基于高分辨率格点数据的1961-2013年青藏高原雪雨比变化

Change of snowfall/rainfall ratio in the Tibetan Plateau based on a gridded dataset with high resolution during 1961-2013

基于国家气象信息中心发布的1961-2013年全国0.5°×0.5°逐日降水量和日平均气温格点数据集以及气象站点日降水量和日平均气温实测资料,采用森斜率、M-K突变分析、IDW空间插值以及小波分析等方法,对近53年来青藏高原的降水量、降雨量、降雪量以及雪雨比的时空变化、突变和周期等特征进行了分析。结果表明:1从时间尺度上看,青藏高原的降水量和降雨量总体呈增加趋势,增加幅度分别为0.6 mm·a^(-1)(p<0.05)和1.3 mm·a^(-1)(p<0.001);而降雪量和雪雨比均呈下降趋势,下降幅度分别为0.6 mm·a^(-1)(p<0.01)和0.5%a^(-1)(p<0.001)。2从空间分布上看,青藏高原的大部分地区降水量和降雨量呈增加趋势,而降雪量却呈现减少趋势。因此,雪雨比在青藏高原相应呈现减少趋势。3突变和周期分析表明,青藏高原降水量、降雨量、降雪量和雪雨比的突变时间分别出现在2005、2004、1996和1998年左右,而周期变化集中为5年、10年、16年、20年左右。4青藏高原降水量倾向率和降雨量倾向率均随海拔的升高呈现出先降低后升高的变化趋势,降雪量倾向率随海拔的升高而降低,雪雨比倾向率随海拔的升高呈微弱的下降趋势。

Based on the daily precipitation and temperature from a 0.5° × 0.5° gridded dataset and meteorological stations during 1961- 2013 released by the National Meteorological Information Center, the spatial and temporal changes of annual precipitation, rainfall, snowfall and snowfall/rainfall ratio（S/R） in the Tibetan Plateau in recent 53 years were analyzed by using Sen slope, Mann- Kendall mutation test, IDW（inverse distance- weighted） interpolation and Morlet wavelet. The results indicated that：（1） Generally, precipitation and rainfall show increasing trends by 0.6 mm · a-1（p 〈 0.05） and 1.3 mm · a-1（p 〈 0.001）, respectively, while snowfall and S/R show decreasing trends by 0.6 mm·a-1（p 〈 0.01） and 0.5%·a-1（p 〈 0.001）,respectively.（2） Precipitation and rainfall show increasing trends, but snowfall shows a decreasing trend in most parts of the Tibetan Plateau. On this basis, S/R shows a decreasing trend.（3） Abrupt and cycle analysis shows that the abrupt changes of the precipitation, rainfall,snowfall and S/R occurred in 2005, 2004, 1996 and 1998, respectively. The precipitation,rainfall, snowfall and S/R present cycles of approximately 5 years, 10 years, 16 years and 20 years, respectively.（4） As elevation increases, the trend magnitude for precipitation and rainfall shows a decrease-to-increase trend, and that for snowfall and S/R shows a decreasing trend.