大气环流模式(GCMs)模拟预测的气候变化情景, 必须经过降尺度处理后才能得出次网格尺度上未来气候变化的时空分布细节, 才能满足评估气候变化对资源、环境和社会经济等影响的需要。本文在简单介绍了目前降尺度模型的研究现状后, 重点分析了统计降尺度方法的优缺点及适用性, 并应用黄河源区7个站点1961-1990年的实测地区最高气温和最低气温资料, 对统计降尺度模型(SDSM)的应用进行了分析和验证。首先利用SDSM建立大尺度气候要素和地面气温变量间的统计转换关系, 确定模型应用的预报因子变量, 然后用独立的观测资料验证模型的可靠性, 最后把建立好的统计关系应用于英国Hadley中心海气耦合模式(HadCM3 SERS B2)的输出, 分别生成了黄河源区7个站点未来3个时段2020s, 2050s和2080s的气温变化情景。在此基础上, 应用Arc/GIS的Kriging插值方法获得整个区域的气温变化情景进行分析。结果表明, 日最高气温模拟值随时间推移增幅很快, 3个时段(2020s, 2050s和2080s)的平均气温变化情景分别为1.34, 2.60和3.90℃, 而日最低气温变化相对不明显, 3个时段的平均气温变化情景分别为0.87, 1.49和2.27℃。表现在每个季节和每个月的变化情景又各不相同, 日最高气温以春季和秋季变化最显著, 而日最低气温则以夏季和秋季的变化最为明显。
Direct outputs of climate change simulation from general circulation models(GCMs) are inadequate for the assessment of land-surface impact on regional scale. Statistical downscaling technique is proposed as one of the tools to establish the relationship between the mesoscale GCM output(frequently atmospheric circulation data) and sub-grid-scale surface variables(such as precipitation), under the assumption that the GCM output are more reliable than the latter. After analyzing both advantage and disadvantage of the statistical downscaling technique and its applicability, the daily maximum and minimum temperatures were downscaled from GCM grid to local area using the Statistical Downscaling Model(SDSM) in this paper. In order to analyze its reasonability, a resolution with the grid of 2.5 in latitude and 3.75 in longitude in source of the Yellow River was selected and the 30-year daily temperature series at 7 stations were used. First, a statistical transfer function between the large scale predictors and the local temperature was established by using the SDSM. Then, the transfer function was validated by using individual observed data. Finally, the temperature scenarios for future periods(2020s, 2050s and 2080s) were estimated using the validated transfer function from output of the HadCM3 SERS B2 at 7 stations. On the basis of above analysis, the temperature scenarios for the whole source of the Yellow River was developed by using the Kriging interpolation in ArcGIS. The results show that the downscaled maximum temperature quickly increase, and the average scenarios for the future periods are 1.34, 2.60 and 3.90℃, respectively. However, the average change of daily minimum temperature is relatively unconspicuous, and the scenarios for the future periods are 0.87, 1.49 and 2.27℃, respectively. The scenarios for different months and seasons are quite different. The scenarios for the maximum temperature are remarkable in spring and autumn, and the scenarios for the minimum temperature are distinct in summer and autumn.
赵芳芳, 徐宗学. 黄河源区未来地面气温变化的统计降尺度分析[J]. 高原气象, 2008, 27(1): 153-161.
ZHAO Fang-fang, XU Zong-xue. Statistical Downscaling of Future Temperature Change in Source of the Yellow River Basin. PLATEAU METEOROLOGY, 2008, 27(1): 153-161.
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