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| 国家基本/基准站地面气温资料城市化偏差订正 | |
| 引用本文： | 温康民,任国玉,李娇,任玉玉,孙秀宝,周雅清,张爱英.国家基本/基准站地面气温资料城市化偏差订正[J].地理科学进展,2019,38(4):600-611. |
| 作者姓名： | 温康民 任国玉 李娇 任玉玉 孙秀宝 周雅清 张爱英 |
| 作者单位： | 1. 中国地质大学(武汉)环境学院大气科学系,武汉 430074 2. 国家气候中心,北京 100081 3. 辽宁省铁岭市气象局,辽宁 铁岭 112000 4. 中国科学院南海海洋研究所,广州 510000 5. 山西省晋中市气象局,山西 晋中 030600 6. 北京市气象局,北京 100089 |
| 基金项目： | 国家自然科学基金项目(41575003) |
| 摘 要： | 城市化偏差是中国地面气温观测记录中最大的系统性偏差,订正该偏差可为大尺度气候变化监测和研究提供准确的基础资料。论文介绍了用于单站地面月平均气温序列城市化偏差订正的一个方法,并利用该方法订正了685个国家基本/基准站1961—2015年地面年及月平均气温序列中的城市化偏差。采取自东往西迭代订正的方法,即从东往西逐经度订正,订正完的目标站也可作为参考站。首先,规定目标站的参考站在300 km范围内,并利用2站的去线性趋势年均气温的相关系数作为标准,规定相关系数最大且通过信度水平为0.005显著性检验的4个候选参考站作为该目标站的参考站;然后,对各个参考站年均气温与其对应目标站年均气温求相关,并以其平方为权重计算各参考站月和年均气温的平均值序列,即为各目标站年和月平均地面气温参考序列;其次,利用目标站气温序列趋势及其参考序列趋势之差作为总的订正值,订正目标站气温序列中包含的城市化偏差。较大的城市化偏差出现在华北地区、华中部分地区、东北北部、西南及西部部分地区,介于0.1~0.3 ℃/10 a;在中国西北部分地区、西藏西部及南部、东北南部、华南沿海、华东及华中个别站存在负偏差;对整个中国而言,相对城市化偏差为19.6%。以北京、武汉、银川、深圳作为华北、华中、西北和华南地区的大城市代表站,发现其在过去55 a的相对城市化偏差分别为67.0%、75.4%、32.7%和50.3%,与前人针对单站评估城市化影响的结果基本一致,说明论文的订正方法较为合理。论文介绍的城市化偏差订正方法,可用于订正中国等快速城市化地区地面气温观测资料的系统偏差,订正后的气温数据在很大程度上消除了城市化因素引起的不确定性。 |
| 关 键 词： | 国家基本/基准站 地面气温 月均气温 城市化偏差 订正方法 |
| 收稿时间： | 2018-05-10 |
| 修稿时间： | 2018-12-20 |
Adjustment of urbanization bias in surface air temperature over the mainland of China |
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| Kangmin WEN,Guoyu REN,Jiao LI,Yuyu REN,Xiubao SUN,Yaqing ZHOU,Aiying ZHANG.Adjustment of urbanization bias in surface air temperature over the mainland of China[J].Progress in Geography,2019,38(4):600-611. | |
| Authors: | Kangmin WEN Guoyu REN Jiao LI Yuyu REN Xiubao SUN Yaqing ZHOU Aiying ZHANG |
| Abstract: | This study developed a method for correcting urbanization bias of station monthly mean surface air temperature data. By using this method and the data of 143 reference stations obtained from a previous research, we corrected urbanization bias of annual and monthly mean temperature data from 1961 to 2015 for 685 national reference climate and basic weather stations (national stations). We explained the rationality and evaluated the effect of the adjustment in the monitoring and analysis of surface air temperature change in the mainland of China. This study adopted a method of iterative correction that corrects longitudinally from east to west, and a revised target station can also serve as a reference station. First, the reference stations of a target station was set as within the range of 300 km, and the correlation coefficients of the detrended annual mean temperature between the target and candidate reference stations were taken as the criteria for selecting the reference stations. Second, by using the correlation coefficients of the reference stations as weights, the weighted average of the annual and monthly mean temperature of all the reference stations around a target station was calculated, obtaining annual and monthly mean temperature reference series for each of the target stations. Third, urbanization biases of the target stations were adjusted by using the linear temperature trend differences of the target station and the reference series as the total correction amounts. The areas with large adjustments are located in North China, part of Central China, northern Northeast China, part of Southwest China, and western China, ranging from 0.1 to 0.3 ℃/10 a, and negative urbanization bias exists in some areas of northwestern China, western and southern Tibet, southern Northeast China, coastal South China, and a few stations in East and Central China. For the whole of China's mainland, the relative urbanization bias is 19.6%. As representative metropolis observational sites in North China, Central China, Northwest and South China, respectively, Beijing, Wuhan, Yinchuan, and Shenzhen stations are found to have larger relative adjustments of 67.0%, 75.4%, 32.7%, and 50.3% respectively in the past 55 years. The adjustments are in line with the results of previous studies on assessment of impacts of urbanization for the stations. The results show that the adjustment is reasonable. The corrected temperature data largely have eliminated the uncertainties caused by urbanization biases. |
| Keywords: | national stations surface air temperature monthly mean temperature urbanization bias correction method |
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