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地理科学    2018, Vol. 38 Issue (10) : 1715- 1723
北京地区城市热岛强度长期变化特征及气候学影响机制
黄群芳1,2,, 陆玉麒2
1.江苏第二师范学院城市与资源环境学院,江苏 南京 210013
2.南京师范大学地理科学学院,江苏 南京 210023

作者简介:黄群芳(1979-),女,湖南益阳人,博士,主要从事城市热岛效应与城市宜居研究。E-mail: flyingfangzi@163.com

摘要

选择北京地区为研究区,基于1967~2016年城市站(北京站)和城郊农村站(密云站)的长期气象观测数据,研究平均气温、最高气温、最低气温对应的城市热岛强度长期变化特征及其气候学影响机制。研究发现,过去50 a平均气温和最低气温对应的城市热岛强度显著增加,增温率分别为0.29℃/10a和0.45℃/10a,而最高气温对应的城市热岛强度则没有明显变化趋势;统计分析显示过去50 a北京地区相对湿度、风速和日照时数显著降低以及气温显著上升有利于城市热岛的形成,强化城市热岛强度;未来全球变暖和快速城市化背景下北京地区城市热岛效应将进一步加剧,形成更频繁和持续时间更长的夏季城市高温热浪,严重危及城市居民生产生活和生命健康。

关键词: 城市热岛强度; 长期趋势; 相对湿度; 风速; 气压; 北京地区;
Long-term Trend of Urban Heat Island Intensity and Climatological Affecting Mechanism in Bejing City
Huang Qunfang1,2,, Lu Yuqi2
1. College of Urban, Resources and Environmental Science, Jiangsu Second Normal University, Nanjing 210013, Jiangsu, China
2. College of Geographical Science, Nanjing Normal University, Nanjing 210023, Jiangsu, China
Abstract

Urban heat island (UHI) has an important effect on urban eco-environment, living and production, and physical and mental health of the residents. In addition, urban warming especially summer heat wave caused by UHI significantly affects many aspects of the global economy, such as energy and water consumption, transportation, and social economy. Understanding of long-term trend of urban heat island intensity and its climatological driving mechanism will help the rational urban planning, urban livable construction, and urban sustainable development. Beijing is the center of the Beijing-Tianjin-Hebei metropolitan area, and has experienced a rapid urbanization process in the past few decades. This study aims to elucidate the long-term trends of UHI intensities of mean air temperature, minimum air temperature, and maximum air temperature and the climatological driving mechanism based on 50 years (1967-2016) meteorological observation data from urban station (Beijing station) and rural station (Miyun station). In the past five decades, the UHI intensities of mean air temperature, and minimum air temperature showed a significant increasing trend with the increasing rates of 0.29℃/decade (r2=0.59, P<0.001) and 0.45℃/decade (r2=0.62, P<0.001) respectively. In contrast, no marked variability trend was observed for the UHI intensities of maximum air temperature. Statistical analysis has shown that relative humidity, wind speed, and sunshine duration decreased significantly and air temperature increased significantly in Beijing over the past 50 years, which is conducive to the formation of UHI and the enhancement of UHI intensity. Multiple stepwise linear regressions showed that relative humidity, maximum wind speed, and atmospheric pressure were the key climatological factors controlling UHI intensities of mean air temperature and minimum air temperature, which could explain 92.4% and 87.6% of variabilities respectively. Atmospheric pressure, relative humidity, and sunshine duration were the key climatological factors controlling UHI intensities of maximum air temperature. Under the background of global warming and rapid urbanization, UHI effect in Beijing will further intensify, resulting in more frequent and prolonged summer urban heat waves, which will seriously endanger urban residents' production, life and health. Therefore, it is necessary to consider the effects of UHI on the future urban planning and construction. By optimizing urban layout, carrying out reasonable road system planning, energy planning and ecosystem planning and other measures, we can alleviate UHI effects and reduce high temperature and heat waves harm caused by UHI.

Keyword: urban heat island intensity; long-term trend; relative humidity; wind speed; atmospheric pressure; Beijing City;

IPCC第五次评估报告表明,近60 a来地面气温每十年上升0.12℃,约为过去100 a气温增温率的2倍[1]。相对于全球气候变暖,作为土地利用/土地覆盖变化最激烈形式的城市地区经历了更剧烈的增温[2,3,4],形成了城市气温显著高于周边乡村和郊区的城市热岛现象。城市热岛效应广泛存在于全球各类、各级别城市中,据研究,全球超过1 100个城市、中国98.9%的城市都观测到了城市热岛效应[5,6]。在当前城市快速发展的背景下,2050年全球城市化率将达到70%[7],未来的城市热岛效应强度和发生范围将进一步强化,可以预见,城市热岛将成为当今世界面临的巨大挑战。因此,在城市热岛效应被发现的100多年时间内,国内外不同领域包括气象、城市规划、环境保护、园林设计和医疗卫生等专家学者从各个角度对城市热岛的形成和生态环境效应开展了大量的研究。在Web of Science数据库以“Urban heat island”作为关键词进行检索,1997~2016年与城市热岛相关的研究论文呈现快速增长态势,由1997年的20篇左右增加到2016年的500余篇,翻了近30倍。

相对于全球气候变暖,居民更容易受到区域和地区气候热环境的影响,如全球变暖速度最快的10 a(2003~2012年)温度增长了0.78℃(相对于1850~1900年),而城市热岛带来的短时增温可达15.4℃[1,8]。城市高温会加剧大气环境污染、促进流行病的爆发、加剧夏季高温热浪,从而对区域生态环境、人们生活健康带来重要的影响[9,10,11],甚至会造成重大人员伤亡[12,13,14]。此外,热岛带来的城市增温特别是极端高温热浪还会影响能源、交通、水消费和社会经济生活的各个方面[15]。因此,研究城市热岛的形成机制及对策建议对于区域气候变化的响应和适应、全球社会经济生活和城市生态系统的健康、持续发展至关重要[16]

已有的研究表明日最大热岛强度发生在反气旋、静风、少云的夜晚[17,18],有研究基于此理想条件筛选出的气象数据进行了城市热岛效应的气候学机制分析[19,20,21],但关于正常大气状态对城市热岛效应的长时间作用机制的认识还存在不足。有研究通过对城市热岛效应案例进行强弱分组并与气象条件进行关联来探讨气象因素对城市热岛效应的影响[22],但研究结果一定程度上也混淆了其他环境因素对城市热岛的影响。此外,一些研究通过对比典型强热岛日和弱热岛日地面气象要素的变化来得到影响城市热岛形成、减弱和消失的核心气象因子[23],加强了典型气象要素对城市热岛效应发生、发展的认识,但有限的数据限制了结果的说服力。因此,迫切需要丰富不同气候区、不同等级城市的城市热岛强度长期变化数据集和成因机制解释。

北京是京津冀城市群的核心城市,过去的许多研究已显示北京城区是典型的“热岛”,其热岛强度比中国沿海城市明显,其结果是造成北京冬季寒冷期缩短和夏季炎热期增强,未来城市高温热浪等灾害更加频繁[13,24,25],但关于其长期气候学形成机制还不甚明了。因此,本文选择城市化快速发展的北京地区为研究对象非常具有代表性,基于1967~2016年长期气象观测数据,研究城市热岛强度长期变化特征,筛选和厘清其影响的主要气象因子,构建基于主要气象因子的城市热岛强度估算模型,以更好服务于区域气候变化的响应和适应研究,也为城市规划、园林景观设计和城市高温热浪的应对管理提供科学依据。

1 数据与方法
1.1 研究区域

研究基于城区的北京站和郊区的密云站开展研究,其中北京站(116°28′E,39°48′N,海拔31.3 m)位于北京市东南部高度城市化区域,周边高楼大厦环绕,土地利用以工业用地为主,是典型的城市站(图1a)。密云站(116°52′E,40°23′N,海拔71.8 m)位于北京市东北部临近森林和水库的远郊区,周围建筑多为低矮平房(图1a),土地利用以农业用地和森林用地为主,属于典型乡村站(图1a)。此外,基于中国科学院资源环境科学数据中心(http://www.resdc.cn/)提供的2010年人口分布数据也可以发现北京站人口密度显著高于密云站,其分别能作为城市站和乡村站代表(图1b)。由于2个站点海拔高度非常接近,因此本研究在开展比较分析时没有对数据进行海拔校正。

图1 气象站位置和北京市土地利用类型(a)及总人口(b)空间分布 Fig.1 Location of two meteorological stations and distributions of land use type (a) and total population (b) in Beijing City

1.2 数据资料与方法

1957~2016年2个站点长期日观测数据均来自于中国气象局下属的中国气象数据网(http://data.cma.cn/[2017-08-16])。考虑到1957~1966年日最大风速缺测较多,为了保证数据的连续性、一致性和完整性,选取1967~2016年数据进行研究。此外,中国气象局对中国气象数据网公布的数据进行严格质量控制,校正和调整了数据的不连续性,以确保资料的代表性、准确性和可比性[26,27],也被广泛应用于各类研究和气候变化评价中[21,23~25]

城市热岛强度通过城区的北京站气温减去郊区的密云站气温得到,分别计算获得年日平均气温、年日最高气温和年日最低气温分别对应的城市热岛强度。在探讨城市热岛强度长期变化的气候学影响机制时,仅考虑利用城区北京站各气象要素与城市热岛强度进行趋势和统计相关分析。

1.3 统计分析

采用SPSS 16.0软件对数据结果进行统计分析,包括线性趋势拟合,单要素线性相关分析和逐步多元线性回归分析。单要素线性相关分析用于阐明城市热岛强度与气象要素间是否存在显著相关,而多元线性回归分析用于从众多气象要素中遴选出影响最显著的关键气象要素,用于城市热岛强度的预测。

采用ArcGIS 9.2绘制北京地区土地利用和人口空间分布图,利用Origin 8.5软件绘制其他数据图。

2 结果与讨论
2.1 城市热岛强度和主要气象要素长期变化特征

北京城区和密云站长期气温观测数据显示,北京地区存在明显的城市热岛效应(图2)。整体而言过去50 a北京地区平均气温和最低气温对应的热岛强度显著增加,热岛强度增温率分别为0.29℃/10a和0.45℃/10a,城市夜间增温更明显(图2a、2c)。可以看出,不同时段城市热岛强度变化也存在一些波动,未表现出均一的增加趋势,如1980年之前是一个缓慢的增加过程,1980年之后城市热岛强度快速增加,在1997~2003年城市热岛强度曾出现停滞甚至有时下降,但在2004年后又呈现逐渐增加趋势。此外,2011~2016年北京地区城市最低气温热岛强度为3.01℃(图2c),说明当前城区夜间气温比乡村要高3℃,这一数值与以往关于北京城市热岛强度研究结果较为一致[28]。相反,过去50 a白天最高气温对应的热岛强度变化不大,没有显著的增加趋势,城市和乡村站最高气温相差约0.5℃(图2b)。

图2 日平均气温(a)、日最高气温(b)和日最低气温(c)计算得到的城市热岛强度长期变化 Fig.2 Long-term trends of urban heat island intensities of daily mean temperature (a), daily maximal temperature (b), and daily minimal temperature (c)

北京地区夜间最低气温城市热岛强度明显高于白天最高气温城市热岛强度这一研究结论与国际上同类研究结果一致[17,29],平均气温城市热岛强度0.29℃/10a的增温率与雅典平均气温城市热岛强度0.21~0.22℃/10a的增温率比较接近[29]。此外,北京作为世界上快速城市化地区和大都市聚集区,其目前城市热岛强度与国际上已高度城市化的大都市聚集区比较接近[30],如日本东京都市区20世纪90年代初,晴朗静风的夏季夜间城市热岛强度为3℃,白天的城市热岛强度为1.0℃[31,32],同样在欧洲雅典,夜间最低气温热岛强度在3℃以上[29],而在纽约都市区夏秋季城市热岛强度为3℃,冬春季为4℃[33]。但与一些中小城市相比,北京地区城市热岛强度要明显高得多,如临近的廊坊市晴朗无风时城市平均热岛强度最大,只有1.25℃[19],而济宁市城市热岛强度仅为0.79℃[20],反映了随着城市等级和密集程度的增加,城市热岛强度明显增加,相应的未来受夏季城市高温热浪影响的风险也在增加。

城市热岛的形成与气象条件密切相关,晴朗少云、静风、气压梯度小等天气条件会促进城市热岛效应的形成和发展[34,35]。反过来,城市化建设改变了城市的下垫面状况,坚硬、不透水下垫面的增加及建筑物的增多、增密、增高,导致城市下垫面粗糙度增大,也会改变城市的风速、相对湿度和气温等气象条件进而影响到城市热岛效应。北京城区站长期气象观测数据显示,过去50 a北京市气象条件也发生了明显变化,主要表现为日照时数、风速和相对湿度的显著降低(图3b、3c、3e),和平均气温、最低气温和最高气温的显著增加(图3d);北京市的降水和气压呈现轻微降低趋势(图3a、3f),但统计检验显示不显著。日照时数显著下降与全球变暗和北京地区雾霾污染密切相关[36,37],而风速的显著降低与全国范围内南北增温差异以及城市建筑物引起地面粗糙度变化有关[38,39]

图3 主要气象要素长期气候学变化特征 Fig.3 Long-term climatology of main climatological factors

2.2 城市热岛强度与主要气象要素间相关分析

城市热岛强度主要受3方面因素影响:① 城市物理与形态特征,包括城市建筑材料的反射率、城市街谷结构、城市周边地形、城市用地组成;② 气象条件,包括风速、云量、相对湿度、降水、大气稳定性、热对流状况等;③ 人为热排放:工业能源消费产生的热量、交通道路车辆废热、居民生活排热及人类自身新陈代谢产生的热量。在既定城市下垫面条件下,一定时间点的热岛强度主要受背景气候和天气条件的影响,因此本文主要分析城市热岛强度与主要气象要素间的相关性,探讨城市热岛强度长期变化的气候学影响机制。

根据线性相关分析结果,对于平均气温和最低气温热岛强度而言,除了降水以外的其他气象要素均与之存在显著线性关系,其中与相对湿度线性相关的决定系数最高,其次是最低温、平均温、最大风速和平均风速;相比较而言,最大风速线性相关决定系数要高于平均风速,最低气温线性相关决定系数要高于平均气温和最高气温(表1、图4)。而对于最高气温热岛强度,只有相对湿度和平均气压与之存在显著线性关系,且与平均气压的相关性更好(表1、图4)。整体而言,城市热岛强度与日照时数、风速、相对湿度和气压存在负相关,而与气温则存在正相关。据IPCC第五次报告情景预估未来全球气候将继续变暖[1],因此在未来全球变暖背景下,可以预见北京城市化过程中城市热岛效应还将进一步加强,进而强化夏季城市高温热浪强度、频次和持续时间,给城市居民健康带来极大危害[13,40]。因此,未来城市规划和建设中应充分考虑城市热岛效应影响,通过优化城市布局、进行合理道路系统规划、能源规划和生态系统规划等措施减缓城市热岛效应影响。

表1 城市热岛强度与城区北京站主要气象要素间的线性相关决定系数和显著性水平 Table 1 Determination coefficient and significance level of linear relationships between urban heat island intensity of daily mean temperature, daily maximal temperature, and daily minimal temperature and main meteorological factors of urban station (Beijing station)

图4 城市热岛强度与气象要素关系 Fig.4 Linear relationships between urban heat island intensities and climatological factors

2.3 城市热岛强度多元线性回归模拟

为进一步遴选影响城市热岛强度的关键气象要素,本文对城市热岛强度与主要气象要素进行了逐步多元回归分析,考虑到最大风速和最低气温相关性要更好,多元线性逐步回归风速和气温分别取最大风速和最低气温进行分析。结果表明,影响平均气温和最低气温热岛强度的核心气象要素依次是相对湿度、最大风速和气压,3个要素对城市热岛强度的总解释率分别为92.4%和87.6%。而对于最高气温热岛强度,影响最大的气象要素依次是气压、相对湿度和日照时数,3个要素对城市热岛强度的总解释率52.5%。与单要素线性相关分析相比,多元逐步回归遴选出来的3个关键气象要素对城市热岛强度的解释率显著上升(表1),特别是平均气温和最低气温城市热岛强度的解释率都在87%以上,由此得到如下多元线性回归模型,以便对未来城市热岛强度进行预测。

UHIIMean=-0.065RH-0.397WSMax-0.160AP+

168.90 (r2=0.924,P<0.001,n=50) (1)

UHIIMin=-0.101RH-0.572WSMax-0.201AP+

213.93 (r2=0.876,P<0.001,n=50) (2)

式中,UHIIMeanUHIIMin分别为平均气温和最低气温城市热岛强度,RHWSMaxAP分别为北京城市站平均相对湿度、最大风速和平均气压。

为了描述3个气象要素对城市热岛强度的组合影响,本文研究了平均气温、最低气温和最高气温城市热岛强度随3个关键气象要素的变化(图5)。图中显示城市热岛强度与3个关键气象要素均呈负相关关系,相对干燥的空气、静风、微风条件有利于城市热岛效应的形成,对应的城市热岛强度大。综合起来,相对湿度、最大风速和气压是影响北京城市热岛强度的关键气象要素。相对湿度、风速和云量是影响城市热岛强度的关键气象要素[17,30],因云量数据的缺失无法判断其对北京市城市热岛强度的影响,但相对湿度和风速对城市热岛强度的影响与以往研究结果一致,并突出了气压对北京市城市热岛强度的影响。

图5 城市热岛强度随关键气象要素变化 Fig.5 Urban heat island intensity charge with key meteorological factors

3 结论

利用北京站和密云站1967~2016年的长期气象观测数据分析了北京市热岛强度的长期变化趋势并探讨其气候学形成机制,结果如下:

1) 过去50 a,北京地区平均气温和最低气温的城市热岛强度显著增加,并且最低气温的城市热岛强度增温率明显高于平均气温的城市热岛强度,其增温率分别为0.45℃/10a 和0.29℃/10a,而最高气温的城市热岛强度没有明显变化趋势。

2) 日照时数、风速、平均相对湿度和平均气压与平均气温和最低气温的城市热岛强度均存在不同程度的显著负相关,而气温与平均气温和最低气温的城市热岛强度则存在显著的正相关,反映随着全球气候变暖城市热岛强度存在增加趋势。

3) 平均相对湿度、最大风速和平均气压是影响北京地区平均气温和最低气温城市热岛强度的控制性气象因子,而平均气压、平均相对湿度和日照时数是影响最高气温城市热岛强度的核心气象因子。

致谢:感谢中国科学院南京地理与湖泊研究所的张毅博和邓建明帮助绘制了部分图件。

The authors have declared that no competing interests exist.

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利用1992—2008年沈阳站和新城子站逐日4个时次的平均气温、平均风速、降水量、云量和能见度资料,对不同天气条件下沈阳的城市热岛效应进行研究。结果表明:除雾和浓雾天气条件下,沈阳城市热岛强度在08时最弱外,其他天气条件下均表现为20时最强,14时最弱;不同天气条件下,夜间城市热岛强度均高于白天;晴朗无风条件下昼夜城市热岛强度差最大,为0.73℃。四季相比,除雾条件下秋季城市热岛强度最强外,其他天气条件下均为冬季最强;除大雨条件下春季城市热岛强度最弱外,其他条件下均为夏季最弱。沈阳城市热岛强度随降水量的增加而减弱,随能见度的降低而减弱,随着风速的增加而减弱。白天和夜间两个时次的差值表现为,1~3级风夜间变化幅度大于白天,0级和4~5级风速有相反规律,其他天气条件下无明显规律。
DOI:10.3969/j.issn.1674-7097.2011.01.009      [本文引用:2]
[Li Liguang, Liang Zhibing, Wang Hongbo et al. Urban heat island characteristics in Shenyang under different weather conditions. Transactions od Atmospheric Sciences, 2011, 34(1): 66-73.]
[22] Morris C J G, Simmonds I. Associations between varying magnitudes of the urban heat island and the synoptic climatology in Melbourne, Australia[J]. International Journal of Climatology: A Journal of the Royal Meteorological Society, 2000, 20(15): 1931-1954.
[本文引用:1]
[23] 李兴荣, 胡非, 舒文军, . 北京秋季城市热岛效应及其气象影响因子[J]. 气候与环境研究, 2008, 13(3): 291-299.
应用北京地区地面气象观测台1990~2004年10月的气温资料,分析了近15年来北京秋季城市热岛的特征,结果表明,北京秋季夜间城市热岛要强于白天.此外,对比分析了一个强热岛和一个弱热岛的特征及其气象影响因子,结果表明,北京秋季夜间特定条件下存在强热岛,白天城市强热岛会减弱消失,城市强热岛的日变化非常明显.夜间城市强热岛形成和维持是多个因子综合作用的结果.白天日照充足的晴夜,北京城郊地面风场很弱(≤1.0 m·s-1),同时城区垂直方向47 m以下大气风场持续很弱(≤1.0 m·s-1),城区320 m以下大气持续存在强逆温.日落后郊区地面大气降温速率和幅度远大于城区,促使夜间强热岛的形成和维持.白天日出后太阳辐射的加热作用所引起的郊区地面大气升温速率和幅度大于城区,城区大气稳定度的减弱以及城区大气逆温的消失是夜间强热岛减弱并最终消失的重要原因.
DOI:10.3878/j.issn.1006-9585.2008.03.07      [本文引用:2]
[Li Xiongrong, Hu Fei, Shu Wenjun et al. Characteristics of urban heat island effect and its meteorological influencing factors over Beijing in autumn. Climatic and Environmental Research, 2008, 13(3): 291-299.]
[24] Liu W, Ji C, Zhong J et al. Temporal characteristics of the Beijing urban heat island[J]. Theoretical and Applied Climatology, 2007, 87(1-4): 213-221.
This paper describes the inter-annual trend, and the seasonal and hourly variation of the near surface urban heat island (UHI) in Beijing. The surface air temperature data (mean, maximum, and minimum) from one urban (downtown Beijing) and one rural (70塳m from downtown Beijing) station were used for the period 1977 and 2000. It is found that the temperatures in both urban and rural stations show an increasing tendency. Specifically, minimum temperature shows the greatest tendency at the urban station whereas maximum temperature shows the greatest increase at the rural station. The UHI intensity obtained by calculating the difference in temperatures between the two stations identifies that the intensity is greatest and has the greatest increasing trend for minimum temperature, while the UHI intensity of maximum temperature shows a slow decrease over time. UHI intensity for minimum temperature has a strong positive correlation with the increase in the urban population and the expansion of the yearly construction area. Seasonal analyses showed the UHI intensity is strongest in winter. This seasonal UHI variation tends to be negatively correlated with the seasonal variation of relative humidity and vapor pressure. Hourly variation reveals that the strongest UHI intensity is observed in the late nighttime or evening, while the weakest is observed during the day.
DOI:10.1007/s00704-005-0192-6      [本文引用:1]
[25] You Q, Jiang Z, Kong L et al. A comparison of heat wave climatologies and trends in China based on multiple definitions[J]. Climate Dynamics, 2017, 48(11-12): 3975-3989.
Heat waves (HWs) can have disastrous impacts on human activities and natural systems, and are one of the current foci of scientific research, particularly in the context of global warming. However, there is no standard definition of a HW, which makes assessment of temporal trends a challenge. In this study, based on daily mean, maximum and minimum temperature, and relative humidity datasets from China Meteorological Administration, the patterns, trends and variations of HW in China during 19612014 are investigated. Sixteen previously published HW indices (HIs) are calculated, which are divided into two types using relative and absolute threshold temperatures, respectively. During 19612014, both relative and absolute threshold HIs show the highest number of HW in Jianghua and South China, geographically consistent with the climate characteristics of China. The majority of HIs shows negative/positive trends of HW days before/after 1990 over the whole of China, but especially in Jianghua and South China, which reflects rapid warming since 1990. There are significant correlations among different HIs in the same type (both absolute and relative), but correlations are weak between relative and absolute threshold HIs. Because relative and absolute HIs show contrasting trends, the choice of HI is therefore critical for future analysis
DOI:10.1007/s00382-016-3315-0      [本文引用:2]
[26] Li Q, Dong W.Detection and adjustment of undocumented discontinuities in Chinese temperature series using a composite approach[J]. Advances in Atmospheric Sciences, 2009, 26(1): 143-153.
Annually averaged daily maximum and minimum surface temperatures from southeastern China were evaluated for artificial discontinuities using three different tests for undocumented changepoints. Changepoints in the time series were identified by comparing each target series to a reference calculated from values observed at a number of nearby stations. Under the assumption that no trend was present in the sequence of target-reference temperature differences, a changepoint was assigned to the target series when at least two of the three tests rejected the null hypothesis of no changepoint at approximately the same position in the difference series. Each target series then was adjusted using a procedure that accounts for discontinuities in average temperature values from nearby stations that otherwise could bias estimates of the magnitude of the target series step change. A spatial comparison of linear temperature trends in the adjusted annual temperature series suggests that major relative discontinuities were removed in the homogenization process. A greater number of relative change points were detected in annual average minimum than in average maximum temperature series. Some evidence is presented which suggests that minimum surface temperature fields may be more sensitive to changes in measurement practice than maximum temperature fields. In addition, given previous evidence of urban heat island (i.e., local) trends in this region, the assumption of no slope in a target-reference difference series is likely to be violated more frequently in minimum than in maximum temperature series. Consequently, there may be greater potential to confound trend and step changes in minimum temperature series.
DOI:10.1007/s00376-009-0143-8      [本文引用:1]
[27] Li Q, Liu X, Zhang H.Detecting and adjusting temporal inhomogeneity in Chinese mean surface air temperature data[J]. Advances in Atmospheric Sciences, 2004, 21(2): 260-268.
Adopting the Easterling-Peterson (EP) techniques and considering the reality of Chinese meteorological observations, this paper designed several tests and tested for inhomogeneities in all Chinese historical surface air temperature series from 1951 to 2001. The result shows that the time series have been widely impacted by inhomogeneities resulting from the relocation of stations and changes in local environment such as urbanization or some other factors. Among these factors, station relocations caused the largest magnitude of abrupt changes in the time series, and other factors also resulted in inhomogeneities to some extent. According to the amplitude of change of the difference series and the monthly distribution features of surface air temperatures, discontinuities identified by applying both the E-P technique and supported by China station history records, or by comparison with other approaches, have been adjusted. Based on the above processing, the most significant temporal inhomogeneities were eliminated, and China most homogeneous surface air temperature series has thus been created. Results show that the inhomogeneity testing captured well the most important change of the stations, and the adjusted dataset is more reliable than ever. This suggests that the adjusted temperature dataset has great value of decreasing the uncertaities in the study of observed climate change in China.
DOI:10.1007/BF02915712      [本文引用:1]
[28] 宋艳玲, 张尚印. 北京市近40年城市热岛效应研究[J]. 中国生态农业学报, 2003, 11(4): 126-129.
利用北京市近40年气候资料研究分析北京市市区与郊区平均气温日、季、年际和年代变化特征发现,40年中1995年11月24日市区与郊区日平均气温温差最大,达4.6℃;季变化市区与郊区温差冬季最大,为1.11℃,春季最小,仅为0.26℃;年际变化1961~1977年市区与郊区温差较小,而1978~2000年市区与郊区温差达0.62℃,热岛效应明显增强;年代变化市区与郊区温差60年代最小,仅为0.13℃,90年代最大,为0.78℃.近年虽高温(≥35℃)日数明显增多,但年最高气温变化较小,仅有1997年、1999年和2000年年最高气温>38℃.近40年市区与郊区年平均气温明显上升,市区气温平均10年升高0.43℃,郊区气温平均10年升高0.21℃,北京市市区年平均气温序列中存在明显的12年周期.
[本文引用:1]
[Song Yanling, Zhang Shangyin.The study on heat island effect in Beijing during last 40 year. Chinese Journal of Eco-Agriculture, 2003, 11(4): 126-129.]
[29] Founda D, Pierros F, Petrakis M et al. Interdecadal variations and trends of the Urban Heat Island in Athens (Greece) and its response to heat waves[J]. Atmospheric Research, 2015, 161: 1-13.
61The seasonal and temporal variability and trends of UHI in Athens was studied.61UHI accounts for almost half of Athens' warming.61Nocturnal and daytime UHI reveal different patterns.61UHI increased hot days frequency.61Heat waves amplify UHI intensity during night time.
DOI:10.1016/j.atmosres.2015.03.016      [本文引用:3]
[30] Kim Y H, Baik J J.Daily maximum urban heat island intensity in large cities of Korea[J]. Theoretical and Applied Climatology, 2004, 79(3-4): 151-164.
This study investigates the characteristics of the daily maximum urban heat island (UHI) intensity in the six largest cities of South Korea (Seoul, Incheon, Daejeon, Daegu, Gwangju, and Busan) during the period 19732001. The annually-averaged daily maximum UHI intensity in all cities tends to increase with time, but the rate of increase differs. It is found that the average annual daily maximum UHI intensity tends to be smaller in coastal cities (Incheon and Busan) than in inland cities (Daejeon, Daegu, and Gwangju), even if a coastal city is larger than an inland city. A spectral analysis shows a prominent diurnal cycle in the UHI intensity in all cities and a prominent annual cycle in coastal cities. A multiple linear regression analysis is undertaken in order to relate the daily maximum UHI intensity to the maximum UHI intensity on the previous day (PER), wind speed (WS), cloudiness (CL), and relative humidity (RH). In all cities, the PER variable is positively correlated with the daily maximum UHI intensity, while WS, CL, and RH variables are negatively correlated with it. The most important variable in all cities is PER, but the relative importance of the other three variables differs depending on city. The total variance explained by the multiple linear regression equation ranges from 29.9% in Daejeon to 44.7% in Seoul. A multidimensional scaling analysis performed with a correlation matrix obtained using the daily maximum UHI intensity data appears to distinguish three city groups. These groupings are closely connected with distances between cities. A multidimensional scaling analysis undertaken using the normalized regression coefficients obtained from the multiple linear regression analysis distinguishes three city groups. Notably, Incheon and Busan form one group, whose points in the two-dimensional space are very close. The results of a cluster analysis performed using the multivariate data of PER, WS, RH, and CL are consistent with those of the multidimensional scaling analysis. The analysis results in this study indicate that the characteristics of the UHI intensity in a coastal city are in several aspects different from those in an inland city.
DOI:10.1007/s00704-004-0070-7      [本文引用:2]
[31] Kimura F, Takahashi S.The effects of land-use and anthropogenic heating on the surface temperature in the Tokyo Metropolitan area: A numerical experiment[J]. Atmospheric Environment, 1991, 25(2): 155-164.
The diurnal variation of the simulated surface air temperature agrees well with the observed value; an average over 36 days which represent typical summer days; i.e. negligible gradient winds and almost clear skies. The model shows that the contribution of anthropogenic heat is much larger at night, in spite of the lower energy consumption as compared to daytime use. Due to the scarcity of green vegetated areas in the central part of the city, the surface air temperature is enhanced in this region during daytime, however this enhancement is small after midnight.
DOI:10.1016/0957-1272(91)90050-O      [本文引用:1]
[32] Yamashita S.Detailed structure of heat island phenomena from moving observations from electric tram-cars in Metropolitan Tokyo[J]. Atmospheric Environment, 1996, 30(3): 429-435.
In this study, the detailed horizontal structure, i.e. cliffs and plateaux of the heat island of the Metropolitan Tokyo area is investigated. According to Oke (1977), cliff is steep temperature gradient at the rural/urban boundary and plateau is a steady but weaker horizontal gradient of increasing temperature towards the city center. However, these features are not always evident, e.g. large city like Tokyo. To elucidate such aspects, moving observations of the horizontal distribution of air temperature from electric trains of the transportation network of Metropolitan Tokyo during late evening or early morning were thus conducted. In total, 16 railroad lines were used for the moving observations. The observations were done in two phases for sectional and horizontal distributions. Results show that three cliffs exist in the heat island of Metropolitan Tokyo, although the location of these cliffs should be taken into consideration for urban planning or urban redevelopment.
DOI:10.1016/1352-2310(95)00010-0      [本文引用:1]
[33] Gedzelman S D, Austin S, Cermak R et al. Mesoscale aspects of the Urban Heat Island around New York City[J]. Theoretical and Applied Climatology, 2003, 75(1-2): 29-42.
09A mesoscale analysis of the Urban Heat Island (UHI) of New York City (NYC) is performed using a mesoscale network of weather stations. In all seasons the UHI switches on rapidly in late afternoon...
DOI:10.1007/s00704-002-0724-2      [本文引用:1]
[34] 彭保发, 石忆邵, 王贺封, . 城市热岛效应的影响机理及其作用规律——以上海市为例[J]. 地理学报, 2013, 68(11): 1461-1471.
以上海市为例,从土地利用规模和强度的变化、类型和布局的变化、利用方式的变化三个方面揭示其对热岛效应的影响机理;实证分析结果表明:(1)土地城市化是上海城市热岛强度的主要影响因素;就建成区扩张对热岛强度的具体影响而言,累积效应大于其增量效应;(2)工业化、房地产开发、人口增长对上海城市热岛强度均具有较大的影响;就经济发展和能源消耗对城市热岛强度的具体影响而言,密度效应通常大于其规模效应;就全社会房屋竣工面积、20层以上高层建筑数量对热岛强度的影响而言,累积效应小于增量效应;就人口增长对城市热岛强度的具体影响而言,密度效应与规模效应大体相近;(3)土地利用和城市发展模式的差异导致了城市热岛效应的空间差异。
DOI:10.11821/dlxb201311002      [本文引用:1]
[Peng Baofa, Shi Yishao, Wang Hefeng et al. The impacting mechanism and laws of function of urban heat islands effect: A case study of Shanghai. Acta Geographica Sinica, 2013, 68(11): 1461-1471.]
[35] Morris C J G, Simmonds I, Plummer N. Quantification of the influences of wind and cloud on the nocturnal urban heat island of a large City[J]. Journal of Applied Meteorology, 2001, 40(2): 169-182.
Analyses taken over all observed weather conditions of daily 0600 EST climate data from a network of monitoring stations in and around the large city of Melbourne, Australia, revealed a 20-yr mean urban heat island (UHI) value of 1.13C. The UHI varied seasonally between summer (1.29C), spring (1.25C), autumn (1.02C), and winter (0.98C). Investigations undertaken with daily wind speed and cloud amount data enabled a detailed investigation of the relative importance of factors such as the turbulent and radiative exchanges on Melbourne's UHI. Analysis of variance and regression techniques were used to explore these processes and to predict the behavior of the UHI in numerical terms for mean seasonal and annual periods between 1972 and 1991. Over the 20-yr period, analyses of the association among Melbourne's UHI, wind, and cloud revealed that the UHI was inversely proportional to approximately the fourth root of both the wind speed and the cloud amount. This relationship explained more of the UHI variance during summer and the least variance during winter. Increases in the amount of cloud cover and in the frequency of wind speeds in excess of 2.0 m sresulted in a statistically significant (95% confidence level) reduction in UHI magnitude. The influence of wind in limiting Melbourne's UHI magnitude was greatest during clear to near-clear sky conditions. Similarly increases in cloud were most restrictive to UHI development during calm to low wind speeds. Unlike most previous studies, the linear regression analysis presented here revealed that cloud was more limiting than the wind speed to UHI development for all seasons except summer. Contour plots of the UHI are presented for the various associations between each category of cloud and wind. These plots enable a clear visual presentation of the most to least favorable conditions for UHI intensity and development. The analyses indicate that low wind speeds and little or no cloud were typically associated with the largest UHI development. Eight octas of cloud and wind speeds in excess of 5.0 m swere usually associated with modest (but still apparent) UHI development.
DOI:10.1175/1520-0450(2001)0402.0.CO;2      [本文引用:1]
[36] Wild M. Global dimming and brightening: A review[J]. Journal of Geophysical Research Atmospheres, 2009, 114(D10):D00D16.
[本文引用:1]
[37] Yang Y H, Zhao N, Hao X H et al. Decreasing trend of sunshine hours and related driving forces in North China[J]. Theoretical and Applied Climatology, 2009, 97(1-2): 91-98.
Global dimming is currently an active area of research in climate change. Trends of temporal (on the order of decades, years, seasons or even months) and spatial patterns in sunshine hours and associated climatic factors (average air temperature, relative humidity, precipitation and wind speed) over North China are evaluated for the period 1965~1999 based on data from 81 standard meteorological stations. The results show that: (1) North China is experiencing decreasing sunshine hours (82.855h/decade); (2) seasonally, decline in sunshine hours is highest in summer and lowest in winter; (3) spatially, decrease in sunshine hours is highest in inland and plain regions and lowest in the northwest mountain and coastland regions; (4) sunshine hours have a high correlation with precipitation, relative humidity and wind speed, with wind speed having the strongest influence on sunshine hours implicit in the close correlation (temporally and spatially) between the two variables; (5) cloud cover could not be any significant driver of sunshine-hour decline because it is more or less stable; (6) spatially and seasonally, wind speed is an important driving factor of decreasing sunshine hours in North China. Furthermore, the interactions between wind speed and aerosol loading may be an enabling factor of wind speed in driving (strongly) the changes in sunshine hours.
DOI:10.1007/s00704-008-0049-x      [本文引用:1]
[38] Vautard R, Cattiaux J, Yiou P et al. Northern hemisphere atmospheric stilling partly attributed to an increase in surface roughness[J]. Nature Geoscience, 2010, 3(11):756-761.
Surface winds have declined in China, the Netherlands, the Czech Republic, the United States and Australia over the past few decades. The precise cause of the stilling is uncertain. Here, we analyse the extent and potential cause of changes in surface wind speeds over the northern mid-latitudes between 1979 and 2008, using data from 822 surface weather stations. We show that surface wind speeds have declined by 5-15% over almost all continental areas in the northern mid-latitudes, and that strong winds have slowed faster than weak winds. In contrast, upper-air winds calculated from sea-level pressure gradients, and winds from weather reanalyses, exhibited no such trend. Changes in atmospheric circulation that are captured by reanalysis data explain 10-50% of the surface wind slowdown. In addition, mesoscale model simulations suggest that an increase in surface roughness-the magnitude of which is estimated from increases in biomass and land-use change in Eurasia-could explain between 25 and 60% of the stilling. Moreover, regions of pronounced stilling generally coincided with regions where biomass has increased over the past 30years, supporting the role of vegetation increases in wind slowdown.
DOI:10.1038/ngeo979      [本文引用:1]
[39] Xu M, Chang C P, Fu Cet al. Steady decline of east Asian monsoon winds, 1969-2000: Evidence from direct ground measurements of wind speed[J]. Journal of Geophysical Research: Atmospheres, 2006, 111(D24):doi:10.1029/2006JD007337.
[1] It is commonly believed that greenhouse-gas-induced global warming can weaken the east Asian winter monsoon but strengthen the summer monsoon, because of stronger warming over high-latitude land as compared to low-latitude oceans. In this study, we show that the surface wind speed associated with the east Asian monsoon has significantly weakened in both winter and summer in the recent three decades. From 1969 to 2000, the annual mean wind speed over China has decreased steadily by 28%, and the prevalence of windy days (daily mean wind speed &gt; 5 m/s) has decreased by 58%. The temperature trends during this period have not been uniform. Significant winter warming in northern China may explain the decline of the winter monsoon, while the summer cooling in central south China and warming over the South China Sea and the western North Pacific Ocean may be responsible for weakening the summer monsoon. In addition, we found that the monsoon wind speed is also highly correlated with incoming solar radiation at the surface. The present results, when interpreted together with those of recent climate model simulations, suggest two mechanisms that govern the decline of the east Asian winter and summer monsoons, both of which may be related to human activity. The winter decline is associated with global-scale warming that may be attributed to increased greenhouse gas emission, while the summer decline is associated with local cooling over south-central China that may result from air pollution.
DOI:10.1029/2006JD007337      [本文引用:1]
[40] 李双双, 杨赛霓, 张东海, . 近54年京津冀地区热浪时空变化特征及影响因素[J].应用气象学报, 2015(5): 545-554.
It indicates that hot summers will become more frequent in eastern China in the future. The region will face a great risk in the absence of any adaptation measures taken towards reducing its vulnerability to effects of extreme heat. Beijing Tianjin Hebei Region is identified as the biggest metropolitan in northern China. Rapid urbanization and the recent frequent occurrence of hot summers in the region raises questions about influencing factors at the regional scale and the spatiotemporal variability of heat waves. Using the newly developed Heatwave Index (HI), a statistical analysis is conducted on the temporal and spatial distribution characteristics of heat waves in the Beijing Tianjin Hebei Region over a period from 1960 to 2013. More specifically, based on the history of relocations, the heat wave trends between Beijing and Fengning is compared to investigate the influence of urbanization, and also analyse the relationship between atmospheric circulation anomalies and observed heat wave trends. It shows that based on variations in heat wave trends, two distinct phases are identified in Beijing Tianjin Hebei Region. Owing to some abrupt changes in the mid 1970s, the frequency of heat waves decrease from 1960 to 1973, and then increase from 1974 to 2013. Heat waves show a decreasing trend in the southern part and an increasing trend in the northern part of Beijing Tianjin Hebei Region. A significant increasing trend is found in the northern and western biological conservation area, and decreasing trend in the south eastern plains. At the regional scale, urbanization and relocations affects the occurrence of slight to moderate rather than extreme heat waves. In the period of global warming and rapid urbanization, the frequency of heat waves in Beijing is higher than that of Fengning. In recent global warming hiatus, the frequency of heat waves in Beijing is lower than Fengning. Driving factors behind the temporal and spatial patterns are deemed complicated. The inter decadal variations are significantly and closely related to the offsetting of western Pacific subtropical high (WPSH) ridge and the anomalous anticyclone over the Tibetan Plateau (TPAI) in summer. In other words, there is a positive correlation between the number of heat wave days and WPSH and TPAI. Furthermore, the probability of a summer with a mega heat wave would increase with the 坅nomalies in WPSH and TPAI.
DOI:10.11898/1001-7313.20150504      [本文引用:1]
[Li Shuangshuang, Yang Saini, Zhang Donghai et al.Patiotemporal variability of heat waves in Beijing-Tianjin-Hebei region and influencing factors in recent 54 years. Journal of Applied Meteorological Science, 2015(5): 545-554.]