The northeastern China is a sensitive region of climate change, whose detailed trend of climate changes is highly interesting. In this study, this kind of variation trend was analyzed. Potential evapotranspiration (PE) and moisture index (MI) were modeled by using Thornthwaite scheme based on the observation data of 1961-2004 from 94 meteorological stations. To describe the climate fluctuation in the northeastern China in 1961-2004, the linear regression method was used to analyze the variation trends of mean annual temperature, mean annual precipitation, PE and MI. Mann-Kendall method was used to test the significant difference. The results show a general increasing tendency in mean annual temperature, mean annual precipitation, PE and MI. However increasing tendency was more significant in mean annual temperature and PE than in mean annual precipitation and MI. Analysis of seasonal climate variation indicates that there showed positive trends in winter and in spring, while the positive trend was more significant in winter than in spring. Furthermore, the relations between climate changes and geographical factors were analyzed, the results show that both climate factors and their interannual variability were correlated to latitude, longitude and altitude, suggesting that latitude is the most climate factor affecting climate changes, followed by altitude and longitude.
Using five well-replicated Qilian juniper (Sabina przewalskii Kom.) tree-ring width index se- ries, monthly normalized difference vegetation index (NDVI) of grassland, and climatic data from 1982 to 2001, the relationships between tree-ring width index, NDVI of grassland, and climatic data were analyzed firstly. Then, the relationship between tree-ring width index and NDVI of grassland was explored. The re- sults showed that: (1) Temperature and precipitation in June influenced tree-ring width index and NDVI of grassland deeply in Delingha. (2) There were sig- nificant relationships between five tree-ring width index series (DLH1-DLH5) and monthly NDVI of grassland from June to September, with the most significant relationship being between tree-ring width index series and NDVI of grassland in August. (3) The PC1 (the first principal component derived from DLH1-DLH5 series) exhibited good agreement with monthly NDVI of grassland in the grass growth sea- son (from June to September) and the averaged NDVI in the growth season, which was attributed to their common responses to water-supply limit in Delingha. This study may allow an increase in studying the past dynamics of grassland in Delingha in that the variation of grassland NDVI during the last millennium has been reconstructed from PC1.
HE Jicheng1,3 & SHAO Xuemei1,21. Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
