(CMATG2006Z03). 作者简介: 沈艳, 主要从事气候变化研究. Email: sheny …

资助课题: 国家级气象科学数据资源建设(2005DKA31700-02), 中国气象局气象新技术推广项目(CMATG2006Z03). 作者简介: 沈艳, 主要从事气候变化研究. Email: sheny …内容摘要：

中国 55 年来地面水汽压网格数据集的建立及精度评价 沈 艳 1 熊安元 1 施晓晖 2 刘小宁 1 1. 国家气象信息中心，北京, 100081 2. 中国气象科学研究院灾害天气国家重点实验室，北京, 100081 摘要 对气象要素网格化是气候变化研究中避免空间抽样误差的有效方法之一。文中采用薄盘光 滑样条插值法（ANUSPLIN），在考虑站点经度、纬度和海拔高度的基础上，对中国 55 年 来地面水汽压站点资料进行空间插值，得到了中国陆地水汽压年和月平均值 1°×1°网格数 据集。精度检验表明：中国年水汽压插值误差普遍小于 0.3 hPa；而月水汽压的插值误差由 于受水汽压周期变化的影响，表现出周期性变化的特点。一般夏季较大，最大误差在 0.5 hPa 左右，冬季较小,约为 0.2 hPa。在考虑站点海拔与对应网格 DEM 差值大小的基础上，建 立实测水汽压值与对应网格水汽压值年序列，并进一步分析二者的相关关系，表明：（1） 二者具有很好的相关性，相关系数为0.88—0.96；（2） 能很好地模拟地形影响，得到 的网格水汽压可以较好地代表实测水汽压的变化趋势。由此建立了中国近 55 年来地面水汽 压的年序列。其趋势表明：近 55 年来中国年平均水汽压呈增加趋势，其线性趋势为 0.52 hPa/(100 a)，其中西部增加趋势大于东部，且以夏季的增大趋势最为显著。结合近 50 年来 气温的变化趋势说明：在中国，气温每增加 1 ℃，大气中年平均水汽含量约增加 3.15%。 关键词 网格数据集，地面水汽压，气候变化，趋势系数 资助课题：国家级气象科学数据资源建设(2005DKA31700-02)，中国气象局气象新技术推 广项目(CMATG2006Z03)。 作者简介：沈艳,主要从事气候变化研究.Email: sheny@cma.gov.cn 2007-05-28 收稿,2007-07-30 改回. 中图法分类号 P468.0+2 Development of the grid based ground water vapor pressure over China in recent 55 years and its accuracy evaluation 1 SHEN Yan XIONG Anyuan 1 SHI Xiaohui 2 LIU Xiaoning 1 1. National Meteorological Information Center, CMA, Beijing 100081, China 2. State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China Shen Yan,Xiong Anyuan,Shi Xiaohui,Liu Xiaoning.2008.. Abstract The detection of large-area average changes of meteorological data is always hampered because of the different record series of long-term in-situ measurements, which can often cause the spatial sampling errors. Fortunately, more and more scientists explore that the grid-based dataset can reduce the spatial sampling error through given interpolation method. Substantial progress has been made in the last two decades in quantitatively documenting analysis of different meteorological factors. Ground water vapor pressure is an important meteorological factor that can control some physiological, ecological and water balance process in ecosystem. In this article, using the quality controlled observational monthly and yearly mean ground water vapor pressure data series over China Mainland, through the ANUSPLIN software developed by the Australian National University based on the thin-plate smooth spline method, the datasets of yearly and monthly grid-based ground water vapor pressure are established over China in recent 55 years from the 1951 to 2005. Cross-validation tests show that this gauge-based analysis has high quantitative quality, which annual interpolation error is typically less than 0.3 hPa except from 1951 to 1953 and monthly error has periodic variation with biggest in summer and smallest in winter. In spring and autumn its monthly error value is between the others. The research results include: (1) The relationship between measured and its corresponding grid cell indicates that they are a good linear correlation passing the 0.01-level confidence check. The grid value can represent the pattern of the measured one. (2) Using the 30-year normal from the 1971 to 2000, annual and seasonal variation based on the grid dataset suggests that average annual change shows increasing trend over the 55 years with its linear trend 0.52 hPa per 100-year. The increasing trend in western China is more obvious than that in eastern China. For the seasonal scales, the summer trend is the most dramatic, which is estimated linear trend 0.98 hPa per 100-year over the whole China. While the spring's is lowest which is 0.42 hPa per 100-year. Considering the temperature rising in recent 50 years, 3.15% of water vapor will be increased when temperature warms up 1 degree over China, which is lower than the globe average value. There are two reasons for it, one is the stronger warming trend in China than in the world the other maybe arises from drought enhancement in China. This developed datasets are helpful to explore the spatial and temporal distributions of the ground water vapor pressure. It can be used in a wide range of applications, including weather/climate monitoring, climate analysis, numerical model verifications, ecological assessment, and hydrological studies. Key words Grid dataset, Ground water vapor pressure, Climate change, Trend coefficient