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The Applicability of Two Statistical Downscaling Methods to the Qinling Mountains
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摘要: 基于ASD（automated statistical downscaling）统计降尺度模型提供的多元线性回归和岭回归两种统计降尺度方法，采用RCP4.5（representative concentration pathways 4.5）和RCP8.5情景下全球气候模式MPI-ESM-LR输出的预报因子数据、NCEP/NCAR再分析数据和秦岭山地周边10个气象站观测数据，评估两种统计降尺度方法在秦岭山地的适用性及预估秦岭山地未来3个时期（2006-2040年、2041-2070年和2071-2100年）的平均气温和降水。结果表明：率定期和验证期内，两种统计降尺度方法均可以较好地模拟研究区域的平均气温和降水的变化特征，且多元线性回归的模拟效果优于岭回归。在未来气候情景下，两种统计降尺度方法预估的研究区域平均气温均呈明显上升趋势，气温增幅随辐射强迫增加而增大。降水方面，21世纪未来3个时期降水均呈不明显减少趋势，但季节分配发生变化。综合考虑两种统计降尺度方法在秦岭山地对平均气温和降水的模拟效果和情景预估结果，认为多元线性回归降尺度方法更适用于秦岭山地气候变化的降尺度预估研究。Abstract: The Qinling Mountains is not only the dividing line of northern China and southern China, but also the dividing line between monsoon climate of medium latitudes and subtropical monsoon climate in China, i.e., the dividing line between China's warm temperate and subtropical regions. It also has abundant natural resources because of its special geographical location and complex climate environment. In the context of global warming, impacts of climate change on forest ecosystems in the Qinling Mountains are of great significance. Global climate model which is widely used in large-scale climate simulation studies, cannot be applied in this region due to low resolution. Statistical downscaling model can be used to provide local-scale daily temperature and precipitation for studying climate change impacts of this region. Different statistical downscaling models have different principals, as well as different predictors. Therefore, it is necessary to compare different downscaling models and to select more appropriate downscaling model to obtain reasonable simulation results. Focusing on the future daily mean temperature and precipitation for the Qinling Mountains, the multiple linear regression and the ridge regression downscaling approaches based on ASD (automated statistical downscaling) model are implemented. Outputs from the general circulation model (MPI-ESM-LR) under RCP4.5 and RCP8.5 scenarios are analyzed. Simulation results of two statistical downscaling approaches during calibration and validation periods are analyzed and future climate change projections in periods of 2006-2040, 2041-2070 and 2071-2100 are generated. During the calibration and validation periods, both statistical downscaling approaches perform well in simulating the mean temperature and precipitation. However, the multiple linear regression perform better than the ridge regression, and the mean of simulated temperature is better than that of precipitation. Both statistical downscaling approaches project an increase for the mean temperature and its magnitudes depending on the emission scenarios, i.e., RCP8.5 resulting in a higher temperature than RCP4.5. The annual precipitation would slightly decrease but not statistically significantly, while the seasonal distribution of annual precipitation will change, a slightly increase in spring and a decrease in other seasons, especially in summer. In summary, the multiple linear regression is more suitable for statistical downscaling research in the Qinling Mountains.
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图 2 率定期秦岭山地两种统计降尺度方法模拟与观测的月平均气温(a)和标准差(b)比较
Fig. 2 Comparison of monthly mean value(a) and standard deviation(b) of temperature between the observed and the simulated by two statistical downscaling approaches in the Qinling Mountains during calibration period
图 3 率定期秦岭山地两种统计降尺度方法模拟与观测的降水量各特征量比较
Fig. 3 Comparison of different climate variables of precipitation between the observed and the simulated by two statistical downscaling approaches in the Qinling Mountains during calibration period
图 4 验证期秦岭山地两种统计降尺度方法模拟与观测的平均气温(a)和标准差(b)的比较
Fig. 4 Comparison of mean value(a) and standard deviation(b) of temperature between the observed and the simulated by two statistical downscaling approaches in the Qinling Mountains during validation period
图 5 验证期秦岭山地两种统计降尺度方法模拟与观测的降水各特征量比较
Fig. 5 Comparison of different climate variables of precipitation between the observed and the simulated by two statistical downscaling approaches in the Qinling Mountains during validation period
图 6 两种统计降尺度方法预估的不同情景下秦岭山地未来3个时期平均气温变幅
(a)多元线性回归，(b)岭回归
Fig. 6 Future mean temperature change in the Qinling Mountains generated by two statistical downscaling approaches under different scenarios in different periods
(a)the multiple linear regression, (b)the ridge regression
图 7 两种统计降尺度方法预估的不同情景下秦岭山地未来3个时期降水量变化
(a)多元线性回归，(b)岭回归
Fig. 7 Future precipitation change in the Qinling Mountains generated by two statistical downscaling approaches under different scenarios in different periods
(a)the multiple linear regression, (b)the ridge regression
表 1 NCEP/NCAR和MPI-ESM-LR预报因子
Table 1 Predictors used for NCEP/NCAR and MPI-ESM-LR
序号 变量 1 500 hPa相对湿度 2 700 hPa相对湿度 3 850 hPa相对湿度 4 海平面气压 5 500 hPa温度 6 700 hPa温度 7 850 hPa温度 8 近地面温度 9 500 hPa纬向风速 10 700 hPa纬向风速 11 850 hPa纬向风速 12 近地面纬向风速 13 500 hPa经向风速 14 700 hPa经向风速 15 850 hPa经向风速 16 近地面经向风速 17 500 hPa位势高度 18 700 hPa位势高度 19 850 hPa位势高度 
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表 2 多元线性回归的解释方差和均方根误差
Table 2 Explained variance and root mean square error of the multiple linear regression in calibration period
站点 气温 降水量 解释方差 均方根误差 解释方差 均方根误差 宝鸡 0.969 0.0060 0.208 0.648 西安 0.968 0.0063 0.182 0.569 华山 0.919 0.0093 0.241 1.14 略阳 0.961 0.0043 0.266 2.30 汉中 0.958 0.0053 0.227 2.08 佛坪 0.956 0.0053 0.184 0.955 商县 0.973 0.0040 0.196 0.826 镇安 0.962 0.0059 0.153 1.24 石泉 0.964 0.0075 0.297 1.95 安康 0.957 0.0082 0.204 1.21
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表 3 岭回归的解释方差和均方根误差
Table 3 Explained variance and root mean square error of the ridge regression in calibration period
站点 气温 降水量 解释方差 均方根误差 解释方差 均方根误差 宝鸡 0.969 0.0157 0.207 0.899 西安 0.968 0.0188 0.179 0.749 华山 0.919 0.0164 0.240 1.35 略阳 0.961 0.0227 0.267 2.55 汉中 0.959 0.0256 0.234 2.73 佛坪 0.956 0.0180 0.182 1.18 商县 0.973 0.0175 0.196 1.12 镇安 0.962 0.0211 0.155 1.61 石泉 0.962 0.0231 0.297 2.02 安康 0.957 0.0253 0.204 1.97
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