中文摘要：

利用兰州市区及周边5个气象站的气象数据,通过重标极差分析（R/S）和非周期循环分析,计算兰州市气温、降水和城市热岛强度时间序列的Hurst指数、分维数和非周期循环的平均循环长度,分析气温、降水和城市热岛强度时间序列的变化趋势、长期记忆效应和记忆周期。结果表明：兰州市四季和年平均气温呈现上升趋势,变化趋势具有持续性特征。年均、春、夏、秋、冬季气温序列的平均循环长度分别为9 a、4 a、8 a、5 a和6 a。兰州市春、冬季降水量仍将持续递减,夏季降水持续递增;年平均和秋季降水量变化过程具有反持续性,未来的年平均和秋季降水量将出现增加的趋势,年均、春、夏、秋、冬季降水量序列的平均循环长度分别为9 a、7 a、10 a、12 a和13 a。兰州热岛强度冬季最强,夏季最弱,春秋居中;1月平均热岛强度为1.6℃,7月平均热岛强度为0.1℃。近50 a兰州市热岛强度逐渐增强,线性趋势达到0.46℃.（10a）-1,未来兰州市热岛强度的变化趋势将以目前的速率持续增强。将定量描述长程依赖性的重标极差分析和非周期循环分析二者结合起来,应用于气候变化的趋势预测研究,是一种新颖、科学、可行的非线性研究方法。

英文摘要：

Based on two methods of R/S and non-periodic cycle analysis, the values of Hurst parameter H, fractal dimension D and non-cycle average cycle length of temperature and precipitation at time series in Lanzhou City were calculated, and then the variation trends, long-term memory effects and memory cycles of temperature and precipitation at the time series were analyzed. The results show that the values of H and D of average seasonal and annual temperature varied in ranges of 0.5 - 1 and 1 - 1.5 respectively, there was a fractal structure, and average seasonal and annual temperature was in a continuous increase trend. Average cycle lengths of average annual and spring, summer, autumn and winter temperature at time series were 9 years, 4 years, 8 years, 5 years and 6 years respectively. Precipitation in spring and winter will continuously decrease but increase in summer. H and D of average annual and autumn precipitation series with long-term negative correlation will vary in ranges of 0 - 0.5 and 1.5 - 2 respectively, the change process has an anti-persistent character, the average annual and autumn precipitation in the future will in an increase trend, average cycle lengths of average annual, spring, summer, autumn and winter precipitation at time series will be 9 years, 7 years, 10 years, 12 years and 13 years respectively. Precipita- tion in the past will affect that in the future for a long time. The study revealed that the methods of R/S and non- periodic cycle analysis are new, scientific and reliable in predicting regional climate change.