上海市城市热岛的时间多尺度分析与数值试验
|
摘要
人类活动对气候的影响是气候学研究中的重要课题。城市气候便是在区域气候的背景下,在人类活动和城市化的共同影响下形成的特殊局地气候。城市居民和各项设施在每天的生活和生产中消耗大量能源,排放越来越多的人为热。上海作为国际金融中心,快速发展的主要特征之一便是原有的土地利用方式的改变。其密集的人口、交错的道路、林立的建筑物等共同形成了性质特殊的下垫面,并且随着城市化进程日益加速,城市热岛现象愈发严重。
文章正是从这一角度出发,在对大量气象数据进行小波和EMD经验模态多尺度分析的基础上,发现问题,探讨城市下垫面在城市气候中所起的作用。通过使用ENVI、IDL和ARCGIS等软件,数字化上海市航片得到完整下垫面建筑信息；在此基础上定量定性的分析计算城市人为热排放与城市气温及都市空调使用之间的关系；最后修正新一代中尺度模式系统(ARWWRF)中下垫面土地利用类型数据,并耦合城市冠层模型(UCM)的进行数值模拟研究和对比试验,探讨下垫面数据、城市覆盖层模型UCM和人为热各自对城市气候要素的影响。主要工作及结论如下：
(1)研究发现上海近136年来的气温变化总体处于一个持续增暖的过程中,近30年来的增温速度超过了以往任何一个时期。其次存在着准70a周期振荡,该时间尺度上气温主要表现为冷-暖-冷-暖四个阶段,40年代的暖期和60年代的冷期构成了上海20世纪气温变化的主要特征。此外还存在着准2a的周期震荡。在不同的时间尺度上,在1920’s后期和1959’s后期有较为明显的温度突变点。
(2)上海近136年来的降水量变化以短时间尺度的周期振荡为其主要变化规律,分别是2-3a、5a、8-10a和40a。其中以40a的周期振荡最为显著,特别是在20世纪初到中后期振荡变化清晰。在更长的时间尺度上,这一地区的降水量正处于上升阶段,而20a以下的时间尺度则相反。
(3)对2004年和2009年上海冬季和夏季的城市热岛分别进行小波和EMD的多尺度分析,小波中心的正负振荡形式很好的与热岛形成对应关系,在不同尺度下冬夏两季均呈现出明显的日变化周期。冬季热岛主要发生于傍晚及夜间,而夏季热岛主要在午后发展强盛,符合春夏较弱,秋冬强的特性。同时由于城市化的影响,2009年热岛强度较之2004年总体上升0.4-0.5℃,并且表现出夏季热岛强度增大更快的特征。
(4)在ENVI、IDL和ARCGIS的辅助下,利用上海市航片进行了上海市下垫面建筑信息数字化。得到相对完整的上海市建筑物分布、高度数据,为研究都市人为排热奠定了基础,并由此对ARWWRF模式中的土地利用数据做了修正,扩大城区面积,使其更准确的反应实际情况。
(5)利用上海市近50年各区县月均温数据,统计分析了各区与崇明夏季每五年均温差,发现热岛效应由市区中心向郊区延伸,范围越来越大,特别是20世纪80年代开始温差呈较大幅增加,城市热岛现象显著；绘制人为热排放流程图,并将城市人为热源进行分类,分析所有可能成为人为热排放源的设施以便定性及控制研究；对供给,消费和排出三种阶段的计算方法进行对比归纳,根据目的不同分别使用；估算上海近30年燃油排热、燃煤排热以及人为热排放总量；统计上海市区和郊区的年均温及年均温差随时间变化及其与人为热排放之间的关系,发现两者之间有很好的相关性；并根据上海工业区、建筑物以及道路的空间分布,得出上海人为热的空间分布。
(6)就上海市夏季空调使用排热对上海市温度影响进行了定性研究,在数字化航片得到的建筑物信息基础上假设楼层和空调密度之间存在相关性,发现空调排热与地面温度反演有着非常良好的一致性,同时也是造成上海城市高温的主要因素之一。
(7)耦合城市冠层模型UCM,利用ARWWRF模式对上海地区春季大气边界层的风场、温度场以及边界层高度等特征值分别进行了土地利用数据修正前后的数值模拟研究,对比结果,发现下垫面土地利用类型由耕地等类型变为城市后,水平风速显著减小,呈现明显城市拖曳作用；垂直速度增大,并由于白天陆面加温作用更为显著,在地面风场的作用下高温中心出现偏于下风方向；近地面气温和边界层高度均更贴近实测值。
(8)为进一步说明城市化对城市气候的影响,设计不同实验,结果显示,使用修正后数据、启动城市冠层模式并加入人为热得出的温度模拟结果最为接近观测值,对于分别代表市区、郊区及快速城市化地区的冬、夏季模拟结果与实测值相关系数R均达到0.9以上。各因子对城市气温的影响根据其所占百分比不同依次为UCM城市模块>下垫面土地利用类型>人为热排放。城市下垫面较之非城市下垫面的增温作用显著,海风登陆后通过相同距离不同性质下垫面后,经过上海市区的增温幅度可达4.61℃,远远大于经过非市区的1.81℃。
基于以上工作,我们对城市化有了更为直观的理解,也对城市气候的特殊性、复杂性有了更深刻的认识。城市作为我们人类生活聚居的区域,其内部机制、时空分布规律、能量交换都极为复杂。快速的城市化正成为城市气候学者们研究的主要方向之一,而统计方法、数值模拟则发挥着无可取代的作用。怎样合理的利用模式、修改模式也成了我们工作中重要的科学问题。
The effect on climate by human activities is the key part of climatology, while urban climate is a special local climate formed by hurman activities and unbanization, under the background of regional climate. More and more anthropogenic heat has been emitting into environment since a huge amount of energy is consumed each day by urban residents and city construction. Shanghai, the international commercial center, one of which main characteristics in its rapid development is the change of the original land use function. A dense population, crossing roads, intensive high structures, etc consist the particular underlying surface, which causes a more severe urban heat island with the urbanization's getting ever more acute.
Analysis in this paper started from multiple time sacle by wavelet and EMD, and then problems were brought out to the exploration into the function of underlying surface in urban climate. Softwares including ENVI, IDL and ARCGIS were used to help gain the complete information of constructures, based on which urban anthropogenic heat emittion and the relationship between urban temperature and the usage of air-conditioning both in qualitative and quantitative were made. Finally the data of land use categories in ARWWRF was modified to made numerical simulation cases and contrast experiments with UCM coupled. The main conclusion can be drawn as below.
(1) Researches show that general temperature changes in recent 136 years of Shanghai is a steady increase, with a faster rate than ever in the last 3 decades. Secondly, the main fluctuation cycles are around 70 years, at which the temperature mainly shows four stages as cold-warm-cold-warm. The warmer period in 1940s' and the colder period in 1960s' made up the main features of temperature changes in 20th century in Shanghai. Lastly, a quasi-periodic oscillation of 2 years was also found to exist. At multiple time scales, noticeable abrupt changes of temperature lie in late 1920s' and 1959s'.
(2) Main variation law of precipitation in recent 136 years of Shanghai is oscillations at micro-scale including 2-3a,5a,8-10a and 40a, among which 40a is the most obvious, especially during the middle 20th century. The precipitation is in ascend phase at larger time scale but just the opposite for those under 20a scales.
(3) Wavelet and EMD methods were used to gain multiple scale analysis of Shanghai heat island both in winter and summer of year 2004 and 2009 respectively. Results shows good corresponding relationships between the wavelets' center oscillation and generation of heat island, displaying obvious diurnal variations in both winter and summer under different time scale. Heat island always happens in evening and midnight in winter, while it turns stronger in the afternoon in summer, which is in accordance with the characteristic of its being weaker in spring and summer but stronger in winter and autumn. Meanwhile, with the effets of urbanization, the value of heat island of 2009 is 0.4-0.5℃higher than that of the year 2004, and the increasment in summer is bigger than in winter.
(4) With the help of ENVI, IDL and ARCGIS, Shanghai aerial photograph was processed to obtain complete digital information of structure distribution and height in Shanghai, which help to build the basis of researches on urban anthropogenic heat emission and the land use data in ARWWRF. The urban and built-up land was enlarged according to the digital data, which is tally with the actual situation.
(5) 50 years' month average temperature data of 11 districts in Shanghai was analyzed, as a result, temperature difference of every 5 years between each station and Chongming was got, showing the extend of heat island from city center to rural areas, especially from 1980's,20th century, with a larger increase of difference in temperature. On the basis of existed theories, one of the main mechanisms of heat island generation, anthropogenic heat emission, was analyzed. All possible heat resources and facilities in urban were studied and categorized based on the flow chart, then methods of calculating and concluding of 3 phases were compared for different purposes. Heat emitted by carbon and fuel in recent 30 years were calculated respectively in order to estimate the total amount of anthropogenic heat. Meanwhile, annual average temperature of both downtown and suburb and temperature difference were analyzed, which is proved to be positively correlated to anthropogenic heat. Spacial distribution of anthropogenic heat emission in Shanghai was gained according to the special distribution of industrial area, constructures and roads.
(6) Finally, effects on temperature by the usage of air-conditioning in summer Shanghai were studied. Based on digital construction distribution information of Shanghai, supposing a relativity between floors and air-conditioning density, heat emission by air-conditioning has a quite good consistency with surface temperature retrieval, which was also proved to be one of the main reasons of the high temperature in Shanghai city.
(7) With UCM coupled, numerical simulations of wind field, temperature, and other characters of PBL in spring of Shanghai were made by ARWWRF with both original and modified underlying surface data. Results showed that horizontal wind velocity decreases obviously, displaying the urban dragging effects. Meanwhile, the vertical wind velocity increases, and tends to be bigger affected by surface warming effects. The higher temperature centers appear at the downwind direction by the effects of surface wind fields. Surface temperature and PBL height are both closer to the observation values.
(8)To make further sense of the effects on urban climate by urbanization, different experiements were designed. Results showed that, with modified land use data, UCM coupled and anthropogenic heat added, temperature simulated is the closest to the observation values. Simulation values of areas represented city center, surbub and urbanizing land are close to the observation values both in winter and summer, with correlation coefficient R larger than 0.9. In the light of influence extent of the 3 factors on urban temperature, the order should be UCM＞underlying surface landuse data＞anthropogenic heat emission. Heating effects of urban underlying surface is obviously than that of others. Data shows that, within same distance, the increasment of temperature of sea-land breeze passing through urban areas can reach 4.61℃, notably higher than blowing over other surfaces.
Urbanization, particularity and the complexity of urban climate can be better and intuitionisticly understood based on above work. Cities, as the living regions of human, have extremely complex internal mechanism, Space-time Distribution and energy exchange function. Rapid urbanization has been turning into one of the major subjects in urban climate, while the statistical menthods and numerical simulations have been brought the important role to play, thus, how to use and modify models rationally is an important scientific issue.
文章正是从这一角度出发,在对大量气象数据进行小波和EMD经验模态多尺度分析的基础上,发现问题,探讨城市下垫面在城市气候中所起的作用。通过使用ENVI、IDL和ARCGIS等软件,数字化上海市航片得到完整下垫面建筑信息；在此基础上定量定性的分析计算城市人为热排放与城市气温及都市空调使用之间的关系；最后修正新一代中尺度模式系统(ARWWRF)中下垫面土地利用类型数据,并耦合城市冠层模型(UCM)的进行数值模拟研究和对比试验,探讨下垫面数据、城市覆盖层模型UCM和人为热各自对城市气候要素的影响。主要工作及结论如下：
(1)研究发现上海近136年来的气温变化总体处于一个持续增暖的过程中,近30年来的增温速度超过了以往任何一个时期。其次存在着准70a周期振荡,该时间尺度上气温主要表现为冷-暖-冷-暖四个阶段,40年代的暖期和60年代的冷期构成了上海20世纪气温变化的主要特征。此外还存在着准2a的周期震荡。在不同的时间尺度上,在1920’s后期和1959’s后期有较为明显的温度突变点。
(2)上海近136年来的降水量变化以短时间尺度的周期振荡为其主要变化规律,分别是2-3a、5a、8-10a和40a。其中以40a的周期振荡最为显著,特别是在20世纪初到中后期振荡变化清晰。在更长的时间尺度上,这一地区的降水量正处于上升阶段,而20a以下的时间尺度则相反。
(3)对2004年和2009年上海冬季和夏季的城市热岛分别进行小波和EMD的多尺度分析,小波中心的正负振荡形式很好的与热岛形成对应关系,在不同尺度下冬夏两季均呈现出明显的日变化周期。冬季热岛主要发生于傍晚及夜间,而夏季热岛主要在午后发展强盛,符合春夏较弱,秋冬强的特性。同时由于城市化的影响,2009年热岛强度较之2004年总体上升0.4-0.5℃,并且表现出夏季热岛强度增大更快的特征。
(4)在ENVI、IDL和ARCGIS的辅助下,利用上海市航片进行了上海市下垫面建筑信息数字化。得到相对完整的上海市建筑物分布、高度数据,为研究都市人为排热奠定了基础,并由此对ARWWRF模式中的土地利用数据做了修正,扩大城区面积,使其更准确的反应实际情况。
(5)利用上海市近50年各区县月均温数据,统计分析了各区与崇明夏季每五年均温差,发现热岛效应由市区中心向郊区延伸,范围越来越大,特别是20世纪80年代开始温差呈较大幅增加,城市热岛现象显著；绘制人为热排放流程图,并将城市人为热源进行分类,分析所有可能成为人为热排放源的设施以便定性及控制研究；对供给,消费和排出三种阶段的计算方法进行对比归纳,根据目的不同分别使用；估算上海近30年燃油排热、燃煤排热以及人为热排放总量；统计上海市区和郊区的年均温及年均温差随时间变化及其与人为热排放之间的关系,发现两者之间有很好的相关性；并根据上海工业区、建筑物以及道路的空间分布,得出上海人为热的空间分布。
(6)就上海市夏季空调使用排热对上海市温度影响进行了定性研究,在数字化航片得到的建筑物信息基础上假设楼层和空调密度之间存在相关性,发现空调排热与地面温度反演有着非常良好的一致性,同时也是造成上海城市高温的主要因素之一。
(7)耦合城市冠层模型UCM,利用ARWWRF模式对上海地区春季大气边界层的风场、温度场以及边界层高度等特征值分别进行了土地利用数据修正前后的数值模拟研究,对比结果,发现下垫面土地利用类型由耕地等类型变为城市后,水平风速显著减小,呈现明显城市拖曳作用；垂直速度增大,并由于白天陆面加温作用更为显著,在地面风场的作用下高温中心出现偏于下风方向；近地面气温和边界层高度均更贴近实测值。
(8)为进一步说明城市化对城市气候的影响,设计不同实验,结果显示,使用修正后数据、启动城市冠层模式并加入人为热得出的温度模拟结果最为接近观测值,对于分别代表市区、郊区及快速城市化地区的冬、夏季模拟结果与实测值相关系数R均达到0.9以上。各因子对城市气温的影响根据其所占百分比不同依次为UCM城市模块>下垫面土地利用类型>人为热排放。城市下垫面较之非城市下垫面的增温作用显著,海风登陆后通过相同距离不同性质下垫面后,经过上海市区的增温幅度可达4.61℃,远远大于经过非市区的1.81℃。
基于以上工作,我们对城市化有了更为直观的理解,也对城市气候的特殊性、复杂性有了更深刻的认识。城市作为我们人类生活聚居的区域,其内部机制、时空分布规律、能量交换都极为复杂。快速的城市化正成为城市气候学者们研究的主要方向之一,而统计方法、数值模拟则发挥着无可取代的作用。怎样合理的利用模式、修改模式也成了我们工作中重要的科学问题。
The effect on climate by human activities is the key part of climatology, while urban climate is a special local climate formed by hurman activities and unbanization, under the background of regional climate. More and more anthropogenic heat has been emitting into environment since a huge amount of energy is consumed each day by urban residents and city construction. Shanghai, the international commercial center, one of which main characteristics in its rapid development is the change of the original land use function. A dense population, crossing roads, intensive high structures, etc consist the particular underlying surface, which causes a more severe urban heat island with the urbanization's getting ever more acute.
Analysis in this paper started from multiple time sacle by wavelet and EMD, and then problems were brought out to the exploration into the function of underlying surface in urban climate. Softwares including ENVI, IDL and ARCGIS were used to help gain the complete information of constructures, based on which urban anthropogenic heat emittion and the relationship between urban temperature and the usage of air-conditioning both in qualitative and quantitative were made. Finally the data of land use categories in ARWWRF was modified to made numerical simulation cases and contrast experiments with UCM coupled. The main conclusion can be drawn as below.
(1) Researches show that general temperature changes in recent 136 years of Shanghai is a steady increase, with a faster rate than ever in the last 3 decades. Secondly, the main fluctuation cycles are around 70 years, at which the temperature mainly shows four stages as cold-warm-cold-warm. The warmer period in 1940s' and the colder period in 1960s' made up the main features of temperature changes in 20th century in Shanghai. Lastly, a quasi-periodic oscillation of 2 years was also found to exist. At multiple time scales, noticeable abrupt changes of temperature lie in late 1920s' and 1959s'.
(2) Main variation law of precipitation in recent 136 years of Shanghai is oscillations at micro-scale including 2-3a,5a,8-10a and 40a, among which 40a is the most obvious, especially during the middle 20th century. The precipitation is in ascend phase at larger time scale but just the opposite for those under 20a scales.
(3) Wavelet and EMD methods were used to gain multiple scale analysis of Shanghai heat island both in winter and summer of year 2004 and 2009 respectively. Results shows good corresponding relationships between the wavelets' center oscillation and generation of heat island, displaying obvious diurnal variations in both winter and summer under different time scale. Heat island always happens in evening and midnight in winter, while it turns stronger in the afternoon in summer, which is in accordance with the characteristic of its being weaker in spring and summer but stronger in winter and autumn. Meanwhile, with the effets of urbanization, the value of heat island of 2009 is 0.4-0.5℃higher than that of the year 2004, and the increasment in summer is bigger than in winter.
(4) With the help of ENVI, IDL and ARCGIS, Shanghai aerial photograph was processed to obtain complete digital information of structure distribution and height in Shanghai, which help to build the basis of researches on urban anthropogenic heat emission and the land use data in ARWWRF. The urban and built-up land was enlarged according to the digital data, which is tally with the actual situation.
(5) 50 years' month average temperature data of 11 districts in Shanghai was analyzed, as a result, temperature difference of every 5 years between each station and Chongming was got, showing the extend of heat island from city center to rural areas, especially from 1980's,20th century, with a larger increase of difference in temperature. On the basis of existed theories, one of the main mechanisms of heat island generation, anthropogenic heat emission, was analyzed. All possible heat resources and facilities in urban were studied and categorized based on the flow chart, then methods of calculating and concluding of 3 phases were compared for different purposes. Heat emitted by carbon and fuel in recent 30 years were calculated respectively in order to estimate the total amount of anthropogenic heat. Meanwhile, annual average temperature of both downtown and suburb and temperature difference were analyzed, which is proved to be positively correlated to anthropogenic heat. Spacial distribution of anthropogenic heat emission in Shanghai was gained according to the special distribution of industrial area, constructures and roads.
(6) Finally, effects on temperature by the usage of air-conditioning in summer Shanghai were studied. Based on digital construction distribution information of Shanghai, supposing a relativity between floors and air-conditioning density, heat emission by air-conditioning has a quite good consistency with surface temperature retrieval, which was also proved to be one of the main reasons of the high temperature in Shanghai city.
(7) With UCM coupled, numerical simulations of wind field, temperature, and other characters of PBL in spring of Shanghai were made by ARWWRF with both original and modified underlying surface data. Results showed that horizontal wind velocity decreases obviously, displaying the urban dragging effects. Meanwhile, the vertical wind velocity increases, and tends to be bigger affected by surface warming effects. The higher temperature centers appear at the downwind direction by the effects of surface wind fields. Surface temperature and PBL height are both closer to the observation values.
(8)To make further sense of the effects on urban climate by urbanization, different experiements were designed. Results showed that, with modified land use data, UCM coupled and anthropogenic heat added, temperature simulated is the closest to the observation values. Simulation values of areas represented city center, surbub and urbanizing land are close to the observation values both in winter and summer, with correlation coefficient R larger than 0.9. In the light of influence extent of the 3 factors on urban temperature, the order should be UCM＞underlying surface landuse data＞anthropogenic heat emission. Heating effects of urban underlying surface is obviously than that of others. Data shows that, within same distance, the increasment of temperature of sea-land breeze passing through urban areas can reach 4.61℃, notably higher than blowing over other surfaces.
Urbanization, particularity and the complexity of urban climate can be better and intuitionisticly understood based on above work. Cities, as the living regions of human, have extremely complex internal mechanism, Space-time Distribution and energy exchange function. Rapid urbanization has been turning into one of the major subjects in urban climate, while the statistical menthods and numerical simulations have been brought the important role to play, thus, how to use and modify models rationally is an important scientific issue.
引文
[1]Core Writing Team, Pachauri, R.K and Reisinger, A. (eds.). IPCC,2007:Climate Change 200 7:Synthesis Report. Contribution of Working Groups Ⅰ, Ⅱ and Ⅲ to the Fourth Assessm ent Report of the Intergovernmental Panel on Climate Change [EB/OL]. http://www.ipcc.ch/publications_and_data/publications_ipcc_fourth_assessment_repo rt_synthesis_report.htm,2007.
[2]田苹,李绍云,李耀宁,等.气候变化对人类生存环境的影响分析[J].环境保护与循环经济,2009,3：48-50.
[3]周淑贞,束炯.城市气候学,北京；气象出版社,1994
[4]丁正兴,郝志敏.浅谈土地利用/土地覆被变化研究现状及发展趋势.水土保持应用技术,2008年第5期
[5]Simulation of Efects of LandUse Change on Climate in China by a Regional Climate Model。 Gaoxuejie, Luoyong, LINwantao, ZHAO zongci, and Filippo GIORG1, Advances in Atmospheric Sciences,20(4),583—592
[6]曹嘉宏.台湾土地利用形态对MM5模拟局部环流之影响[D].国立中央大学.大气物理研究所,2007,18.
[7]中国当代土地利用对区域气候影响的数值模拟.高学杰,张冬峰,陈仲新,Jeremy S. Pal, Filippo Giorgi,中国科学,2007年第37卷第3期：397-404
[8]Fang-Yi Cheng, Soon-Tae Kim, Daewon Byun. A numerical study to understand impact of meteorological fields on Houston's high O3 problem using CMAQ/MM5[R]. Extended Abstract,2007 AMS Conf.,San Antonio.
[9]Chen, F., J. Dudhia, Coupling an advanced land surface-hydrology model with the Penn Rev. State-NCAR MM5 modeling system[R]. Part Ⅰ:Model implementation and sensitivity. Mon. Wea.2001,129:569-585,.
[10]Nielsen-Gammon, John W. Meteorological modeling for the August 2000 Houston-Galveston ozone episode:PBL Characteristics, nudging procedure and performance evaluation. Report to the Technical Analysis Division[R], Texas Natural Resource Conservation Commission.2002,109.
[11]Lam J.S.L., A.K.H. Lau,J.C.H. Fung.Application of refined land-use categories for high resolution mesoscale atmospheric modeling[J]. Boundary-Layer Meteorology,2006,119:263-288.
[12]ZHAO M, ZENG X M. A Theoretical Analysis on the Local Climate Change Induced by the Change of Landuse[J]. Advances in Atmospheric Science.2002,19(1):1075-1082.
[13]董平,王凤娇,魏敏.夏季不同下垫面最高温度变化规律及影响因子[J].安徽农学通报,2009,15(13)：160-161.
[14]朱西存,赵庚星.局地不同下垫面对气象要素的影响及其气候效应[J].中国生态农业学报,2009,17(4)：760-764.
[15]陈声海,魏信,王修信等.小区地表温度与下垫面结构关系研究[J].国土资源遥感,2009,3：49-53.
[16]王今殊.北京地区陆表温度空间分布特征与下垫面的关系[J].徐州师范大学学报,2009,27(4)：66-70.
[17]闫敏华,张伟,陈泮勤.RegCM3对三江平原下垫面变化的敏感性检验[J].东北师大学报,2010,42(1)：141-145.
[18]戴俐卉,洪景山,庄秉洁,等.WRF模式中台湾地区土地利用型态更新与个案研究[C].2006年天气分析与预报研讨会论文汇编,“中央气象局”,2006.2-43.
[19]刘太中,荣平平,刘式达,等.气候突变的子波分析[J].地球物理学报,1995,38(2)：158-162.
[20]丁一汇,戴晓苏.中国近百年来的温度变化[J].气象,1994,20(12)：19-26.
[21]王绍武.近百年我国及全球气温变化趋势[J].气象,1990,6(2)：11-15.
[22]姜晓艳,等.中国东北地区近百年气温序列的小波分析[J].气候变化研究进展,2008,4(2)：122-125.
[23]王文,张薇,蔡晓军.近50a来北京市气温和降水的变化[J].干旱气象,2009,27(4)：350-353
[24]窦浩洋,张晶晶,赵听奕.珠江三角洲城市热岛空间分布及热岛强度研究[J].地域研究与开发,2010,29(4)：72-77
[25]王桂新,沈续雷.上海城市化发展对城市热岛效应影响关系之考察[J].亚热带资源与环境学报,2010,5(2)
[26]张慧霞,王鹏.基于遥感的城市热岛效应研究—以广州市为例[J].衡阳师范学院学报,2010,31(3)
[27]胡张保,余炳丰,秦临香.西安夏季和冬季城市热岛的多时间尺度变化特征[J].土木建筑与环境工程,2010,1：111-115
[28]骆杨,周锁铨.孙善磊等.杭州城市热岛空间分布及时域-频域多尺度变化特征[J].生态环境学报,2009,18(6)：2200-2205
[29]Henderson-Sellers A, Gornitz V. Possible climate impacts of land cover transformations, with particular emphasis on tropical deforestation. Climatic Change,1984,6:231-258
[30]Dickinson R E 1984. Modeling evapotranspiration for three dimen——sional global climate models [M]//Hansen、E, Takahashi T. Climate Processes and Climate Sensitivity. Amer. Geophys. U—ion, Washington DC,58—72.
[31]Dickinson R E, Kennedy PJ, Sellers A H, et al.1986. Biosphere—atmosphere transfer schelne(BATS)for the NCAR community climate model[R]. Teeh. Rep. NcARE/TN— 275+STR, National Center for Atmospheric Researck
[32]Sellers P J, Mintz Y, Sud Y C, et al.1986. A simple biosphere model(SiB)for use within general circulation models[J]. J. Atm08. Sci.,43(6):505—531.
[33]Xue Y, Sellers P, Kinter J, et al.1991. A simplified biosphere model for global climate studies[J]. J. Climate,4{345—364.
[34]Dai YJ, Zeng QC 1997. A land surfaee model(IAP94)for climate studies. Part I:Formulation and validation in off line experiments[J]. Adv. Atmos. Sei.,14:433—460.
[35]Bonan GR 1995. A land surface model(LSM Version 1.0)for ecological, hydrological。 and atmospheric studies:Technical deseription and user's guide [R]. NCAR Tech Note NCAR /TN—417+STR,165pp.
[36]Sellers P J, Los SO, Tucker CJ, et al.1996. A revised land surface parameterization(SiB2)for atmospheric oCMS Part II:The generation of global fields of terrestrial biophysical parameters from satellite data[J-I. J. Climate.9:706—737.
[37]Dai Y J, Zeng X B, Dickinson R E. et aL 2003. The conu'non land model [J]. Bull. Amer. Meteor. Soc.,84:1013—1023.
[38]Oleson KW, Dai YJ, Bonan G, et al.2004. Technical description of the Community hlld Model(CLM)ER]. NCAR/TN--461+STR,173pp.
[39]Dickinson R E, Oleson KW, Bonan G, et al.2006. The community land model and its climate statistics as a con7Iponent of the community climate system model D]. J. Climate, 19(11):2302—2324.
[40]Sud Y C, Molod A. A GCM simulation study of the influence of Saharan evapotranspiration and surface-albedo anomalies on July circulation and rainfall. Mon Wea Rev, 1988,116:2388～2400
[41]Dickinson R E, Henderson-Sellers A. Modelling tropical deforestation:A study of GCM land-surface parameterization. Quare J Roy, Meteor Soc,1988,114:439～462
[42]Lean J, Warrilow D A. Simulation of the regional climatic impact of Amazon deforestation. Nature,1989,342:411～413
[43]Shukla J, Nobre C, Sellers P J. Amazon deforestation and climate change. Science,1990,247:1322～1325
[44]Nobre C A, Sellers P J, Shukla J. Amazonian deforestation and regional climate change. J Climate,1991,4:957～988
[45]Henderson-Sellers, Dickinson R E, Durbidge T B, et al. Tropical deforestation:Modeling local-to regional scale climate change. J Geophys Res,1993,98:7289～7315
[46]Lean J, Rowntree P R. A GCM simulation of the impact of Amazonian deforestation on climate using an improved canopy representation. Quare J Roy Meteor Soc, 1993,119:509～530
[47]Xue Y, Shukla J. The influence of land-surface propreties on Sahel Climate,Part I:Desertification. J Climate,1993,6:2232～2245.
[48]Dirmeyer P A, Shukla J. The effect on regional and global climate of expansion of the world's deserts. Quare J Roy Meteor Soc,1996,122:451～482
[49]Lynn B H, Rind D, Avissar R. The importance of mesoscale circulation generated by subgrid-scale landscape heterogeneities in general circulation models. J Climate, 1995,8:191～205
[50]Xue Y, Shukla J. The influence of land-surface properties on Sahel Climate,Part ILAfforestation. J Climate,1996,9:3260～3275
[51]Xue Y. The impact of desertification in the Mongolian and the Inner Mongolian Grassland on the regional climate. J Climate,1996,9:2173～2189
[52]Sud Y C, ang K, Walker G K. Impact of in situ deforestation in Amazonia on the regional climate:eneral circulation model simulation study. J Geophys Res,1996,01:7095～7109
[53]Dirmeyer P A, Shukla J. Albedo as a modulator of climate response to tropical deforestation. J Geophys Res,1994,99:20863～20877
[54]Varejao-Silva M A, Franchito S H, Rao V B. A coupled biosphere-atmosphere climate model suitable for studies of climate change due to land-surface alterations. J Climate, 1998,11:1749～1767
[55]薛永康,曾凡荣,Schlosser AC.一个植被模式的检验和它在陆气相互作用中的应用.大气科学,1998,22(4)：575-585
[56]吕世华,陈玉春。西北植被覆盖对我国区域气候变化影响的数值模拟,1999,第18 卷第3期
[57]苏从先,胡隐樵.河西地区绿洲的小气候特征和“冷岛效应”.大气科学,1987,11：390-396
[58]傅抱璞.人类活动对大气降水影响的估算.全国气候变化学术会议文集.北京：科学出版社,1981.143-151
[59]吕世华,陈玉春.绿洲和沙漠下垫面状态对大气边界层特征影响的数值模拟.中国沙漠,1995,15(2)：116-123
[60]范广洲,吕世华,罗四维.西北地区绿化对该区及东亚、南亚区域气候影响的数值模拟.高原气象,1998,17(3)：300-309
[61]符淙斌袁慧玲恢复自然植被对东亚夏季气候和环境影响的一个虚拟实验科学通报。2001年4月第46卷第8期
[62]WangG X, Liu JQ, Kutota J, etal. Efects of land—Use charges on hydrological processes in the middle basin of the Heihe River, northwest China[J]. Hydrulogical ProcesSes,2007, 21(1010):1370—1382.
[63]于兴修,杨桂山,王瑶.土地利用/覆被变化的环境效应研究进展与动向[J].地理科学,2004,24(5)：627—633.
[64]李丽娟,姜德娟,李九一,等.土地利用/覆被变化的水文效应研究进展[J].自然资源学报,2007,22(2)：211—223.
[65]秦丽杰,张郁,许红梅,等.土地利用变化的生态环境效应研究[J].地理科学,2002,22(4)：508-512.
[66]吴运金,张甘霖,赵玉国,等.城市化过程中土地利用变化对区域滞洪库容量的影响研究——以南京市河西地区为例[J].地理科学,2008,28(1)：29-33.
[67]高超,朱继业,戴科伟,等.快速城市化进程中的太湖水环境保护：困境与出路[J].地理科学,2003,23(6)：746-750.
[68]Cheng Feng, Xie Zhenghui 2009. A land coverdataset based on Chinese vegetation data and its impact on land surface simulations [J]. Chinese Journal of Atmospheric Sdences(in Chinese),33(4):681—697.
[69]权瑞松,刘敏,侯立军,陆敏,张丽佳,欧冬妮,许世远,俞立中.土地利用动态变化对地表径流的影响——以上海浦东新区为例.灾害学,Mar.2009, Vol.24 No.1
[70]陈莹,许有鹏,尹义星.土地利用/覆被变化下的暴雨径流过程模拟分析——以太湖上游西苕溪流域为例.地理科学,Feb.,2009, Vol.29 No.1
[71]文伟俊,沈桐立,丁治英,宋沽慧.城市化对广西夏季海陆风影响的数值试验.热带气象学报,Vol.25, No.3, Jun.,2009
[72]佟华,陈仲良,桑建国.城市边界层数值模式研究以及在香港地区复杂地形下的应用.大气科学Vol.28 No.6 Nov.2004
[73]佟华,桑建国,等城市人为热对北京热环境的影响.气候与环境研究,2004,9(3)：409-421.
[74]杨玉华,徐祥德.翁永辉.北京城市边界层热岛的日变化周期模拟.应用气象学报,2003,14(1)：61-68
[75]陈炯,郑永光,邓莲堂.城市建筑物对城市边界层三维结构影响的数值模拟.北京大学学报(自然科学版),第43卷,第3期,2007年5月
[76]罗军辉,冯平,哈力旦·A等matlab7.0在图像处理中的应用[M].北京：机械工业出版社,2005.
[77]胡昌华,张军波,夏军,张伟.基于matlab的系统分析与设计——小波分析[M].西安：西安电 子科技大学出版社.1999.
[78]刘莉红,郑祖光.近百余年我国气温变化的突变点分析[J].南京气象学报,2003,26(3)：378-383.
[79]Donald B. Percival & Andrew T.Walden时间序列分析的小波方法[M].北京：机械工业出版社,2004.5.
[80]魏凤英.现代气候统计诊断与预测技术[M].气象出版社,2007：182-184.
[81]覃军,张录军,胡江林.武汉近百年来气温变化的多时间尺度分析[J].气象科学,2001,21(2)：206-210.
[82]刘德地,李梅等.近50年来浙江省降雨特性变化分析[J].自然资源学报,2009,24(11)：1973-1983.
[83]衡彤,王文圣,丁晶.降水量时间序列变化的小波特征[J].长江流域资源与环境,2002,11(5)：466-470.
[84]邵晓梅,许月卿,严昌荣.黄河流域降水序列变化的小波分析[J].北京大学学报(自然科学版)2006,1
[85]张健,章新平,王晓云,张剑明.近47年来京津冀地区降水的变化[J].干旱地区资源与环境,2010,2(24)
[86]牛存稳,张利平,夏军.华北地区降水量的小波分析[J].干旱地区地理,2004,27(1)
[87]石柱鲜,黄红梅,朴粉丹.基于小波的我国经济周期波动的分析与预测[J].吉林大学社会科学学报,2009,49(3)
[88]张凌云,覃峥嵘,李家文.近58年柳州市汛期降水特征分析[J].气象研究与应用,2009,30(3)
[89]徐东霞,章光新,冯夏清.嫩江流域径流量多时间尺度特征分析[J].资源科学,2009,31(9)
[90]王秀杰,杨敏,崔海军.黄河潼关汛期水沙变化周期及其趋势分析[J].自然资源学报,2009,24(2)
[91]国世友,邹立尧,吴琼.近百年黑龙江省气候变化特征[J].黑龙江气象,2003,4：8-11
[92]HUANG N E,SHEN Z,LONG S R,et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationaty series analysis[J].Proc R Soc Lond A,1998,454: 899-995.
[93]HUANG N E, SHEN Z,LONG S R.A new view of nonlinear water waves:The Hilbert spectrum[J]. Annual Review of Fluid Mechanics,1999,31:417-457.
[94]邓拥军,王伟,钱成春,等.EMD方法及Hilbert变换中边界问题的处理[J].科学通报,2001,46(3)：257-263.
[95]林振山,汪曙光.近四百年北半球气温变化的分析(EMD方法的应用)[J].热带气象学报,2004,2：90-96.
[96]熊学军,郭炳火,胡莜敏,等.EMD方法和HILBERT谱分析法的应用与探讨[J].黄渤海海洋.2002,2：12-21.
[97]冯平,丁志宏,韩瑞光.基于EMD的洮河年径流量变化多时间尺度分析[J].干旱区资源与环境,2008,22(12)：73-76.
[98]刘莉红,郑祖光,琚建华.基于EMD方法的我国气温和东部年降水量序列的振荡模态分析[J].高原气象,2008,27(5)：1060-1065.
[99]李强,吴健,等.利用EMD方法提取太阳活动周期成分[J].空间科学学报,2007,27(1)：1-6.
[100]Hadas S.Spatial Distribution and Microscale Characteristics of the Urban Heat Island in Tel —Aviv,Israel [J]. Landscape and Urban Planning,2000,48(1—2):1—18.
[101]ERNESTO J. Heat Island Development in Mexico City[J]. Atmospheric Environment, 1997,31(22):3821—3831.
[102]张光智,徐祥德,王继志等.北京及周边地区城市尺度热岛特征及其演变[J].应用气象学报,2002,13(1)：43-50.
[103]季崇萍,刘伟东,轩春怡.北京城市化进程对城市热岛的影响研究[J].地球物理学报,2006,49(1)：69—77.
[104]Unger J, Sfimeghy Z, Gulyrs A, et al. Land-use and Meteorological Aspects of the Urban Heat Island[J]. Meteorological Applications,2001,8(2):189-194.
[105]陈云浩,史培军,李晓兵.基于遥感和GIS的上海城市空间热环境研究[J].测绘学报,2002,31(2)：139-144.
[106]王传琛,刘际楹.杭州城市气候[J].地理学报,1982(37)：164-173.
[107]张景哲,刘启明.北京城市气温与下垫面结构关系的时项变化[J].地理学报,1988(43)：159-168
[108]陈沈斌,潘莉卿.城市化对北京平均气温的影响[J].地理学报,1997(52)：27-36
[109]于今殊,李贵才,刘玉洁,曹广真.北京地区陆表温度空间分布特征[J],测绘科学,2009(34)：66-70
[110]张穗,何报寅,杜耘.武汉市城区热岛效应的遥感研究[J].长江流域资源与环境,2003,12(5)：445-449.
[111]周淑贞.上海城市气候中的“五岛”效应[J].中国科学：B辑,1988(11)：1226-1234.
[112]束炯,江田汉,杨晓明.上海城市热岛效应的特征分析[J].上海环境科学,2000,19(11)：532-534.
[113]江田汉,束炯,邓连堂.上海城市热岛的小波特征[J].热带气象学报,2004,20(5)：516-522.
[114]孙娟,束炯,乐群.上海市城市热岛效应的时间多尺度特征[J].华东师范大学学报,2007,132(2)：36-43.
[115]刘玲,吴坤悌,黄光瑞,陈庭甫,张庆国.合肥城市发展对热岛强度的影响[J].长江流域资源与环境,2008,17(A01)：88-93.
[116]丁金才,周红妹,叶其欣.从上海市热岛演变看城市绿化的重要意义[J].气象,2002,28(2)：22-24.
[117]许世远,束炯,王铮,等.上海城市自然地理图集[M].中华地图学社,2004.
[118]束炯,江田汉,杨晓明.上海城市热岛效应的特征分析[J].上海环境科学,2000,19(11)：532-534.
[119]Kusaka H. and Kimura F. Coupling a single-layer urban canopy model with a simple atmospheric model:Impact on urban heat island simulation for an idealized case. Journal of the Meteorological Society of Japan,2004,82(1):67-80.
[120]周荣卫,蒋维楣,何晓凤、刘罡.应用城市冠层模式研究建筑物形态对城市边界层的影响[J].气象学报,2008,66(4)：89-99
[121]陈家宜,王介民,光田宁.一种确定地表粗糙度的独立方法.大气科学,1993,17(1)：21-26.
[122]贾立,王介民.卫星遥感结合地面资料对区域表面动量粗糙度的估算.大气科学,1999,23(5)：632-640.
[123]张强,吕世华.城市地表粗糙度长度的确定.高原气象,2003,22(1)：24-32.
[124]高志球,卞林根,逯昌贵等.城市下垫面空气动力学参数的估算.应用气象学报,2002,13(特刊)：26-33.
[125]韩素芹,刘彬贤,解以扬等.利用255m铁塔研究城市化对地面粗糙度的影响.气象,2008,34(1)：54-58.
[126]周淑贞,吴林.气象卫星在上海城市气候研究中的应用初探[J].城市气候区域气候论文集[C]上海：华东师范大学出版社,1989,194-213.
[127]佟华,刘辉志,桑建国等.城市人为热对北京热环境的影响[J].气候与环境研究.2004,9(3)：409-420.
[128]Small. R.D. Environmental impact of fires in Kuwait[J]. Nature,1991,350:11-12.
[129]中华人民共和国国家统计局.统计数据,年度数据,能源.http://www.stats.gov.cn/tjsj/ndsj/2009/indexch.htm
[130]龙惟定,张蓓红,钟婷.上海住宅空调能源的现状与发展[J].全国暖通空调制冷2002年学术文集[C].2002,1999-2007
[131]上海统计年鉴.统计数据,城市建设,主要年份用电量[2000-2007].http://www.stats-sh.gov.cn
[132]钟婷,龙惟定.上海市住宅空调的相关调查及其耗电量的估算[J].建筑热能通风空调,2003,22(3)：22-24
[133]王凡,许进.华东电网2004-2005年用电结构分析[J].华东电力,2005,33(11)：16-19
[134]于航,刘淑娟,李祎或,于靓.从上海市民的居住生活习惯看能源消耗特征[J].上海节能,31-1500,26-30
[135]国家电网,上海电力公司,电网运行情况[2006].http://www.sh.sgcc.com.cn
[136]王宝军,施斌,姜洪涛,赵理政.近30年南京市浅层地温场变化规律研究[J].高校地质学报,2009,15(2)：199-205
[137]周红妹,周成虎,葛伟强,丁金才.基于遥感和GIS的城市热场分布规律研究[J].地理学报,2001,56(2)：189-197.
[138]谷清.风速与烟源有效高度、地面浓度的关系[J].上海环境科学,1988,7(6)：16-19.
[139]寇正,欧阳子济.暴发性气旋发展中科压强迫与潜热释放作用的数值试验[J].气象科学,1999,19(3)：259-269.
[140]周淑贞,余碧霞.上海城市对风速的影响[J].华东师范大学学报(自然科学版),1988,3：30-41.
[141]Landsberg,H.E, The Urban Climate[M].Academic Press,1981,147.
[142]李兆元.西安城市气候研究分报告,中国不同气候区域城市气候的研究[C].1990.
[143]陈燕,蒋维楣,吴涧,等.用区域边界层模式对杭州市热岛的模拟研究[J].高原气象,2004,23(4)：519-528.
[144]周淑贞,王行恒.上海大气环境中的城市干岛和湿岛效应[J].华东师范大学学报(自然科学版),1996(4)
[2]田苹,李绍云,李耀宁,等.气候变化对人类生存环境的影响分析[J].环境保护与循环经济,2009,3：48-50.
[3]周淑贞,束炯.城市气候学,北京；气象出版社,1994
[4]丁正兴,郝志敏.浅谈土地利用/土地覆被变化研究现状及发展趋势.水土保持应用技术,2008年第5期
[5]Simulation of Efects of LandUse Change on Climate in China by a Regional Climate Model。 Gaoxuejie, Luoyong, LINwantao, ZHAO zongci, and Filippo GIORG1, Advances in Atmospheric Sciences,20(4),583—592
[6]曹嘉宏.台湾土地利用形态对MM5模拟局部环流之影响[D].国立中央大学.大气物理研究所,2007,18.
[7]中国当代土地利用对区域气候影响的数值模拟.高学杰,张冬峰,陈仲新,Jeremy S. Pal, Filippo Giorgi,中国科学,2007年第37卷第3期：397-404
[8]Fang-Yi Cheng, Soon-Tae Kim, Daewon Byun. A numerical study to understand impact of meteorological fields on Houston's high O3 problem using CMAQ/MM5[R]. Extended Abstract,2007 AMS Conf.,San Antonio.
[9]Chen, F., J. Dudhia, Coupling an advanced land surface-hydrology model with the Penn Rev. State-NCAR MM5 modeling system[R]. Part Ⅰ:Model implementation and sensitivity. Mon. Wea.2001,129:569-585,.
[10]Nielsen-Gammon, John W. Meteorological modeling for the August 2000 Houston-Galveston ozone episode:PBL Characteristics, nudging procedure and performance evaluation. Report to the Technical Analysis Division[R], Texas Natural Resource Conservation Commission.2002,109.
[11]Lam J.S.L., A.K.H. Lau,J.C.H. Fung.Application of refined land-use categories for high resolution mesoscale atmospheric modeling[J]. Boundary-Layer Meteorology,2006,119:263-288.
[12]ZHAO M, ZENG X M. A Theoretical Analysis on the Local Climate Change Induced by the Change of Landuse[J]. Advances in Atmospheric Science.2002,19(1):1075-1082.
[13]董平,王凤娇,魏敏.夏季不同下垫面最高温度变化规律及影响因子[J].安徽农学通报,2009,15(13)：160-161.
[14]朱西存,赵庚星.局地不同下垫面对气象要素的影响及其气候效应[J].中国生态农业学报,2009,17(4)：760-764.
[15]陈声海,魏信,王修信等.小区地表温度与下垫面结构关系研究[J].国土资源遥感,2009,3：49-53.
[16]王今殊.北京地区陆表温度空间分布特征与下垫面的关系[J].徐州师范大学学报,2009,27(4)：66-70.
[17]闫敏华,张伟,陈泮勤.RegCM3对三江平原下垫面变化的敏感性检验[J].东北师大学报,2010,42(1)：141-145.
[18]戴俐卉,洪景山,庄秉洁,等.WRF模式中台湾地区土地利用型态更新与个案研究[C].2006年天气分析与预报研讨会论文汇编,“中央气象局”,2006.2-43.
[19]刘太中,荣平平,刘式达,等.气候突变的子波分析[J].地球物理学报,1995,38(2)：158-162.
[20]丁一汇,戴晓苏.中国近百年来的温度变化[J].气象,1994,20(12)：19-26.
[21]王绍武.近百年我国及全球气温变化趋势[J].气象,1990,6(2)：11-15.
[22]姜晓艳,等.中国东北地区近百年气温序列的小波分析[J].气候变化研究进展,2008,4(2)：122-125.
[23]王文,张薇,蔡晓军.近50a来北京市气温和降水的变化[J].干旱气象,2009,27(4)：350-353
[24]窦浩洋,张晶晶,赵听奕.珠江三角洲城市热岛空间分布及热岛强度研究[J].地域研究与开发,2010,29(4)：72-77
[25]王桂新,沈续雷.上海城市化发展对城市热岛效应影响关系之考察[J].亚热带资源与环境学报,2010,5(2)
[26]张慧霞,王鹏.基于遥感的城市热岛效应研究—以广州市为例[J].衡阳师范学院学报,2010,31(3)
[27]胡张保,余炳丰,秦临香.西安夏季和冬季城市热岛的多时间尺度变化特征[J].土木建筑与环境工程,2010,1：111-115
[28]骆杨,周锁铨.孙善磊等.杭州城市热岛空间分布及时域-频域多尺度变化特征[J].生态环境学报,2009,18(6)：2200-2205
[29]Henderson-Sellers A, Gornitz V. Possible climate impacts of land cover transformations, with particular emphasis on tropical deforestation. Climatic Change,1984,6:231-258
[30]Dickinson R E 1984. Modeling evapotranspiration for three dimen——sional global climate models [M]//Hansen、E, Takahashi T. Climate Processes and Climate Sensitivity. Amer. Geophys. U—ion, Washington DC,58—72.
[31]Dickinson R E, Kennedy PJ, Sellers A H, et al.1986. Biosphere—atmosphere transfer schelne(BATS)for the NCAR community climate model[R]. Teeh. Rep. NcARE/TN— 275+STR, National Center for Atmospheric Researck
[32]Sellers P J, Mintz Y, Sud Y C, et al.1986. A simple biosphere model(SiB)for use within general circulation models[J]. J. Atm08. Sci.,43(6):505—531.
[33]Xue Y, Sellers P, Kinter J, et al.1991. A simplified biosphere model for global climate studies[J]. J. Climate,4{345—364.
[34]Dai YJ, Zeng QC 1997. A land surfaee model(IAP94)for climate studies. Part I:Formulation and validation in off line experiments[J]. Adv. Atmos. Sei.,14:433—460.
[35]Bonan GR 1995. A land surface model(LSM Version 1.0)for ecological, hydrological。 and atmospheric studies:Technical deseription and user's guide [R]. NCAR Tech Note NCAR /TN—417+STR,165pp.
[36]Sellers P J, Los SO, Tucker CJ, et al.1996. A revised land surface parameterization(SiB2)for atmospheric oCMS Part II:The generation of global fields of terrestrial biophysical parameters from satellite data[J-I. J. Climate.9:706—737.
[37]Dai Y J, Zeng X B, Dickinson R E. et aL 2003. The conu'non land model [J]. Bull. Amer. Meteor. Soc.,84:1013—1023.
[38]Oleson KW, Dai YJ, Bonan G, et al.2004. Technical description of the Community hlld Model(CLM)ER]. NCAR/TN--461+STR,173pp.
[39]Dickinson R E, Oleson KW, Bonan G, et al.2006. The community land model and its climate statistics as a con7Iponent of the community climate system model D]. J. Climate, 19(11):2302—2324.
[40]Sud Y C, Molod A. A GCM simulation study of the influence of Saharan evapotranspiration and surface-albedo anomalies on July circulation and rainfall. Mon Wea Rev, 1988,116:2388～2400
[41]Dickinson R E, Henderson-Sellers A. Modelling tropical deforestation:A study of GCM land-surface parameterization. Quare J Roy, Meteor Soc,1988,114:439～462
[42]Lean J, Warrilow D A. Simulation of the regional climatic impact of Amazon deforestation. Nature,1989,342:411～413
[43]Shukla J, Nobre C, Sellers P J. Amazon deforestation and climate change. Science,1990,247:1322～1325
[44]Nobre C A, Sellers P J, Shukla J. Amazonian deforestation and regional climate change. J Climate,1991,4:957～988
[45]Henderson-Sellers, Dickinson R E, Durbidge T B, et al. Tropical deforestation:Modeling local-to regional scale climate change. J Geophys Res,1993,98:7289～7315
[46]Lean J, Rowntree P R. A GCM simulation of the impact of Amazonian deforestation on climate using an improved canopy representation. Quare J Roy Meteor Soc, 1993,119:509～530
[47]Xue Y, Shukla J. The influence of land-surface propreties on Sahel Climate,Part I:Desertification. J Climate,1993,6:2232～2245.
[48]Dirmeyer P A, Shukla J. The effect on regional and global climate of expansion of the world's deserts. Quare J Roy Meteor Soc,1996,122:451～482
[49]Lynn B H, Rind D, Avissar R. The importance of mesoscale circulation generated by subgrid-scale landscape heterogeneities in general circulation models. J Climate, 1995,8:191～205
[50]Xue Y, Shukla J. The influence of land-surface properties on Sahel Climate,Part ILAfforestation. J Climate,1996,9:3260～3275
[51]Xue Y. The impact of desertification in the Mongolian and the Inner Mongolian Grassland on the regional climate. J Climate,1996,9:2173～2189
[52]Sud Y C, ang K, Walker G K. Impact of in situ deforestation in Amazonia on the regional climate:eneral circulation model simulation study. J Geophys Res,1996,01:7095～7109
[53]Dirmeyer P A, Shukla J. Albedo as a modulator of climate response to tropical deforestation. J Geophys Res,1994,99:20863～20877
[54]Varejao-Silva M A, Franchito S H, Rao V B. A coupled biosphere-atmosphere climate model suitable for studies of climate change due to land-surface alterations. J Climate, 1998,11:1749～1767
[55]薛永康,曾凡荣,Schlosser AC.一个植被模式的检验和它在陆气相互作用中的应用.大气科学,1998,22(4)：575-585
[56]吕世华,陈玉春。西北植被覆盖对我国区域气候变化影响的数值模拟,1999,第18 卷第3期
[57]苏从先,胡隐樵.河西地区绿洲的小气候特征和“冷岛效应”.大气科学,1987,11：390-396
[58]傅抱璞.人类活动对大气降水影响的估算.全国气候变化学术会议文集.北京：科学出版社,1981.143-151
[59]吕世华,陈玉春.绿洲和沙漠下垫面状态对大气边界层特征影响的数值模拟.中国沙漠,1995,15(2)：116-123
[60]范广洲,吕世华,罗四维.西北地区绿化对该区及东亚、南亚区域气候影响的数值模拟.高原气象,1998,17(3)：300-309
[61]符淙斌袁慧玲恢复自然植被对东亚夏季气候和环境影响的一个虚拟实验科学通报。2001年4月第46卷第8期
[62]WangG X, Liu JQ, Kutota J, etal. Efects of land—Use charges on hydrological processes in the middle basin of the Heihe River, northwest China[J]. Hydrulogical ProcesSes,2007, 21(1010):1370—1382.
[63]于兴修,杨桂山,王瑶.土地利用/覆被变化的环境效应研究进展与动向[J].地理科学,2004,24(5)：627—633.
[64]李丽娟,姜德娟,李九一,等.土地利用/覆被变化的水文效应研究进展[J].自然资源学报,2007,22(2)：211—223.
[65]秦丽杰,张郁,许红梅,等.土地利用变化的生态环境效应研究[J].地理科学,2002,22(4)：508-512.
[66]吴运金,张甘霖,赵玉国,等.城市化过程中土地利用变化对区域滞洪库容量的影响研究——以南京市河西地区为例[J].地理科学,2008,28(1)：29-33.
[67]高超,朱继业,戴科伟,等.快速城市化进程中的太湖水环境保护：困境与出路[J].地理科学,2003,23(6)：746-750.
[68]Cheng Feng, Xie Zhenghui 2009. A land coverdataset based on Chinese vegetation data and its impact on land surface simulations [J]. Chinese Journal of Atmospheric Sdences(in Chinese),33(4):681—697.
[69]权瑞松,刘敏,侯立军,陆敏,张丽佳,欧冬妮,许世远,俞立中.土地利用动态变化对地表径流的影响——以上海浦东新区为例.灾害学,Mar.2009, Vol.24 No.1
[70]陈莹,许有鹏,尹义星.土地利用/覆被变化下的暴雨径流过程模拟分析——以太湖上游西苕溪流域为例.地理科学,Feb.,2009, Vol.29 No.1
[71]文伟俊,沈桐立,丁治英,宋沽慧.城市化对广西夏季海陆风影响的数值试验.热带气象学报,Vol.25, No.3, Jun.,2009
[72]佟华,陈仲良,桑建国.城市边界层数值模式研究以及在香港地区复杂地形下的应用.大气科学Vol.28 No.6 Nov.2004
[73]佟华,桑建国,等城市人为热对北京热环境的影响.气候与环境研究,2004,9(3)：409-421.
[74]杨玉华,徐祥德.翁永辉.北京城市边界层热岛的日变化周期模拟.应用气象学报,2003,14(1)：61-68
[75]陈炯,郑永光,邓莲堂.城市建筑物对城市边界层三维结构影响的数值模拟.北京大学学报(自然科学版),第43卷,第3期,2007年5月
[76]罗军辉,冯平,哈力旦·A等matlab7.0在图像处理中的应用[M].北京：机械工业出版社,2005.
[77]胡昌华,张军波,夏军,张伟.基于matlab的系统分析与设计——小波分析[M].西安：西安电 子科技大学出版社.1999.
[78]刘莉红,郑祖光.近百余年我国气温变化的突变点分析[J].南京气象学报,2003,26(3)：378-383.
[79]Donald B. Percival & Andrew T.Walden时间序列分析的小波方法[M].北京：机械工业出版社,2004.5.
[80]魏凤英.现代气候统计诊断与预测技术[M].气象出版社,2007：182-184.
[81]覃军,张录军,胡江林.武汉近百年来气温变化的多时间尺度分析[J].气象科学,2001,21(2)：206-210.
[82]刘德地,李梅等.近50年来浙江省降雨特性变化分析[J].自然资源学报,2009,24(11)：1973-1983.
[83]衡彤,王文圣,丁晶.降水量时间序列变化的小波特征[J].长江流域资源与环境,2002,11(5)：466-470.
[84]邵晓梅,许月卿,严昌荣.黄河流域降水序列变化的小波分析[J].北京大学学报(自然科学版)2006,1
[85]张健,章新平,王晓云,张剑明.近47年来京津冀地区降水的变化[J].干旱地区资源与环境,2010,2(24)
[86]牛存稳,张利平,夏军.华北地区降水量的小波分析[J].干旱地区地理,2004,27(1)
[87]石柱鲜,黄红梅,朴粉丹.基于小波的我国经济周期波动的分析与预测[J].吉林大学社会科学学报,2009,49(3)
[88]张凌云,覃峥嵘,李家文.近58年柳州市汛期降水特征分析[J].气象研究与应用,2009,30(3)
[89]徐东霞,章光新,冯夏清.嫩江流域径流量多时间尺度特征分析[J].资源科学,2009,31(9)
[90]王秀杰,杨敏,崔海军.黄河潼关汛期水沙变化周期及其趋势分析[J].自然资源学报,2009,24(2)
[91]国世友,邹立尧,吴琼.近百年黑龙江省气候变化特征[J].黑龙江气象,2003,4：8-11
[92]HUANG N E,SHEN Z,LONG S R,et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationaty series analysis[J].Proc R Soc Lond A,1998,454: 899-995.
[93]HUANG N E, SHEN Z,LONG S R.A new view of nonlinear water waves:The Hilbert spectrum[J]. Annual Review of Fluid Mechanics,1999,31:417-457.
[94]邓拥军,王伟,钱成春,等.EMD方法及Hilbert变换中边界问题的处理[J].科学通报,2001,46(3)：257-263.
[95]林振山,汪曙光.近四百年北半球气温变化的分析(EMD方法的应用)[J].热带气象学报,2004,2：90-96.
[96]熊学军,郭炳火,胡莜敏,等.EMD方法和HILBERT谱分析法的应用与探讨[J].黄渤海海洋.2002,2：12-21.
[97]冯平,丁志宏,韩瑞光.基于EMD的洮河年径流量变化多时间尺度分析[J].干旱区资源与环境,2008,22(12)：73-76.
[98]刘莉红,郑祖光,琚建华.基于EMD方法的我国气温和东部年降水量序列的振荡模态分析[J].高原气象,2008,27(5)：1060-1065.
[99]李强,吴健,等.利用EMD方法提取太阳活动周期成分[J].空间科学学报,2007,27(1)：1-6.
[100]Hadas S.Spatial Distribution and Microscale Characteristics of the Urban Heat Island in Tel —Aviv,Israel [J]. Landscape and Urban Planning,2000,48(1—2):1—18.
[101]ERNESTO J. Heat Island Development in Mexico City[J]. Atmospheric Environment, 1997,31(22):3821—3831.
[102]张光智,徐祥德,王继志等.北京及周边地区城市尺度热岛特征及其演变[J].应用气象学报,2002,13(1)：43-50.
[103]季崇萍,刘伟东,轩春怡.北京城市化进程对城市热岛的影响研究[J].地球物理学报,2006,49(1)：69—77.
[104]Unger J, Sfimeghy Z, Gulyrs A, et al. Land-use and Meteorological Aspects of the Urban Heat Island[J]. Meteorological Applications,2001,8(2):189-194.
[105]陈云浩,史培军,李晓兵.基于遥感和GIS的上海城市空间热环境研究[J].测绘学报,2002,31(2)：139-144.
[106]王传琛,刘际楹.杭州城市气候[J].地理学报,1982(37)：164-173.
[107]张景哲,刘启明.北京城市气温与下垫面结构关系的时项变化[J].地理学报,1988(43)：159-168
[108]陈沈斌,潘莉卿.城市化对北京平均气温的影响[J].地理学报,1997(52)：27-36
[109]于今殊,李贵才,刘玉洁,曹广真.北京地区陆表温度空间分布特征[J],测绘科学,2009(34)：66-70
[110]张穗,何报寅,杜耘.武汉市城区热岛效应的遥感研究[J].长江流域资源与环境,2003,12(5)：445-449.
[111]周淑贞.上海城市气候中的“五岛”效应[J].中国科学：B辑,1988(11)：1226-1234.
[112]束炯,江田汉,杨晓明.上海城市热岛效应的特征分析[J].上海环境科学,2000,19(11)：532-534.
[113]江田汉,束炯,邓连堂.上海城市热岛的小波特征[J].热带气象学报,2004,20(5)：516-522.
[114]孙娟,束炯,乐群.上海市城市热岛效应的时间多尺度特征[J].华东师范大学学报,2007,132(2)：36-43.
[115]刘玲,吴坤悌,黄光瑞,陈庭甫,张庆国.合肥城市发展对热岛强度的影响[J].长江流域资源与环境,2008,17(A01)：88-93.
[116]丁金才,周红妹,叶其欣.从上海市热岛演变看城市绿化的重要意义[J].气象,2002,28(2)：22-24.
[117]许世远,束炯,王铮,等.上海城市自然地理图集[M].中华地图学社,2004.
[118]束炯,江田汉,杨晓明.上海城市热岛效应的特征分析[J].上海环境科学,2000,19(11)：532-534.
[119]Kusaka H. and Kimura F. Coupling a single-layer urban canopy model with a simple atmospheric model:Impact on urban heat island simulation for an idealized case. Journal of the Meteorological Society of Japan,2004,82(1):67-80.
[120]周荣卫,蒋维楣,何晓凤、刘罡.应用城市冠层模式研究建筑物形态对城市边界层的影响[J].气象学报,2008,66(4)：89-99
[121]陈家宜,王介民,光田宁.一种确定地表粗糙度的独立方法.大气科学,1993,17(1)：21-26.
[122]贾立,王介民.卫星遥感结合地面资料对区域表面动量粗糙度的估算.大气科学,1999,23(5)：632-640.
[123]张强,吕世华.城市地表粗糙度长度的确定.高原气象,2003,22(1)：24-32.
[124]高志球,卞林根,逯昌贵等.城市下垫面空气动力学参数的估算.应用气象学报,2002,13(特刊)：26-33.
[125]韩素芹,刘彬贤,解以扬等.利用255m铁塔研究城市化对地面粗糙度的影响.气象,2008,34(1)：54-58.
[126]周淑贞,吴林.气象卫星在上海城市气候研究中的应用初探[J].城市气候区域气候论文集[C]上海：华东师范大学出版社,1989,194-213.
[127]佟华,刘辉志,桑建国等.城市人为热对北京热环境的影响[J].气候与环境研究.2004,9(3)：409-420.
[128]Small. R.D. Environmental impact of fires in Kuwait[J]. Nature,1991,350:11-12.
[129]中华人民共和国国家统计局.统计数据,年度数据,能源.http://www.stats.gov.cn/tjsj/ndsj/2009/indexch.htm
[130]龙惟定,张蓓红,钟婷.上海住宅空调能源的现状与发展[J].全国暖通空调制冷2002年学术文集[C].2002,1999-2007
[131]上海统计年鉴.统计数据,城市建设,主要年份用电量[2000-2007].http://www.stats-sh.gov.cn
[132]钟婷,龙惟定.上海市住宅空调的相关调查及其耗电量的估算[J].建筑热能通风空调,2003,22(3)：22-24
[133]王凡,许进.华东电网2004-2005年用电结构分析[J].华东电力,2005,33(11)：16-19
[134]于航,刘淑娟,李祎或,于靓.从上海市民的居住生活习惯看能源消耗特征[J].上海节能,31-1500,26-30
[135]国家电网,上海电力公司,电网运行情况[2006].http://www.sh.sgcc.com.cn
[136]王宝军,施斌,姜洪涛,赵理政.近30年南京市浅层地温场变化规律研究[J].高校地质学报,2009,15(2)：199-205
[137]周红妹,周成虎,葛伟强,丁金才.基于遥感和GIS的城市热场分布规律研究[J].地理学报,2001,56(2)：189-197.
[138]谷清.风速与烟源有效高度、地面浓度的关系[J].上海环境科学,1988,7(6)：16-19.
[139]寇正,欧阳子济.暴发性气旋发展中科压强迫与潜热释放作用的数值试验[J].气象科学,1999,19(3)：259-269.
[140]周淑贞,余碧霞.上海城市对风速的影响[J].华东师范大学学报(自然科学版),1988,3：30-41.
[141]Landsberg,H.E, The Urban Climate[M].Academic Press,1981,147.
[142]李兆元.西安城市气候研究分报告,中国不同气候区域城市气候的研究[C].1990.
[143]陈燕,蒋维楣,吴涧,等.用区域边界层模式对杭州市热岛的模拟研究[J].高原气象,2004,23(4)：519-528.
[144]周淑贞,王行恒.上海大气环境中的城市干岛和湿岛效应[J].华东师范大学学报(自然科学版),1996(4)