1961～2017年雅鲁藏布江河谷地区夏季气候暖干化趋势

基于1961～2017年青藏高原腹地雅鲁藏布江河谷地区4个站（拉萨、日喀则、泽当和江孜）夏季（6～8月）月平均气温、降水和相对湿度等观测资料,分析了该地区夏季气候年际和年代际演变特征,并探讨了气温、降水和相对湿度在年际和年代际时间尺度上的相互关系以及与总云量和地面水汽压的联系。结果表明:（1）1961～2017年该地区夏季气候出现了暖干化趋势。气温（相对湿度）显著升高（下降）,降水趋势变化不明显;本世纪初气温（相对湿度）均发生了显著的突变。（2）该地区夏季气候因子间在年际和年代际时间尺度上存在密切关系:气温与相对湿度和降水均存在明显的负相关,降水与相对湿度为正相关。（3）该地区夏季气候因子间的年际和年代际变化与同期总云量和地面水汽变化有关。1961～2017年总云量持续减少是气温显著升高的主要原因之一,气温的显著升高和降水变化不明显又造成了相对湿度的显著下降。     

Comparison of precipitation products to observations in Tibet during the rainy season

Precipitation is an important component of global water and energy transport and a major aspect of climate change. Due to the scarcity of meteorological observations, the precipitation climate over Tibet has been insufficiently documented. In this study, the distribution of precipitation during the rainy season over Tibet from 1980 to 2013 is described on monthly to annual time scales with meteorological observations. Furthermore, four precipitation products are compared to observations over Tibet. These datasets include products derived from the Asian Precipitation-Highly-Resolved Observational Data(APHRO), the Global Precipitation Climatology Centre(GPCC), the University of Delaware(UDel), and the China Meteorological Administration(CMA). The error, relative error, standard deviation, root-mean-square error, correlations and trends between these products for the same period are analyzed with in situ precipitation during the rainy season from May to September. The results indicate that these datasets can broadly capture the temporal and spatial precipitation distribution over Tibet. The precipitation gradually increases from northwest to southeast. The spatial precipitation in GPCC and CMA are similar and positively correlated to observations. Areas with the largest deviations are located in southwestern Tibet along the Himalayas. The APHRO product underestimates, while the UDel, GPCC, and CMA datasets overestimates precipitation on the basis of monthly and inter-annual variation. The biases in GPCC and CMA are smaller than those in APHRO and UDel with a mean relative error lower than 10% during the same periods. The linear trend of precipitation indicates that the increase in precipitation has accelerated extensively during the last 30 years in most regions of Tibet. The CMA generally achieves the best performance of these four precipitation products. Data uncertainty in Tibet might be caused by the low density of stations, complex topography between the grid points and stations, and the interpolation methods, which can also produce an obvious difference between the gridded data and observations.