| The Tibetan Plateau (TP) is the biggest and highest snow-covered region in the mid-latitude in the North Hemisphere. Changes of snow cover on TP have important effects on the synoptic climate in China, thus, to evaluate the variations of snow cover and diagnose the main atmospheric causes are undoubtedly crucial to climate predictions. Based on the snow cover data from 115 meteorological stations on TP covering 1957?2006 and ERA-40 atmospheric circulation products for the period from 1958?2002, this study mainly investigates the characteristics of snow depth variations on TP and the key factors in the atmospheric circulation that would throw impacts on it, so as to offer references for complicated climate predictions. The main conclusions are as follows:1. Work on the spatial distribution of snow depth on TP shows that there are four basic centers with heavy snow on TP, which is different from the previous definitions according to fewer meteorological stations. Among the four centers, the two lying in the middle and east TP accumulate most snow in autumn, and the other two lying in the Himalayas and the Pamirs increase rapidly in winter, as a result, forms the snow distribution pattern on TP as more in the center and edge of TP and less in the other regions.2. The spectral and wavelet analysis disclose that there are quasi-three years period for the snow depth on TP, and the two breaks in the recent 50 years took place in 1971 and 1998, respectively. Temporally, the annual snow depth on TP presents an increase trend before the middle of 1990s, and the rate reaches 0.06cm/10a?about 1.8% of annual mean snow depth. After 1997, the annual mean snow depth on TP decreases continually, and all go beyond ?1 standardized deviation except for 1998. Composing years with heavy and light snow on TP, the distribution of differences between heavy snow and light snow characters as more in the Himalayas and in the middle of TP. Among the four seasons, winter mean snow depth contributes the most to the annual one?the correlation coefficient between them reaches to 0.96, and spring and autumn snow are in the next place.3. Researches on the relationship between annual mean snow depth and air temperature indicate that there is negative correlation between them, but not very significant. However, statistical analyses based on seasonal scale reveal that, except for winter, the correlations are all reach 99.9% significant level and the correlation coefficients in spring, autumn and summer are ?0.49, ?0.48 and ?0.64, respectively. Among the 26 years with higher air temperature than normal in spring, there are 22 years with less snow depth than normal (about 85%), and in autumn the rate is 16/20=80%, and in summer it is 71%. Among all the 90+ effective stations, about 98% of stations in autumn and more than 92% of stations in spring show negative correlation between snow depth and air temperature, in which more than 63% and 72% stations, respectively, reach 95% significant level.4. Analysis of synoptic correlation between snow depth on TP and the North Atlantic Oscillation (NAO) Index indicate the positive correlations are significant in January, February and March. When NAO is stronger, the snow depth is bigger widely on TP, and the regions reaching 95% significant level distribute mainly in the middle of TP zonally from the southwest to the northeast TP. Composing years with stronger and weaker NAO, results indicate that when NAO is stronger than normal, the meridional disturbances on 500hPa level in the mid-high latitude of the North Hemisphere are abnormal. First, the trough in the mid-high latitude of the North Atlantic Ocean strengthens and extends southward, and the ridge in the front of trough moves northward and expands eastward, as a result, the European Trough in about 20°E moves eastward to about 50°E, and the ridge in Ural Mountains moves eastward to Baikal Lake. The accelerated the subtropical westerly jet in the upstream of TP brings more warm-wet airflow for TP. Consequently, the circulation pattern being propitious to the abundant snow on TP forms.5. The positive correlation between snow depth and snowfall on TP in spring is significant, and the correlation coefficient reaches 0.61. The years with the same phase for them are 34 reaching 70% of the total samples. Close relationship between snowfall on TP and the India-Burma Trough (IBT) in May is revealed. There are 18 years with more snowfall on TP among the 29 years with stronger IBT, and on the contrary, 11 out of 16 years with less snowfall when there is weaker IBT. When IBT is stronger than normal in May, an important climate feature on 500hPa level is that the trough over India-Burma region strengthens and extends southward to 10°N or so and the trough line lies around 90°E, and stronger cyclonic vorticity occurs in the trough area; Contrarily, there is no evident trough in the corresponding area then IBT is weaker than normal. The differences in geopotential height between stronger and weaker IBT can reach to over 30 meters.6. With the severity of climate warming, the response of snow cover on TP to the increasing air temperature becomes a crucial issue arousing extensive attention. Defining the critical climate condition of"at-risk"snowfall and snow cover being fit for TP by citing"at-risk"snow concept, this paper not only discusses the sensitivity of snow cover on air temperature under the current climate status, but also predicts the"at-risk"regions of snow cover on TP when the air temperature increases 2.2?2.6°C by 2050. Results indicate that, under the current climate condition, stations on TP with"at-risk"snowfall in autumn and spring have reached 77.8% and 81.1%, respectively, and 32.8% and 36.3% for"at-risk"snow cover. Spatially, the southeast TP is the main"at-risk"region, and parts of south and north TP are main"at-risk"snowfall regions. By 2050, almost all the stations in the"at-risk"regions will convert to"at-risk"status, that is to say, the snow cover will decrease to a great extent.
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